Nesrine Charradi
ABSTRACT
With the advent of the web 2.0, user-centric, consumers are increasingly becoming producers of information. He can express his opinions on the monetary exchange of goods, services and information through annotations. An annotation takes many different forms and is used for many different functions. This annotative activity is carried out by systems specially developed to annotate the products or services of an online commerce web site. In the literature, many tools have been developed to annotate various products and services. In this article, we present a classification of thirty annotation tools developed by industry and academia. This organization of annotation tools is built on the basis of functionalities that they offer. From this classification, we present our observations and the limits of these systems.
- B. Bickart and R. M. Schindler, 2001. Internet forums as influential sources of consumer information, Journal of interactive marketing, vol. 15, no. 3, pp. 31--40. https://doi.org/10.1002/dir.1014Google Scholar
Cross Ref
- J. Chaparro-Peláez, A. Hernández-García and A. Urueña-López, 2015. The role of emotions and trust in service recovery in business-to-consumer electronic commerce, Journal of Theoretical and Applied Electronic Commerce Research, vol. 10, no. 2, pp. 77--90. http://dx.doi.org/10.4067/S0718-18762015000200006 Google ScholarDigital Library
- G. Cui, H. K. Lui, and X. Guo, 2012. The effect of online consumer reviews on new product sales, International Journal of Electronic Commerce, vol. 17, no. 1, pp. 39- 58. https://doi.org/10.2753/JEC1086-4415170102 Google ScholarDigital Library
- F. Lehuédé, 2009. L'internet participatif redonne confiance aux consommateurs, Consommation et modes de vie, no. 222.Google Scholar
- J. P. Singh, S. Irani, N. P. Rana, Y. K. Dwivedi, S. Saumya, and P. K. Roy, 2017. Predicting the "helpfulness" of online consumer reviews, Journal of Business Research, vol. 70, pp. 346--355. https://doi.org/10.1016/j.jbusres.2016.08.008Google ScholarCross Ref
- R. A. Hendrawan, E. Suryani, and R. Oktavia, 2017. Evaluation of e-commerce product reviews based on structural, metadata, and readability characteristics, Procedia Computer Science, vol. 124, pp. 280--286. https://doi.org/10.1016/j.procs.2017.12.157Google ScholarCross Ref
- T. Beauvisage, J.S. Beuscart, V. Cardon, K. Mellet, and M. Trespeuch, 2013. Notes et avis des consommateurs sur le web, Réseaux, no. 1, pp. 131--161.Google Scholar
- Kalboussi, N. Omheni, O. Mazhoud, and A. H. Kacem, 2015.How to organize the annotation systems in humancomputer environment: Study, classification and observations. In Proceedings of the 15th IFIP TC.13 International Conference on Human-Computer Interaction, INTERACT'15, Lecture Notes in Computer Science (LNCS), Springer, (pp. 115--133). https://doi.org/10.1007/978-3-319-22668-2_11Google Scholar
- Bai, Y., Zhang, G., Wang, P., Chen, J., Zhang, T., Cai, F., ... & Liu, L, 2014. The Comparative Analysis between Sensory and Cognitive in Online Customer Reviews of Baby Formula. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2).Google Scholar
- A. Kalboussi, O. Mazhoud, and A. H. Kacem, 2013. Annotative Activity as a Potential Source of Web Service Invocation. In Proceedings of the 9th International Conference on Web Information Systems and Technologies, WEBIST'13, SciTePress, (pp.288-292).Google Scholar
- Mudambi, S. M., & Schuff, D. 2010. Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185--200. https://doi.org/10.2307/20721420 Google ScholarDigital Library
- Beauvisage, T., Beuscart, J. S., Cardon, V., Mellet, K., & Trespeuch, M. Online Consumer Reviews Design and Consequences of a New Valuation Device.Google Scholar
- S. Kotha, 1998. Competing on the internet: The case of amazon. com, European Management Journal, vol. 16, no. 2, pp. 212--222. https://doi.org/10.1016/S0263-2373(97)00089-3Google ScholarCross Ref
- A. Lendle, M. Olarrega, S. Schropp, and P. L. Vezina, 2013. ebay's anatomy, Economics Letters, vol. 121, no. 1, pp. 115--120.Google ScholarCross Ref
- C. Vasquez, 2011. Complaints online: The case of tripadvisor, Journal of Pragmatics, vol. 43, no. 6, pp. 1707--1717. https://doi.org/10.1016/j.pragma.2010.11.007Google ScholarCross Ref
- J. Fogel and S. Zachariah, 2017. Intentions to use the yelp review website and purchase behavior after reading reviews, Journal of theoretical and applied electronic commerce research, vol. 12, no. 1, pp. 53--67. http://dx.doi.org/10.4067/S0718-18762017000100005Google ScholarCross Ref
- N. Dutta and A. K. Bhat, 2014. Flipkart: journey of an indian ecommerce start-up, Emerald Emerging Markets Case Studies. https://doi.org/10.1108/EEMCS-03-2014-0064Google Scholar
- E. Jensen, 2015. Tripadvisor og trustpilot bestemmer ferie. digital. business. dk. retrieved 17.01. 2018.Google Scholar
- B. Tan, S. L. Pan, X. Lu, and L. Huang, 2015. The role of is capabilities in the development of multi-sided platforms: the digital ecosystem strategy of alibaba. com, Journal of the Association for Information Systems, vol. 16, no. 4, p. 2. https://doi.org/10.17705/1jais.00393Google ScholarCross Ref
- M. Singh, B. Ghutla, R. L. Jnr, A. F. Mohammed, and M. A. Rashid, Walmart's sales data analysis-a big data analytics perspective, in 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2017, pp. 114--119. https://doi.org/10.1109/APWConCSE.2017.00028Google ScholarCross Ref
- J.P. Mellinas, S.M. M. María-Dolores, and J. J. B. García, 2015. Booking. com: The unexpected scoring system, Tourism Management, vol. 49, pp. 72--74. https://doi.org/10.1016/j.tourman.2014.08.019Google ScholarCross Ref
- R. E. Petty, J. T. Cacioppo, and D. Schumann, 1983. Central and peripheral routes to advertising effectiveness: The moderating role of involvement, Journal of consumer research, vol. 10, no. 2, pp. 135--146. https://doi.org/10.1086/208954Google ScholarCross Ref
- U. Matzat and C. Snijders, 2012. Rebuilding trust in online shops on consumer review sites: Sellers' responses to usergenerated complaints, Journal of Computer Mediated Communication, vol. 18, no. 1, pp. 62--79. https://doi.org/10.1111/j.1083-6101.2012.01594.x Google ScholarDigital Library
- V. Sharma and H. Karnick, 2016. Automatic tagging and retrieval of e-commerce products based on visual features, in Proceedings of the NAACL Student Research Workshop, pp. 22--28. https://doi.org/10.18653/v1/N16-2004Google Scholar
- A. Kalboussi, O. Mazhoud, and A. H. Kacem, 2016. Comparative study of web annotation systems used by learners to enhance educational practices: features and services. International Journal of Technology Enhanced Learning, 8(2), 129--150. http://dx.doi.org/10.1504/IJTEL.2016.078081 Google ScholarDigital Library
- K.P. Yee, 2002. Critlink: advanced hyperlinks enable public annotation on the web, University of California, Berkeley.Google Scholar
- S. Asharaf and M. S. Abdulla, 2013. Scope of ontological annotation in e-commerce, International Journal of Business Information Systems, vol. 14, no. 3, pp. 335--347. http://dx.doi.org/10.1504/IJBIS.2013.056721 Google ScholarDigital Library
- K. Tanaka, K. Kunze, M. Iwata, and K. Kise, 2014. Memory specs: an annotation system on google glass using document image retrieval, in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp'14, pp. 267--270. https://doi.org/10.1145/2638728.2638775 Google ScholarDigital Library
- B. Hernàndez, J. Jiménez, and M. J. Martin, 2010. Customer behavior in electronic commerce: The moderating effect of e-purchasing experience, Journal of business research, vol. 63, no. 9-10, pp. 964--971. https://doi.org/10.1016/j.jbusres.2009.01.019Google ScholarCross Ref
- K. Tanaka, M. Iwata, K. Kunze, M. Iwamura, and K. Kise, 2013. Share me-a digital annotation sharing service for paper documents with multiple clients support, in 2013 2nd IAPR Asian Conference on Pattern Recognition. IEEE, pp. 779--782. https://doi.org/10.1109/ACPR.2013.182 Google ScholarDigital Library
- T. Hayashi, Y. Wang, Y. Kawai, and K. Sumiya, 2018. An ecommerce recommender system using complaint data and review data. in IUI Workshops.Google Scholar
- S.C. Necula, V.D. Pavaloaia, C. Strîmbei, and O. Dospinescu, 2018. Enhancement of e-commerce websites with semantic web technologies, Sustainability, vol. 10, no. 6, p. 1955. https://doi.org/10.3390/su10061955Google ScholarCross Ref
- R. East, K. Hammond, and M. Wright, 2007. The relative incidence of positive and negative word of mouth: A multi-category study, International journal of research in marketing, vol. 24, no. 2, pp. 175--184. https://doi.org/10.1016/j.ijresmar.2006.12.004Google Scholar
- M. Lee, S. Rodgers, and M. Kim, 2009. Effects of valence and extremity of ewom on attitude toward the brand and website, Journal of Current Issues & Research in Advertising, vol. 31, no. 2, pp. 1--11. https://doi.org/10.1080/10641734.2009.10505262Google ScholarCross Ref
- H. Liu, Y. Feng, X. Song, and L. Chen, 2019. The impact of merchant's response to negative reviews on consumers' purchase intention, in the 16th International Con- ference on Service Systems and Service Management (ICSSSM). IEEE, pp. 1--6. https://doi.org/10.1109/ICSSSM.2019.8887857Google Scholar
- C. M. Cheung, M. K. Lee, and C. Wagner, Introduction to social media and e-business transformation minitrack, in 2014 47th Hawaii International Conference on System Sciences. IEEE, 2014, pp. 550--550. https://doi.org/10.1109/HICSS.2015.94 Google ScholarDigital Library
- M. N. Hajli, 2014. A study of the impact of social media on consumers, International Journal of Market Research, vol. 56, no. 3, pp. 387--404. https://doi.org/10.2501/IJMR-2014-025Google ScholarCross Ref
- N. Hajli, 2015. Social commerce constructs and consumer's intention to buy, International Journal of Information Management, vol. 35, no. 2, pp. 183--191. https://doi.org/10.1016/j.ijinfomgt.2014.12.005 Google ScholarDigital Library
- P. Pu, L. Chen, and R. Hu, 2011. A user-centric evaluation framework for recommender systems, in Proceedings of the fifth ACM conference on Recommender systems, pp. 157--164. https://doi.org/10.1145/2043932.2043962 Google ScholarDigital Library
- Y. Lu, R. Dong, and B. Smyth, 2018. Coevolutionary recommendation model: Mutual learning between ratings and reviews, in Proceedings of the 2018 World Wide Web Conference WWW'18, pp. 773--782. https://doi.org/10.1145/3178876.3186158 Google ScholarDigital Library
- S. Aciar, D. Zhang, S. Simoff, and J. Debenham, 2007. Informed recommender: Basing recommendations on consumer product reviews, IEEE Intelligent systems, vol. 22, no. 3, pp. 39--47. https://doi.org/10.1109/MIS.2007.55 Google ScholarDigital Library
- X. Fang and J. Zhan, 2015. Sentiment analysis using product review data, Journal of Big Data, vol. 2, no. 1, p. 5. https://doi.org/10.1186/s40537-015-0015-2Google ScholarCross Ref
- Kalboussi, A., Mazhoud, O., Omheni, N., & Kacem, A. H. 2014. A new annotation system based on a semantic analysis of a learner's annotative activity to invoke web services. International Journal of Metadata, Semantics and Ontologies, 9(4), 350--370. https://doi.org/10.1504/IJMSO.2014.065447 Google ScholarDigital Library
- Kalboussi, A., Omheni, N., Mazhoud, O., & Kacem, A. H. 2015a. An interactive annotation system to support the learner with web services assistance. In Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies, ICALT'15, (pp. 409--410). http://dx.doi.org/10.1109/ICALT.2015.57 Google ScholarDigital Library
- Kalboussi, A., Mazhoud, O., & Kacem, A. H. 2016b. Functionalities provided by annotation systems for learners in educational context: An overview. International Journal of Emerging Technologies in Learning, 11(2), 4--11. https://doi.org/10.3991/iiet.v11i02.5166Google ScholarCross Ref
- Chehab, K., Kalboussi, A., & Kacem, A. H. (2018). Study of Annotations in e-health Domain. In Proceedings of the 16th International Conference on Smart homes and health Telematics, ICOST'18, Lecture Notes in Computer Science (LNCS), Springer, (pp. 189--199). https://doi.org/10.1007/978-3-319-94523-117Google Scholar
Index Terms
- Web-based Applications and Services of Annotation in Electronic Commerce
Recommendations
Semantic web service offer discovery for e-commerce
ICEC '08: Proceedings of the 10th international conference on Electronic commerceSemantic Web Services (SWS) are an important part of the Semantic Web, traditionally focused on discovery and composition of e-services. In the area of e-commerce services, it is necessary to go past the granularity of service discovery and also to ...
Selecting services for web applications: the open hypermedia case
As long as the volume of the distributed information in the Internet increases, the need for useful and easy-to-use 3rd party services in Web Applications will be growing. Web developers adopt tactics for integrating external services into their ...
E-commerce Recommendation with Personalized Promotion
RecSys '15: Proceedings of the 9th ACM Conference on Recommender SystemsMost existing e-commerce recommender systems aim to recommend the right products to a consumer, assuming the properties of each product are fixed. However, some properties, including price discount, can be personalized to respond to each consumer's ...
Comments