Example of a Quantitative Research Paper for Communication Students

Quantitative Research Paper Example For Communication Students. Example of a Quantitative Research Paper for Students. Quantitative research and communication examples. How to write a quantitative research paper. Quantitative research paper about communication.

Quantitative Research Paper Elements

The elements of  Quantitative Research Papers for Communication Students are:

1. Research Topic
2. Introductions
3. Literature Review
4. Hypothesis Development
5. Conceptual Model
6. Methodology
7. Results and Discussions

8. Conclusion

9. References

 

Quantitative Research Paper Example For Communication Students

 

Research Topic: An Examination of the Relationship between Social Media Engagement and Citizen Journalism Practice

Social media has been an indispensable communication channel for sharing and consuming news. The evolution of social media platforms has boosted digital journalism practice globally. This study intends to examine factors influencing social media engagement to practice citizen journalism by adopting a prominent technology adoption model-the unified theory of acceptance and use of technology (UTAUT).

The researchers designed online survey questionnaires and distributed them among university students through Google Forms to collect data, where 301 valid samples were collected. The structural equation modeling (SEM- PLS) approach was utilized to ascertain the presented model based on the empirical data found from respondents. The findings show that performance expectancy, effort expectancy, and social influence significantly affect social media users’ practice of citizen journalism. The results clearly indicate that social media engagement significantly influenced citizen journalism practice. People practice citizen journalism on social media sites to report real-time news, entertain friends, educate people, and shape public opinion.

Keywords: Social Media, Engagement, Citizen Journalism, UTAUT, Online news.

Introduction

Social media platforms change people’s news sharing and consuming behaviors. A study ascertained that around 45 per cent of Americans consume news from Facebook (Chen 2020). Social media enables people to share and consume news easily and instantly. Citizen journalists accumulate and disseminate news on social media sites, including Facebook, WeChat, WhatsApp, and Instagram, to inform netizens. People practice citizen journalism to share opinions, transfer mainstream media content, shape public opinion, report crime events and entertain each other. Sometimes, citizen journalists generate creative news stories assembled with text, pictures, and videos to raise social awareness in society.

In Malaysia, digital journalism has become a powerful source to shape public opinion through sharing informative news events on social media sites during the COVID-19 pandemic (Raza et al. 2021: 144). The Malaysian government controls the mass media outlets and their practitioners, directly and indirectly, during broadcasting news and information (Jalli 2017). The intention of sharing news on social media is growing daily. It is reckoned that more than 80 per cent of youth in Malaysia use social media (Ismail et al. 2019: 508). However, citizen journalism roles like a double-edged sword that has both positive and adverse consequences in society (Barry 2017). Because of social media availability, false content and disinformation spread faster than authentic information (Daud et al. 2020). In Malaysia, fake news spreading via social networking sites has increased significantly during the pandemic period (Raj 2020).

Findings from past studies show that people use specific social media sites such as WeChat, Facebook, and WhatsApp to share diverse news content (Kümpel, Karnowski and Keyling 2015; Peng and Miller 2021: 1). Previous research focuses on the motivation to adopt social media to practice citizen journalism; for example, people practice citizen journalism to shape public opinion (Jalli 2017), to spread fake news during the COVID-19 pandemic (Raza et. 2021: 145), and to promote cultural integration (Mahamed and Omar 2017: 675).

The present study attempts to identify the factors that influence citizens to adopt social media platforms for citizen journalism practice. It is designed to establish the connection between social media engagement and citizen journalism practice. The unified theory of acceptance and use of technology (UTAUT) was applied to determine the factors affecting social media engagement in the practice of citizen journalism. The current study proposes a model that intends to contribute a theoretical perception of social media engagement to the practice of citizen journalism.

Literature Review

Social Media Engagement

Social media are widespread platforms for publishing news content and entertainment. One study indicated that 35% of people aged 18-29 refer to online as a primary source of news acquisition (Shearer 2018). According to Albaalharith et al. (2021), half the 7.7 billion people worldwide use diverse social media platforms. Social media refer to internet-based communication networking platforms that facilitate communication through computer and mobile applications (Aichner et al. 2021). It is reckoned that about 4.0 billion citizens utilize social media sites and Facebook managed to rank top among them followed by YouTube, WeChat, LinkedIn, etc. (Kobiruzzaman 2021). According to Kobiruzzaman (2021), approximately 2.74 billion monthly users access Facebook globally.

Kalsnes and Larsson (2018) stated that Facebook merges as the most effective site for news sharing, following Twitter. It allows users to become opinion leaders and gatekeepers. A study shows that social media encourage users to act as the source of news (Oeldorf-Hirsch and Sundar 2015). People adopt social networking sites for sharing, liking, and commenting on news content for convenience. Nowadays, users adopt social networking platforms to share real-time news events to enhance involvement (Kobiruzzaman et al. 2018).

Anyone can share a live news event on Facebook through live-video features. Alexander (2014) claimed that 80% of Americans and 70% of social media users acknowledge that social networking sites are useful tools to disseminate news content during disaster management crises. According to a study around 26.69 million people use internet services in Malaysia, almost 85% of the whole population (Kamaruddin and Rogers 2020). The role of citizen journalists has increased with the rise of using smartphones (Allan 2017).

Malaysian people adopt social media to consume news and information for their immediate and convenient services (Mahamed et al. 2020). Another study shows that social media have become indispensable for sharing and consuming news (Kümpel et al. 2015). These social networking sites have ease-of-use features for sharing content. Individuals and media organizations utilize convenient features for posting news content.

Citizen Journalism Practice (CJP)

Citizen journalists utilize websites and mobile applications of social media to disseminate news events quickly. It is a process of gathering and reporting hard and soft news content. Citizen journalists also collect news and analyze them to report as news on digital platforms to inform others (Allan 2017; Lacy, Duffy, Riffe, Thorson and Fleming 2010).  People prefer social media over websites to practice digital journalism for its convenient features for sharing content. It offers a stress-free interaction and sharing news content opportunity for citizens. Citizen journalists contribute to filling the lack of news broadcasted by the mass media industry.

Malaysian political parties control the mainstream media firmly; therefore, journalists cannot play the role independently (Balaraman et al. 2015). Hence, many people are prone to use social media to report crime and real-time news. Citizen journalism in Malaysia has become a vital tool for political parties to spread promotional news events on social media to attract supporters and potential voters (Chinnasamy and Roslan 2015). In 2013, the 13th General Election in Malaysia notified the importance of online media to disseminate reliable information, whereas mainstream media are biased in broadcasting political news (Kee and Nie 2017).

Citizen journalism activities promote cultural integration among Malaysians from various cultural backgrounds (Mahamed & Omar 2017). It promotes harmony and peace in Malaysia through sharing information via social media platforms. Many students become citizen journalists to share information regarding covid-19 affected people and the coronavirus death toll on social media. The convenient tools of social media influence students to become digital journalists. Besides the advantages, social media-based citizen journalism significantly spreads fake and fabricated news. Widespread misinformation was disseminated through Facebook, and Twitter, during the 2016 US election.

The expansion of technology and social media sites has changed people’s news-consuming behavior. It is crucial to study people’s needs to comprehend their behaviors. Social and psychological needs influence people to set their behavior regarding social media use. People can adopt social media to report news content for numerous purposes. The news content can be short or long based on the topics. Citizens modify traditional media content and share them again to spread the news in their community. Sometimes, traditional media create news based on citizen journalist reports. In this study, we considered all types of news created by citizens and shared on social media to meet personal, social, political, organisational, and informational goals.

We diagnosed social media-based citizen journalism from a uses and gratifications viewpoint. Michailina, Andreas, and Christos (2015) identified that people utilize social networking sites for four needs; information, discussion, entertainment, and surveillance.

Based on the literature review, the UTAUT model has been demonstrated as a valid and robust theory in understanding the factors influencing people to utilize social media platforms to practice citizen journalism. Hence, the relationship between social media engagement and citizen journalism practice can be examined using the UTAUT model.

Unified Theory of Acceptance and Use of Technology Model

Social media and communication technologies are interconnected intensely for their practical function in society (Ou, Sia and Hui 2013). People adopt social media to meet individual, social, political and commercial needs. Previous research validates the unified technology acceptance and use of technology theory as a comprehensive theoretical model to predict the adoption intention of social media platforms. Peng and Miller (2021) indicate that UTAUT is a perfect model to explain people’s news use behavior on WeChat, a famous social media site in China.

Consequently, the UTAUT model certainly provides an in-depth understanding of factors that predict citizen journalism practice on social media. Venkatesh, Davis, Morris and Fred D. Davis established the UTAUT model in 2003 based on the eight well-known technology adoption models (Venkatesh et al. 2003). Based on the literature study on user acceptance, the technology adoption models are broadly popular in identifying factors that influence the users to adopt new information and communication technology (Rauniar, Rawski, Yang and Johnson 2014). Venkatesh et al. (2003) mentioned the four constructs that directly and indirectly determine the user’s system use motivation. The three constructs (1. Performance Expectancy, 2. Effort Expectancy, and 3. Social Influence) determine the intention to use the technology through behavioral intention.

The fourth characteristic (Facilitating Conditions) directly determines the intention to use the technology. The UTAUT model also presents four moderator variables (Age, Gender, Experience and voluntariness of use). The current study investigated the intention of social media usage to practice citizen journalism depending on users’ perception of the exercise. Hence, we applied the UTAUT model to describe why and how users adopt social media platforms to practice citizen journalism. Venkatesh et al. (2003) mentioned that performance expectancy, effort expectancy, facilitating conditions, and social influence are four determinants of technology usage intention. Social media sites have become very popular for practicing citizen journalism for performance expectancy, effort expectancy, and social influence determinants. The UTAUT model is applicable to identify the determinant elements and consequences of using social media sites to practice citizen journalism.

Hypothesis Development

Performance Expectancy (PE)

Performance expectancy considers how the new system will assist users in improving performance to complete the task (Venkatesh et al. 2003). It validates why the new system is advantageous for individual performance and improves efficacy. It also simulates the decision-making process of why they use the system. In a social context, people accept new technology when got benefit from it. Performance expectancy describes the importance of new media and technology, influencing a person to use the latest technology (Mortenson and Vidgen 2016). Social media are definitely advantageous technology in practicing citizen journalism.

Peng and Miller (2021) stated that people use the WeChat application for social media news because they perceive it as an advantageous tool. Following this statement, this research hypothesized that when people consider social media platforms as helpful tools, they will utilize social media to exercise citizen journalism.

H1: Performance expectancy will influence social media users to practice citizen journalism positively.

Effort Expectancy (EE)

Effort expectancy considers how much the new technology will be easy and effortless to complete the test (Venkatesh et al. 2003). Researchers employ this construct to examine the level of ease of use. It represents the extent to which users determine how simple modern technology is to learn and operate (Ismail et al. 2021). Effort expectancy demonstrates the ease and effortlessness of using the new technology. People will adopt new technology if those tools are easy to use, affecting their decision to adopt particular technology.

However, the users might not adopt the new system if it is challenging to operate and takes much time compared to the previous technique. Effort expectancy is a crucial factor in making an adoption decision at the beginning stage. According to Lane and Coleman (2012), people prefer to use social networking platforms for social and business applications that are easy to operate. Based on the above discussion, we hypothesized that if people perceive that social media are trouble-free and effortless tools to share news events, they will adopt social media to practice citizen journalism.

H2: Effort expectancy will positively influence social media users to practice citizen journalism.

Social Influence (SI)

Social influence is the degree to which users prioritize other beliefs, that why they should utilize the new system (Venkatesh et al. 2003). It directly affects others’ behavioral intentions to adopt the technology. People change technology usage behaviors when considering that others benefit from the new technology (Peng & Miller, 2021; Mortenson & Vidgen, 2016). Social influence can come from friends, colleagues, family members, relatives, and managers. It happens at the initial stage when people are expected to meet their own and others’ expectations.

Peng and Miller (2021) indicated that people adopt the WeChat tool for social media news use because they get recommended and suggested by peers. Therefore, this study hypothesized that people embrace social media to exercise citizen journalism if they observe others utilize these sites for the same purposes.

H3: Social influence will influence social media users to practice citizen journalism positively.

Conceptual Model

The researchers presented a conceptual model for this study based on a literature review and adapted from the UTAUT model. The model shows an explicit relationship between independent variables (Performance expectancy, Effort expectancy, and Facilitating conditions) and dependent variables (Citizen Journalism Practice). Mainly, it presents a direct relationship between the performance expectancy of social media engagement and citizen journalism practice.

Additionally, there is a connection between the effort expectancy of social media engagement and citizen journalism practice. The model also shows an evident connection between the facilitating conditions of social media engagement and citizen journalism practice. Furthermore, this conceptual framework examines the influence of the three independent variables on the dependent variables.

Methodology

Sampling and Data Collection

The theoretical goal of this research is to investigate factors affecting social media engagement in the practice of citizen journalism using the UTAUT model. A quantitative research approach was administered to gather and analyze numerical data. Additionally, a convenience sampling strategy was employed to collect the data using online survey questionnaires through Google Forms. The researchers generated a self-administered questionnaire and transformed it into Google Forms. Afterward, the Google Form link was shared among university students through email and WhatsApp.

The online questionnaire link was sent to the students who use social networking sites to exercise citizen journalism. The researchers adopted the Structural equation model (SEM-PLS) to investigate causal connections between independent and dependent variables and validate the proposed conceptual model. Kline (2015) stated that at least 150 respondents were suggested for a satisfactory data analysis with a structural equation model (SEM) tool. Since this research included 17 observable variables, the minimum sampling size was 17×10=170; as most scholars suggested using a sample size of 10 cases per parameter.

However, the researchers suggested 301 samples for this study to analyze the conceptual model. The data were collected from 301 university students in Malaysia from different levels of study, including bachelor’s, master’s, and PhD programs. The research topic is related to new technology and social media; therefore, it was perceived that university students have enough knowledge and experience in using social media to practice citizen journalism.

Questionnaire Development

The authors used research instruments to collect information to understand the university students’ perception of social media engagement in practicing citizen journalism. The online survey questionnaire represents a quick, cheap, and efficient way of getting information from many people. The research model contained three independent variables and one dependent variable estimated by 17 items adapted from previous studies (Venkatesh et al. 2003; Peng and Miller 2021; and Puriwat and Tripopsakul 2021).

Researchers modified the items to fit the context of social media use for citizen journalism. In this study, the researchers employed the 5-point Likert scale to measure the 17 different items of three independent (PE, EE, SI) and one dependent variable (CJP). The 5-point Likert scale was utilized ranging from 1= strongly disagree, 2= disagree, 3=somewhat agree, 4=agree and 5= strongly agree. In the conceptual framework, the independent variables include three constructs: performance expectancy (four items), effort expectancy (four items), and social influence (five items).

The dependent variable includes constructs: citizen journalism practice (four items). In order to measure the independent variable performance expectancy, item PE1 was adapted from Venkatesh et al. (2003), items PE2 and PE3 were adapted from Peng and Miller (2021), and Item PE4 was adapted from Puriwat and Tripopsakul (2021). Additionally, item EE1 is adapted from Venkatesh et al. (2003), Item EE2 was adapted from Peng and Miller (2021), and Items EE3 and EE4 were adapted from Puriwat and Tripopsakul (2021) to measure the independent variable effort-expectancy.

Moreover, items SI1 and SI2 were adapted from Venkatesh et al. (2003), item SI3 was adapted from Peng and Miller (2021), and items SI4 and SI5 were adapted from Puriwat and Tripopsakul (2021) to measure the independent variable social influence. Finally, items CJP1 and CJP2 were adapted from Peng and Miller (2021), and items CJP2 and CJP3 were adapted from Puriwat and Tripopsakul (2021) to measure the dependent variable of citizen journalism practice. The total of 17 observed variables of four constructs are presented in Table 1.

Table 1. The details of constructs and observable variables in the study.

Results and Discussions

Demographic Details of Respondents

Most respondents were female, with 55.8%, 168, and male, with 44.2%, 133 out of 301 students. Additionally, most respondents fell under the age group of 18-23 years, who are youth. The majority of respondents who contributed to this study are undergraduate students with 76.1%, followed by the foundation with 8.0%, STPM with 7.0%, Diploma with 4.0%, Matric with 3.0%, and Masters and PhD with 1.0%.  Moreover, for the year of study, most students were in their second year with 33.9%, 102 students, and the respondent’s highest per cent of family monthly income is RM2001 – 4000 with 38.9%, 117 respondents. The demographic statistics reports are detailed in Table 2.

Table 2: Respondents’ Demographic Details

Construct Reliability and Validity Analysis

PLS-SEM (SmartPLS 4 version) statistical tool was employed to estimate the construct reliability and validity of the instrument. The Average Variance Extracted (AVE) was measured in order to validate the convergent validity of the constructs. Table 3 shows the item loading score surpasses 0.5, composite reliability (CR) surpasses 0.7 and Cronbach’s Alpha (CA) score surpasses 0.7. These scores provide sufficient evidence of the reliability and validity of the constructs. The reliability and validity outcome mentioned in Table 3 validated the consistency and accuracy of independent variables including three constructs: performance expectancy, effort expectancy, and social influence and the dependent variable including one construct: citizen journalism practice of the conceptual model.

Table 3. Reliability and Validity of Constructs.

Table 3 represents the reliability and validity of the four constructs. Composite reliability (CR), Cronbach’s Alpha (CA), and Average Variance Extracted (AVE) were employed to estimate the reliability and validity of the conceptual model’s constructs. Tavakol and Dennick (2011) mentioned that CR and CA values are acceptable when they exceed 0.7. The researchers found that all the CR and CA item values were above 0.7 mentioned in Table 3.

Fornell and Larcker (1981) stated that the AVE value is accepted when it exceeds 0.5, and the outcome of this study revealed that AVE scores for all the constructs were above 0.6. Hence, the authors accepted items that outer loadings values were in the acceptable range. The data analysis results confirmed 17 items, including PE (4 items), EE (4 items), SI (5 items) and CJP (4 items). The researchers deleted three items due to obtaining standardized factor loading score less than 0.5.

Discriminant Validity

The Discriminant Validity of all the variables has been examined through the Fornell Larcker criterion and Heterotrait-monotrait ratio (HTMT) which are presented in Table 4 and 5.

Table 4: Fornell Larcker Criterion

In general, the Fornell-Larcker criteria are used to measure the extent to which latent variables in a model share variance (Fornell & Larcker 1981). The Fornell-Larcker criteria value shows that the square roots of the AVE of all variables are greater than their respective intercorrelations (Henseler et al. 2015: 122). Consequently, the validity and reliability assessments indicate that the measurement model is acceptable, and the results confirm this conclusion.

Table 5: Heterotrait-Monotrait Ratio (HTMT)

HTMT is an alternate way to assess the discriminant validity of the constructs. Henseler et al. (2015) mentioned that HTMT value is acceptable when it is less than 1. In the current study, the minimum and maximum value of HTMT is 0.616 and 0.969, which confirms the validity of the constructs of this research model.

Assessment of the Structural Model

Table 6: Coefficient of Determination (R²)

Table 6 represents the value of the Coefficient of Determination (R2) which means the good fit of the model. The adjusted R2 value was found 0.756 (76%) which is above 25%. Cohen (2013) mentioned that the R2 value higher than 0.26 is significant to accept the model. In this study, the R2 (0.756) and R-square adjusted value (0.753) was substantially acceptable prediction levels for empirical research.

 Table 7: Effect size (f²)

The value of effect size (f2) was presented in Table 7. According to Cohen (2013), the f2 value higher than 0.34 represents the large effect size, the f2 value above 0.14 and below 0.34 represents the medium effect size, and the f2 value above 0.01 and below 0.14 represents the small effect size. In this study the range of effect sizes EE, PE, and SI had medium, small, and large effect sizes on CJP respectively.

 Table 8: Multicollinearity Statistics (Inner VIF)

The Inner VIF value of the multicollinearity test is presented in Table 8. According to Pallant and Manual (2020), the VIF value above 10 and below 0.1 indicates the presence of multicollinearity. The minimum and maximum value of multicollinearity were found 1.549 and 2.305 respectively, which confirms the appearance of multicollinearity among the independent variables.

 Table 9: Predictive Relevance (Q²)

Q² value is presented in Table 9, and it isa  predictive relevance that indicates whether a model has predictive relevance or not. In the current study, the Q² values is found 0.745 which is higher than zero (0), and Q² value greater than zero indicates the presence of predictive relevance (Chin 1998).

Table 10: Hypothesis Test

Figure 2: Path Analysis Result

Results

The PLS-SEM analysis outcome indicated the proposed model has fit adequately for its standardized beta value, T value, and p-value mentioned in Table 10. The proposed model, adopted from the UTAUT, explained the relationship between social media engagement and citizen journalism (R2=0.75,6 see Table 6). Table 10 and Figure 2 illustrate the results of the hypothesis test. Hypotheses are accepted when the T statistics are more than 1.96 and all the p-values are below 0.05 (Greenland et al. 2016). The standardized beta value indicates that among all the hypotheses, PE, EE, SI, and CJP have the strongest relationship.

H1 hypothesized that performance expectancy will influence social media users to practice citizen journalism positively. The PLS-SEM data analysis validated the connection between performance expectancy and social media engagement to practice citizen journalism (T= 4.122 and p < 0.001). Hence, H1 is significantly accepted as the T value was 4.122 and the p-value was 0.000 (see Table 10). The result shows that the intention of citizen journalism practice increased with social media engagement, because it enhances users’ performance.

H2 posited that effort expectancy will influence social media users to practice citizen journalism positively. According to PLS-SEM data analysis, effort expectancy was positively connected to social media usage in citizen journalism (T= 6.501 and p < 0.001). More specifically, H2 was accepted as the T value was 8.372 and the p-value was 0.000 (see Table 10). The citizens use social media to practice citizen journalism for its easy-to-use features.

H3 proposed that social influence will influence social media users to practice citizen journalism positively. Social influence was also positively related to social media use to practice citizen journalism (T= 8.372 and p < .001).   Moreover, H3 was accepted as the T value was 8.372 and the p-value was 0.000 (see Table 10). People share and consume news events on social media when others suggest following them.

The current study developed three hypotheses, and all hypotheses were accepted based on their statistical value. The results illustrated that performance expectancy, effort expectanc,y and social influence were significant motivators in social media usage to practice citizen journalism. Nowadays, people prefer to utilize social media platforms to generate and share news rather than blogs. Hence, the conceptual model of this study can be followed for future research in other contexts.

Discussion

This study examined factors influencing social media adoption to practice of citizen journalism. The results show that social media engagement is related to citizen journalism. The findings confirm that this study is consistent with previous research. Venkatesh et al. (2003) mentioned that performance expectancy (PE), effort expectancy (EE), and social influence (SI) directly and positively influence usage behavior (UB) in the UTAUT model. Peng and Miller (2021) suggested that effort expectancy, task-technology fit, facilitating conditions, and social influence are potent motivators of social media news use behavior.

The empirical data of this study represented that performance and effort expectancies and social influence positively affect social media adoption to practice citizen journalism (CJ). Overall, the findings strongly supported all three hypotheses (H1, H2, &H3). The PLS-SEM analysis demonstrates that the unified theory of acceptance and use of technology (UTAUT) helps examine the correlation between social media engagement and citizen journalism. The authors proposed a new model to explain social media users’ influence on citizen journalism. The findings show that respondents practice citizen journalism on social media to report real-time news, inform and entertain friends, educate people for social awareness, shape public opinion, and search for academic news using more than one account.

Hypothesis 1 predicts that performance expectancy (T= 4.122, p= 0.000) affects social media users to practice citizen journalism. According to the results, students who adopt social media perceived that it is easy to use to report news content. The findings also show that Facebook is the most useful platform to share news content, following Twitter, LinkedIn, Instagram, WhatsApp, and WeChat. Social media allows people to report crimes and real-time news to share with friends and co-workers. The result showed that citizen journalists were satisfied with the performance expectancy of social media engagement to practice citizen journalism.

According to hypothesis 2, effort expectancy (T= 6.501 and p= 0.000) was a powerful predictor to stimulate social media users to practice citizen journalism. The findings were supported by previous studies; for example, Peng and Miller (2021) proposed that WeChat’s convenient feature influences the individual to engage in news sharing on it. The news on social media is straightforward and understandable. Findings also showed that citizen journalists were satisfied with the expected effect of social media involvement that they experienced practically. The finding from hypothesis 3 represented (T=8.372, p=0.000) that social influence mainly affects social media users to be involved with citizen journalism.

People practice citizen journalism on social networking sites such as Facebook, WeChat, WhatsApp, and Twitter, motivated by friends. Individuals adopt WeChat to consume news motivated by close friends who use and recommend it to others (Peng & Miller, 2021). Social influence believes that social media users practice citizen journalism because they prefer to maintain relationships with others who respect them (Venkatesh and Davis 2000; Chen 2020). People become motivated when friends get benefit from reporting on social media. According to findings, the suggestions from a favorite person influence people to adopt news-consuming behavior, and this result was in accordance with previous research.

Conclusion

This study investigates the relationship between social media engagement and citizen journalism practice using the unified theory of acceptance and use of technology (UTAUT). The study validated the hypotheses that Effort Expectancy, Performance Expectancy and Social Influence positively influence citizens to use social media to practice citizen journalism. The results showed that citizen journalists were satisfied with effort expectancy, performance expectancy and social influence of social media engagement to practice citizen journalism.

This research contributed to the theoretical knowledge of the UTAUT model since only a few studies were conducted on social media-based citizen journalism practice. Thus, the current research contributes to filling the literature gap by validating all three hypotheses that performance expectancy (PE), effort expectancy (EE), and social influence (SI) affect social media users’ behaviors. The expansion of social networking platforms has triggered an increase in digital journalism practice worldwide. Our study certainly offers managerial significance to practitioners and policymakers. The government authorities and policymakers can train digital journalists using social media platforms. Educational institutes can utilize social media for learning purposes, as the findings showed that students spend massive time on social media.

Although this research offered theoretical and practical implications for society, still, there have some limitations. The empirical data was gathered from university students in Malaysia; hence, the outcome of this study is generalized based on Malaysian culture. Future studies can examine social media engagement to practice online journalism in other cultures and countries. Additionally, future research can investigate the technological factors that contribute to engaging citizens in digital journalism. A qualitative research method can also be adapted to investigate social media engagement in citizen journalism. The rigorous interview tool of qualitative research can provide in-depth perception and findings concerning social media-based journalism. Other research may also investigate the relationship between the use of smartphone and online journalism through content analysis.

Contributor Details

M M Kobiruzzaman currently studies at the Department of Communication, Universiti Putra Malaysia. His research jurisdiction includes Journalism, Social Media Communication and Corporate Communication. He has published some creative and academic articles. He is ready to impart his knowledge to other people.

Contact: Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia. Email: mmkobiruzzaman@gmail.com. ORCID ID: https://orcid.org/0000-0001-9681-3820

Mastura Mahamed currently works at the Department of Communication, Universiti Putra Malaysia. She does research in Communication and Media, particularly in journalism, as well as youth and media. She was also involved in the study of Social Policy, and Qualitative Social Research.

Contact: Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia. Email: mastura.mahamed@upm.edu.my. ORCID ID: https://orcid.org/0000-0002-1858-9939

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