1. INTRODUCTION

Social networking sites (SNSs) such as Twitter, Instagram, Facebook, Google+, and Myspace permit individuals to create profiles in view of sharing photographs, personal information, and joining friends’ groups (Hew, 2011; Ortiz et al., 2023), and can be viewed as a place of acquiring social benefit (Hoque & Hossain, 2023; Lee, 2024). According to Ibáñez-Sánchez et al. (2022), SNSs are utilized not only for transmitting messages and creating content but also for leisure, communication, and people-finding purposes. They are emerging as one of the most popular media of online social interaction and emotion sharing (Hoadley et al., 2010), and managing friendships as well. Mainly, the design of SNSs is to simplify communication and content sharing within networks of users, who via an individual network share ideas, interests, activities, or events on a virtual platform (Chen et al., 2012).

Statista (2024) report that SNS usage worldwide has surpassed 60%, and the highest penetration rates were found in 2023 in Northern and Western Europe, followed by Eastern Asia and Southern Europe. Globally, there were over 4.6 billion SNSs users in 2022, and that number is expected to rise to over 5.8 billion by 2027, a 26% growth in only five years. Facebook, which has the largest market size, had 2.96 billion active monthly users as of 2023, and its parent company, Meta, recorded 116.6 billion US dollars in revenue in 2022, which is more than double that of 2018 (Statista, 2024). Due to the critical function that these websites serve in increasing global communication, removing barriers between people, and facilitating conversation on a public platform, SNSs are currently the most popular communication route among educated youngsters. These websites serve as a conduit for these people to connect with friends and widen their social network (Dong et al., 2014).

Internet services such as social networking services, emailing, portal services, or other groundbreaking Internet services, are effortlessly available, providing various benefits in ubiquitous environments, and van der Heijden (2004) argue that there is a strong relationship between services and new information technologies. In addition, understanding the reasons behind people’s actions and their level of engagement and stickiness with SNSs requires an understanding of the gratification that users of these platforms derive from their use (Silveira et al., 2022). Yet, despite the increasing necessity to understand the specific individual benefits a person can obtain using a certain SNS at a more insightful level, research in this facet still remains less studied. Prior research review reveals that several studies were conducted in the SNS area regarding post adoption (Chang & Zhu, 2012; Lin & Lu, 2011), and continuance usage (Mouakket, 2015), with even less research in the setting of net individual benefits. It is worth noting that teenagers love SNSs, and cases of cyberbullying frequently occur on these sites as well.

Hence, the primary objectives of this research are to offer a thorough understanding of the benefits that individuals can derive from using SNSs and how to utilize them responsibly. The authors feel that there is a growing need to comprehend the unique advantages that each user of a SNS may derive from using it, as these platforms have become popular means of connection with the intention of receiving reciprocal favors. Over the past ten years or so, the global netizen population, especially of social media, has grown geometrically due to the rising popularity of the Internet among youths. Every day, new people for a variety of reasons from all ages enter cyberspace. In light of this, the topic of comprehending the personal net benefits of utilizing SNSs has garnered interest and is logically sound.

Therefore, to fill the research gap, this current study develops a research framework applying the modified DeLone & McLean information system (D&M IS) success model (DeLone & McLean, 2003) to investigate specific individual impacts obtained using SNSs. The DeLone and McLean (2003) model focuses mainly on measuring the success of e-commerce and net paybacks achieved from using IS. Thus, the model is considered one of the most well-known frameworks for explaining individual net benefit expectations of IS (DeLone & McLean, 2003). It is becoming popular among researchers adopting it for IS success scenarios such as knowledge management systems (Kulkarni et al., 2006), online learning systems (Lin, 2007), e-commerce (DeLone & McLean, 2004), and e-government systems (Wang & Liao, 2007) by specifying different constructs aiming to make the D&M model more comprehensive and usable in different contexts. This model also acts as the underpinning for investigation in the domain of SNS contexts. However, limited research has applied the D&M IS success model within an individual benefits setting. As a result, the current study primarily builds upon D&M’s IS model with some extensions, suggesting that when the quality is sufficient, users become content and increase their intention to use, which in turn influences how much they believe they have gained (e.g., enjoyment, expected relationship, and reputation). Thus, we contribute a theoretical extension of D&M’s IS success model to examine empirically the effect of computer systems on use and satisfaction, and how continuance usage and user satisfaction can create net benefits represented by relationship formation, information sharing, and social presence.

The overall purpose of the study is to examine the net benefits users can obtain from using SNSs by applying the updated DeLone and McLean updated model (2003) in the SNS context. Particular attention is paid to the following research questions (RQs) in this paper.

RQ1: What elements will have an impact on individual net benefits in the IS success model?

RQ2: What notable individual net benefits do SNS users experience?

The current study makes the following contributions. Firstly, by illuminating the distinct dynamics of users’ relationships with social media, it will offer insights into the various aspects of SNS quality that influence users’ satisfaction and intention to use the platform. Secondly, this study will contribute to a deeper comprehension of the fundamental benefits and advantages that support users’ intentions to utilize SNSs. Thirdly, in order to effectively interact with and support users in the digital age, academics, practitioners, educators, and policymakers will find these insights to be highly beneficial. Fourthly, knowing what increases SNS usage might help marketers adjust their approach to successfully attract and retain digital consumers. Finally, by applying these ideas, platform designers can create user interfaces that customize SNSs to each user’s preferences and needs, increasing their allure and intrigue.

2. THEORETICAL BACKGROUND

With regard to contributing an overall and inclusive meaning of IS success that reconciles information systems assessment from multiple perspectives, DeLone and McLean (2002) revised the current model and its measures and separated them into six leading sorts. Consequently, they provided a framework that can be used from multidimensional perspectives and which has interdependencies among constructs. Reviewing all the modifications and empirical studies, DeLone and McLean (2003) updated their original model accordingly, revealing a rationalized framework of IS success pertaining to these newly added dimensions. The six paradigms of the updated model include information quality, service quality, system quality, user satisfaction, intension to use, and net benefits.

DeLone and McLean (2002) argue that user satisfaction is a critical indicator of computer system success. Satisfaction refers to the comparison between perceived performance and expectations. The attitudes of users toward computer systems within their surroundings are a key component of user satisfaction. According to DeLone and McLean (2003), one of the most important quality factors for determining a system’s success is system or information quality. The following criteria were used to assess system quality: importance, flexibility, accessibility, dependability, ease of use, and integration. It is anticipated that improved system quality will increase user satisfaction and usage, which will boost personal productivity. On the other hand, the accuracy, context, relevancy, timeliness, completeness, and accessibility of the information were the metrics used to gauge information quality. The objectivity of a website or information system is increased by great information quality (Dong et al., 2014).

Furthermore, regardless of whether the assistance is given to an Internet service provider, a new structural unit, or an IS department, service quality is a full support process offered by its provider. According to Grönroos (1984), service quality is defined as the general assessment and opinion of customers regarding the performance of a service provider, that has significant impacts on customer satisfaction in the SNS context. Due to the fact that users are now customers rather than employees, bad user assistance results in lost sales and funds, which is a crucial aspect in an e-commerce setting (DeLone & McLean, 2004). Additionally, service quality in e-commerce gauges the entire assistance provided by the website. The ability for a user to simply add people to a list of friends who are known to him or her, and to communicate with other users, are the most crucial features offered in an SNS setting. According to DeLone and McLean (2002), user satisfaction, which considers how users perceive computers in the context of their surroundings, is essential to the success of computer systems. Recently, user information pleasure has taken the place of model of information systems success and effectiveness (DeLone & McLean, 2003). User satisfaction was identified in this current study as the primary indicator of an information system’s success. In the D&M model, user happiness has been expanded to include any web service that enables online communication, including email or instant messaging.

The current study extends the D&M IS success model by adding particular benefit constructs for a number of causes. First, DeLone & McLean suggested in the conclusion of their study that a number of research works should conduct investigating and incorporation to measure net benefits. Seddon (1997) clearly argued that different stakeholders may have dissimilar views about what brings them benefit. Therefore, researchers must carefully and precisely specify the stakeholders and the setting in which they calculate the net advantages of an IS (DeLone & McLean, 2003). This result was backed up by Yuthas and Young (1998), who emphasized that examining usage and satisfaction metrics is insufficient as a substitute for accurately assessing performance or net benefits. Progress should be made in developing and testing net benefit measures on individual, group, industry, firm, and national levels (DeLone & McLean, 2003). In particular, this present study proposes to expand net benefits on an individual level basis.

First, Mouakket (2015) has pointed out that the use of SNSs should include the construct of enjoyment as an important factor that motivates users to keep using SNSs. According to Li (2011), enjoyment is “the extent to which the activity of using information technology is perceived to be enjoyable.” Lin and Lu (2011) mentioned that perception of enjoyment is a vital factor in using any SNS. In other words, the individual adopts information technology as he/she perceives the chances of getting enjoyment from it. Second, expected relationship refers to the closeness or strength of the relationship that is predicted to increase consumer loyalty (Hennig-Thurau, 2000). Wilson et al. (2012) emphasize that crystallizing ephemeral relationships are advantageous in SNSs in order to retain strong and weak ties binding people. On the other hand, as Hennig-Thurau et al. (2010) report, SNSs have become a tool for users to create and maintain relationships with friends, family, and other people. Furthermore, Dunne et al. (2010) mentioned that maintaining relationships is a key driver of using SNSs.

Finally, an online user’s reputation refers to how their peers in the virtual community perceive them as valuable members (Lin et al., 2017). According to Madden and Smith (2010), 71 percent of SNS users are between the ages of 18 and 29 and have modified their privacy settings to prevent others from viewing their online activity. This data shows that SNS users are concerned about their online reputation. Reputation is not built on sight; rather, users spend extensive time to share their status, information, and images, aiming to build self-reputation. This demonstrates a long-lasting relationship with individual SNS usage behavior; however, reputation has not been examined as an antecedent of continued SNS use behavior. Therefore, this study perceives reputation as an important individual benefit achieved by continuously using SNSs. In particular, this study includes enjoyment, expected relationships, and reputation as antecedents of user satisfaction and use intention in SNSs.

Based on these discussions, the current study offers a comprehensive and multidimensional research model (Fig. 1) for measuring the net individual benefits of using SNSs, where the information, system, and service quality of any SNS is allegedly the key to satisfaction and use intention, ultimately providing benefits including enjoyment, expected relationships, and reputation.

Fig. 1

The proposed framework.

3. HYPOTHESES DEVELOPMENT

4. RESEARCH METHODOLOGY

5. EMPIRICAL RESULTS

5.1. Measurement Model

5.1.1. Convergent Validity

A measurement model output is shown in Fig. 2, showing that the CFA factor loads with their respective constructs surpass the 0.70 threshold value; this represents that the data are highly loaded and enabled to generate higher reliability and validity (Hair et al., 2010). However, a few items of some constructs are removed due to being below the loading of 0.70. To retain consistency, our eight-construct model had five questions for each construct. Information quality, system quality, service quality, use of SNSs, enjoyment, expected relationship, and reputation all had one item removed. Additionally, two items were removed from the satisfaction variables.

 

Fig. 2

Measurement model.

 

Cronbach’s alpha (α), composite reliability (CR), average variance extracted (AVE), and coefficients are all displayed in Table 3 (Browne & Cudeck, 1993; Doll et al., 1994; Hair et al., 2010) for each summed scale. Cronbach’s alpha, which quantifies internal consistency reliability, ranges from 0.86 to 0.93, exceeding the cutoff point of 0.7 and suppressing higher internal consistency (Hair et al., 2010). The CR and AVE values should be in the 0.70 and 0.50 ranges, respectively, or higher (Fornell & Larcker, 1981). The study’s findings reveal that the CR value varies from 0.86 to 0.93 and the AVE varied between 0.61 and 0.77, showing higher measuring instrument validity.

 

Table 3

Reliability and validity statistics

Construct	α	CR	AVE	IQ	SQ	SeQ	USE	SAT	EJ	ER	RP
IQ	0.88	0.88	0.66	0.81a)
SQ	0.86	0.86	0.61	0.36	0.78a)
SeQ	0.92	0.92	0.75	0.60	0.42	0.87a)
USE	0.90	0.91	0.71	0.40	0.69	0.44	0.84a)
SAT	0.89	0.90	0.75	0.69	0.71	0.59	0.77	0.87a)
EJ	0.89	0.90	0.70	0.57	0.65	0.47	0.74	0.82	0.83a)
ER	0.91	0.92	0.73	0.61	0.50	0.54	0.58	0.72	0.71	0.85a)
RP	0.93	0.93	0.77	0.64	0.26	0.47	0.27	0.55	0.50	0.72	0.88a)

Model fit indices		Obtained value			Recommended value			Reference
CMIN/DF		2.546			≤3			Hair et al. (2010)
CFI		0.942			≥0.90			Hair et al. (2010)
AGFI		0.828			≥0.80			Doll et al. (1994)
NFI		0.909			≥0.90			Hair et al. (2010)
GFI		0.859			≥0.80			Doll et al. (1994)
RMSEA		0.061			≤0.08			Browne & Cudeck (1993)

[i]

α, Cronbach’s alpha; CR, composite reliability; AVE, average variance extracted; IQ, information quality; SQ, system quality; SeQ, service quality; USE, use of social networking site; SAT, satisfaction; EJ, enjoyment; ER, expected relationships; RP, reputation; CMIN/DF, the ratio of chi-square to degrees of freedom; CFI, comparative fit index; AGFI, adjusted GFI; NFI, normalized fit index; GFI, goodness-of-fit index; RMSEA, root mean square error of approximation.

[ii]

^a)The values in the diagonal line are average variance extracted.

5.1.2. Discriminant Validity

The results of the analysis demonstrate that the square correlations for each construct are less than the variance that each construct’s indicators have recovered. Additionally, values of the variance inflation factor that are within its cutoff value of 10 show the model’s achieved discriminant validity. Based on the aforementioned validity and reliability, it can be said that the measurement model has reached acceptable convergent and discriminant validity.

5.1.3. Model Fitness Test

To have a suitably great model fit for the measurement model, the overall model fit was evaluated in terms of the goodness-of-fit index (GFI), adjusted GFI (AGFI), comparative fit index (CFI), and the root mean square error of approximation (RMSEA). As demonstrated in Table 3 (Browne & Cudeck, 1993; Doll et al., 1994; Hair et al., 2010)’s lower panel, all of the model fit indices fall within their respective recommended ranges, indicating that the suggested model fits the sample data well.

5.2. Structural Model

In the structural model, the relationships between latent variables are taken into consideration to test the hypotheses, where the values of the causal paths are shown in Fig. 3. The structural model also produced good model fit indices (e.g., the ratio of chi-square to degrees of freedom=3.31, GFI=0.824, AGFI=0.793, CFI=0.911, incremental fit index=0.911, and RMSEA=0.074). Variance explained by use, satisfaction, enjoyment, expected relationship, and reputation were at 71%, 66%, 69%, 60%, and 52% respectively, representing that the model has good explanatory power.

 

Fig. 3

Structural model. Straight line, significant paths; dash line, non-significant path.

 

The path analysis showed that the information quality has a significant positive effect on satisfaction (β=0.537, p<0.001) and negative effect on use intention (β=-0.319, p<0.001), thus accepting H1a and rejecting H1b. System quality has a significant impact on satisfaction (β=0.564, p<0.001) and use (β=0.189, p<0.001), supporting H2a and H2b. Despite having a significant impact on satisfaction (β=0.240, p<0.001), service quality had a negligible effect on use intention (β=-0.046, p>0.001), hence showing accepting H3a and rejecting H3b.

Satisfaction was found to have a substantial negative influence on enjoyment (β=-0.265, p<0.001), but a positive influence on expected relationships (β=0.65, p<0.001), reputation (β=0.36, p<0.001), and use intention (β=0.83, p<0.001), thereby rejecting H4a and supporting H4b, H4c, and H4d. Furthermore, use intention was found to have a noteworthy positive influence on enjoyment (β=0.143, p<0.001) and expected relationship (β=0.492, p<0.001), but negative on reputation (β=-0.72, p<0.001), providing support for H5a, H5b, and rejecting H5c.

6. DISCUSSION

Examining SNS success determinants and understanding the user benefits which can be gained through continuous use intension and satisfaction were this study’s primary goals. The study findings are consistent with the notion that user satisfaction and use intention are mostly determined by the three system success dimensions of SNSs, namely, information quality, system quality, and service quality. Again, satisfaction and use intention were found to have a significant impact on user benefits. Overall, our study demonstrated flawless prediction capabilities with a higher variance explained in use, satisfaction, enjoyment, expected relationship, and reputation (71%, 66%, 69%, 60%, and 52%, respectively).

The study discovered the considerable impact of information quality on satisfaction and a negligible detrimental effect on use intention, which reveal the fact that user psychological acceptance and a favorable attitude, such as satisfaction, is largely generated by the information shared in SNSs. These findings are in line with the findings of Dong et al. (2014) and Zheng et al. (2013), who found that the virtual world values information quality when it comes to sharing knowledge. Thus, it is widely accepted that valid, reliable, need-based, and problem-solving information are the building blocks for achieving SNS users’ satisfaction. However, the present study finds a negative relationship between information quality and use intention. Our data indicate negligible influences, contrary to Wang et al. (2014) who report a beneficial impact of information quality on use intention. The possible reasons could be explained as aggressive information sharing in different SNSs nowadays, such as self-promotional news by various parties, repetitive promotions and commercials for numerous unpopular products and services, and huge numbers of unimportant viral messages hampering user comfortability, thus creating users’ disinterest.

This study interestingly finds a significant positive effect of system quality on satisfaction and use intention, consistent with the findings of Wang et al. (2014) and Dong et al. (2014). They regarded system quality as a requirement for the SNS platform, suggesting the fact that users seeking online platform engagement have strongly focused on usability, functionality, ease of use, and many others technical features of the system. Reliable, trusted, and secured systems in the virtual world are the top priority of users. Again, service quality appeared to be a substantial predictor of satisfaction, which is consistent with Mouakket (2015). Nonetheless, it was discovered that the impact of service quality on usage intention was negligible, which does not corroborate Dong et al. (2014). The findings imply that service quality, associated with the actual outcome received by users from SNSs and the conditions under which the service is rendered, are the significant predictors of satisfaction. Another possible explanation could be that the way users interact with service providers is the prime dimension and constitutes a crucial factor in the development of user attitude. However, excessively misleading information, uninteresting viral news, fear of loss of privacy, and lack of direct interaction with service providers may be the causes of the negligible impact of service quality on usage intention.

The satisfaction of SNS users is a powerful factor in establishing expected relationships, reputation, and use intention. These results are similar to the findings of Lin et al. (2017), DeLone and McLean (2003), and Zheng et al. (2013). According to their arguments, when it comes to information sharing on SNSs, individuals are primarily concerned with their own happiness and thereby achieve individual benefits. Individuals perceive that joining an SNS will enable them to connect with new users, make friendships with them, strengthen the relationship, build their social status, and cultivate a positive self-image, which ultimately results in continued use of the SNS (Jahan & Kim, 2021). These findings reveal that as much as users are satisfied with a particular SNS, they perceive that they have benefited personally, socially, and that they will continue to use the SNS as they can.

However, satisfaction does not show a positive impact on enjoyment benefit, because users may find enjoyment from different sources rather than an SNS, or as the SNS industry matures, users may not think it is as enjoyable as perceived. On the other hand, use intention works as a powerhouse of enjoyment and expected relationship benefits, but not reputation. In line with our result, Lin and Lu (2011) noted that a major motivator for using SNSs is enjoyment. However, Lin et al. (2017) contended that reputation is a key component of SNS use, which runs counter to our findings. A user may exhibit greater enjoyment, feel engaged, expect strong relationships with other users, and be open to forming mutually beneficial relationships on SNSs, and as a result spend much more time on that SNS. Although users may spend much more time on an SNS, they do not feel credible merely because of their enrolment in an SNS, but rather for doing something contributory to other users or to the community.

7. CONCLUSION

Using the updated DeLone and McLean updated model (2003) in the context of SNSs, the goal of this study was to investigate the net benefits that users can receive from using SNSs. The current study sheds light on the individual benefits highlighting the enjoyment, expected relationships, and reputation derived from the satisfaction and use intention of SNS users. This study explores the causes and effects of user satisfaction and usage intention using the D&M IS model as a theoretical foundation. In this study, the information, system, and service quality of SNSs are primarily considered as the antecedents of user satisfaction and use intention, with system quality having the greatest impact on both. Thus, successfully addressing (RQ1), what elements will have an impact on individual net benefits in the IS success model? Our research has successfully demonstrated that the main pillars of information quality, system quality, and service quality are what enable users to use SNSs in the IS success model and thus realize personal net benefits and increase user happiness. In addition, it has been demonstrated that satisfaction and usage intention are the antecedents of enjoyment, expected relationships, and reputation. The expected relationship is the advantage that respondents reported having the most concern about, according to the survey. It answered the questions being considered regarding RQ2, what notable personal advantages do SNS users experience? Overall, our research indicates that people use SNSs mostly for fun, to fulfill expectations with other users, and to establish a reputation for themselves ahead of others.

Thus, it is important to draw the conclusion that people typically use SNSs for personal enjoyment, forming connections with others, and attempting to enhance their online identities. Accordingly, understanding the advantages of SNSs is essential for organizations looking to use these platforms for marketing, customer interaction, and general organizational performance, given that consumers continue to place a high value on SNSs.

Despite the significant contributions and implications, there are certain limitations to the current study that may provide guidance for future studies. First, it was a survey-based study, which inherently produces a few drawbacks related to actual responses and intended responses. As this study collected data using a self-administrated and structured questionnaire, respondents may have faced hesitation to provide their responses, and they may have provided responses that may not be what they intended. Second, although this study produces unique evidence of SNS quality and individual benefits using the D&M IS model, other important theories may not have been considered, such as social capital, social exchange, or other factors. Third, demographic characteristics were not considered in the discussion that may indicate a lack of knowledge in this study.

Therefore, future researchers can consider the limitations of this study and validate this model in a more integrated manner. Future researchers may collect data from SNS users with an interconnected process to avoid the limitations mentioned above. Second, future trials can assess individual benefits from a social capital or social exchange perspective, which may generate interesting insights into SNS literature. Third, the daily use of SNSs, how long users have been connected, how often users log into SNSs, etc. can be considered as an actual use intention in future studies.