The factors that are related to women’s online purchase
intention of high-end shoe start-ups – Eijk Amsterdam
case study
Abstract
This study was aimed to find out which factors influence women’s online purchase intention of high-end shoe start-ups. Since consumers are gaining experience with the online retail channel nowadays, and new e-commerce technologies are developed, the established relationships in the e-commerce literature might have become invalid. Therefore a
combination of inductive and deductive approaches was used in order to give a valid answer to the research question. Data was gathered through a case study at the Dutch high-end shoe start-up Eijk Amsterdam. Their target group was addressed by conducting semi-structured interviews while approaching the web-store. Moreover, a netnography study was done
although this solely functioned as background information due to some errors in the data. The findings of the interviews demonstrated that consumers in general trust the shoe industry and therefore have a positive attitude towards online shoe stores in general. Whether they were familiar with the start-up status of the company had several implications. Although consumers perceive product risk, there are various factors that enhance their trust. Noticeable is the extended pre-purchase process, which mitigates all product risk. Moreover, experience has shown to have various implications. The positive and negative perceptions were balanced through the valence framework, which ultimately determines the consumers’ online purchase intention. The implications of this research are especially interesting for online shoe web-stores and particularly for start-ups. Although the findings are low in generalizability, it contributes to the e-commerce literature by offering new insights in today’s context.
___________________________________________________________________________
Isabel de Bruijn June 29th 2016
10340440 Willem Dorresteijn
Statement of originality
I, Isabel de Bruijn, hereby declare to have solely written and produced this thesis and take full responsibility for all included content. I acknowledge the originality of this thesis and state to not have made use of any sources aside the sources referred to in this document.
The faculty of Economics and Business of University of Amsterdam does not hold itself responsible for the content of this paper as they have had a guiding role in the process of creating this thesis up till the point of submission.
Table of contents
1. Introduction 5 2. Literature review 7 2.1 Study-related factors 7 2.1.1 The feminine consumer 7 2.1.2 Online purchase intention 8 2.1.3 Implications for start-ups 9 2.1.4 High-end shoes implications 10 2.2 Constraints to the literature 11 2.3 Development of the conceptual model 12 2.3.1 Basic theoretical framework 12 2.3.2 Extensions to the theoretical framework 13 2.3.3 Conceptual model 15 2.4 Elaboration of the constructs 16 2.4.1 Risk 16 2.4.2 Trust 18 2.4.3 Trust propensity 20 2.4.4 Perceived benefit 20 2.4.5 Experience 21 2.4.6 Online store image 21 3. Method 22 3.1 Case study – Eijk Amsterdam 22 3.2 Research design 23 3.3 Sample 24 3.3.1 General sampling criteria 24 3.3.2 Specific sampling criteria 24 3.3.3 Participants 25 3.4 Data collection and analysis 27 3.5 Interview procedure 28 4. Results 29 4.1 The implications of being familiar with the company status 29 4.1.1 Trust in start-ups in relation to brand involvement 29 4.1.2 Benefits of high-end shoe start-ups 29 4.2 Implications for high-end shoes 30 4.3 Perceived benefits of the online retail channel 30 4.4 Perceived disadvantages of the online retail channel 31 4.5 The twofold effect of experience 31 4.6 Perceived extended pre-purchase process through experience 32 4.7 Risk 32 4.7.1 Information risk 33 4.7.2 Time Risk 33 4.7.3 Financial risk 33 4.7.4 Product risk 33 4.8 Trust 34 4.8.1 Trust in internet and e-commerce 34 4.8.2 Trust in shoe-business 34 4.8.3 Initial trust through familiarity with the web-store 34 4.8.4 Trust in third parties 35 4.8.5 Trust through online store image 35 4.8.6 Trust through social media page 35 4.8.7 Trust in product features through imagination 36 4.9 The implications of pricing 364.10 Personal factors 37 4.10.1 Trust propensity 37 4.10.2 The influence of personality 37 4.10.3 The influence of self-congruency 37 4.10.4 Factors resulting in irritation 37 4.11 Factors directly related to online purchase intention 38 4.12 Valence framework 38 6. Discussion 39 6.1 Theoretical implications 39 6.2 Managerial implications 40 6.3 Limitations and directions for future research 40 7. Conclusion 42 References 42 Appendices 47 Appendix A 47 Appendix B 49 Appendix C 52 Appendix D 55 Appendix E 57 Appendix F 58 Appendix G 59 Appendix H 61 Appendix I 68
1. Introduction
Starting a consumer goods company is challenging as high upfront costs are needed and high competition is faced when entering the market place. Therefore it is crucial in the beginning phase to establish a brand name in order to build a favorable reputation and to acquire customers (Bresciani & Eppler, 2010). A strong brand name can be generated by directly managing the consumers’ experience with the brand (Rawson, Ducan & Jones, 2013). This can be done for example by opening a retail store. Beside the direct influence on branding, this also leads to a higher contribution margin and cash flow since no fee to the external retailer has to be paid, (Quix & Van der Kind, 2014). However, having an own store might have its own financial risks in the beginning phase.
But a new opportunity has emerged as more and more customers started to shop online (Richard et al., 2010). The Internet penetration is world widely featured by immense growth rates as, 260% from 2000 to 2011 in Europe. In 2011 the average Internet penetration in Europe was 67% and in the Netherlands 88%. Already by 2003, Corbitt, Thanasankit and Yi argued that it is essential to exploit the Internet as business platform. Thus this new channel has great potential if it is well implemented (Endo, Yang & Park, 2012), since it enables a start-up to exploit the above-mentioned advantages.
In case a consumer goods start-up ultimately succeeds in attracting customers to their online store, they might face additional challenges. Namely, the distinctive characteristics of the online retail channel might have implications for the online shopping behavior of the consumer (Ganesh, Reynolds, Luckett & Pomirleanu, 2010). First consumers perceive more risk when shopping online than in brick and mortal stores (Bhatnagar & Ghose, 2004) and therefore a trust might be more important in e-commerce (Gefen & Straub, 2004). This is especially prevalent for the products that are high in experience features (Pascual-Miguel, et al., 2015). High-end shoes are an example of these experience goods (Endo et al., 2012) as consumers can’t be satisfied with the shoes if only one thing goes wrong, as the size runs too small (Endo & Kincade, 2008). Especially luxury products yields high expectations. The consumers’ inability to physically examine shoes in combination with the absence of a well-established brand reputation (Derbaix, 1983, as cited in Agheyan-Simonian, Forsythe, Kwon & Chattaraman, 2012), might result in challenges to exploit the online retail channel for start-ups. However, since e-commerce is still a relatively new phenomenon, there are in general more challenges to overcome. Therefore it is relevant to address the consumer’s viewpoint as it is ultimately the consumer whom decides whether to pursue an online transaction with the start-up or not (McCole, Ramsey & Williams, 2010; Kim, Song, Braynov & Rao, 2005).
The current literature offers many insights on branding in general and simultaneously on entrepreneurship however, the intersection of these two fields is an underexplored area (Rode & Vallaster, 2005; Merrilees, 2007). Moreover, there is barely research done on consumer perceived implications of transactions with start-ups (McGregor, 2005) and
especially not in the online retail setting. Although in general there is much research done on consumers’ perceived risk in the online retail setting, but it yields inconsistent findings (Kim, Ferrin & Rao, 2008; Aghekyan-Simonian et al., 2012). The same counts for the precise relationship between risk and trust (Lim, 2003).
Besides these gaps in the literature, there is another important constraint. Namely, due to the rapidly changing context, as e-commerce has been only recently introduced, customers’ perceived risk and trust theories are constantly changing (Johns, 2006; Alvesson & Kärreman, 2007). An example is the increased experience of consumers with e-commerce, however its implications are barely researched (Toaylor & Todd, 1995; Vijayasarathy, 2004, as cited in Hernández, Jiménez & Martín, 2010). Ultimately due to the changing context, existing theories relating to factors in consumers pre-purchase process might have become invalid nowadays (Corbitt et al., 2003).
Due to these gaps and possible errors in the literature, while shoppers around the world are showing strong enthusiasm to purchase apparel related products online (Park & Stoel, 2005), it is essential to gain more insights into this area. Since various researchers argue that men and women respond differently to the characteristics of the online retail channel (Dittmar, Long & Meek, 2004; Venkatesh, Thong, Xu, 2012; Pascual-Miguel et al., 2015), whereas women show a more negative attitude than men do (Dittmar et al., 2004) it is especially relevant to address the feminine perspective. Therefore this study is aimed to address the feminine consumers’ cognitions of high-end shoe start-ups while shopping online, in today’s context. This leads to the research question: “What are the factors that influence
women’s online purchase intention of start-ups in the high-end shoe industry?”
In order to approach the problem in a real life context, the research question was answered through a case study in a Dutch high-end shoe start-up named Eijk Amsterdam. Therefore 6 women of Eijk’s target group were approached by semi-structured interviews whereas some experimental conditions were applied. In order to impede error due to the constraints in the literature, relating to the changing circumstances (Corbitt et al., 2003; Johns, 2006; Alvesson & Kärreman, 2007) a combination of inductive and deductive approaches was used (Saunders et al., 2012; Boeije, 2010). Therefore based on the established topics, which were derived from current theories, progressive insights were gained into the research area in
order to retrieve undiscovered relationships and validate existing relationships. Moreover the traffic on Eijk’s website and online marketing channels were traced, although this solely functioned as background information due to several constraints. The data of the semi-structured interviews was analyzed based on open, axial and selective coding in order to answer the research question.
The study is organized as follows. First the relevant factors and current theories are outlined in order to derive at the conceptual model, whereas the specific constructs are address in detail afterwards. In the third section, the methodology is addressed including the case, the research design, the sample, interview procedure and the data collection and analysis. Afterwards the results are presented based on theoretical coding. In the discussion, the theoretical and practical implications are addressed including the limitations and
suggestions for future research, whereas the conclusion is presented at the end.
2. Literature review
In this section, first the specific components that are relevant to the research question will be addressed. Next, the constraints to the e-commerce literature will be examined which lead to the development of the conceptual model. After the presentation of the conceptual model, the specific constructs will be elaborated in the last sub paragraph.
2.1 Study-related factors 2.1.1 The feminine consumer
The online retail channel enables to absorb and process product information and transactions quicker and easier, however it might also have implications for consumers. For example there is an absence of direct contact between the seller and the consumer and a delay between the purchase moment and the receipt of the products (Chang, 1998; Qui & Li, 2008, as cited in Pascual-Miguel et al., 2015). Whether these implications of e-commerce might result in a favorable or unfavorable attitude depends on the personal factors of the consumer (Ganesh et al., 2010).
One important factor is gender as several researchers suggest that men and women respond differently to certain marketing stimuli (Meyers-Levy, 1989; Putrevu, 2001, 2004; as cited in Richard et al., 2010) and also typically to the characteristics of e-commerce (Dittmar et al., 2004; Venkatesh et al., 2012; Pascual-Miguel et al., 2015). The origin of these
differences is related to the gender differences in sociology and biology (Richard et al., 2010; Venkatesh et al. 2012).
Related to sociology, several authors suggest that men and women have different social roles and therefore exhibit different role behaviors (Bakan, 1966; Deaux & Lewis, 1984; as cited in Venkatesh et al., 2012). Especially older women tend to be more involved in shopping and are more price-sensitive, due to their social roles as housekeepers (Slama and Tashchian, 1985, as cited in Venkatesh et al., 2012). Moreover, women tend to be more interdependent, cooperative and consider more details (Bakan, 1966; Deaux & Kite, 1987 as cited in Venkatesh et al., 2012). Awad and Ragowsky (2008) add that women tend to perceive online shopping less favorably due to the reduced social interaction at the online channel (Awad & Ragowsky 2008, as cited in Murphy & Tocher, 2011).
According to biological theories relating to information processing, the gender differences can be explained by differences in brain functioning. Since the two brain
hemispheres are more integrated by women, they tend to process information more holistic, and give equal weight to self- versus other- generated information. Moreover since women tend to take more details into account (Richard et al., 2010) they have shown to be more sensitive to new cues in their environment (Venkatesh et al., 2012). Relation to the online context, other researchers suggest that especially older women, tend to rely more on
facilitating conditions, since they tend to be less willing and less cognitively capable to learn to use a new technology (Notani, 1998; Morris et al, 2005; Plude & Hoyer, 1985 as cited in Venkatesh et al., 2012). Moreover trust has shown to have a stronger effect on online
purchase intention for women than for men (Awad & Ragowsky, 2008 as cited in Chen, Yan, Fan & Gordon, 2015).
2.1.2 Online purchase intention
The recent introduction of e-commerce, and its inherent implications for consumers, is an extensively researched field. The implications of e-commerce can be approached through different view points since four parties are present; the consumer, the technology, third parties and the seller (Kim et al., 2005). Moreover a distinction can be made between various phases of the transaction, as the pre-purchase phase and the post-purchase phase (Endo et al., 2012). This study is specifically focused on consumer behavior in the pre-purchase phase.
Two important constructs are addressed in the e-commerce literature relating to the prpurchase phase; purchase intention and purchase behavior. According to various e-commerce studies and theories as the Technology Acceptance Model of Davis (1989), the
Theory of Planned Behavior (Fishbein & Ajzen, 1975) and the Theory of Reasoned Action (Arzen, 1991) consumers’ purchase intention is a strong prerequisite of actual purchase behavior (Pavlou, Fygenson, 2006; Venkatesh, Thong, Xu, 2012; Venkatesh et al., 2003; Ajzen, 1991; Kim et al., 2008). This is based on the assumption that “human beings attempt to make rational decisions based on the information available to them” (Kim et al., 2008, p. 546). But intention is not the only factor that results in behavior. However, these other factors fall outside the scope of this study. Therefore this study is solely focused on factors resulting in online purchase intention.
Various studies addressed the antecedents of purchase intention. Some researchers focused on specific e-commerce variables as perceived risk and trust. Others thrived to develop holistic approaches of various variables, which determine online purchase intention. One important theory in this field is the Unified Theory of Acceptance and Use of
Technology (Venkatesh, Morris, Davis & Davis, 2003; Venkatesh et al., 2012). Pascual-Miguel et al. (2015) modified and extended this theory to the e-commerce context and developed their “E-commerce acceptance model”. Other recently developed holistic
theoretical models developed by Achekyan-Simonian et al. (2012) and by Kim et al. (2008). The first examined specifically the effect of product brand image and online store image on various types of risk in relation to purchase intention of a multi-brand apparel webstore. The latter one encompasses the various antecedents of consumer’s trust in a particular webstore. The net balance of trust, risk, benefits and prior experience, leads to a consumers’ purchase intention and ultimately purchase behavior (Kim et al., 2008). Their theoretical framework was based on the Valence mechanism (Peter & Tarpey, 2975; Lewin, 1943; Bilkey, 1953; 1955, as cited in Kim et al., 2008). This implies that consumers are thriving to maximize the outcome of their decision and therefore they base their purchase decision on the net balance of the positive and negative attributes of the transaction. This balancing mechanism is also acknowledged by other recent studies as Chen et al. (2015) and Pascual-Miguel et al. (2015).
2.1.3 Implications for start-ups
Companies address the needs of their target group by offering a value proposition; a set of benefits that satisfy those needs (Kotler & Keller, 2012). Part of a company’s value
proposition is made tangible by offering a product, service or experience. But the company’s brand image may also add relevant intangible value for the consumer (Kotler & Keller, 2012; Aghekyan-Simonian et al., 2012). Keller (1993) defines product brand image as “the
3). Brand association has shown to enhance purchase intention (Del Rio et al., 2001; Keller, 1993, as cited in Aghekyan-Simonian et al., 2012) and even purchase behavior (Lee & Tan, 2003).
Due to the lack of a well-established brand name, consumer goods start-ups can solely rely on the tangible part of their value proposition, thus the product they offer. However, this might be challenging for start-ups at the online retail channel as their product features can be less easily examined by customers, whereas various researchers argue that a strong brand name, positively bias the consumers’ evaluation of the product features (Chattopadhyay & Basu, 1990; Kwon and Lennon, 2009; Beckwith et al., 1978, as cited in Aghekyan-Simonian et al., 2012). Moreover, product brand image has shown to function as a risk reliever
(Derbaix, 1983; Roselius, 1971, as cited in Aghekyan-Simonian et al., 2012) and has shown to be an important indicator of product quality in apparel context (Aghekyan-Simonian et al., 2012). Aghekyan-Simonian et al. (2012) found that it especially lowers product risk, financial risk and time risk for online apparel purchasing. Moreover, start-ups may face challenges in building consumer trust due to their lack of a well-established brand name (Drori, Honig & Sheaffer, 2009; Stinchcombe, 1965, as cited in Murphy & Tocher, 2011). On the other hand, Endo et al. (2012) argue that consumers have a more positive attitude towards online multi-brand shoe retailers due to their perceptions of good customer service and their extended product assortment, which might result in an additional constrain for high-end shoe start-ups.
2.1.4 High-end shoes implications
Pascual-Miguel et al. (2015) argues that a specific product type affects a consumer’s online purchase behavior since not every product type is equally suitable for e-commerce. Especially experience attribute dominated products are shown to be less appropriate (Citrin et al., 2003; Moon, Chadee & Tikoo, 2008 as cited in Endo et al., 2012; Phau & Poon, 2000; Brown et al., 2003 as cited in Chen et al., 2015). Shoes are a typical example of a product high in
experience attributes (Endo et al., 2012). Namely, as mentioned in the introduction, fit and size of shoes are, amongst others, critical determents to satisfaction with shoes (Endo & Kincado, 2008). This might even be more apparent when the shoes could be categorized as high-end or luxious (Stathopoulou & Balabanis, 2016; Okonkwo, 2009) and therefore characterized with high quality, a distinctive design in a higher price class.
Although shoes are a typical consumer product that is high in experience features and might therefore have commercial implications, there is barely research done on shoes and
especially not on high-end shoes. Solely Endo et al. (2012) investigated online shoe sales but focused on differences in satisfaction between online shoe manufacturers and shoe retailers, based on reviews in the post-purchase process. However, research on experience goods in e-commerce context has shown that the inability to physically examine the critical product features when shopping online, might result in perceived product risk for the consumer (Aghekyan-Simonian et al., 2012; Bhatnagar et al., 2000; Forsythe & Shi, 2003; Choi, Geistfeld, 2004; Lim, 2003). Product risk has shown to be a significant barrier for the online purchase of apparel related goods (Aghekyan-Simonian et al., 2012). Forsythe and Shi (2003) add that product risk is likely to rise when the price of the product is high and when desired product information is limited. However through time, the product risk for experience goods has been reduced due to the development of new technologies (Endo et al., 2012). Although, pictures, video’s, descriptions et cetera provide the consumer with a variety of valuable information, experience goods, as high-end shoes still require interaction between the product and the consumer (Endo & Kincade, 2008). Therefore consumers prefer to shop experience goods, in normal stores (Heung, 2003; Rajamma, Paswan and Ganesh, 2007), which in turn might lower their online purchase intention.
2.2 Constraints to the literature
While studying the current literature, several flaws relating to e-commerce theories and consumer behavior appeared. On the one hand, Johns (2006) and Alvesson & Kärreman (2007) suggest that a changing contextual background can result in necessary changes in the literature and therefore the need to generate new knowledge since existing relationships might break down. According to Corbitt et al. (2003) the introduction of e-commerce is indeed a dynamic and radical process and therefore the literature is unable to keep pace in building valid trust and risk models related to e-commerce. Therefore Hernández et al. (2010) argue that the increased experience of consumers with e-commerce might have implications for the aspects that they value and are relevant during their pre-purchase process.
On the other hand, there are various inconsistencies in the e-commerce literature due to the different viewpoints and definitions of the important e-commerce constructs risk and trust, and simultaneously its specific relationship (Kim et al., 2008; Aghekyan-Simonian et al., 2012; Lim, 2003). Moreover there is limited research done onto the e-commerce implications of high-end shoes and simultaneously on start-up implications. Next, various moderators are addressed which influence the relationships between factors leading to online purchase intention as age, gender, and prior experience (Venkatesh et al., 2012; 2003). By
addressing the research question from a single viewpoint, the adolescent feminine customer with some prior online experience, the effect of moderation can be mitigated in order to develop reliable and valid theory. However, limited research is done on the feminine consumer and the implication of age in the e-commerce context. Moreover as addressed before, increased experience with the e-commerce might have resulted in invalid relationships in the literature (Hernández et al., 2010; Corbitt et al., 2003; Johns, 2006; Alvesson &
Kärreman, 2007).
2.3 Development of the conceptual model
Due to these various constraints to the current literature, fresh insights are needed in order to give a valid and reliable answer to the research question. In order to develop theory, an inductive approach is needed (Boeije, 2010; Saunders et al., 2012; Ali & Birley, 1999). However, as there is relevant literature available, the current literature will be used to retrieve relevant topics for inductive reasoning (Ali & Birley, 1999). Moreover starting from the current literature facilitates initial theory building of the study and prior knowledge can accumulate by the findings of this study (Boeije, 2010; Eisenhardt, 1989 as cited in Ali & Birley, 1999). But, intentionally no hypotheses will be developed from the current literature as this might impede the explorative power of theory development through inductive
reasoning (Ali & Birley, 1991). Therefore the current literature is solely studied in order to retrieve topics, which will give initial guidance to the research.
2.3.1 Basic theoretical framework
Since the research question of this study is related to the purchase intention of a particular website as referent, the “Trust based consumer decision model” of Kim et al. (2008) might function as a suitable theoretical framework to retrieve relevant constructs. However the research question is related to factors in general that influence online purchase intention, and not solely the antecedents of trust. Moreover as the specific implications of start-ups and experience goods as high-end shoes are addressed in this study, their theoretical framework might be incomplete and therefore unsuitable for this study
However, Kim et al. (2008) based their theoretical framework on the valence
framework (Peter and Tarpey, 1975; Bilkey, 1953, 1955; Lewin, 1943) and found support for its functioning. This mechanism implies that consumers are thriving to maximize the outcome of their decision and therefore they base their purchase decision on the net balance of the
positive and negative attributes of the transaction (Peter & Tarpey, 2975; Lewin, 1943; Bilkey, 1953; 1955, as cited in Kim et al., 2008). This balancing mechanism is also acknowledged by other recent studies as in Chen et al. (2015) and Pascual-Miguel et al. (2015). Therefore the valence mechanism will also function as a suitable starting point to build the conceptual model for this study.
But some adoptions are needed as this study solely addresses the factors relation to online purchase intention. As intention is direct predictor of behavior but not the only
predictor, the other relevant factors fall outside the scope of this study. Therefore relationship between purchase intention and purchase behavior will be removed in order to build the conceptual model. Moreover, the literature addresses various inconsistencies relating to the precise relationship between risk and trust (Lim, 2003), however this falls outside the scope of this study as well. Therefore consumer trust is addressed in this study as on important
mechanism to overcome risk and insecurity in an online transaction. (Kim et al., 2008; Luhmann, 2000; Mcknight, Choudhury & Kacmar, 2002, as cited in Chen et al., 2015). Moreover, trust is also directly related to consumers online purchase intention
(Bahattacherjee, 2002; McKnight, Cummings & Chervany 1998; Gefen, 2000; Kim et al., 2008) as well as perceived risk (Kim et al., 2008; Pascual-Miguel et al., 2015; Aghekyan-Simonian et al., 2012). Last, as trust is not involved in all risk-taking behaviors, consumers’ online purchase intention might be affected by more than solely risk and trust, as benefit (Kim et al., 2008). Therefore the main constructs, trust, risk and net benefit and its relationships to online purchase intention will function as a suitable starting point for the conceptual
framework of this study.
2.3.2 Extensions to the theoretical framework
Although the construct “risk” is inherent to e-commerce, and is an extensively researched factor, there are inconsistencies in the literature, which might be due to limited
conceptualizations of the risk constructs (Aghekyan-Simonian et al., 2012; Lim, 2003; Kim et al., 2008). However, due to the increased experience with the Internet and newly introduced technologies as Paypal for example, some types of risk might be less experienced nowadays (Hernández et al., 2010). Therefore I addressed recent theories in order to retrieve relevant risk classifications. First, as product type might influence online purchase intention (Pascual-Miguel et al., 2015), experience goods as high-end shoes might result in perceived product risk (Choi & Geistfeld, 2004 as cited in Pascual-Miguel et al., 2015). Next Aghekyan-Simonian et al. (2012) made a distinction between perceived product risk, financial risk and
time risk by addressing the implications of product brand image and online store image on online purchase intention. Moreover, various researchers argue that besides the risk constructs as just mentioned, information risk is also shown to be predominant in e-commerce
(Bhatnagar et al., 2000; Forsythe & Shi, 2003). Altogether, a distinction will be made between product risk, financial risk, time risk and information risk.
Besides a definition of the various risk constructs, a conceptualization of trust is also needed. Namely various researchers argue that at the online retail channel consumer’s trust in internet, trust in the web-store en trust in third parties is needed before an online purchase decision is made (Kim et al., 2005; Grabner, Krauter and Kaluscha, 2003; Urban et al., 1999; as cited in McCole et al., 2010 - McCole et al., 2010). This might especially prevalent for start-ups, as they might not benefit from their brand name as a risk reliever (Derbaix, 1983; Roselius, 1971, as cited in Aghekyan-Simonian et al., 2012). Therefore this nuance in the trust construct will be added to the conceptual model.
Various researches used “trust propensity” as a personality oriented antecedent of trust in their theoretical framework (Kim et al., 2008; Gefen, 2000; McKnight et al., 1998; Chen et al., 2015). This might be a relevant personal variable to add in relation to the personal
perceptions of implications of start-up and high-end shoes in e-commerce
Due to the implications of increased experience with the e-commerce on various factors related to online purchase intention (Hernández et al., 2010; Corbitt et al., 2003; Johns, 2006; Alvesson & Kärreman, 2007), the construct experience will be added to the conceptual model as well. However, due to its constraints to the current literature and since there is limit research done onto its precise implications (Hernández et al., 2010), it will be added as a separated construct.
As already addressed in section 2.1.3; “Implications for start-ups” the absence of a well established brand name might have implications to factors relating to online purchase intention (Aghekyan-Simonian et al., 2012). However, due to the limited research done into the implications for start-ups, “product brand image” will added as a separated construct to the conceptual model. The same holds for the implications of high-end shoes at the online retail channel as discussed in section 2.1.4. Product type has shown to be related to online purchase intention since products high in experience features are argued to be less suitable to sell online (Citrin et al., 2003; Moon, Chadee & Tikoo, 2008 as cited in Endo et al., 2012; Phau and Poon, 2000; Brown et al., 2003 as cited in Chen et al., 2015). However the precise implications of high-end shoes on purchase intention are not addressed yet in the literature.
Therefore “product type” will be added as a separated construct to the conceptual model as well.
Last, online store features have shown, to be related to purchase intention (Liang and Lai, 2002; Richard, 2005; Ranganathan & Ganapathy, 2002; Kwon and Lennon, 2009; Verhagen & Van Dolen, 2009 as cited in Aghekyan-Simonian et al., 2012). Murphy and Tocher (2011) argued that women are especially sensitive to particular website features wheras start-ups can enhance their trustworthiness and legitimacy concerns by strategically adapting their online store image. However, in a multi-brand apparel context, online store image has solely shown to be a risk reliever and not being directly related to online purchase intention (Aghekyan-Simonian et al., 2012). However, online store image might be relevant consumer touch point for online high-end shoe start-ups (Rawson, Ducon & Jones, 2013). Due to these inconsistencies “online store image” will be added as a separated construct to the conceptual model.
2.3.3 Conceptual model
Based on these constructs, the conceptual model is developed as shown is Figure 1. The various constructs are summarized by the basis of the valence framework. But the conceptual model should solely be threated as summation of relevant and related concepts and not as established relationships between variables (Ali & Birley, 1999). All the constructs will be elaborated below in order to provide background information for inductive reasoning. The paragraphs in which the topics are addressed are indicated in the figure.
Figure 1. Initial conceptual model of the relevant constructs.
2.4 Elaboration of the constructs 2.4.1 Risk
Consumers experience risk due to uncertainty and potential undesirably outcomes of their behavior (Taylor, 1974; Dowling, Staelin, 1994, as cited in Lim, 2003, p. 218). Risk is an inherent concept in e-commerce since transactions in web stores are even more uncertain by nature than transactions in traditional retail stores (Chen et al., 2015, Bhatnagar & Ghose, 2004). Therefore Forsythe & Shi (2003) define risk in e-commerce as “the subjectively determined expectation of loss by an internet shopper in contemplating a particular online purchase” (p. 869). Consumer perceived risk is a powerful prediction of a consumer’s actual behavior as consumers are often more motivated to avoid mistakes than to maximize their utility while shopping (Mitchell, 1999, as cited in Lim, 2003), which is in line with the valence theory (Peter & Tarpey, 1975, as cited in Kim et al., 2008). Some researchers argue that since women expect higher negative consequences, they perceive more risk in
Sheehan, 2000; Dittmar et al, 2004; as cited in Chen et al., 2015) and this is especially the case for apparel products (Aghekyan-Simonian et al., 2012).
2.4.1.1 Information risk
Information risk is associated with the inappropriate use of personal data, which is provided to or generated from the webstore (Lim, 2003). An example is the perceived risk of credit card fraud (Grady & Fram, 1997, as cited in Kim et al., 2008).
2.4.1.2 Financial risk
Financial risk encompasses the potential monetary loss arising from shopping online (Lim, 2003; Forsythe & Shi, 2003; Kim et al., 2008) and is common across product categories (Bhatnagar & Ghose, 2004 as cited in Aghekyan-Simonian et al., 2012). For example,
transactions might not be fulfilled, resulting in a financial loss or when due to a technological error, the double amount of money might be retrieved from a bank account.
2.4.1.3 Time risk
Forsythe and Shi (2003) and Lim (2003) describe time risk in e-commerce as the amount of time loss due to difficulties in the navigation on the website, ordering the product, waiting for delivery, and the possible time needed to return a unsatisfactory product.
2.4.1.4 Product risk
As already examined in section 2.1.4; “High-end shoe implications”, perceived product risk refers to a consumers’ belief that he or she will suffer losses due to purchasing a certain product online (Bhatnagar et al., 2000; Lee, Park & Ahn, 2001; Raijas, 2002, as cited in Lim, 2003). Thus product risk comes from the inability to physically examine the product features (Kim et al., 2008; Forsythe & Shi, 2003; Aghekyan-Simonian et al., 2012; Bhatnagar et al., 2000; Pascual-Miguel et al., 2015), since we are unable to use all our five senses when shopping online (Chen et al., 2015). As already elaborated, this might be especially prevalent for high-end shoes, as these are a typical good which is high in experience features (Citrin et al., 2003; Moon, Chadee and Tikoo, 2008 as cited in Endo et al., 2012 - Phau & Poon, 2000; Brown et al., 2003 as cited in Chen et al., 2015).
2.4.2 Trust
The many different definitions and conceptualizations of trust that are available in the literature, show that “trust” is a multidimensional construct (McCole et al., 2010; Hong & Cho, 2011). Kim et al. (2008) defines trust as “a consumer’s subjective belief that the selling party or entity will fulfill its transactional obligations as the consumer understands them” (p. 545). In general one can say that “trust exist in an uncertain and risky environment”
(Bhattacharya et al., 1998, p. 461, as cited in McCole et al., 2010) and it is often associated with integrity, empathy, competence, ability and predictability (Gefen et al., 2003; Lee and Turban, 2001; McKnight et al., 2002; Urban et al., 1999, as cited in McCole et al., 2010).
According to the Social Exchange Theory (Benassi, 1999; Zucker, 1986, as cited in Chen et al., 2015) and many other researchers (Gefen, 2002; Jarvenpaa et al., 1999; Kim et al., 2005; Urban & Sultan, 2000; as cited in Kim et al., 2008) trust is a prerequisite in any business relationship since a transaction only takes place if both parties trust each other. Due to the distinctive characteristics of the online channel, as there is no face-to-face contact and a time spam between payment and delivery, consumers’ trust might be even more important than in traditional retail channels (Kim et al., 2008). Therefore Kim et al. (2005) argued that a customer’s trust in web stores is based on the transaction process whereas trust in traditional retail stores is based on personal contact with the seller. Therefore particularly in the online retail environment, a customer’s trust in the internet, trust in the web-store and trust in third parties that safeguard the exchange, is needed before an online purchase decision is made (Kim et al., 2005; Grabner, Krauter & Kaluscha, 2003; Urban et al., 1999; as cited in McCole et al., 2010 - McCole et al., 2010). Ultimately trust has shown to be positively related to online purchase intention (Kim et al., 2008; Bahattacherjee, 2002; Gefen, 2002; McKnight, Choudhury & Kacmar, 2002).
2.4.2.1 Trust in internet
According to Williamson (1985, as cited in McCole et al., 2010), consumers perceive less trust in internet when they have higher privacy and security concerns. Due to the perceived absence of protection at the online channel, some customers might refrain from doing online transactions due to the perceived risk and danger of the Internet (McCole et al., 2010).
2.4.2.2 Trust in web-store
Trust in the online seller is important for consumers to accept the risk of the transaction and therefore influences one’s online purchase intention (Gefen & Straub, 2004; Reichheld and Schefter, 2000; Gefen and Heart, 2006; Jarvenpaa et al., 1999, 2000; as cited in McCole et al., 2010). A consumer’s trust in a web-store is build upon prior purchases or based on an
affective feeling (Lewis and Weigert, 1985, as cited in McCole et al., 2010). Several
researchers argue for the various positive factors of a well-established brand name as already discussed in section “2.1.3 Implications for start-ups”. Therefore, online start-ups in particular may face challenges in building initial consumer trust, due to newness and legitimacy
concerns (Drori, Honig & Sheaffer, 2009; Stinchcombe, 1965 as cited in Murphy & Tocher, 2011). But start-ups can enhance their consumer’s trust by strategically adopting their online store image (Murphy and Smart, 2000 as cited in Murphy and Tocher, 2011; Aghekyan-Simonian et al., 2012).
2.4.2.3 Trust in third parties
Legitimacy can be gained through the association with another trusted party (Choi and Shepherd, 2005; Rutherford and Buller; 2007, as cited in Murphy and Tocher, 2011). When a web-store communicates certain assurances related to personal information and payment security this might enhance a consumers’ trust and therefore might impede the perceived information risk (Murphy & Blessinger, 2003, as cited in Murphy & Tocher, 2011). This is important since Martin and Camerero (2008, as cited in Murphy & Tocher, 2008) argue that security and privacy are the most prevalent factors in determining a consumers’ trust in a web-store Garbarino and Strahilevitz (2004, as cited in Murphy & Tocher 2011) suggest that this is particularly relevant for women since they perceive higher information risk than men.
Relating to trust in other independent parties, Lim (2003) mentioned the benefit of online information search as suggested by Taylor (1974) and Roselius (1971). Moreover, according to Quix and Van der Kind (2014) consumers rely nowadays more and more on the opinion of others users during their pre-purchase process and therefore companies should focus on review marketing as reviews will reduce a customers’ uncertainty. Last
recommendations of friends and relatives to shop online (Venkatesh et al., 2012) or at a particular web store (Pascual-Miguel et al., 2015; Murphy & Tochner, 2011) have also shown to enhance a consumers’ trust perception.
2.4.3 Trust propensity
Trust propensity refers to someone’s general inclination to adopt a trusting attitude towards others and simultaneously to someone’s faith in humanity (McKnight et al., 1998 as cited in Chen et al., 2015; Gefen, 2000). The first concept implies the extent to which someone is judging others and their tolerance of mistakes of others. The latter implies the belief that people have good intentions in the first place and are reliable (Chen et al., 2015; Kim et al., 2008). These two components combined result in their initial level of trust, which someone has before and during a transaction (Gefen, 2000; Falcone, Singh & Tan, 2001; McKnight & Chervany; 2001, as cited in Chen et al., 2015). Thus when consumers’ trust propensity is high, they have a positive attitude and accepting new things at the first sight, although risk threats might be present (Graziano & Tobin, 2002, as cited in Chen et al., 2015).
2.4.4 Perceived benefit
Kim et al. (2008) defines perceived benefit as “a consumer’s belief about the extent to which he or she will be better off from a transaction with a certain website” (p. 547). This might include for example cost- and time- savings, increased convenience and a wide product selection (Margherio, 1998 as cited in Kim et al., 2008). This is related to performance expectancy, which implies the expected degree to which using the technical application will provide benefits to the consumer in performing certain activities (Venkatesh et al., 2012). According to Hernández et al., (2010) when customers gain experience with e-commerce, the perceived rational benefits will have a strong effect on their purchase intention.
One particular benefit, which is well researched in the literature are the hedonic motivations or related concepts as perceived playfulness and the enjoyment of shopping online (Pascual-Miguel et al., 2015). Richard et al. (2010) found that women enjoy the various challenges in order to get all the relevant product information while shopping online. Therefore “hedonic motivation” has shown to positively influence someone’s online purchase intention (Morosan & Jeong, 2008; Ha & Stoel, 2009; Hwang, 2010 as cited in Pascual-Miguel, 2015).
Endo et al. (2012) note that particularly for online shoe retailing, a wide product assortment, as a variety in size, category and color, is the most critical factor for consumer overall satisfaction in their pre-purchase phase. Moreover the ease of searching online due to the extended, product information, the self-service technologies as search options and ability to easily search for shoes with special requirements, which are not readily available in brick and mortar stores are found to be relevant benefits (Endo et al., 2012). Thus the perceived
benefits result in a major incentives to shop online and therefore has a direct influence on the consumers’ purchase intention (Kim et al., 2008).
2.4.5 Experience
Part of the literature suggests that experience with e-commerce has a positive effect on online purchase intention (Blake et al., 2005, as cited in McCole et al., 2010). Moreover Hernández, (2010) argue that perceptions relating to online purchase intention evolve over time due to the accumulated experience. Factors as effort expectancy, the mental effort needed to shop online (Pascual-Miguel et al., 2015), and a similar construct called “perceived ease of use”
(Venkatesh et al., 2012; Hernández et al., 2010), are shown to reduce in magnitude when consumers gain online shopping experience due to the reduced perceived cognitive
complexity of the technology (Townsend et al., 2001; Hausman & Siekpe, 2009, as cited in Hernández et al., 2010). This is in line with Hernández et al. (2010) findings implying that when e-commerce experience is gained, certain aspects of the online shopping process
become more important while others decrease in importance for the consumer. Repetitiveness of behavior led consumers to feel more in control and therefore more comfortable and capable to purchase online (Liao, Paliva & Lin, 2006 as cited in Hernández et al. (2010). Others add that experience with e-commerce results indirectly in self-efficacy (Montoya-Weiss et al., 2003; Rodgers et al., 2005 as cited in Hernández et al., 2010), which has ultimately an effect on final behavior and therefore enhances intentional behavior (Taylor and Todd, 1995; Bandura, 1986, Wu et al., 2010, as cited in Hernández et al., 2010).
2.4.6 Online store image
According to Murphy and Smart (2000, as cited in Murphy & Tocher, 2011, p. 27)
“entrepreneurial B2C firms can improve their perceived legitimacy and trustworthiness by adopting expected conventions in design and performance and through strategically manipulating information cues communicating transference, ability and competency,
structural assurances, values and goal congruence, and fulfillment” Aghekyan-Simonian et al. (2012) found a similar effect; besides product brand image does online store image also positively bias consumers’ perceptions of product attributes when they cannot be evaluated directly. Moreover previous studies have found a positive relationship between website features related to functionality, security and communication and enhanced purchase intention of the web-store. Examples are product selection, payment methods, security and privacy
concerns, web design and information content (Liang & Lai, 2002; Richard, 2005;
Ranganathan and Ganapathy, 2002; Kwon and Lennon, 2009; Verhagen & Van Dolen, 2009 as cited in Aghekyan-Simonian et al., 2012). Murphy and Tocher found that especially women are sensitive for the website features in relation to the trust in the web-store (2011). This suggest that when start-ups pay attention to the design of their website, it can enhance the trust of women and therefore indirectly it’s purchase intention.
3. Method
3.1 Case study – Eijk Amsterdam
Data was collected through a case study at a Dutch high-end shoe start up named Eijk
Amsterdam. A case study strategy was chosen in order to address the research question within its’ real life context (Saunders et al., 2012). Therefore it enabled to gain a rich understanding of all the relevant processes and factors relating to the topic and thus improved the validity of the data (Saunders et al., 2012). By doing a marketing and sales internship for the company, experience and knowledge is acquired on the characteristics of the target group and
specifically on their online behavior.
Eijk is a high-end shoe company, based in Amsterdam. Jolanda van Eijk found the company in 2014. She thrived to design stylish and fashionable heels for women, which could be worn all day long. Therefore she designed heels, which have an optical stiletto look. Due to the extra support of the heel and the high-quality materials that are used, Eijk shoes are sophisticated, yet comfortable to wear. Due to the distinctive design, the quality and the price range, they could be named, high-end shoes (Stathopoulou & Balabanis, 2016). The target group is women aged from around 30-65 years old, which have a busy life and want to have comfortable shoes, yet looking stylish. The price range of the shoes varies from 249 to 699 Euros.
Eijk doesn’t have an own physical store yet but solely an online store through the website page (http://www.eijk-amsterdam.com/nl/shop/). Moreover shoes are sold through various physical retailers, online retailers and pop-up stores which are mostly based in the Netherlands. Some retailers buy Eijk’s shoes however most often they are sold in
consignment arrangements. Most of the products are sold through retailers. Since the launch of the website in February 2014, 84 pairs of shoes are sold through the web store
(08/06/2016). More than half of the online clients were Dutch and almost one third was French and the rest came from outside Europe. Eijk uses newsletters, Facebook, Instagram,
Twitter and Pinterest as online marketing tools. It is mostly focused on Instagram (939
followers, 26/06/16) and Facebook (839 likes, 26/06/16). At the moment there are two people working for the company, the founder and I as a part-time marketing and sales intern.
3.2 Research design
The case study was a cross-sectional multi-method design (Saunders et al, 2012) consisting of semi-structured interviews (Saunders et al., 2012) in combination with a netnography method (La Rocca, Mandelli, & Snehota, 2014; Weijo, Hietanen, & Mattila, 2014). In order to
develop theory, semi-structured interviews were used. This enabled to enter into the perspective of the target population in order to gain relevant information on factors, which influence their online purchase intention (Patton, 2002).
Since the established theories in the literature might have become outdated as
addressed before, research was conducted through various phases in order to gain progressive insights into the research area. However, as already explained in section “2.3 Development of the conceptual model”, using the current literature to retrieve topics, facilitates initial theory building (Boeije, 2010; Eisenhardt, 1989 as cited in Ali & Birley, 1999). Moreover it results in the accumulation of knowledge through the findings of this study (Boeije, 2010). Therefore a combination of inductive and deductive approaches was applied in several data collection and data analysis-rounds in order to gain progressive insights into the problem area (Boeije, 2010).
Based on the constructs as visualized in the conceptual model, as is shown in figure 1, an interview guide was developed which includes the relevant topics to be considered in the first round of interviews (Patton, 2002). However, the topics are generally defined and should therefore not be treated as variables in order to keep room for the generation of unintended topics, which might be relevant to the research question (Ali & Birley, 1999). Moreover since solely one researcher did the interviews, a checklist of topics was useful in order to ensure that all relevant topics were questioned (Patton, 2002). The interview guide of the first round is provided in Appendix A.
Moreover, the online behavior of the target population is researched by using a netnography method (Weijo, Hietanen & Mattila, 2014). However, due to the low quality of the netnograpy data, due to several errors, the netnography findings are solely used as background information. The reason is that data triangulation with the qualitative findings might reduce, instead of enhance the validity of the results of this study (Saunders et al., 2012). The netnography study is attached in Appendix B.
3.3 Sample
3.3.1 General sampling criteria
Participants were selected based on purposive sampling in order to gain relevant insights in the problem area of this study (Boeije, 2010). Since a case study approach offers the
opportunity to address the problem within its real life context (Saunders et al., 2012), the target population of this study is part of Eijk’s target group. The reason behind this choice is that Eijk’s marketing communications are tailored to their target group in order to enhance their purchase intentions, where upon reliable data can be obtained by addressing specifically them (Ali & Birley, 1999).
However in the current literature relating to online purchase intention, various moderators and mediators as age, gender and life phase, are addressed (Venkatesh et al., 2003; 2012; Dittmar et al., 2004; Pascual-Miguel et al., 2015). But the age of Eijk’s target group is broadly defined; 30-65 years old women. Therefore the target population of this study is limited to the target group aged, between 45 and 60 years old in order to gain reliable insights (Boeije, 2010). Due to their life phase they may have more money to spend and moreover, it is not a prerequisite that they are grown up with the frequent use of the Internet and therefore they are a good target population to test the effect of online experience.
Therefore the general sampling criteria were; Dutch women, aged between 45-60, who like to have a nice style but also enjoy comfort and who are able to afford the price class of the shoes.
3.3.2 Specific sampling criteria
Specific participants within the target population were selected to the needs in the various phases of the study, and were therefore not determined beforehand. Participants were mostly acquaintances, which facilitated besides the conversation, also the ability to configure whether they were indeed within the target and had the specific characteristics, which were needed in certain phases of the study. In this sense a wide range of perspectives are addressed in order to gain progressive insights to answer the research question (Boeije, 2010).
First, since brand familiarity might enhance purchase intention and may lower the risk perception of the consumer (Aghekyan-Simonian et al., 2012; Kim et al., 2008), a distinction is made between participants who are familiar versus unfamiliar with Eijk. Moreover, due to the likeliness that consumers experience product risk while buying shoes online, Endo &
Kincade, 2008; Endo et al., 2012), physical examination of Eijk shoes might have
implications to the participant’s online purchase intention (Kim et al., 2008; Forsythe & Shi, 2003; Aghekyan-Simonian et al., 2012; Bhatnagar et al., 2000; Pascual-Miguel et al., 2015). Therefore another relevant selection criterion is whether the participant physically examined Eijk shoes or not. Moreover, Eijk is mainly focused on social media channels as Instagram and Facebook for their marketing communications. Therefore it would be relevant to consider the effect of their social media practices on online purchase intention of the brand. Therefore another relevant selection criterion is whether the participants follows Eijk on social media or is active on social media. Last, although addressing an actual online client of Eijk might provide additional insights, its is not possible to filter out the factors which were related to purchase behavior and not solely to purchase intention (Venkatesh et al., 2012). Due to this error, no clients were approached.
Last, the current literature states that experience is relevant to purchase intention in various ways (Blake et al., 2005, as cited in McCole et al., 2010; Hernández, et al., 2010). Hernández et al., (2010) made a distinction between two types of behavior relating to experience, namely adoption of e-commerce and online repurchase behavior. This is in line with Venkatesh et al., 2012 who developed a distinct theory relating to e-commerce adoption; the Unified theory of Acceptance 2. Due to the implications of these different types of
behavior, I decided to focus on participants, which made at least one prior online purchase before. The magnitude of prior experience is therefore also a relevant selection criterion as consumers may vary from limited experienced to very experienced with e-commerce, which might have implications. However, a participant with no prior e-commerce experience might be a relevant negative case, which might ultimately strengthen the other outcomes as it can validate the other findings (Boeije, 2010).
3.3.3 Participants
3.3.3.1 Participants round 1
Participant 1 is a 48-year-old woman, working as a ceramist and who lives in a small village near Eindhoven. She has never heard of Eijk before. In general she likes to have a nice style but sees shopping more as a necessity. She says to be confident in surfing online and
mentioned the increased availability of products as benefit of the online retail channel. However she only buys products when she knows the brand beforehand due to the perceived
risk, due to prior bad experiences with e-commerce and the perceived unease of the sending and return procedure.
Participant 2 is a 55-year-old woman, occupied as a teacher and who lives in a small village near Breda. She has heard of Eijk before and also physically examined a pair of shoes. She likes to try out new brands and recently discovered the advantages of shopping online. Although she is relatively inexperienced of surfing online, she really enjoys the advantages and says that it is addictive, challenging and fun. Moreover she perceived low risks and was high in trust in the web-store and the Internet in general.
Participant 3 is a 57-year-old woman, occupied as a recruiter and who lives in Breda. She has never heard of Eijk. In general she likes to shop and when shopping online she used to start at for her known websites. She enjoys the ease of online shopping but can get annoyed by the various pop-ups and cookies. She is open to try out new brands while gaining trust in the product and company through several ways as through reviews, product information, pictures, searching on Google and the returning conditions.
3.3.3.2 Participants round 2
Participant 4 is a 47-year old woman, occupied as a receptionist and who lives in a suburb of Rotterdam. She has never heard of Eijk before. She used to shop a lot online since she
embrace the convenience of shopping goal directed by applying filters. She trusts new brands when she gets in touch with them by multi-brand websites or search engines whom, she has good experience with.
Participant 5 is a 51 year-old women, working as a surgeon and who lives in a village near Rotterdam. Initially she has never heard of Eijk before. Offline shopping is a relaxing and fun activity to her however she also acknowledged the advantages of the online retail channel. In general she likes to spend money on luxury products and moreover she like to support small stores and start-ups due to the online and offline franchise giants. Online she is high in trust and perceives low risk due to her sufficient e-commerce experience.
3.3.3.3 Participant round 3
Participant 6 is a 60 year-old women, working as a ceramist and teacher, who lives in Breda. She has never heard of Eijk before. Although she noted the importance of here style reflecting her personality, shopping in general is not in here interests sphere and especially she has never shopped online yet. When she shops she used to go always to the same store due to the
good personal service and the apparel supply. She mentioned that personal service is important for self-congruency and ease. Although she doesn’t think that online shopping is risk full, it is just not part of her life style. Moreover when she is involved with the philosophy of a starting brand or a retailer, she is highly loyal and doesn’t experience any risk.
3.4 Data collection and analysis
The first round of interviews had a more explorative character in order to examine the current state of factors, which aimed to be relevant to the online purchase intention for women of start-ups in the high-end shoe industry. Participants were purposively selected, as one was familiar with Eijk and the other two not. The interviews were conducted with the help of the initial interview guide, as provided in Appendix A. Moreover, the guidelines for conduction semi-structured interviews of Patton were studied beforehand (2002). The data was analyzed by applying open coding and by placing memos to the transcripts of the interviews (Boeije, 2010). Since there was only one researcher, which might impede the reliability of the coding process (Boeije, 2010), open coding was carefully applied and revised in several cycles in order to get an agreed list of initial open codes and therefore saturation was reached (Boeije, 2010).
Based on this initial open coding list of round 1, as is shown in Appendix C, new topics came up that were not identified yet by the initial theories used to build the conceptual model. Next, the interview guide was modified and new participants were purposively selected in order to gain progressive insights into the relevant topics of the second round of interviews, as provided in Appendix D. By constant comparison and modification of the initial open coding list, the open coding list was supplemented by the findings of the second interview, as provided in Appendix E, until saturation was reached (Boeije, 2010). Next axial coding was applied by combining the open codes into categories, whereby relevance was examined by distinguishing between main codes and sub codes (Boeije, 2010).
Based on some conceptual gaps in the axial coding list, focused data collection was applied by addressing an additional participant with specific characteristics to answer the question marks (Boeije, 2010). The modified interview guide and the cumulative open coding lists are provided in Appendix F and Appendix G. Based on the analysis of the additional insights, saturation of the axial coding scheme was reached, implying that the distinctions between the main codes and sub-codes and between categories were clear and that no further adjustment was needed in order to improve the axial coding scheme as is shown in Appendix H (Boeije, 2010).
Last, based on the axial coding scheme, theoretical coding was applied in order to develop relevant theory from the generated data (Strauss & Corbin, 2007, as cited in Boeije, 2010). In this final phase all the loose axial coding schemes were revised and combined into theoretical concepts until theoretical saturation was reached (Saunders et al., 2012; Boeije et al., 2010). Therefore the recent literature was addressed in order to establish and validate relationships and factors. Through the cyclical character of the research method in order to gain progressive insights, valid theory was developed regarding the factors which influences consumer’s purchase intention of start-ups in the high-end feminine shoe industry. The exact steps and decisions, which were taken during the several data collection and analysis phases, are provided in Appendix I.
3.5 Interview procedure
The interviews took place at a convenient time and place for the participant in order to facilitates the conversation and therefore lowering participants bias (Saunders et al, 2012) Moreover, all interviews were done in Dutch, the native language of both the participants and the interviewer, in order to impede interviewer- and participant errors (Saunders et al., 2012).
The topic of the interview was solely introduced as factors they consider while purchasing online shoes. Additionally they were told that I was especially interested in their opinion and feelings towards certain criteria in order to reduce the likelihood of social desirable answers and therefore participant bias, since they were mostly familiar that I did an internship at Eijk Amsterdam (Saunders et al., 2012). After some general questions regarding their shopping behavior, their experience with internet and specifically their e-commerce experience, participants were asked to visit Eijk’s online store. They were asked to search a pair of shoes of which they could have the intention to buy it, while taking all the steps that they ought to be relevant in their pre-purchase process. By using a similar approach as Gefen (2000, p. 730) this experiment facilitated the conversation with the participant in order to address relevant issues and therefore enhances the content validity of this study (Saunders et al., 2012). Beforehand they were guaranteed that no actual purchase had to be made in order to keep them relax. Moreover they were intentionally not informed about the fact that Eijk is a start-up since this might bias their perceptions (McGregor, 2005). Moreover, they wouldn’t notice it while shopping online and in case they are interested in the company status, they could discover it by their selves.
Since the predetermined topics solely functioned as guidance, there was much room for a free conversation in order to get insights into undiscovered areas. Moreover, sometimes
some experimental manipulations took place as some participants were intentionally shown a pair of Eijk shoes or they were intentionally told about the company status in order to test the effect of these implications.
4. Results
In this section, theoretical coding is applied through the revision of the axial coding scheme and through the verification with the current literature. This ultimately yields the relevant factors, and its underlying mechanisms. The balancing mechanism of the valence framework will be addressed at the end of this section. This ultimately results in online purchase intention of a high-end shoe start-up.
Consumers might be unfamiliar with the company status while visiting an unknown web-store. However, this might have implications. Therefore the first factor relates to whether the consumer is familiar with the start-up status of the web-store. Afterwards, it will be
threated as the implications of an unknown single-brand web-store and thus not specifically as a start-up.
4.1 The implications of being familiar with the company status 4.1.1 Trust in start-ups in relation to brand involvement
In case participants were familiar that Eijk Amsterdam was a start-up, this had implications. Some mentioned to be indifferent for the company status as they were high in trust of e-commerce in general in case of good conditions. They mentioned that they have to promote their self in the market place and cannot rely on their brand name. Participant 5 mentioned; “bad publicity on internet might have severe effects for starting companies”. However some perceived more risk by doing an online transaction with small and especially start-up brand as they had less trust in their facilitating conditions.
! Underlying mechanism: Start-up"Trust / Start-up"Risk
4.1.2 Benefits of high-end shoe start-ups
The implications of company status of a high end-shoe start-up might also have benefits for the feminine consumer. Some participants mentioned to enjoy supporting entrepreneurial brands. Participant 2 mentioned, “in case the conditions are good, trying out new brands is fun, it gives an extra dimension”. Moreover, since the mass consumer is not familiar with the brand yet, participant 2 mentioned; “by buying shoes of a new brand, you have something
special”. Furthermore, some participants had more trust in the price/quality rate of start-up brands; “products of big brands might be overpriced, therefore I wait for sales”.
! Underlying mechanism: Start-up!Benefit / Start-up!Trust in price/quality rate
4.2 Implications for high-end shoes
Specifically relating to high-end shoes, participants mentioned the importance of design, color, fit, comfort, quality and the extension to the current wardrobe. Moreover, the
importance of self-congruence was mentioned as participant 6 mentioned; “shoes should be an extension of my personality, it is a way to express myself”. Participants mentioned to be relatively indifferent for shoe brands, as they select on design. However a brand name has shown to enhance trust and lowers risk when buying shoes at the online retail channel, which is similar to the findings of Aghekyan-Simonian et al. (2012).
! Underlying mechanisms: Design"Purchase intention / Product brang image"Trust
4.3 Perceived benefits of the online retail channel
Participants noted that online shopping has other benefits than offline shopping while some are its inverse. One benefit, which was often addressed, was the possibility to apply filters and use search engines (Endo et al., 2012), as participant 4 mentioned: “I don’t like to spend much time on shopping, and that’s why I often shop online, as it facilitates to shop goal directed”. This is in line with Venkatesh et al. (2012) notion of “facilitating conditions”, which are found to have a direct effect on consumers’ behavioral intention (Venkatesh et al., 2012; Ajzen, 1991) and might be especially beneficial for the needs of older women (Notani, 1998; Morris et al, 2005; Plude and Hoyer, 1985 as cited in Venkatesh et al., 2012). The
transparency of the Internet is also mentioned to be beneficial for comparing prices and to get in touch with new brands due to the wide assortment that is reachable online (Endo et al., 2012; Margherio, 1998 as cited in Kim et al., 2008). Additional benefits are the timing and time-savings and the general ease of shopping online (Margherio, 1998 as cited in Kim et al., 2008). Other mentioned benefits were the absence for the need to carry around shopping bags and the absence of annoying store staff. Last, participant 2 mentioned the fun aspect of shopping online due to her limited experience with e-commerce as she mentioned “as I don’t have much experience with e-commerce, I see it as a new challenge”. This was in line with Richard et al., 2010 as she enjoyed the challenges while it also enhanced her purchase intention (Morosan & Jeong, 2008; Ha & Stoel, 2009; Hwang, 2010 as cited in
