Personalization in e-commerce is currently at a stage at which the economic potential for its future application seems to be promising, based on the assumptions that electronic retailing (e-tailing) companies’ investment in it will be appreciated by consumers and that those companies will eventually yield rewards (Moon, Chadee, & Tikoo, 2008). However, the sector’s optimism has been shaken by findings in recent empirical studies that show mixed results regarding the effect of personalization, and it has been indicated that personalized services may lead to neutral or even negative user responses (Samah, Yahaya, & Ali, 2011). Comparatively, the boundaries of personalization in e-tailing have been expanded and have been assumed to have great potential to influence consumer attitude and behavior, in the sense that personalization may help consumers to communicate with a company on a multidimensional level (Ansari & Mela, 2003). In particular, personalization effects may vary depending on consumers’ psychological and/ or behavioral characteristics and experiences (Rose, Hair, & Clark, 2011). Some consumers may view this e-commerce phenomenon negatively, as an invasion of privacy, and their hesitation could be a critical impediment in implementing personalized services. Despite the perception that personalization is a necessary condition for e-tailers, little academic literature exists to support the condition of e-tailers implementing appropriate online personalization strategies, modified based on consumer characteristics (Chellappa & Sin, 2005). With this motivation, our primary goal was to consider the online features that influence consumer attitude and intention towards personalization, taking into account the moderating effects of both privacy concern and willingness to share information in the personalization process. It was also our aim to profile an online consumer base with two consumer characteristics—namely, privacy concern and willingness to share information—as well as categorizing five dimensions of e-personalizing features, in order to identify segments of online consumers and examine the behavioral characteristics of online consumers in each of these segments and to explore the discrepancies in attitudes toward e-tailers’ person- alization efforts across the segments. We compared the segments by probing consumers’ evaluation of e-tailers, including the website features essential to personalization. We anticipated that the new data about consumer privacy and information-sharing concerns contained in our findings would help to fill a gap and address discrepancies in the literature on personalization. Furthermore, our profiling of active online consumers according to core behavioral characteristics will contribute to expanding the current knowledge in terms of how to provide advanced and efficient personalized strategies online.

Literature Review

Personalization and Privacy

In traditional brick-and-mortar retailing, the focus of personalization is on social interactions between service employees and their customers to generate positive psychological responses (Pappas, Giannakos, & Chrissikopoulos, 2013). In this situation, besides forming a rapport from warm welcoming, small talk, or eye contact, personalized experiences at various other service levels are not available, because of the limited technological support in traditional retailing. Personalization has proven to be an important element of the online store environment. It involves a process of gathering consumer information during the online interaction with the consumer, which is then used to deliver appropriate content and services, customized to the consumer’s needs (Murthi & Sarkar, 2003; Smith, 2006). Online store features that serve the purpose of e-personalization include wish lists for future purchases, site entry features, promotion and event notifications, and the ability to personalize the online store through preference (Kramer, Noronha, & Vergo, 2000). The aim of all these features is to better serve the customer by anticipating needs and expectations to make the interaction efficient and satisfactory for both parties and to build a relationship that encourages the customer to return for subsequent purchases (Komiak & Benbasat, 2006). With a strong basis in machine interactivity, personalization in e-commerce has been approached based on a narrow definition of interaction that focuses on the process of gathering, storing, and analyzing information about consumers and, based on the analysis, delivering the right information to each consumer at the right time (Thirumalai & Sinha, 2011).

Consumers’ concerns regarding the exposure of personal information and invasion of their privacy have been among the most important impediments to marketing with information systems in technology-based environments (Park, 2011). Akhter (2015) pointed out that privacy concern is a core determinant of self-efficacy and Internet involvement. Such concern may stem from a lack of trust in e-tailers as well as ignorance about how e-tailers utilize their information (Wu, Huang, Yen, & Popova, 2012). Thus, because mechanisms for personalization fundamentally rely on tracking website users’ behavior history, privacy concern is likely to be a major obstacle to consumers’ appreciation of personalization in e-commerce (Stead & Gilbert, 2001). Suspicion about where personal information is stored by websites may trigger privacy concerns among individual consumers and lead them to remove their information, spread negative feedback, or complain to a third-party (Sheehan & Hoy, 1999). Barnett White, Zahay, Thorbjørnsen, and Shavitt (2008) found that customers with a high level of privacy concern showed little appreciation of personalized emails, particularly if they considered an email not to be legitimate. Thus, consumers’ fears about privacy may also affect the validity and completeness of the information they choose to provide, which could ultimately lead to inaccurate targeting, wasted effort, and frustrated consumers (Awad & Krishnan, 2006). Therefore, with the assumption that personalization on the Internet requires knowledge and information at an individual level, it is important for e-tailers to identify the features resulting in consumer preferences and/or differences, such as privacy concerns.

E-tail and Evaluating Features

According to Farquhar, Langmann, and Balfour (1998), consumers have basic needs related to e-tailers that include ease of use, consistency in user interfaces, privacy, security, cost transparency, reliability, design tailored to the type of customer, order confirmation, and system status information. Information resources necessary to implement personalization vary depending upon the features provided on a website. In line with the existing literature, we explored five core website features: navigation, personalization, privacy and security, customer care, and promotion. Navigation is a tool that operates on the website to help customers to broaden the scope of their purchases. This feature can be considered a necessary function to reduce customer effort and cost, as e-tailers offer a greater variety of products compared to typical offline retailers. Personalization is identified as an important mediator in the formation of customer attitude, satisfaction, and loyalty behavior (Riecken, 2000). It determines the extent to which an e-tailer’s website can tailor an overall shopping experience to one specific consumer, based on their individual information and preferences. Another important attribute of the online environment is customer care. A customer care center could be operated on the website to ensure the availability and effectiveness of the service provided to customers by supporting all customer interface activities, such as orders and delivery tracking services. Service failures can have serious negative effects, such as the loss of customers, time, and money (Goetzinger, Park, & Widdows, 2006). Conversely, successful care of customers may strengthen the relationship between the customer and e-tailer and promote customer retention. Customer care operationality refers to the availability and effectiveness of customer service tools to support all the pre- as well as postpurchase customer interface activities, such as order and delivery tracking services. Interactive customer services are very important, especially for online stores (Yoo, Lee, & Park, 2010). Finally, promotion includes all kinds of sales promotions and events, such as reward programs and the ability to design bespoke customer promotions. On the basis of a review of the current literature, we proposed the following research questions:
Research Question 1: What is the role of consumer privacy and security concerns in the adoption of personalization features in an online shopping context?
Research Question 2: What is the impact of consumers’ willingness to share their personal information with e-tailers on their behavior when using personalization features in an online shopping context?

Method

Participants and Data Collection

The population targeted for this study was online shoppers aged over 18 years. Data were collected using an online survey. Over 7,000 people were asked to participate, and 1,659 respondents completed the survey (response rate = 23.7%). The average income of the sample was US$41,886 and the average age was 41.8 years (range = 18–70 years). In terms of marital status, 55.7% were married and 33.6% were single, and 10.7% did not provide a response to this item. Of the respondent group, 47% were high school graduates, 28% held a bachelor’s degree, and 16% held a vocational or technical degree, and 9% did not respond to this item. The majority of the respondents (73.8%) were women. We developed 13 survey items to measure attitudes toward the following five dimensions of e-tailing by evaluating features identified in the previous literature (Richard, 2005): personalization, navigation, promotion, customer care, and security and privacy. The calculation of Cronbach’s alpha coefficient yielded reliabilities of .821, .840, .742, .866, and .947, respectively.

Results

First, we performed a confirmatory factor analysis to validate the five dimensions of e-tail evaluation criteria. The scores given to each of the five dimensions by the online user sample were then used to determine two essential behavioral constructs in personalization, which were privacy concern (PC) and willingness to share information (WTSI). The overall scores for all five dimensions were then divided by the mean scores of PC and WTSI, which resulted in the retrieval of four consumer segments: Cluster 1: n = 644 (38.8%), high PC and high WTSI; Cluster 2: n = 189 (11.4%), low PC and high WTSI; Cluster 3: n = 2,391 (4.4%), low PC and low WTSI; and Cluster 4: n = 587 (35.4%), high PC and low WTSI. The number of respondents assigned to Clusters 1 and 4 (74.2%) showed that the online user sample in our survey had a high level of privacy concern, regardless of the degree to which these respondents were willing to reveal their information to an e-tail website. Furthermore, the large number in Clusters 1 and 4 implies that a majority of current online consumers feel comfortable providing their information to an e-tailer, and that this is the case even for the 38.8% of those concerned about invasion of their privacy.

The next step in the profiling process was a multivariate analysis of variance (MANOVA) and discriminant analysis, which were conducted to check the statistical significance of group differences and to determine the characteristics of the respective clusters.

Confirmatory factor analysis for the five dimensions

In addition to the chi-square/degrees of freedom value (χ² = 715.640, df = 55, p < .01), the various fitness criteria examined were goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). The results were as follows: GFI = .938, AGFI = .897, indicating a reasonable fit (Hayduk, 1988). Further, the CFI value of .953 was higher than the expected threshold of .90, and, therefore, was shown to be a good fit for the data applied to the proposed model. RMSEA was .085, which is above the standard of .05 recommended by Browne and Cudeck (1993), but it was close to a value of .08 and, thus, represents a reasonable amount of error of approximation in the population.

MANOVA and Discriminant Analysis

The MANOVA results suggested that the mean vectors of predictor variables were different in each of the four clusters. Table 1 shows that, except for the number of passwords for e-commerce and self-perception in e-commerce, there were significant mean differences for most of the variables across the clusters. All 12 significant variables were entered with a simultaneous estimation approach based on the research interest for classifying the groups determined by all the predictors from the MANOVA results, rather than by choosing the most discriminant variable. Thus, three discriminant functions were generated, two of which were statistically significant. The centroids of the clusters with the same level of privacy concern (Cluster 1 + Cluster 4 vs. Cluster 2 + Cluster 3) tended to be closer to each other. A close examination of the first discriminant function loadings revealed that the first function corresponded most closely to privacy concern, customer care, navigation, and promotion. The second function explained an additional 24.1% of the variance (eigenvalue = 0.251, canonical correlation = .448, Wilks’s Lambda = .791; chi square = 386.865, df = 22, p < .001). The centroids were found to be -.457, -.600, .290, and .573 for each cluster with order (see Table 2). The centroid of Cluster 2 (low PC, high WTSI) was relatively far from the other clusters. The second function mainly corresponded to comfortableness toward e-tailers, expenditure on Internet shopping, personalization, and number of searched e-tailer websites. The third function explained an extra 1% of the variance (eigenvalue = 0.010, canonical correlation = .101, Wilks’s Lambda = .990, chi square = 16.794, df = 10, p = .079) with an nonsignificant result at p > 0.05 level. However, when comparing the distances between the centroids of each cluster, the second cluster seems to be more distanced from the other clusters. Repeatedly, the postcomparison analysis showed differences in the roles distinguishing the four segments among the five dimensions of attitude towards e-tailers’ websites and Internet users’ characteristics.

Discussion

The results show that the response of consumers to personalized services may vary significantly depending on the level of an individual’s privacy concern and willingness to share information. In terms of the potential to generate profit for the e-tailer, the customers on whom it would be most rewarding to focus when developing personalized e-tail programs would be those who have a high concern about privacy but who also have abundant experience with Internet shopping. Furthermore, we found that customers with less concern than others had about privacy and a greater willingness to share their information responded positively to online personalization strategies. With the rapid advancement of personalization features in e-commerce, there is a valid demand for further research to identify the relevant contextual information needed to implement effective personalization strategies.

The profiling results in our study identified online user segments with different attitudes towards e-tailers’ websites, differentiating them by privacy concern and willingness to share information. In particular, those consumers in Cluster 1, that is, the group with a high level of privacy concern and a high level of willingness to share information, were more likely than those in the three other clusters to regard website features as very important factors in their evaluation of e-tailers. Although these respondents had security and privacy concerns, simultaneously, they were very willing to reveal their personal information to e-tailers and, in terms of both the number of retailers they were registered with and the extent of their Internet shopping expenditure, they seemed to have active e-shopping behavior. These findings are in line with those of Awad and Krishnan (2006), who noted that privacy issues were one of the most important factors affecting consumer attitude to personalization. Among these people with high concern about privacy and high willingness to share information, their privacy concern seems to stem not from the fear or distrust of Internet shopping or e-tailers, but from their degree of interest in, or direct experience with Internet shopping.

This argument is further validated when this group is compared with the Cluster 3 consumers, that is, the group who were reluctant to share their information with e-tailers despite their low privacy concerns. People in this group were not concerned about the features of e-tailers’ websites; they were uncomfortable with, and barely had any contact with, e-tailers. It can thus be interpreted that even though privacy and security issues are considerations of paramount importance for e-tailers, it would be a wasted effort for them to try to satisfy this group of consumers who seem to lack interest in online shopping and whose fear and distrust do not seem to stem from direct experience. On the other hand, if this fear and distrust leads this cluster of consumers to be cautious or apprehensive about Internet shopping, e-tailers should prepare a specific strategy with the aim of providing information that reassures and persuades them to trust Internet shopping. According to our results, the only apparent difference between consumers in the high privacy concern and high willingness to share information group and the group with high privacy concern and low willingness to share information was the extent to which they were happy to share information. Consumers who do not want to provide personal information because of security and privacy concerns do not place importance on the personalization and promotional features that are very dependent on the amount and quality of customer information available. Finally, we found that the members of Cluster 2, who had low privacy concern and high willingness to share information, placed relatively less value on e-tailers’ web features than those in the high privacy concern and high willingness to share group (Cluster 1).

From these results, it can be logically concluded that the more experienced online users are with Internet shopping, the more willing they are to share information with e-tailers; good experiences with e-tailers builds online users’ trust. However, these results show that this kind of trust does not automatically imply the loss of privacy and security concerns. These results also suggest that e-tailers should explicitly emphasize their privacy policies and security arrangements to their loyal customers who may constantly worry about these issues.

Implications

There have been mixed results in previous studies on the effect of personalized services in e-tail environments, with some researchers claiming that personalized services may lead to neutral or even negative responses (Samah et al., 2011). We identified the behavioral characteristics of online consumers and categorized them into segments. We found empirical evidence of important differences in consumer attitude toward e-tailers’ personalization efforts across the segments. When analyzing the personalization effect, most previous researchers did not consider consumer characteristics and their effect on consumer responses and attitudes to personalized services (Smith, 2012) and, given the significant role of the individual characteristics identified in this study, this omission may help to explain the existence of the discrepancies in the previous literature.

Our results support the critical role of both privacy concern and willingness to share information in online shopping situations. Because these two factors appear to be crucial to companies in their execution of e-service personalization, our results may provide an influential reference for both academics and practitioners considering how to improve their personalized website marketing programs. The critical role of privacy concern confirmed in this study is in line with extant reports identifying privacy issues as one of the most important factors affecting consumer attitude towards personalization, which, from a broader perspective, is a critical antecedent to shoppers’ hesitation to purchase via the Internet (see e.g., Chang & Wu, 2012). On the basis of the overall study results, it could be argued that, in terms of increasing profit margins, the customers for whom companies would find it most advantageous to establish a personalization strategy, would be people with a high level of privacy concern and considerable Internet shopping experience. It might also be a fruitful profit-making strategy for e-tailers to adopt personalization strategies tailored to the preferences of online consumers who are willing to share their personal information and who have a low level of privacy concern. However, when it comes to the segment of online consumers who have rarely used the e-tail environment, we found that these people had a high level of privacy concern about the Internet overall and, hence, very little intention of offering their personal information.

Limitations

A limitation in our study is that the framing of the website choice question may have resulted in biased responses at the higher or lower end of the scale. Specifically, respondents were asked to give their responses for general e-tailers’ websites, regardless of their familiarity or preference. Secondly, we adopted only perceptual measures for all variables. Although perceptual measures are clearly important for generating insights into how customers perceive and value e-tailers’ websites, the bottom-line orientation of direct marketers demands that future researchers use other constructs, such as actual revenue generated by a website, or return on investment of a website, as the dependent measure.

References

Jungkun Park, College of Technology, University of Houston, 110 Cameron Building, Houston, TX 77204-6020, USA. Email: [email protected]