Tag Archives: recommendations

From e-commerce to social commerce


A matter of trust

The advancement of Web 2.0 social networks brought new developments to e-commerce. In recent years, e-commerce has transformed to social commerce. Social commerce is a new stream and subset of traditional e-commerce, which combines e-commerce with Web 2.0 social networks.

Social commerce, trust & buying intention

Thanks to social networks consumers can now communicate, rate other products, review others’ opinions, participate in forums, share their experiences and recommend products and services. By bringing the features of Web 2.0 social networks to e-commerce, consumers can support each other in the acquisition of products and services in an online context. This results in more customer-oriented business models where customers can share knowledge, experiences and information about their products and services.

Social commerce has three main constructs that empower customers and increase the sociability of e-commerce:

  • Forums and communities: Online discussion sites that support information sharing;
  • Ratings and reviews: Provide comprehensive information about a product for potential customers;
  • Referrals and recommendations: Unlike brick and mortar stores, in online stores it is not possible to interact with staff, so customers rely more on other customers’ recommendations.

Trust is a central issue in e-commerce. Social commerce has helped to establish more trust in e-commerce platforms. Customers experience higher levels of trust as they can support each other through information exchange. This is because interactions and interconnectivity reduce the perceived risk in online transactions. Reviews, ratings and recommendations can indicate the trustworthiness of an online seller as customers consider reviews from other customers to be more reliable than information from a commercial website.

Hajli (2015) found that the three social commerce constructs significantly positively influence the user’s intention to buy. Trust appeared to be a mediating variable. Social commerce constructs have a positive effect on user’s trust, which in turn positively influences the intention to buy (Figure 1). To arrive at these findings, Hajli (2015) conducted a survey study with four constructs: intention to buy, social commerce constructs, perceived usefulness and trust. A five point Likert-scale was used in the questionnaire. Data was collected at universities in the UK. The final sample consisted of 243 completed and usable questionnaires. Next, Structural Equation Modelling (SEM) was used for data analysis. The hypotheses were tested with the Partial Least Squares (PLS) method. The findings underline that social commerce constructs, like customer reviews, are more likely to increase trust, and in turn increase customers’ intention to buy.

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Amazon customer reviews

From a practical perspective, this study encourages online businesses to make a plan for reviews and to manage social networks effectively as it significantly impacts customers’ purchasing decisions. It recommends them to engage with their customers through reviews to develop trust. Other research indeed shows that 91 percent of customers read online reviews and that 84 percent trusts online reviews as much as a personal recommendation (Bloem, 2017) In practice, this implies that not offering customer reviews is similar to ignoring 84 percent of your buying population by not giving them the information they want to support them in their buying decision (DeMers, 2015).

To illustrate, Amazon optimised its business model based on customer reviews and ratings. Customer reviews are one of the most important ranking factors in Amazon’s A9 algorithm. It ranks product search results based on the positivity of customer reviews and rating. (Grosman, 2017)

Fake review problem

A weakness in the study of Hajli (2015) is that it does not consider that information related to the identity of the reviewers influences the perceived trustworthiness of a review.  The paper simply finds that more reviews increases trust, which in turn increases the buying intention.  However, in reality, it might not be that straight forward anymore with the rise of fake product reviews. Nowadays, it is hard for customers to decide which reviews to trust. There is looming crisis of confidence in online product reviews, which used to be a key factor in customers’ buying decision. (Silverman, 2017) As customers cannot trust reviews anymore, it can be questioned whether the positive relation between the number of reviews, trust and buying decision still holds.

Increasingly, customers pay careful attention to reviews, e.g. looking for reviews with a Verified Purchase tag. Nearly 66.3 percent of Amazon reviews are five-star ratings, which is highly unrealistic. Reviews on Amazon are a key factor when making a purchasing decision and without reviews it is difficult for online retailers to gain sales. In an attempt to boost sales, retailers offer reviewers free or discounted samples in return for a positive customer review. So, it is no surprise that 96 percent of paid reviews on Amazon is rated four- or five-star.  (Cipriani, 2016)

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Source credibility

Many authors have investigated the positive impact of online reviews on sales of products and services. However, given the importance of source credibility, I believe more research is needed on trustworthiness of reviewers as an important construct. The source credibility theory explains how a recommendation persuasiveness is affected by the perceived credibility of its source. Actually, customers accept reviews depending on the perceived trustworthiness of the reviewer, which consequently impacts the buying decision. Reviewer trustworthiness is therefore an important moderating variable that positively moderates the impact of review-based online reputation. (Banerjee, Bhattacharyya, & Bose, 2017)

Concluding, instead of solely increasing the number of (positive) customer reviews, online retailers should also build a good review-based online reputation that encourages and identifies top trustworthy reviewers and that ranks reviews based on reviewer trustworthiness.

This post was inspired by: Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191

References:

Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness. Decision Support Systems, 17-26.

Bloem, C. (2017, July 31). 84 Percent of People Trust Online Reviews As Much As Friends. Here’s How to Manage What They See. Opgehaald van Inc.: https://www.inc.com/craig-bloem/84-percent-of-people-trust-online-reviews-as-much-.html

Cipriani, J. (2016, March 14). Why You Shouldn’t Trust All Amazon Reviews. Opgehaald van Fortune: http://fortune.com/2016/03/14/paid-amazon-reviews/

DeMers, J. (2015, December 28). How Important Are Customer Reviews For Online Marketing? Opgehaald van Forbes: https://www.forbes.com/sites/jaysondemers/2015/12/28/how-important-are-customer-reviews-for-online-marketing/#35eccc711928

Grosman, L. (2017, February 28). Five Tips To Improve Your Ranking On Amazon. Opgehaald van Forbes: https://www.forbes.com/sites/forbescommunicationscouncil/2017/02/28/five-tips-to-improve-your-ranking-on-amazon/#3079c5f89fed

Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191.

Silverman, D. (2017, April 20). A Matter of Trust: Amazon Declares War on Fake Product Reviews. Opgehaald van Clavis Insight: https://www.clavisinsight.com/blog/matter-trust-amazon-declares-war-fake-product-reviews

Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions


Recommendations: who has not seen them? Whenever you go online, different recommendations appear for you: with similar products, with different products, based on your past purchases, or based on what other people viewed. But did you know that all these types of recommendations have different names and different effects?

Li and Karahanna (2015) review 40 empirical studies between 1990 and 2013, that focussed on the understanding of online recommendation systems (RS). An RS is basically a web-based technology, that has the ability to advise and offer a certain product that would satisfy the individual users’ needs.

Based on past literature, three stages in this so called recommendation process have been found. Stage 1 involves the understanding of the consumer (including the collection of consumer data and creating a consumer profile), as well as the delivery of recommendations to this consumer (which are match making approach and the recommendation system presentation). This is followed by a personalized recommendation (stage 2). In stage 3, the impact of the recommendation system is assessed. Stage 3 ‘flows’ back to Stage 1 in the form of feedback.

I think especially the recommendation system presentation and its effect are particularly interesting. Multiple types of RS are discussed within academic literature, such as content-based, visual, collaborative-based and social-network based recommendations. According to Li and Karahanna (2015), these types often overlap in practice, creating hybrid RS.

The content-based recommendation takes into account a consumer’s preferences, as well as his past search and purchase behaviour. A collaborative-based recommendation system does the same, but also takes into account other customers (Other customers also bought…). One main difference is that for the latter, much more data are needed, since you need data on not only one, but more customers.

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An example of recommendations on Amazon 

While collaborative recommendations have as a disadvantage that new products do not have such links yet and some customers have atypical behaviour, collaborative RS are often used when it comes to alternative-based and cross-sell recommendations. The latter means that recommended items are generated across multiple, different categories, whereas the first is are mostly based on multiple customer ratings and purchases. The algorithms used for alternative-based recommendations are further based on a bunch of different customers’ clickstream data to detect preferences.

An example of a content-based recommendation is the visual recommendation. While content-based recommendations take past behaviour into account, visual recommendations do not. As expected, this type of RE shows products that are similar to another product a consumer has viewed.

So, which recommendation do you think is most effective?

That is up to you to find out (if you are still looking for a thesis topic)! While a lot of research has been done on the types of RS, limited empirical research exists on which strategies to implement to optimally use the different types of recommendation systems.

Based on some other papers and past theses I have read, I think that the visual recommendation works the least well – it will not increase the sales of one consumer, but I believe it rather shows alternatives to something they were thinking of purchasing (black dress 1, 2 or 3: that is the question). Further, while it might be nice to know what other consumers bought or viewed, I often find it irrelevant to myself. I’d rather shop-the-look if a complete outfit is shown on a model for example. However, with products other than clothes (such as books or videos) it might be different. Hence, go ahead and pick a nice thesis topic regarding these recommendations in different product categories!


Li, S., & Karahanna, E. (2015, February). Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions. Journal of Association for Information Systems, 72-107.

 

Research Framework, Strategies, And Applications Of Intelligent Agent Technologies (IATs) In Marketing


What is an agent?

Anything that perceives its environment through sensors and in return acts upon it(Russell and Norvig 1995).

What is an intelligent agent? An agent that displays machine learning abilities.

Does perhaps Amazon Alexa, Apple’s Siri, Google Assistant, Microsoft’s Cortana ring a bell?

In “Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing applied” the authors attempt to define how are these intelligent agent technologies used in the context of marketing and how can marketers understand and exploiting them. First step towards that direction was to try and establish a marketing centric definition. Hence, Intelligent Agent Technologies are according to authors:

Systems that operate in a complex dynamic environment and continuously perform marketing functions such as:

  • dynamically and continuously gathering any data that could influence marketing decisions
  • analyzing and learning from data to provide solutions/suggestions
  • implementing customer-focused strategies that create value (for customers and firms)

The second step was to classify all marketing applications of IATs in a way that would demonstrate relationships and differences among them. A useful and understandable tool for researchers and managers, the proposed marketing taxonomy is depicted below:

 

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To answer all these research questions the authors reviewed the existing literature and then conducted 100 in depth interviews with managers form 50 randomly selected companies. Two independent researchers analyzed the interview data, that were then used to shape the taxonomy and the below framework.They also made some propositions that would help researchers and mainly managers to utilize IATs and ultimately drive company performance.

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Overall, implementing the right IAT can assist the progress of numerous marketing functions permitting companies to achieve a sustainable competitive advantage. Both firms and customers can benefit from them. Companies are in a position to understand and put customers’ interest first (through collaborative filtering, personalization, recommendation systems) and in return gain customer loyalty and trust.On the other hand IATs offer consumers value, by providing them with convenience, better information, customized selection and less information overload (e.g price-comparison engines or agents that configure and customize their computer systems on the basis of their preferences).

Strengths and Weaknesses:

%ce%b4%ce%b9%ce%b1%cf%86%ce%ac%ce%bd%ce%b5%ce%b9%ce%b11Since there was no concrete research or a fully developed theory surrounding IATs in marketing and subsequently no certain phenomena or existing theoretical frameworks to test, the authors rightfully opted for the grounded theory approach.So in contrast to the traditional research method they tried to construct a theory by discerning which ideas and concepts are repeatedly used in the interview data. These patterns were then grouped into categories that formulated their theory and shaped both the taxonomy and the framework.

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Although the authors reasonably based their analysis on grounded theory, whether they applied it correctly is another question. The fact that they reviewed the existing literature in order to formulate the interview questions somehow conflict with the grounded theory methodology. The goal of this approach is to discern natural patterns. However, the used questionnaires possibly inhibited this since they kind of predisposed the managers’ answers since the queries were literature related.

%ce%b4%ce%b9%ce%b1%cf%86%ce%ac%ce%bd%ce%b5%ce%b9%ce%b13Given further progress in recommender systems (or other means of reducing costs for the customer), a situation might arise in which a “ready-made” solution provided by the system delivers higher preference fit than a customer-designed product—which, on the other hand, delivers the advantage of enabling “I designed it myself” feelings.” (Franke, N., Schreier, M. and Kaiser, U, 2010). This poses a very serious question for companies. When it is preferable to let an agent customize, decide or recommend a product/website? How quickly and how frequently should the agents respond and adjust to user needs? Ultimately what is more beneficial for both parties, implementing agents or give consumers the freedom to tailor products and and websites to their needs. Perhaps, technological advancements and the machine learning capabilities of IATs could soon enable companies them to successfully distinct these two categories of consumers and accordingly present them the proper interface.

 

References:

Franke, N., Schreier, M. and Kaiser, U. (2010). The “I Designed It Myself” Effect in Mass Customization. Management Science, 56(1), pp.125-140.

Kumar, V., Dixit, A., Javalgi, R. and Dass, M. (2015). Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing. Journal of the Academy of Marketing Science, 44(1), pp.24-45.

Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. 1st ed. Prentice Hall, p.31.

Why Recommendation Agents Should Let Us Participate


“I see you are looking at our infinite range of stuffed animals, may I help you find what you need?” They are the salespeople of the online retailers; recommendation agents (RA’s). By capturing our perceived preferences based on browsing patterns or interests, RA’s aim to understand our needs. Not an unnecessary luxury of any sort, as the complexity and amount of information we are confronted with often exceeds our limited information-processing capacities and thus the benefits of RA’s can turn into costs. (Dabholkar, 2006; West et al., 1999). If there would be a Maslow pyramid for online shopping needs, it would be the bottom layer; a basic need, indeed.

However, one recommendation agent does not fit all. Different websites use different types of RA’s and the extent to which we can interact with these systems is heavily influenced by the interface design and its dialogue initiation process. Ranging from extensive questionnaires to not even a “hello, I’m here”, the possibility to participate in a two-way dialogue depends on the online salesperson you have encountered. But does the quality and quantity of customers’ input really matter?

In their lab based experiment, using existing RA’s in a controlled setting, Dabholkar and Sheng (2011) show that greater consumer participation in using RA’s leads to more satisfaction, greater trust and higher purchase intentions with respect to the recommended products and the system itself. Existing research already elaborates on the effects of participation in decision making on satisfaction, trust and purchase intentions in the offline and online context (Driscoll, 1978; Chang et al., 2009; Yoon 2002). In addition, much research has been conducted in the RA field, but upon this point failed to combine these two topics.

A great strength in Dabholkar and Shengs’ research, is the fact that there is a significant importance in understanding these relationships in the RA field as they are of huge strategic importance to online marketers. Therefore this topic is highly relevant. Moreover, by adding the dimension of financial risk, the authors are able to also identify that higher product prices moderate the need of participation in the RA context. This gives marketers insight for which products their recommendation agents should have high/low levels of possible interaction and therefore are able to personalize their RA’s per product and possibly increase purchases.

But, there are also a few limitations that need to be taken into account. One could argue that the used sample is non-representative for the online shopping population, as it completely consisted of college students with an average age of 21.91. Although the authors highlight the fact that the largest share of the Internet population is aged 18-32, it is not unthinkable that a student’s perception of financial risk differs from a middle aged person with substantially more spending power. Besides, students perceptions of trust in the online shopping context may be not completely representative, as they grew up with the Internet.

Summarizing, Dabholkar and Sheng give great insights in the effects of consumer participation in RA’s on satisfaction, trust and even purchase intentions. However, generalizability at this point is questionable, so further research across different age groups needs to be conducted to validate these results. But for now; Does your customer base primarily consist of students? Then it is time to revaluate your online salespeople. Get them to communicate with us, we would love to talk!

 

Chang, C. C., Chen, H. Y., & Huang, I. C. (2009). The interplay between customer participation and difficulty of design examples in the online designing process and its effect on customer satisfaction: Mediational analyses. Cyber Psychology & Behaviour, 12(2), 147-154.
Dabholkar, P. A. (2006). Factors influencing consumer choice of a ‘rating web site’: An experimental investigation of an online interactive decision aid. Journal of Marketing Theory & Practice, 14(4), 259-273.
Dabholkar, P. A., & Sheng, X. (2011). Consumer participation in using online recommendation agents: effects on satisfaction, trust and purchase intentions. The Service Industries Journal, 32(9), 1433-1449.
Driscoll, J. W. (1978). Trust and participation in organizational decision making as predictors of satisfaction. Academy of Management Journal, 21(1), 44-56.
West, P. M., Ariely, D., Bellman, S., Bradlow, E., Huber, J., Johnson, E., . . . Schkade, D. (1999). Agents to the Rescue? Marketing Letters, 10(3), 285-300.
Yoon, S. J. (2002). The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing, 16(2), 47-63.

“The Filter Bubble: exploring the effects of using recommender systems on content diversity” (Nguyen et al. 2014)


The article chosen addresses the so-called bubble effect identified by Pariser (2012). This bubble effect suggests that by using recommender systems (RS), users are exposed to only a few products that they will like and miss out of many others.  The paper wants to investigate this through understanding the content diversity at an individual level provided by collaborative filtering. It suggests to be the first study observing the effects of this phenomenon on an individual level.

From the study conducted by Lee and Hosanagar (2015) we have understood that there are many opposing views existing in the literature on content diversity on an individual level. Therefore, as the current article claims to be the first one studying this phenomenon on an individual level it is interesting to see how the study has been conducted and what their conclusions are compared to the article of 2015.

The paper addresses very well the debate in regard of the bubble effect: whether recommender systems may be harmful to users. First the behavioral aspects of people who are exposed to similar content and what the effect is on their individual behavior is addressed. As they want to measure the effects on an individual level it is important to recognize what has been found in regard of individual behaviors on content exposure.

For this study, they use the long-term users of MovieLens as they need longitudinal data to draw conclusions on the user’s behavior over time. Two research questions are addressed; 1. Do recommender systems expose users to narrower content over time? 2. How does the experience of users using recommender systems differ of those who do not rely on recommender systems?

The article uses the “tag genome” developed by Vig et al. (2012) to analyze the diversity of the movies that are recommended and consumed (rated). This appears to be a strong measure as it identifies the content of the movie and identifies the similarities content-wise. Multiple articles have used the movie genres (Lee and Hosanagar, 2015) or the ratings given (Adamopoulos and Tuzhilin, 2014) to identify similarities which seems to be less generalizable as the content of the movies still can vary greatly when using these metrics.

The article describes clearly how the findings should be interpreted and addresses multiple questions that have risen based on the findings. This leads to a well-rounded study where the effect of item-item collaborative filtering is exposed. First, the article addresses whether recommender systems expose users to a narrower content over time through comparing the content diversity at the beginning with the content diversity at the end of the user’s rating history. This comparison can show the development of content consumption of the user over time. It has been found that the content diversity of both user groups (using RS or not to rate movies) becomes quite similar over time. Furthermore, it identifies whether using RS reduces the total content-diversity consumed of that user. The conclusion is that users using RS over time consume more diverse content then users ignoring RS. Finally, the experience of the two user groups are evaluated and it is observed that users using RS seem to consume more enjoyable movies based on their ratings given.

As the limitations of the article suggest itself it would be interesting to study the phenomenon in a more experimental setting where the behavior of users can be observed in more detail. This would help in understanding the reasons of the decisions made by the users based on the recommendations. The multiple studies conducted in the field of RS mostly focus on collaborative filtering as this RS is the most commonly used (Lee and Hosanagar, 2015) but research should also focus on other recommender systems to make sure that those used benefit the user the most.

 

References

Adamopoulos, P. and Tuzhilin, A., 2014, October. On over-specialization and concentration bias of recommendations: Probabilistic neighborhood selection in collaborative filtering systems. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 153-160). ACM. http://dl.acm.org/citation.cfm?id=2645752

Lee, D. and Hosanagar, K., 2015. ‘People Who Liked This Study Also Liked’: An Empirical Investigation of the Impact of Recommender Systems on Sales Diversity.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2603361

Nguyen, T.T., Hui, P.M., Harper, F.M., Terveen, L. and Konstan, J.A., 2014, April. Exploring the filter bubble: the effect of using recommender systems on content diversity. In Proceedings of the 23rd international conference on World wide web (pp. 677-686). ACM. http://dl.acm.org/citation.cfm?id=2568012

“Drink Socially!”


Let’s face it, most of us like to share our positive experiences with our friends, and we like to use social media to reach as much of them as possible. Companies try to help us fulfill these needs with social media plugins or applications. The subject of this blog is a social media application. It is a combination of sharing your experience, rating the product and recommending (or not!) it to other users of the application. The best thing about it is however the subject: BEER! I am talking about the application Untappd, a social media app that enables its users to share and explore the world of beers (Mather & Crunchbase Staff, 2016).

Continue reading “Drink Socially!”

M-Commerce – Creating new opportunities?


Introduction

Mobile commerce is growing rapidly and at a faster pace than e-commerce (Brohan, 2016). Currently, mobile commerce accounts for one third of total e-commerce sales. This percentage is expected to exceed the 50% mark soon. In other words, mobile commerce optimization is not a competitive advantage anymore, but a competitive imperative for companies (Roggio, 2016). Due to mobile commerce, you can buy everything you want, whenever you want. What would you buy via your mobile device? Do you have any wishes on your shopping list?

Continue reading M-Commerce – Creating new opportunities?

Blogging: Influencing readers


Do online reviews matter? Yes they do. Word-of-Mouth is a concept most of you are familiar with. WOM occurs when an individual communicates his or her preferences on an item, product or service that they have previous experiences with (Abălăesei, 2014). The traditional definition states it is ‘oral, person-to person communication between receiver and a communicator whom the receiver perceives as non- commercial, regarding a brand, product or service’ (Kaijasilta 2013, p.7). However with the popularity of the internet increasing several studies introduced the concept of eWOM.

Continue reading Blogging: Influencing readers

Which one do you think I should pick ?


Today is Saturday, it is sunny and warm outside. Perfect day to go shopping ! You are obviously going to spend this afternoon with your favorite side-kick. Your mission is to find the perfect outfit that will fit your shape, your personality, and evidently match your preferences.

You finally find these really cool jeans, but you are still hesitant about the top : the blue and black, or the white and gold one ? The retailer comes to you and says that the jeans and the blue and black top would be the perfect match. On the other hand, your friend who has similar tastes as you – so similar you have a couple of clothes in common – votes for the second option.

What would you do ? Who would you trust ? How to make sure which one is the right choice ?

As a matter of fact, when you are browsing the internet, you might encounter the same problem-resolving process.

When a Youtube user surfs on Youtube, they first type the title of the video. On the right side of the video, a section displays other videos. In this section, two types of recommendations are presented :

  • Featured / Related videos :

They are based on the Youtube’s product network ; that is to say that they are based on the site recommendation algorithm only.

  • “Recommended for you” :

These are based on the Youtube’s social network. In fact, another user marked the video you just watched as favoriteas well as another one. Therefore, the second video will be “recommended for you”.

Thus, which of these videos matches users’ preferences the best ?

In a study, participants were asked to watch videos on Youtube, and rate each of them from one star – Poor – to 5 stars – Awesome. One group had access to the product network only – Related-Featured videos – the second group to both product network and the social network – Dual Network – and the third group to user-generated links only – Recommended for you.

What emerges from the study is illustrated in the graph below :

The fact that the second group curve is the lowest shows that finding a liked video takes less time when using the dual network than any of the other networks. Thus, rather than proposing either one or the other, offering both at the same time gives the user more possibilities, more choice, and therefore, more opportunities to reach the right video.

Therefore, the most efficient way for Youtube to satisfy its users, is to offer them as many choices as possible using different methods : the product network as well as the social network.

So, next time you will go shopping and do not know what choice to make, try the clothes on in the fitting rooms ! – or buy both.


References :

Jacob Goldenberg, Gal Oestreicher-Singer, Shachar Reichman, The Quest for Content : How User-Generated Links Can Facilitate Online Exploration, Journal of Marketing Research, August 2012, 462-468, 17p.

Yang Sok Kim, Ashesh Mahidadia, Paul Compton, Alfred Krzywicki, Wayne Wobcke, Xiongcai Cai, Michael Bain,People-to-People Recommendation Using Multiple Compatible Subgroups, AI 2012 : Advances in Artificial Intelligence, 2012.

Searching in Choice Mode: the faster and superior way?


good bad choice

With the rise of the internet, consumers can consider an ever growing amount of alternatives of a certain product they intend to buy.  Sequentially, recommendation systems have become a widely observed phenomenon in electronic commerce. Much is known about the effect of these product recommendation on the outcome of the purchase decision of the consumer, but research now also concludes that the process of the product search is transformed in the presence of recommendation systems. EUR Professor Benedict Dellaert  and Gerard Haubl from the University of Alberta explain how we traditionally approach searching for a product and how RAs transform the way we search. We trade in the well documented normative model of consumer search (i.e. Hauser & Wernerfelt, 1990), where we continue to search for additional alternatives until we believe that the potential improvement of our product selection, which the newly examined alternative might represent, does not weigh up against the effort of search we need to put in, for a model where we search in choice mode. In choice mode, we let the recommendations guide us on which alternatives to consider and we continuously compare the alternatives we have assessed among each other. Furthermore, we consider a smaller set of alternatives than we would without the RAs and consider these in more depth. The higher the variability in the (perceived)  attractiveness of the alternatives, the stronger this effect becomes, a finding that contrasts the prediction of normative search theory (see Weitzman, 1979). grfiek Luckily for us, the research indicates that the choices we make using the choice mode match our preferences a lot better than the normative model search, following the average match of 82% with the RA versus a mere 47% without the RA. On top of that, we save time as we do so. Though, there might be a fly in the ointment; more recent research from Adomavicius, Bockstedt, Curley & Zhang (2013) reports that the recommendation system can manipulate consumer preferences and can give rise to a bias. The consumer may choose for a particular alternative, because he believes that its high position on the recommendation system means that that particular alternative is a ‘correct’ answer to the uncertainty he faces. Whether that is true depends greatly on the quality of the recommendations. Also, the consumer might be biased when reporting its satisfaction with the product, as subconsciously he adapts his preferences to the product characteristics of the product bought using a recommendation system. In the end, it seems we can make truly better and more time-efficient product choices with the help of recommendation systems, on the conditions that we critically asses the quality of these systems and remain loyal to our original preferences.

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Resources :

Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2013). Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, Vol. 24, No. 4, pp. 956-975.

Dellaert, B. G.C.,  Häubl, G., (2012) Searching in Choice Mode: Consumer Decision Processes in Product Search with Recommendations. Journal of Marketing Research, Vol. 49, No. 2, pp. 277-288.

Hauser, J.R., & Wernerfelt, B ., An Evaluation Cost Model of Consideration Sets,  Journal of Consumer Research, Vol. 16, No. 4 (Mar., 1990), pp. 393-408 Weitzman, M., Optimal Search for the Best Alternative, (1979), Econometrica, Vol.  47, No. 3, pp. 641–54.

Do Recommander Systems Manipulate Consumers Preferences?


Recommender systems are important in the decision making. Those systems provide the customers suggestions. As a result of this, firms can better serve their customers. This will also lead to an increase in sales. Research has focused on the development and the improvement of recommender systems. But there is not quite much studied about the behavioral implications of using recommender systems. Are those recommender systems manipulating the customers?

Most recommender systems use the consumers ratings items as input. Those recommendations the system provide present an expectation of how well a customer will like an item. There is also a feedback loop, those are the actual ratings of the customer after the purchase has been made. The recommendations are based on the actual ratings of a customer who already has tried this product or item.

There is a possibility that people are influenced by elements in the environment when they make a decision. The first one is the anchoring issue. People are maybe consumed drawn, because the system is presenting an item to them and they choose it. It is important to know if a customer really likes it or just chooses it because it is presented in a advantageous way. It is difficult for people to see if an item is reasonable for them. It could be presented as a advantageous choice, but it will maybe be the opposite!

When there is uncertainty, a customers seeks for the most plausible item. The suggested item is viewed as the ‘correct’ answer, therefore a lot of people will choose this. The users belief that the recommender system will choose the right option for them, therefore they choose what the systems presents to them.

Users that will receive a high recommendation from the recommender system, will also give higher rating after they bought/used it and vice versa. What they saw as a rating, has influence on their own rating. They are biased. This is although not symmetric.

There is an significant effect when the recommendation is raised, but not when the recommendation is lowered. It is notable that this effect is not only taking place when the uncertainty is high, it also operates at the point of consumption.

Also is the reliability of the system important. When the system is known as reliable, the customers’ ratings will be more close to the recommendation the customer has seen before. When the system is thus less reliable, the ratings given by the customer will be less close to the recommendation.

Sources

Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2013). Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, 24(4), 956-975.

Cosley D, Lam S, Albert I, Konstan JA, Riedl J (2003) Is seeing believing? How recommender interfaces affect users’ opinions. Cockton G, Korhonen P, eds., CHI 2003 Conference, Fort Lauderdale FL (ACM, New York), 585–592.

Experiencing clothes behind a computer screen


Simply everything can be bought online nowadays. From mass production products to specific niche products. However, about a third of all online transactions are returned (1). Buying online can result in certain disadvantages compared to buying in an actual store, one of which is the impossibility to physically hold the product, which can in the case of online clothing retailers, result in product returns. E-commerce clothing retailers like Zalando and Wehkamp.nl use a lot of different kind of recommendations to help the customers make satisfactory choices.

When buying clothes online, a customer can not try the article behind his/her computer screen. A customer will not know if the fabric is what the customer wants or how the product will actually look when he/she wears it. Does it look fancy? Slobby? Casual? Formal? And most of all, a customer will never be sure if the clothing actually fits unless he/she tries it on. This disadvantage has to do with the fact that clothing is an experience product characterized by the attributes that need to be experienced before the purchase, like taste, softness or fit (2). According to Xiao and Benbasat (2007), the use of recommendation agents influences the choice of users to a greater extent in the case of these products. What kind of recommendation agents do Zalando and Wehkamp.nl actually have to help customers choose? Continue reading Experiencing clothes behind a computer screen

Choices, Choices!


Whether shopping for clothes or electronics, books or microwave food, consumers have many more options than ever before. The variety of choices today gives us a much better chance of finding something that exactly suits our needs, our personalities, our activities and our bodies. However, those choices can be overwhelming. In his book  “The Paradox of Choice: Why More Is Less,” Barry Schwartz emphasizes the downside of these huge assortments. According to him, as the number of choices grows further, consumers feel overloaded and, as a result, choice becomes a burden rather than a freedom.

However, since we (i.e. the consumers) have easier access to more and more information and alternatives, the chances that there is a product perfectly fitting our preferences exists out there. Traditionally, the rule of 80-20 was overruling all markets. The underlying idea is that 20% of the products make up 80% of the revenues. 

Continue reading Choices, Choices!