Authors retain the copyright without restrictions for their published content in this journal. HSSR is a SHERPA ROMEO Green Journal.
DECISION MAKING IN THE ERA OF INFOBESITY: A STUDY ON INTERACTION OF GENDER AND PSYCHOLOGICAL TENDENCIES
Corresponding Author(s) : Sana Maidullah
Humanities & Social Sciences Reviews,
Vol. 7 No. 5 (2019): September
Purpose: This study examines information processing during consumer decision making on online platforms as influenced by gender differences and psychological tendencies. Further exploration is ‘how much information is too much information; leading to infobesity.’
Methodology: The methodology to address the objective included the questionnaires for assessment of psychological tendencies and naturalistic experiments to measure decision making in online conditions. An online marketplace prototype was created for mobile purchase, named ‘mobile bazaar,’ and another for hotel booking, named ‘backpackers.’ The prototype was designed in such a way that the manipulation of information presented to the participant is possible. Participants were recruited with purposive and snowball sampling method depending upon their willingness and familiarity with online market platforms. Final data were collected from Three hundred sixty-eight participants during the period of October 2017- March 2018. The data from questionnaires and the computerized task was scored and analyzed with SPSS version 21 with t-test, chi-square and logistic regression analysis methods.
Main findings: The present study shows the influence of psychological tendencies (i.e., need for closure, exploratory tendencies, and uncertainty avoidance) and gender difference in decision making. Female seems to follow ‘process less to process better’ strategy, whereas, men seem to follow ‘process more to get better’ strategy. The findings also provided input to the debate of information measurement in consumer research.
Implications: Understanding decision making features of Indian consumers can not only contribute to the understanding of the naturalistic decision-making process itself but also can provide inputs to the market researchers, designers, and policymakers.
Novelty /originality of the study: The study was novel in terms of its use of the online marketplace prototype as a naturalistic decision making study method. This method allowed the researchers to examine participants' behavior (of information processing and decision making) in real like scenarios and yet had the luxury of manipulation of presenting information as per research design. Therefore the findings of present study will have more generalizability.
- Bettman, J. R. (1979). Memory factors in consumer choice: A review. The Journal of Marketing, 37–53. https://doi.org/10.1177/002224297904300205 DOI: https://doi.org/10.1177/002224297904300205
- Pilli, L. E., & Mazzon, J. A. (2016). Information overload, choice deferral, and moderating role of need for cognition: Empirical evidence. Revista de Administração, 51(1), 036–055. https://doi.org/10.5700/rausp1222 DOI: https://doi.org/10.5700/rausp1222
- Resnick, M. (2001). Recognition Primed Decision Making in E-Commerce. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 45(4), 488–492. https://doi.org/10.1177/154193120104500447 DOI: https://doi.org/10.1177/154193120104500447
- Li, N., & Zhang, P. (2002). Consumer online shopping attitudes and behavior: An assessment of research. Eighth Americas Conference on Information Systems, August, 508–517. https://doi.org/10.1016/S0022-4359(01)00056-2 DOI: https://doi.org/10.1016/S0022-4359(01)00056-2
- Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81. https://doi.org/10.1037/h0043158 DOI: https://doi.org/10.1037/h0043158
- Melinat, P., Kreuzkam, T., & Stamer, D. (2014). Information Overload : A Systematic Literature Review Information Overload : A Systematic Literature Review, (September). https://doi.org/10.1007/978-3-319-11370-8_6 DOI: https://doi.org/10.1007/978-3-319-11370-8_6
- Speier, C., Valacich, J. S., & Vessey, I. (1999a). The influence of task interruption on individual decision making: An information overload perspective. Decision Sciences, 30(2), 337–360. https://doi.org/10.1111/j.1540-5915.1999.tb01613.x
- Speier, C., Valacich, J. S., & Vessey, I. (1999b). The Influence of Task Interruption on Individual Decision Making: An Information Overload Perspective. Decision Sciences, 30(2), 337–360. https://doi.org/10.1111/j.1540-5915.1999.tb01613.x DOI: https://doi.org/10.1111/j.1540-5915.1999.tb01613.x
- Milord, J. T., & Perry, R. P. (1977). A methodological study of overloadx. The Journal of General Psychology, 97(1), 131–137. https://doi.org/10.1080/00221309.1977.9918509 DOI: https://doi.org/10.1080/00221309.1977.9918509
- Jacoby, J., Speller, D. E., & Kohn, C. a. (1974). Brand Choice Behavior as a Function of Information Load: Replication and Extension. Journal of Marketing Research, 11(1), 63–69. https://doi.org/10.2307/3150994 DOI: https://doi.org/10.2307/3150994
- Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. Information Society, 20(5), 325–344. https://doi.org/10.1080/01972240490507974 DOI: https://doi.org/10.1080/01972240490507974
- Wright, P. (1975). “Consumer choice strategies: Simplifying vs. optimizing”. Journal of Marketing Research, 60–67. https://doi.org/10.1177/002224377501200109 DOI: https://doi.org/10.1177/002224377501200109
- Malhotra, N. K. (1982). Information Load and Consumer Decision Making. Journal of Consumer Research, 8(4), 419. https://doi.org/10.1086/208882 DOI: https://doi.org/10.1086/208882
- Lee, B., & Lee, W. (n.d.). The Effect of Information Overload on Consumer Choice Quality in an On-Line Environment, 21(March 2004), 159–183. https://doi.org/10.1002/mar.20000 DOI: https://doi.org/10.1002/mar.20000
- Rudd, J. M. (2009). Page 1 of 8 ANZMAC 2009. Management, (2008), 1–8. https://doi.org/10.1080/09652540903511290 DOI: https://doi.org/10.1080/09652540903511290
- Hwang, M. I., & Lin, J. W. (1999). Information dimension, information overload and decision quality. Journal of Information Science, 25(3), 213–218. https://doi.org/10.1177/016555159902500305 DOI: https://doi.org/10.1177/016555159902500305
- Huang, M.-H. (2000). Information load: its relationship to online exploratory and shopping behavior. International Journal of Information Management, 20(5), 337–347. https://doi.org/10.1016/S0268-4012(00)00027-X DOI: https://doi.org/10.1016/S0268-4012(00)00027-X
- Kock, N. (2001). Information Overload : A Decision Making Perspective Information Overload : A Decision Making, (August 2015). https://doi.org/10.1007/978-3-642-56680-6 DOI: https://doi.org/10.1007/978-3-642-56680-6
- Putrevu, S. (2001). Exploring the Origins and Information Processing Differences Between Men and Women: Implications for Advertisers. Academy of Marketing Science Review, 10(July 2015), 1–16. https://doi.org/https://login.ezproxy.napier.ac.uk/login?url=http://search.proquest.com.ezproxy.napier.ac.uk/docview/200857003?accountid=16607
- Kim, D.-Y., Lehto, X. Y., & Morrison, A. M. (2007). Gender differences in online travel information search: Implications for marketing communications on the internet. Tourism Management, 28(2), 423–433. https://doi.org/10.1016/j.tourman.2006.04.001 DOI: https://doi.org/10.1016/j.tourman.2006.04.001
- Everhart, D. E., Shucard, J. L., Quatrin, T., & Shucard, D. W. (2001). Sex-related differences in event-related potentials, face recognition, and facial affect processing in prepubertal children. Neuropsychology, 15(3), 329. https://doi.org/10.1037/0894-418.104.22.1689 DOI: https://doi.org/10.1037/0894-422.214.171.1249
- Geary, D. C. (1996). Sexual selection and sex differences in mathematical abilities. Behavioral and Brain Sciences, 19(2), 229–247. https://doi.org/10.1017/S0140525X00042400 DOI: https://doi.org/10.1017/S0140525X00042400
- Schumacher, P., & Morahan-Martin, J. (2001). Gender, Internet and computer attitudes and experiences. Computers in Human Behavior, 17(1), 95–110. https://doi.org/10.1016/S0747-5632(00)00032-7 DOI: https://doi.org/10.1016/S0747-5632(00)00032-7
- Darley, W. K., & Smith, R. E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of Advertising, 24(1), 41–56. https://doi.org/10.1080/00913367.1995.10673467 DOI: https://doi.org/10.1080/00913367.1995.10673467
- Else-Quest, N. M., Hyde, J. S., Goldsmith, H. H., & Van Hulle, C. A. (2006). Gender differences in temperament: A meta-analysis. Psychological Bulletin, 132(1), 33–72. https://doi.org/10.1037/0033-2909.132.1.33 DOI: https://doi.org/10.1037/0033-2909.132.1.33
- Meyers-levy, J., & Loken, B. (2014). ScienceDirect Revisiting gender differences : What we know and what lies ahead ☆. Journal of Consumer Psychology. https://doi.org/10.1016/j.jcps.2014.06.003 DOI: https://doi.org/10.1016/j.jcps.2014.06.003
- ChanLin, L.-J. (1999). Gender differences and the need for visual control. International Journal of Instructional Media, 26(3), 329.
- Gysler, M., Brown Kruse, J., & Schubert, R. (2002). Ambiguity and gender differences in financial decision making. Working Papers/WIF, 2002(23).
- Coley, A., & Burgess, B. (2003). Gender differences in cognitive and affective impulse buying. Journal of Fashion Marketing and Management: An International Journal, 7(3), 282–295. https://doi.org/10.1108/13612020310484834 DOI: https://doi.org/10.1108/13612020310484834
- Tifferet, S., & Herstein, R. (2012). Gender differences in brand commitment, impulse buying, and hedonic consumption. Journal of Product & Brand Management. https://doi.org/10.1108/10610421211228793 DOI: https://doi.org/10.1108/10610421211228793
- Park, J., Yoon, Y., Lee, B., Park, J., Yoon, Y., & Shavitt, S. (2009). Association for consumer research, 36, 362–366.
- Downing, K., Chan, S., Downing, W., Kwong, T., & Lam, T. (2008). Measuring gender differences in cognitive functioning. Multicultural Education & Technology Journal. https://doi.org/10.1108/17504970810867124 DOI: https://doi.org/10.1108/17504970810867124
- Huang, J. H., & Yang, Y. C. (2010). Gender differences in adolescents’ online shopping motivations. African Journal of Business Management, 4(6), 849–857.
- Javadi, M. H. M., Rezaie Dolatabadi, H., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. R. (2012). An Analysis of Factors Affecting on Online Shopping Behavior of Consumers. International Journal of Marketing Studies, 4(5), 81–98. https://doi.org/10.5539/ijms.v4n5p81 DOI: https://doi.org/10.5539/ijms.v4n5p81
- Roets, A., & Van Hiel, A. (2007). Separating ability from need: Clarifying the dimensional structure of the need for closure scale. Personality and Social Psychology Bulletin, 33(2), 266–280. https://doi.org/10.1177/0146167206294744 DOI: https://doi.org/10.1177/0146167206294744
- Buhr, K., & Dugas, M. J. (2002). The intolerance of uncertainty scale: Psychometric properties of the English version. Behaviour Research and Therapy, 40(8), 931–945. https://doi.org/10.1016/S0005-7967(01)00092-4 DOI: https://doi.org/10.1016/S0005-7967(01)00092-4
- Patton, J.H., Stanford, M.S., & Barratt, E.S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51(6), 768-74. https://doi.org/10.1002/1097-4679(199511)51:6<768::AID-JCLP2270510607>3.0.CO;2-1 DOI: https://doi.org/10.1002/1097-4679(199511)51:6<768::AID-JCLP2270510607>3.0.CO;2-1
- Raju, P.S., & Venkatesan, M. (1980). Exploratory behavior in the consumer context: A state of the art review. In Advances in Consumer Research, Vol. 7 Ed. J.C. Olson, Ann Arbor. MI: Association for Consumer Research, 258-263.
- Lurie, N. (2002). Decision making in information-rich environments: The role of information structure. ACR North American Advances.
- Maidullah, S. & Sharma, A. (2019). Gender difference in information processing limit during online decision making. Journal of Management Research and Analysis, 6(1), 1-10.
- Shashaani, L. (1997). Gender differences in computer attitudes and use among college students. Journal of Educational Computing Research, 16(1), 37–51. https://doi.org/10.2190/Y8U7-AMMA-WQUT-R512 DOI: https://doi.org/10.2190/Y8U7-AMMA-WQUT-R512