The Theme of Personalization in Technology Annotated Bibliography

Ho, S. Y., & Bo doff, D. (2014). The effects of Web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer search theory. MIS quarterly, 38(2).

Ho & Bo doff (2014) combine consumer search theory and the elaboration likelihood model to study the impacts of internet personalization on consumer attitude and norms. They say "This research extends the theory on web personalization ...a user's attitude and behavior toward the personalization agent (p.497)." The authors assert that web personalization is crucial in advertising. The authors say "Web personalization can achieve two business goals: increased advertising revenue and increased sales revenue (p.497)." They conclude that this research addresses the trade-off between item choosing and sampling and offers practical guidance on how to drive users towards the norms and attitudes that will fulfill their business objectives (Ho & Bo doff, 2014).

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I agree with the study that it examines web personalization to a certain extent. However, I disagree with the effectiveness of the study to investigate the impacts of internet personalization on user norm and attitude. The authors say "our setup allowed participants to select exactly one item per log-on session (p.518)." This limitation did not allow the researcher to examine the number of items bought. As Ho & Bo doff (2014) say, "our sample frame may limit the generalizability of the findings (p.518)." This fault may restrain the findings of the study to the user norms who are acquainted with the provider (Ho & Bo doff, 2014).

Thongpapanl, N., & Ashraf, A. R. (2011). Enhancing online performance through website content and personalization. Journal of computer information systems, 52(1), 3-13.

Thongpapanl & Ashraf (2011) address the concept of improving online performance via personalization. They say "but still there exists little consensus regarding the amount of information and the level of customization needed to optimize customers' satisfaction and their purchase intention (p.3)." The authors apply a structural equation modeling analysis using a sample of established American retailer websites to understand the most resourceful ways to design and implement e-retailers website aspects. The authors say "Internet Retailer reports that in the United States... in 2007 rose by 21.8% and 8.8% respectively (p.3)." The study concluded that client satisfaction acts as a driving factor to the retailer's online sales performance (Thongpapanl & Ashraf, 2011).

I disagree with the study of personalization and web context because it constitutes various limitations. The authors say "We recognize that the number of features identified may not represent an exhaustive list of online features implemented by Web retailers (p.10)." I believe there are other online characteristics that can increase the role of internet personalization and information content. The scholars say "we believe that future research can extend our conceptual model by including and evaluating these other online features (p.10)." these limitations will generate results that are biased hence not giving the right approach towards web personalization (Thongpapanl & Ashraf, 2011).

Shani, N. S., Wheeler, S. C., & Chintagunta, P. (2018). Personalization in Email Marketing: The Role of Noninformative Advertising Content. Marketing Science, 37(2), 236-258.

Sahni et al., (2018) study the content of email advertising used to tailor to the advertisement messages through experiments. The authors said, "we conduct randomized field experiments in which experimentally tailored email messages are sent to millions of individuals (p.236)." The results of the study are that including the name of the message recipient increases the likelihood of the recipient opening by 0.2 which reflects 31% increase in sales leads a 17% decrease in the number of persons unsubscribing from the email campaign. The authors say "...such content increases the attention consumers pay to the other content in the rest of the advertising message (p.236)." They conclude the psychology-ingrained research in marketing by forming the robustness of lab results in field environments (Shani et al., 2018).

I disagree that email marketing a good approach to personalize messages. The data on these experiments raises various questions about the 'boundary' of the impact of personalization. The authors say "...under what conditions do we expect the effects to vanish? Our data shed some light on this issue (p.261)." The previous research on confidentiality implies some boundary conditions. The authors say "...in the context of email marketing, consumers may expect the sender to know their name, and not be surprised to see it in the email (p.261)." The concerns of privacy might hurt the advertiser because the clients have observed that their data are used on undesirable motives by companies (Shani et al., 2018).

Aguirre, E., Maher, D., Grewal, D., de Rutter, K., & Wetzel's, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49.

Aguirre et al., (2015) conduct an explorative field study on Facebook to unravel the personalization paradox by explaining the role of information gathering and its effect on vulnerability and click-through frequency. The authors say that "when firms engage in overt information collection, participants exhibit greater click-through intentions in response to more personalized advertisements (p.34)." They claim that greater personalization augments service relevance and client adoption, though paradoxically, it might increase client's sense of vulnerability and fewer adoption frequencies. They say "to investigate the personalization paradox, this study uses three experiments that ...collecting information from social media websites is a crucial determinant of how customers react to online personalized advertising (p.34)." The study concluded that the emotions of vulnerability that clients encounter when companies embark on undisclosed information collection tactics (Aguirre et al., 2015).

I disagree with the effectiveness of the explorative field study on Facebook. The respondents took into account a theoretical buying case study. The authors say "...consumer decision making in the real world may differ (p.69)." it is difficult to state if the results of the study generalize to other contexts particularly those in which personalization depicts distinct levels of information sensitivity. The authors say "....it is difficult to say whether the results generalize to other contexts... (p.69)" I am not satisfied with the explorative study to explain the concept of web personalization in technology. These limitations suggest that the authors should look for alternatives to understand the concept of web personalization (Aguirre et al., 2015).

Sharma, S., & Rana, V. (2017). Web Personalization through Semantic Annotation System. Advances in Computational Sciences and Technology, 10(6), 1683-1690.

According to Sharma & Rana (2017), a web result ranking can be substantially improved by taking into account the individual behavior signals including past queries and traffic's hits for a website. They say 'there is need to personalize the search results (p.1683).' The authors argue that it is more complicated to extract appropriate information from the coexisting knowledge. They state that 'semantic web and data mining techniques play an important role in mining valuable information from the web (p.1683)." The scholars conclude that a creative semantic personalization mining technology will help to satisfy clients wants more efficiently and effectively, making interactions quickly and eventually increasing client satisfaction and the potential of repeat visit (Sharma & Rana, 2017).

I agree that the semantic model is ideal for web personalization because it is grounded on long-run behavioral signals. Sharma & Rana (2017) say that "we describe a general architecture for automatic Web personalization based on the proposed modules (p.1689)." They state that the system constituted of query processing, user identification, domain analysis, content similarity and pattern determine and analysis. I like the model because it can be used to perform various functions. The authors say "...discuss solutions to the problems of data redundancy, knowledge extraction, and making recommendations based on the extracted knowledge (p.1689)." the model obtains the perception and the desired outcomes offered to the user (Sharma & Rana, 2017)

References

Aguirre, E., Maher, D., Grewal, D., de Rutter, K., & Wetzel's, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49.

Ho, S. Y., & Bo doff, D. (2014). The effects of Web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer search theory. MIS quarterly, 38(2).

Shani, N. S., Wheeler, S. C., & Chintagunta, P. (2018). Personalization in Email Marketing: The Role of No informative Advertising Content. Marketing Science, 37(2), 236-258.

Sharma, S., & Rana, V. (2017). Web Personalization through Semantic Annotation System. Advances in Computational Sciences and Technology, 10(6), 1683-1690.

Thongpapanl, N., & Ashraf, A. R. (2011). Enhancing online performance through website content and personalization. Journal of computer information systems, 52(1), 3-13.