• No results found

The Attraction of Internet Personalization to Web Users

N/A
N/A
Protected

Academic year: 2021

Share "The Attraction of Internet Personalization to Web Users"

Copied!
10
0
0

Loading.... (view fulltext now)

Full text

(1)

A b s t r a c t Numerous companies are taking advantage of interactive technology to personalize their interactions with users. However, in contrast to the proliferation of personalized web services worldwide, Information Systems (IS) research on users’ attitudes towards personalized service is minimal. It remains an empirical question as to whether online firms can use personalization as a marketing strategy to attract new users. Our research examines how users’ web experi-ence and their perceptions towards perso-nalization influence their attitudes towards switching to a personalized website. A survey study with 238 subjects was con-ducted. The findings provide empirical evidence showing that if users have involved interactions with their current site, then personalized services are not attractive enough to motivate them to migrate from one site to another. Users want useful personalized services, but at the same time, they are concerned with the manner in which firms use their data for personaliza-tion. Practical implications of the findings are discussed.

Keywords: personalization, involvement, information accessibility, perceived usefulness, perceived ease of use, privacy

A u t h o r s

Shuk Ying Ho

(suho@unimelb.edu.au) is Lecturer for Accounting and Business Information Systems at the University of Melbourne. Her research interests include

e-commerce and personalization technology in IS research. Her papers are forthcoming inInformation Systems

Research,European Journal of

Operational Research, andJournal of

Organizational Computing and

Electronic Commerce.

The Attraction of Internet Personalization to

Web Users

SHUK YING HO

INTRODUCTION

What is Internet personalization?

In the past decade, many online firms have emerged in almost all industries. As the competition inten-sifies, customer relationship

manage-ment (CRM) becomes crucial.

Online firms are investing large amounts of money in providing better services for their users. To remain competitive, Internet busi-nesses have been adopting differen-tiating strategies to attract and retain users (Bakos 1991). Figure 1 pro-vides a taxonomy of CRM systems based on the level of automation and individualization. In this study, we focus on Internet personalization which is at the highest level in Figure 1. Personalization aims to tailor content to individual needs, and to have content arrive at the users at just the right moment. Its immediate objectives are to under-stand and to deliver highly focused, relevant content matched to users’

needs and contexts (Albert et al.

2004). The long-term objective is to generate more business opportu-nities.

Companies employ personaliza-tion agents in different ways to

generate business opportunities.

Some companies use the agents as advice-giving systems to provide

recommendations to each individual and to generate up-sell and cross-sell opportunities. The main objective is to maximize online merchants’ rev-enue. One example is Amazon, which greets the users by name and lists some book recommendations on their individualized pages. The

recommendations are generated

based on the users’ previous pur-chases and the preferences of like-minded people, and no extra work is imposed on the users. Amazon con-tinues to establish its personalization system, and more filtering mechan-isms are being added to make the book recommendations more rele-vant and useful. As stated by Jeff Bezos, the CEO of Amazon, the mission of Amazon is to be the most user-centric company in the world (Anonymous 2002). Other persona-lization agents are used to (dynami-cally) structure the index of product pages based on click-stream analysis to minimize the users’ search efforts. One example is My Yahoo. My

Yahoo provides a personalized

‘space’ for each user. It automatically

generates personalized content

based on the user’s profile. For instance, it provides information on the horoscope for the correct star sign matched with a person’s date of birth. Apart from automatic perso-nalization, it also presents users with an array of choices and allows the

Copyright ß 2006 Electronic Markets Volume 16 (1): 41–50. www.electronicmarkets.org DOI: 10.1080/10196780500491162

(2)

users to select what is of interest to them. The users can personalize not only the content (e.g., weather, finance, sport), but also the layout (e.g., color, background). Recently, the My Yahoo service has been enhanced with personalized searching. That is, the users can save pages of search results to a ‘personal web’ and block URLs from appearing on the result list. My Yahoo is considered to be one of the forerunners among the growing number of personalized websites that have been emerging on the Internet over the past few years

(Manberet al. 2000).

Existing information systems (IS) research on Internet

personalization

The proliferation of personalized web services worldwide is drawing increased attention from IS researchers. Previous work related to personalization falls into three main categories. The first category includes applications of personalization technology. Personalization agents are found to be useful in different domains. These

include information dissemination (Light and

Maybury 2002; Loeb 1992), entertainment recommen-dations (Smyth and Cotter 2000), search engines

(Manber et al. 2000), and medicine (Bental and

Cawsey 2002).

The second category focuses on philosophical issues, such as privacy regulations and privacy ethics related to

data collection and processing (Cingilet al. 2000; Kobsa

2002; Volokh 2000). In practice, website-tracking is performed in an unobtrusive manner without the awareness of the users. Although users demand more customized services, they are increasingly concerned about the threats to their privacy and how online

merchants use their data. These studies address issues centred around this dilemma, which is not just a social science issue but also a concern for IS design as restrictions on data collection affect the web mining process. Further, this stream of work proposes technol-ogy frameworks to minimize privacy problems related to personalization. One of the well-known standards is the Platform for Privacy Preferences (P3P), which enables web users to specify their expectations concerning the use of their personal data, and websites to specify their

disclosure practices of user data (Cingil et al. 2000;

Kobsa 2002).

The third category focuses on technologies for mining the enormous amount of user transactions and deriving efficient rules to generate personalized content (Eirinaki and Vazirgiannis 2003; Perkowitz and Etzioni 2000; Ramakrishnan 2000). Works in this area concentrate on computational procedures to analyze transactions and personal profiles. While these studies investigate various aspects of personalization applications, little attention has been given to the basis for understanding the relationship between personalization and user responses.

Examples of commercial personalization software

packages/systems

Many commercial software packages for personalization are available for small-sized and medium-sized compa-nies. Examples include One-to-One (Broadvision),

Dynamo Relationship Commerce Suite (Art

Technology Group), Personalization Manager (Net Perceptions) and ADAPTe (ResponseLogic), all of which supply the full range of e-commerce personaliza-tion applicapersonaliza-tions. These packages use artificial intelli-gence and rule-based systems to specialize applications such as personalization of advertising and product recommendations at online sites. They provide one-to-one marketing and sales solutions and customer-driven websites with targeting engines. The e-businesses can ultimately operate more efficiently by aligning supply chains with data concerning the composition of the market and purchase patterns to build user profiles, which in turn determine personalized recommendations (see Figure 2).

The concept of personalization is not limited to online stores, and it can be applied to search engines. In 2004, two personalized search engines, A9.com by an Amazon subsidiary and MyJeeves by Ask Jeeves, were launched to let users store individual search results and then provide personalized web searches. Search engine analysts expect other major search engines, such as Google and Yahoo, to soon follow suit with their own personalized services. Global investment in personalization technologies will grow from USD500 million in 2001 to USD2.1 billion in 2006 (Anonymous 2001).

Figure 1. Taxonomy of customer relationship management systems for e-commerce

(3)

Motivations and research questions

There is much publicity about delivering personalized services over the web and the stakes are high for vendors selling related products. In spite of that, our under-standing of the impacts of Internet personalization is far from conclusive. Advocates of Internet personalization claim that personalization agents have changed the web into a personal communication medium. By providing individualized content, offerings and services, Internet personalization helps to control aimless surfing activity

(Light and Maybury 2002; Pitkowet al. 2002; Shahabi

and Banaei-Kashani 2003). Also, personalizing web content empowers online merchants to deliver user value and to achieve profitable growth (Greer and Murtaza 2003; Peppers and Rogers 1997). It has been reported that e-commerce websites using personaliza-tion technology have seen annual revenue increases of up to 52% (Parkes 2001).

But there remains scepticism on the prospects of Internet personalization. Nielsen (1998) claimed that

Internet personalization was highly over-rated.

Personalization technology is unnecessary if the web architecture is designed and content is well-organized. Festa (2003) remarked that online merchants seeking to personalize their websites in the hope of boosting online sales are not getting the expected payback. It costs about four times as much to personalize a website than to run a comparable adaptive site. A recent report by Jupiter Research (2003) indicated that only 14% of users think that personalized offers or recommendations on shopping websites lead them to purchase more frequently.

Is Internet personalization an effective marketing strategy to attract new users? What are the reasons that users switch to a personalized website? Our work conducts a survey study among web users to address these questions.

The remainder of the paper is organized as follows: the following section provides the theoretical back-ground and states the research hypotheses. The study is then introduced, followed by a consideration of the implications of the study. A conclusion and discussion of some limitations of the study are presented in the final section.

BACKGROUND AND HYPOTHESES

Website switching occurs when a user or group of users switches their allegiance from one website providing a certain type of services/products to another. This website switching may be temporary or permanent. For instance, if the Amazon website is not available, a user may browse or even purchase from Barnes and Noble. The switch may be limited to just this one occasion, or it may be longer lasting.

In this research we develop a model of website-switching behaviour to study the situation in which users currently use site A that has no personalization, and to see whether they will switch to site B that does have personalization. We believe the features of both the current website and the available website are determin-ing factors influencdetermin-ing users’ switchdetermin-ing behaviour. On one hand, users rely on experience and their often long-held attitudes about the current website. Personal Figure 2. Generation of personalized recommendations

(4)

experience can be an important factor influencing users’ attitudes. On the other hand, incorporation of persona-lization technology in a website acts as advertising. Personalization can play an important role in attracting new users. That is, if the users are unsatisfied with their current site and there exists an alternative that uses personalization and the users perceive it to be valuable, then website-switching may take place.

In the following, we divide the factors that motivate or inhibit a user’s switching to a personalized website into two categories: (1) experience with current websites, and (2) perceptions of personalized websites.

Experience with current websites

This category of variables focuses on the features of the current websites. Marketing literature on brand switch-ing considers two groups of variables, satisfaction derived out from the website and involvement level (Shukla 2004). Thus, our study explores how users’ pleasant navigation experiences and their involvement in the current website influence their switching decision. We anticipate that unpleasant experiences caused by difficulty in locating relevant content at the current website and low involvement will lead to a higher tendency toward website switching.

Level of involvement (I).

User involvement is of interest to researchers because of its potential impact on willingness to revisit the website. Prior research shows that users’ level of involvement has a direct, positive and significant impact on their willingness to purchase (Gammack and Hodkinson 2003), their motivation to process the information (MacInnis and Jaworski 1989), the way they process information (Johnson and Eagly 1989, 1990; MacInnis and Jaworski 1989), and their switching behaviours from one brand/product

to another (Keaveney and Parthasarathy 2001).

Involvement is a motivational factor. If users are involved in a website, then they recognize the good features of the site easily, and prefer and insist upon using it. Ultimately, they repeatedly visit it with little thought, and this leads to higher reluctance to switch to other websites.

Hypothesis 1: If a user is highly involved in a website, then it is less likely that he/she will switch to a personalized website.

Accessibility of relevant information (R).

Internet users lack physical contact with the stores or products. Thus, users’ experience is based purely on website information. This causes users to rely heavily on technology and information quality to keep them interested and serviced as they explore the site with ease and pleasure

(McKinney et al. 2002). The design elements of

the site, such as ease of navigation, can, therefore,

be a crucial factor affecting their web experience (Wolfinbarger and Gilly 2001).

An important website design element is the organiza-tion of informaorganiza-tion (Garrett 2002). The tremendous amount of information released on the web creates the problem of information overloading. This greatly reduces the navigation capabilities of online users, which in turn negatively affects their satisfaction towards the website. Thus, IS researchers proposed and confirmed

that information relevance (McKinneyet al. 2002) and

accessibility (Yanget al. 2005) are important web quality

factors affecting user satisfaction. As mentioned earlier, personalization is a newly developed technology which aims to solve the above problem. That is, personalization tailors content to individual needs, reduces the amount of delivered content and increases information accessi-bility. This aligns with the claims by Jeff Bezo, personalization can dramatically improve their users’ chances of finding what they want (even if they didn’t know it was what they wanted) from a 1,000 to 1 chance in a regular bookstore, to a 50 to 1 chance at Amazon.com (Anonymous 1998). Thus, we anticipate that the low accessibility of relevant content of the current website is a factor motivating one to move to a personalized site.

Hypothesis 2: If a user cannot access relevant content in the current website, it is more likely that he/she will switch to a personalized website.

Perceptions of the personalized website

This category of variables covers how web users perceive the new personalized websites. Compared with a physical channel, e-commerce moves the information technology to the foreground and web users interact with the technology directly (Koufaris 2002). Thus, the two variables of the Technology Acceptance Model (Davis 1989), perceived usefulness and perceived ease of use, are included in our study. In addition, since personalization arouses a list of social ethical issues, we also include a construct called ‘privacy concerns of the users’ and study its effects on users’ switching decision.

Perceived usefulness (U) and perceived ease of use (E)

of Internet personalization.

In IS literature, innovation adoption research provides a theoretical framework with which to identify the innovation-related factors in order to assess their impacts on adoption decisions. One of the established models is the Technology Acceptance Model (TAM), which has been applied extensively to identify factors that facilitate or inhibit the adoption of an innovation (Al-Gahtani and King 1999; Bajaj and Nidumolu 1998; Chin and Gopal 1995; Venkatesh et al. 2003). In this study, two of its constructs,

(5)

perceived usefulness and perceived ease of use of personalization services, are studied. Perceived useful-ness is defined as the degree to which a person believes that using a particular system would enhance his/her job

performance (Davis 1989; Venkateshet al. 2003). In the

context of a personalized website, perceived usefulness is regarded as the level of matching between personalized offers and one’s preferences. Perceived ease of use is defined to be ‘the degree to which a person believes that using a particular system would be free of effort’ (Davis

1989; Venkateshet al. 2003). The ease of use of

user-centric software is very important (Agarwal and Karahanna 2000). In our context, it refers to the ease of operating a personalization agent on the client side. The following hypotheses are proposed:

Hypothesis 3: When a user perceives personalized services to be useful, he/she will switch to a personalized website.

Hypothesis 4: When a user perceives personalized services to be easy to use, he/she will switch to a personalized website.

Privacy concerns (C).

Apart from the technological factors, much IS research also focuses on ethical issues related to personalization, and related technical

solu-tions (Caulfieldet al. 2000; Cingilet al. 2000; Kobsa

2002; Kramer et al. 2000; Stewart and Segars 2002;

Volokh 2000). Users face a dilemma: although they demand more customized services, they are increasingly concerned about privacy infringements and how their information is being used by online merchants. There are concerns that their purchase histories and navigation behaviours could be analysed and abused (Nash 2000;

Pitkow et al. 2002). Thus, we hypothesize that these

concerns will affect the probability of switching. Hypothesis 5: Privacy concerns discourage a user from switching to a personalized website.

THE STUDY METHODOLOGY

Samples

A self-administrated survey was conducted. The ques-tionnaire consisted of 45 questions and all questions were measured on a 5-point Likert scale. The time required to complete the whole questionnaire was 20 minutes. In order to have a larger coverage, we disseminated the survey in two ways: electronic survey and paper survey.

First, we sent emails to 350 full-time staff and students in a public university. The subjects were invited to take part in the study and to complete the questionnaires

online. From these 350, 205 (558.57%) completed

responses were returned. Since users who use emails for

communications generally have higher computer knowl-edge, the results would obviously be biased towards intensive Internet usage and active web behaviours. To reduce the bias, we distributed a paper survey on the university campus. We had a random sample of 200

people, and collected 33 (516.5%) responses. Overall,

we collected 238 completed responses.

Of the respondees, 160 were male and 78 female. A Kruskal-Wallis test revealed no significant differences in the gender ratio (approximately 66% males and 34% females) of subjects across the two methods of data collection. Their average age was 26.39 years old. All subjects were computer users. On average, they spent 4.32 hours on computer work or web browsing every day and they had 4.17 years of Internet browsing. At a 5% level of significance, F-tests showed that age, Internet experience, and Internet usage of the subjects did not differ significantly across the two groups.

Nearly all of them had general knowledge of online shopping, online payment and e-banking, and 70.59%

(5168/238) reported that they had shopped online,

such as movie tickets purchase and grocery shopping from online supermarkets. More than 60% had experi-ence with or had heard about personalized websites, such as Amazon and My Yahoo.

Measurements of variables

The statistical analysis was performed with SPSS version 9.0. To assess convergent and discriminant validity, we factor-analysed the items for the instruments. Table 1 shows results of the factor analysis and reveals that the item loadings were consistent with five distinct theore-tical constructs. A five-factor solution was obtained with all component eigenvalues greater than one. The factors were level of involvement (I), accessibility of relevant information (R), privacy concerns (C), perceived useful-ness (U) and perceived ease of use (E) of Internet personalization. These factors explained 68.23% of the

total variance in the survey. Items loaded highly (.0.70)

on their associated factors. The Cronbach alpha values for the constructs are shown in Table 2. Consequently we concluded the instruments were statistically valid and reliable.

Results

A multiple regression was conducted, with the likelihood of switching to a personalized website as the dependent variable. The R-square of the model was 0.316. Table 3 shows the regression model.

Involvement with the current website was a significant factor that influenced whether users switched to a website or not. Users who were highly involved in the website showed higher resistance against switching to a

(6)

new website. The result was consistent with Hypothesis

1 (t522.95,p,0.05).

Inconsistent with Hypothesis 2, accessibility of relevant information is not a significant factor affecting

users’ decision (t520.695,p.0.1). That is, users would

not switch to a personalized site from a site whose content was difficult-to-access or badly organized. There can be two reasons. First, users do not consider personalization agents to be a tool to solve the information-overload problem. Second, in addition to personalization agents, there are many other alternatives,

such as search engines and taxonomy hierarchies, to facilitate their user searching, so that the accessibility of relevant information would be greatly increased. Personalization is not necessary.

Participants who perceived personalization to be useful were willing to switch to a personalized website

(t53.53,p,0.01), supporting Hypothesis 3. Generally,

the score of the usefulness of a personalization agent was

not high (mean52.66), and the standard deviation was

large (SD51.59). This illustrates that the subjects had

various opinions on the usefulness of the agents. To probe further into the topic, we asked some subjects to specify their expectations of the functionality of a personalization agent, and compared the expectations from subjects who perceived the agent to be useful and those who perceived it to be useless. Those perceiving the agent to be useless had high expectations, including recommendations matching contexts and tasks, and layout customization. Those perceiving the agent to be useful had general expectations, including preference-matching recommendations, personal purchase history tracking, and delivery-tracking. A large difference in expectation of functionality leads to a large difference in usefulness perception.

Table 1. Rotated matrix component from factor analysis

U E R C I I1 0.303 0.113 20.022 0.089 0.888 I2 0.073 0.124 20.042 0.245 0.907 R1 0.076 20.032 0.843 20.053 0.020 R2 0.039 0.003 0.727 20.019 20.050 R3 20.119 20.084 0.736 20.070 20.058 R4 20.013 20.008 0.826 0.026 0.032 U1 0.964 0.031 20.009 0.115 0.094 U2 0.920 0.040 20.075 0.062 0.044 U3 0.886 0.093 0.033 0.161 0.137 U4 0.949 0.030 0.036 0.098 0.099 U5 0.914 0.079 20.003 0.145 0.086 E1 0.080 0.903 20.075 20.021 0.041 E2 0.048 0.743 20.078 0.019 0.084 E3 0.046 0.880 20.040 0.079 20.025 E4 0.003 0.910 0.054 0.014 0.021 E5 0.027 0.770 20.064 0.027 0.094 E6 0.068 0.866 0.056 20.059 0.075 C1 0.248 0.015 20.085 0.935 0.150 C2 0.222 0.016 20.041 0.936 0.184

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 5 iterations.

Level of Involvement (I)

Accessibility of Relevant Contents (R)

Perceived Usefulness of Internet Personalization (U) Perceived Ease of Use of Internet Personalization (E) Privacy Concerns (C)

Table 2. Reliability

Constructs Items Reliability

Level of involvement (I) 2 0.872

Accessibility of relevant contents (R) 4 0.791 Perceived usefulness of Internet

personalization (U)

5 0.964

Perceived ease of use of Internet personalization (E)

6 0.922

Privacy concerns (C) 2 0.945

(7)

Contradictory to Hypothesis 4, ease of use was not a

significant factor (t520.64, p.0.1). Usefulness

domi-nated ease of use, and this aligns with some IS studies

(e.g., Davis 1993; Elbeltagi et al. 2005). From a

practical point of view, it may imply that users use a technology mainly because of the usefulness of the technology itself. From a research point of view, the difference between the findings of prior studies (sig-nificant influence of ease of use on behavioural inten-tion) and this study (insignificant influence) may be due to the nature of the sample. Web users today are generally more computer literate than their counterparts five to ten years ago. This was supported by the descriptive statistics. The subjects perceived

personaliza-tion agents as not difficult to use (mean54.51,

SD51.44). Hence, ease of use may have been less of

an issue for this sample than it would have been for the samples used in prior studies. Owing to this general improvement of computer literacy among web users, the relationships found to be valid in prior work may need to be re-examined.

Privacy infringement from personalization impacted negatively on users’ decisions to switch to a personalized

website, supporting Hypothesis 5 (t522.12,p,0.05).

The subjects were concerned with how online merchants used their purchase transactions to generate the

perso-nalized recommendations (mean53.41, SD5.76).

Some subjects raised questions about how much data were tracked by online merchants. Some even ques-tioned whether the merchants would sell their profiles and transaction details to some marketing survey companies or other online merchants.

PRACTICAL IMPLICATIONS

In contrast to the widespread adoption of personaliza-tion software and the strong advocacy by management

gurus (Albert et al. 2004; Bakos 1991) on the use of

personalization services as a differentiating strategy, little has been done to assess the effectiveness of Internet

personalization. The current work is among only a few pioneering efforts (Moe 2003) that empirically assess the effects of Internet personalization. We believe that the findings of the current work are applicable to a wide range of web-based services that target the attraction of users. There are five practical implications:

1. Our work indicates that personalization can be a

successful marketing strategy in some types of website only. The nature of the interactions occurring among web users and their sites was an important factor. If the interactions are frequent and involved, web users are more reluctant to switch to a new site, even though the new site provides personalized services. Those e-businesses cannot use personalization as a marketing strategy to attract new users from a high-involvement site to a personalized one. Examples of high-involvement websites included network games and online chat rooms. Online stores selling high-involvement products, such as cosmetics, are also included.

2. Content overloading is not a reason to incorporate

a personalization agent into a site. Examples of content-overloading sites include knowledge-based websites (e.g., Microsoft.com) and online stores with millions of products (e.g., eBay.com). This aligns with the argument by Nielsen (1998). If the owners of content-overloading websites plan to facilitate the navigation of the users, to retain the users and to increase their satisfaction, they could choose to restructure the information architecture of the site or to use a search engine, rather than to employ a personalization agent.

3. Our empirical findings show that perceived

useful-ness of personalization is a significant factor in attracting new users. Thus, it is worthwhile for firms to invest in data mining to analyse the transaction patterns among like-minded people. However, web visitors may not look for a product for purchase. They navigate the web for different purposes, such as random browsing to kill time. Thus, simply analysing the transaction log may not generate the Table 3. Multiple regression model

Unstandardized Coefficients

Std Coeff

t Sig.

B Std. Error Beta

(Constant) 3.880 .529 7.342 .000

Level of Involvement (I) 20.270 .091 2.184 22.952 .003

Accessibility of Relevant Contents (R)

20.047 .067 2.043 2.695 .488

Perceived Usefulness (U) 0.262 .074 .220 3.525 .001

Perceived Ease of Use (E) 20.005 .082 2.004 2.064 .949

Privacy Concerns (C) 20.143 .067 2.132 22.123 .035

(8)

best personalized recommendations. Online firms need to understand both users’ past transactions and their current context in order to generate useful recommendations. Therefore, it becomes a chal-lenge to integrate server-side logged data with real-time click stream analysis. This leads to more room for future research.

4. While Internet personalization has been shown to

be effective in attracting new users, its use should be balanced by taking a proactive approach towards protecting data privacy on the users’ side. According to our findings, privacy concern is found to be a statistically significant factor and it negatively affects subscribers’ decisions to switch to a personalized website. However, the standar-dized coefficient of perceived usefulness of perso-nalization is 1.5 times that of privacy concern. This implies that regarding personalized recommenda-tions, our sample has more concerns about its usefulness, and its likely relevance and accuracy of preference-matching, than about privacy issues. Since our subjects have higher IT knowledge than an average user, they understand the personaliza-tion process more, and thus, have fewer concerns about privacy. Whether users with lower IT knowl-edge still consider perceived usefulness to be more important than privacy concerns can be an interest-ing question for future research. A recent survey conducted by the Personalization Consortium reports that while 76% of the respondents find it helpful for websites to remember basic personal information such as names and email addresses, only 50% read the privacy statement and only 38% of the respondents agree that privacy statements are easy to understand. Online firms should make sure that users have equitable access to their personal information and should provide a mechanism for users to quickly change and keep track of inaccurate or unauthorized personal information.

5. Lastly, the implications are not limited to a

business-to-consumer context. It can be applied to an Intranet portal of a company. For instance, personalization can be used to improve the infor-mation flow of in-house services and interactions between a firm and its employees. A large corporate Intranet can personalize its content to attract the employees to maximize the effectiveness of infor-mation dissemination.

CONCLUSIONS AND FUTURE RESEARCH

To summarize, our work investigates the motivating and inhibiting factors for online users to switch to a personalized website. Our work provides empirical evidence showing that users want useful personalized

services, but at the same time, they are concerned with the manner in which firms use their data for personaliza-tion. If users have involved interactions with their current site, then personalized services are not attractive enough to motivate them to migrate from one site to another. It provides insights to online merchants who plan to incorporate personalization tools in their websites.

However, this study has three limitations. First, although we conducted both email and paper surveys, the number of respondents for the email survey was much more than for the paper survey. Hence, there might be a significant bias in favour of IT use and expertise. This might also be the reason why our sample considers personalization agents to be easy to use. Further studies on how different groups of users (e.g., IT experts vs. IT novices) evaluate personalization agents in different contexts (e.g., online shops vs. information websites) would be interesting. Second, this paper only addressed users’ decisions to switch to a new website. Would they rely on the personalized recommendations after switching to the new websites? Would they purchase more? Future studies on the difference in exploration between personalized and general content could be interesting. Third, since our work focuses on whether personalization influences users’ behaviours to switching to a personalized website, we do not know how the users behave after switching to a personalized site. Can personalization services retain them? Would they develop a sense of loyalty and trust in the long run? Or would they switch back to a website providing general content? A longitudinal field study might contribute much to this area of research.

References

Agarwal, R. and Karahanna, E. (2000) ‘Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage’,MIS Quarterly

24(4): 665–94.

Al-Gahtani, S. and King, M. (1999) ‘Attitudes, Satisfaction and Usage: Factors Contributing to Each in the Acceptance of Information Technology’,Behavior and Information

Technology18(4): 277–97.

Albert, T. C., Goes, P. B. and Gupta, A. (2004) ‘GIST: A Model for Design and Management of Content and Interactivity of User-Centric Websites’,MIS Quarterly

28(2): 161–82.

Anonymous (1998) ‘Earth’s Most Customer Centric Company: Differentiating with Technology’ online at http://mitworld.mit.edu/video/1/, accessed 12 July 2005.

Anonymous (2001) ‘Personalization Technology Market Booming’ online at http://www.nua.ie/surveys/

index.cgi?f5VS&art_id5905357178&rel5true, accessed 1 October 2005.

(9)

Anonymous (2002) ‘A Bookstore by Any Other Name’ online at http://www.commonwealthclub.org/archive/98/98-07bezos-intro.html, accessed 12 July 2005.

Bajaj, A. and Nidumolu, S. R. (1998) ‘A Feedback Model to Understand Information System Usage’,Information and

Management33(4): 213–24.

Bakos, J. Y. (1991) ‘A Strategic Analysis of Electronic Marketplaces’,MIS Quarterly15(3): 295–310. Bental, D. and Cawsey, A. (2002) ‘The Adaptive Web:

Personalized and Adaptive Systems for Medical Consumer Applications’,Communications of the ACM45(5): 62–3. Caulfield, B., Carr, D. F. and Murphy, K. (2000) ‘Playing with

Plutonium: How to Handle User Data without Inviting a Privacy Meltdown’,Internet World6(14): 52–8.

Chin, W. W. and Gopal, A. (1995) ‘Adoption Intention in GSS: Relative Importance of Beliefs’,The Data Base for

Advances in Information Systems26(2/3): 42–63.

Cingil, I., Dogac, A. and Azgin, A. (2000) ‘A Broader Approach to Personalization’,Communications of the ACM

43(8): 136–41.

Davis, F. D. (1989) ‘Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology’,MIS

Quarterly13(3): 319–39.

Davis, F. D. (1993) ‘User Acceptance of Information Technology: Systems Characteristics, User Perceptions and Behavioral Impacts’,International Journal of Man-Machine

Studies38(3): 475–87.

Eirinaki, M. and Vazirgiannis, M. (2003) ‘Web Mining for Web Personalization’,ACM Transactions on Internet

Technology3(1): 1–27.

Elbeltagi, I., McBride, N. and Hardaker, G. (2005) ‘Evaluating the Factors Affecting DSS Usage by Senior Managers in Local Authorities in Egypt’,Journal of Global

Information Management13(2): 42–65.

Festa, P. (2003) ‘Report Slams Web Personalization’ online at http://www.news.com.com/2100-1038-5090716.html, accessed 21 October 2004.

Gammack, J. and Hodkinson, C. (2003) ‘Virtual Reality, Involvement and the Customer Interface’,Journal of End

User Computing15(4): 78.

Garrett, J. J. (2002)The Elements of User Experience:

User-CenteredDesignfortheWeb,1stedn,NewYork:NewRidersPress.

Greer, T. H. and Murtaza, M. B. (2003) ‘Web Personalization: The Impact of Perceived Innovation Characteristics on the Intention to Use Personalization’,Journal of Computer

Information Systems43(3): 50–5.

Johnson, B. T. and Eagly, A. H. (1989) ‘Effects of

Involvement on Persuasion: A Meta-analysis’,Psychological

Bulletin106: 290–314.

Johnson, B. T. and Eagly, A. H. (1990) ‘Involvement and Persuasion: Types, Traditions, and the Evidence’,

Psychological Bulletin107: 375–84.

‘Jupiter Research Reports That Web Site ‘‘Personalization’’ Does Not Always Provide Positive Results’ Jupiter Research Press Releases, online at: http://www.jupitermedia.com/ corporate/releases/03.10.14-newjupresearch.html, accessed 1 October 2005.

Keaveney, S. M. and Parthasarathy, M. (2001) ‘Customer Switching Behavior in Online Services: An Exploratory Study of the Role of Selected Attitudinal, Behavioral, and Demographic Factors’,Academy of Marketing Science

29(4): 374–90.

Kobsa, A. (2002) ‘The Adaptive Web: Personalized

Hypermedia and International Privacy’,Communications of

the ACM45(5): 64–7.

Koufaris, M. (2002) ‘Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior’,

Information Systems Research13(2): 205–23.

Kramer, J., Noronha, S. and Vergo, J. (2000) ‘A User-Centered Design Approach to Personalization’,

Communications of the ACM43(8): 44–8.

Light, M. and Maybury, M. T. (2002) ‘The Adaptive Web: Personalized Multimedia Information Access’,

Communications of the ACM45(5): 54–9.

Loeb, S. (1992) ‘Architecting Personalized Delivery of Multimedia Information’,Communications of the ACM

35(12): 39–47.

MacInnis, D. J. and Jaworski, B. J. (1989) ‘Information Processing from Advertisements: Toward an Integrative Framework’,Journal of Marketing53(4): 1–24. McKinney, V., Yoon, K. and Zahedi, F. M. (2002) ‘The

Measurement of Web-Customer Satisfaction: An

Expectation and Disconfirmation Approach’,Information

Systems Research13(3): 296–315.

Manber, U., Patel, A. and Robison, J. (2000) ‘Experience with Personalization on Yahoo!’,Communications of the ACM

43(8): 35–9.

Moe, W. W. (2003) ‘Buying, Searching, or Browsing: Differentiating between Online Shoppers Using In-Store Navigational Clickstream’,Journal of Consumer Psychology

13(1/2): 29–39.

Nash, K. S. (2000) ‘Personalization: Clash of the Killer Ps’,

Computerworld34(25): 66–7.

Nielsen, J. (1998) ‘Personalization is Over-rated’ online at: http://www.useit.com/alertbox/981004.html, accessed 1 May 2005.

Parkes, C. (2001) ‘The Power of Personalization: Web Technologies Get Personalized to Increase Profits’ online at: http://www.esj.com/features/

article.asp?EditorialsID535, accessed 1 May 2005. Peppers, D. and Rogers, M. (1997)Enterprise One to One:

Tools for Competing in the Interactive Age, 1st edn, New

York: Doubleday.

Perkowitz, T. M. and Etzioni, O. (2000) ‘Adaptive Websites’,

Communications of the ACM43(8): 152–8.

Pitkow, J., Schu¨tze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E. and Breuel, T. (2002) ‘Personalized Search’,Communications of the ACM45(9): 50–5. Ramakrishnan, N. (2000) ‘Pipe: Web Personalization by

Partial Evaluation’,IEEE Internet Computing4(6): 21–31. Shahabi, C. and Banaei-Kashani, F. (2003) ‘Efficient and

Anonymous Web-Usage Mining for Web Personalization’,

Journal of Computing15(2): 123–47.

(10)

Shukla, P. (2004) ‘Learning and Information Dissemination in Logistics Alliances’,Asia Pacific Journal of Marketing and

Logistics16(4): 82–104.

Stewart, K. A. and Segars, A. H. (2002) ‘An Empirical Examination of the Concern for Information Privacy Instrument’,Information Systems Research

13(1): 36–49.

Smyth, B. and Cotter, P. (2000) ‘A Personalized Television Listings Service’,Communications of the ACM43(8): 107–11.

Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003) ‘User Acceptance of Information Technology:

Toward a Unified View’,MIS Quarterly

27(3): 425–78.

Volokh, E. (2000) ‘Personalization and Privacy’,

Communications of the ACM43(8): 84–5.

Wolfinbarger, M. and Gilly, M. (2001) ‘Shopping Online for Freedom, Control and Fun’,California Management

Review43(2): 34–55.

Yang, Z., Caib, S., Zhouc, Z. and Zhoua, N. (2005) ‘Development and Validation of an Instrument to Measure User Perceived Service Quality of Information Presenting Web Portals’,Information and Management42(4): 575–89.

References

Related documents

Complete machine tool expertise and support since 1968 Broad range of high quality and reputable machine tools Long standing relationships with machine tool suppliers..

Berdasarkan pembobotan di atas diketahui bahwa sebesar 40,71% merupakan Tingkat Kerentanan Tinggi, 44,79% memiliki Tingkat Kerentanan Sedang, sebesar 14,50% memiliki

Second, we find that the change in environmental policy induced by a binding term limit depends on the composition of the state population in the manner suggested by the

ETH-CC (Ethernet Continuity Check - Fault detection) ETH-LB (Ethernet Loopback - Fault acknowledgement) ETH-LT (Ethernet Link Trace - Fault location).

program enhanced students’ reading, writing, vocabulary, and grammar skills; (b) that the condensed environment made it easier for teachers to target specific learning areas; (c)

Enhancing knowledge, developing self-management skills, and building confidence are considered to be important variables associated with improving outcomes, including health-

The activity described below aims to teach high-intermediate and advanced learners of English how to understand American housing ads.. The main target group is members of the