ONLINE RECOMMENDATION SYSTEMS’ USAGE BY COMPANIES IN BALTIC COUNTRIES

Elina Gaitniece

Abstract


Global retailers are using sophisticated online recommendation systems (ORS) which enhance customers' loyalty towards the specific site. Online markets in Baltic countries are growing fast, but Baltic e-commerce sites are not using a wide enough range of eWOM tools. The aim of this paper is – to evaluate how eWOM through ORS is perceived and used by digital marketing specialists and e-commerce players in the Baltics. Research methods used were: literature analysis on ORS’s influence on consumer purchase decisions, and an expert survey and monitoring study. The research results revealed major barriers for advanced ORS usage in the Baltics, as they discovered a gap between experts' opinion and the current reality in the Baltics. The article provides recommendations to online retailers in the Baltics on improvements that are needed

Keywords


recommendation system, eWOM, Baltic countries

Full Text:

PDF

References


Berger, J. (2013). Contagious: Why Things Catch On. New York: Simon & Schuster.

Berger, J., Sorensen, A. T., & Rasmussen, S. J. (2010). Positive Effects of Negative Publicity: When Negative Reviews Increase Sales. Marketing Science, 29(5), 815-827.

Burrow, J. L., & Fowler, A. R. (2012). Marketing. Boston: Cengage Learning.

Chatterjee, P. (2001). Online reviews: do consumers use them? Advances in Consumer Research Volume, 28, 129-133.

Flanagin, A. J., & Metzger, M. J. (2013). Trusting expert- versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Com puters in Human Behavior, 29, 1626–1634.

Floh, A., Koller, M., & Zauner, A. (2013). Taking a deeper look at online reviews: The asymmetric effect of valence intensity on shopping behaviour. Journal of Marketing Management, 29, 646–670.

Kaufman, I., & Horton, C. (2015). Digital Marketing: Integrating Strategy and Tactics with Values. New York: Routledge.

Liao, S.-h., & Chang, H.-k. (2016). A rough set-based association rule approach for a recommendation system for online consumers. Information Processing and Management(52), 1142–1160.

Oestreicher-Singer, G., & Sundararajan, A. (2012). The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets. Management Science, 58(11), 1963 - 1981.

Pang, J., & Qiu, L. (2016). Effect of Online Review Chunking on Product. International Journal of Electronic Commerce, 20(3), 355-383.

Park, C., & Lee, T. (2009). Information direction, website reputation and eWOM effect: A moderating role of product type. Journal of Business Research, 62(1), 61–67.

Pine, B. J., Peppers, D., & Rogers, M. (1995). Do You Want to Keep Your Customers Forever? Boston: Harvard Business Review Press.

Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender Systems in E-Commerce. New York: Proceedings of the 1st ACM conference on Electronic commerce.

Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers online choices. Journal of Retailing, 80(2), 159-169.

Strauss, J., & Frost, R. (2014). E-Marketing. Harlow: Pearson Education Limited.

Thurau, T. H., Gwiner, K., Walsh, G., & Gremler, D. (2004). Electronic word-of-mouth via Consumer-opinion Platforms: What Motivates Consumers To Articulate Themselves on the Internet? Journal of Interactive Marketing, 38-53.




DOI: http://dx.doi.org/10.12955/cbup.v5.913

Refbacks

  • There are currently no refbacks.


Print ISSN 1805-997X, Online ISSN 1805-9961

(c) 2016 Central Bohemia University