Last couples of weeks I have worked on developing research ideas and planning out overall study procedures. This has been more specified after meeting with Prof. Ponto on Monday.

Given the time constrains, we decided to keep the overall procedure relatively simple for the STAR project.

This progress report 4 is organized as follows. First, I will discuss the series of behavioral data that can be measured and analyzed for the STAR project. Although original research plan was simply to compare shopper’s in-store behaviors in both 3D virtual store and actual store, I would like to develop the study further. On Monday meeting, we discussed other potential aspects to be examined in order to get the bigger picture of store design research. This would be what I will largely cover for the last half of this progress report.

The popularly accepted industry adage is that “unseen is unsold”. Consistent with this belief, retailers have developed the strategies to drag shoppers’ attention and expose them to more products. Suppose a shopper enters the university store to buy notebooks. When passing by electronics aisle, she remembers that her computer mouse got broken last week and need a new one. In-store stimuli such as products or promotion banners can guide shopper’s overall shopping paths as it triggers further wants and needs. Along these lines, for the STAR project, I would like to collect series of behavioral data that show how shoppers use physical products in the store as external memory cues that create new needs or trigger forgotten needs.

Specific plans for data collections are as follows.

In both conditions, participants will be asked to check the 3 product categories they planned to purchase during the current shopping trip. The main purpose of limiting the number of products in both conditions is to facilitate easier comparison in regard to the shopping paths deviation. However, this approach can invite some criticism. It would be necessary to think about valid reasons that can back up such manipulation.

Through meeting on Monday, we concluded that calculating reference/actual shopping paths can be rather complicated. In such case, we will compare the number of items that shoppers pay attention to, approach to and examine.

In order to develop this research further, I would like to examine other variables that can have impacts on in-store behaviors.

1) Store familiarity in terms of product location and layout

2) Shopping goal

The situation variable, shopping goal, affects in-store decision making in various ways. Along with the mind set and construal level theory, it is generally known that individuals who are in abstract state are more susceptible to environmental cues as undecided individuals do not know yet what they want to buy. Research consistently reports that shopper use physical products in the store as external memory cues that create new needs or trigger forgotten needs (Hui, Inman, Huang, and Suher, 2013; Inman and Winer, 1998; Park, Iyer and Smith, 1989). My central premise is that shoppers who are less certain of their goal would shop more, because they are more open to in-store stimuli and guided by external memory that triggers wants or needs. When consumer shops with concrete goal in their mind, product search should be guided by internal memory. In contrast, when consumers are not fully aware of what they want, search activities should be guided by external memory. This in turn will increase susceptibility to attractive marketing promotion for shopper.

With respect to shopping goal, I suppose following hypothesis can be tested in both actual and 3D virtual store.

Hypothesis 1: Compare to consumers who have concrete shopping goals, those who have abstract shopping goals browse and search product in a less selective manner.

Hypothesis 1a: Compare to who have concrete shopping goals, those who have abstract shopping goals pay attention to more diverse categories of products.

Hypothesis 1b: The probability that visual attention (noting) leads to following searching behavior (approaching and examination) is higher for consumers who have abstract overall shopping goal compare to consumers who have concrete shopping goals.

*) We also discussed the possibility of studying online shopping behavior. Surprisingly, there is no research that explicitly compare the searching behavior in offline and online shopping contexts to my knowledge. I will discuss it more about this on next post.

*) I got IRB reviews & comments. As it says that I need letter of permission to conduct the study at the bookstore, I got a signature from Duane and secured the permission today. I will be working on revising IRB for next couples of weeks.

I worked on IRB and developed questionnaire.

Following Alex and Prof. Ponto’s advice I tried to describe overall procedures rather broadly than providing detailed description, however since all documents(i.e. survey questions, consent forms, and recruitment materials) should be coherent(those things get stamped at the end) it was quite challenging to make overall IRB proposals and all these documents sensible and go well with each other. As any changes on those documents mean a change request, I tried to asked for everything I might want to do which took me a quite time. IRB has been submitted on last Monday.

As I stated in IRB proposal, throughout the study I intend to

1) Explore shopper’s in-store shopping paths and purchase behaviors both in an actual store and different types of 3D virtual stores

2) Compare and analyze the static measures

3)  Assess the validity of measuring in-store behaviors in computer simulated virtual retail environments.

I did not fully describe plans for data analysis however I intend to produce two different studies through this project. The overall structure of the project would be as following.

During stage one, I will investigate the validity of measuring in-store behavior in 3D VR environment by comparing static measures that include 1) walking path 2) deviation from optimal walking path 3) number of item examined 4) category of items examined 5) time spent in the store environment to same data set obtained from actual store.

This is an original & primary plan for STAR project. The biggest challenge is how to assess the validity of outcome measures. With the small number of participants (about 20 participants in each condition) absolute value comparison can not be quite meaningful. As what I am interested in is shopping pattern (e.g. do participants examine VR store in a similar manner as they do in actual store) descriptive analysis could be more meaningful.

Stage two, in which I newly developed my interest, involves exploratory look into in-store behavior. Surprisingly, to my knowledge, there are only handful of researches that examined causal relationship between in-store shopping path and purchase behavior. Even  existing pertinent study on shopping path majorly focused on relationship between in-store shopping distance and unplanned purchase behavior. “Unseen is unsold” has become an industry adage however there is no research that explicitly studied how seeing lead to buying. Using the instrumental approach, head mount action camera, I will be able to estimate the direct impact of in-store attention to subsequent searching behavior. To develop a valid research frame work, I am currently working on investigating other variables(i.e. prior exposure to the brand, consumer impulsivity) that have potential impact on such attention-searching relation.

Walking path Data) 

Reference path or optimal path: It refers to shoppers planned in-store path that minimize the distance he or she must cover to pick up all planned purchases.

Actual shopping path(=reference path+error): While in the store, the shopper may deviate from the reference path. For example, the shopper who planned to buy winter jacket may be attracted to warm winter hat displayed in the store and thus incur additional travel distance.

Deviation for the shopping path(error): this is primary interest of mine. I would like to see how it relates to unplanned purchase behavior and store layout.

Survey question

(1) familiarity with the University bookstore in terms of product location and store layout

(2) last time visited university bookstore

(3) mental map (do not know how to use this but just included it just in case)

(4) whether participants have shopping list for current shopping trip or not

(5) expected expenditure for the shopping

(6) all the product they plan to purchase

3D scanning of University book store 2

I revisited the university bookstore last Sunday (9^th of November) morning and conducted 3D scanning.  Total number of scan is 17 and this was done from 7:50 to 11:15 AM.

Problems

Section 3 was mostly covered however I could not capture the large part of section 2(Art supplies, pen and notebook etc.) The overall store doesn’t look so bad without these school supplies area in Scene though. Since Duanne won’t be able to make a time until Thanksgiving, I might visit the store again early December in case needed.

As the number of data set increases, the computer often freeze and get really slow making it unable to do additional editing. Markus recommended me to use another computer in the Lab.

The merchandises were changed a lot. As the store has changed the location of shelves and items, many items are overlapping creating messy look. It seems like it will take quite a lot of time deleting overlapping item and reorganizing the store manually.

Literature Review

1. Location of Items in the store

“Consumer Fit Search, Retailer Shelf Layout, and Channel Interaction”

Gu, Z., & Liu, Y. (2013), Marketing Science, 32(4), 652-668.

This is recently published study on Marketing Science and I found out that this study actually examined what I wanted to explore. This study explored the strategic implication of retailer shelf layout decision. That retailers are more likely to benefit from displaying competing products (e.g. coach and Michal Kors) in distance locations. While displaying competing product in the same location allow consumer to inspect various products all at once, in distance location consumer are induced to inspect one product first and then decide whether to incur the travel to inspect another one.

2. Product design on Choice

“The Impact of Product Design on Choice: A Dual-Process Explanation”

CLAUDIA TOWNSEND and SANJAY SOOD

UCLA Marketing Ph. D dissertation, Accepted to Journal of Consumer Science

Although this study is not quite relevant to my study, I found it fascinating since there are limited number of marketing or retailing studies that explored impact of design on choice.  This study divided product attribute into functional attributes and design (aesthetic) attribute and conducted 4 studies examining differences between these two attributes. The study results imply that consumers may not be fully aware of the effect of aesthetics on choice although design hugely influences their choice. Respondents are sensitive to variations in the price premium for function attributes, with design response to price premium variations is negligible across large ranges in values.

Plan for Next Week

-Working on 3D scanned data-clean up overlapping merchandise

-Develop questionnaire and detailed plan for study

3D scanning of University book store

I scanned the University bookstore(711 State St, Madison, WI 53703) on 25th of November using FARO 3D laser scanner. The total number of scanned files are 15(took 8-10 minutes per each). It took about 4 hours(from 7:00-11:20AM) as a whole. Since the battery was ran out during the scanning job, I had to stop scanning and charge it for about 20-30 minutes. (*Make sure fully charge two batteries next time before starting scanning work).

I roughly divided the store in three sections.

Section 1:Left side of the store-Fashion apparel items/ Section 2:Back side of the store-School supplies and gift shop/ Section 3: Right side of the store-Fashion apparel items and badger items.

Section 1 area are mostly covered except women’s apparel section. Additional scanning job is necessary for the rest of areas.

I planed to revisit the store upcoming Sunday(November 8th) from 7:30 to 11:30. Since this could be last chance of scanning(Duane won’t be able to have time until Christmas), I will try to scan as many as possible probably about 18-20 files (5, 6 scan per hour).

Registering Scanned data

Using software SCENE(specially designed software for FARO focus 3D), I registered scanned data set. Since one data set was ruined due to the low battery during scanning, 14 scanned data out of 15 were used for registering job.

Although overall visualization of the store seems to nice, scanner couldn’t capture the whole appearance of apparel items. As items were displayed so densely, capturing images from whole perspective was challenging. Just in case, I took pictures of individual set of item for the possibility of using PhotoScan. (But I don’t know how to import PhotoScan files into SCENE and combine two different data set or vise versa, I will explore this later)

Plan for next week 

November 6th and 7th – Literature review on shopping path studies and develop questionnaire. Idea development for shopping path study.

November 8th – Scan the bookstore

November 10th- Register the scanned data