The authors would like to acknowledge the Royal Thai government, the European Regional Development Fund, and the Dutch provinces Gelderland and Overijssel (Grant number 2011P017004) for financial support. The content of the paper reflects only the views of the authors. The authors also appreciate help from Andrea Poelstra of GreenDino and Tobias Heffelaar of Noldus Information Technology for their valuable input on technical topics.

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The authors have nothing to disclose.

The virtual store is one of the more advanced computer technologies that have been developed to create virtual environments in which people can experience and react to close-to-reality objects. Generally, the desktop virtual store consists of user-friendly interfaces that require a short time to understand. However, a number of critical points need to be accounted for. First, clear research objectives are needed beforehand to specify the starting points when building the virtual store. This includes a plan about the products; the type, placement, and number of shelves; the location of product categories on these shelves; the type and location of products within the product categories; and other elements (e.g., poster, signage, and special displays). Moreover, it is important to decide which model (2D or 3D) of a digital representation of objects will be used (see Figure 14). The 3D models are virtual representations, with height, width, and depth, in which all sides are represented in detail. In contrast, the 2D model gives the illusion of a 3D representation by presenting an object in a cube frame (3D shape), with realistic visuals of the front of the object. The other sides of the 2D models are roughly shown without detail. Different forms of representations give rise to different user experiences and different senses of immersion. The 3D model that shows all details of an object may give a higher sense of presence and immersion (PI and Psi) than the 2D model. However, the 2D model is flexible and easy for a researcher to use, and the size of the cube frame can be easily adjusted. Thus, the choice of the virtual representation depends upon the research aims. Second, after all stores are built, the researcher should run and test all versions of the virtual store by visiting each store and picking up, selecting, and returning products to verify that the data is stored correctly. Third, because the study consists of several steps, clear instructions and detailed virtual store manuals are extremely important. The instructions should indicate what participants should and should not do in each step. Fourth, the practice session is vital to familiarizing participants with the virtual store and minimizing biases generated from different computer skills. Last, researchers should be cautioned to save data as frequently as possible to avoid any potential data loss. 
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/55719/55719fig11.jpg" /> 
Figure 14: &#160;An example of a product in a 3D model (left) and a 2D model (right). When participants click on a product, the 3D model can be rotated on-screen to illustrate all sides of the product, whereas the 2D model illustrates only the front side of the product and cannot be rotated. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The use of virtual stores in consumer research has advantages over more traditional research methods. A virtual store is a tightly controlled yet realistic environment17,19, thereby providing the internal validity of a controlled experiment while maintaining a high degree of external validity as well. It thus combines the advantages of both field and laboratory approaches20. This implies that consumer behavior can be observed and measured in a realistic context, with less concern for socially desirable answers than in other research methods, such as surveys and focus groups21. A recent study has indicated that, compared to a method of using photographs to display a store shelf, the use of virtual reality results in consumer in-store behavior that more closely resembles the behavior demonstrated in a physical store, based on several parameters(i.e. feelings of presence, type of brands selected, and responses to the location of products in the display)18. An additional advantage of using virtual reality is that changes in the store environment can be made without having to rely on complex implementation processes in real-life settings22,23. This provides flexibility for the researcher. As a result, the use of a virtual store has clear benefits when the objective of a study is to examine consumer responses to products that are not yet available in the market (e.g., in early stages of new product development), to examine consumer responses to factors that are costly or difficult to change in real life (e.g., overall store layout), and/or to examine routinized behaviors in familiar environments.
Despite the stated advantages of the virtual store, several limitations need to be carefully considered. The major limitations, at this stage of development, relate to: 1) the time and space needed per participant, 2) the potential skill-related bias, 3) the costs involved in adapting new environments, and 4) real behavior and incentives. Currently, the virtual store can be used by only one person at a time. In particular, a number of participants are sampled in a virtual laboratory or an experimental area in order to run simulations. This limitation of time and physical space for the virtual store experiment restricts sample size and types of target groups. In addition, the restriction on the types of target groups is also caused by the skills required for participants to use the computer. Gamers or younger participants are likely to be able to handle the program more efficiently than the elderly or persons with low computer skills. Another limitation of the virtual store is that the adaptation of the store and the product library is in the development stage. If one wants to use a complex store design or store elements or products that are different from the available templates (e.g., enlarging the store size or including new store elements, such as display tables), the program needs to be adjusted. Thus, cost and time are incurred for the preparation of data collection. Lastly, even though previous studies have shown that the virtual store reflects behavior in the physical store more closely than does an experiment using pictorial stimuli, participants tend to buy more products in the laboratory setup than they do in actual stores. Thus, although the use of a virtual store increases realism compared to the use of pictures, several differences from real-life behavior remain18. To be cautious, this must be considered when interpreting results from a study using the virtual store.
There is a vast range of different technological features and systems for virtual reality applications. These systems mainly vary on aspects of equipment mobility, user interfaces, and development costs. The costs for equipment and licenses vary and are subject to drastic changes due to technological developments. In general, the costs per participant are higher when more behavior data is needed with higher-level 3D simulations. The use of a different system or interface may counteract some of the mentioned limitations, but at a cost in terms of money or flexibility. Specifically, the first limitation, on the time and space needed per participant, can be counteracted by using smartphone technologies. Smartphones, in combination with a designated headset, can render a full, immersive, 360&#176; environment. Limitations on space are as low as possible, since it does not cost more space than what one would normally use. Due to the widespread use of smartphones and the low cost of designated headsets, multiple people can use it at the same time. The downside of this technology is that smartphones have a lower computing power and thus can only handle less-difficult environments. The second limitation is the potential skill-related bias, a limitation that any system must deal with. Some systems, such as the Cave system, simulate natural movements24, which potentially could reduce this bias. The Cave system uses projector screens and head tracking, which allows participants to physically move through a limited space and to orient their head arbitrarily. Such a system, however, is not or is hardly mobile and requires much more developmental and hardware costs. The third limitation, the costs that are involved with adapting the store products and environment, are dependent upon the degree of simulation. It is possible to simulate a stationary environment based on a picture, but as soon as more detail, such as a 3D world or 3D products, are needed, one is dependent upon the availability of these objects in 3D. The last limitation, the simulation of real behavior and incentives, are likely dependent on the aforementioned factors of mobility, skill bias, and, in general, the degree of immersion. Mobile units can be used in a relevant context (e.g., in the actual supermarket), thereby making the incentive and the purpose of the visit real (e.g., buying a product virtually results in actually buying the product in real life). Furthermore, it can be expected that, when the user interface closely resembles natural movement, it will better resemble real-life behavior. Lastly, the level of immersion achieved by the current virtual store is between those of a regular desktop and a semi-immersive virtual reality projection8. Since other virtual store systems are in the early stages of development, studies describing and comparing different virtual store systems are scarce. A comparison of shopping behavior under different levels of immersion is yet to be conducted.
As virtual reality has become a widely used technology, outside the scope of computer games, virtual reality technology is likely to enter the market of home users (e.g., by television, internet or mobile application). This will potentially enable researchers to do virtual reality testing outside the laboratory. Moreover, this development opens up ample opportunities to measure, research, and understand the behavior of people on a broader scale in terms of groups and areas (e.g., in developing countries or rural areas with limited accessibility to technology). The external validity of the research will consequently be enhanced. With the advancement of this technology on the consumer market, virtual reality research could further develop from supporting simulations to the direct measurement and tracking of real behavior. Just like people surfing on the web or consumers choosing in a web shop are already intensely tracked to predict or influence behavior, the same type of behavioral measures exist (and will come to exist) for simulated virtual worlds. Another potential development is foreseen in the area of generating personalized environments. Several websites are already automatically adjusted to the individual who visits them. Examples of such websites are online retailers that give suggestions based upon aspects such as location, previous purchases, and Facebook (i.e., a social media and networking platform), which personalizes not only the advertisements, but also other content to fit personal preferences. The same could happen for virtual worlds. In practice, people could, for example, select personalized supermarkets, design or choose the manner in which they would prefer to be guided (e.g., &#34;guide me towards sustainable product choices&#34;), or even limit the choices they can make (e.g., only products for people with a specific disease).
In summary, unravelling the mysteries of consumer behavior cannot be achieved by any stand-alone research method. Thus, to compare or combine insights, various data collection tools must be used. The virtual reality developments have taken great steps in the last few years. Now, it is the time to link these methods to traditional methods so that new insights can emerge. There are multiple options of the virtual store, all with their respective advantages and disadvantages. The virtual store described here is unique in that there is an easy editor to build a virtual store that includes a range of options in order to collect behavioral data. An example of research with the virtual store presented here lays the groundwork as a universal way of measuring consumer behavior in virtual-reality research.

It is undeniable that understanding consumers&#39; in-store behavior is of critical importance to achieve effective retail marketing. To aid in this understanding, advanced virtual reality technology, known as the virtual store, can enable studies of consumer behavior using computationally created virtual environments. The virtual-store approach uses a virtual reality system to generate realistic and immersive three-dimensional virtual store environments in which people can interact with the objects in the store. In such virtual store environments, people experience artificially created sensory experiences. Virtual store environments can be either realistic representations of store environments that exist in reality, or imaginary store environments. In addition, the virtual store can be seen as an intermediate tool between traditional consumer research (i.e., text-based surveys, focus groups, or lab experiments), controlled field experiments (i.e., in mock store environments), and field studies (i.e., video captures, personal observations, or tests of product sales promotion)1.
Virtual reality applications have considerable research history. As early as 1965, Sutherland2&#160;described his &#34;ultimate display&#34; concept, which includes a virtual world that provides sound and tactile feedback. Originally, attention was mainly focused on the technological hardware, but as this does not provide insights into the effects of virtual reality systems, attention has shifted to the human experience3,4. The sense of &#34;presence,&#34; of being in the computer-generated world, has consequently become a key to virtual-reality experiences5,6. Presence has been defined as the &#34;subjective experience of being in an environment, even when one is physically situated in another&#34;.7 From this point of view, &#34;sense of presence&#34; can be retrieved from a participant and refers to the extent to which a person perceives him/herself to be in an environment. Alternatively, Slater8 has distinguished between the concepts of presence and immersion, called &#34;place illusion&#34; (PI) and &#34;plausibility illusion&#34; (Psi). PI relates to having a sensation of being in a real place. It is assessed by a set of valid actions or responses that participants can perform to change their perceptions or the environment (e.g., moving the head and eye to change the gaze direction or grasping some object to move it). PI is high when a similar set of responses to change perceptions are required in the virtual reality system compared to the response expected in an equivalent physical environment. Psi accounts for what is perceived in the virtual reality, referring to the illusion that it is actually occurring. A vital component that can lead to Psi is for the virtual reality to provide the illusion that events in the virtual environment over which a participant does not have direct control refer directly to him/herself. Psi can be measured by tracing any actions or responses that people manifest in response to changes in the virtual reality that originated from outside. For example, if people&#39;s heart rates increase when they see an avatar in the virtual environment, this can represent a similar reaction to the real world. Thus, this virtual reality system provides high Psi.
The virtual store technology has been introduced in business and academics to serve several purposes. It can be used as a managerial aid, for instance to assist category managers of companies in developing a shelf plan for their products. Virtual stores also have their use in clinical settings, to measure emotional responses to food for patients with an eating disorder1 or as a screening tool for mild cognitive impairment9. A more common use of virtual stores in research, however, is to assess consumer in-store behavior and consumer responses to changes in the store environment, such as price changes10,11,12, different setups of point-of-sale displays13, different packaging options14, different nutritional labels on the backsides of product packages15, and stock levels16. In addition, the virtual store is currently used to help create and test public health interventions to stimulate healthier food choices among children17. Due to various benefits stated previously, virtual store technology and hardware are in rapid development. Therefore, this paper will focus on the human experience and describe the essential elements of studies using virtual reality in general. All essential information obtained from the current virtual store system will be demonstrated.
Currently available virtual store systems can be briefly categorized as: 1) non-immersive (e.g., desktop), 2) semi-immersive (e.g., projection, CAVE-systems), and 3) fully-immersive (e.g., head-mounted displays). Each system likely brings different levels of immersion, presence, PI, and Psi depending upon the support system. However, because the measures of immersion, presence, PI, and Psi are bound to the specific sensorimotor contingencies that each system supports, a comparison of these indicators across different systems has been deemed impossible8. In recent years, desktop virtual stores have received more attention and have been used increasingly in research. Even though the virtual store has been regarded as a promising tool for in-store consumer behavior research, expertise on how to use such a virtual store is required to ensure the timely and correct preparation and implementation of experiments. However, up to now, reported studies that comprehensively describe the procedure to conduct virtual store experiments are very scarce. Therefore, this work aims to describe a protocol for conducting consumer research with the desktop virtual store, which is of vital importance.
Generally, research with a virtual store requires: 1) equipment to display the virtual environment, 2) an editor program to enable researchers to build the virtual environment, 3) a virtual representation of the studied object (e.g., several elements of a store and products), 4) a consumer interface to navigate the virtual environment and make choices, 5) procedures for running the data collection itself, and 6) a data management system that facilitates data storage and analysis. Most of these will likely be managed by a virtual shop company and a programmer. Researchers should know: 1) how to create a retail store for an experiment in an editor program, 2) how to run data collection with the consumer interface, and 3) how to organize all outputs in the data management program and export outputs to be put into a statistical program. The current paper will address this information by giving detailed protocol steps for conducting experiments with the desktop virtual store. Additionally, advantages and limitations of using the virtual store in consumer research will be discussed. The detailed protocol described in this paper can be used to help researchers start and conduct virtual store research.
The desktop virtual store used in this paper requires hardware (i.e., personal computers (PC), liquid-crystal display (LCD) screens, a three-dimensional (3D) space navigator, a mouse, and a keyboard) and software (i.e., to design a shop and to shop like a consumer in a 3D virtual store). This particular system has been used in prior studies14,18.

<table><tbody><tr><td>Virtual Supermarket Software</td><td>GreenDino BV</td><td>http://www.greendino.nl/virtual-labs.html</td><td>This software consists of editor, product library and consumer interface.&amp;nbsp;</td></tr><tr><td>Data Management Software: Observer XT&amp;nbsp;</td><td>Noldus Information Technology</td><td>http://www.noldus.com/human-behavior-research/products/the-observer-xt&amp;nbsp;</td><td>This software records observational data and facilitates the exportation of researcher-specified data sets using filters</td></tr><tr><td>3D SpaceNavigator</td><td>3Dconnexion</td><td>http://www.3dconnexion.eu/index.php?id=26&amp;amp;redirect2=www.3dconnexion.eu</td><td>A 3D SpaceNavigator allows participants to walk and make turns in the virtual store. In addition, it can be used by participants to adjust their eye-level during a shopping trip.</td></tr><tr><td>3D moddeling software (e.g. Blender or 3DS Max)</td><td>Blender Foundation / Autodesk</td><td>https://www.blender.org/ http://www.autodesk.nl/products/3ds-max/overview</td><td>In case 3D models need to be made or adjusted 3D modeling software is needed. Many objects can be found online under different licencing agreements.&amp;nbsp;</td></tr><tr><td>Contract Reseach&amp;nbsp;</td><td>Wageningen Univeristy and Research</td><td>http://www.wur.nl/en/Expertise-Services/Research-Institutes/Economic-Research.htm</td><td>The socio-economic research institute (Wageningen Economic Research)&amp;nbsp; with experience in conducting the consumer research with the virtual store.&amp;nbsp;</td></tr></tbody></table>

using a virtual store as a research tool to investigate consumer in-store behavior

People&#39;s responses to products and/or choice environments are crucial to understanding in-store consumer behaviors. Currently, there are various approaches (e.g., surveys or laboratory settings) to study in-store behaviors, but the external validity of these is limited by their poor capability to resemble realistic choice environments. In addition, building a real store to meet experimental conditions while controlling for undesirable effects is costly and highly difficult. A virtual store developed by virtual reality techniques potentially transcends these limitations by offering the simulation of a 3D virtual store environment in a realistic, flexible, and cost-efficient way. In particular, a virtual store interactively allows consumers (participants) to experience and interact with objects in a tightly controlled yet realistic setting. This paper presents the key elements of using a desktop virtual store to study in-store consumer behavior. Descriptions of the protocol steps to: 1) build the experimental store, 2) prepare the data management program, 3) run the virtual store experiment, and 4) organize and export data from the data management program are presented. The virtual store enables participants to navigate through the store, choose a product from alternatives, and select or return products. Moreover, consumer-related shopping behaviors (e.g., shopping time, walking speed, and number and type of products examined and bought) can also be collected. The protocol is illustrated with an example of a store layout experiment showing that shelf length and shelf orientation influence shopping- and movement-related behaviors. This demonstrates that the use of a virtual store facilitates the study of consumer responses. The virtual store can be especially helpful when examining factors that are costly or difficult to change in real life (e.g., overall store layout), products that are not presently available in the market, and routinized behaviors in familiar environments.

The virtual store displayed using a PC with three 42-in LCD screens has been applied to examine the effects of supermarket layout on consumer shopping behavior (e.g., total shopping time, movement duration and speed, total number of products examined, and total number of products purchased) and perceived shopping experience. The virtual store enables the researcher to flexibly modify the attributes of store shelves (i.e., shelf length and shelf orientation) and to examine these effects in a laboratory setting.
As an example, results from the store layout study are provided. In the study, supermarket stores were built using 4 different layouts, in which shelf length (short versus long shelves) and shelf orientation (paralleled arrangement versus unparalleled arrangement) were varied. These stores are depicted in Figure 7.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/55719/55719fig7.jpg" /> 
Figure 7: Pictures of four store layouts in the store layout experiment. The layouts differ in shelf length and shelf orientation: 1) store with long and parallel shelves, 2) store with short and parallel shelves, 3) store with long and non-parallel shelves, and 4) store with short and non-parallel shelves. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The study was performed in accordance with the &#34;Generic Protocol Food Choice Simulator&#34; and approved by the Social Sciences Ethics Committee of Wageningen University. All participants signed an informed consent form prior to participating in the experiments. In the present example, participants (n = 241, 71% female) were divided into four groups; each group visited one of four store layouts. Participants were trained on how to use the virtual store in a practice session. Next, they completed a shopping motivation manipulation task that asked them to recall shopping trips with either hedonic or utilitarian shopping motivations. Subsequently, the participants started the main test, in which they were requested to shop for a dinner using a shopping list. Participants were asked to imagine that they were shopping with either hedonic or utilitarian motivation (the same motivation as in the previous recall task was assigned). The shopping list consisted of fixed-choice (8 pre-determined types of products) and free-choice products (undetermined products from the fruit and vegetable category). The free-choice products were used to test the effects of store layout on the number of products purchased. Once the participants finished shopping, they filled in a computer-based questionnaire to evaluate their shopping experiences, perceptions about the store, and willingness to revisit the store.
The data management program recorded shopping behavior (e.g., total shopping time, moving speed, and total number of products purchased). Afterwards, variables were exported from the data management program to 3 separated tables: Table 1, Table 2, and Table 3. Table 1 presents the total shopping time, the total number of products examined, and the total number of products purchased by each participant. Table 2 presents the total movement duration (i.e., shopping time) that was selected from a filter of speeds higher than 0.001 m/s. Table 3 presents the moving speed that can subsequently be used to calculate the walking distance (walking distance (m) = average moving speed (m/s) x total moving time (s)).
<img alt="Table 1" src="/files/ftp_upload/55719/55719tbl1.jpg" /> 
Table 1: Examples of shopping-related behavioral data from each participant (i.e., total shopping time, total number of products examined, total number of products purchased, and total number of products returned), exported from the data management program. All shopping-related behavioral data from each participant should be organized in one row before transferring it to SPSS or other statistical programs. This exported data will be stored to the file called &#34;Behavioral data&#34; in the export folder of the data management program. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719tbl1large.jpg" target="_blank">Please click here to view a larger version of this table.</a>
<img alt="Table 2" src="/files/ftp_upload/55719/55719tbl2.jpg" /> 
Table 2: Examples of movement-related data (i.e., the moving speed and the walking position of each participant), exported from the data management program. The movement-related data is selected when participants moved with speeds higher than 0.100 m/s. This selection filters out all data that occurred when participants stood still. All movement-related data from each participant should be organized in one row before being transferred to SPSS or other statistical programs. This exported data will be stored to a file called &#34;Numerical data&#34; in the export folder of the data management program. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719tbl2large.jpg" target="_blank">Please click here to view a larger version of this table.</a>
<img alt="Table 3" src="/files/ftp_upload/55719/55719tbl3.jpg" /> 
Table 3: Examples of movement duration (indicated in the shopping duration column), exported from the data management program. The movement duration is retrieved from the behavioral data table that filters out the time during which participants did not move (speed &#60;0.100 m/s). This duration is shorter than the total shopping duration. The exported data will be stored to a file called &#34;Behavioral data&#34; in the export folder of the data management program. <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719tbl3large.jpg" target="_blank">Please click here to view a larger version of this table.</a>
Once the data was exported, univariate ANOVA was applied to analyze the effects of shelf length and shelf orientation on in-store shopping behavior. The effects of store layout can be presented in various forms, such as bar charts and tables.
Figure 8 displays the total number of products examined and the total number of products purchased in the supermarkets with different store layouts. The results from the virtual store confirmed that store layout attributes, specifically the interaction of shelf length and shelf orientation, influenced the number of products examined (F (1,237) = 4.66, p &#60; .05, &#951;p&#178; = .02) and the number of products purchased (F (1,237) = 3.47, p = .06, &#951;p&#178; = .01). The findings showed that when shelves were placed in parallel, the length of the shelves did not affect the number of products examined (Mshort &#177; SDshort = 16.12 &#177; 5.37, Mlong &#177; SDlong = 17.12 &#177; 5.99, F (1,237) = 0.81, p = .37, &#951;p&#178; = .00), nor the number of products purchased (Mshort &#177; SDshort = 12.00 &#177; 2.77, Mlong &#177; SDlong = 12.22 &#177; 2.37, F (1,237) = 0.24, p = .63, &#951;p&#178; = .00). In contrast, when the orientation of the shelves was unparalleled, shorter shelf lengths stimulated a higher number of products examined (Mshort &#177; SDshort = 17.62 &#177; 6.48, Mlong &#177; SDlong = 15.23 &#177; 6.45, F (1,237) = 4.65, p &#60; .05, &#951;p&#178; = .02) and purchased than longer shelf lengths (Mshort &#177; SDshort = 12.30 &#177; 2.15, Mlong &#177; SDlong = 11.35 &#177; 2.37, F (1,237) = 4.61, p &#60; .05, &#951;p&#178; = .02).
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/55719/55719fig8.jpg" /> 
Figure 8: The total number of products examined (left) and the total number of products purchased (right) in a supermarket with different store layouts (short versus long shelves placed in a paralleled or in an unparalleled orientation). The total number of products examined (packages or items) increased every time the participants clicked on a product. This number differs from the total number of products purchased (packages or items), by which the number of products in the purchase basket was recorded. Participants were allowed to return any selected products. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In addition to product choice behaviors, the virtual store can also record time and movement-related behaviors, such as, the shopping time and the walking distance. Figure 9 and Figure 10 show the effects of shelf attributes on the shopping time and walking distance of participants, respectively.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/55719/55719fig9.jpg" /> 
Figure 9: Total shopping time (s) participants spent in the supermarket with different shelf lengths and shelf orientations. The total shopping time accounts for the time participants spent between entering the store and leaving the store. The data management program also allows researchers to filter out the time that participants spent in a specific area. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/55719/55719fig10.jpg" /> 
Figure 10: The walking distance of participants in the supermarket with different shelf lengths and shelf orientations. The walking distance was determined by multiplying the moving time (s) with the average shopping speed (m/s). The duration of the moving time used to calculate walking distance differs from the total shopping time because the moving time is exclusively recorded during participant movement. In contrast, the total shopping time accounts for the movement time and the time spent viewing and selecting products. Thus, the total moving time can be attained by only selecting the time during which participants move faster than 0.100 m/s. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In addition to the effects of shelf attributes, the current research also focuses on shopping motivations to understand their influence on in-store shopping behavior. The results reveal significant main effects of shopping motivations on all in-store behavioral variables. Consumers with a hedonic motivation searched for (i.e., clicked on) (Mhedonic &#177; SDhedonic = 17.97 &#177; 6.93) and purchased more products (Mhedonic &#177; SDhedonic = 12.25 &#177; 2.42) than consumers with a utilitarian motivation (products examined: Mutilitarian &#177; SDutilitarian = 15.10 &#177; 4.82, products purchased: Mutilitarian &#177; SDutilitarian = 11.69 &#177; 2.43, see Figure 11). They also spent more time (Mhedonic &#177; SDhedonic = 607.18 &#177; 205.07 s, Mutilitarian &#177; SDutilitarian = 480.94 &#177; 134.25 s, see Figure 12) and walked longer distances (Mhedonic &#177; SDhedonic = 89.87 &#177; 31.15 m, Mutilitarian &#177; SDutilitarian = 80.73 &#177; 34.08 m, see Figure 13). The interaction effect of shopping motivation and store shelf attributes was not significant.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/55719/55719fig12.jpg" /> 
Figure 11: The total number of products examined (left) and the total number of products purchased (right) by participants with utilitarian and hedonic shopping motivation. The number of products examined and purchased are presented across all store layouts. Participants were assigned to shops under either utilitarian or hedonic shopping motivation prior to a shopping task. The shopping motivation was manipulated by a motivation manipulation task and a shopping situation. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/55719/55719fig13.jpg" /> 
Figure 12: Total shopping time (s) spent in the supermarkets by participants with utilitarian or hedonic shopping motivation. The total shopping time accounts for the entire time that participants with different shopping motivations spent in the virtual supermarket across all store layouts. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/55719/55719fig14.jpg" /> 
Figure 13: The distance that participants with utilitarian and hedonic shopping motivation walked. This figure shows the average walking distance across all store layouts. p &#60;0.10+, p &#60;0.05*, p &#60;0.01**, p &#60;0.001*** <a href="http://cloudflare.jove.com/files/ftp_upload/55719/55719fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.

People&#39;s responses to products and/or choice environments are crucial to understanding in-store consumer behaviors. Currently, there are various ...

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11.3K Views.  Wageningen University and Research. This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.

Video: Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new conceptualization of this search process, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We refer to this conceptualization as the 3S Model and illustrate its usefulness through three eye-tracking studies, one from each level of analysis. Our experimental examples, which range from manipulating certain stimuli on a single product (e.g., the placement of textual and pictorial packaging elements) to manipulating the entire shopping trip for customers during their stay in a store (e.g., through more or less specific shopping tasks), highlight the broad applicability of this alternative approach for understanding customers&#8217; in-store search behavior. Thus, our model can be seen as a helpful tool for researchers interested in how to conduct experimental eye-tracking studies that shed light on the perceptual processes preceding product choices and purchase decisions. The 3S Model is equally suitable in controlled lab conditions and under ecologically valid settings in the real retail environment. Furthermore, it can be used from the micro level, with a focus on the meaningful metrics on a particular product, through the intermediate level, with the emphasis on the area surrounding products in shelves and other in-store spaces, all the way to the macro level, examining customers&#8217; navigational paths throughout a store as a function of their shopping tasks, cognitive capacity, or ability to acquire in-store information.

This article presents a new conceptualization of the in-store search process, the 3S Model, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We illustrate the usefulness of our conceptualization through three eye-tracking studies, one from each level of analysis in the 3S Model.

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new ...

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional (2-D) images. In everyday life, however, humans interact predominantly with real-world solid objects, not images. Currently, we know very little about whether images of objects trigger similar behavioral or neural processes as do real-world exemplars. Here, we present methods for bringing the real-world into the laboratory. We detail methods for presenting rich, ecologically-valid real-world stimuli under tightly-controlled viewing conditions. We describe how to match closely the visual appearance of real objects and their images, as well as novel apparatus and protocols that can be used to present real objects and computerized images on successively interleaved trials. We use a decision-making paradigm as a case example in which we compare willingness-to-pay (WTP) for real snack foods versus 2-D images of the same items. We show that WTP increases by 6.6% for food items displayed as real objects versus high-resolution 2-D colored&#160;images of the same foods -suggesting that real foods are perceived as being more valuable than their images. Although presenting real object stimuli under controlled conditions presents several practical challenges for the experimenter, this approach will fundamentally expand our understanding of the cognitive and neural processes that underlie naturalistic vision.

We describe methods for presenting real-world objects and matched images of the same objects under tightly-controlled experimental conditions. The methods are described in the context of a decision-making task, but the same real-world approach can be extended to other cognitive domains such as perception, attention, and memory.

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional ...

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization through marketing technology. The purpose of this study is to examine how to conduct consumer research using an online survey and analysis of data. This study examines consumers&#39;&#160;perceived benefits while customizing a product as well as emotional product attachment, attitudes toward a customization program, and loyalty intentions in the context of online retailing. In addition, this study investigates how consumer responses are different based on individual characteristics such as fashion innovativeness. An online survey company in South Korea recruited 290 female apparel shoppers who purchased apparel online. To enhance external validity, this study used an existing retail website with a well-established mass customization program. After completing the customization program, participants complete the online questionnaire. Structural equation modeling (SEM) and latent mean analyses (LMAs) are then performed for analyses. This study stresses the importance of testing measurement invariance for mean comparisons. Before the SEM and LMA, this study follows the hierarchy of invariance tests (configural invariance test, metric invariance test, and scalar invariance test), which are not considered by traditional approaches such as ANOVA. These statistical analyses provide applicability of the invariance test procedures and LMA to consumer behaviors. The conclusions of mean differences have integrity and validity because they are guided by a sophisticated statistical procedure to ensure measurement invariance.

Presented here is a protocol to examine consumer responses toward mass customization in the context of online retailing. The protocol details the online survey procedure and how to analyze data using structural equation modeling and group differences using latent mean analyses.

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization ...

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. Although direct behavioral observation may increase the external validity and generalizability of a study, it may be time-consuming and be subject to experimenter or observer bias. To address these issues, the use of data loggers as an alternative to natural observation can allow researchers to conduct broad studies without interrupting the participants&#39; naturally occurring behaviors. This article describes one of such tools&#8212;the occupancy and light data logger&#8212;with its technical description, deployment protocol, and information about its possible applications in psychological experiments. The results of testing the reliability of the logger in comparison to human observation is provided alongside an example of the gathered data during a 15-day measurement in public restroom (N = 1,148) that includes: 1) room occupancy changes; 2) indoor light changes; and 3) room occupancy time.

This article describes a procedure for using and deploying an occupancy and light data logger which allows collecting data about light-switching behavior of participants in field settings.

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. ...

Measuring Engagement of Spectators of Social Digital Games

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of participants with motion tracking systems. In the context of games that are not confined within a screen, measuring the different dimensions of engagement such as physiological arousal can be challenging. The focus of the study is made on the spectators of the game and the differences in their engagement according to interactivity. Engagement is measured with physiological and self-reported arousal, as well as an engagement questionnaire at the end of the experiment. Physiological arousal is measured with electrodermal activity (EDA) sensors that record the data on a portable device (EDA box). Portability was essential because of the nature of the game, which is akin to a life-size pong and includes many participants that move. To have an overview of the events of the game, three cameras are used to film three angles of the playing field. To synchronize the EDA data with events happening in the game, boxes with digital numbers are used and put in the frames of cameras. Signals are sent from a sync box simultaneously to the EDA boxes and to light boxes. The light boxes show the synchronization numbers to the cameras, and the same numbers are also logged on the EDA data file. That way, it is possible to record EDA of many people that move freely in a large space and synchronize this data with events in the game. In our particular study, we were able to assess the differences in arousal for the different conditions of interactivity. One of the limitations of this method is that the signals cannot be sent farther than 20 meters away. This method is, therefore, appropriate for recording physiological data in games with an unlimited number of players but is restricted to a limited space.

We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of ...

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

This study introduces an experimental paradigm for a usability test of emerging technologies in a management information system (MIS). The usability test included both subjective and objective evaluations. For the subjective evaluation, a usability questionnaire and a NASA-TLX scale were adopted. For the objective evaluation, methods of Neuro-Information-Systems (NeuroIS) were used. From a NeuroIS perspective, this study used mobile fNIRS and eye tracking glasses for multimodal measurements, which solved the problem of ecological validity of cognitive neuroscience tools used in real-world behavior experiments. This study used Augmented Reality (AR) integrated into the Internet of Things (IoT) as an experimental object. Comparing the differences in the neuroimaging data, the physiological data, the usability questionnaire, and the NASA-TLX scale data between the two information-search modes (AR versus a website), information search with AR had a higher efficiency and a lower cognitive load compared with information search with a website during the process of consumption decision-making. The usability experiment results demonstrate that AR, as an emerging technology in retail, can effectively enhance consumer experiences and increase their purchase intention. The experimental paradigm, combining both subjective and objective evaluations in this study, could be applied to a usability test for emerging technologies, such as augmented reality, virtual reality, artificial intelligence, wearable technology, robotics, and big data. It provides a practical experimental solution for the user experience in human-computer-interactions with the adoption of emerging technologies.

This study presents an experimental paradigm for&#160;a&#160;usability test combining subjective and objective evaluations. The objective evaluation adopted Neuro-Information-Systems (NeuroIS) methods, and the subjective evaluation adopted a usability questionnaire and a NASA-Task Load Index (NASA-TLX) scale.

This study introduces an experimental paradigm for a usability test of emerging technologies in a management information system (MIS). The usability test ...

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing clinical and performance-based IADL assessments are not feasible for integration into clinical medicine. Virtual reality (VR) is a powerful yet underutilized tool that could advance the diagnosis and treatment of neurological disease. An impediment to the adoption and scaling of VR in clinical neurology is VR-related sickness resulting from sensory inconsistencies between the visual and vestibular systems (i.e., locomotion problem).
The Cleveland Clinic Virtual Reality Shopping (CC-VRS) platform attempts to solve the locomotion problem by coupling an omnidirectional treadmill with high-resolution VR content, enabling the user to physically navigate a virtual grocery store to simulate shopping. The CC-VRS consists of Basic and Complex shopping experiences; both require walking 150 m and retrieving five items. The Complex experience has additional scenarios that increase the cognitive and motor demands of the task to better represent the continuum of activities associated with real-world shopping. The CC-VRS platform provides objective and quantitative biomechanical and cognitive outcomes related to the user&#39;s IADL performance. Initial data indicate that the CC-VRS results in minimal VR-sickness and is feasible and tolerable for older adults and patients with Parkinson&#39;s disease (PD). The considerations underlying the development, design, and hardware and software technology are reviewed, and initial models of integration into primary care and neurology are provided.

Virtual reality (VR) is a powerful yet underutilized approach to advance the diagnosis and treatment of neurological disease. The Cleveland Clinic Virtual Reality Shopping platform combines state-of-the-art VR content with an omnidirectional treadmill to quantify instrumental activities of daily living-a proposed prodromal marker of neurological disease.

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing ...

Engineering

Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these approaches often appear poorly accurate in scientific terms. Neuroscience is increasingly applied to the investigation of the neurophysiological bases of the perception of and reaction to commercial stimuli to support traditional marketing methods. In this context, a particular sector or marketing is represented by public service announcements (PSAs). The objective of this protocol is to apply electroencephalography (EEG) and autonomic signal analysis to study responses to selected antismoking PSAs. Two EEG indices were employed: the frontal alpha band EEG asymmetry (the Approach Withdrawal (AW) index) and the frontal theta (effort index). Furthermore, the autonomic Emotional Index (EI) was calculated, as derived from the Galvanic Skin Response (GSR) and Heart Rate (HR) signals. The present protocol describes a series of operational and computational steps required to properly estimate, through the aforementioned indices, the emotional and cerebral reaction of a group of subjects towards a selected number of antismoking PSAs. In particular, a campaign characterized by a symbolic communication style (classified as "awarded" on the basis of the prizes received by specialized committees) obtained the highest approach values, as estimated by the AW index. A spot and an image belonging to the same PSA campaign based on the "fear arousing appeal" and with a narrative/experiential communication style (classified as "effective" on the basis of the economical/health-related improvements promoted) reported the lowest and highest effort values, respectively. This is probably due to the complexity of the storytelling (spot) and to the immediateness of the image (a lady who underwent a tracheotomy). Finally, the same "effective" campaign showed the highest EI values, possibly because of the empathy induced by the testimonial and the explicitness of the message.

The following protocol describes a series of operational and computational steps required to properly estimate the emotional and cerebral reaction of a group of subjects towards a selected number of Public Service Announcements (PSAs) against smoking, aired in the USA and Europe during the period between 1998 and 2015.

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these ...

Neuroscience

Buying Behavior

Assael&#39;s Consumer Buying Behavior model, by Henry Assael, categorizes consumer buying behavior into four types based on involvement and brand/product differences.
The four types of buying behavior are:
Complex Buying Behavior: This behavior occurs when there is a significant difference between brands. Consumers extensively research, compare brands, and evaluate product attributes before purchasing. Examples include buying a new car, a house, or other high-value products.
Dissonance-Reducing Buying Behavior: Consumers are highly involved, but brands have little perceived difference. Post-purchase, consumers may question their decision, seeking reassurance or additional information to alleviate doubts. Examples include electronic gadgets.
Habitual Buying Behavior: Consumers have low involvement and low perceived brand/product differentiation. Consumers often exhibit brand loyalty or habitual purchasing based on habit, convenience, or routine. Household items, groceries, or personal care products often fall into this category.
Variety-Seeking Buying Behavior: Low involvement and significant brand/product differences characterize this behavior. Consumers are not brand loyal and may seek variety for change or experimentation. Common in snack, fashion accessories, and entertainment product purchases.
Understanding buying behavior guides marketers in tailoring strategies. For complex buying, detailed information and brand differentiation are emphasized, while habitual buying emphasizes brand loyalty and convenience.