Information & Management 46 (2009) 448–452

Psychological reactance to online recommendation services§

Gyudong Lee, Won Jun Lee *

SKKU Business School, Sungkyunkwan University, Seoul, South Korea

A R T I C L E I N F O

Article history:

Received 7 April 2008

Received in revised form 2 March 2009

Accepted 31 July 2009

Available online 6 September 2009

Keywords:

Threat to freedom

Psychological reactance

Recommendation

Personalization

Online shopping

A B S T R A C T

Adoption of online recommendation services can improve the quality of decision making or it can pose

threats to free choice. When people perceive that their freedom is reduced or threatened by others, they

are likely to experience a psychological reactance where they attempt to restore the freedom. We

performed an experimental study to determine whether users’ expectation of personalization increased

their intention to use recommendation services, because their perception of expected threat to freedom

caused by the recommendations reduced their intention to participate. An analysis based on subjects’

responses after using a hypothetical shopping website confirmed the two-sided nature of personalized

recommendations, suggesting that the approach and avoidance strategies in persuasive communica-

tions can be effectively applied to personalized recommendation services on the web. Theoretical and

practical implications are discussed.

� 2009 Elsevier B.V. All rights reserved.

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1. Introduction

By using IT, firms can now provide personalized services to theire-customers, based on their individual needs, preferences, orpurchase history. While interacting with customers throughmultiple channels such as web and wireless devices, firms cancollect vast amounts of customer data at the individual level. Byutilizing the preference information hidden in the customer data,firms can satisfy the specific needs of their customers by providingpersonalized services and enhancing the quality of their custo-mers’ web experience.

Despite all the euphoria expressed about the value of onlinepersonalization, consumers’ reactions seem disappointing. Nunesand Kambil [7] reported that only 5.6% of the consumers wanted toreceive any personalized web service. According to another surveyby Jupiter Research [4], just 8% of consumers said that personaliza-tion increased their repeat visits to news or entertainment websites.This apparent gap between the high expectation of companies andthe lukewarm responses of their customers warrants an investiga-tion of why consumers are less than enthusiastic.

We used psychological reactance theory to capture animportant source of the users’ negative feeling about personaliza-tion services. We tried to provide a better understanding of thereasons for limited success of recommendation engines from theuser’s perspective.

§ The corresponding author was supported by Samsung Research Fund,

Sungkyunkwan University, 2006.

* Corresponding author. Tel.: +82 2 760 0459; fax: +82 2 760 0440.

E-mail addresses: sku00@skku.edu (G. Lee), leewj@skku.edu (W.J. Lee).

0378-7206/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2009.07.005

In general, people show a tendency to loss aversion, being moresensitive to losses than gains. Therefore, the influence of negativefactors could be stronger than one might presume, especially whenthe users have the freedom not to use the IS or have alternatives tochoose from. The empirical results of our work show this two-edged nature of personalization technology. The user’s expectationof personalized service induces the perception of usefulnessbecause choosing from many alternatives can be a nuisance to thedecision maker. At the same time, a personalization service doescause its users to experience psychological reactance, whichrestrains their use of personalized recommendations.

2. Theoretical background

2.1. Psychological reactance

Freedom in thought or behavior is one of the most importanthuman values. When freedom is threatened or restricted, peopleare generally motivated to restore it. Desiring to restore threatenedfreedom is termed psychological reactance. The degree of reactanceis determined by the intent to exercise freedom in future, theimportance of the freedom, the strength of the threat to freedom,and the likelihood of subsequent threats. Because psychologicalreactance is a motivational state, it is frequently measured by theeffect of threat on the behavior or attitude.

Much past research on the role of reactance can be found inrelated literature. In marketing literature, the pop-up advertise-ments in websites were shown to produce user’s psychologicalreactance [1]. Similarly, psychological reactance resulting fromunsolicited advice makes consumers ignore recommendations [2].Psychological reactance induced during sales promotion activities

Fig. 1. Research model.

Table 1Operational definition of constructs.

Construct Definition

Threat to future use The extent to which one thinks behavioral

choice will be threatened or restricted.

Expected personalization The extent to which <the IS> results

represent his or her personal needs.

PEU The degree to which a person believes that

using <the IS> easy to us

PU The degree to which a person believes that

using <the IS> would enhance performance.

INT The degree of a person’s intention to use

<the IS>.

G. Lee, W.J. Lee / Information & Management 46 (2009) 448–452 449

caused consumers to select a low value reward option [5]. In thedomain of relational marketing, the perceived threat to consumers’autonomy was shown to have a negative impact on the firms’customer retention programs [12].

2.2. Online recommendation

Online recommendation services are the common form ofpersonalization and they are viewed as a persuasive communica-tion during which companies try to achieve their business goals[9]. From a decision making perspective, recommendation serviceshelp individuals search for the correct products or service. Decisionmaking incurs the cost of thinking and other cognitive effort.Further, when customers are given incomplete information, theymay be unsatisfied with their decision. With the help ofrecommendation engines they can browse and evaluate theiroptions, deal with information overload, and enhance the qualityof their decision making. Technological advances in the recom-mendation engines have allowed e-commerce firms to graspbusiness opportunities by collecting customers’ preference infor-mation and managing customer relationships effectively. For areview of recommendation agents’ impact on customer decisionprocess and decision outcomes, see Xiao and Benbasat [13].

3. Research hypotheses and research model

Expected personalization, how well a recommendation systemwill predict and represent a customer’s personal needs, wasassumed to have a positive effect on the system’s perceivedusefulness. Accurate recommendations reduce information over-load and bring order to a multitude of choices. When the decisionmaker is provided with accurate recommendations, he or she willbe able to make better decisions and achieve shopping objectivesin less time. This leads to the hypothesis.

H1. Expected personalization will have a positive effect on per-ceived usefulness of the recommendation service.

Psychological reactance theory indicates that perception of athreat to behavioral freedom has negative influence on the attitudeor behavior. If individuals find recommendations to be restrictiveor feel heavily pressured to accept them, they consider them asbarriers to free choice or behavior. In such cases, they experience astate of reactance, negatively evaluating the recommendation,refusing to accept it in an attempt to restore their freedom tochoose and, even choosing the opposite of what was beingrecommended. Web users can also perceive web recommenda-tions as restricting of their free will, even when the recommenda-tions are relevant, accurate, and timely. When this occurs, theyenter a motivational state where they form a negative attitudetoward accepting the proposed recommendations. We thereforeposited that the perception of threat to freedom generated byrecommendations will be negatively related to the intention to usethe recommendation service.

H2. Perceived threat to freedom will decrease the intention to usethe recommendation service.

The research model of our study includes the typical relation-ships of perceived usefulness (PU), perceived ease of use (PEOU),and INT to use; see Fig. 1.

4. Research model

4.1. The measures

All the measurement items were validated in prior research.Items by Silvia [8] were modified to measure threat to future usage(TFU). Expected personalization (EP) was measured by the threeitems of Komiak and Benbasat [6]. For the three perceptionconstructs (PU, PEOU, and INT), we modified the items of Weiquanand Benbasat [11] so that they reflected our study context. Allquestionnaire items were organized in Likert-style type 7-pointscales (from 1: strongly disagree to 7: strongly agree). Theoperational definitions and questionnaire items are shown inTables 1 and 2, respectively.

4.2. The experiment and participants

A shopping website was developed using a web hosting servicethat provided online access to a commercial shopping mallpackage. This website represented a hypothetical shopping mallwhich targeted mass market, but sold only product categories thatwould be purchased by typical college students (clothes, cos-metics, and electronic devices). The website provided theinformation that one would normally find in Internet e-commercesites (the company’s name, phone, and address; its website privacypolicy; instructions regarding member registration, order place-ment, payment, and delivery).

Participants were self-selected, voluntarily, from our pool of600 students, 205 participated in the experiment. The sampleconsisted of 205 students, 117 male subjects and 88 femalesubjects. Their average age was 21.4 years (S.D. = 3.4) and 90.7% ofthem had experience in online purchasing. They were first asked tobrowse the website, following which each participant was asked toanswer the questionnaire, presented on a set of web pages.

Table 2Measured items.

Naming Construct/items

Threat to future use

TFU1 <This Service> will restrict my use of the website.

TFU2 <This Service> will bother me in using the website.

TFU3 <This Service> will interfere in my using the website.

Expected personalization

EP1 <This Service> will select according to my needs.

EP2 <This Service> will know what I want.

EP3 <This Service> will take my needs as its own preferences.

Perceived ease of use

PEOU1 <This Service> is easy to understand.

PEOU2 Learning to use <This Service> is easy.

PEOU3 <This Service> is easy to use.

Perceived usefulness

PU1 Using <This Service> enables me to find goods more quickly.

PU2 Using <This Service> enables me to find goods more easily.

PU3 Using <This Service> is helpful to purchase goods.

Intention to use

INT1 I will use <This Service> in purchasing goods.

INT2 I intend to use <This Service>.

INT3 I will frequently use <This Service>.

G. Lee, W.J. Lee / Information & Management 46 (2009) 448–452450

Participants were randomly assigned to one of the twotreatment groups representing high or low threat conditions.The 103 subjects who were assigned to the high threat group wereinformed by a web page before answering the questionnaire thatthe following three types of personal data would be collected andused as input to the recommendation services: registrationinformation, transaction data, and browsing history. The 102participants who were assigned to the low threat group wereinformed that only the registration information would be used forthe individualized recommendation. In our experiments wemanipulated the degree of threat using the amount of informationcollected by websites as surrogate.

5. Data analysis and results

SPSS and PLS (Partial Least Square) graph 3.0 were used in ourdata analysis. PLS has gained wide acceptance because of its abilityto model latent constructs under conditions of non-normality andsmall to medium sample sizes; also it is more relevant forexploratory studies than other SEM analyses.

Table 3Descriptive statistics and correlations among constructs in high threat condition.

# of items Mean S.D. Cronbach’s alpha com

TFU 3 3.6 0.24 0.95 0.97

EP 3 4.1 0.09 0.91 0.94

PU 3 4.5 0.04 0.94 0.96

PEOU 3 4.4 0.12 0.91 0.95

INT 3 4.0 0.20 0.92 0.95

Note. TFU: threat to future usage, EP: expected personalization, PU: perceived usefulnea Diagonal elements: the squared root of AVE.

Table 4Descriptive statistics and correlations among constructs in low threat condition.

# of items Mean S.D. Cronbach’s alpha Com

TFU 3 3.8 0.08 0.87 0.92

EP 3 3.8 0.10 0.91 0.94

PU 3 4.5 0.07 0.91 0.94

PEOU 3 4.2 0.06 0.94 0.96

INT 3 3.7 0.21 0.92 0.95

Note. TFU: threat to future usage, EP: expected personalization, PU: perceived usefulnea Diagonal elements: the squared root of AVE.

Manipulation was checked by the perceived threat of informa-tion usage, because users could feel threatened when they felt thattheir personal information would be more extensively scrutinized.The item ‘this website will use much information about me’ wasmeasured using a 7-point Likert-type scale (from 1: stronglydisagree to 7: strongly agree). The values of the mean were 4.3(S.D. = 1.27) and 3.7 (S.D. = 1.19) for the high and low threattreatment groups, respectively. The difference between the twogroups is statistically significant (t = 3.68, p < 0.01).

5.1. Measurement model

Validation checks were conducted for the two treatmentgroups. Descriptive statistics and correlations among the con-structs for the high and low threat groups are shown in Tables 3and 4, respectively. All constructs exceeded 0.7 for Cronbach’salpha (construct validity), 0.7 for composite reliabilities, and 0.5 forAverage Variances Extracted (convergent validity). Also, thesquared root of AVE for each construct was larger than all othercross-correlation values (discriminant validity). In addition, theloadings and cross-loadings of PLS, as presented in Appendices Aand B, also satisfy the conditions that indicators load more on theirtheoretical construct than on the other constructs and the loadingsof the indicators are above 0.7.

5.2. Structural model

The relationships among the constructs of the structural modelwere tested by the bootstrap technique with 2000 subsamples toobtain the t-values of the paths. The results of PLS analysis areshown in Fig. 2 with the path coefficients and their correspondingt-values in parentheses.

For the high threat treatment group, the R2 values for INT andPU were 71.1% and 39.6%, respectively. In addition to the R2, weperformed the Stone-Geisser Q2 test for the predictive relevancefor the endogenous constructs [10]. Q2 values for INT and PUrevealed a good level of model-fitness (0.59 for INT and 0.22 forPU). As shown in Fig. 2, expected personalization has a significantpositive relation with perceived usefulness (H1; t = 2.69, p < 0.01).The path from threat to future usage to INT is significant andnegative (H2; t = �2.68, p < 0.01). The results for the low threatgroup in Fig. 2 also indicate that a significant amount of the totalvariances in INT and PU is explained: the R2 values were 32% and36%, respectively. Their Q2 values also showed a reasonable model-

posite reliability TFU EP PU PEOU INT

0.93a

�0.10 0.94�0.08 0.51 0.92�0.16 0.46 0.55 0.96�0.25 0.59 0.82 0.52 0.92

ss, PEOU: perceived ease of use, INT: intention to use.

posite reliability TFU EP PU PEOU INT

0.92a

0.28 0.920.06 0.38 0.95�0.07 0.13 0.51 0.89

0.08 0.51 0.55 0.30 0.92

ss, PEOU: perceived ease of use, INT: intention to use.

Fig. 2. PLS results. ***p < 0.01. Note: The path coefficients and corresponding t-values (in parentheses) are presented in the order of high/low threat conditions.

G. Lee, W.J. Lee / Information & Management 46 (2009) 448–452 451

fitness (0.09 for INT and 0.16 for PU). Furthermore, thehypothesized positive effect of expected personalization on PUwas shown to be statistically significant (H1: t = 3.51, p < 0.01).However, the path from threat to future usage to INT was notsignificant (t = 0.37).

For both groups, the paths from PU to INT and from PEOU to PUare significantly positive. However, the relationship between PEOUand INT is not significant in either of the groups.

6. Discussions and implications

Recommendation systems reduce a large number of alternativesinto a few that are supposed to match the users’ preference. Theyalso allow individual consumers to determine the best alternativeswith the least effort, overcoming the information overload problem.On the other hand, they may make some users feel that theirfreedom to choose products is restricted and threatened. Our studyverified that online personalized recommendation services can haveboth effects. On the positive side, under both high and low threatconditions, the expected personalization of the recommendationservice enhances its PU (H1), which in turn increases the consumer’sintention to use the personalized service. On the negative side, thethreat to future usage decreases the consumer’s intention to use therecommendation services, but this was shown to be the case only inthe high threat group (H2). Table 5 summarizes the results of thehypothesis tests.

Although not formally hypothesized in our research model, webelieved that PEOU would increase the intention to use therecommendation services. According to our statistical analysis, itwas not a significant indicator of the intention to use.

The effect of threat tofutureusageonthe intention was significantonly in the high threat condition where a larger amount of diverseinformation must be collected. This moderating role of the users’perception of information collection suggests that alleviation of thisusers’ perception can subdue the negative effect of psychological

Table 5Summary of testing hypotheses.

Hypotheses Result

H1 Expected personalization!perceived usefulness (+)

Supported

High threat group Supported

Low threat group Supported

H2 Threat to future usage!intention to use (�)

Partially supported

High threat group Supported

Low threat group Not supported

reactance. Similarly, Hui et al. [3] reported, in their study of privacy inthe use of websites, that the amount of information requested had anegative influence on information disclosure.

Credibility is one of the major characteristics of the commu-nicators that have significant influence on persuasive attempts,which generally requires time and effort to establish. In this regard,companies with established credibility have an advantage, but aresubject to a greater risk when wrong contents are presented.Development of credibility with the users and assurance of relevanceand accuracy of recommendations will be critical to their success.

The practical implications of this study are that firms shouldmonitor the effect of personalized offers. Although personalizedoffers are often convenient and accurate, they can also generatefear of a threat to a customer’s free behavior. Firms need to be farmore careful when using the web to interact with individualconsumers because the users’ psychological reactions are difficultto notice through online interactions. Second, due to the lack ofsocial cues and the reduced social identity in online environments,customers could become more aggressive when they think theyare threatened than they would in the offline world. Firms ought toprovide personalized services with care and pay close attention tocustomer complaints and negative word-of-mouth. Finally,managing credibility is closely related to the outcomes ofrecommendation services. Our results suggest that prematureinteraction with customers may cause negative outcomes. Firms,especially at the early stage of customer relationship development,should be cautious about collecting data from and interacting withtheir customers.

7. Conclusion

The main purpose of our study was to show the existence ofpsychological reactance in the context of personalized recom-mendation services. Using a hypothetical shopping website, weconducted an experimental study and verified the two-edge natureof web recommendation services: while personalization improvesusefulness and the intention to use the personalized recommen-dation service, it can also decrease the intention when the usersperceive too much of their information is collected and used.

Our findings must be understood in the light of the samplingmethod employed. Although students were randomly assigned toone of the two conditions, they were voluntary participants. Thesample data was collected from college students based on theirexperience in scanning a hypothetical shopping website. Collegestudents in general tend to have extensive experience in using theInternet and their shopping behaviors can be different from thoseof average online shoppers due to their socioeconomic status. Thus,

G. Lee, W.J. Lee / Information & Management 46 (2009) 448–452452

our sample may be limited in the generality of the behavior ofInternet shoppers. In addition, we manipulated the amount ofpersonal information requested in an attempt to explore its effecton threat to future usage and expected personalization. However,the role of the expected and experienced personalization inevaluation and adoption of the information service may bedifferent. Therefore, our findings may be more applicable to theinitial stage of a relationship between firms and consumers.

Appendix A. PLS loadings and cross-loadings in high threatcondition

TFU EP PU PEOU INT

TFU1 0.94 �0.10 �0.08 �0.15 �0.25

TFU2 0.97 �0.11 �0.08 �0.16 �0.25

TFU3 0.96 �0.06 �0.08 �0.13 �0.21

EP1 �0.13 0.94 0.43 0.36 0.52

EP2 �0.06 0.93 0.45 0.40 0.49

EP3 �0.06 0.90 0.55 0.51 0.61

PU2 �0.04 0.50 0.95 0.54 0.77

PU3 �0.13 0.50 0.93 0.50 0.79

PU1 �0.06 0.45 0.96 0.53 0.76

PEOU1 �0.09 0.43 0.53 0.92 0.50

PEOU2 �0.17 0.45 0.52 0.96 0.50

PEOU3 �0.17 0.37 0.49 0.89 0.43

INT1 �0.17 0.51 0.80 0.45 0.90INT2 �0.28 0.57 0.75 0.54 0.95INT3 �0.24 0.55 0.74 0.46 0.94

Note. TFU: threat to future usage, EP: expected personalization, PU: perceived

usefulness, PEOU: perceived ease of use, INT: intention to use.

Appendix B. PLS loadings and cross-loadings in low threatcondition

ETFU EP PU PEOU INT

ETFU1 0.87 0.28 0.12 0.02 0.10

ETFU2 0.94 0.23 0.02 �0.06 0.07

ETFU3 0.88 0.23 0.02 �0.15 0.04

EP1 0.31 0.95 0.35 0.10 0.46

EP2 0.31 0.95 0.32 0.08 0.42

EP3 0.13 0.86 0.39 0.17 0.53

PU2 0.05 0.30 0.96 0.50 0.48

PU3 0.10 0.39 0.85 0.38 0.54

PU1 0.02 0.36 0.95 0.50 0.52

PEOU1 �0.10 0.13 0.49 0.95 0.32

PEOU2 �0.02 0.11 0.47 0.95 0.25

PEOU3 �0.08 0.20 0.48 0.95 0.29

INT1 0.08 0.54 0.59 0.32 0.91INT2 0.01 0.43 0.52 0.34 0.94INT3 0.13 0.46 0.42 0.17 0.93

Note. TFU: threat to future usage, EP: expected personalization, PU: perceived

usefulness, PEOU: perceived ease of use, INT: intention to use.

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Lee, Gyudong received PhD in Business Administration

(Management Information Systems) from Sungkyunk-

wan University, South Korea. He has work experience

for several years in a global electronics company in

Seoul. His research interests include psychology of IS

users, social impacts of technology, personalization

technology, and information providing.

Won Jun Lee is a professor of MIS at SKKU Business

School. Professor Lee received a bachelor’s degree in

Business from SKKU, MBA from University of Michigan-

Ann Arbor, and PhD from Indiana University-Bloo-

mington. His publications have appeared in journals

such as Decision Sciences, Production and Operations

Management, DSS, and Asia Pacific Journal of Informa-

tion Systems. His current research interests include

issues related to web personalization, service interac-

tions and system design, reverse auction and procure-

ment innovations in B2B E-commerce, among others.