Perceived usefulness of social media in financial decision-making: differences and similarities
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DOIhttp://dx.doi.org/10.21511/im.16(4).2020.13
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Article InfoVolume 16 2020, Issue #4, pp. 145-154
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Financial decision-making through social media blogs and opinions is an area not much explored by the researchers. This study intends to understand the perceived usefulness of social media in financial decision-making amongst individuals and groups based on demographic similarities and social parameters. This paper aims to understand the perception of various subgroups in society within the large population sample. The paper applies Mann-Whitney and Kruskal-Wallis non-parametric tests to examine the proposed research questions from a dataset of 201 individuals residing in two most populated states in Northern India. The analysis reveals the differences between different groups categorized based on generation, financially dependent, educational background, occupation, and geographical location. In terms of social media’s perceived utility in financial decision-making, results suggest that segregated groups based on cohort generation and occupation have significant variations relative to others. Based on the educational context, all other segments, number of financially dependent, geographical location, were found insignificant. The novelty of the paper lies in investigating the perceived usefulness of social media in financial decision-making amongst various homogenous groups based on demographics in a developing country. The study outcomes can be useful for the financial service providers and social media platforms in comprehending consumer behavior to devise an innovative marketing strategy for financial products targeting specific segments through enhanced coordination between them.
Acknowledgment
This paper was supported by Internal Grant Agency of FaME TBU No. IGA/FaME/2019/002.
- Keywords
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JEL Classification (Paper profile tab)D14, L82, M31, M37
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References44
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Tables5
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Figures0
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- Table 1. Tests of normality
- Table 2. Measurement scale
- Table 3. Sample profile
- Table 4. Mann-Whitney U test results
- Table 5. Kruskal-Wallis test results
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- Ahmed, M. A., & Zahid, Z. (2014). Role of social media marketing to enhance CRM and brand equity in terms of purchase intention. Asian Journal of Management Research, 04(3), 533-549.
- Ali, M. M., Binti, M., & Maideen, H. (2019). A Study on Factors Inluencing the Adoption of a Crowdsourcing Mobile Application among Generation Y and Z in Maldives.
- Arvola, A., Vassallo, M., Dean, M., Lampila, P., Saba, A., Lähteenmäki, L., & Shepherd, R. (2008). Predicting intentions to purchase organic food: The role of affective and moral attitudes in the Theory of Planned Behaviour. Appetite, 50(2-3), 443-454.
- Bai, L., & Yan, X. (2020). Impact of Firm-Generated Content on Firm Performance and Consumer Engagement: Evidence from Social Media in China. Journal of Electronic Commerce Research, 21(1), 56-74.
- Bejtkovský, J. (n.d.). The Current Generations : The Baby Boomers, X , Y and Z in the Context of Human Capital Management of the 21st Century in Selected Corporations in the Czech Republic.
- Bishnoi, S. (2013). An Empirical Study on Investors’ Behaviour in National Capital Region (NCR). International Journal on Global Business Management & Research, 1(2), 14-26.
- Blankespoor, E., Miller, G. S., & White, H. D. (2014). The role of dissemination in market liquidity: Evidence from firms’ use of TwitterTM. Accounting Review, 89(1), 79-112.
- Chan, R. Y. K. (2001). Determinants of Chinese consumers’ green purchase behavior. Psychology and Marketing, 18(4), 389-413.
- Chen, A., & Peng, N. (2012). Green hotel knowledge and tourists’ staying behavior. Annals of Tourism Research, 39(4), 2211-2216.
- Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic Word-Of-Mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1).
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). United States of America: L. Erlbaum Associates.
- Dean, M., Raats, M. M., & Shepherd, R. (2012). The Role of Self-Identity, Past Behavior, and Their Interaction in Predicting Intention to Purchase Fresh and Processed Organic Food. Journal of Applied Social Psychology, 42(3), 669-688.
- Gardiner, S., Grace, D., & King, C. (2015). Is the Australian domestic holiday a thing of the past? Understanding baby boomer, Generation X and Generation Y perceptions and attitude to domestic and international holidays. Journal of Vacation Marketing, 21(4), 336-350.
- Gordon, J., & Anderson, T. (2004). Cross-Cultural, Cross-Cultural Age and Cross-Cultural Generational Differences in Values between the United States and Japan. Journal of Applied Management and Entrepreneurship, 9(1), 21.
- Hanafizadeh, P., Khosravi, B., & Badie, K. (2019). Global discourse on ICT and the shaping of ICT policy in developing countries. Telecommunications Policy, 43(4), 324-338.
- Hasan, M. R., Haq, M. R., & Rahman, M. Z. (2019). Impact of social network on purchase decision: a study on teenagers of Bangladesh. Journal of Business & Retail Management Research, 14(01), 20-32.
- Horn, I. S., Taros, T., Dirkes, S., Hüer, L., Rose, M., Tietmeyer, R., & Constantinides, E. (2015). Business reputation and social media: A primer on threats and responses. Journal of Direct, Data and Digital Marketing Practice, 16(3), 193-208.
- Ismail, S., Sham, R., & Wahab, S. N. (2018). Impacts of Online Social Media on Investment Decision in Malaysia.
- Jiri, B. (2016). The Employees of Baby Boomers Generation, Generation X, Generation Y and Generation Z in Selected Czech Corporations as Conceivers of Development and Competitiveness in their Corporation. Journal of Competitiveness, 8(4), 105-123.
- Johnston, R. B. (2016). Arsenic and the 2030 Agenda for sustainable development. In Arsenic Research and Global Sustainability – Proceedings of the 6th International Congress on Arsenic in the Environment, AS 2016 (pp. 12-14).
- Kapil, Y., & Roy, A. (2014). A Critical Evaluation of Generation Z at Workplaces. International Journal of Social Relevance & Concern, 2(1), 1.
- Kaur, P., Virani, S., & Fazalbhoy, S. (2016). Psychological Traits and Demographic Factors Do They Affect Investor’s Behavior? Indian Journal of Management Science, 6(1), 46-54.
- Khan, Akhtar, Dey, I. (2020). Financial Anxiety, Financial advice, and E-payment use : Relationship and perceived differences between males & females of Generation Z. Journal of Critical Reviews, 7(18), 1812-1820.
- Khan, K. A., & Akhtar, M. A. (2020). Electronic payment system use : a mediator and a predictor of financial satisfaction. Investment Management and Financial Innovations, 17(3), 246-262.
- Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480-1486.
- Kulkarni, M. S. (2014). A Study of Investment Behaviour Based on Demographics. Journal of Commerce and Accounting Research, 3(4).
- Kwahk, K. Y., & Kim, B. (2017). Effects of social media on consumers’ purchase decisions: evidence from Taobao. Service Business, 11(4), 803-829.
- Lan, Q., Xiong, Q., He, L., & Ma, C. (2018). Individual investment decision behaviors based on demographic characteristics: Case from China. PLoS ONE, 13(8), 1-17.
- Levickaite, R. (2010). Y, X, Z kartos: Pasaulio be sienu idejos formavimas naudojantis socialiniais tinklais (Lietuvos atvejis). Limes, 3(2), 170-183.
- Martin, C. A. (2005). From high maintenance to high productivity: What managers need to know about Generation Y. Industrial and Commercial Training, 37(1), 39-44.
- Metawa, N., Hassan, M. K., Metawa, S., & Safa, M. F. (2019). Impact of behavioral factors on investors’ financial decisions: case of the Egyptian stock market. International Journal of Islamic and Middle Eastern Finance and Management, 12(1), 30-55.
- Mostafa, M., M. (2006). Antecedents of Egyptian Consumers’ Green Purchase Intentions: A Hierarchical Multivariate Regression Model. Journal of International Consumer Marketing, 19(2), 97-126.
- Mostafa, M. M. (2009). Shades of green: A psychographic segmentation of the green consumer in Kuwait using self-organizing maps. Expert Systems with Applications, 36(8), 11030-11038.
- Paul, J., Modi, A., & Patel, J. (2016). Predicting green product consumption using theory of planned behavior and reasoned action. Journal of Retailing and Consumer Services, 29, 123-134.
- Paul, T. (2015). The Effect of Social Media on Trading Behavior : Evidence From Twitter The Effect of Social Media on Trading Behavior : Evidence From Twitter.
- Pradhan, S. K., & Kasilingam, R. (2015). Corporate Action and Investment Decision: A Study Based on Demographic Characters of Investors. ASBM Journal of Management, 8(1), 43.
- Reports, J. D. P. (2014). New-Vehicle Buyers Who Spend the Most Time Shopping Online Also Visit the Most Dealerships WESTLAKE.
- Social Statistics Division, G. of I. (2017). Youth in India 2017.
- Stolper, O. A., & Walter, A. (2017). Financial literacy, financial advice, and financial behavior. Journal of Business Economics, 87(5), 581-643.
- Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176.
- Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends in Sport Sciences, 1(21), 19-25.
- Turner, A. (2018). Generation Z: Technology and Social Interest.
- van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101, 449-472.
- World Economic Forum. (2019). Global Gender Gap Report 2020: Insight Report.