alexa
Reach Us +44-1522-440391
Who Do You Choose Your Social Media for? An Analysis of How Users Gratification and Social Norms Influences Social Media Choices
E-ISSN: 2223-5833

Arabian Journal of Business and Management Review
Open Access

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
  • Analysis   
  • Arab J Bus Manage Rev 2017, Vol 7(4): 321

Who Do You Choose Your Social Media for? An Analysis of How Users? Gratification and Social Norms Influences Social Media Choices

Sadhwin Sharma*
College of Business, California State University, Monterey Bay, 5108 Fourth Avenue, Marina, Canada
*Corresponding Author: Sadhwin Sharma, College of Business, California State University, Monterey Bay, 5108 Fourth Avenue, Marina, Canada, Tel: 831-582-3000, Email: [email protected]

Received Date: Aug 28, 2017 / Accepted Date: Oct 16, 2017 / Published Date: Oct 28, 2017

Abstract

People continue to use online social networks to satisfy different personal and social needs and disregard negative features like the risk of privacy and security. Thus, the purpose of this study is to understand the continuation of OSN usage from the aspect of gratification and flow, interactivity with the OSNs and the influence of social norms. The following constructs, from the theories mentioned above, will be used to measure their effects on satisfaction and flow: human-human interaction, human message interaction, human-community interaction, human-systems interaction, identification, compliance, and internalization. Satisfaction and flow will be measured to determine if there is a positive effect on the continued use of OSNs.

Keywords: Satisfaction; Internalization; Compliance; Humansystems interaction; Human-human interaction; Human message interaction; Human-community interaction

Introduction

The popularity and use of online social network platforms like Facebook, Instagram, and Twitter continue to increase. Individuals have the ability to connect, share, and build relationships with other users through these platforms. The use of online social network sites has become a daily norm for the millions of users. Aside from the technical features that attract individuals to use these platforms, there are social and communal aspects users seek from the social media platforms [1]. Online Social Networks (OSNs) allow users to customize their image, share their status, seek information, and be a part of a community. Individuals use these online social network platforms to satisfy those needs. While the exponential growth of OSNs has given people a new medium of communication, it has its share of positive and negative influences [2]. There is no denying the fact that the popularity of OSNs has introduced substantial new challenges. Earlier studies on an OSN primarily focused on investigating the privacy and security concerns of its users. The personal information shared on an OSN such as personal pictures, phone numbers, addresses, status update, personal opinion, and political ideologies can easily reach to advertising agents, employers, law enforcement agents, creditors and tax authorities and thus, violates an individual’s privacy. The consequences of privacy and security issues can be felt on personal and family lives and users’ reputation and can result in identity theft and psychological pain such as embarrassment and addiction [2]. With the increasing use of OSNs, users have also experienced negative impacts on their wellbeing. More and more people are falling into depression, loneliness, dissatisfaction and poor self-esteem because of the frequent use of OSNs.

The increased use of OSNs has led scholars to study why individuals continue to use the online social network platforms. The continuance of OSNs can be explained by the satisfaction and gratification one receives and the flow one has towards the OSNs being used. An important part of OSNs is interaction among individuals, the interaction of individuals with the types of the messages and contents, and the interaction of individuals with the type of system and technology. The level of engagement and interactions one has towards the OSNs impact their decision to continue using OSNs. For example, studies have shown that motivations and perceived interactivity have a positive effect on satisfaction which leads to continued use of OSNs [3]. Similarly, there have been limited studies that describe online social network continuance using the Social Influence Processes Theory. For example, a study explains how subjective norms, group norms, and social identity lead to the use of OSNs [4]. People continue to use online social networks to satisfy different personal and social needs and disregard negative features like the risk of privacy and security.

The purpose of this study is to measure OSN continuance from the aspect of gratification and flow, interactivity with the OSNs and the influence of social norms. The following constructs, from the theories mentioned above, will be used to measure their effects on satisfaction and flow: human-human interaction, human message interaction, humancommunity interaction, human-systems interaction, identification, compliance, and internalization. Satisfaction and flow will be measured to determine if there is a positive effect on the continued use of OSNs.

Literature Review and Hypothesis Development

The exponential growth of online social networks has motivated a burgeoning stream of research in this field. Research studies on OSN are as old as the OSN itself. The initial research on the online social network was informative in nature as the researchers were figuring out the impact of it in general people. Ellison et al. [5] studied the relationship between the use of and OSN and the development and continuation of social capital. Valkenburg et al. [6] studied the relationship between OSN platforms and the effects on the users' selfesteem and well-being. Research on OSN began by examining and understanding the primary use of it and the impact and effect it had on people and their social relationships.

Similarly, there has been increasing research on post-adoptive behaviors of online social network users specifically focusing on the continuation of online social network usage [7]. IS literature has shown that the antecedents for the adoption of online social network and continuance of online social network are different. Wu et al. [7] used the UTAUT model for investigating the antecedents of online social network continuance. The research found that all the key constructs of UTAUT, namely, social influence, performance expectancy, effort expectancy and facilitating conditions are direct determinants of users’ continued use of online social networks. Kim [8] used the expectationconfirmation model for understanding the continuous use of online social network and found that perceived usefulness, enjoyment, satisfaction, influence and confirmation affects the intention to continue using the online social network. Similarly, there have been studies that focus on the flow, or habit that influences the continuance intention use of OSN [9,10].

However, several of these research studies are focused on only one facet of adoption of social media. Some studies have been limited in analyzing why a user chooses a social media and how his surrounding and social norms affect his choices [7]. Other studies are focused on how self-gratification impacts ones’ choice of OSNs [3,11]. This study bridges the gap by measuring continuation of OSN from the aspects of gratification and flow and how social norms and interactivity feature of OSNs impact their intention to continue using the social network.

Flow theory

Among the several antecedents of using social media, satisfaction and flow can be considered as two of the relatively important factors [11]. As proposed by Csikszentmihalyi [12], flow theory defines flow as the holistic experience that people feel when they act with total engagement and involvement into an activity. In an online social network setting, flow implies how absorbed a user is to a social media. Satisfaction has been defined in terms of a receiver's affective reactions and is related to intent to use the internet, social media, and other devices [9]. As proposed by DeLone and Ephraim [13] in IS success model and further tested and expanded by other researchers in Users and Gratification theory [11], satisfaction has a positive relationship with continuance intention. While satisfaction is an affective belief, flow reflects a cognitive state of being engaged. When people are satisfied and are in the state of holistic experience where they are so engaged and involved that nothing else matters, they show more intention to continue using social media [11,14] . Users who are happy and comfortable with the social media and who are consumed by it, seem to continue using OSNs [9] (Figure 1).

business-management-conceptual-model

Figure 1: Conceptual model.

H1: The degree of flow positively influences the continuation of OSN usage.

H2: The degree of satisfaction positively influences the continuation of OSN usage.

Interactivity theory

Perceived interactivity can be defined as “the extent to which users perceives their experiences as a simulation of interpersonal interaction and sense they are in the presence of a social other” [15]. While perceived interactivity can have several dimensions [16], it usually implies a human-interactivity that occurs between humans through a medium such as OSNs and message interactivity which implies sharing and finding right kind of messages and content [17]. Comprehensively, for this study, we would add another dimension to the interactivity theory named as human-community interaction.

Human-human interaction implies the reciprocal communication between senders and receivers through the functions of the online social network. Social media provides users the ease of communicating and interacting with other users through social networks. OSNs can use a medium such as Facebook, Twitter, Snapchat and Instagram for interpersonal communication that allows exchanging comments, creating friends lists, and engage in other type’s interactions. Higher the human-human interaction, the higher the satisfaction a user receives from the OSNs and the higher his/her engagement and absorption to the OSNs [11,18].

Human-message interaction implies the ability of users to interact with messages such as browsing and sharing messages and contents through an online social network. It also implies the ability to effectively share, search, organize, and find information at will. Interactions in the form of ease of browsing and effectively finding information through social networks help to engage users and to increase users’ satisfaction [11]. Users use social media as searching tools (finding old friends), reading news, reading new trends, and receiving product/service feedback. With social media acting as “go to” platform for messages, emails, news, content sharing, product/service feedback, audio-video chats and several other messaging functions, users find satisfaction and engagement on the OSNs that fulfil what they are looking for. Previous research studies have found a positive relationship between humanmessage and satisfaction and flow [3,10,18].

The human-community interaction implies the use of OSNs to conduct social interaction with the community and create a sense of belongingness. Users of OSNs are interested in interacting, building or joining communities with common interests. Several users choose OSNs depending on what kind of community they can create or join in those OSNs. The users using Snapchat, for example, are focused on building a community where they can share “unfiltered” videos or pictures that disappear with one viewing by other party/parties. The human-community interaction has been found by previous research studies to have a positive relationship with the satisfaction and flow of users in using OSNs [10,11,18].

H3a and H3b: The degree of human to human interaction positively influences the degree of flow and the degree of satisfaction.

H4a and H4b: The degree of human to message interaction positively influences the degree of flow and the degree of satisfaction.

H5a and H5b: The degree of human to communication interaction positively influences the degree of flow and the degree of satisfaction.

Social influence processes theory

The Social Influence Processes Theory focuses on the influence others have on individuals’ habits and behaviors. Social influence theory has been used in previous research to describe the use of OSNs. In fact, The Social Influence Processes Theory focuses on the influence others have on individuals’ habits and behaviors. Social influence theory has been used in previous research to describe the use of OSNs. In fact, Cheung and Lee (2009) studied how subjective norms, group norms, and social identity lead to the use of OSNs. In this study, the factors used from the social influence theory are identification, compliance, and internalization. While UGT is one important factor that implies users look for their own gratification while adopting and continuing OSN, their choices, however, are affected by the fact that what their peers think about them. Thus, the social influence plays an important part while adopting and continuing OSN [19]. The technology acceptance model (TAM) by Davis [20] highlighted the positive impact of social influence on adoption and acceptance of a technology. Their study of social influence was based on the social influence theory proposed by Kelman [21] that identified three social influence processes: compliance, identification, and internalization, which are often represented by subjective norm, social identity and group norm, respectively [22,23]. However, IS research have focused mainly on the effect of compliance (or subjective norm). In this study, we are following some of the lead by research studies such Dholakia et al. [22] and Shen et al. [23] to use all the three components of social influence. Compliance, also known as subjective norm, is the act of agreeing with others in the same group based on explicit or implicit invitation [21]. Venkatesh et al. [24] expanding on TAM, discovered that social influence derived from subjective norm is an important determinant of intention to adopt technology. Identification, also known as social identity, is defined as the process of being influenced by others in the same social group [21]. Previous research studies have found that the users’ internalization process has positive effects on their intentions to use social networking sites [2]. Internalization, also known as group norm, implies the process of accepting a belief or behavior of other users as it is consistent with one’s value system [21]. All the three components of social influence, compliance, identification, and internalization have been found to impact intention to continue using technology [22,23,25]. Previous research studies have also found the impact of social influence factors on continuation of online social networks [26,27]. H6: The degree of compliance positively influences the continuation of OSN usage. H7: The degree of internalization positively influences the continuation of OSN usage. H8: The degree of identification positively influences the continuation of OSN usage.

Methods

To understand how gratifications, social influence, and interactivity affect continuation of OSNs, we have developed an instrument and pilot tested it. In order to test the conceptual research model and answer the research questions, the study requires the use of a rigorous empirical method that ensures valid and reliable results. This section provides a detailed description of the measurement scales used for the pilot test and analyzes the data.

Measures and instrumentation

A focus group survey was conducted to see how much of our research model created through the literature review explained the views and adoption behavior of the users of the online social network. After revising the research model based on the feedback of the focus group, an online questionnaire survey was developed to collect the pilot data and perform an empirical test of the relationship as suggested by the research model presented in the paper. An online survey targeted at OSN users such as the one in this study would lead to a higher generalizability but a medium realism and lower precision. As a part of our complete research, we are planning in combining quantitative and qualitative research methods that may help to complement, compensate, extend and develop our finding. Respondents were first asked about the OSN they used the most. Based on their choice of the OSN, they were asked questions personalized to that website. The items presented to all the users were the same except the changes in few wordings based on the types of OSN they choose as their primary OSN. The respondents’ OSN usage information, demographic information, computer experiences, experiences with privacy invasions at past, etc. were also collected during the survey. The following latent constructs were measured with multi-items: human-human interaction, humanmessage interaction, human-community interaction, flow, satisfaction, compliance, internalization, identification, and continuation of OSNs usage. We started our survey item development with a comprehensive literature review to determine the existing measurement scales that have proven reliable and valid. Following Churchill’s [28] guideline for developing better measures and items, we started the items adoption, modification, and development by defining these constructs. Scales for human-human interaction, human-message interaction, and humancommunity interaction were adapted from Hsu et al. [10]. Scales for flow were also adopted from Hsu et al. [10]. Scales for satisfaction were adapted from Hsu et al. [10], Chang et al. [3], and Yen [29]. Scales for continuation of OSN usage were adapted from Han et al. [30]. Scales for compliance, internalization, and identification were adapted from Christy [4]. Each of the items above was measured using a five-point Likert scale ranging from “strongly disagree” to “strongly agree”.

Common method bias

Collecting data using single method and at single time period may lead to common method bias. This is especially true given the nature of the self-reporting online survey used for this research. Following the ex-ante remedies [31], we randomized the items within the instrument to mitigate order effect. The survey provided anonymity to the respondent which also helped to reduce the social desirability bias. A post hoc analysis was performed to see if there were respondent who completed the survey in unreasonable time or failed the attention trap questions.

We will be using ex-post remedies on the final data collection. We will be using both Harman’s single factor test as well as marker variable test [32] to examine if our data consist of common method biases.

Participants for pilot testing

After conducting the expert panel review and focus group studies, we conducted our pilot test. This pilot test would help us understand the additional required changes that we may have to perform before collecting the final data. To collect data for our pilot study, several attention trap questions were asked to respondents at different stages of the online survey. Incorrect answers to any of the attention trap questions were deleted. We send our online survey hosted in Qualtrics to approximately 80 students of three different classes of a public university located in the United States. Out of those, 60 respondents completed the survey. Majority of the respondents were in between the age of 18-24 and meet the demographic of people who uses OSNs. We will continue using similar respondent demographics for our final data collection as well.

Data Analysis of Pilot Study

This portion of the study explains the data analysis techniques used, including descriptions of instrument validity assessment, construct validity tests, and analysis of the conceptual model for pilot study data. A similar data analysis will be conducted once the full data is collected. Results are further illustrated in model and tabular presentations.

Instrument validity

Reliability for the constructs was measured using composite reliability score and Cronbach's alpha. The Cronbach’s alpha and composite reliability examine the internal consistency among the data. Where Cronbach’s alpha and the composite reliability are above 0.7, it shows the reliability of the instrument. Convergent validity is confirmed when the items load much higher on their hypothesized construct than any other constructs. One item from compliance variable and one item from flow variable have loadings lower than 0.70 and may need to be revised before full data collection. All other items that load together showed loading of 0.7 and higher. Also, average variance extracted (AVE) for all the constructs exceeded the threshold of 0.5 for all the constructs used in the study. Discriminant validity was established when the square root of AVE for each construct were greater than the inter-construct correlation corresponding off-diagonal correlations of the construct to their latent variables. Although some cross-loading was evident between constructs, each construct’s AVE exceeded .5 and was greater than any variance shared with other constructs. Shared variance and AVEs for each construct are depicted in Tables 1-3.

  CL COMP FL HC HH HM IDEN INT SF Composite reliability
CL1 0.976 - - - - - - - - 0.96
CL2 0.923 - - - -        
CL3 0.95 - - - - - - - -
CL4 0.875 - - - - - - - -
COMP1 - 0.524 - - - - - - - 0.84
COMP3 - 0.866 - - - - - - -
COMP4 - 0.95 - - - - - - -
FL1 - - 0.889 - - - - - - 0.76
FL3 - - 0.5856 - - - - - -
FL4 - - 0.8042 - - - - - -
HC1 - - - 0.9052 - - - - - 0.9
HC2 - - - 0.7592 - - - - -
HC3 - - - 0.8257 - - - - -
HC4 - - - 0.8283 - - - - -
HH1 - - - - 0.9274 - - - - 0.95
HH2 - - - - 0.9008 - - - -
HH3 - - - - 0.942 - - - -
HH4 - - - - 0.8765 - - - -
HM1 - - - - - 0.774 - - - 0.86
HM3 - - - - - 0.88 - - -
HM4 - - - - - 0.78 - - -
IDEN1 - - - - - - 0.8921 - - 0.95
IDEN2 - - - - - - 0.861 - -
IDEN3 - - - - - - 0.8727 - -
IDEN4 - - - - - - 0.9172 - -
IDEN5 - - - - - - 0.8718 - -
INT1 - - - - - - - 0.799 - 0.92
INT4 - - - - - - - 0.822 -
INT5 - - - - - - - 0.898 -
INT6 - - - - - - - 0.914 -
SF1 - - - - - - - - 0.866 0.94
SF2 - - - - - - - - 0.883
SF3 - - - - - - - - 0.909
SF4 - - - - - - - - 0.906

CL: Continuation of OSN usage; FL: Flow; SF: Satisfaction; IDEN: Identification; INT: Internalization; Comp: Compliance; HH: Human-human interaction; HM: Humanmessage interaction; HC: Human-community interaction.

Table 1: Loadings and cross-loadings.

  COMP CL FL HC HH HM IDEN INT SF AVE
COMP 0.8 0 0 0 0 0 0 0 0 0.64
CL 0.55 0.93 0 0 0 0 0 0 0 0.87
FL 0.33 0.27 0.73 0 0 0 0 0 0 0.53
HC 0.41 0.63 0.25 0.83 0 0 0 0 0 0.69
HH 0.46 0.74 0.49 0.5 0.91 0 0 0 0 0.83
HM 0.51 0.58 0.34 0.28 0.43 0.82 0 0 0 0.67
IDEN 0.75 0.52 0.44 0.74 0.48 0.47 0.89 0 0 0.79
INT 0.72 0.68 0.4 0.76 0.57 0.51 0.83 0.86 0 0.74
SF 0.49 0.69 0.3 0.66 0.51 0.48 0.56 0.8 0.89 0.79

CL: Continuation of OSN usage; FL: Flow; SF: Satisfaction; IDEN: Identification; INT: Internalization; Comp: Compliance; HH: Human-human interaction; HM: Humanmessage interaction; HC: Human-community interaction.

Table 2: AVE and shared variance of latent constructs.

Hypothesis (with Direction) Path coefficient T-statistics P-value Supported?
H1: FL → CI 0.015 0.002 p>0.05 Not Supported
H2: SF → CI 0.431 2.742 p<0.05 Supported
H3a: HH → FL 0.403 2.097 p>0.05 Supported
H3b: HH → SF 0.159 1.02 p<0.05 Not Supported
H4a: HM → FL 0.177 0.961 p<0.05 Not Supported
H4b: HM → SF 0.275 4.33 p>0.01 Supported
H5a: HC → FL 0.022 0.049 p<0.05 Not Supported
H5b: HC → SF 0.509 5.74 p>0.01 Supported
H6: COMP →  CI 0.205 1.524 p<0.05 Not Supported
H7: INT → CI 0.402 1.87 p<0.05 Not Supported
H8: IDEN → CI -0.208 0.979 p<0.05 Not Supported

Table 3: Hypothesis support.

PLS analysis

Just to see how the pilot data looks like, we tested our structural model using the pilot data. We used SmartPLS for this purpose. We used a bootstrapping resampling technique within SmartPLS to test our hypothesis. H2, H3a, H4b, and H5b were supported, while the remaining hypotheses were not found to be significant. As this is just a pilot data with 60 respondents, we have decided not to take away a lot of information from this testing. Thus, we would defer explaining our results and explanation for this study here. Further data need to be collected after dropping and revising the few items that cross-loaded. Further data analysis of the structure model testing is required after it.

Discussion

Overall findings

As we only analyzed the pilot data and found only four of the hypothesis is supported, we will defer explaining the overall findings till we revise our items and instrument and then collect or final data. Explaining the findings with pilot data would make very limited sense.

Implications

Despite the recent research focus on some relevant behavioral topics related to social network, this study adds value to the existing research by analyzing users’ intention to engage in social media based on their gratification towards the social media and the impact of social influence on them. On a theoretical level, there has been limited research that has looked at adoption of social media from flow, gratifications, interactivity, and social influence angle. This research study expands on those theories and present an empirically tested model that will help scholars further understand why some people continue using OSNs despite several dark side of the OSNs being discussed in the media all the time.

Conclusion

Despite OSNs have several negatives to offer, users seems to continue using OSNs. This research studies the factors that may be behind the reasons for these users to keep continuing using OSNs. This study focuses mainly on interactivity the social media provides and the influence of people around on the usage of OSNs.

References

Citation: Sharma S (2017) Who Do You Choose Your Social Media for? An Analysis of How Users’ Gratification and Social Norms Influences Social Media Choices. Arabian J Bus Manag Review 7: 321.

Copyright: © 2017 Sharma S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Select your language of interest to view the total content in your interested language

Post Your Comment Citation
Share This Article
Relevant Topics
Article Usage
  • Total views: 3271
  • [From(publication date): 0-2017 - Dec 06, 2019]
  • Breakdown by view type
  • HTML page views: 3163
  • PDF downloads: 108
Top