Online and Offline Purchase Intention: Master’s Thesis

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Online and Offline Purchase Intention:

The Case of Verbal, Visual, and Social Elements on the Apparel Product Page

Master’s Thesis

Olivia A. Lin

s3602052

o.lin@student.rug.nl

University of Groningen

Faculty of Economics & Business

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Abstract

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1. Introduction

From year 1995 onwards, the world saw an exponential growth in Internet usage along with the Internet as a medium for various sectors, one of which being retail. As several major e-commerce sites like Amazon and eBay launched, they led the way to mainstream online retailing (Ellis-Chadwich, 2013). In 2014, there were 1.32 billion digital buyers worldwide with 1.336 billion USD in online retail sales (Statista, 2018-a, 2018-b). By 2021, digital buyers are predicted to climb to 2.14 billion with 4.878 billion USD in online retail sales, a major increase in just seven years. In the offline market, however, a record number of more than 7,000 brick-and-mortar stores across the U.S. closed in 2017, with the previous record occurring during the 2008 financial crisis. This signaled a major shift from the offline channel to online (Isidore, 2017). It is unclear if the gain in traction of online shopping is moving to dominate the on- and off-line retail industry since some products are better/easier bought offline. One thing for certain is the surge in competition and, therefore, a push for brands to develop new and superior strategies to compete with the constant shifts in retailing space. This could entail matching the needs of ever-changing consumer preferences, and making channel-specific product purchases more accessible in the opposite channel, or simply a move to multichannel marketing.

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Doesn’t it make sense for brands to prioritize strengthening their online retail setting? It’s true that consumers may take a preference for channel depending on the item of expense, but maybe it is only because the online stores are not yet designed well enough for (or cater to the needs of) consumers to convince them that online is indeed efficient, safe, and just as good as offline for even the experiential products like apparel items. Since companies are able to use the online channel to morph, provide recommendations, and actually match individual shopper preferences, it’s likely the best place to start with trying to perfect that customer experience, and eventually, benefit companies.

Additionally, multichannel retailing and multichannel marketing are growing, and recent research shows that retailers who utilize both channels are actually more successful than companies that are only available in one channel (Jin et al., 2009). In the offline setting, considerations about the flow of the store, positioning of products, lighting, music played, and customer service are among the most important factors that affect the buying decision. In the online setting are the considerations about the advertisement design elements, look and flow of the website, ease of use, quality of the verbal and visual information, morphing strategies, and level of effort generated by the brand to be connected with their prospective and returning customers at play. These two channels work together and can lead to synergistic effects, making it a worthwhile strategy. The difficulty arises during the decision-making and implementation processes, knowing how much of what and which items to include for each channel. That is the basis of this research.

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2. Background

2.1 The online and offline shopping channels

It cannot be said that one channel, in its entirety, is better than the other. Rather, many factors are considered when making consumer decisions. The rise of multichannel marketing is a result of the highly beneficial synergistic effects that come from this form of retailing (Rangaswamy & Bruggen, 2005). Firms have greater success when they can market to consumers from more than one channel because it mirrors the steps taken by the shopper. This form of marketing can be characterized really as a shift from a channel-focused view to customer-centric (Schoenbachler & Gordon, 2002). Not to be confused with omnichannel marketing, multichannel is simply about being able to reach the consumer from multiple outlets. Most literature that surrounds this topic touch on its importance, effectiveness, and potential strategies (Verhoef et al., 2015; Rangaswamy & Bruggen, 2005; Levin et al., 2005). Important are the strategies to maximize channel effectiveness through the comprehension of consumer behavior. What are the individual preferences that make up the majority? What leads an individual to choose one channel over the other? To begin this background section, the topic’s links to consumer behavior will be briefly discussed. After, key points of the customer journey will be reviewed.

2.1.1 Links to consumer behavior

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(i.e. not much affective sensory stimulation online), while perceived risks are inflated. In this sense, those with high hedonic shopping values are likely to avoid online shopping because there are more risks involved. Perhaps retailers need to increase the hedonic aspects of shopping on the online channel (i.e. make the website more experiential) if they want to create more consistency between channels, and close the gap.

As technology becomes the norm and more offline retailers are being offered online, the switch to (and/or adoption of) online shopping is becoming more effortless due to the tools available and late adopter benefits. Although the case, a large contrast in purchase behavior, depending on category, for each marketing channel remains. It is clear that there are certain products, consumer attitudes, and shopping stages that match better with one than the other. As it is necessary for marketers to know the specific mixes of these factors, Levin et al. (2005) conducted an experiment on perceived importance of certain product attributes at different shopping stages for the online and offline channels (see Table 2-1 for an adaptation of these results). They found that the offline environment is dominated with attributes having to do with experience and delivery, such as being able to see, touch, and handle the product; quickly retrieve the products; have no exchange issues; and experience an enjoyable experience compared to online. The online environment is more aligned with search tasks meaning consumers are able to shop quickly, have a large selection, and look/find the best deals. These factors lead consumers to choose offline over online when buying clothes, but online over offline when looking for purchases such as airline tickets.

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perceptions about online stores for consumers, a questionnaire was distributed and found that providing quality information can strengthen commitment in the online context (Park & Kim, 2003). That is, having quality information that satisfies the customer when navigating any product page can allow the individual to be more likely to feel less risks and more benefits in making the online purchase. One study found that perceptions of risk can be mitigated simply if “hassle-free exchanges” are guaranteed (Levin et al., 2003). This is taken into account when developing the survey experiment and will be mentioned in a later section.

Offline Online

Perceived benefits Attributes related to experience and delivery procedure.

• Being able to see, touch, and

handle the product

• Quick retrieval of products • No exchange issues

• Enjoyable shopping experience

easier achieved (than online)

Attributes related to search task.

• Being able to shop quickly • Having a large selection • Being able to find the best prices

When is the channel most preferred?

• When shopping for clothing • When shopping for airline tickets • (For all products included in the

study) during the search stage

Table 2-1. Perceived benefits and risks of each channel based on Levin et al. (2005).

It is the firm’s role to shift consumer perceptions about online shopping to make it more consistent with the offline sector, in the sense of increased benefits, and lack of risks it provides. Providing a webpage that is professional, easy to navigate (Hsieh & Tsao, 2013), and informative (Bleier et al., 2018) are necessary steps to alleviate consumer doubts (Harridge-March, 1983). In the next section, implications of these points will be elaborated on.

2.2 Consumer experience in online and offline shopping channels

2.2.1 Product page features and their importance in the online customer experience

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which companies try to lead website visitors to make a purchase by the end of their visit (Bleier et al., 2018; Rose, 2012; Urban et al., 2009). Looking at the offline shopping channel, some factors are quite similar to online, while others are much more complicated to duplicate. Just as the layout of the store should be easy to navigate, the same can be said for the online store. The biggest difference between channels, after the dimensionality of it, arises when pertaining to the product analysis stage. More for the apparel category than most others, the need to engage with the product to fully capture whether its “usage benefit” is high (Levin et al., 2005). Therefore, the focus should be set on the features included on each product page in order to create the optimal online customer experience. Previous research has touched on the importance of these design elements and included features and functions on web pages in general, but not directly pertaining to apparel websites (Bleier et al., 2018; Hauser et al., 2009).

At the product page touchpoint, consumers look over the product and make the decision of whether, or not, to add the item to their cart (Bleier et al., 2018). Depending on how much, what type, and the quality of the information on the page, it dictates how stimulated the visitor becomes and what they are able to productively gain from the moment they decide to click on the item. In this sense, being able to properly understand the product with what is given on the product page can make this process more fluid and ease the feeling of perceived risks especially for experiential goods like clothing items. Most apparel product page layouts include a photo (or more) of the product, name of the product, a short description, measurements of the garment, details of the garment material, sizing information, etc. Some companies opt out on many of these features, and include only the bare minimum. Identifying whether, or not, the inclusion of these aspects has any effect on the consumer can provide useful information to the firm (and its marketing team) on where to start with changes. If perceptions are too strong, then the webpage information may not be enough to shift the mindsets of the individual.

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to the consumer. Often, shopping is a social activity. A qualitative study by Dennis et al. (2010), more directed at women (but generalized also to men), found that combining online shopping with social networking can help increase behavioral intention and provide a competitive advantage to the firm. In fact, social influence has a moderating role on online shopping decisions and also shape the attitudes about it (Lee et al., 2011). On the other hand, many consumers prefer offline shopping because of the social implications (i.e. The shopper can spend time with their friends or family at a mall or shopping center and get advice on what to buy). The salesperson is a key player in this setting in providing the help and care necessary for the experiential and social connection. In the online channel, linking a social media platform to the product page can provide a sense of inspiration and influence on purchase decision, while a review section could simulate the critiques present and quality reassurance. The following section incorporates previous sections and elaborates on methods to improve the online customer experience.

2.2.2 Design elements that affect the online customer experience

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sort of connectivity to the customer in a social aspect and affect the online experience. In a two-part experiment, they tested how, and which, product web page design elements affected purchase behavior and created a design guide based on their results (found in Table 2-2). The table shows how Bleier et al. (2018) incorporated the experiences and which actually led to an effect. This research is based mostly on these aspects but focuses on the online apparel industry and where offline strategies can be implemented online.

Online customer experience design

Type How to build each experience...

Information experiences

• Include more descriptive details

• Use five bulleted features for key attributes summarization • Include key attributes comparison matrix

• Include recommendations section Entertaining

experiences

• (No strong effects were found)

Social experiences • Use more conversational language (i.e. adjectives, self-reflection, pronouns) • Include lifestyle/context photos

• Avoid using content filters (that allows the user to decide what, when, and how much they see on the web page)

Sensory experiences • Create product videos

• Provide cropped images of products that focus on key characteristics

Table 2-2. Adapted from effective online customer experience design guide by Bleier et al. (2018)

2.3 Product quality and its relevance

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no difference per channel. The difference in product type causes the optimal experience needed to differ (Bleier et al., 2018). There is a shift in trustworthiness of the product quality, along with the brand itself. Since some goods are more receptive on one channel than the other, customization to consumer preferences is needed.

Multichannel marketing is a broad topic with many areas to navigate. Research often emphasize the importance of utilizing both channels, but mostly discuss them as separate entities in effects. The analyses in the previous sections of this report were built in succession starting with the links to consumer behavior, how the firm plays a role in this, and what could/should be done to merge the gap between the online and offline channels. Next, discussion of the factors put into the survey experiment, method, discussion, and conclusion will take place.

3. Development of variables and hypotheses

Less attention has been given to the apparel industry, compared to other product categories, when discussing the online channel. The high risks and lack of experiential aspects lead companies to accept that clothing shopping is best dealt with offline. The offline environment has features that allow the consumer to fulfill their hedonic needs in terms of saving time and socializing (Kim & Forsythe, 2007; Rohm & Swaminathan, 2004). For online apparel shopping, the adventurous shopper is actually more motivated by the innovativeness of the Internet rather than the clothing itself (Goldsmith & Flynn, 2004). Product brand image along with online store image play an impactful role on perceived risks of online shopping and can be influential in purchase intentions (Aghekyan-Simonian et al., 2012). By integrating techniques and features into the individual online product pages, the gap between channels can be minimized. The trick is to meet as many of the offline touchpoints as possible, but in an online format. By doing this, the retailer can create an entertainment aspect that provides a more pleasurable and beneficial online experience for the consumer.

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risks attached to the online channel to create a more trustworthy space, it can change the perceptions of online shopping. In this sense, bridging the online and offline channels and making online more like offline would allow for maximization of benefits in both channels. In the context of apparel clothing, which have higher risks due to its hedonic aspects, there is a greater gap to fill.

3.1 Independent variables and moderator

In the commercial world, flow is an aspect in which many companies try to incorporate into their offline and online channels due to its effective nature in creating positive feelings that are related to the intent to purchase. Flow Theory is explained as a state when individuals are “immersed in their activity and current actions transit flawlessly into another, displaying an inner logic of their own [thus] creating harmony” during an experience (Bilgihan et al., 2013). It involves the features provided on the product page and add to the risks or benefits that exist to the visitor.

Purchase intention is affected by the components presented to the customer, and has been used in several research to measure the effectiveness of these components (Bleier et al., 2018; Forsythe et al., 2006; Voss et al., 2003; Aghekyan-Simonian et al., 2012). Forsythe et al. (2006), after developing a scale to measure perceived risks and benefits, found that those who shop frequently or at large quantities online perceive the Internet to have greater benefits and less risks; those positive perceptions predicted future intentions to visit and purchase online again. This means that high usage of the online channel leads to familiarity along with a greater level of flow. Bleier et al. (2018) in their research, more specific to the product page, evaluated effects of the online customer experience along with how product type and brand trustworthiness affect purchase. In the case of apparel product pages, it is hypothesized that including more features on the product page would decrease the feeling of risks (and increase benefits) which elevates the online customer experience and increases online purchase intention.

H1. Increased apparel product page features will have a positive effect on online purchase intention.

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et al., 2018). Similarly, a deeper level of sensory experiences (such as a model wearing the piece of clothing, zoomed-in features of the product, lifestyle videos, etc. rather than a single flat image of the product) can provide the consumer with a better understanding of the article and how it would feel on them.

H1a. Increased verbal details (more descriptive experience elements of product) on an apparel product page will have a positive effect on online purchase intention.

H1b. Increased visual elements (more sensory experience elements of product) on an apparel product page will have a positive effect on the online purchase intention.

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H2. Increased apparel product page features has a greater positive effect on purchase intention when moderated by customer-generated content (compared to company-generated content).

This can be further broken down to include elements from Table 2-2. For this research, adding the social aspect descriptively means including customer generated reviews/descriptions. Visually, it is providing images that customers post on social media and share voluntarily.

H2a. Increased descriptive details on an apparel product page will have a greater positive effect on purchase intention when moderated by customer-generated content (compared to company-generated content).

H2b. Increased visual elements on an apparel product page will have a greater positive effect on purchase intention when moderated by customer-generated content (compared to company-generated content).

3.2 Carry-over effects across channels

Cross-channel synergy entails that the use of “one channel enhances the purchase experience on another channel” (Verhoef et al., 2007). Especially with the rise in multichannel shopping, understanding carry-over effects from channel to channel is crucial. Dinner et al. (2019) analyzed advertising expenditures (for both channels) and its direct and indirect impacts for firms. They found that traditional advertising (in the offline channel) is less effective than online methods and can even lead marketers to mis-calculate the true impact of the online channel as an advertising space due to these cross-over effects. Additionally, online advertising can “effectively grow the offline channel”, especially when used as a research platform (coined ‘research shopping’). This is presented to note that both channels play a major role in the customer shopping experience and should not be deemed as more important than the other.

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on the online channel, but the convenience of shopping in a physical store can override the lowered risk. This further emphasizes the importance of acknowledging existing cross-over effects. Even though consumers may want to continue making their purchases offline, what they see in the online channel can be a determining factor in what they buy or who they buy from. In a randomized experiment, Lewis & Reiley (2014) found that online advertising increased purchases by 5%, but 93% of those purchases were made in the physical store and 78% of those increases were from consumers who never actually clicked on the online advertisement. The information on the product page acts as a form of advertisement; therefore, it would be interesting to see how its quantity and type can affect offline purchase intentions. Since consumers can become reluctant in navigating both channels once they’ve built a strong familiarity with one, companies should seek on a balanced spread in focus rather than one over the other (Melis, 2015). This is important to implement, otherwise cross-channel integration no longer stays effective for sales growth. Based on previous research about cross-channel advertising effects, it is hypothesized that online implementations (on the apparel product page) will have a positive cross-over effect in terms of offline purchase intention.

H3. Increased apparel product page features will have a positive effect on offline purchase intention.

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4. Methodology

Understandingly so, purchase intention has been researched and tested immensely in the field of marketing. This is not the case for the link between multi-channel marketing factors and apparel purchase intention; most research has had greater focus on other product categories. As expressed previously, this research is an extension from Bleier et al. (2018) and draws upon techniques used in their experimentation portion while focusing on the purchase intention of clothing on- and off-line. An experiment and questionnaire were created for the purpose of this study. First a clothing product web page with varying variable levels was presented to the participant for evaluation. To check for purchase intention and get better insights on consumer behavior, three sets of survey questions were provided.

4.1 Construct measures

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translated into showing “Instagram users” who purchased and uploaded images of themselves wearing the product, and the opportunity for the shoppers (or participant) to have their photo featured as well. Lastly, the no social conditions didn’t include the social elements block. Before being presented with the stimuli, participants were told to imagine a situation in which they were shopping for a “light jacket” to prime and prevent them from making implicit assumptions about the product.

Condition level

Design elements Low High

Visual elements Baseline image Baseline image

No video Image crops

Video clip

Verbal elements Basic information only

Descriptions have emotional tone (None of the high factors) # of words 25% more

At least 5 bullet points Return information Social elements No social aspect Social aspect (Instagram)

Table 4-1. Experimental stimuli factors.

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(2008), and Rohm & Swaminathan (2004). Table 4-2 presents all items from the questionnaire section along with their appropriate sources.

Measurement items

Purchase intention (adapted from Bleier et al. 2018)

PI1. I would purchase this product from the online store. PI2. I would purchase this product from the offline store. PI3. I would rather purchase this product offline than online.

Moderator (Adapted from Rose et al. 2012)

S1. In general, being able to connect with other consumers who share similar interests

in the same products is a positive feature of online shopping (e.g. social media posts, reviews).

Price consciousness (Konus et al. 2008)

PC1. In general, I compare the prices of various clothing products before I make a choice.

Experience dimensions (Bleier et al. 2018)

ED1. Before purchase, it is important to me to touch the product to evaluate how it will

perform.

ED2. Before purchase, it is important to me to test this product to evaluate how it will perform. ED3. I can adequately evaluate this product using only information provided by the web page

about the product’s attributes and features.

ED4. I can evaluate the quality of this product simply by reading information about the

product.

Informativeness

INFO1. Overall, I like the amount of product information provided. (Adapted from Bleier et al.

2018)

INFO2. In general, information about the clothing product is important in my purchase

decision. (Adapted from Rose et al. 2012)

INFO3. In general, I like to take my time when I do shopping. (Adapted from Rohm &

Swaminathan 2004)

Relevance

R1. The product shown previously is relevant to me.

Table 4-2. Measurement items included in survey experiment.

4.2 Data Collection

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with an average age of 25 (M=25.36, SD=6.867). Students (47.8%) made up a majority of respondents, while the remainder were either working full-time (31.6%), working part-time (17.6%), or unemployed (2.9%).

Status of participants

Gender (by %) Male 27.2%

Female 72.8%

Status (by %) Full-time worker 31.6%

Part-time worker 17.6%

Student 47.8%

Unemployed 2.9%

Age (in years) Min value 16

Max value 56

Mean 25.36

Table 4-3. Status of participant

4.2.1 Data preparation

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This will be further discussed later in the Limitations section. Lastly, to take account of the moderator and any interaction effects present on purchase intention, interaction variables were computed for the experimental variables: Verbal_Visual’, ‘Verbal_S’, ‘Visual_S’, and ‘Verbal_Visual_S’.

4.3 Validation

The conditions randomly assigned to the respondents measured the effects of the independent variables on the dependent variables for this study. All other variables involved were separate from the hypotheses and included to test for any further effects on purchase intention as control variables. Validation tests were performed overall to examine the quality of the measures, but analyses of the data will be first elaborated on the variables important to the research questions before doing so for those remaining.

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To account for the strong correlation between ED1 and ED2, the items were averaged together to form AvgED1ED2. The dependent variables, although highly correlated with each other, were left alone since a moderate correlation between DVs are usually appropriate for certain analyses such as this one (Maxwell, 2001). Lastly, PI3 is thrown out due to the similar nature as PI2.

5. Results

A hierarchical multiple regression modeling approach was chosen due to the nature of the data and research. Multiple regression allows for the prediction of a dependent variable (DV) based on a multitude of independent variables (IVs). This version of regression was selected because it allows a sort of progression in the understanding of the variables included. This way, analyses of only the conceptual model are done before adding the interaction effects and subsequent filler items. Analysis was first done for online purchase intention, followed by offline. Before running the regression, eight assumptions needed to be taken into consideration. The first two applied for both. Indeed, there was one dependent variable (DV) considered continuous for the purpose of this research, and two or more independent variables (IVs) being measured at the continuous or nominal level. The remaining assumptions are checked per DV.

5.1 In the case of online purchase intention

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Lastly, the P-P Plot allows for the assumption of normality to be met. With all assumptions satisfied, the hierarchical multiple regression could be proceeded with.

Results of model fit are found in Table 5-1. Model 1 included only the experimental variables from the conceptual model, and was not statistically significant (p>0.05). Model 2 included all experimental variables along with the subsequent items and was statistically significant, R²=0.398,

F(12,103)=5.667, p<0.001; adjusted R²=0.328. Model 3 included the addition of interaction

effects and was statistically significant, R²=0.403, F(16,99)=4.169, p<0.001; adjusted R²=0.306.

Research from Cohen (1988) suggests that effect sizes for Models 2 and 3, 32.8% and 30.6%, were considered small, but acceptable.

Online PI - Model fit

Model 1 Model 2 Model 3

R² .004 .398 .403

F .165 5.667*** 4.169***

R²-adjusted -.022 .328 .306

Note. N=116, ***p<0.01

Table 5-1. Online PI, Model fit

Table 5-2 shows the regression results of the different models. Confirming only H2b, visual

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Online Purchase Intention hierarchical multiple regression analysis

B β B β B β Verbal .061 .026 .150 .064 -.119 -.050 Visual .150 .064 .429** .182 .335 .142 Social -.040 -.017 -.065 -.028 .462 .196 Age .006 .035 .012 .069 .014 .081 Gender .603** .226 .363 .136 .366 .137 Status -.040 -.039 .062 .047 .076 .058 R1 .386**** .375 .404**** .392 ED3 .175 .162 .190 .177 ED4 .179** .184 .180* .184 PC1 -.079 -.061 -.092 -.070 INFO1 .337*** .243 .337*** .243 INFO2 -.153 -.123 -.167 -.134 INFO3 -.015 -.015 -.012 -.011 S1 .068 .077 .070 .079 AvgED1ED2 -.011 -.010 .007 .006 Verbal*Visual .300 .281 Verbal*Social -.123 -.114 Visual*Social -.253 -.228 Verbal*Visual*Social .010 .018

Note. N=116, B=unstandardized regression coefficient; β=standardized coefficient. *p<0.1, **p<0.05, ***p<0.01, ****p<0.001

Table 5-2. Online purchase intention; significant values are bolded.

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5.2 In the case of offline purchase intention

Subsequent graphs for assumptions check can be found in Appendix D, 9-14 to 9-29. A Durbin-Watson value of 2.029 is again considered relatively normal and suggests no significant autocorrelation. There was linearity as assessed by partial regression plots between offline purchase intention and each of the IVs. Visual inspection of a plot of studentized residuals against the predicted values showed no homoscedasticity. There was no evidence of multicollinearity (except for model 3, which included interaction effects) with VIF scores lower than 10. There were no studentized deleted residuals greater than ±3 standard deviations; twenty-one observations were considered “high” leverage outliers with values greater than 0.2, but checking Cook’s distance showed no values greater than 1 in terms of being influential so the outliers were left in the sample. Lastly, evaluating the P-P Plot satisfied the normality assumption. With all assumptions fulfilled, continuation with the hierarchical multiple regression procedure was allowed.

Offline PI - Model fit

R²

F R²-adjusted

.028 .224 .244

.517 1.922** 1.633**

-.026 .107 .095

Note. N=116, **p<0.05

Table 5-3. Offline PI Model fit

Model fit results are found in Table 5-3. Model 1 only included the experimental model and was not statistically significant (p>0.05). Model 2 had the addition of the subsequent variables and was statistically significant, R²=0.224, F(15,100)=1.922, p<0.05; adjusted R²=0.107. Model 3

included the addition of interaction effects and was statistically significant, R²=0.244,

F(19,96)=1.633, p<0.05; adjusted R²=0.095. According to Cohen (1988), effect sizes for Models

2 and 3, 22.4% and 24.4%, were considered small but acceptable.

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channel, product relevance and information-provided had a statistically significant positive effect on offline purchase intention seen in Models 2 and 3 (p<0.001 and p<0.10). Additionally, testing/touching the product for evaluation had a statistically significant positive effect on offline purchase intention (p<0.05). These results will be further examined in the discussion section. Predictions were made to determine offline purchase intention for those who strongly agreed that they were able to evaluate the quality of the product, felt the information provided was enough,

and that testing/touching the product before purchase was important. Mean offline purchase

intention was predicted as 4.102 (95% CI, 3.625 to 4.578) on the 5-point Likert scale, meaning strongly agree to purchase on that channel. The multiple regression run to predict offline purchase intention from product relevance, information provided, and importance of testing/touching the product before purchase predicted statistically significantly, F(1,114)=327.748, p<0.001, adjusted

R²=0.137. All four variables added statistically significantly to the prediction, p<0.05.

Offline Purchase Intention hierarchical multiple regression analysis

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Visual*Social -.392 -.369

Verbal*Visual*Social .273 .506

Note. N=116, B=unstandardized regression coefficient; β=standardized coefficient. *p<0.1, **p<0.05, ***p<0.01

Table 5-4. Offline purchase intention; significant values are bolded.

6. Discussion

6.1 Online context

An effective online customer experience is developed through consideration of a series of elements that convey information best to the consumer. Bleier et al. (2018) emphasized the importance of the right combination and amount of verbal, visual, and social aspects (amongst others) on the product page. In their 16-part experiment, they were able to find significant evidence of these design elements on individual online purchase decision. In an attempt to recreate these results, by implementing the recommended design guide (but centered towards the apparel sector), the results of this research were unable to confirm the previous work. Instead, other elements of interest were found and will be further discussed in this section.

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social media images or emotion-evoking text) than an electronic item, such as headphones. The emotional and social appeals bring life to the webpage (Bleier et al., 2018) and induce a sort of human-warmth feeling that affect purchase behavior positively (Hassanein & Head, 2007; Hutter et al. 2013). Perhaps the lack of significance can be derived from and directed at the many restrictions present in this research, and will surely be discussed in the limitations section. Having tested for other possible effects on online PI, product relevance was included as a control variable. If personal relevance to the product was low to the individual, they would likely be biased and less willing to make the online purchase (Ajzan et al., 1996). Product relevance can be incorporated into a consumer’s shopping experience by providing product filters, implementing accurate recommendation sections, and using morphing strategies. Being able to evaluate the quality of the product from the product page they were provided significantly and positively affected the online PI decision. Liking the amount of information provided to them also significantly and positively affected the online PI decision. These two items expressed the importance of individual awareness in the quality and quantity presented on the product page, confirming Bleier et al. (2018) findings. In only the first model, which did not include any control variables, gender was found to have a significant positive effect on online PI. A study by Hernandez et al. (2011) found that socioeconomic characteristics have no effect on purchase intention once acquired online shopping experiences were considered. Contrarily, Garbarino & Strahillevitz (2004) found that gender differences indeed affect the perceptions of negative outcomes and lead females to perceive higher risk than males in terms of online purchase intention.

6.2 Offline context

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Once the control variables were included (Model 2), product relevance and liking the amount of information provided in the condition had a positive significant effect on offline PI. There is difficulty in being able to present the most relevant product to a customer when in-store; however, relevancy can be provided online before the individual decides to go offline to purchase the item. Being able to utilize both channels affects overall customer satisfaction (Montoya-Weiss et al., 2003). Regarding the information provided, even if the individual felt like it was enough, they agreed that they would purchase the item offline suggesting that purchase intention is not exclusive to one channel (Pauwels et al., 2011; Schoenbachler & Gordon, 2002; Park, 2003). Different from online PI, those that agreed over the importance of being able to touch and try the product before purchase led them to be likely to purchase the item offline, confirming previous research about perceived risks of the online channel (Forsythe & Shi, 2003; Levin et al., 2005; Soopramanien & Robertson, 2007). Individuals feel risk over the purchase even with enough product page information. This can lead to offline PI.

6.3 Remaining notes

In individual text responses, from participants, to the question asking why one channel may be preferred over the other, many expressed the positives of online to be ‘convenience’ and ‘ease of use’ while those who chose offline mainly discussed the uncertainty of ‘not being able to understand/analyze the product fully’ from the online information. As previously mentioned, this research was an attempt to see if it would be possible to eliminate those online shopping risks through the inclusion of certain variables. It seems as if respondents agreed with the need for informational context on the product page; perhaps the methods used in the research, or other factors, averted the individuals from wanting to purchase the product on one, or either, channel. Lastly, a few responses emphasized their dislike of the product in general; thus, they would make the purchase neither online nor offline. These factors should be considered in future studies.

7. Limitations

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in age, profession, and gender would be favorable. Future experimental designs should include more articles of clothing to account for different preferences in the styles of the participants, along with models of different races and body shapes to wear the product. Although information provided on the product page could influence purchase decision, the individual must have some sort of interest in the product. A research design that simulates a shopping web page and has the respondent choose and evaluate different items could show more useful behavioral effects for managerial implications.

Bleier et al. (2018) mentioned the lack of return policy information effects in their study (i.e. the information didn’t have any significant effect on purchase). The same occurred in this research. Although displayed on the product page, some individuals who belonged to the conditions with the information responded that they would not be likely to purchase the product since they were unable to test it first. Perhaps failure to read the return information provided, or the lack of consideration in their responses, prevented the understanding of the ability to return the product if it did not fit their liking in the end. Future research should account for these variables in a more obvious manner for the participant. Although, in reality, it could be that customers often don’t take in the information fully from the product page and decide not to make a purchase due to their own beliefs about the site.

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8. Conclusion

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10. APPENDIX

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Appendix B. Correlations table

Table 9-1. Correlations between dependent variables, independent variables, and control variables. Significant correlations p <0.05are in bold.

Verbal Visual SC PI1 PI2 PI3 R1 ED1 ED2 ED3 ED4 PC1 INFO1 INFO2 INFO3 S1

Pearson’s Correlatio n [Control variables = Age, Gender, Status] Verbal 1.000 .034 022 .028 .013 .082 -.087 -.035 .076 -.103 -.063 .037 .167 .068 -.024 -.136 Visual 1.000 -.006 .066 .043 .005 -.089 .164 .202 -.164 -.007 .095 -.147 .112 .105 -.027 SC 1.000 -.017 -.130 -.008 .066 .099 -.094 -.024 .036 .047 -.025 -.004 -.024 -.116 PI1 1.000 .599 -.022 .449 -.134 -.124 .412 .391 .112 .291 .085 .066 .093 PI2 1.000 .405 .269 .234 .155 .075 .142 .175 .231 .069 .001 .055 PI3 1.000 -.060 .608 .501 -.368 -.157 .217 .074 .093 .137 -.024 R1 1.000 -1.72 -.075 .298 .250 .190 .148 .244 .046 .008 ED1 1.000 .773 -.297 -.170 .291 .065 .230 .179 .065 ED2 1.000 -.307 -.250 .325 .161 .283 .139 .057 ED3 1.000 .620 .094 .248 .129 .202 .252 ED4 1.000 .220 .100 .080 .154 .218 PC1 1.000 .256 .333 .369 .185 INFO1 1.000 .222 .128 -.020 INFO2 1.000 .338 .325 INFO3 1.000 .222 S1 1.000 Sig.

2-tailed Verbal . Verbal .721 Visual SC .819 .767 PI1 PI2 .888 .387 PI3 R1 .357 ED1 .715 ED2 .426 ED3 .277 ED4 .509 .695 PC1 INFO1 .077 .472 INFO2 INFO3 .799 S1 .152

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Appendix C. Online purchase intention

Table 9-2. Normal P-P Plot

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Table 9-4. Partial regression plot for Visual condition against online purchase intention

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Table 9-6. Partial regression plot for information evaluation against online purchase intention

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Table 9-8. Partial regression plot for price comparison against online purchase intention

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Table 9-10. Partial regression plot for information important in purchase decision against online purchase intention

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Table 9-12. Partial regression plot for social against online purchase intention

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Appendix D. Offline purchase intention

9-14. Normal P-P Plot

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Table 9-16. Partial regression plot for need for gender against offline purchase intention

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Table 9-18. Partial regression plot for need for Verbal condition against offline purchase intention

Table 9-19. Partial regression plot for need for Visual condition against offline purchase intention

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Table 9-20. Partial regression plot for need for Social condition against offline purchase intention

Table 9-21. Partial regression plot for need for product relevance against offline purchase intention

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Table 9-22. Partial regression plot for need for information evaluation against offline purchase intention

Table 9-23. Partial regression plot for need for quality evaluation against offline purchase intention

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Table 9-24. Partial regression plot for need for price comparison against offline purchase intention

Table 9-25. Partial regression plot for need for product information against offline purchase intention

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Table 9-26. Partial regression plot for need for product info in purchase decision against offline purchase intention

Table 9-27. Partial regression plot for need for taking-time against offline purchase intention

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Table 9-28. Partial regression plot for need for social against offline purchase intention

Online and Offline Purchase Intention: Master’s Thesis