Tag Archives: Purchase intentions

Which products are best suited to mobile advertising?

No doubt it is the first thing you look at when you wake up in the morning and the last thing you see before you go to sleep: your mobile phone. Imagine when checking the weather app before you leave the house a banner pops-up showing an ad for a new movie. What will be the chance that this banner will have any effect on you?

Mobile phones have become a part of our lives. According to eMarketer (2015a) US adults spend an average of 3 hours per day on mobile devices. Therefore, it is not surprising that global spending on mobile advertising has been rapidly growing up to more than $100 billion in 2016 (eMarketer, 2015b). Marketers want to grab the opportunity to capture your attention while browsing on your phone. However, marketers are not very happy with the effectiveness of their mobile advertising campaigns. Most of the time, they do not know what they are doing and are using a so-called ‘spray-and-pray’ mentality.

Because of the large-scale investments that are being made in mobile advertising, a better understanding of factors affecting mobile advertising campaign performance is needed. This is where Bart et al. (2014) saw an opportunity for research. Their main question is: ‘Under what product-related conditions are mobile display advertisements effective in changing consumers’ product related attitudes and purchase intentions?’.

Bart et al. (2014) used a multi-campaign, multi-industry data set from a large U.S. market research agency. 39,946 consumers participated in their field study and completed a questionnaire about products featured in 54 mobile display ads from 2007 to 2010. This large and double mixed data set is one of the main strengths of the paper. The authors estimated Average Treatment Effects (the difference between the mean attitude or intention ratings in the exposed and control conditions) for attitude and intention, moderated by product involvement (high or low) and product type (utilitarian or hedonic).

The most striking result from their study is that mobile display advertisements tend to only be effective for products that are both utilitarian and have high involvement, such as washing machines and family cars. These ads, on average, increased positive attitude by 4.5% and intention to buy by 6.7%, while hedonic and low involvement product ads (such as a sports car or toilet paper) had no effect.

An underlying psychological reason for this result is that high involvement goods are relevant and consumers are likely to have retained information about them. Even though mobile display ads do not include a lot of information they do have the ability to influence consumers by triggering their memory about a product that has already been assessed. In addition, a higher involvement with the product tends to be processed cognitively rather than emotionally, which is why mobile display ads work better for utilitarian products rather than hedonic products.

The main managerial implications of this study are that given a product, marketers can now better understand whether a mobile display ad will be effective or not. Also, given the decision of a marketer to invest in a mobile display advertisement campaign, they can have a better sense of what product to use and how to position this product in order to maximize campaign effectiveness.

So after reading these results and interesting findings, what do you think, will a banner on your phone showing a movie ad have an effect on you?


Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research51(3), 270-285.

eMarketer (2015a) Growth of Time Spent on Mobile Devices Slows available at: https://www.emarketer.com/Article/Growth-of-Time-Spent-on-Mobile-Devices-Slows/1013072

eMarketer (2015b) Mobile Ad Spend to Top 100 Billion Worldwide in 2016, 51% of Digital Market available at: (https://www.emarketer.com/Article/Mobile-Ad-Spend-Top-100-Billion-Worldwide-2016-51-of-Digital-Market/1012299

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.