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The Integration of Social Media to the Theory of Planned Behavior: A Case Study in Indonesia

  • SIHOMBING, Sabrina O. (Faculty of Economics and Business, Universitas Pelita Harapan) ;
  • PRAMONO, Rudy (Pelita Harapan University)
  • 투고 : 2021.01.15
  • 심사 : 2021.04.01
  • 발행 : 2021.05.30

초록

Leader and leadership are one of the important aspects in the life of a country. This study aims to predict the intention of young voters to vote for state leader elections by expanding the theory of planned behavior to the Indonesian context. Apart from the importance of the presidential election, research rarely uses the theory of planned behavior, and to the best of researchers' knowledge, there are no studies that have applied the theory of planned behavior to predict the intention to vote for the president. Therefore, this study is an attempt to fill that gap. Two hundred questionnaires were distributed using non-probability purposive sampling. Data analysis was carried out using the structural equation modeling (SEM) approach. The results showed that attitude and behavior control were positively related to voters' intention to elect presidential candidates. Furthermore, information from social media also has a positive relationship with the attitude of choosing presidential candidates. However, the results also show that subjective norms do not have a significant relationship with voters' intention. This study contributes knowledge to researchers, practitioners, and policymakers about the factors that influence youth intention to vote in Indonesia, namely, attitudes, perceived behavior control, and information from social media.

키워드

1. Introduction

Leaders and leadership are important in many aspects of social life. The stability of a country is closely related to the stability of business, politics, society, and others. The president is the highest leader of a country. In this regard, the presidential election is important because the position of the president is the head of state and head of government. Therefore, many things depend on the leadership of the president. The success or failure of the president has an impact on many social aspects such as economy, politics, culture, democracy, and others. Regarding democracy, Indonesia is often seen as a beacon of democracy and pluralism in Southeast Asia (Arifianto et al., 2019; Takahashi, 2019).

The presidential elections are important in many ways. Many say that the presidential election is a party of democracy in Indonesia (e.g., Dedi, 2019; Khodijah & Yusuf, 2019; Setyawan, 2019; Natsir & Ridha, 2018; Soebagio, 2008), but there are impacts of the presidential election. First, studies show that many presidential elections create polarization in society (Smidt, 2017; Jones, 2015; Abramowitz & Saunders, 2008). In the context of Indonesia, the year of the presidential election is always a “hot” year for the people in Indonesia. Especially since 2014, the presidential election resulted in heavy voter polarization until the presidential election in 2019. Tensions between family, friends and the community are examples of how the polarization of certain presidential voters arises in daily life. This polarization is believed to continue until the upcoming presidential election (De Jong, 2019; Warburton, 2019; Fata, 2018). Second, presidential elections affect many aspects such as economic and business performance (Harymawan et al., 2020; Evelyn & Basana, 2018; Obradovi & Tomic, 2017; Sagita, 2017; Shen et al., 2017; Chieo et al., 2014; Imelda et al., 2014), public health issues (DeJonckheere et al., 2018; Mette & Bertolini, 2018William & Medlock, 2017), public security (Masters, 2018; Nwolise, 2007), work engagement and job performance (Beck & Shen, 2018; Petrillo, 2016), and others.

One of the main targets for political parties is young people or millennial (Wanasida et al, 2021), considering the significant number of young people. Specifically, the youth population (15–35 years old) in Indonesia accounts for 34.5 to 50 percent of the total population, which shows a significant size. Youth voters are usually perceived as being half way from enthusiasm and political apathy. Indonesian youth is often portrayed as pragmatic and less interested in politics (Irawanto, 2019; Hasyim, 2018; Morissan, 2016). It can be said that they seemed eager and curious about the election. However, this enthusiasm is not necessarily in line with the reality of political behavior. Young people are identified as voters who change their minds easily (swing voters). They are easy to find and receive information from the Internet.

2. Literature Review

This research applied one theory of consumer behavior, namely, the theory of planned behavior (TPB), in the context of the intention to elect a presidential candidate. This is because researchers in marketing and consumer behavior show that voters can be analyzed as consumers in political markets (Peng & Hackley, 2009; O’Cass & Pecotich, 2005; Newman, 1985). Not only that, but researchers have also used theories in consumer behavior to predict voting behavior since the 1970s (Newman, 1985; Luc, 2020; Wu et al., 2020). Specifically, planned behavior theory is one of the most parsimonious and widely used theories in predicting behavior (Hasbullah et al., 2014; Connee & Armitage, 1998). Furthermore, this theory is also open for the addition of other variables. Ajzen (1991, 1988) states that the expansion of the theory of planned behavior can be done in an effort to understand more deeply people’s behavior. Researchers have broadened the theory of planned behavior by adding variables that fit their research context such as habits (Xin et al., 2019; Soh et al., 2018; Foltz et al., 2016; Moons & De Pelsmacker, 2015), future behavior (Parkinson et al., 2018; Raut et al., 2018; Wang & Zhang, 2016; Patiro & Sihombing, 2014; Sommer, 2011), knowledge (Chiu et al., 2019; Setyawan et al., 2018; Maichum et al., 2017; Park et al., 2017), trust (Ha et al., 2019; Rameez & Kulathunga, 2019), emotions (Parkinson et al., 2018; Londono-Roldan et al., 2017; Moon & De Pelsmacker, 2015), perceived benefits (Ha et al., 2019; Gangwal & Bansal, 2016) environmental care (Chen & Tung, 2014), moral norms (Botetzagias et al., 2015), among others.

Despite the importance of presidential elections, research rarely applies TPB and there is no research in applying extended theories to predict intention to choose a president. Furthermore, understanding young voters is important. They are future leaders. They are at the forefront in changing the country. Their role is important in politics where they are not just voters, but active actors in politics themselves (Lestari & Arumsari, 2018; Skelton, 2010; McFarland & Thomas, 2006). Therefore, this study aims to predict the intentions of young voters in electing presidential candidates by expanding the TPB. This study applied the TPB by adding a research variable, namely, information from social media. In this study, TPB was added with information from social media as an antecedent variable of attitude. The addition of this variable is because social media is the main element in the daily lives of young people. And social media is also becoming the dominant channel in political marketing (Kardashian, 2019; Harris & Harrigan, 2015). Specifically, social media is also used as an important tool in political campaigns where they can be used for sending political campaign advertisements (Bright et al., 2019; Dasli, 2019; Weeks et al., 2017; Vonderschmitt, 2012). Further, social media is believed to shape individual choices especially in election context (Biswas et al., 2014). Several studies have shown the influence of social media in presidential and legislative elections (Sekarwulan et al., 2020; Adinugroho et al., 2019; Munzir & Zetra, 2019; Ratnamulyani & Maksudi, 2018).

3. Research Methods and Materials

This study applied a quantitative paradigm (see Figure 1). Specifically, descriptive research with the aim of testing hypotheses is the type of research carried out. Furthermore, the sampling method in this study is a nonprobability sampling method, that is, purposive sampling which aims to select respondents with certain criteria that are consistent with the study (Sekaran & Bougie, 2019). In this study, the sample used was students from private universities who had the right to vote in the 2019 Presidential Election. Young people were selected in this study given the important role of young people in nation building. Young people live with social media and are quickly influenced by information from social media. The sample size was 200 respondents. One guidelines of sample size is samples less than 100 are “small” samples, 100 to 200 are “medium,” and more than 200 are “large” (Kline, 2005). Other researchers stated that a range of sample size from 30 to 460 is appropriate (Wolf et al., 2013). Thus, the samples in this study have met the requirements of the number of samples.

OTGHEU_2021_v8n5_445_f0001.png 이미지

Figure 1: Research Model

This study applied items from previous research to measure the research variables (Hsu et al., 2015; Ajze, 2006). To measure the answers of respondents, the questionnaire used the interval scale because this scale shows a clear distance between each scale point (scale point) that is in the respondent’s answer (Hair et al., 2007). The type of interval scale used in this study is a Likert scale. Likert scale is a method of measurement that will indicate the response of the respondents to their attitude towards an object. The measurement method using the Likert scale consists of a range of answers from “strongly agree” to “strongly disagree,” which will be answered by respondents (Hair et al., 2007). Likert scale is used because it is a method of measuring attitudes that is simple and convenient (Chyung et al., 2017).

Before testing hypotheses, reliability and validity tests are performed first to ensure the research indicators are reliable and valid (Sekaran & Bougie, 2019). The reliability test conducted in this study used the Cronbach’s alpha method. Furthermore, validity tests were conducted by using Composite Reliability and AVE to test convergent validity and Fornell-Larcker Criterion to test discriminant validity (Hair et al., 2010). Then, data were analyzed using structural equation modeling (SEM). This is because many important variables in social science cannot be observed directly (latent variables), for example, the variables of attitude, intention, motivation, and so others. These variables will then be measured by various indicators that may contain measurement errors. Structural equation modeling (SEM) has become the main tool for testing and understanding the relationship between latent variables (Deng et al., 2018; Byrne, 2010; Guo & Lee, 2007).

4. Results and Discussion

4.1. Results

A total of 200 questionnaires were returned, resulting in a response rate of 82.5%. However, two questionnaires could not be used because they were not filled out. Thus, 163 questionnaires could be used or a response rate of 81.5%. Two thirds of respondents are women and one third are men. All respondents are students from a private university.

The research instrument test was conducted by testing the reliability and validity of the indicators used in measuring each variable. Table 1 shows that the indicators used for the variables are reliable and valid. Specifically, the table shows that each construct demonstrated internal consistency with a Cronbach’s alpha value of α in the range of 0.671 to 0.855. These alpha values were described as reasonable (Taber, 2018).

Table 1: Reliability and Validity Results

OTGHEU_2021_v8n5_445_t0001.png 이미지

Table 1 also shows that all composite reliability values were above 0.8 and AVE values above 0.6. Thus, it can be stated that convergent validity had been achieved in this study (Hair et al., 2010). Discriminant validity was assessed using the Fornell and Larcker criteria by comparing the square root of each AVE in the diagonal with the correlation coefficient (off-diagonal) for each construct in the relevant row and column. Table 2 shows the estimated correlations between constructs, and the square root of the extracted mean variance (AVE) on diagonal values. AVE is between 0.699 and 0.856 (Table 1), which is above the required value of 0.50 (Fornell & Larcker, 1981). The square root AVE, which is from 0.836 to 0.925, is higher than the value of the correlation, which is from -0.020 to 0.582. Thus, it can be stated that the discriminant validity of the research construct has been achieved.

Table 2: Discriminant Validity (Fornell-Larcker Criterion & Heterotrait-Monotrait Criterion)

OTGHEU_2021_v8n5_445_t0002.png 이미지

Square Roots of AVE (Diagonally in Bold).

Hypothesis testing is conducted after the goodness of the data is examined by using the reliability and validity tests. Structural equation modeling is applied to test the structural relationships between variables. This research applied T-statistics and p-value to test significance coefficient hypothesis. The results of the structural equation modeling analysis are presented in Table 3.

Table 3: Result of Hypotheses Testing

OTGHEU_2021_v8n5_445_t0003.png 이미지

4.2. Discussion

The research aims to examine the extended Theory of Planned Behavior by adding variables information from social media to predict the intention to choose a presidential candidate. This research confirms the first hypothesis that shows the relationship between information from social media and attitude toward choosing a president candidate. This result is consistent with attitude theory that information is the basis for attitude formation. Specifically, a person processes information about an object before he arrives at the object’s evaluation and forms his attitude towards the object (Jacoby et al., 2002; Lutz, 1978). This result of the relationship between information and attitude is consistent with previous findings (e.g., Utami & Rahyuda, 2019; Sihombing, 2017; Elliot & Speck, 2005).

The results of this study confirm the second hypothesis that shows a positive relationship between attitude and intention to behave. Attitude theories such as Theory of Reasoned Action, Theory of Planned Behavior, and Theory of Acceptance Model show that attitude is a predictor of intention to behave (Ijzen & Fisbein, 2005; Davis, 1989). The results of this study are also consistent with previous studies that show a positive relationship between attitude and intention to behave (e.g., Cho & Son, 2019; Nguyen et al., 2019; Natalia & Sihombing, 2018; Hussein, 2017; Patiro & Sihombing, 2016; Gunadi & Sihombing, 2015; Sihombing, 2011; Hansen & Jensen, 2007).

The results show that the third hypothesis is not supported. The unsupported hypothesis is related to the relationship between subjective norms and the intention to choose a presidential candidate. Subjective norms refer to the influence of people who are considered important (e.g., family, siblings, and friends) that will affect one’s behavior. The results of this study indicate that subjective norms do not have a significant relationship with the intention to choose a presidential candidate. This result can be explained through the current political situation in Indonesia. It can be said that everyone who has the right to vote already has made their “choice”. In other words, families of fathers and mothers and their children can have different choices. Each choice is often considered a result of political polarization. Political polarization between supporters of two presidential candidates Jokowi and Prabowo was sharper in the 2019 presidential election. This was because the battle between the two was a continuation of the previous period in 2014. The battle continued in the 2019 presidential election.

The battle between the two camps (i.e., Jokowi vs. Prabowo) created acute fanaticism, which divided the community into two camps in the 2019 presidential election. Jokowi won the presidential election in 2014 ago with a narrow margin. The difference in votes between Jokowi and Prabowo in 2014 was the thinnest among presidential elections since 1998, marking intense competition between the two camps. Prabowo’s reluctance to admit his defeat in the 2014 presidential election was considered a trigger for the continuation of political segregation during the Jokowi administration in 2014–2019 and created polarization in society (Triwibowo, 2019). It has been reported in many newspapers or other forms of media that the president’s choice is different between husband and wife or parents with children or with close friends has made family relations or friendships can heat up during the election.

The results of this study indicate that there is a positive relationship between perceived behavioral control and intention to behave. It is related to the context of the general election (president, regional head, and others) in Indonesia, which adheres to the “LUBER” principle, which stands for “Direct (L), General (U), Free (B) and Confidential (R)” (Fatayati, 2017). Specifically, “Free” means that voters are required to vote without coercion from any party. Free also reflects control from within the individual. The results of this study are also consistent with past studies that show a positive relationship between perceived behavioral control and behavioral intention (e.g., Saputra & Sihombing, 2018; Patiro & Sihombing, 2016; Sihombing, 2011; Liao et al., 2010; Wang, 2010).

5. Conclusion

This study aims to predict the voting intention in a presidential election using an extended planned behavior theory. The results show that only one hypothesis is not supported, namely, the relationship between subjective norms and the intention to choose a presidential candidate. Supported hypotheses show that attitude and behavioral control have a positive relationship with the intention to choose a presidential candidate. Information from social media also has a positive relationship with attitudes to choose presidential candidates.

There are two main limitations of the study. First, this study uses non-probability sampling by selecting student respondents at a private university. Therefore, the results do not reflect the general voting student population in Indonesia. Thus, future studies can replicate research by using young voter respondents with different characteristics, for example, young voters who are already working. Thus, the results of research using different respondents can give the same or different results, and are useful to provide an understanding of the intention to vote. Second, data for this study were collected in only one period (cross-sectional study). Therefore, this study only describes the phenomenon at one time. Future research can try to develop causality.

참고문헌

  1. Abramowitz, A., & Saunders, K. (2008). Is Polarization a Myth? The Journal of Politics, 70(2), 542-555. https://doi.org/10.1017/S0022381608080493
  2. Adinugroho, B., Prisanto, G. F., Irwansyah, & Ernungtyas, N. F. (2019). Social Media and the Internet in Political and Election Information Involvement. Jurnal Representamen, 5(2), 80-95.
  3. Ajzen, I. (2006). Constructing a TpB Questionnaire: Conceptual and Methodological Considerations. Retrieved June 27, 2011 from the World Wide Web: http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf
  4. Ajzen, I., & Fishbein, M. (2005). The Influence of Attitudes on Behavior. In: the Handbook of Attitudes. Mahwah, NJ: Lawrence Erlbaum.
  5. Azjen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  6. Ajzen, I. (1988). From Intentions to Actions: Attitudes, Personality, & Behavior. Chicago: Dorsey Press.
  7. Arifianto, A. R., Pepinsky, T., Laksmana, E. A., Anwar, D. F., & Murphy, A. M. (2019). Indonesia after the 2019 Election. Asia Policy, 14(4), 43-87.
  8. Beck, J. W. and Shen, W. (2018). The Effects of Presidential Elections on Work Engagement and Job Performance. Applied Psychology, 68(3), 547-576. https://doi.org/10.1111/apps.12167
  9. Biswas, A., Ingle, N. & Roy, M. (2014). Influence of Social Media on Voting Behavior. Journal of Power, Politics & Governance, 2(2), 127-155.
  10. Botetzagias, I., Dima, A. & Malesios, C. (2015). Extending the Theory of Planned Behavior in the Context of Recycling: The role of Moral Norms and of Demographic Predictors. Resources, Conservation and Recycling, 95, 58-67. https://doi.org/10.1016/j.resconrec.2014.12.004
  11. Bright, J., Hale, S., Ganesh, B., Bulovsky, A., Margetts, H., & Howard, P. (2019). Does Campaigning on Social Media Make a Difference? Evidence from Candidate Use of Twitter During the 2015 and 2017 U.K. Elections. Communication Research. https://doi.org/10.1177/0093650219872394
  12. Byrne, B. (2010). Structural Equation Modeling with Amos (2nd ed.). London: Routledge.
  13. Chen, M., & Tung, P. (2014). Developing an Extended Theory of Planned Behavior Model to Predict Consumers' Intention to Visit Green Hotels. International Journal of Hospitality Management, 36, 221-230. https://doi.org/10.1016/j.ijhm.2013.09.006
  14. Chieo, W., Mayer, R.W., & Wang, Z. (2014). Stock Market, Economic Performance, and Presidential Elections. Journal of Business & Economics Research, 12(2), 159-170. https://dx.doi.org/10.19030/jber.v12i2.8530
  15. Chiu, Y., Chou, Y., Chang, Y., Chu, C., Lin, F., Lai, C., Hwang, S., Fang, W., & Kao, S. (2019). Using an Extended Theory of Planned Behavior to Predict Smoking Cessation Counsellors' Intentions to Offer Smoking Cessation Support in the Taiwanese Military, BMJ Open. Retrieved from: https://bmjopen.bmj.com/content/bmjopen/9/5/e026203.full.pdf
  16. Cho, E., & Son, J. (2019). The effect of social connectedness on consumer adoption of social commerce in apparel shopping. Fashion and Textiles, 6(14). https://dx.doi.org/10.1186/s40691-019-0171-7
  17. Chyung, S. Y., Roberts, K., Swanson, I., & Hankinson, A. (2017). Evidence-Based Survey Design: The Use of a Midpoint on the Likert Scale. Performance Improvement, 56(10), 15-23. https://doi.org/10.1002/pfi.21727
  18. Connee, M., & Armitage, C. J. (1998). Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. Journal of Applied Social Psychology, 28(15), 1429-1464. https://doi.org/10.1111/j.1559-1816.1998.tb01685.x
  19. Dasli, Y. (2019). Use of Social Media as a Tool for Political Communication in the Field of Politics. Ordu University Journal of Social Science Research, 9(1), 243-251.
  20. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and End User Acceptance of Information Technology. MIS Quarterly, 13, 318-339. https://doi.org/10.2307/249008
  21. De Jong, A. (2019). Indonesia's Election and Polarization. International Viewpoint - Online Socialist Magazine. Available at http://www.internationalviewpoint.org/spip.php?article6137
  22. Dedi, A. (2019). Analysis of the 2019 Concurrent General Election System. Jurnal Moderat, 5(3), 213-226. http://dx.doi.org/10.25147/moderat.v5i3.2676
  23. Deng, L., Yang, M., & Marcoulides, K.M. (2018). Structural Equation Modeling with Many Variables; a Sstematic Review of Issues and Developments. Frontiers in Psychology, 9, 580. https://doi.org/10.3389/fpsy.2018.00580
  24. DeJonckheere, M., Fisher, A., & Chang, T. How has the presidential election affected young Americans? Child and Adolescent Psychiatry and Mental Health, 12(8). https://doi.org/10.1186/s13034-018-0214-7
  25. Elliott, M., & Speck, P. (2005). Factors That Affect Attitude toward a Retail Web Site. Journal of Marketing Theory and Practice, 13(1), 40-51. Retrieved July 27, 2020, from www.jstor.org/stable/40470185 https://doi.org/10.1080/10696679.2005.11658537
  26. Evelyn, E., & Basana, S. R. (2018). Effect of 2008 and 2016 U. S. Presidential Election in the Indonesian Stock Market. Jurnal Manajemen dan Kewirausahaan, 20(1), 16-22. https://dx.doi.org/10.9744/jmk.20.1.16-22
  27. Fatayati, S. (2017). The Relevance of Election Principles as Efforts to Create Democratic Elections with Integrity. Tribakti: Jurnal Pemikiran KeIslaman, 28(1), 147-165. https://doi.org/10.33367/tribakti.v28i1.472
  28. Fata, M. K. (2018). Reading the Polarization of Santri in the Presidential Election Contest 2019. Jurnal Dinamika Penelitian: Media Komunikasi Sosial Keagamaan, 18(02), 325-346.
  29. Foltz, B., Newkirk, H. E. & Schwager, P. H. (2016). An Empirical Investigation of Factors that Influence Individual Behavior toward Changing Social Networking Security Settings. Journal of Theoretical and Applied Electronic Commerce Research, 11(2), 1-15. https://dx.doi.org/10.4067/S0718-18762016000200002
  30. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
  31. Gangwal, N., & Bansal, V. (2016). Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India. In: Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016), 2, 357-367. https://dx.doi.org/10.5220/0005627503570367
  32. Gunadi, A. P., & Sihombing, S. O. (2015). Prediction of Actual E-Toll Card Use with Structural Model Equation Approach. Jurnal Manajemen Teknologi, 14(2), 151-172. https://doi.org/10.12695/jmt.2015.14.2.3
  33. Guo, S., & Lee, C. K. (2007). Statistical power of SEM in social work research: Challenges and strategies. Paper presented at the Eleventh Annual Conference of the Society of Social Work Research. San Francisco.
  34. Ha, N. T., Nguyen, T. L. H., Nguyen, T. P. L., & Nguyen, T. D. (2019). The Effect of Trust on Consumers' Online Purchase Intention: An Integration of TAM and TPB. Management Science Letters, 9, 1451-1460. https://doi.org/10.5267/j.msl.2019.5.006
  35. Hagger, M. S. (2019). The reasoned action approach and the theories of reasoned action and planned behavior. In: Dunn, D. S. (Ed.), Oxford Bibliographies in Psychology. New York, NY: Oxford University Press. https://dx.doi/10.1093/OBO/9780199828340-0240
  36. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
  37. Hair, J. F, Money, A. H., Samouel, P., & Page, M. (2007). Research Methods for Business. Chichester, UK: John Wiley and Sons.
  38. Hansen, E. R., & Tyner, A. (2018). Educational Attainment and Social Norms of Voting. Presented in the Annual Meeting of The American Political Science, August 20-September 2, Available at https://static1.squarespace.com/static/5b953c5f9d5abb8c51d6d8c6/t/5b957f1203ce64eefd1fba92/1536524051067/Hansen_Tyner_Norms_Voting.pdf
  39. Harris, L., & Harrigan, P. (2015). Social Media in Politics: The Ultimate Voter Engagement Tool or Simply an Echo Chamber? Journal of Political Marketing, 14(3), 251-283. https://doi.org/10.1080/15377857.2012.693059
  40. Harymawan, I., Nasih, M., Suhardianto, N., & Shauki, E. (2020). How does the presidential election period affect the performance of the state-owned enterprise in Indonesia? Cogent Business & Management, 7(1), 1750330. https://doi.org/10.1080/23311975.2020.1750330
  41. Hasbullah, N., Mahajar, A. J., & Salleh, M. I. (2014). A conceptual framework of extending the theory of planned behavior: the role of service quality and trust in the consumer cooperatives. International Journal of Business and Social Science, 5(12), 142-148.
  42. Hansen, T., & Jensen, J. M. (2007). Understanding Voters' Decisions: A Theory of Planned Behaviour Approach. Innovative Marketing, 3(4), 87-93.
  43. Hasyim, S. (2018). The Role of Millennial Generation in Determining Victory in the Presidential Election 2019. Available at https://www.matamatapolitik.com/peran-generasi-milenial-dalam-menentukan-kemenangan-di-pilpres-2019/
  44. Hsu, M., Chang, C., Lin, H., & Lin, Y. (2015). Determinants of Continued Use of Social Media: The Uses and Gratifications Theory and Perceived Interactivity. Information Research, 20(2). Available at http://informationr.net/ir/20-2/paper671.html#.XlXju0pS_IU
  45. Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159-164. https://doi.org/10.1016/j.procs.2017.01.196
  46. Imelda, I., Siregar, H., & Anggraeni, L. (2014). Abnormal Returns and Trading Volume in the Indonesian Stock Market in Relation to the Presidential Elections in 2004, 2009, and 2014. Bisnis & Birokrasi Journal, 21(2), 64-76. https://doi.org/10.20476/jbb.v21i2.4319
  47. Irawanto, B. (2019). Young and Faithless: Wooing Millenials in Indonesia's 2019 Presidential Election. ISEAS Perspective, 1, 1-10.
  48. Jacoby, J., Morrin, M., Jaccard, J., Gurhan, Z., Kuss, A., & Maheswaran, D. (2002). Mapping Attitude Formation as a Function of Information Input: Online Processing Models of Attitude Formation. Journal of Consumer Psychology, 12(1), 21-34. https://doi.org/10.1207/S15327663JCP1201_03
  49. Jones, D. (2015). Partisan Polarization and the Effect of Congressional Performance Evaluations on Party Brands and American Elections. Political Research Quarterly, 68(4), 785-801. Retrieved July 14, 2020, from www.jstor.org/stable/24637816 https://doi.org/10.1177/1065912915601896
  50. Kaplan, A. M., & Haenlein, M. (2010). Users of the World, Unite! The Challenges and Opportunities of social media. Business Horizons, 53(1), 59-68. https://doi.org/10.1016/j.bushor.2009.09.003
  51. Kardashian, K. (2019). Three Social Media Themes to Watch in 2020. Retrieved from: https://www.tuck.dartmouth.edu/news/articles/three-social-media-themes-to-watch
  52. Khodijah, S. N., & Yusuf, A. (2019). Comparative Study of Implicit Attitude Evaluation with Intention to Choose 2019 Presidential Election Candidates (Jokowi-Amin, Prabowo-Sandi, and Golput) in University of Indonesia Students. Jurnal KSM Eka Prasetya UI, 1(3).
  53. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford.
  54. Lestari, E. Y., & Arumsari, N. (2018). Political Participation of Beginner Voters in Semarang Mayor Election in Semarang City. Integralistik, 1(XXIX), 63-72.
  55. Liao, C., Lin, H., & Liu, Y. (2010). Predicting the Use of Pirated Software: A Contingency Model Integrating Perceived Risk with the Theory of Planned Behavior. Journal of Business Ethics, 91(2), 237-252. Retrieved July 27, 2020, from www.jstor.org/stable/27749793 https://doi.org/10.1007/s10551-009-0081-5
  56. Liew, A. (2007). Understanding Data, Information, Knowledge and Their Inter-Relationships. Journal of Knowledge Management Practice, 8(2). http://www.tlainc.com/articl134.htm
  57. Londono-Roldan, J. C, Davies, K., & Elms, J. (2017). Extending the Theory of Planned Behavior to examine the role of anticipated negative emotions on channel intention: The case of an embarrassing product. Journal of Retailing and Consumer Services, 36, 8-20. https://doi.org/10.1016/j.jretconser.2016.12.002
  58. Luc, P. T. (2020). Outcome Expectations and Social Entrepreneurial Intention: Integration of Planned Behavior and Social Cognitive Career Theory. Journal of Asian Finance, Economics and Business, 7(6), 399-407. https://doi.org/10.13106/jafeb.2020.vol7.no6.399
  59. Lutz, R. (1978). Attitude Change or Attitude Formation? An Unanswered Question: Rejoinder. Journal of Consumer Research, 4(4), 276-278. https://doi.org/10.1086/208708
  60. Maichum, K., Parichatnon, S., & Peng, K. (2017). Developing an Extended Theory of Planned Behavior Model to Investigate Consumers; Consumption Behavior toward Organic Food: A Case Study in Thailand. International Journal of Scientific of Technology Research, 6(1), 72-80.
  61. Masters, J. (2018). Russia, Trump, and the 2016 U.S. Election. Available at https://www.cfr.org/backgrounder/russia-trump-and-2016-us-election
  62. McFarland, D., & Thomas, R. (2006). Bowling Young: How Youth Voluntary Associations Influence Adult Political Participation. American Sociological Review, 71(3), 401-425. https://doi.org/10.1177/000312240607100303
  63. Mette, K., & Bertolini, K. (2018). Fear, Anxiety, and the 2016 Presidential Election: What are the Effects on Student Achievement? Empowering Research for Educators, 2(1), 28-36. Available at: https://openprairie.sdstate.edu/ere/vol2/iss1/5, 28-36
  64. Moons, I., & De Pelsmacker, P. (2015). An Extended Decomposed Theory of Planned Behaviour to Predict the Usage Intention of the Electric Car: A Multi-Group Comparison. Sustainability, 7, 6212-6245. https://doi.org/10.3390/su7056212
  65. Morissan. (2016). Level of Political and Social Participation of the Young Generation. Jurnal Visi Komunikasi, 15(01), 96-113.
  66. Munzir, A., Asmawi, A., & Zetra, A. (2019) Various Roles of Social Media in Political World in Indonesia. JPPUMA: Jurnal Ilmu Pemerintahan dan Sosial Politik UMA (Journal of Governance and Political Social UMA), 7(2), 173-182.
  67. Natalia, E., & Sihombing, S. O. (2018). The Relationship between Entrepreneurship Education and Mentoring toward Entrepreneurship Intention. Jurnal Manajemen, XXII (03), 340-359. https://doi.org/10.24912/jm.v22i3.426
  68. Natsir, M., & Ridha, M. (2018). Stuck in Electoralism: Notes on Indonesian Democracy in the Last Two Decades. Jurnal Politik Profetik, 6(2), 234-248.
  69. Newman, B. I. (1985). An Historical Review of the Voter as a Consumer. In: Sheth, J. N. and Tan, C. T. (Eds.), SV - Historical Perspective in Consumer Research: National and International Perspectives. Singapore: Association for Consumer Research, 257-261
  70. Nwolise, O. B. C. (2007). Electoral Violence and Nigeria's 2007 Elections. Journal of African Elections, 6(2), 155-179. https://doi.org/10.20940/JAE/2007/v6i2a9
  71. Obradovic, S., & Tomic, N. (2017). The Effect of Presidential Election in the USA on Stock Return Flow - A Study of a Political Event. Journal Economic Research, 30(1), 112-124. https://doi.org/10.1080/1331677X.2017.1305802
  72. O'Cass, A., & Pecotich, A. (2005). The Dynamics of Voter Behavior and Influence Processes in Electoral Markets: A Consumer Behavior Perspective. Journal of Business Research, 58, 406-413. https://doi.org/10.1016/j.jbusres.2003.08.003
  73. Park, J. Y., Chiu, W., & Won, D. (2017). Sustainability of Exercise Behavior in Seniors: An Application of the Extended Theory of Planned Behavior. Journal of Physical Education and Sport, 17(1), 342-347.
  74. Parkinson, J., Russell-Bennett, R., & Previte, J. (2018). Challenging the Planned Behavior Approach in Social Marketing: Emotion and Experience Matter. European Journal of Marketing, 52(3/4), 837-865. https://doi.org/10.1108/EJM-05-2016-0309
  75. Patiro, S., & Sihombing, S. (2016). Predicting Intention to Purchase Counterfeit Products: Extending the Theory of Planned Behavior. International Research Journal of Business Studies, 7(2). https://doi.org/10.21632/irjbs.7.2.109-120
  76. Peng, N., & Hackley, C. (2009). Are Voters, Consumers? A Qualitative Exploration of the Voter-Consumer. Qualitative Market Research: An International Journal, 12(2), 171-186. https://doi.org/10.1108/13522750910948770
  77. Petrillo, L. (2016). Presidential Election Is Stressing Out Workers, Hurting Productivity. https://www.shrm.org/resourcesandtools/hr-topics/employee-relations/pages/politics-at-work-.aspx
  78. Rameez, M., & Kulthunga, D. (2019). Customers' Online Purchase Intention: Applying Extended Theory of Planned Behavior Model. Information and Knowledge Management, 9(10), 23-36.
  79. Ratnamulyani, I. A., & Maksudi, B. I. (2018). The Role of Social Media in Increasing Participation of Beginner Voters Among Students in Bogor Regency. Sosiohumaniora, 20(2), 154-161.
  80. Raut, R. K., Das, N., & Kumar, R. (2018). Extending the Theory of Planned Behavior: Impact of Past Behavioral Biases on the Investment Decision of Indian Investor. Asian Journal of Business and Accounting, 11(1), 265-291. https://doi.org/10.22452/ajba.vol11no1.9
  81. Sagita, V. D. (2017). Trump's Elected Shock Effect in Indonesian Stock Market. Journal of Indonesian Applied Economics, 7(1), 71-83. https://dx.doi.org/10.21776/ub.jiae.2017.007.01.5
  82. Saputra, T., & Sihombing, S. (2018). Application of the Theory of Planned Behavior for Predicting the Intention of International Entrepreneurship: Global Mindset and Cultural Intelligence as Moderation Variables. Asia Pacific Management and Business Application, 7(2), 59-80. https://doi.org/10.21776/ub.apmba.2018.007.02.1
  83. Sekaran, U., & Bougie, R. (2019). Research Methods for Business. Hoboken, NJ: Wiley.
  84. Shen, C., Bui, D. G., & Lin, C. (2017). Do political factors affect stock returns during presidential elections? Journal of International Money and Finance, 77(C), 180-198. https://dx.doi.org/10.1016/j.jimonfin.2017.07.019
  85. Sekarwulan, A., Azzahra, A. A., Syifafasya, N., & Safitri, D. (2020). The Use of Social Media for the Participation of UNJ Communication Science Students in Determining the 2019 Presidential Candidates and Cawapres. Kanal: Jurnal Ilmu Komunikasi, 8(2), 50-57. https://doi.org/10.21070/kanal.v8i2.139
  86. Setyawan, A., Noermijati, N. Sunaryo, S., & Aisjah, S. (2018). Green Product Buying Intentions Among Young Consumers: Extending the Application of Theory of Planned Behavior. Problems and Perspectives in Management, 16(2), 145-154. https://dx.doi.org/10.21511/ppm.16(2).2018.13
  87. Setyawan, A. (2019). Let's Succeed in the 2019 Democracy Party. Available at https://setkab.go.id/mari-sukseskan-pesta-demokrasi-2019/
  88. Sihombing, S. O. (2017). Predicting intention to share news through social media: An empirical analysis in Indonesian youth context. Business and Economic Horizons, 13(4), 468-477. http://dx.doi.org/10.15208/beh.2017.32
  89. Sihombing, S. O. (2011). Understanding Knowledge Sharing Behavior: an Examintai8on of the Extended Theory of Planned Behavior. Journal the Winners, 12(1), 24-39. https://doi.org/10.21512/tw.v12i1.681
  90. Skelton, T. (2010). Taking young people as political actors seriously: Opening the borders of political geography. Area, 42(2), 145-151. Retrieved July 20, 2020, from www.jstor.org/stable/27801455. https://doi.org/10.1111/j.1475-4762.2009.00891.x
  91. Smidt, C. (2017). Polarization and the Decline of the American Floating Voter. American Journal of Political Science, 61(2), 365-381. Retrieved July 17, 2020, from www.jstor.org/stable/26384737. https://doi.org/10.1111/ajps.12218
  92. Soebagio, H. (2008). Implications of the White Group in the Perspective of Democratic Development in Indonesia. Makara, Sosial Humaniora, 12(2), 82-86.
  93. Soh, P., Koay, K. Y., & Lim, V. K. G. (2018). Understanding Cyberloafing by Students through the Lens of an Extended Theory of Planned Behavior. First Monday, https://doi.org/10.5210/fm.v23i6.7837
  94. Sommer, L. (2011). The Theory of Planned Behavior and the Impact of Past Behavior. International Business & Economics Research Journal, 19(1), 98-110. https://dx.doi.org/10.19030/iber.v10i1.930
  95. Taber, K. S. (2018). The Use of Cronbach's Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48, 1273-1296. https://dx.doi.org/10.1007/s11165-016-9602-2
  96. Takahashi, T. (2019). Indonesia's presidential election puts SE Asia's democracy to the test, Retrieved from https://asia.nikkei.com/Spotlight/Comment/Indonesia-s-presidential-electionputs-SE-Asia-s-democracy-to-the-test2
  97. Triwibowo, W. (2019). Preventing Political Polarization after the 2019 Presidential Election is Sharpening. https://nasional.kompas.com/read/2019/04/23/13291151/mencegah-polarisasi-politik-pasca-pilpres-2019-semakin-tajam?page=all
  98. Utami, P. D. P., & Rahyuda, K. (2019). International Research Journal of Management, IT & Social Sciences, 6(4), 107-117. https://dx.doi.org/10.21744/irjmis.v6n4.663
  99. Vonderschmitt, K. (2012). The Growing Use of Social Media in Political Campaigns: How to use Facebook, Twitter, and YouTube to Create an Effective Social Media Campaign. Honors College Capstone Experience/ Thesis Projects. Paper 360. http://digitalcommons.wku.edu/stu_hon_theses/360
  100. Wanasida, A. S., Bernarto, I., Sudibjo, N., & Pramono, R. (2021). Millennial Transformational Leadership on Organizational Performance in Indonesia Fishery Startup. Journal of Asian Finance, Economics and Business, 8(2), 555-562. https://doi.org/10.13106/jafeb.2021.vol8.no2.0555
  101. Wang, L., & Zhang, Y. (2016). An extended version of the theory of planned behaviour: the role of self-efficacy and past behaviour in predicting the physical activity of Chinese adolescents. Journal of Sports Sciences, 34(7), 587-597. https://doi.org/10.1080/02640414.2015.1064149
  102. Wang, E. (2010). The Effects of Browsing Frequency and Gender on the Relationship between Perceived Control and Patronage Intentions in E-tail. International Journal of Electronic Commerce, 14(3), 129-144. Retrieved July 27, 2020, from www.jstor.org/stable/20749974 https://doi.org/10.2753/JEC1086-4415140306
  103. Warburton, E. (2019). Polarisation in Indonesia: What if Perception is Reality. Available at https://www,newmandala.org/how-polarised-is-indonesia/
  104. Weeks, B. E., Ardevol-Abreu, A., & de Zuniga, H. G. (2017). Online Influence? Social Media Use, Opinion Leadership, and Political Persuasion, International Journal of Public Opinion Research, 29(2), 214-239. https://doi.org/10.1093/ijpor/edv050
  105. William, D. R., & Medlock, M. M. (2017). Health Effect of Dramatic Societal Events - Ramifications of the Recent Presidential Election. The New England Journal of Medicine, 376, 2295-2299. https://doi.org/10.1056/NEJMms1702111
  106. Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. Educational and Psychological Measurement, 76(6), 913-934. https://doi.org/10.1177/0013164413495237
  107. Wu, W. Y., Do, T. Y., Nguyen, P. T., Anridoh, N., & Vu, M. Q. (2020) An Integrated Framework of Customer-based Brand Equity and Theory of Planned Behavior: A Meta-analysis Approach. Journal of Asian Finance, Economics and Business, 7(8), 371-381. https://doi.org/10.13106/jafeb.2020.vol7.no8.371
  108. Xin,Z., Liang,M., Zhanyou,W., & Hua,X. (2019). Psychosocial factors influencing shared bicycle travel choices among Chinese: An application of theory planned behavior. PLOS ONE, 14(1), e0210964. https://doi.org/10.1371/journal.pone.0210964