The effect of technology on consumer purchase intention in the fashion retail industry

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The Effect of Technology on Consumer Purchase Intention in the Fashion Retail

Industry

Hidde Wien

Student number: 10798072

Submission date: August 16th_{2018, Final version}

MSc. Business Administration – Marketing Track University of Amsterdam

First supervisor: drs. F.W.J. Quix Second supervisor: R.E.W. Pruppers MSc

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Statement of Originality

This document is written by Student Hidde Wien who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

The fashion retail industry is a very dynamic environment and is continually changing. In the past years several forms of digitalization shook up this industry. A customer journey from the past and today’s customer journey is like night and day. These ongoing changes and developments require retailers to adapt and respond to be able to create and deliver satisfying customer journeys, as a positive customer experience tends to increase consumer purchase intention. This research aims to develop a stronger understanding of current customer journeys in the Dutch fashion retail industry and investigates what the impact of digitalization is on purchase intention. To achieve this goal, interviews with customers and fashion retailers are conducted. These interviews reveal insights on current journeys and forms of digitalization that have the potential to have an impact on these journeys. A quantitative study based on a survey is conducted to reveal whether certain forms of digitalization affect consumer’s purchase intention and therefore has impact on the customer journey. This research shows that digitalization has impact on purchase intention in the Dutch fashion retail industry and that consumers of different ages have different wants and needs with regard to digitalization. The study reveals that two out of the five tested technologies considerably outperform the others and are labeled as the most value-adding forms of technology by consumers, i.e. increase purchase intention.

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1 Introduction

1.1 Digitalization in Retail

Digitalization is changing the face of retail. The days of consumers using the physical store as the only channel of interaction between them and retailers are over. Several consumer shifts are occurring and changing the way business is done in retail. McKinsey & Company and The Business of Fashion (BoF) (2017) state that “The global fashion industry is moving into a decisive phase of digital adoption by the mainstream consumer, and online sales of apparel and footwear is projected to grow rapidly”. In addition, they write that the consumer purchase journey has changed from a traditional linear model, to a more complex journey across online and offline touchpoints. Consumers are more demanding than ever. Today’s customer expects perfect functionality and immediate support at all times from retailers. In addition, consumers have 24/7 access to information for comparing products and services. According to McKinsey & Company and BoF (2017), consumers are becoming less brand-loyal due to these developments. They state that 67% of the millennials are willing to switch brands for being offered a minimum price discount of 30%. As stated before, the fashion industry is projected to grow rapidly, but this generation has high expectations of companies. They demand convenience, quality, values, orientation, newness and a low price from firms (McKinsey & Company, BoF, 2017, p.17) .

In the past years the role of digitalization has become more prominent in the retail industry. However, in literature the focus with regard to digitalization has thus far been mainly on e-commerce (Hagberg et al., 2017). As stated by McKinsey & Company and BoF

(2017) e-commerce is still projected to grow rapidly, but according to Pauwels et al., (2011) the implications of digitalization reach further than solely e-commerce. Hagberg et al., (2017) for example found that consumers use mobile applications in fixed store settings while

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shopping rather than using it solely for e-commerce. Regarding investments in AI, ‘Retail is expected to increase average spending on AI by 5% in the next three years, way behind the level of pace-setting sectors such as financial services, transport and logistics and auto assembly’ (McKinsey & Company and BoF, 2017).

1.2 Fashion Retail Industry

The fashion retail industry is an interesting industry to investigate because it is way behind other sectors with regard to the implementation of certain technologies, such as AI, yet many success cases are there to be found in the fashion industry by firms who invested in technology. For example Amazon acquired Kiva, a company that manufactures mobile robotic fulfillment systems based on AI (for $775 million) and ‘reduced the “Click to ship” cycle time from over 60 minutes (human handling) to 15 minutes, and increased inventory capacity by 50 percent’ (McKinsey & Company and BoF, 2017). Another area that has the potential to improve customer experience is Image analytics. For example ‘Myntra’s brand Moda Rapido is powered by AI to offer computer generated designs without human intervention and now has the highest gross margins compared to all other 14 brands under the Myntra portfolio’ (McKinsey & Company and BoF, 2017). These examples emphasize the potential that technology has to offer for the fashion industry. But why is the fashion industry way behind other industries when it comes to company investments in technology? Is it due to the fact that consumers are not ready for the implementation, or perhaps the firms or retailers? Maybe the technologies are not sophisticated enough to provide consumers substantiated added value? This study tries to find answers on these questions and therefore, the research question of this thesis is formulated as follows:

What is the effect of technology on consumer purchase intention in the retail fashion industry?

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1.3 Contributions

The aim of this study is to contribute to existing literature on digitalization in the retail industry by investigating the impact of digitalization on consumer purchase intention. As already mentioned, Hagberg et al., (2017) state that the focus of current literature mainly has been on digitalization with regard to e-commerce. This study investigates whether the impact of digitalization on the fashion retail industry goes beyond solely e-commerce and has impact on offline retail settings, i.e. physical stores. This thesis builds upon the findings of Pauwels et al., (2011) that digitalization reach further than solely e-commerce and the findings from Hagberg et al., (2017) that nowadays mobile applications are more often being used by consumers when shopping offline.

To practitioners, this study aims to provide concrete guidelines for retailers by showing what the effect is of certain types of technologies on consumer purchase intention. This reveals useful insights for retailers because this shows them what kind of technologies are suitable for what type of consumer.

1.4 Structure

This thesis consists of several research phases. The first phase of the research is an exploratory research and is used to develop an independent variable, which is used in the conceptual model (chapter 3). This exploratory research is conducted in the form of interviews. These interviews are held with two fashion retailers to reveal what technologies have the biggest potential to be useful for consumers, i.e. increase their purchase intention. In the following chapter, an elaborate review of the available literature will be provided. The forms of technology (retrieved from the exploratory research) are discussed in chapter 2. In chapter 3 the expected effects of the technologies on purchase intention are presented in a conceptual model with hypotheses that are based on the literature. In chapter 4 the methodology of this thesis is discussed. The results of the quantitative analysis are presented

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in chapter 5. The results will be discussed in chapter 6, divided in theoretical and managerial implications. Finally, a conclusion and limitations of this study are provided with suggestions for future research in chapter 7.

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2 Literature review

In the literature review the Dutch retail landscape is first discussed to get an overview of the environment in which retailers operate. Second, retail trends that are shaping the retail environment are addressed. Third, the term ‘customer journey’ is described and it is discussed what the impact of digitalization on the customer journey has been thus far. In this section, two customer journey models are discussed. Furthermore, the concept ‘Zero Moment of Truth’ is discussed and how this concept has changed the customer’s decision journey. Fourth, it is described how channel strategies have changed over time and evolved into omnichannel strategies. Additionally, the effects of these developments on the fashion industry are being discussed. Lastly, several forms of technology (technologies that have the potential to digitalize the customer journey) will be discussed. These forms of technology are expected to have an impact on future customer journeys, which is based on the extensive literature review and interviews with fashion retailers. The impact of these forms of digitalization is statistically tested in this study.

2.1 Retail landscape

Retail is of great importance in the Dutch economy. In 2015, 27.2% of total consumption was spent in the retail industry (Quix, 2016, p. 39). It is a complicated and big industry in the Netherlands, with approximately 113,000 retailers in the beginning of 2016. The retail industry is therefore the ‘biggest employer’ of the Netherlands (Quix, 2016, pp.58). In 2007 nearly one million companies (987,345) existed in the Netherlands, of which 92,255 were retailers (9.3%). In 2016 a total of 1,536,050 companies existed, of which 113,350 were retailers (7.4%) (CBS, 2016). According to these numbers it seems that the retail industry is of decreasing importance. But when looking at the numbers on employment in the retail industry a marked increase is observed. From 1990 to 2014 the employment rate in the Dutch retail industry increased with 68% (from 461,000 to 775,000). Furthermore, it can be stated

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that the Dutch retail industry is responsible for 10% of total employment in the Netherlands. (Quix, 2016, p.59). The stagnant growth in amount of retailers in combination with the increase of the employment rate in retail indicates that retailers are significantly up-scaling.

The increase in amount of companies in the Netherlands can be partly explained by the explosive growth of online retailers (henceforth tailers). In 2000 the amount of registered e-tailers was 800. In 2010, 7,100 registered e-e-tailers were active in the Dutch retail industry (HBD, 2000, 2010). A low entry barrier for e-tailers is a possible explanation for this growth.

The numbers above and cases on retailers show that the retail industry is a very dynamic environment and that it is continually changing. Macro developments are the drivers that affect society and occur gradually, but can also have impact in the short term. Retailers have little to no impact on these macro developments. Important for retailers is how they respond to these developments. Many macro developments stimulate the needs of consumers and with that, opportunities for retailers to respond to those needs with new forms of services and innovations. The retailer that can successfully combine a ‘macro-driver’ with a consumer’s need using new forms of service or innovations seems to have a bright future (INretail, 2018).

Traditional retailers are not the only source of products, services and information for todays’ consumer. Due to digitalization consumers are able to use several resources and platforms to access products, services and information. For traditional retailers products were following a linear value chain from producer, to wholesale, to retailer and eventually to the consumer.

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Figure 1: Quix, F. (2018). Lecture 1 - Is there a future for retail? [Powerpoint slides]. Retrieved from https://www.bit.ly/2sdqens.

Nowadays we see a shift from this value chain (figure 1) to a sort of ‘value network’ (figure 2). The consumer is the key player in this network and can access all these parties from the value chain himself. This shift is the result of the ‘consumer empowerment’ trend (discussed in the next section). Consumers are becoming more and more empowered in their search for products, services and information, which is stimulated by digital developments. Consumers can get in contact with other consumers, retailers, producers, platforms and even foreign players are increasing in popularity.

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Figure 2: Quix, F. (2018). Lecture 1 - Is there a future for retail? [Powerpoint slides]. Retrieved from https://www.bit.ly/2sdqens.

2.2 Retail trends

Several macro developments and forms of digitalization resulted in trends that had an impact on the retail industry. As stated by Michiel Muller, co-founder of Picnic, a Dutch retailer: ‘Disruption is not a bad thing, not so much the technologies are disruptive, but the speed at which these developments occur are disrupting the retail environment’. According to the report of INRteail (2018) there are four underlying movements that strongly influence the retail environment. These are the movements that consumers are moving from Consumerism to Consciousism, the increase in consumer empowerment, the increase of digitalization and introduction and adoption of Blockchain.

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Social pressure and the increase of conscious consumers are the driving forces behind the shift from Consumerism to Consciousism. This shift is described in the next section.

As illustrated above in the shift from the value chain to a value network, consumer empowerment is increasing. Information is readily available for consumers and they have access to it anywhere at anytime. Retailers can no longer ‘fool’ consumers and have to be more transparent than ever in their business. The driving force behind these changes is digitalization. Society is becoming more digital every day. This increases the amount of data that is held by companies such as Microsoft, Apple, Google, Amazon, Facebook and Alibaba (MAGAFA) and therefore these companies are becoming more dominant. Based on data and artificial intelligence these platforms will shape the future of how supply and demand is being brought together. Blockchain is, according to experts, the biggest technological innovation since the Internet (INretail, 2018). Blockchain is able to deliver trust, control and transparency on a decentralized level. The concept ‘Blockchain’ is more elaborately discussed in chapter 1.6.4.

In the report ‘Retail richting 2030’ from INretail (2018), 12 prospected dominant retail shifts are described for the Dutch Retail industry. The retail shifts that had or might have an impact on the fashion industry are covered in the following section.

The first shift is ‘from Consumerism to Consciousism’. This shift implies that consumers are becoming more conscious about their purchasing behavior but also about from what companies they buy their products. Everything that a firm or consumer does is becoming more and more visible. In other words, the individual footprint is becoming more visible and therefore it is becoming more important to ‘do well’ (in a social responsible manner). Due to the attention on conscious consuming by consumers, firms, media and politics, the social pressure is increasing and making it even more important. So not only buying fewer (irrelevant) products is becoming more important, but fairer products as well (products that

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are created under fair conditions for every party in the supply chain). The key term describing this shift is transparency. By creating transparency in the supply chain, consumers are able to see how their products are made, where value is created and what the price of this is. Therefore, customers are expected to be willing to pay for this transparency. According to the report of INretail (2018) 59% of the asked participants appreciates openness by retailers on product origin. An example of a Dutch retailer based on this transparency principle is Bellamy Gallery. Bellamy Gallery is a Dutch company offering products with complete transparency on the cost structure, no ‘mysterious margins’, no discounting but ‘always offering the best price’. No pieces of clothing that could be thrown away after a few weeks (which is the result of fast fashion) but high quality key pieces, and no unnecessary parties involved in the supply chain, but direct from the producer to the customer. Bellamy Gallery furthermore states that the Dutch retail industry needs to be reformed. With Bellamy Gallery, the founders want to go back to what they call the essence of retail: Offering the best product for the best price. Everything around this process should be as simple and transparent as possible. For Bellamy Gallery, a small company that has just started their business, it is easier to step in on this trend than for the bigger fashion retailers in the industry.

The second shift is ‘from one-way to recycling’. The throwaway society is not sustainable and more consumers are becoming aware of that, which is also repeatedly confirmed by media. To respond to these developments we see several retailers being established based on the principles of recycling. A clear example is the Dutch company ‘Hulaaloop’, a retailer in baby clothing. Babies tend to grow out of their clothes very fast. Buying clothes for babies is therefore relatively expensive and not sustainable. Hulaaloop offers a subscription based business model on children clothing. Subscribers fill in the sizes of children and the amount of outfits they want to receive. When children grow out of the outfits, the clothing can be sent back to Hulaaloop. Hulaaloop will clean the outfits; send bigger sized

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clothing to the subscribers and will send the received outfits to other subscribers. This process based on subscription is more sustainable and environment friendly.

The third shift is ‘from transaction to attraction’. The traditional function of a store has always been purely transactional. Due to increase in diversity of offerings, a physical store has to offer more to consumers than solely transaction. According to INretail (2018), the physical store will transform from points of sale into points of engagement. Physical stores will act more as showrooms in which customers can engage with the brand in a more emotional way. Customers can interact with the brand, experience products and services. An example of a retailer offering this concept to consumers is in the U.S. based B8ta. B8ta brings consumers and producers together and state that they are “showcasing new and innovative products in a unique space that has become a destination for visitors” (Ebeltoft Group, SAS, 2018, pp.14-15). This way producers can test their products and innovations at physical stores without having to invest in own stores. Consumers can test and provide feedback on these new products and eventually buy them. This concept is especially interesting for early adaptors.

The fourth shift is ‘from consumer to prosumer’. Due to technological innovations firms can offer more tailored products to consumers. Consumers can become a part of the designing process and therefore become a ‘prosumer’ (a combination of producer and consumer). People often want to create unique and personal products to express their identity and creativity (or at least have the feeling they are doing so). An example of a firm offering this service is Oakley. Customers can choose their own frame for sports glasses, their own lens and in their preferred colors. Within days customers receive their new glasses. Another example of where consumers are becoming prosumers is NikeID. Nike offers a service on their website on which consumers can ‘design their own shoe’. Consumers can pick their

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preferred shoe model and customize different parts of the shoe with colors, materials and custom embroidery.

The fifth and last shift is ‘from social networks to social commerce’. Due to the digitalization of society customers are always connected to other customers and firms. Social networks are transforming to social (influenced) commerce platforms. Bloggers and Vloggers have a huge range these days. Companies noticed opportunities in this range for advertising and transformed blogger’s and vlogger’s user generated content into sponsor generated content. Nowadays companies contact famous social network users to promote their products. They can add ‘buy buttons’ into the content from which viewers can directly purchase the sponsored products. By doing so a customer journey can take place purely on a social media platform.

2.3 Customer journey

“Existing consumer decision-making models were developed in pre-internet days and have remained for the most part unquestioned in the digital marketing discourse.” (Wolny, Charoensuksai, 2014). In the pre-internet days the customer journey was seen as a ‘Purchase Funnel’, a rather simple linear journey that will be explained in the next section. The importance of creating a strong customer experience is shown by a recent study by Accenture (2015; in cooperation with Forrester). “Improving the customer experience received the most number one rankings when executives were asked about their top priorities for the next 12 months” (Lemon, Verhoef, 2016). Lemon and Verhoef conceptualize the customer experience as “a customer’s ‘journey’ with a firm over time during the purchase cycle across multiple touch points.” They also conceptualize it as a dynamic process. According to Lemon and Verhoef (2016), the customer experience goes through different phases starting from the prepurchase phase to purchase phase to postpurchase phase.

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The prepurchase phase contains all the consumer’s points of interaction with the brand before purchasing. This phase encompasses the customer’s recognition of the need to consideration of satisfying that need (Hoyer (1984), Pieters, Baumgartner and Allen (1995) in Lemon, Verhoef (2016)). Examples are: becoming aware due to advertising or word-of-mouth (WOM) or searching for information (online or offline).

The purchase phase contains all the customer’s interactions with the brand during the purchase process. Choosing, placing an order and paying for the product or service are events in this phase. There has been a lot of attention to this phase, which focused on how marketing activities can influence the purchase decision (Lemon, Verhoef, 2016).

The postpurchase phase contains all the customer’s interactions with the brand or other consumers after the purchase of the product (usage, consumption, service requests, after sales, writing reviews, engaging in a community among other customers/consumers etc.).

The consumer decision-making process is thus seen as a process that goes trough different phases. A model illustrating this process is the ‘Purchase funnel’ by Mooney & Rollins (2008), which is discussed in the next section.

In addition, Lemon and Verhoef (2016) state that ‘recent studies mainly considered the drivers of customer satisfaction or value (e.g., Baker et al. 2002; Hunneman, Verhoef, and Sloot 2015) but have not considered the drivers of customer experience as a broad construct’. They state that researchers should look at how ‘specific elements of the customer experience (e.g., sensory, affective, cognitive) combine to influence the customer at different points in the journey?’.

One factor that is strongly influencing the customer journey is digitalization. According to Van Bommel, Edelman and Ungerman (2014) there will be a radical integration of the consumer experience across physical and virtual environments. Nowadays, customers have anywhere and at anytime access to online information. For retailers, digital channels are

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therefore no longer solely a cheap source of interaction with customers, but have much more to offer.

Digitalization of the physical shopping environment of customers can significantly change the customer experience. “Customers will for example be able to search for products by image, voice, and gesture” (Van Bommel, Edelman, Ungerman, 2014). It is essential for retailers to think about whether digitalization can improve their customer’s journeys and how, because two-thirds of the decisions a customer makes are informed by the quality of his experiences all along his customer journey (Van Bommel, Edelman, Ungerman, 2014). The impact of digitalization on the traditional customer journey models is discussed in the following sections.

2.3.1 Purchase funnel

The purchase funnel as illustrated by Mooney & Rollins (2008) illustrates the consumer decision-making process of the pre-internet period. According to this model a consumer goes through different phases in his or her decision-making process. First a consumer will become aware of a certain product or service. Second, the consumer might consider buying the product and makes a decision in the third phase. These first three phases can be seen as the ‘prepurchase phase’ as mentioned by Lemon & Verhoef (2016). The purchase is being made in the fourth phase, the ‘purchase phase’. Finally, in the fifth phase the customer decides to either come back or not. This last phase is the ‘postpurchase’ phase as mentioned by Lemon & Verhoef (2016), which contains all consumer interactions with the brand after the purchase such as aftersales service, but also the interaction of the customer with others (WOM, reviews and sharing information/the product).

Quix (2016) states that marketers mainly have impact on the beginning of the funnel, especially on the awareness phase. At this stage mass media are used to raise awareness among consumers. In the second phase, the consideration phase, WOM is the most important

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factor influencing consumers. Marketers will traditionally still have influence on the decision-making process but a consumer’s friends and relatives will have a strong impact at this stage and in the next phase as well. They will make recommendations to consumers like employees of a store make recommendations or come up with suggestions. When a consumer is in-store, the packaging and in-store advertising will influence the process. Eventually, the customer will be satisfied or dissatisfied with the purchased good or service and decide to come back or not (the retention phase). Quix (2016) adds to this that it is important to measure the level of satisfaction among customers during their customer journey because it is more profitable retaining customers than acquiring new ones.

The search process of consumers has changed and therefore the traditional purchase funnel no longer holds. Due to increase in Internet adoption among consumers and the enormous amount of information sources available online such as social media, comparison websites and review sites, the shape of the traditional purchase funnel changes. Consumers acquire online information during the consideration and decision phase and therefore the funnel does not get narrower but wider. This is in line what McKinsey Company & BoF (2017) found, as stated in the introduction; the consumer purchase journey has changed from a traditional linear model, to a more complex journey across online and offline touchpoints. Due to this new media-mix, the shape of today’s model looks more like a ‘fish-shape’, which is called the customer journey model (Quix, 2016).

2.3.2 Customer journey model (Fish model)

As stated in the previous section, the amount of information sources increased, and the way consumers acquire information has changed as well (social media, comparison sites and review sites). Quix (2016) states that therefore Mooney and Rollins (2008) converted the purchasing funnel into a new customer-journey model, the fishmodel (figure 3). This model starts more narrow and focused and widens at the orientation phases. After these phases the

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model narrows again when a good or service is being purchased. In contrast to the traditional model, the model widens again after the purchasing phase. This is due to the fact that customers share their experience, and thus influence other consumers (Quix, 2016, pp.438-439). This ‘wavy pattern’ of the fish getting narrower and wider remains present according to Mooney and Rollins (2008) because the end of person A’s journey, is the start of person B’s journey.

Figure 3: Translation of Q&A’s edit of model from Mooney, K. and Rollings, N. (2008) The Open Brand.

Mooney updated her customer journey model in 2012. She mentions that we have entered a new era and only the firms that understand the new journey will continue to exist (Quix, 2016). The updated version of the customer journey (figure 4) thinks of a consumer that is either one of two types of consumers in the first phase of his or her customer journey. A consumer is either searching for products or discovering products (or services). In the first situation, when a consumer is searching for a product, this person is in need of that product. When a consumer is discovering products, that person wants that product. It is important for

Visitor’s

perspective Inspiration Orientation Decision Visiting Feedback

Retailer’s

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retailers to make this distinction between two types of customers, because a retailer should use different ways of communication in different journeys.

Figure 4: The updated Customer-journeymodel by Mooney, K. (2012)

2.3.3 Zero moment of truth

As stated in the introduction, today’s customers expect more than ever from companies regarding convenience, quality, values, orientation, newness and a low price. This in combination with the fact that consumers spend on average six hours per week researching fashion online; searching for products, gathering information, inspiration, comparing prices, etc. (Amed & Berg, 2017). The increase of digital devices used by consumers puts pressure on firms to move towards an omnichannel strategy. The omnichannel concept will be discussed in the next section.

A concept showing the impact of digitalization on consumer’s decision-making processes is the ‘Zero Moment of Truth’ (ZMOT). This concept was introduced into the classical three-step marketing process of stimulus, shelf and experience by Lecinski (Winning

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the Zero Moment of Truth, 2011). This is an important concept that has changed the way we look at the buying decision journey of customers. Lecinski defines the ZMOT as ‘a moment where marketing and information happens, and where consumers make choices that affect the success and failure of nearly every brand in the world.’ (Wolny, Charoensuksai, 2014). In his book, Lecinski writes that there is a 3-step mental model, which contains the three critical moments during a customer’s decision journey. The first step is the stimulus. This step contains any form of advertising that raises awareness to consumers about a product or service (television, print, radio). The second step is the ‘First Moment of Truth’ (FMOT). This step is the moment at which consumers stand in front of the shelf of the store and decide which product to buy. The third and last step is the ‘Second Moment of Truth’ (SMOT), in which the customer experiences the purchased good and evaluates it either as a positive or negative experience.

Figure 5: The traditional 3-step mental model (From Winning the Zero Moment of Truth, 2011).

Due to increases in smartphone usage of consumers this traditional 3-step model no longer suffices. Besides smartphones, consumers are gathering information from any device. Data revealed that an average shopper in 2011 used 10,4 sources of information as a foundation for their purchasing decisions, which was 5,3 sources in 2010 (Lecinski, 2011). The nearly doubling amount of information sources used shows the relevance of this concept.

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Therefore, a fourth step is added to the traditional 3-step model, which is called the ‘Zero Moment of Truth’ (ZMOT). Prior to consumers visiting stores to asses products and deciding whether to buy or not, they search online on their mobile devices (or computers/laptops) for information in the form of reviews, blogs, video’s etc. These sources of information are often output from the SMOT. Reviews are written based on a customer’s experience and are used as input for consumers in the ZMOT step. Figures in Lecinski’s book (2011) show that 70% of Americans look at product reviews before making a purchase. Additionally, 79% of consumers use a smartphone while shopping. These figures imply that mobile cannot be left out of the decision making process and therefore the fourth step, ZMOT, is added to the existing model.

Figure 6: The new 4-step mental model (From Winning the Zero Moment of Truth, 2011).

The increase of consumer’s mobile usage and the importance of the ZMOT in a customer’s decision journey should ring some bells for retail managers. Consumers are able to access information anywhere at anytime. Showrooming (assessing products in-store and buying

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online), Webrooming (assessing products online and buying it in-store) and ZMOT are terms, which imply that consumers are exposed to more complex multi-channel shopping journeys (Wolny, Charoensuksai, 2014). Therefore, integration of these developments into today’s customer journey demands an appropriate strategy, which is a unified omnichannel strategy.

2.4 Channel retailing

Prior to 1994, only one channel of communication between a brand and its consumers existed; the physical store. Communicating solely via one channel is called a singlechannel strategy. In 1994 it became possible to order and buy goods online. This development changed the way retailers communicate with their customers (Quix, 2016, p.200). Quix mentions several follow-up strategies. First of all, the multichannel strategy, which is the follow-up strategy of singlechannel. With this strategy consumers can be served via different channels (in-store OR online OR via catalogues). The idea behind this strategy is that consumers can be fully served via one channel. Second, he mentions the crosschannel strategy, which is the follow-up of multichannel. With this strategy, the consumer is still the starting point, but can be served via different channels, which the consumer perceives as separate channels. Finally, Quix states that the ‘ultimate’ channel-strategy is omnichannel. With this strategy the consumer does not experience the channels, but experiences the brand or retail formula as a whole. This is in line with the article of Piotrowicz and Cuthbertson (2014), in which the authors state that under an omnichannel strategy the customer journey should be smooth and should provide a seamless and unified customer experience, regardless of the channels used. Verhoef et al. (2015) define omnichannel management as “the synergetic management of the numerous available channels and customer touchpoints, in such a way that the customer experience across channels and the performance over channels is optimized”. They further acknowledge that different channels interact with each other and are used simultaneously.

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Despite the rise of online channels, customers still demand a physical/human touch in their journey. According to Piotrowicz and Cuthbertson (2014), customers want to see the product, feel and touch it and try the product before buying it. Furthermore, they state that customers want to experience the atmosphere of the shop as well. Fashion products are typical examples in which customers are seeking this physical touch. Blázquez (2014) states that the fashion industry had difficulties during the transformation from offline to online. Retailers had trouble translating the in-store experience to the online environment. Additionally, the fashion industry was slow in adopting e-commerce compared to other sectors. But due to new innovative technologies consumers are now able to “evaluate fashion online while undergoing an exciting and interactive shopping experience” (Blázquez, 2014).

The role technologies play in-store is increasing as well. According to Piotrowicz and Cuthbertson (2014), these technologies should interact fully with the customer experience. They provide a few examples of technologies that can be used in-store: interactive screens, augmented reality, “magic mirrors”, as well as technologies for the staff. Continuous digitalization will impact the way consumers interact with retailers. Digitalization of aspects in the offline world and the integration of online in offline environments have impact on customer journeys. Certain touchpoints or phases in customer journeys might be labeled as points of friction in which the customer journey can be improved by digitalization and thus improve the customer experience.

2.5 Fashion industry

The fashion industry was the slowest retail sector in adopting e-commerce (Sender, 2011 in Blázquez, 2014). The main factor causing this was the difficulty of translating the in-store experience to the online environment. Furthermore, clothing is considered to be a high-involvement product category (Keng et al, 2003, in Blázquez, 2014) in which customers want to see, feel, touch (Citrin et al., 2003, in Blázquez, 2014) and try-on the product. Attempts to

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overcome the problems concerning the translation of offline experiences to the online environments have taken place such as using augmented reality, and 3D virtual models to improve the online experience. These developments have consequences for brick-and-mortar stores. According to Blázquez (2014) ‘There is a gap in understanding the extent to which online experiences influence consumers’ expectations for their multichannel shopping experiences’.

We have seen that electronic commerce had an impact on retailing but what will be the effect of digitalization in physical retail stores? Will digitalization be able to make the in-store experience of customers more convenient and/or faster? More broadly: will digitalization have an impact on the customer experience and therefore on the customer journeys of customers, and will this impact differ across different branches in the retail industry?

This study is looking at the fashion industry. In this industry “sensory elements are especially important, as consumers look for entertainment when they buy clothing” (Drapers, Technology in Fashion Report, 2012). It is uncertain how future physical stores will look and it will depend on the industry. The aim of this research is to identify what kind of customer journeys occur in the fashion industry, how long they take and which phases occur in these journeys. The role digitalization will play in creating and customizing the ‘perfect’ customer journey in this category is discussed (making products more accessible, more convenient to purchase (offline as well as online), creating a certain atmosphere in-store, and creating an integrated experience between channels).

2.6 Forms of Technologies

Based on the literature review and the interviews with the two retailers and customers (chapters 3.1 and 3.2), several forms of technologies will be discussed that might play a role

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in the digitalization of customer journeys. Based on this information and the discussion several forms of technologies are introduced that will be tested using statistical analysis. 2.6.1 Artificial intelligence

Nowadays, a lot of forms of digitalization based on AI are being introduced by innovative startups but also conventional players are investing in this form of digitalization. Many firms are becoming aware of the possibilities that AI offers across the entire value chain (McKinsey & Company, BoF, 2017). AI is becoming more popular and is increasingly taking over the creative part of designing fashion.

Firms such as Amazon, startup Syte and Slyce are active in this area. Amazon has introduced a camera with an application that acts as a personal style assistant: The Amazon Echo Look. When a person stands in front of the camera he or she can ask Echo Look (via voice-control) to take a picture of his or her outfit and based on the picture come up with style advise. The camera sends the picture to the application. In the application the user can compare two different outfits. Based on trends and personal preferences the application provides recommendations on which outfit is the ‘best’, using machine learning. The application also allows sharing the pictures with friends on social media. The application of Syte is based on AI. By using image recognition the application comes up with recommendations based on pictures of certain styles uploaded by the user (CB Insights, 2018). Another example showing the practical relevance of these kinds of applications in a fashion retail-shopping environment is the application Slyce. Slyce cooperates with retailer Macy’s to create an application that customers of Macy’s can use in-store. Within the app, customers can take pictures of products to find visually similar products. The application provides information on the in-store and online availability via a chat-bot in messenger and comes up with other product recommendations. Additionally, customers can find reviews and buy these products instantly (CB Insights, 2018).

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These forms of digitalization acting as style assistants are not new, but due to the fact that the technology behind it is becoming more and more sophisticated and more people are using it, the amount of data to work with is increasing. The increase of data allows these applications to come up with more relevant recommendations, which in turn creates more users and therefore a ‘data-adding loop’ arises; the bigger the data set, the better the recommendations. Better recommendations will attract more users and therefore again increase the dataset.

2.6.2 Augmented reality and Virtual reality

As mentioned by Blázquez (2014) fashion retailers had trouble transferring the offline experience to the online environment. Several forms of digitalization have been developed and adapted by retailers trying to enhance this experience transfer. Augmented reality (AR) and virtual reality (VR) are examples of technologies that are used by retailers to build or strengthen the connection between them and their customers (CB Insights, 2018). AR and VR are used in the fashion industry to transfer digital experiences into stores but also to create in-store experiences online. Neha Singh, the founder and CEO of Obsess (a startup helping brands creating environments for experiential shopping) stated that: “What we’re trying to show here is that shopping in the future will be a combination of some elements of what physical stores have today, like visual merchandising and curated pieces, but then they have all of these other things that are not possible in physical stores.” An example of digitalization in-store enhancing the customer experience is a AR-mirror. These mirrors can for example, with the use of AR, project different colors of a piece of clothing on a customer, so a customer does not need to get undressed to try on different colors. These mirrors have different features as well. Ralph Lauren for example, introduced interactive smart mirrors in their flagship store in New York. Customers of this store could enter a fitting room after picking up clothes to try on. With the use of RFID tags on the clothing, the mirror displayed

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the items on the mirror with available in-store sizes and colors. There is also a button to request assistance of an employee to for example grab a different size or color. On top of that, the mirror suggests other items that would suit/complement the customer’s look.

Such a smart mirror is transferring the information a customer could easily access when shopping online to the in-store environment. This example shows that the lines between offline and online shopping are blurring and it is emphasizing the urge of adapting an omnichannel strategy as Quix (2016) mentioned. Adapting an omnichannel strategy creates opportunities to create seamless online and offline integrated customer experiences.

2.6.3 RFID

The fashion industry is running at a pace faster than ever before. Apparel is produced using hyper-rapid manufacturing systems. This also requires new inventory management tools, and according to CB Insights (2018), Radio Frequency Identification technology (RFID) is one technology that will be widely adopted. As mentioned in the interview by retailer 1 (see chapter 3.2), it is expected that RFID will be widely used for digitalization of inventory management, as it will decrease a retailer’s cataloging times. Production of RFID tags is becoming more affordable, and therefore making it possible to use on a larger scale. Fashion retailer Zara reported that employees needed 40 hours to scan barcodes in a particular store, but after adoption of the RFID system this same employee only needs around 5 hours for completing the same task, adding to the efficiency of retailers (CB Insights, 2018). When all items of a retailer are RFID tagged (or for a particular product group for example), the inventory accuracy will increase and the out-of-stocks will significantly reduce.

The way in which RFID is used in this context is not directly influencing the customer journey. It is more having impact on a companies’ back-office level, smoothening the supply chain and indirectly influencing the customer journey. More accurate levels of inventory lead

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to more efficient order picking, faster shipping time and therefore a more convenient customer journey.

A way in which RFID can be used to directly influence a customer’s journey is by using beacons for proximity marketing. With proximity marketing retailers are pushing advertising content towards customers passing by using beacons. These beacons connect with a customer’s smartphone via RFID (these beacons can be based on other technologies as well such as Bluetooth and Wifi).

A retailer that is currently using these kinds of beacons is Hudson’s Bay. When customers have the Hudson’s Bay application on their smartphone and enter the store, the application connects with the in-store beacon system. The application helps customers with navigation through the store while it is collecting valuable data for Hudson’s Bay. Based on the acquired information in the application, the app provides a hyper personalized service for the customer. In addition, the entire solution is integrated seamlessly with various external applications, such as e-commerce, a live chat tool and a real-time event calendar.

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3 Hypotheses and Conceptual Model

The goal of this study is to investigate what kinds of technologies increase a consumers’ purchase intention. The technologies used in this study are based on the exploratory research in the form of interviews. In order to be able to measure the effects of technology on purchase intention, several hypotheses are presented in this chapter. Three variables function as moderators and are expected to have an impact on the relationship between certain type of technologies and purchase intention. These moderators are personal innovativeness, performance expectancy and privacy concern. In figure 7, the expected effects are illustrated in the conceptual framework. In the following paragraphs, the motivation for the chosen variables and hypotheses is discussed.

Figure 7: Conceptual framework 3.1 Choice of Technologies

This thesis consists of several research phases. The first phase of the research is an exploratory research and is used to develop an independent variable, which is used in the conceptual model. This exploratory research is conducted in the form of interviews. Interviews are held with consumers and two fashion retailers to reveal what technologies have the biggest potential to be useful for consumers and increase their purchase intention. The interviews are constructed in a semi-structured way because ‘interviews are most likely to provide the depth of information that might be useful. Interviews are also a good method to

Technology Purchase

Intention Personal

Innovativeness Performance Expectancy Concern Privacy

H1

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resolve seemingly conflicting information, because the researcher has the direct opportunity to ask about the apparent conflict’ (Harrell & Bradley, 2009). Furthermore, Harrel & Bradley (2009) state that “a semi-structured interview collects detailed information in a style that is somewhat conversational, and these interviews allow a researcher to delve deeply into a topic and understand thoroughly the answers provided”. During the interviews other interesting questions could emerge from the dialogue with the retailers. By using a semi-structured interview there is enough room for discussion, which results in an interesting conversation and insights.

3.1.1 Consumer interviews

Consumer interviews were conducted to get a clear picture of current customer journeys and to find what points of friction in the customer journey exists and thus can be improved. The goal of the consumer interviews is to reveal what kind of technologies could possibly overcome current points of friction in a consumer’s shopping journey. Later on, these technologies will be discussed with the retailers in the retailer interviews. The outcomes of these interviews provide insights on which technologies have the potential to improve customer journeys and ultimately increase consumer’s intention to purchase. These technologies act in the conceptual model as an independent variable.

For the consumer interviews, a semi-structured interview was used. The interviews revealed how the customer journeys look like and what the main frustrations of consumers are while shopping for fashion items. The interviewees reported both online and offline customer journeys. From the consumer interviews it can be concluded that a separation can be made between age groups. Younger consumers tend to prefer shopping in an ‘omni-channel’ way (as discussed in chapter 1.4 on channel retailing). It has been reported multiple times that these consumers use different store channels interchangeably. For example after visiting the website or application of a store they plan to visit a store or the other way around. They

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furthermore reported that they visit websites to compare prices from competitors or check availability of products online on their smartphone while being in store (Showrooming). The younger consumers also mentioned more often to prefer online shopping to offline shopping compared to older consumers.

In contrast, older consumers often reported that they preferred to visit physical stores, most of the time due to the fact that they can assess the products and try them on. Furthermore, it was often mentioned that these consumers go to city-centers, physical stores, because it is an experience for them; while visiting stores with friends or family they later have a drink or dinner in the city, which is a real day out.

The most frequently mentioned frustrations for offline shopping were crowded stores, waiting for dressing rooms and not having products in stock. For online shopping the most frequently mentioned frustration was not knowing how a product fits (if shipping and returns are free this is less of a problem, but still ‘a thing’). All of the mentioned frustrations require physical and/or cognitive effort, which consumers perceive as a cost.

Based on these findings it seems that online shopping functions more as convenience shopping, which should be fast and easy (with free shipping and returns). This seems especially the case for younger consumers (millennials), as this generation is known for their impatience and they are ‘spoiled’ with technologies that encourage speed and convenience. Offline shopping seems to function more as experience shopping, because it was often mentioned that visiting stores was often done in combination with leisure activities such as visiting other parts of a city, having dinner or sitting on a terrace.

3.1.2 Retailer Interviews

In addition to the customer interviews, two interviews were conducted with fashion retailers to get a professional perspective on the developments, current customer journeys and what the impact of digitalization on purchase intention is (and will be) in the Dutch fashion retail

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industry. Semi-structured interviews were also used for the interviews with the retailers. Using a semi-structured interview was especially important with the retailers because according to Harrell & Bradley (2009) “interviews are most likely to provide the depth of information that might be useful” and a “semi-structured interview collects detailed information in a style that is somewhat conversational, and these interviews allow a researcher to delve deeply into a topic and understand thoroughly the answers provided”. The retailers have very specific and deep knowledge about the retail industry and therefore it would be obstructive to use structured (closed) questions.

From the interview with retailer 1 it can be concluded that online commerce is increasingly playing a more important role in customer journeys of their target group (students). Their website as well as social commerce is becoming more important. In the past, the entire customer journey could take place offline, but these days the entire customer journey (of a student for example) could take place online, or even entirely on a social platform. Especially younger consumers are for example becoming aware of brands via social media, such as Instagram. These posts are influencing them, and with a ‘buy button’ in these posts they are able to directly purchase these products without leaving the social platform. This is in line with the shift ‘from social networks to social commerce’ as explained in the literature review.

Retailer 1 thinks that customers of the same age as their target group (students) are definitely ready for digitalization of their customer journey. RFID, AR and for example a Visual search application seem very promising according to retailer 1. When these forms of technologies are becoming more affordable and fit into the format of a retailer, the manager believes that these technologies will definitely play a role in digitalizing the customer journey. Something entirely different can be concluded from the interview with retailer 2. From this interview it can be concluded that the target group of retailer 2 (35 and older) still very

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much appreciates the human service in a physical store while shopping. Retailer 2 does offer the ability to purchase products online, but their focus is on physical store service. According to retailer 2 this age group does not necessarily require further digitalization of their customer journey as the focus lies on human interaction between customer and employee.

From the interviews with the retailers it can be concluded that there is certainly a certain degree of interest among consumers in digitalizing the customer journey, but a difference in interest between young and older consumers exists. Therefore, the same distinction between age groups has to be made as concluded from the customer interviews. Meaning that younger consumers (young millennials) are more willing to adapt and experiment with digitalization in their customer journey as they seek for speed, convenience and enjoy these technologies while shopping. Older consumers (35+) do not require digitalization as they often shop in offline stores to get personal advice (or on a website). This does not mean that their customer journey cannot be digitalized. Therefore, the quantitative study should point out whether certain forms of digitalization will impact the customer journey and whether these differ among age groups.

Additionally, when combining the findings of the consumer interviews and the interviews with the retailers, it again can be concluded that online shopping seems to function more as convenience shopping, while offline shopping seems to function mainly as experience shopping. The findings of these interviews in combination with the literature review form the basis of the chosen measures and variables for the quantitative study.

3.2 Purchase Intention & Technology

Purchase intention is defined by Pavlou (2003) as a consumers’ intention to visit a store to gain information, and subsequently purchase a product. Furthermore, the study of Pavlou (2003) confirmed that purchase intention is a good estimate for predicting a consumers’ actual purchase, therefore purchase intention is used as the dependent variable in the conceptual

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framework. As stated in the introduction, Pauwels et al., (2011) state that the implications of digitalization reach further than solely e-commerce. To investigate what kind of technologies could be useful for consumers while shopping (i.e. increase purchase intention), the exploratory research has been conducted.

The interviews revealed that four technologies are considered to have the highest potential to be useful in fashion retail, meaning that these technologies are expected to have a positive effect on consumer purchase intention. These technologies are Artificial intelligence (AI), Radio Frequency Identification technology (RFID), Augmented Reality (AR) and Virtual Reality (VR). The findings from this exploratory research resulted in the categorical variable ‘Technology’, which is categorized in five forms of technology based on the four different technologies. Especially, AI is expected to have a strong positive effect on customer purchase intention as a study forecast that ‘by 2020, 85% of customer interaction in retail will be managed by AI’ (McKinsey & Company, BoF, 2017).

Previous research by Inman & Nikolova (2017) showed that different forms of technology lead to different levels of purchase intention. Based on the findings from previous literature and the interviews with the retailers it is expected that ‘Technology’ will have a positive effect on consumer purchase intention. It is expected that different forms of technology will have different effects on purchase intention; resulting in the following hypothesis:

H1: Means of purchase intention differ among forms of technology

These different effects of the different technologies on purchase intention will be further explored by performing a post-hoc test (See results section, chapter 5).

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3.3 Personal Innovativeness

Personal innovativeness is a variable “that potentially affect how people respond to innovations” (Jeong, Yoo & Heo, 2009). According to Jeong, Yoo & Heo (2009) the operational definition of personal innovativeness centers on “the willingness of individuals to adopt an innovation”. Additionally, the study of Limayem, Khalifa & Frini (2000) found strong support for “the positive effects of personal innovativeness on intentions to shop online”. Based on the findings of this literature it is therefore expected that higher (lower) levels of personal innovativeness result in higher (lower) levels of purchase intention. Hence, the following hypothesis is formulated for the main effect of personal innovativeness:

H2a: Higher levels of personal innovativeness result in higher levels of purchase intention.

Besides the main effect of personal innovativeness on purchase intention it is also expected that higher levels of personal innovativeness result in more pronounced differences in purchase intention between the five different technologies than when they are lower. Hence, the following hypothesis is formulated:

H2b: The differences in purchase intention between the five technologies are more

pronounced when personal innovativeness is higher than when it is lower.

3.4 Performance Expectancy

According to Juaneda-Ayensa et al. (2016), performance expectancy is defined as “the degree to which using different channels and/or technologies during the shopping journey will provide consumers with benefits when they are buying fashion” (Venkatesh et al., 2003, 2012, in Juaneda-Ayensa et al., 2016). Juaneda-Ayensa et al., (2016) furthermore state that

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“performance expectancy has consistently been shown to be the strongest predictor of behavioral intention (e.g., Venkatesh et al., 2003, 2012; Escobar-rodriguez and Carvajal-Trujillo, 2014) and purchase intention (Pascal-miguel et al., 2015). Based on the findings from these studies it is therefore expected that higher (lower) levels of performance expectancy result in higher (lower) levels of purchase intention. The following hypothesis is formulated for the main effect of performance expectancy:

H3a: Higher levels of performance expectancy result in higher levels of purchase intention

Besides the main effect of performance expectancy on purchase intention it is also expected that higher levels of performance expectancy result in more pronounced differences in purchase intention between the five different technologies than when they are lower. Hence, the following hypothesis is formulated:

pronounced when performance expectancy is higher than when it is lower.

3.5 Privacy Concern

Previous research by Hoffman, Novak & Peralta (1999) stated that “privacy concerns represent a key barrier to consumer e-commerce”. According to Slyke et al., (2006) “Consumer concerns for information privacy are reducing participation in e-commerce and that mechanisms that reduce these concerns will lead to increased willingness to purchase from Web merchants (Milne and Boza, 1999, Miyazaki and Fernandez, 2001)”. Based on the literature it is therefore important to incorporate privacy concern in the conceptual model as a variable that moderates the effect of the technologies on consumer purchase intention.

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Additionally, Cespedes and Smith (1993) found that “consumers’ privacy concerns are likely to increase as consumers become aware that marketers have somehow obtained information about them without their awareness or permission”. The necessity to include privacy concern in the model is emphasized by the fact that consumer privacy is a hot topic during the research period (may 2018). This is especially important for retailers with the introduction of the General Data Protection Regulation (GDPR) on the 25th_{of May 2018 in}

Europe. Based on previous literature, which revealed that consumer concerns for information privacy reduce participation in e-commerce it is expected that privacy concern has a negative main effect on purchase intention. Hence, the following hypothesis is formulated:

H4a: Higher levels of privacy concern result in lower levels of purchase intention

Besides the main effect of privacy concern on purchase intention it is also expected that higher levels of privacy concern result in more pronounced differences in purchase intention between the five different technologies than when they are lower. Hence, the following hypothesis is formulated:

pronounced when privacy concerns are higher than when they are lower.

3.6 Additional Analyses NPS and NLS scores

Besides the quantitative study that is based on the conceptual model and conducted to test the hypotheses as formulated in the previous sections, information on the Net Promoter Score and the Net Loyalty Score is acquired by means of the survey. With

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information on these scores several analyses are being conducted which have the aim to provide useful insights for retailers in practice. The NPS and NLS scores will be introduced in the second part of the results section and after that the analyses on these scores will be discussed (chapter 5.4).

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4 Methodology

This chapter describes and explains the chosen research method to answer the research question what the impact of technology is on consumer purchase intention in the fashion retail industry and test the hypotheses that originate from the literature review and interviews.

4.1 Research process

The entire research consists of several phases. First, an exploratory research has been conducted in the form of interviews with consumers and retailers. These interviews were held to uncover and understand current customer journeys and to get a view on what forms of digitalization might be used in future customer journeys. Based on this information the variables and forms of digitalization for the quantitative study were chosen. Furthermore, a preliminary study was done. In this study a literature review has been conducted. The extensive literature review creates an understanding of the current literature on customer journeys in the fashion retail industry.

4.2 Research design quantitative study

The aim of the quantitative study is to explain the relationship between forms of digitalization (technologies) and a consumer’s purchase intention. ‘Purchase intention’ was chosen as independent variable in this research, which is based on the assumption that the more positive a customer’s experience throughout his customer journey is, the higher his level of purchase intention will be. Furthermore, this study will explain the effect of moderators on this relationship. A quantitative approach is being conducted on primary data collected using an online survey. The online survey was constructed and administered in cooperation with Q&A Insights and Consultancy, a company doing research and consultancy work in the retail industry in the Benelux.

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4.3 Sample

The primary data for the quantitative study was collected in cooperation with Q&A Insights and Consultancy. Q&A has an enormous database at their disposal, which makes the findings of this study strong and relevant for retail in practice. Persons in Q&A’s database were asked to fill in the survey of 70 questions and took around 10 minutes. Forms of technologies were clearly and elaborately defined in the introduction texts. A pilot test was conducted before the survey went online. In this pilot test 50 random respondents from the database of Q&A were asked to fill in the survey. After completing the survey these respondents could make notes and comment on the survey. The only comment made a few times was that respondents sometimes find it hard to pretend using the forms of technologies when they did not know the specific form of technology. The survey could be filled in during week 17 and week 18 and had a completion rate of 60%. In total 272 respondents completed the survey. Each respondent answered questions on five forms of technology (Smart mirrors, Beacons, Visual search, AR and VR) resulting in 5 x 272 = 1360 observations.

4.4 Technologies

The technologies that are being investigated in this study are based on the extensive literature review and the interviews with consumers and retailers. The technologies that will be considered are RFID, AI, AR and VR.

RFID was presented in the survey in the form of an RFID-interactive mirror in dressing rooms on which consumers could assess size and color availability, recommendations of other products and request help from employees. Another form of technology based on RFID presented to the respondents were beacons. These beacons connect to consumer’s smartphones pushing advertising content and personalized offers while in-store (proximity marketing). AI was presented as an application that works with visual search (similar to the application ‘Slyce’, see chapter 1.6.1). With this application consumers can

The effect of technology on consumer purchase intention in the fashion retail industry