ABSTRACT
Research has shown that persuasive strategies are more effective in bringing about a change in attitude or behavior when they are tailored to individuals or groups of similar individuals. Several domains such as exercise and health domains use the demographic data of users to tailor influence strategies such as their age, gender, and culture. However, in domains such as e-commerce where the users’ demographic data is unknown, there is a need to identify other factors that can be used to tailor persuasive strategies. To contribute to research in this area, this work-in-progress paper investigates the use of shoppers’ level of involvement in the shopping process as a potential factor for tailoring persuasive strategies in e-commerce. We present preliminary results from a game-based study that compares the response to Cialdini's persuasive strategies for people with high and low levels of involvement. Our results suggest that people with high levels of involvement in the shopping process are influenced differently from those with low level of involvement, making level of involvement a potential trait that can be used in tailoring persuasive strategies in e-commerce. The shoppers who are highly involved in the shopping process responded to more authority messages compared to other strategies, while those with low level of involvement responded to more commitment messages than other strategies. Also, the highly involved shoppers shopped for healthier foods for themselves and a child while they shopped the least healthy for a significant other while the low involved shoppers shopped healthier for their significant other and less healthy for themselves.
- Adaji, I., Kiron, N. and Vassileva, J. 2020. Evaluating the Susceptibility of E-commerce Shoppers to Persuasive Strategies. A Game-Based Approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020), 58–72.Google Scholar
- Adaji, I., Oyibo, K., Intelligence, J.V.-F. in A. and 2020, U. 2020. E-Commerce Shopping Motivation and the Influence of Persuasive Strategies. Frontiers in Artificial Intelligence. 3, (2020), 67.Google Scholar
- Adaji, I. and Vassileva, J. 2016. Evaluating personalization and persuasion in e-commerce. International Workshop on Personalized Persuasive Technology (Salzburg, 2016), 107–113.Google Scholar
- Alkış, N. and Taşkaya Temizel, T. 2015. The impact of individual differences on influence strategies. Personality and Individual Differences. 87, (Dec. 2015), 147–152. DOI:https://doi.org/10.1016/J.PAID.2015.07.037.Google Scholar
- Beharrell, B. and Denison, T.J. 1995. Involvement in a Routine Food Shopping Context. British Food Journal. 97, 4 (Mar. 1995), 24–29. DOI:https://doi.org/10.1108/00070709510085648.Google ScholarCross Ref
- Busch, M., Mattheiss, E., Reisinger, M., Orji, R., Fröhlich, P. and Tscheligi, M. 2016. More than Sex: The Role of Femininity and Masculinity in the Design of Personalized Persuasive Games. 219–229.Google Scholar
- Cialdini, R. 2001. Harnessing the science of persuasion. Harvard business review. 79, 9 (2001), 72–81.Google Scholar
- Cialdini, R. 2001. The science of persuasion. Scientific American. (2001).Google Scholar
- Cialdini, R.B. 2009. Influence: Science and practice. Pearson Education Boston.Google Scholar
- Conlin, R. and Labban, A. 2019. Clustering Attitudes and Behaviors of High/ Low Involvement Grocery Shopper. Journal of Food Products Marketing. 25, 6 (Jul. 2019), 647–667. DOI:https://doi.org/10.1080/10454446.2019.1629558.Google ScholarCross Ref
- Freedman, J.L. and Fraser, S.C. 1966. COMPLIANCE WITHOUT PRESSURE: THE FOOT-IN-THE-DOOR TECHNIQUE 3. Journal ol Personality and Social Psychology. 4, 2 (1966), 155–202.Google Scholar
- Hirsh, J.B., Kang, S.K. and Bodenhausen, G. V. 2012. Personalized Persuasion: Tailoring Persuasive Appeals to Recipients’ Personality Traits. Psychological Science. 23, 6 (Jun. 2012), 578–581. DOI:https://doi.org/10.1177/0956797611436349.Google ScholarCross Ref
- Kaptein, M. 2011. Adaptive persuasive messages in an e-commerce setting: the use of persuasion profiles. European Conference on Information Systems (Helsinki, 2011), 183.Google Scholar
- Kaptein, M. and Eckles, D. 2012. Heterogeneity in the Effects of Online Persuasion. Journal of Interactive Marketing. 26, 3 (2012), 176–188. DOI:https://doi.org/10.1016/j.intmar.2012.02.002.Google ScholarCross Ref
- Kaptein, M., Ruyter, B. De and Markopoulos, P. 2012. Adaptive persuasive systems: a study of tailored persuasive text messages to reduce snacking. ACM Transactions on. (2012).Google Scholar
- Kinley, T.R. and Josiam, B.M. 2010. Shopping behavior and the involvement construct Spring Break Tourism View project Movie Induced Tourism View project. Article in Journal of Fashion Marketing and Management. 14, 4 (2010), 562–575. DOI:https://doi.org/10.1108/13612021011081742.Google Scholar
- Kramer, T. and Spolter-Weisfeld, S. 2007. The effect of cultural orientation on consumer responses to personalization. Marketing Science. 26, 2 (2007), 246–258.Google ScholarDigital Library
- O'Cass, A. 2000. An assessment of consumers product, purchase decision, advertising and consumption involvement in fashion clothing. Journal of Economic Psychology. 21, 5 (Oct. 2000), 545–576. DOI:https://doi.org/10.1016/S0167-4870(00)00018-0.Google Scholar
- Orji, R. 2016. The impact of cultural differences on the persuasiveness of influence strategies. Proceedings of the 11th International Conference 2016 on Persuasive Technology (2016), 38–41.Google Scholar
- Orji, R., Mandryk, R. and Vassileva, J. 2014. Gender and persuasive technology: Examining the persuasiveness of persuasive strategies by gender groups. International Conference on Persuasive Technology (2014), 48–52.Google Scholar
- Phillips, D.M. and Stanton, J.L. 2004. Age-related differences in advertising: Recall and persuasion. Journal of Targeting, Measurement and Analysis for Marketing. 13, 1 (Sep. 2004), 7–20. DOI:https://doi.org/10.1057/palgrave.jt.5740128.Google ScholarCross Ref
- Scarborough, P. and Matthews, A. 2015. Reds are more important than greens: how UK supermarket shoppers use the different information on a traffic light nutrition label in a choice experiment. nternational Journal of Behavioral Nutrition and Physical Activity . 12, 1 (2015), 151.Google Scholar
- Silayoi, P. and Speece, M. 2004. Packaging and purchase decisions: An exploratory study on the impact of involvement level and time pressure. British Food Journal. Emerald Group Publishing Limited.Google Scholar
- Smith, K., Dennis, M. and Masthoff, J. 2016. Personalizing reminders to personality for melanoma self-checking. Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (2016), 85–93.Google ScholarDigital Library
- de Vries, R.A.J., Truong, K.P., Zaga, C., Li, J. and Evers, V. 2017. A word of advice: how to tailor motivational text messages based on behavior change theory to personality and gender. Personal and Ubiquitous Computing. 21, 4 (Aug. 2017), 675–687. DOI:https://doi.org/10.1007/s00779-017-1025-1.Google ScholarDigital Library
- Zaichkowsky, J.L. 1985. Measuring the Involvement Construct. Journal of Consumer Research. 12, 3 (Dec. 1985), 341. DOI:https://doi.org/10.1086/208520.Google ScholarCross Ref
Index Terms
- Level of Involvement and the Influence of Persuasive Strategies in E-commerce: A Game-Based Approach
Recommendations
Towards Improving E-commerce Users Experience Using Personalization & Persuasive Technology
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationWith the increase in the number of e-commerce companies over the last decade, there is stiffer competition for e-businesses to win and retain customers. Companies have to give clients reasons to shop with them and become return customers. The use of ...
Shopping Motivation and the Influence of Perceived Product Quality and Relative Price in E-commerce
UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and PersonalizationUnderstanding a consumer's motivation to shop online with a vendor can help an e-business better understand the attitude of customers and what they look out for in their shopping decision-making process. Equally important in the shopping decision making ...
The Effect of Gender and Age on the Factors That Influence Healthy Shopping Habits in E-Commerce
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and PersonalizationPeople typically eat what they shop for; if consumers shop for healthy foods, they will likely eat healthy foods. In order to influence healthier eating habits among consumers, it is important to identify the factors that influence them to shop for ...
Comments