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Article Digital Environment in Global Markets: Cross-Cultural Implications for Evolving Customer Journeys Hyoryung Nam and P.K. Kannan Abstract Digital technologies and digital media are changing the environments in which firms interact with customers. However, the evolution of digital organizational forms, customer technology use, and the nature of customer journeys differ significantly across global markets. Drawing on observations of customer journeys across different international markets, the authors propose a framework to explain the observed differences in terms of the cross-cultural and socioeconomic factors that influence customer journeys. The authors put forth several propositions built on logical extensions of the extant research findings and identify areas for future academic research. In addition, they outline the managerial implications arising from the application of the framework for multinational firms seeking to market their products and services across global markets. Keywords customer journey, cross-cultural marketing, AI, omnichannel marketing, privacy Introduction The explosive growth of innovative digital technologies over the past two decades has revolutionized the way customers browse for information, compare products and services, make purchases, and engage with firms and other customers. Cus- tomers today interact with firms and other customers through multiple online touch points in multiple channels and media. Although the basic technologies underlying digital innovations are much the same all over the world, the nature of customers’ interactions with different touch points in a digital environment differ significantly across global markets. For instance, in some global markets, customers interact with standalone touch points that each have a distinct focus on ecommerce, social media, search, or entertainment, whereas in other markets, customers interact with one large ecosystem or a hub that has integrated all these functionalities. Within a single economic market, cus- tomers tend to switch between different online and offline channels, resulting in omnichannel touch points playing a greater role. In addition, customers’ use of technology-driven touch points (e.g., virtual agents such as Alexa and Siri) differs across global markets. Such differences have important impli- cations for how firms approach each market, design their touch points, and acquire and retain customers. We approach the question of understanding cross-cultural differences in customers’ behaviors in a digital environment from the perspective of the customer journey. Customer jour- ney is defined as customers’ experiences with a firm across multiple touch points in multiple channels and media through- out the purchase stages (Lemon and Verhoef 2016). Analyzing the customer journey in different markets provides useful insights for understanding how customers in different markets interact with various touch points throughout the journey—first in motivation, then in search and consideration, then in pur- chasing a product, and lastly in continuing to interact with the firm and other customers after the purchase. In this article, we seek to understand how customer journeys vary in different cultural contexts as customers progress from the pre-purchase stage to the purchase stage and continue on to the post-purchase stage. By doing so, we explain the observed dif- ferences in the customer journey in terms of cross-cultural, socioeconomic, and privacy factors and identity the role of dif- ferent types of touch points and emerging technologies in each stage of the customer journey across different cultures. We base these observations on extant academic studies, consultant Hyoryung Nam is Assistant Professor of Marketing, School of Business, University of Washington Bothell (email: hnam1@uw.edu). P.K. Kannan is Dean’s Chair in Marketing Science, R.H. Smith School of Business, University of Maryland (email: pkannan@rhsmith.umd.edu). Journal of International Marketing 2020, Vol. 28(1) 28-47 ª American Marketing Association 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1069031X19898767 journals.sagepub.com/home/jig

reports, and personal notes, highlighting the differences across markets. The contribution of this article is twofold. First, although customer journeys have been studied in detail in many contexts (Hamilton et al. 2020; Lemon and Verhoef 2016; Shankar and Tsai 2018), our article is the first systematic study of the dif- ferences in customer journeys across global markets. Despite the importance of this topic, how the customer journey differs between cultures is underexplored (Lemon and Verhoef 2016). Our framework provides an important theoretical basis for studying these differences. Second, the propositions we set forth should motivate future research in this area by providing a basis for such analyses. In addition, our integrative framework will help multinational companies understand how cross-cultural, socioeconomic, and privacy factors influence the customer journey, and it will thus help in developing better digital marketing strategies for different countries. A recent report by the Boston Consulting Group (Jain et al. 2018) pre- dicts that by 2022 more than three billion customers from emerging markets will be online and are expected to make $4 trillion in online purchases. Thus, for multinational firms, it is crucial to design digital marketing strategies for emerging countries that are founded in a solid understanding of the dif- ferences in cross-cultural, socioeconomic, and privacy factors across countries that may influence the customer journey. We structure the article as follows. First, we briefly define the concept of the online customer journey and the types of cus- tomer touch points. Using secondary research, we describe how online organization forms, the role of technology and technology usage, and customer journeys differ across global markets. Next, our conceptual framework highlights how cross-cultural, socio- economic, and privacy factors influence the motivating pro- cesses within the customer journey. We then present our propositions on the basis of this framework. We follow this with questions and implications for future research arising from the analyses. Finally, we discuss how managers can utilize the pro- posed framework and better design touch points in online cus- tomer journeys in different countries. Customer Journey Stages in the Customer Journey Following prior academic literature and business press articles on the customer journey (Hamilton et al. 2020; Lemon and Ver- hoef 2016; Lee et al. 2018), we consider the customer journey a three-stage process: pre-purchase, purchase, and post-purchase. 1 The pre-purchase stage includes customer interactions in the navigation path before purchase. In this first stage, customers identify needs, discover a product/brand, search for information, and build a consideration set while evaluating choice alternatives. Customers start the journey driven by utilitarian motivations or hedonic motivations (Babin, Darden, and Griffin 1994). After they start the journey, customers engage in a product search to reduce information uncertainty, which often relies on extrinsic cues such as brand credibility (e.g., Erdem and Swait 1998) and information from social sources (e.g., Chen 2017). The purchase stage includes customer interactions with a platform during the purchase event. This second stage involves processes focusing on choice, ordering, and payment (Lemon and Verhoef 2016). As the options for touch points increase in the digital environment, consumers may be faced with choice overload, lack of confidence in their purchase, and dissatisfac- tion with their decision. As prior research (e.g., Broniarczyk, Hoyer, and McAlister 1998) has shown, this may result in cus- tomers abandoning or postponing their search and/or purchase. The post-purchase stage comprises customer interactions with a platform after the purchase. This stage involves usage and consumption behavior, post-purchase engagement, word- of-mouth (WOM) through posting reviews, and so on. There has been extensive research in the context of offline journeys focused on consumption experience (e.g., Holbrook and Hirschman 1982), product returns (e.g., Wood 2001), and cus- tomer engagement (e.g., Pansari and Kumar 2017), all of which could be equally be applied to the digital journeys. Types of Touch Points in the Customer Journey Customers interact with multiple touch points during their jour- ney. Prior studies use a typology to understand the importance of different touch points in the customer journey. For instance, Haan, Wiesel, and Pauwels (2016) compare the role of firm- owned touch points, content-integrated touch points managed by customers, and content-separated touch points managed by customers in different stages in the purchase funnel. Lemon and Verhoef (2016) identify four main touch points (brand- owned, partner-owned, customer-owned, and social/external touch points) and acknowledge that the importance of different types of touch points may differ at each stage in the customer journey. In this article, we focus on three categories of touch points—firm-owned, partner-owned, and social touch points— and we discuss the role of technology-driven touch points in the customer journey. Firm-owned touch points are touch points in which custom- ers’ interactions are under the firm’s control. Examples of firm-owned touch points are firm websites, brand social media channels, email marketing, and loyalty programs. All the pro- motion and marketing activities that firms conduct (e.g., price discount, product package design) can be considered firm- owned touch points. Partner-owned touch points are those in which customers’ interactions are jointly managed by the firm and its partners. Examples of partner-owned touch points are search engines, display advertising, price comparison websites, and referral sites, in which firms collaborate with partners including mar- keting agencies and communication channel partners. 1 We acknowledge that the proposed three stages do not fully depict the customer journey. Customers may iterate between stages, end the journey, or start a journey at any stage. However, we believe that the proposed framework provides a generalizable foundation for understanding the customer journey. Nam and Kannan 29

richly detailed, qualitative feedback,whereasmany Western com- panies focus on quantitative feedback such as Amazon star ratings (Briggs et al. 2017c). Consequently, this allows firms in China to have access to far more data from a wider range of sources along the entire customer journey than their U.S. counterparts. This access enables integration of all such data into a single, wholistic view of individual customers. The larger Chinese platforms are also using bots, AI, and machine learning to process real-time data from social media, purchase transactions, and customers’ feed- back. These differences provide the basis for Chinese firms to obtain greater customer intelligence in terms of customer prefer- ences, unmet needs, and satisfaction. Differences also exist in how consumers view the importance of brand and social influence in their digital purchases. For exam- ple, Japanese customers are willing to pay more for premium brands—Japan is the second largest luxury market in the world—yet they are reluctant to buy private-label brands (Sals- berg 2010). Chinese shoppers consider brand and how society views the brand more importantly than customers in the U.S. (PWC 2018a). Similarly, the role of social influence in the cus- tomer journey is further amplified in Japan and China. In one study, 68 % of Chinese and 45 % of Japanese customers said that their purchase decisions are influenced by social media, whereas 38 % of Americans and 33 % of Germans said the same (Wernau and Woo 2019). In China, influencer marketing through lives- treaming has become one of the most popular advertising strate- gies, whereas such a marketing strategy is still nascent in the U.S. and Europe where influencers still rely on posts and short videos on Instagram and YouTube (Wernau and Woo 2019). These examples show the importance of cultural and socioeconomic differences that could influence the digital customer journey. Thus, our main objective in this article is to explain such differences in customer journeys and how they are influenced by emerging technologies, cultural factors, digital infrastruc- tures, and environments across different geographical/interna- tional markets. What do these differences mean for consumers’ choice of touch points (channels and devices) across cultures? What do they mean for multinational firms as they design channels across markets? What does it mean for firms in terms of competition and competitive market structure, as well as the evolution of these markets over time? If a multi- national firm (e.g., Starbucks) designed touch points in multiple different countries, would there be differences across the coun- tries? (For instance, would managers use different search ad strategies or design online platforms differently in different countries?) If so, what factors should marketing managers con- sider? The integrative framework that we propose in the next section seeks to provide an understanding of how cultural and socioeconomic dimensions are related to the customer journey and how such an understanding can lead to better digital mar- keting strategies for across global markets. Conceptual Framework We present our integrated framework to highlight how various factors influence customer journeys and customers’ interactions with multiple touch points. Figure 1 presents our framework, which views the customer journey as an outcome of several interrelated motivating processes. These include shopping motivation, information search, the tendency to adopt specific technology, use of multiple channels, and post- purchase processes. These motivating processes are, in turn, affected by cross-cultural, socioeconomic, and privacy factors. The way the customer journey evolves affects marketing out- comes such as customer value and firm value. We specifically focus on the cross-cultural factors, as we expect these to inter- act with the other two categories of socioeconomic and privacy factors significantly. We put forth a series of propositions regarding the organizational forms, the importance of brand, and the specific nature of the customer journey. Building on Hofstede’s (1980, 1991, 2001) framework on cross-cultural differences, we identify four main dimensions of culture: individualist–collectivist, power distance, uncertainty avoidance, and masculinity–femininity. Prior studies show that Hofstede’s framework is related to consumer behavior such as product search (e.g., Engelen, Lackhoff, and Schmidt 2013), consumer innovativeness (e.g., Steenkamp, Hofstede, and Wedel 1999), effect of persuasion knowledge (e.g., Briley and Aaker 2006), and susceptibility to WOM and social influence (e.g., Money, Gilly, and Graham 1998). As we show in this section, these dimensions are useful in explaining the differ- ences laid out in our framework. Cross-Cultural Influence on Customer Journey We first focus on how customers’ behaviors in each stage of the customer journey differ across global markets. We present our propositions in terms of Hofstede’s cross-cultural factors. Table 1 summarizes prior studies on the effect of cross- cultural factors on customers’ behaviors in each stage of the customer journey. Motivation in Pre-Purchase Stage Customers start the purchase journey for various reasons. Cus- tomers driven by utilitarian motivations focus on whether their needs can be met efficiently and conveniently, whereas custom- ers driven by hedonic motivations consider shopping an intrin- sically enjoyable process regardless of whether a purchase is completed or not (Babin, Darden, and Griffin 1994; Childers et al. 2001). With the popularity of online shopping, researchers have recognized the role of hedonic motivations in the digital customer journey. Hedonic shopping motivations consist of adventure shopping (shopping for stimulation and adventure), social shopping (shopping as a way to socialize with others), gratification shopping (shopping as a special treat to oneself), idea shopping (shopping to keep up with new trends), role shop- ping (shopping for others), and value shopping (hunting for bargains) (Arnold and Reynolds 2003). Customers driven by hedonic shopping motivations are more likely share positive WOM, show higher loyalty and repurchase intention (Jones, Reynolds, and Arnold 2006), engage more in impulsive buying, Nam and Kannan 31

and purchase a larger volume (Yim et al. 2014) than customers driven by utilitarian motivations. Customers’ shopping motivations vary by different cultures. Although there is scant empirical research on cross-cultural differences in online and mobile shopping motivations, practi- tioners argue that Chinese customers engage more in hedonic shopping online than Western customers (Briggs et al. 2017a). Online shopping is an adventure and entertainment for Chinese customers, whereas for Western customers, it is more a matter of convenience and efficiency. Collectivism and hedonic motivation. We posit that the collectivist– individualist dimension is related to customers’ hedonic motiva- tions. The collectivist–individualist dimension is defined as the extent to which individuals in a culture are integrated into the goals and identity of a group (Hofstede 1980; Hofstede 1991). In individualist cultures, people focus on self-interest and individ- ual preferences, whereas in collectivist cultures, goals of societies and communities are prioritized more highly than goals of indi- viduals (Mooij and Hofstede 2011). In collectivist cultures, the hedonic shopping experience is highly associated with other- oriented role shopping (shopping for others), as opposed to the self-oriented gratification shopping (shopping as a special treat to oneself) that predominates in individualist cultures (Evanschitzky et al. 2014). In collectivist cultures, online shopping is more of a social journey in which customers interact with friends and dis- cover the latest trends. Thus, customers in collectivist cultures find online shopping more intrinsically enjoyable when they are driven by other-oriented motivations such as social shopping (to build and enrich relationships) and role shopping (to shop for others). Conversely, customers in individualist cultures find onlineshoppingmoreintrinsicallyenjoyablewhentheyaredriven by a self-oriented motivation such as gratification shopping. P 1a : Customers in collectivist cultures are more likely to engage in social shopping than in individualist cultures. P 1a can be used to explain the popularity of ecommerce plat- forms that enable social shopping features in collectivist cultures such as China. Chinese customers shop online to interact with friends, and thus, offering social communities and chat functions in Chinese ecommerce platforms is effective for attracting more customers. However, in western cultures, ecommerce platforms offer limited social sharing and relationship building features; instead, such platforms are optimized for efficiency and conve- nience. There are other social media platforms (e.g., Facebook, Instagram) in which social sharing is encouraged, but these plat- forms have a limited ecommerce focus. Socioeconomic factors and hedonic shopping motivation. Though there is scant empirical research on how socioeconomic factors are related to online shopping motivations, prior research sug- gests that socioeconomic factors such as income and age are related to hedonic motivations (Cox, Cox, Anderson 2005). For example, young customers are more likely to engage in adven- ture shopping and social shopping (Arnold and Reynolds 2003). Practitioners also argue that shopping motivations can be distinctively different across generations. According to a report by McKinsey and Company, so-called Generation Z— people born from 1995 to 2010, the first digital natives who grew up along with the rise of social media and the smart- phone—show different shopping behaviors than other genera- tions (Francis and Hoefel 2018). This young tech-savvy Cross-Cultural Factors Privacy Issues Socioeconomic Factors Customer Value Acquisition Retention Sales Rate Firm Value Sales Profits Growth Rate Technological Innovations Internet Mobile AI and Machine Learning IoT VR and AR Pri v a cy I s s ues Soc S i oe c onom i c Factors Figure 1. Conceptual framework of global digital marketing strategy. 32 Journal of International Marketing 28(1)

generation traverses between online communities, engages more in socializing with friends online, and is willing to pay more for brands that embrace social causes (Francis and Hoefel 2018). We expect Generation Z to be more likely to engage in social shopping than other generations; thus, in emerging economies with younger populations, social shopping may become more popular than in mature economies with more rapidly aging populations. P 1b : Social shopping motivation is higher in emerging markets than in mature markets. Information Search and Evaluation Information search. In the pre-purchase stage, customers engage in product search to reduce information uncertainty and per- ceived risk regarding the purchase. Customers proceed to pur- chase only when they believe they have sufficient information to make a purchase decision. The perceived cost of information uncertainty is the key driver of search and information seeking behaviors. Prior studies find that customers are more likely to engage in the search when the costs of information uncertainty are high (e.g., when product quality uncertainty is high), espe- cially when customers are risk averse (Money, Gilly, and Gra- ham 1998; Shimp and Bearden 1982). We posit that uncertainty avoidance is the cultural dimen- sion that is directly related to the perceived costs of information uncertainty and information search behaviors. Uncertainty avoidance is defined as the extent to which a culture avoids or reduces uncertain situations (Hofstede 1980; Hofstede 1991). Individuals in high uncertainty avoidance cultures are less tolerant of ambiguity and feel more threatened by uncer- tainty than those in low uncertainty avoidance cultures. Con- sequently, individuals in high uncertainty avoidance cultures tend to process more information to enhance stability and pre- dictability (Akdeniz and Talay 2013), search for expert opi- nions (Mooij and Hofstede 2011), are less likely to adopt new ideas or new products (Singh 2006; Yeniyurt and Town- send 2003), and tend to be less innovative (Steenkamp, Hof- stede, and Wedel 1999). In high uncertainty avoidance cultures in which customers are very sensitive to uncertain, ambiguous situations, the quality of information is critical (Engelen, Lackhoff, and Schmidt 2013). Customers from high uncer- tainty avoidance cultures tend to be more engaged in informa- tion seeking through product search and product reviews in the pre-purchase stage (Gupta, Pansari, and Kumar 2018; Swee- ney, Soutar, and Johnson 1999). Thus, we propose: P 2a : Customers in high uncertainty avoidance cultures engage in more information searching than customers in low uncertainty avoidance cultures. The collectivist–individualist dimension is related to infor- mation seeking behaviors. People in collectivist cultures tend to exhibit more information search behaviors than in individu- alist cultures (Money, Gilly, and Graham 1998), and they compare and examine products more in the pre-purchase stage (Ackerman and Tellis 2001). Thus, we propose: P 2b : Customers in collectivist cultures engage in more information searching than customers in individualist cultures. Brand credibility and evaluation. Information uncertainty makes customers rely on extrinsic cues such as price, advertising (Zeithaml 1988), and brand credibility (Erdem and Swait 1998; Montgomery and Wernerfelt 1992) to infer product qual- ity in addition to seeking information from peers and friends (e.g., Chen 2017) in the pre-purchase stage. Cultural back- grounds influence the manner in which customers adopt differ- ent methods of reducing the perceived uncertainty. We next focus on cultural differences in the effect of brand credibility on product evaluation. First, we posit that the power of established brands is pro- nounced in high uncertainty avoidance cultures. The signaling power of the brand is stronger in countries where product qual- ity varies widely and thus uncertainty avoidance is high (Max- well 2001). The effect of credible brands on a purchase decision is greater for customers in high uncertainty avoidance cultures than for customers in low uncertainty avoidance cul- tures, as brands help to lower perceived risk and information costs (Erdem, Swait, and Valenzuela 2006). For instance, cus- tomers in Japan (a high uncertainty avoidance culture) have distinctively different tastes from Western customers. Japanese customers are willing to pay more for the premium brands, and the penetration rate of private-label products was just 4 % in 2010, compared with the global average of 20 % (Salsberg 2010). Thus, we propose: P 2c : The effect of brand credibility on product evaluation is more pronounced in high uncertainty avoidance cul- tures than in low uncertainty avoidance cultures. Second, we posit that brand credibility has a greater impact on product evaluation in collectivist cultures. Credible brands add more value to customers in collectivist cultures, as brand ownership contributes to a sense of belonging within society (Erdem, Swait, and Valenzuela 2006). According to the con- sumer survey conducted by PWC (PWC 2018a), 21 % of Chi- nese customers (collectivist culture) chose brand trust as the number one reason for selecting an online retailer (the highest percentage among 27 countries participating in the survey), whereas only 16 % of U.S. respondents (individualist culture) chose brand trust as the reason for shopping at an online retai- ler. In collectivist cultures, building brand trust and credibility is more important than delivering a persuasive advertising mes- sage (Mooij and Hoftstede 2011). Thus, we propose: P 2d : The effect of brand credibility on product evaluation is more pronounced in collectivist cultures than in indi- vidualist cultures. In the case of digital environments and online markets, P 2d implies that in collectivist cultures, smaller, no-name brands 34 Journal of International Marketing 28(1)

may have difficulty in establishing standalone touch points. Thus, in collectivist cultures such as China, establishing a cred- ible brand is important, and Chinese platforms are developed to integrate multiple functions under an established umbrella brand such as Taobao and Tmall. By contrast, in the United States (an individualist, low uncertainty avoidance culture), there are many independent brand-initiated standalone touch points, and thus touch points in the customer journey are more fragmented across many brands, big and small. Social sources and evaluation. Cultural backgrounds influence the extent to which customers rely on information from social sources (i.e., peers and friends) in the pre-purchase stage. First, prior studies show that uncertainty avoidance can strengthen the value of the information from friends and peers through online and offline interactions. Customers in high uncertainty avoid- ance cultures are more likely to rely on information from their friends and peers than information from impersonal and objec- tive sources such as magazines and product reviews (Dawar, Parker, Price 1996). In addition, customers in high uncertainty avoidance cultures are more likely to look for information and opinions endorsed by others through social media platforms (Johnston et al. 2018; Singh 2006). This effect becomes stronger in the absence of strong brands in the market. In emerging mar- kets, where the level of trust in institutions could be low, this effect could be even stronger. Thus, we propose: P 2e : The effect of information from social sources on product evaluation is more pronounced in high uncer- tainty avoidance cultures than in low uncertainty avoid- ance cultures. Second, the collectivist–individualist dimension is related to the value of information from friends and peers through online and offline interactions. Customers in collectivist cultures rely more on interpersonal information exchange, as they value the information from in-group members more highly than from out- group members (Money, Gilly, and Graham 1998). In individu- alist cultures, people are more likely to seek information from multiple sources, including the media, rather than rely on inter- personal communication in the pre-purchase stage (Mooij and Hoftstede 2011; Goodrich and Mooij 2013). Thus, in collectivist cultures, customers prefer to reduce perceived information uncertainty by relying on their family and friends (in-group members) much more than in individualist cultures. This means that online chat and word of mouth between group members are crucial uncertainty reduction strategies. Thus, we propose: P 2f : The effect of information from social sources on product evaluation is more pronounced in collectivist cul- tures than in individualist cultures. P 2e and P 2f illustrate the importance of social touch points in collectivist and/or high uncertainty avoidance cultures. Making good use of social chatting and fostering information exchange between various social groups are key to the success of digital marketing in such markets. Because technology has blurred the lines between in-group opinions and out-group opinions, WOM in online chats has become a substitute for brand building through conventional means. In in collectivist and/or high uncer- tainty avoidance cultures, strong brands can help bring custom- ers online, whereas platforms that start out with social features help create stronger brands through the presence of a strong word-of-mouth effect. Thus, social effects can reinforce the brand and enable the platform to acquire more customers, who in turn help to increase the social effects. This virtuous cycle can explain the emergence of all-purpose mobile platforms such as Meituan-Dianping and other such web hubs in China. Post-Purchase Stage A recent work by Gupta, Pansari, and Kumar (2018) provides a theoretical framework on global customer engagement. This framework provides an extensive theory on how culture influ- ences the relationships between initial purchase expectation and customer experience, customer experience and satisfac- tion, customer experience and emotional attachment, and the role of brand value and convenience in customer engagement. Building on this framework, we next focus on post-purchase evaluation and social sharing. Post-purchase evaluation. Cross-cultural factors have an impact on post-purchase evaluations and customer service expecta- tions. As customers in collectivist cultures expect to build a relationship with the retailer, these customers have high expec- tations for the quality of the services and the relationship (Donthu and Yoo 1998). Customers in high uncertainty avoid- ance cultures or high power distance cultures tend to put more emphasis on trust and fairness in customer–seller relationships (Lund, Scheer, and Kozlenkova 2013). As such, we can expect higher loyalty toward retailers in online and mobile settings in collectivist, high uncertainty avoidance, and high power dis- tance cultures than in individualist, low uncertainty avoidance, low power distance cultures. Recent research on loyalty pro- grams (Wang and Lawani 2019) also supports this by confirm- ing that consumers’ high power distance perception leads to more positive influence on the customer satisfaction of loyalty program members than that of non-loyalty program members. Cross-cultural factors also have an impact on how customers perceive price fairness in the post-purchase stage. Bolton, Keh, and Alba (2010) showed that in a collectivist culture (Chinese consumers in their setting), consumers care more about how much their friend (in-group) pays than how much a stranger (out-group) pays, as they are likely to experience greater gain (or loss) compared to in-group members as compared to out- group members. In other words, Chinese consumers think it is more unfair when their friend pays a lower price than when a stranger pays a lower price. However, in an individualist cul- ture (such as the United States), consumers do not perceive a significant difference between discounts offered to in-group and out-group members. Factors that influence customers’ satisfaction and experience also vary by cultures. For instance, customers in collectivist Nam and Kannan 35

the same. Europe is lagging behind the United States and China in adoption of AI and new technologies mainly because Eur- opeans are more sensitive to privacy concerns than customers in the United States and China (McKinsey and Company 2019). According to a survey by eMarketer (2019), only 4 % of Japanese customers (a high uncertainty avoidance culture) own a smart watch—the lowest among 41 countries participat- ing in the survey. We argue that these observed differences in technology adoption can be explained by different levels of consumer innovativeness across cultures. Researchers find that customers in individualist cultures tend to be more innovative than cus- tomers in collectivist cultures, as individualists tend not to follow others and to initiate new behaviors independently from others (e.g., Steenkamp, Hofstede, and Wedel 1999). Further- more, uncertainty avoidance also hinders new product accep- tance (Yeniyurt and Townsend 2003). Customers in high uncertainty avoidance cultures are less innovative and more concerned about the risk of new products than customers in low uncertainty avoidance cultures (Steenkamp, Hofstede, and Wedel 1999). We argue that in cultures high in both uncertainty avoidance and individualism (e.g., Europe), uncertainty Table 2. Selected Prior Research on Cross-Cultural Differences in Interactions with Touch Points. Topic Study Key Findings Adoption and usage of technology-driven touch points Ashraf et al. (2017) The adoption process of mobile commerce varies by different countries (the countries studied include Australia, India, the United States, and Pakistan). Muk and Chung (2015) Cultural differences exist in the acceptance of SMS advertising between American consumers and Korean consumers. Singh (2006) Customers in high uncertainty avoidance cultures tend to be less innovative and are less likely to adopt new channels. Steenkamp, Hofstede, and Wedel (1999) Individuals in high uncertainty avoidance cultures are less likely to adopt new ideas or new products. Straub (1994) Cultural differences exist in the adoption and usage of email and fax between American consumers and Japanese consumers. Yeniyurt and Townsend (2003) Individuals in high uncertainty avoidance cultures are less likely to adopt new ideas or new products and tend to be less innovative. Interaction with technology-driven touch points Degens et al. (2014) It is important to consider sociocultural dimensions when designing the cognitive process of virtual agents. Hilken et al. (2017) The effect of AR on perceived value is higher for verbalizers than for visualizers. Mascarenhas, Degens, and Paiva (2016) People in collectivist cultures find a collectivist agent more appropriate and trustworthy, but there is no difference in the evaluations of individualist virtual agents across cultures. Omnichannel shopping Kumar and Pansari (2016) Customers in high uncertainty avoidance cultures tend to explore a smaller number of channels and are less likely to shop from many different channels than customers in low uncertainty avoidance cultures. Lu et al. (2018) Customers in high uncertainty avoidance cultures are less likely to adopt online channels than telephone channels, as telephone channels reduce uncertainty and build trust better than online channels. Pick and Eisend (2016) Customers in different cultures respond differently to perceived switching costs. The positive effect of perceived switching costs on WOM and loyalty is weaker in individualist and high power distance cultures. Privacy concerns Milberg et al. (1995) Customers in individualist, high uncertainty avoidance, high power distance cultures are more concerned about potential privacy invasion than customers in collectivist, low uncertainty avoidance, low power distance cultures. Milberg, Smith, and Burke (2000) Cross-cultural differences regarding privacy concerns are related to different levels of government regulations, which in turn have an impact on perceptions of such regulations and corporate privacy management. Smith, Milberg, and Burke (1996) Customers in high power distance cultures show higher privacy concerns than customers in low power distance cultures because they do not trust firms. Nam and Kannan 37

avoidance plays a more significant role in the adoption of technology-driven touch points than individualism, as custom- ers in such markets are more concerned about the risks associ- ated with new technologies such as potential invasions of privacy. Thus, we propose that customers in low uncertainty avoidance cultures are more likely to adopt technology-driven touch points. P 4a : Customers in low uncertainty avoidance cultures are more likely to adopt technology-driven touch points than customers in high uncertainty avoidance cultures. Adoption of technology-driven touch points is also related to socioeconomic factors. Researchers find that age is signifi- cantly related to innovativeness and new product adoption (Steenkamp, Hofstede, and Wedel 1999). Practitioners also argue that customers from Generation Z, the “first generation of true digital natives,” are more innovative and eager to learn and play with innovations (Francis and Hoefel 2018). There- fore, we expect that Generation Z is more likely to adopt emer- ging technologies than other generations and, thus, the adoption of technology-driven touch points is higher in emer- ging economies with younger populations than in mature economies with more rapidly aging populations. P 4b : Adoption of technology-driven touch points is higher in emerging markets than in mature markets. It should be noted, however, that economic factors such as a country’s economic wealth can interact with the previous fac- tors in determining the rate of adoption of these technologies in different countries (e.g., Islam and Meade 2018) and how quickly the technologies affect the customer journey . Further- more, even though the technology is the same across all coun- tries, outcomes differ vastly on the basis of cultural and economic factors. For example, Wlo ¨mert and Papies (2019) show the extent to which differences in economic and cultural factors are associated with different market outcomes in the wake of the proliferation of broadband internet with respect to music revenue and piracy. Interaction with virtual agents. Not only do customers differ in adoption of technology-driven touch points, customers from different cultures also differ in the way they interact with technology-driven touch points. For instance, customers in col- lectivist cultures may try to build a friendship with a virtual agent, whereas customers in individualist cultures may treat an agent as an assistant for completing a task. Researchers show that in collectivist cultures it is important for virtual agents to infer the social status of a user (e.g., in-group vs. out-group, high social status vs. low social status) and adjust the interac- tion accordingly, as perceived social distance (e.g., stranger vs. friend) can influence the user’s trust in the agent (Degens et al. 2014). Accordingly, the virtual agent’s tone should be adjusted across cultures. In masculine cultures, an assertive tone will be better perceived, whereas in a feminine culture, being empa- thetic and supportive is more effective (Degens et al. 2014). Prior studies in computer science note that it is important to consider sociocultural dimensions (i.e., shared knowledge and assumptions in the culture) when designing the cognitive pro- cess of virtual agents (e.g., Degens et al. 2014). Mascarenhas, Degens, and Paiva (2016) studied whether cultural back- grounds influence how customers evaluate the appropriateness of virtual agents. They designed a collectivist agent and an individualist agent by adapting the way the agent greets the user, asks a personal question, and keeps a relational distance from the user. They found that people in collectivist cultures find a collectivist agent more appropriate and trustworthy, yet there is no difference in the evaluations of individualistic vir- tual agents across the cultures. Similarly, Amazon created an alter ego for Alexa and introduced it in Europe (Cakebread 2017). Amazon adjusted Alexa’s humor, vocabulary, slang, and suggestions for events, holidays, and sports so that it would be more tailored to European cultures. For example, the British version of Alexa understands a catchphrase from a British TV show, uses culturally familiar humor, and updates customers on the latest cricket scores. All in all, these examples highlight the importance for global companies of using distinct approaches for different cultures. Interaction with VR and AR. The emerging technologies of VR and AR have blurred the lines between online and offline touch points. Whereas traditional marketing casts customers as observ- ers and browsers, VR and AR technologies immerse customers either in virtual worlds or in an augmented version of the real physical store (Hall and Takahashi 2017). These technologies remove customers’ pain points, enhance the service quality, and offer a more personalized service (McKone, Haslehurst, and Steingoltz 2016). VR and AR technologies will blur the lines between online and offline channels by creating an immersive online experience very similar to the offline experience (Verhoef and Lemon 2016). Prior studies show how VR and AR technologies enhance the perceived value and customer experience. AR technologies enhance the utilitarian value and hedonic value of a product and increase decision comfort (Hilken et al. 2017), enhance perceived usefulness, increase ease of use. Such technologies are also are more informative and effective than hypermedia print ads (Yaoyuneyong et al. 2016). The effect of AR on perceived value is higher for verbalizers than for visualizers (Hilken et al. 2017). Usefulness, aesthetics, and service excel- lence are the key drivers of AR adoption for customers with higher cognitive innovativeness, whereas for customers with lower cognitive innovativeness, playfulness, and ease of use are more important (Huang and Liao 2015). We expect that cultural differences will influence the effect of VR and AR technologies. We posit that AR may enhance the purchase decision process more for customers in collectivist cultures than in individualist cultures. People in collectivist cultures are accustomed to processing product information in a holistic way and focus on context, symbols, and signs, whereas people in individualist cultures are more accustomed to visualizing product information in an analytical way and 38 Journal of International Marketing 28(1)

as Nest record customers’ living patterns. Facial recognition has already become quite commonplace in countries like China (Griffiths 2019) and is being routinely used in other countries for security purposes. However, despite the tremendous opportunities in data- driven marketing, extensive use of customers’ data may back- fire and could lead to significant privacy concerns, especially when the original data is used in inappropriate ways beyond the context of the original purpose. Prior studies show that privacy concerns decrease the effectiveness of targeted advertisements (e.g., Goldfarb and Tucker 2011). When customers realize their information is being used without their consent or outside the original purpose of the data collection, they tend to react nega- tively to personalized ads (e.g., Aguirre et al. 2015). The popu- larly used retargeting tactic—reaching out to customers who abandon their shopping cart—can be counterproductive because it heightens customers’ privacy concerns (Bleier and Eisenbeiss 2015). Privacy concerns can lower the likelihood of purchase (e.g., Pavlou, Liang, and Xue 2007) and cause cus- tomers to be less motivated to share personal information with firms (e.g., Jiang, Heng, and Choi 2013). However, the main question is whether these concerns are universal across global markets. In collectivist cultures, the social norm is to share personal information with friends, family, and other individuals in the same group (e.g., neighborhood, school, company), whereas in individualist cultures, the norm is that every individual has a right to privacy. These cultural differences lead to higher privacy concerns in individualist cul- tures (Milberg et al. 1995; Milberg, Smith, and Burke 2000). Customers in high uncertainty avoidance cultures are more con- cerned about the risk of an invasion of privacy (Milberg et al. 1995; Milberg, Smith, and Burke 2000). Customers in high power distance cultures show higher privacy concerns than cus- tomers in low power distance cultures because they do not trust firms, which are considered a more powerful entity (Smith, Mil- berg, and Burke 1996). Such cross-cultural differences toward privacy concerns are related to different levels of government regulations and corporate privacy management policies (Mil- berg, Smith, and Burke 2000). This ultimately will have an impact on what touch points are permissible in the customer journey and what firms do with the customer data. Thus, we propose that it is especially critical for firms to invest in privacy-enhancing technologies in individualist, high uncer- tainty avoidance, and high power distance cultures. P 6 : Privacy mitigation is more important for customer experience throughout the journey in individualist, high uncertainty avoidance, and high power distance cultures than in collectivist, low uncertainty avoidance, and low power distance cultures. Research Implications Next, we highlight future opportunities for research. Table 3 presents emergent research themes in cross-cultural differences in the customer journey. Understanding Cultural Differences in the Customer Journey Motivation. We see opportunities for additional research to develop the overall understanding of cross-cultural influences in shopping motivation in the pre-purchase stage of the cus- tomer journey. Due to the lack of theoretical and empirical research in this domain, many questions remain unanswered. There is a critical need for an integrative framework on the relationship between cross-cultural factors and utilitarian and hedonic shopping motivation. For instance, which cultural dimension is more relevant to different dimensions of hedonic shopping (e.g., adventure shopping, social shopping, role shopping, idea shopping)? Are there cultural differences in the way social shoppers or adventure shoppers navigate in the customer journey? Furthermore, it is critical to understand the moderators of the effect of cross-cultural shopping motivation on customers’ decisions throughout the journey. For instance, how does technology influence cross-cultural shopping moti- vations? Can we claim that mobile shopping amplifies the difference in hedonic shopping motivation between individu- alist and collectivist cultures? Socioeconomic factors (e.g., income, age) can also increase the difference even within specific cultural settings. These are important questions for future research to examine in the context of shopping motivations. Social interactions and brands. In terms of the impact of social media chatter and interactions (e.g., Nam and Kannan 2014) relative to firm-generated Content (FGC), recent research by Colicev, Kumar, and O’Connor (2019) shows that user- generated content (UGC) has an impact on the awareness and satisfaction stages of the customer journey, whereas firm- generated content has more of an impact on the consideration and purchase intent stages. While this study is performed in the context of global brands with U.S. consumer data, it might be interesting to explore how the various stages of the customer journey are influenced by UGC versus FGC across global mar- kets. Given our discussion on credibility of brands vis-`a-vis social interactions, would we find the results to be different in collectivistic cultures or in high uncertainty avoidance cultures? In the conceptual framework, we have highlighted the rela- tive roles of social interactions and brand in different cultural contexts. There are many important areas where social inter- actions and brand interact, and there have been interesting enquiries within cultural boundaries. For example, Hollenbeck (2018) finds, in the context of hotels in the state of Texas, that as more online WOM has become available, individual non- chain properties have benefited from online reputation mechanisms and gained market share from branded hotel chains . This could imply that social interactions and WOM can be a substitute for brand-building through conventional means. Would this substitution effect be stronger in collecti- vistic cultures? What does this imply for brand building stra- tegies in different cultures? Similarly, researchers have shown 40 Journal of International Marketing 28(1)

social media firestorms with intense negative sentiment can have short- and long-term effects on brand perceptions (Hansen, Kupfer and Hennig-Thurau 2018). Can cultural factors play a moderating role in this relationship? Are all firestorms the same across cultures? Bitterl and Schreier (2018) study the psychological consequences of consumer participation in crowdfunding projects in which brands are built by consumers and, thus, customers have a stronger affiliation with the products of the funded ventures. Under- standing the nuances of this affiliation across cultures could provide more insights into how customer journeys are shaped by growth in such platforms. Understanding Cultural Differences in Interactions with Touch Points Emerging technologies. One focus of future research could be the role of technology-driven touch points in the customer journey across different cultures. It will be critical to see if cross- cultural and socioeconomic factors influence the extent to which emerging technologies enhance customer experience, engagement, and conversion throughout the journey. For instance, researchers can investigate the following questions: Do cultural backgrounds influence the extent to which AI can be used to fill in for the lack of social relationships? Do virtual Table 3. Research Agenda. Topics Research Questions Motivation ± Which cultural dimension is more related to different dimensions of hedonic shopping motivation? What are the moderators? ± How do hedonic shoppers navigate the customer journey? Are there cultural differences? ± Are there cross-cultural differences in the effect of different dimensions of hedonic shopping motivation on the customer journey? For instance, do social shoppers in collectivist cultures navigate through the customer journey differently from social shoppers in individualist cultures? ± How does technology influence cross-cultural shopping motivations? For instance, could mobile shopping increase the difference in hedonic shopping motivation between individualist and collectivist cultures? Social interactions and brands ± What is the effect of brand credibility versus social interactions on customers’ decisions in different stages of the customer journey? Are there cultural differences? ± Are there cross-cultural differences in the effect of FGC and UGC on customers’ decisions in different stages of the customer journey? For instance, in collectivist cultures, does UGC have a stronger effect on consideration and purchase than in individualist cultures? ± Would social interactions and WOM act as a substitute for conventional brand marketing more in collectivist cultures than in individualist cultures? ± Do cross-cultural factors moderate the relationship between social media sentiment and brand perceptions? In which culture is social media sentiment more strongly related to brand perceptions? Emerging technologies ± Do cultural backgrounds influence the extent to which AI can be used to fill in for the lack of social relationships? ± Do virtual agents boost the social influence of peers in collectivist cultures more so than in individualist cultures? ± Do virtual agents have an impact on the role of social touch points in the journey? Do cultural dimensions moderate the effect of virtual agents on the role of social touch points? ± Which stage of the customer journey do virtual agents impact more? ± In which stages of the customer journey do virtual agents have a greater impact? Are there cultural differences? ± How would customers in different cultures react to AI? Would customer satisfaction with AI in collectivist cultures be more negative than in individualist cultures? ± Do cultural dimensions moderate the effect of VR and AR technologies on engagement and conversion? ± Do VR and AR technologies contribute to omnichannel marketing more in collectivist cultures than in individualist cultures? Channel choice ± What determines customers’ channel choice? What is the role of culture in channel selection? ± How do cultural dimensions play a role in the importance of touch points that contribute to conversion, satisfaction, and engagement in the customer journey? ± How do cultural differences influence which channels are synergetic with each other? For instance, in collectivist cultures, are synergies between social touch points and brand-owned touch points higher than in individualist cultures? ± Are there cross-cultural differences in the needs and expectations of omnichannel shoppers? Privacy ± How do privacy concerns affect firms in different cultures? ± How will strategies for using customer data and/or protecting customer privacy in customer journeys influence business outcomes in terms of customer value, sales, and growth, and how will this vary across markets? ± How will privacy regulations evolve in different cultures? ± How does the effect of privacy regulations on the customer journey differ by cultures? Nam and Kannan 41

capitalize on local conditions and technological innovations. This local market learning can also help increase diffusion of such technologies across markets (cf. Wang 1996). Market Intelligence The observed cultural differences influencing the customer jour- ney suggest that multinational firms can gather distinct insights using market intelligence across global markets. Multinational firms should prioritize different elements and functions of the customer journey depending on the characteristics of the journey. For instance, in Western ecommerce markets, the customer jour- ney is broken up across different firms. Thus, firms have limited insights because they gather data only from a portion of the cus- tomer journey. In contrast, firms in China with centralized ecom- merce sites have the advantage of observing the holistic behavior of customers for a larger portion of the customer journey. This allows Chinese firms to better understand the innovations that customers use in their journeys, whereas such a holistic view may not be easily available to Western firms. The centralized ecom- merce firms have a particular advantage when consumers use multiple devices and channels to interact with them. They can integrate the data and derive new insights into customer journey touch points and customer behaviors, whereas firms in individu- alist cultures (in which touch points are more fragmented) face difficulties in obtaining such insights. What can firms in individualist cultures do to derive insights into the whole customer journey? It is clear that not all firms can generate such insights in real-time and track changes in consumer behavior on their own. A good solution is to purchase data from third-party sources that maintain customer panels and capture information on whole customer journeys (e.g., Comscore in the United States in Europe). Some research firms provide identity graphs that firms can apply to their customer base and learn more about their journeys beyond the firms’ walls (websites and platforms). Finally, firms that seek to derive intelligence about customer journeys must look beyond the functions of marketing to mar- keting analytics, data science, and artificial intelligence for building capabilities in digital and analytics. This calls not only for significant investments in these technologies but also for hiring marketing analysts, cross-culture market researchers, behavioral scientists (in addition to the usual data scientists), computer scientists, and engineers to design and monitor cus- tomer journeys. Conclusions This article focuses on how digital technologies and digital media are changing the shopping environments across different cultures to meet the needs and wants of customers. We high- light the observed differences in customer journeys across glo- bal markets. Our proposed framework, which focuses on the motivating factors of the customer journey and how those fac- tors are impacted by cross-cultural, socioeconomic, and pri- vacy factors, explains how different global settings influence the customer journey. We propose a number of propositions in this context, which not only lead to future research issues but also have implications for practice. We also focus at length on the role of technology in the customer journey, highlighting the influence of virtual agents, VR, and AR, and how the impact of such technologies interacts with privacy factors. The content presented herein is not meant to be exhaustive, and our aim was to scratch the surface of this pivotal issue given the evolving global nature of digital commerce, as well as to spur additional research on this important topic. 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