How People Decide What to Buy Online
A purchase on a screen is a string of small decisions. Six studies and one book show where each step helps the shopper and where it runs out.
Answer to the question
What is online consumer behavior?
Online consumer behavior is how people search for, evaluate, choose and return products on the internet. Research finds a helpful side and a limit at each step: filters narrow large shelves, choice overload depends on conditions, ratings bunch near the top, images only partly replace touch, and returns policies reduce risk.
Key takeaways
- Gerald Häubl and Valerie Trifts (2000) found in a simulated store that a recommendation agent cut search effort and consideration set size while raising decision quality.
- Alexander Chernev, Ulf Böckenholt and Joseph Goodman (2015) pooled 99 observations and found that choice overload depends on four conditions, not on set size alone.
- Nan Hu, Jie Zhang and Paul Pavlou (2009) found 72 to 78 per cent of products rated four stars or more, even with over 2,000 reviews.
- Narayan Janakiraman, Holly Syrdal and Ryan Freling (2016) pooled 21 papers and found lenient return policies raised purchases and raised returns by a smaller amount.
Barry Schwartz argued in The Paradox of Choice that a growing number of options can make choosing harder and the result less satisfying. A 2015 meta-analysis of 99 observations later showed the effect depends on conditions. A screen holds more products than any shop, so online consumer behavior is where the question meets daily practice, one small decision at a time.
Narrowing a very large shelf
Online shopping runs in two stages, in the account given by Gerald Häubl and Valerie Trifts: shoppers screen many products, then compare a shortlist in depth. Their controlled experiment, in a simulated online store, tested two interactive aids. A recommendation agent screened products against the preferences a shopper stated, and a comparison matrix laid selected products side by side.
The agent cut search effort and the size of the consideration set while raising the quality of that set and of the purchase decisions made from it. The matrix also shrank the set and improved its quality, with favourable effects on some of the decision measures.
Filters and sorting do the same job at scale in online consumer behavior, and the open question is when more choice stops helping. Alexander Chernev, Ulf Böckenholt and Joseph Goodman pooled 99 observations in a 2015 meta-analysis of choice overload. They identified four conditions that govern it: how complex the set is, decision task difficulty, how uncertain the shopper's preferences are, and what the shopper is trying to achieve. More complexity, more difficulty, more uncertainty and a stronger wish to minimise effort each raised the odds of overload. Once those conditions were included, assortment size had a significant overall effect, which ran against earlier pooled findings of little average effect.
That supports Schwartz's argument and also limits it. Size alone does not decide the outcome. One reading is that a filter helps most the shopper who can state what they want, because stated preferences are what it works from. A shopper who is unsure gets less from it, and sits on the side of the meta-analysis where overload is likelier. Our piece on how many options a shop should offer takes the assortment question from the seller's side.
Reading ratings that cluster high
Reviews add something to online consumer behavior that a shelf lacks, which is the recorded experience of people who already bought. Nan Hu, Jie Zhang and Paul Pavlou looked at the shape of that information in a 2009 article, using book, DVD and video data collected in 2005 from two large online retailers. Between 72 and 78 per cent of products had ratings of four stars or more. The skew held even for products with more than 2,000 reviews, so small samples did not explain it. The authors concluded that the average rating is a weak stand-in for product quality and sales, and they traced the J-shaped pattern to biases in how review systems collect opinions.
Two things follow for online consumer behavior, one on each side. Reviews still carry real content, because the text says what bought items did well or badly, which no specification sheet can say. And the average, taken alone, sorts poorly when most products cluster at the top, so a gap of a few tenths of a star may say less than it seems to. Reading the number of reviews, their spread and the written detail adds what the average hides.
Our earlier piece on why we trust online ratings anyway covers how star averages relate to quality and to sales. Here the point is narrower: in the decision path, ratings work as a screen, and a screen that most products pass leaves the real discrimination to something else.
What a picture cannot replace
A shop lets people pick things up. Joann Peck and Terry Childers set out in 2003 how haptic information, meaning what touch tells a buyer about texture, hardness, temperature and weight, enters product judgements. Their two experiments found that barriers to touching, such as a display case, lowered shoppers' confidence and raised their frustration, most for people who like to touch. Written descriptions and visual images partly substituted for touch with some types of product, and the authors extended the framework to internet and other nonstore retailing.
What a product page can do about the missing touch was the subject of a 2007 study by Zhenhui Jiang and Izak Benbasat. Their model held that vividness and interactivity are the key features of online product presentation, working through perceived diagnosticity, the fit between online and in-store shopping, and enjoyment of the experience. A study largely supported the model: these presentation features and attitudes fed intentions to return to a site and to buy there.
Images, video and interactive trials narrow the touch gap in online consumer behavior, and how far they narrow it depends on the product. A purchase that hinges on feel stays harder to judge on a screen than one that hinges on a specification, which is one reason the pattern differs by category.
Online consumer behavior at checkout and after
The cart is where an online decision becomes visible. A 2012 chapter by Angeline Close, Monika Kukar-Kinney and Timothy Benusa reviews it and treats the shopper's path in three phases: accepting the internet as a place to shop, the hedonic and practical reasons for using a cart, and the checkout itself.
They separate three outcomes of cart abandonment and purchase: a completed purchase, abandonment with no purchase, and abandonment in order to buy the same item from another online or physical store. The chapter cites industry research reporting that most online shoppers have abandoned a cart at some point. An abandoned cart, on this account, is not always a failed sale. It can be a notebook, a wish list or a price check.
Returns policy sits at the other end of the path in online consumer behavior. A summary from the University of Texas at Dallas describes a 2016 meta-analysis of return policy leniency by Narayan Janakiraman, Holly Syrdal and Ryan Freling, which pooled 21 papers. By that summary, more lenient policies increased purchases and increased returns by a smaller amount, and leniency in time reduced return rates.
Read as interpretation, a generous policy moves part of the risk of a wrong guess from the buyer to the seller, which is what touching and trying do in a shop. Sellers pay for that through the returns it brings, and by the summary that increase is smaller than the gain in purchases.
Set side by side, the steps show where each one helps and where it runs out.
| Step | What it offers the shopper | Where it runs out |
|---|---|---|
| Filters and agents | A shorter, better shortlist | Depends on preferences the shopper can state |
| Large assortments | More chance of a close fit | Overload under complexity and uncertainty |
| Ratings | Experience of earlier buyers | Most products bunch near the top |
| Images and trials | A partial substitute for touch | Varies by product type |
| Returns policy | Lower risk of a wrong guess | Returns rise, by a smaller amount than purchases |
Each row is a risk-reducing move that a page can build in and a shopper can use or skip. A shopper who cannot say what they want gets little from a filter, a buyer of something tactile leans on the return window, and a buyer of a commodity can lean on the star count. Which of the five a purchase turns on varies by product, so the effort belongs there.
Common questions
What is online consumer behavior?
Online consumer behavior is how people search for, evaluate, choose and return products on the internet. Research covers filtering and search, assortment size, reviews, images, carts and returns policies.
Do more options online make it harder to choose?
Sometimes. A 2015 meta-analysis of 99 observations found that overload depends on set complexity, task difficulty, preference uncertainty and the shopper's goal, with assortment size counting once those are included.
Why are so many online ratings four stars or higher?
A 2009 study of books, DVDs and videos at two online retailers found 72 to 78 per cent of products rated four stars or more, even with over 2,000 reviews. The authors attributed the pattern to biases in review systems.
Does a lenient returns policy increase purchases?
A 2016 meta-analysis of 21 papers, as summarised by the University of Texas at Dallas, found that more lenient policies increased purchases and increased returns by a smaller amount.
Sources
Schwartz, B. The Paradox of Choice. Seed book; vault hub Books/The Paradox of Choice.
Häubl, G., & Trifts, V. (2000). “Consumer decision making in online shopping environments: The effects of interactive decision aids.” Marketing Science, 19(1), 4-21. doi:10.1287/mksc.19.1.4.15178 https://doi.org/10.1287/mksc.19.1.4.15178
Chernev, A., Böckenholt, U., & Goodman, J. (2015). “Choice overload: A conceptual review and meta-analysis.” Journal of Consumer Psychology, 25(2), 333-358. doi:10.1016/j.jcps.2014.08.002 https://doi.org/10.1016/j.jcps.2014.08.002
Hu, N., Zhang, J., & Pavlou, P. A. (2009). “Overcoming the J-shaped distribution of product reviews.” Communications of the ACM, 52(10), 144-147. doi:10.1145/1562764.1562800 https://doi.org/10.1145/1562764.1562800
Peck, J., & Childers, T. L. (2003). “To have and to hold: The influence of haptic information on product judgments.” Journal of Marketing, 67(2), 35-48. doi:10.1509/jmkg.67.2.35.18612 https://doi.org/10.1509/jmkg.67.2.35.18612
Jiang, Z., & Benbasat, I. (2007). “Investigating the influence of the functional mechanisms of online product presentations.” Information Systems Research, 18(4), 454-470. doi:10.1287/isre.1070.0124 https://doi.org/10.1287/isre.1070.0124
Close, A. G., Kukar-Kinney, M., & Benusa, T. K. (2012). “Toward a theory of consumer electronic shopping cart behavior: Motivations of e-cart use and abandonment.” In A. G. Close (Ed.), Online Consumer Behavior: Theory and Research in Social Media, Advertising and E-Tail. New York: Routledge. https://scholarship.richmond.edu/marketing-faculty-publications/17/
Janakiraman, N., Syrdal, H. A., & Freling, R. (2016). “The effect of return policy leniency on consumer purchase and return decisions: A meta-analytic review.” Journal of Retailing, 92(2), 226-235. doi:10.1016/j.jretai.2015.11.002 https://doi.org/10.1016/j.jretai.2015.11.002