This blog post is based on the article ‘The Effect of Ordering Decisions by Choice-Set Size on Consumer Search by J. Levav, N. Reinholtz, and C. Lin.

Consumers have to make lots of choices nowadays. Making good decisions can be difficult, both for the consumer and for the company that wants to influence the consumer to make a certain choice. There are several theories on how to manipulate consumers during their decision process. In this article it is investigated on how the order of choice set sizes leads to different choices. Several experiments were conducted, of which the first one will be described below.

In this experiment participants were asked to create a CD with 10 songs for a road trip. There were 10 song selection screens, where they could listen to examples of the songs. In each screen the participants had to select one song that they would like to have on the CD. The participants were told that they would receive the CD they created after the experiment. There were two groups of participants, participants of the first group were shown a number of options for the songs that increased by five in every screen, i.e. for the first song the participants had to choose there were five options, for the second song there were 10 options, so for the 10th song there were 50 options. For the other group, there were 50 options for the first song, decreasing to 5 options for the 10th song. The experiment showed that even though both groups of participants had the same amount of choices in total, participants in group 1 sampled on average 149 songs (out of 275 songs), while participants in group 2 sampled on average 101 songs. Participants in the first group also spent more time on making their final decision.

This experiment shows that consumers facing an increasing choice-set size spent more time on searching for the best option. This effect is confirmed by the other experiments in the article. Another outcome of the experiments was that the first choice that a consumer has to make, is the most important one in influencing the mind-set of the consumer for the following choices. The level of increase in choice options is also relevant; abrupt increases tend to reduce the level of search of the consumer.

It can be concluded that ordering decisions in an increasing choice-set size is a simple way for managers to influence choices of consumers, because it increases consumer search and therefore engagement. A disadvantage of this method that has to be mentioned though is that this deeper search of consumers can lead to a lower satisfaction after the purchase.

Reference:

Levav, J.; Reinholtz, N.; Lin, C. (2012) ‘The Effect of Ordering Decisions by Choice-Set Size on Consumer Search’. Journal of Consumer Research, 39(3), pp. 585-599. Available at: http://www.jstor.org/stable/10.1086/664498?origin=JSTOR-pdf

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In the digital age of consumption, the internet has been a platform that has greatly enhanced the choices available to consumers. Despite the internet’s potential to enhance consumer utility, this vast increase in choice can potentially overwhelm customers and lead to worse choices (Lurie 2004). A growing trend has been the use of recommendation systems by online retailers and content providers. These systems seek to predict the rating or preference that potential consumers would give an item and suggest products or content based off those. In the world of e-retail, Amazon’s recommendation system is widely recognised as the industry leader, which helps consumers through item-to-item collaborative filtering, suggesting items based on previous purchases or views by other consumers with potentially similar interests or preferences.

The decision quality of electronic recommendation systems also serves as an indicator of service quality (Zeithaml et al, 2002), however, little academic research has been done in this field. The 2011 paper by Askoy et al ‘Decision Quality Measures in Recommendation Agents Research‘ takes a closer look at the decision quality of such systems. The paper examines empirically examines the relationship of different measures that have been used to date in recommendation agent literature. In the paper, they distinguish between preference-dependent measures (PDM), which require knowledge of the different decision maker preferences as well as the attribute values of alternatives that are available; preference independent measures (PIM), that do not require any knowledge of individual decision maker preferences but do require product attribute values, and subjective measures (SM), which depend neither on the knowledge of consumer preferences nor attribute values (Askoy et al 2011: 111). Through a simulated experiment, participants were assigned to conditions simulated by a recommendation agent for a database of 32 cellphones, which were each defined by different attributes (price, weight, talk time and stand-by time). This allowed the experiment to simulate a wide range of recommendation agents currently offered by online retailers to their customers.

Results which compared PDM and PIM results verified that there are considerable gains from knowing consumer preferences that are gathered by recommendation agents. Furthermore, PIM measure prove to a way of assessing decision quality when decisions are unknown. These results suggest that setting up mechanisms to compare product attribute values may be highly beneficial to online retailers even if recommendation agents are not in use.

Electronic recommendation systems help customers sort through the ever-increasing array of choices and alternatives and make better and more informed choices. Similar to real salespeople, electronic agents can serve dual roles of being both providers as well as collectors of information. The paper serves as an initial step in assessing the decision quality as a metric for gauging the impact of marketing activities and to help marketing practitioners have a better understanding of the effectiveness of such electronic recommendation agents. This is certainly an area that will be a growing area of interest as companies aim to better their own recommendation systems to increase sales and increase utility and satisfaction of customers.

Sources
Askoy, L., Coil, B., Lurie, N. (2011) “Decision Quality Measures in Recommendation Agents Research”, Journal of Interactive Marketing, vol. 25, pp 110-122

Lurie, Nicholas H. (2004), “Decision Making in Information-Rich Environments: The Role of Information Structure,” Journal of Consumer Research, 30, March, 473–86.

Zeithaml, Valarie A., Parsu Parasuraman, and Arvind Malhotra (2002), “Service Quality Delivery through Web Sites: A Critical Review of Extant Knowledge,” Journal of the Academy of Marketing Science, 30, 4, 362–75.

Zhang, T., Agarwal, R., & Lucas Jr, H. C. (2011). The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets. MIS Quarterly-Management Information Systems,35(4), 859.

The Internet is capable of bombarding its users with information, resulting in an information overload or choice overload. Adapting information to the needs of individual consumers alleviates this information overload. Information personalization is practiced to present the product information that individual consumers want to see in the appropriate manner and at the appropriate time (Pierrakos et al., 2003). Online retailers have widely adopted  personalization as a means to enhance the shopping experience of their customers in order to build and maintain a strong customer relationship. Online retailers can implement information personalization by offering real-time personalized product recommendations (PPRs) to their customers.

This article investigated whether PPRs generate value for online retailers, and if so, how. The authors looked at the effects of online retailer learning (in the form of higher quality PPRs) on consumer store loyalty. In their research they manipulated the quality of the retailers’ learning, resulting in varying qualities of PPRs offered.

Below are the conceptual model of the mechanism through which personalized services affect consumer store loyalty (figure 1) and the research model (figure 2) including the tested hypotheses.

Figure 1.

Figure 2.

H1a: Higher quality PPRs are associated with lower consumer product screening cost.

H1b: Higher quality PPRs are associated with higher consumer product evaluation cost.

H1c: Higher quality PPRs are associated with higher consumer decision-making quality.

H2a: Higher website knowledge is associated with lower consumer product screening cost.

H2b: Higher website knowledge is associated with higher consumer decision-making quality.

H3a: Lower consumer product screening cost is associated with higher consumer store loyalty.

H3b: Lower consumer product evaluation cost is associated with higher consumer store loyalty.

H3c: Higher decision-making quality is associated with higher consumer store loyalty.

The authors designed the experiment as a two-phase task. The subjects’ first task was to rate a list of Amazon.com’s DVDs. Their second task was to pick two DVDs from the website.

The authors’ findings show strong support for the proposed model. They find that, indeed, higher quality PPRs are positively associated with consumers’ online product brokering efficiency: higher decision-making cost and lower product screening cost, and ultimately repurchase intention.

The insights derived from this article could serve as guidelines for online retailers to better adjust their IT strategies to improve customer retention. PPRs have the potential to create a virtuous cycle: the more purchases made by consumers, the higher the level of input to the recommender system, the higher the quality of PPRs received by consumers, the higher the consumers’ online product brokering efficiency, the higher decision-making quality and the lower the product screening cost, and, finally, the higher consumers’ repurchase intentions. This brings sustained competitive advantage, because it becomes increasingly more difficult for competitors to imitate. When consumers switch to another store, their shopping efficiency will suffer.

References:

Pierrakos, D., Paliouras, G., Papatheodorou, C., and Spyropoulos, C. D. 2003. “Web Usage Mining as a Tool for Personalization: A Survey,” User Modeling and User-Adapted Interaction (13:4), pp. 311-372.

In this blog post I will describe the most interesting theoretical and practical elements of the paper ‘The penguin’s window: corporate brands from an open-source perspective’ by Pitt, Watson, Berthon, Wynn and Zinkhan (2006), which relates to session 3.

The traditional New Product Development (NPD) model, where companies are exclusively responsible for coming up with new product ideas, is increasingly being challenged. For instance, innovative success stories of Open Source (OS) projects suggest that customer empowerment makes sense economically. This customer empowerment affects internal NPD processes (companies more actively and directly integrate customer ideas about new products), but also how companies are perceived in the marketplace.

The article introduces an OS lens to review traditional corporate brands. The term OS originated in computer programming, ‘in which a program’s source code is open to view and modification, and there is no charge to download and use the program’. Nowadays the term is used to describe movements that have similar philosophical underpinnings to OS software (e.g. Wikipedia). OS brands have been created in nontraditional ways at minimal cost and thus challenge accepted knowledge of brand building and maintenance. It is important for traditional marketers to understand that organizations behind OS brands do not behave like their traditional competitors. They do not compete in the same way, do not produce their offerings in the same way, are frequently difficult to monitor, and are mostly not motivated by the same financial incentives as traditional branded sellers.

Historically, power and control were centralized and hierarchical with corporations: the producers produced and the customers consume. However, OS decentralizes power and control and makes it heterarchical: producers and consumers are merged into ‘prosumers’. Thus, companies should rethink how to respond to customers becoming its coproducers, its collaborators and its competitors.

Therefore the authors came up with sources of an OS offering (physical, text experience and meaning) and considered these in terms of ‘open’ and ‘closed’ dimensions (figure 2). Interestingly, the authors state that ‘even the most traditional corporate brands are not as closed as managers may think’.

A brand’s meaning cannot be solely determined by the corporation, e.g. the meaning of the Harley-Davidson brand is largely determined by the type of people who drive the motorcycles.

Corporations want to direct appropriate experiences in their offerings. According to the authors there is an ‘experience economy’, wherein the participation of the consumer is actively sought and cultivated to create the brand experience (e.g. in Disneyland).

The rise of fast processors and adaptable interfaces has fueled the rise of interactivity, in which text (stories) become partly open and shaped by readers.

The physical aspects of a brand offering have traditionally been closed and determined by corporations. As discussed in class, there has been a rise in mass customization and nowadays in cocreation of physical products.

Using this framework can help to revision corporate branding towards an open perspective wherein prosumers create the physical offering, author the text, generate the experience, and evolve the brand meaning. This results in a loss of organizational control over the brand.

The authors view OS brands based on the social capital theoretical framework and a specific form of ‘gift economies’, where goods and services are given to others with no reciprocal obligation from the recipients (e.g. creativity, innovation, and development services provided by members). Conversely to economic capital, the more one gives away, the more symbolic capital one accumulates in the form of prestige, status, and reputation.

References

Fuchs, C., & Schreier, M. (2011). Customer empowerment in new product development. Journal of Product Innovation Management, 28(1), 17-32

Pitt, L. F., R. T. Watson, P. Berthon, D. Wynn, and G. Zinkhan. 2006. The penguin’s window: Corporate brands from an open-source perspective. Journal of the Academy of Marketing Science 34 (2): 115–27.