International Journal of Marketing and Business Communication

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Understanding the Impact of Social Media on Consumer’s Attitude and Decision Making Process


Affiliations
1 Associate Professor, Department of Commerce, Daulat Ram College, University of Delhi, Delhi, India
2 Assistant Professor, Department of Commerce, Ramanujan College, University of Delhi, Delhi, India
3 Assistant Professor, Department of Commerce, Daulat Ram College, University of Delhi, Delhi,, India
     

   Subscribe/Renew Journal


Consumer attitude is a combination of consumer belief systems, thoughts, and behavioral intent towards a brand. The shopping behavior of consumers today is greatly influenced by social media. Research suggests that there is an increasing reliance of consumers on social media to get information about unfamiliar brands. This study tries to understand how consumer attitude, when combined with social media, helps the customer make the final purchase decision. The present research was undertaken to determine the degree of social media’s effect on customer decision-making for fast-moving consumer products at various phases of the process. The stages included are information search, alternative evaluation and post purchase stages. SEM has been used to evaluate the theoretical model. The model supports a direct relationship between attitude and social media. Also, direct relationship was supported between attitude and different stages of decision making process. Furthermore, the relationship between attitude and information search, and attitude and post purchase behaviour was found to be significantly partially mediated by social media.

Keywords

Consumer Attitude, Social Media, Decision Making Process
Subscription Login to verify subscription
User
Notifications
Font Size


  • Ahire, S. L., Golhar, D. Y., & Waller, M. A. (1996).Development and validation of TQM implementation constructs. Decision Sciences, 27(1), 23-56.

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.

  • Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79-95.

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

  • Baird, C. H., & Parasnis, G. (2011). From social media to social customer relationship management. Strategy & Leadership.

  • Baumgartner, H., & Steenkamp, J. B. E. (1996).Exploratory consumer buying behavior: Conceptualization and measurement. International Journal of Research in Marketing, 13(2), 121-137.

  • Berger, J., & Schwartz, E. M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48(5), 869-880.

  • Bettman, J. R. (1979). Information processing theory of consumer choice. Addison-Wesley Pub. Co.

  • Blackshaw, P. (2006). The consumer-controlled surveillance culture. Retrieved March 22, 2011, from http://www.clickz.com/clickz/column/1706163/ the-consumer-controlledsurveillance-culture

  • Blackshaw, P., & Nazzaro, M. (2006). Word of mouth in the age of the web-fortified consumer. ConsumerGenerated Media (CGM), 101.

  • Bolton, R. N., Parasuraman, A., Hoefnagels, A., Migchels, N., Kabadayi, S., Gruber, T.,... Solnet, D. (2013). Understanding generation Y and their use of social media: A review and research agenda. Journal of Service Management, 24(3), 245-267.

  • Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7(3), 461-483.

  • Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Sage.

  • Chang, Y., & Thorson, E. (2004). Television and web advertising synergies. Journal of Advertising, 33(2), 75-84.

  • Chaturvedi, S., & Gupta, S. (2014). Effect of social media on online shopping behaviour of apparels in Jaipur city: An analytical review. Journal of Business Management, Commerce & Research, 2(7), 1-8.

  • Chawdhary, R., & Dall’Olmo Riley, F. (2015).Investigating the consequences of word of mouth from a WOM sender’s perspective in the services context. Journal of Marketing Management, 31(9-10), 1018-1039.

  • Chen, C., Cribbin, T., Macredie, R., & Morar, S. (2002).Visualizing and tracking the growth of competing paradigms: Two case studies. Journal of the American Society for information Science and Technology, 53(8), 678-689.

  • Chen, J., Xu, H., & Whinston, A. B. (2011). Moderated online communities and quality of user-generated content. Journal of Management Information Systems, 28(2), 237-268.

  • Chen, S. C., & Lin, C. P. (2019). Understanding the effect of social media marketing activities: The mediation of social identification, perceived value, and satisfaction.Technological Forecasting and Social Change, 140, 22-32.

  • Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities.Internet Research, 18(3), 229-247.

  • Chung, J. Y., & Buhalis, D. (2008). Information needs in online social networks. Information Technology & Tourism, 10(4), 267-281.

  • Constantinides, E., & Fountain, S. J. (2008). Web 2.0: Conceptual foundations and marketing issues. Journal of Direct, Data and Digital Marketing Practice, 9(3), 231-244.

  • Craighead, C. W., Ketchen, D. J., Dunn, K. S., & Hult, G. T. M. (2011). Addressing common method variance: Guidelines for survey research on information technology, operations, and supply chain management.IEEE Transactions on Engineering Management, 58(3), 578-588.

  • Darley, W. K., Blankson, C., & Luethge, D. J. (2010). Toward an integrated framework for online consumer behavior and decision making process: A review.Psychology & Marketing, 27(2), 94-116.

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

  • De Valck, K., Van Bruggen, G. H., & Wierenga, B. (2009). Virtual communities: A marketing perspective. Decision Support Systems, 47(3), 185-203.

  • Elliott, M. T., & Speck, P. S. (2005). Factors that affect attitude toward a retail web site. Journal of Marketing Theory and Practice, 13(1), 40-51.eMarketer. (2008). Consumers await on social networks.

  • eMarketer Research. Retrieved from http://www.

  • emarketer.com/Article.aspx? id=1006622 eMarketer. (2018). Social network users and penetration in worldwide. Retrieved from https://tinyurl.com/ ycr2d3v9

  • Facebook. (2019). Company info. Retrieved from https:// tinyurl.com/n544jrt

  • Festinger, L. (1957). A theory of cognitive dissonance.Stanford University Press: Stanford, California.

  • Fornell, C. R., & Lacker, D. F. (1981). Two structural equation models with unobservable variables and measurement error. 18.

  • Gao, H., Tate, M., Zhang, H., Chen, S., & Liang, B. (2018).Social media ties strategy in international branding: An application of resource-based theory. Journal of International Marketing, 26(3), 45-69.

  • Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193-225.

  • Hair, J. F., Celsi, M., Ortinau, D. J., & Bush, R. P. (2010).Essentials of marketing research (vol. 2). New York, NY: McGraw-Hill/Irwin.

  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M.(2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320-340.

  • Hameed, B. (2011). Social media usage exploding amongst fortune 500 companies. Social Times.

  • Hamilton, M., Kaltcheva, V. D., & Rohm, A. J. (2016).Social media and value creation: The role of interaction satisfaction and interaction immersion. Journal of Interactive Marketing, 36, 121-133.

  • Harman, H. H. (1976). Modern factor analysis. University of Chicago Press.

  • Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 4-21.

  • Herhold, K. (2017). How businesses use social media: 2017 survey.

  • Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 21(2), 256-276.

  • Jepsen, A. L. (2006). Information search in virtual communities: Is it replacing use of off‐ line communication? Journal of Marketing Communications, 12(4), 247-261.

  • Joyce, M., & Kirakowski, J. (2015). Measuring attitudes towards the internet: The general internet attitude scale.International Journal of Human-Computer Interaction, 31(8), 506-517.

  • Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark IV.Educational and Psychological Measurement, 34(1), 111-117.

  • Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model.

  • International Journal of Bank Marketing, 30(4), 303-322.

  • Kesharwani, A., & Tiwari, R. (2011). Exploration of internet banking website quality in India: A webqual approach. Great Lakes Herald, 5(1), 40-58.

  • Kim, J. O., & Mueller, C. W. (1978). Factor analysis: Statistical methods and practical issues (no. 14). Sage.

  • Kim, Y. S. (2011). Application of the cognitive dissonance theory to the service industry. Services Marketing Quarterly, 32(2), 96-112.

  • Kiran, P., & Vasantha, S. (2016). Transformation of consumer attitude through social media towards purchase intention of cars. Indian Journal of Science and Technology, 9(21), 1-9.

  • Klein, L. R., & Ford, G. T. (2003). Consumer search for information in the digital age: An empirical study of prepurchase search for automobiles. Journal of Interactive Marketing, 17(3), 29-49.

  • Kotler, P. (1997) Marketing management: Analysis, planning, implementation, and control (9th ed.). Prentice Hall, Upper Saddle River.

  • Kotler, P. (2003). Marketing for hospitality and tourism, 5/e. Pearson Education India.

  • Kozinets, R. V. (1999). E-tribalized marketing? The strategic implications of virtual communities of consumption. European Management Journal, 17(3), 252-264.

  • Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61-72.

  • Krueger, R. A. (1997). Developing questions for focus groups (vol. 3). Sage Publications.

  • Lai, L. S., & Turban, E. (2008). Groups formation and operations in the Web 2.0 environment and social networks. Group Decision and Negotiation, 17(5), 387-402.

  • Lee, E. (2013). Impacts of social media on consumer behavior: Decision making process (Bachelor’s thesis).Turku University of Applied Sciences.

  • Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49(1), 51-70.

  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008).Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458-468.

  • Lu, H. P., & Hsiao, K. L. (2010). The influence of extro/introversion on the intention to pay for social networking sites. Information & Management, 47(3), 150-157.

  • Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357-365.

  • McDonald, R. P., & Ho, M. H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64.

  • Mitsis, A., & Foley, P. (2012). Do generational membership and psychographic characteristics influence positive word of mouth in a university context?

  • Asian Academy of Management Journal, 17(1), 1.

  • Mortimer, K., & Pressey, A. (2013). Consumer information search and credence services: Implications for service providers. Journal of Services Marketing, 27(1), 49-58.

  • Mortimer, K., & Pressey, A. (2013). Consumer information search and credence services: Implications for service providers. Journal of Services Marketing, 27(1), 49-58.

  • Nunnally, J. C. (1978). Psychometric theory (pp. 86-113, 190-255). McGraw-Hill Book Company.

  • O’Leary-Kelly, S. W., & Vokurka, R. J. (1998). The empirical assessment of construct validity. Journal of Operations Management, 16(4), 387-405.

  • Peterson, R. A., Balasubramanian, S., & Bronnenberg,B. J. (1997). Exploring the implications of the internet for consumer marketing. Journal of the Academy of Marketing Science, 25(4), 329.

  • Ramalho, J. (2018). Online shopping and social media: Friends or foes?

  • Ratchford, B. T., Lee, M. S., & Talukdar, D. (2003). The impact of the Internet on information search for automobiles. Journal of Marketing Research, 40(2),193-209.

  • Ridings, C. M., & Gefen, D. (2004). Virtual community attraction: Why people hang out online. Journal of Computer-Mediated Communication, 10(1),JCMC10110.

  • Sandle, T. (January, 2018). Predicting consumer behavior is key to business success. Retrieved from http://www.digitaljournal.com/business/predictingconsumer-behavior-is-key-to-business-success/article/512713

  • Sarantakos, S. (2012). Social research. Macmillan International Higher Education.

  • Ščeulovs, D., & Gaile-Sarkane, E. (2010). Electronic tools for company’s presence, identification and marketingin e-environment: Theory and practice. Economics andManagement, 15, 775-782.

  • Schlosser, A. E. (2005). Posting versus lurking: Communicating in a multiple audience context. Journal of Consumer Research, 32(2), 260-265.

  • Schmidt, J. B., & Spreng, R. A. (1996). A proposed model of external consumer information search. Journal of the Academy of Marketing Science, 24(3), 246-256.

  • Schreiber, J. B. (2008). Core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy, 4(2), 83-97.

  • Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. John Wiley & Sons.

  • Srinivasan, N., & Ratchford, B. T. (1991). An empirical test of a model of external search for automobiles. Journal of Consumer Research, 18(2), 233-242.

  • Stewart, D. W. (1981). The application and misapplication of factor analysis in marketing research. Journal of Marketing Research, 18(1), 51-62.

  • Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213-225.

  • Susarla, A., Oh, J. H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 23(1),23-41.

  • Sweeney, J. C., Hausknecht, D., & Soutar, G. N. (2000). Cognitive dissonance after purchase: A multidimensional scale. Psychology & Marketing,17(5), 369-385.

  • Thackeray, R., Neiger, B. L., Hanson, C. L., & McKenzie, J. F. (2008). Enhancing promotional strategies within social marketing programs: Use of Web 2.0 social media. Health Promotion Practice, 9(4), 338-343.

  • Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions:Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1),41-48.

  • Wilson, T. D. (1997). Information behaviour: An interdisciplinary perspective. Information Processing & Management, 33(4), 551-572.


Abstract Views: 170

PDF Views: 0




  • Understanding the Impact of Social Media on Consumer’s Attitude and Decision Making Process

Abstract Views: 170  |  PDF Views: 0

Authors

Sunita Gupta
Associate Professor, Department of Commerce, Daulat Ram College, University of Delhi, Delhi, India
Pankaj Gupta
Assistant Professor, Department of Commerce, Ramanujan College, University of Delhi, Delhi, India
Renu Yadav
Assistant Professor, Department of Commerce, Daulat Ram College, University of Delhi, Delhi,, India

Abstract


Consumer attitude is a combination of consumer belief systems, thoughts, and behavioral intent towards a brand. The shopping behavior of consumers today is greatly influenced by social media. Research suggests that there is an increasing reliance of consumers on social media to get information about unfamiliar brands. This study tries to understand how consumer attitude, when combined with social media, helps the customer make the final purchase decision. The present research was undertaken to determine the degree of social media’s effect on customer decision-making for fast-moving consumer products at various phases of the process. The stages included are information search, alternative evaluation and post purchase stages. SEM has been used to evaluate the theoretical model. The model supports a direct relationship between attitude and social media. Also, direct relationship was supported between attitude and different stages of decision making process. Furthermore, the relationship between attitude and information search, and attitude and post purchase behaviour was found to be significantly partially mediated by social media.

Keywords


Consumer Attitude, Social Media, Decision Making Process

References