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For the purpose of testing the hypotheses, an experimental online shopping study of 2 (best vs. a new product) by 3 (aligned vs. not-aligned attributes vs.control) between subjects factorial design was constructed.

Qualtrics survey tool (Qualtrics LLC.) was used for collecting self-reported data and for assigning participants randomly between conditions. For the shopping session a custom experimental webshop was designed, programmed and implemented. The products offered on the experimental webshop were real digital cameras provided by Amazon.com via their Product Advertising free API.

4.2.1. Participants

The sampling unit was any adult (age ≥ 18 years) who fulfilled the eligibility criterion: having made at least one online purchase in the last six months. The sampling method was convenience sampling, respondents selected themselves for participating in the main study.

33 The hyperlink to the experimental material together with a short description was distributed on social network sites, and online forums. The experiment was posted also on the University SONA pool (a platform for recruiting participants from undergraduates), students receiving 1 credit point for a complete contribution.

To attain a desired statistical power level of at least .80 while keeping the chance of committing Type I errors at the conventional 5% level (α=.05) and observing a medium effect size of at least .35 (Cohen, 2013), the minimum number of participants for each experimental condition was determined to be 25 (n = 150 participants in total, computed with G*Power software v.3.1.9.2, Franz-Faul, Kiel Univerity; effect size = .35, α=.05, 1-β=.95, J = 6, I=5, estimated actual power = .9513).

4.2.2. Random assignment procedure

Participants were randomly assigned to one of the experimental maximization behavior groups based on the shopping task (instructed to buy the best product vs. not instructed), using Qualtrics tools. Afterwards, participants were redirected to the experimental webshop designed to resemble the Amazon.com website. The website had implemented an algorithm for randomly assigning visitors on one of the three experimental attribute alignment conditions (aligned vs. nonaligned vs. control). The experimental design is presented in table 2.

Table 2

Main Study: Groups and Experimental Conditions

ID

Condition Maximization Behavior a)

Attribute Alignment b) 1. + + 2. + - 3. - + 4. - - 5. + 0 6. - 0

a) Levels: participants instructed for buying the best product (+); participants not instructed to buy the best products (-)

b) Attribute Alignment Levels: Aligned attributes differing on one dimension only (+); Nonaligned attributes differing randomly on all dimensions (-). No manipulation: the products are served exactly as they appear on Amazon.com product feed (0).

4.2.3. Task scenario

In the exploratory study shopping for photo cameras required on average the longest time to

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exploratory study. Therefore, we used digital cameras in the shopping task scenario for the experimental study.

The shopping scenario was: “Your plan is to buy (a new vs. the best) photo-camera from this website for under 300 Euro”. The scenario informed participants that they could choose to buy any product that fits in the budget and preferences; also, as in real life, they could decide to increase the pre-set budget up to 340 Euros if they thought necessary. The complete task scenario is presented in appendix C.

4.2.4. Measuring choice conflicts

We used the following method adapted from Odekerken-Schröder and Wetzels (2003), to measure choice conflicts: the number of product evaluations weighted by the perceived decision difficulty during the shopping session.

We observed in the exploratory study that the difficulty to make a decision varied with the experienced superficial choice conflict. In other words, when consumers scanned fast the list of available products they reported difficulty making a decision. Hence, in the experimental study we used repeated measurements at fixed points in time for capturing and measuring a similar variation of decision difficulty during the shopping session. A single item question was used “up until now, how difficult has it been for you to choose one of the available alternatives” (7 points semantic differential, 1=very easy, 7=very difficult). The question was presented in a modal popup dialog which required user input to continue.

Using the recommendations from Chan et al. (2004) in order to maintain the efficacy of the popup method, the maximum number of popups per shopping session was restricted to 7. From the exploratory study we observed that a typical shopping session lasted around 15 minutes. As such, the repeated measures were made at constant time intervals of 2 minutes.

4.2.5. Measuring Maximizing Tendency

The findings of the exploratory study indicate that a maximizing tendency is not a unidimensional bipolar construct, tending to maximize at the upper end and satisfice at the lower. In the exploratory study we observed that participants manifested a maximization behavior while their individual maximizing tendency measured on the Maximization Tendency Scale (Highhouse et al., 2008) was to satisfice. This suggests that maximization and satisficing are two separate dimensions, in line with Rim et al. (2011). Therefore we used the Maximization Inventory scale developed by

35 Turner et al. (2012) in the main experiment, as this measures the satisficing tendency separately. the Maximization Inventory scale has 34 items which measure three subscales:

- Satisficing tendency (10 items, α=.73), i.e. “I usually try to find a couple of good options and then choose between them”

- Alternative search (12 items, α=.83), i.e. “I take the time to consider all alternatives before making a decision.”

- Decision difficulty (12 items, α=.85), i.e. “I usually have a hard time making even simple decisions.”

All items are 7 points Likert scales, “Strongly disagree-Strongly agree”. The total score for maximizing tendency is computed as the summated score of decision difficulty and alternative search.