Consumer Knowledge of the Web

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Calibration of consumer knowledge of the web

Kishore Gopalakrishna Pillai a,⁎, Charles Hofacker b,⁎

a Department of Marketing, Leeds University Business School, University of Leeds, Leeds, LS2 9JT, United Kingdom b Department of Marketing, College of Business, Florida State University, Tallahassee, FL 32306, USA

Abstract

Calibration of consumer knowledge of the web refers to the correspondence between accuracy and confidence in knowledge of the web. Being

well-calibrated means that a person is realistic in his or her assessment of the level of knowledge that he or she possesses. This study finds that

involvement leads to better calibration and that calibration is higher for procedural knowledge and common knowledge, as compared to

declarative knowledge and specialized knowledge. Neither usage, nor experience, has any effect on calibration of knowledge of the web. No

difference in calibration is observed between genders. But, in agreement with previous findings, this study also finds that males are more confident

in their knowledge of the web. The results point out that calibration could be more a function of knowledge-specific factors and less that of

individual-specific factors. The study also identifies flow and frustration with the web as consequences of calibration of knowledge of the web and

draws the attention of future researchers to examine these aspects.

© 2007 Elsevier B.V. All rights reserved.

Keywords: Calibration; Knowledge; Web; Involvement; Knowledge type

1. Introduction

Consumer knowledge has been a topic of considerable

research interest in marketing for the past three decades (Alba &

Hutchinson, 1987; Bettman & Park, 1980; Brucks, 1985;

Mitchell & Dacin, 1996; Roy & Cornwell, 2004). The

importance of consumer knowledge can be located in the fact

that knowledge is central to understanding consumer behaviors

such as information search and information processing.

Researchers have examined the antecedents and consequences

of prior knowledge of consumers (Alba, 1983; Chase & Simon,

1973) and the different types of consumer knowledge such as

product class knowledge, price knowledge, knowledge of theWorld Wide Web, etc. (Brucks, 1985; Estelami, Lehmann, &

Holden, 2001; Page & Uncles, 2004). Researchers have also

attempted to distinguish between objective knowledge, which

refers to the absolute knowledge possessed by the consumer,

and subjective knowledge, which refers to consumers' percep-tion of their own knowledge (Raju, Lonial, & Mangold, 1995).

An aspect of knowledge that has been omitted from the

discourse in consumer research is knowledge calibration.

Knowledge calibration refers to the correspondence between

accuracy and confidence in knowledge. Notions of correct and

incorrect knowledge tap into only the first dimension, i.e.,

accuracy. A person can be well calibrated even when possessing

inaccurate knowledge. Being well-calibrated means that a

person is realistic in his or her assessment of the level of

knowledge that he or she possesses. This, in turn, is likely to

facilitate appropriate actions such as information search or

optimal decision making. Hence, examination of this construct is likely to unravel processes that have not been brought to light

by the research on consumer knowledge. The latter construct

has attracted considerable research, while research on calibra-

tion of consumer knowledge is relatively new. Alba and

Hutchinson (2000) highlighted the importance of this construct

and its implications in consumer research and called for

research in this area. This paper responds to the call by

examining the calibration of consumer knowledge of the web, a

topic that is of considerable relevance, given the increasing

importance of the web for consumers. The paper proposes a

model of calibration of knowledge of the web and examines the

Intern. J. of Research in Marketing 24 (2007) 254 – 267

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⁎ Corresponding authors. K.G. Pillai is to be contacted at tel.: +44 113

3432636. C. Hofacker, tel.: +1 850 6447864.

E-mail addresses: [email protected] (K.G. Pillai),

[email protected] (C. Hofacker).

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antecedent roles of involvement with the web, experience,

usage, knowledge type, and gender on calibration of consumer

knowledge of the web.

The paper's contribution is threefold. One, it seeks to

empirically examine knowledge calibration in the consumer

domain. Second, in doing so, it adds a new dimension to the

research on knowledge and usage of the web. Third, it contrib-utes to the larger stream of research on knowledge calibration

through the examination of knowledge type as an antecedent of

calibration.

We start by introducing the concept of knowledge calibra-

tion, followed by a brief review of the literature on knowledge

calibration. The hypotheses for the study are presented after-

wards. Subsequently, the method and the results of the study

are presented. The results indicate the antecedent effects of

involvement and knowledge type in enhancing calibration.

Broadly, the results imply that calibration of knowledge of

the web in particular and calibration of knowledge in general

could be determined by knowledge variables such as types of knowledge. The study discounts the role of individual specific

factors, other than involvement, as antecedents of knowledge

calibration.

2. Literature review and conceptual framework

2.1. Knowledge calibration

As defined earlier, calibration of knowledge refers to the

correspondence between accuracy and confidence in knowl-

edge. High accuracy and high confidence together lead to high

calibration. Similarly, low accuracy and low confidence lead

to high calibration. Lack of correspondence between the twolead to low calibration; thus, low accuracy along with high

confidence (overconfidence), and high accuracy along with low

confidence (underconfidence) are instances of poor calibration,

termed as miscalibration. The following diagram, adapted from

Goldsmith and Pillai (2006), captures the above distinctions in a

2×2 matrix.

Several scientists, including statisticians, meteorologists, and

psychologists, have sought to address the measurement and

explanation of judgments of confidence and their relation to

accuracy (e.g., Gigerenzer, Hoffrage, & Kleinbolting, 1991;

Harvey, 1997; Yates, 1990). A recurring finding from many of

these studies is that people are systematically overconfident about the accuracy of their knowledge and judgment (Pillai

& Goldsmith, 2006). Overconfidence can be defined as the

overestimation of the probability of an event. Thus, in a knowl-

edge assessment task, if the subject estimates that 70% of his/

her answers are correct, but only 50% are actually so, the

subject is overconfident.

Reviewing research on calibration, Spence (1996) states that

much research indicates that individuals are overconfident,

though certain professions have displayed high calibration in

judgments. For example, weather forecasters and odds-makers

(those who calculate and set betting odds based on the

prediction of the result of a contest, such as a horserace or an

election) have been shown to be well calibrated (Hoerl & Fallin,

1974; Murphy & Winkler, 1977), whereas doctors' diagnoses

have not (Christen-Szalanski & Busyhead, 1981; Oskamp,

1962). Explanations for this disparate finding include (a) the

repetitiousness of forecasting with immediate, unambiguous

feedback which enables validation and correction, facilitating

calibration (Lichtenstein, Fischoff, & Philips, 1982), (b) dif-

ferent payoff matrices or loss functions for forecasters anddoctors, whereby doctors perceive the penalty for false negative

as more devastating and hence state their opinions with greater

conviction (Keren, 1991; O'Connor, 1989). The several studies

across various domains point to the fact that high calibration is

rarely achieved and moderate levels that include some degree of

systematic bias, usually overconfidence, are the norm (Pillai &

Goldsmith, 2006).

Noteworthy among other domains that have examined the

issue of the confidence–accuracy relationship is forensic

psychology. In criminal investigations, witnesses often attempt

to identify a culprit and give a statement about the certainty

of the identification, expressed as a confidence judgment. It has been demonstrated that a confident witness is regarded as

a credible witness, with a great impact on the outcome of a

trial (Olsson, 2000). Yet many experimental studies of witness

identification have shown that the confidence–accuracy

relationship is not reliably different from zero (Loftus, 1974;

Narby, Cutler, & Penrod, 1996). Consequently, the issue of

calibration or “realism in confidence assessments” has attracted

considerable scholarly attention in this field.

As mentioned earlier, this study is an attempt to apply the

construct of knowledge calibration to consumer research. In this

study, we examine the calibration of consumers' knowledge of

the web. The latter construct is dealt with in the following

section.

2.2. Consumers' knowledge of the web

As noted earlier, examination of consumer knowledge has

a rich tradition in the marketing literature. Specifically, the

influence of product knowledge on consumers' information

processing has been of great interest to researchers (e.g., Alba &

Hutchinson, 1987; Bettman & Park, 1980; Brucks, 1985).

Product knowledge has been found to be influential in con-

sumers' product evaluations (Blair & Innis, 1996), precision of

price estimates (Biswas & Sherrell, 1993), and processing of

advertising messages (Maheswaran & Sternthal, 1990). Re-search on consumers' knowledge of the web is relatively recent,

as the web itself is relatively recent.

During the past decade, pre-existing firms have sought to

realize benefits for themselves and for their customers online by

providing alternative purchasing channels, inexpensive self-

service options, complementary services, longer product lines,

recommendation agents and interactive advertisements. Other

firms have sprung up to provide new services like online auc-

tions, interactive driving directions, video upload and exchange

and e-mail. While we tend to think of virtual channels as being

less expensive than physical channels, these new interfaces

have their own costs in terms of the consumer knowledge that

is required. Not all consumers are ready for this onslaught of

255 K.G. Pillai, C. Hofacker / Intern. J. of Research in Marketing 24 (2007) 254 – 267



technology (Parasuraman, 2000). The examination of consumer

knowledge of the web and the calibration of such knowledge

becomes relevant in this context.

Knowledge has been defined as the body of facts and prin-

ciples (i.e., information and understanding) accumulated by

mankind (i.e., stored in memory) about a domain (Delbridge

& Bernard, 1998). It follows that consumers' knowledge of the web refers to the information and understanding of the

web stored in the consumers' memory. Examples of consumer

knowledge of the web include information stored in a consum-

er's memory about how to use a search engine, what cookies

are, or the benefit of ‘what's new' links.

Researchers in the past have used proxy measures to infer

consumer knowledge of the web, such as number of hours

spent online or months and years that the medium has been

used (Diaz, Hammond, & McWilliam, 1997). Self-perception

judgments of Web skill have also been used to measure

consumer skill on the web ( Novak, Hoffman, & Yung, 2000).

Recently, Page and Uncles (2004) proposed a scale to measureconsumers' web knowledge content. Because the vast array

of knowledge that consumers could possess about the web

imposes limitations on valid measurement, these authors

restricted their focus to knowledge arising from machine

interactivity of the web. While person-to-person interaction

occurs frequently online, we also restrict our study to machine

interactivity (MI), defined as “the extent to which users can

participate in modifying the form and content of a mediated

environment in real time” (Steuer, 1992, p. 84). The focus on

machine interactivity is justified by the fact that e-services,

online retailing and other self-service technologies, as well as

web advertising, all involve interaction between the consumer

and technology, rather than human-to-human interaction.As detailed in the Measures section, we use the scale

proposed by Page and Uncles (2004) to measure consumers'

knowledge of the web. In the following paragraphs, we discuss

each of the antecedent variables of the study and offer propo-

sitions. A model of antecedents of calibration of knowledge is

proposed. While the model is not comprehensive, many of the

more important variables that affect calibration are represented.

This is followed by a test of the model.

It should be noted that the hypotheses discussed in the

following sections are derived from the interpretation of knowl-

edge calibration as the realism of perceptions on knowledge.

The four quadrants presented earlier (see Fig. 1) are not dealt with separately, for two reasons. The first is that accuracy (at an

aggregate, between subjects, level) and confidence are contin-

uous variables, and the dichotomous division was intended

for conceptual clarification. More importantly, respondents in

the study exhibited high levels of accuracy, rendering the

distinction between the two types of calibration redundant.

2.3. Antecedents of calibration of knowledge of the web

We discuss two different types of antecedent variables:

(a) individual-specific factors such as involvement, experience,

usage, and gender, and (b) knowledge-specific factors which

includes the distinctions between declarative and procedural

knowledge on the one hand and common and specializedknowledge on the other. The selection of the antecedents was

determined, in part, by the research focus on these constructs.

For example, involvement is a variable that has attracted con-

siderable attention as a determinant of consumer behavior. As

discussed below, experience, usage, and gender have also been

shown to be of relevance to the stream of research on knowl-

edge. The antecedent role of knowledge type is one of the key

contributions of this study, and we chose those distinctions

that are of considerable import to the study of web knowledge.

Fig. 2 shows the model of the antecedents of calibration of

consumer knowledge of the web. We start with the discussion of

individual-specific factors.

2.3.1. Involvement

Involvement has been defined in terms of personal rele-

vance of the product or the advertisement to the subject.

Zaichkowski (1985) defines involvement as a person's per-

ceived relevance of the object based on inherent needs, values,

and interests. Involvement is generally considered to be a

function of (a) individual characteristics such as interests,

values, and goals, (b) situational factors such as the purchase

occasion or perceived risk, and (c) characteristics of the object

or stimulus (Laurent & Kapferer, 1985). Outcomes associated

with high involvement include more time and effort spent in

search-related activities (Bloch, Sherrel, & Ridgway, 1986),more extensive decision making, greater perceived differences

in product attributes, and a greater likelihood of establishing

brand preferences (Zaichkowski, 1985).

The effects of involvement on the processes mediating the

effects of a persuasive communication about an issue have been

extensively investigated (Maheswaran & Sternthal, 1990;

Miniard, Bhatla, Lord, Dickson, & Unnava, 1991; Okechuku,

1992; Verlegh, Steenkamp, & Meulenberg, 2005). A robust

finding of this literature is that uninvolved message recipients,

compared to those who are highly involved, are characterized

by (a) less attention to and less cognitive elaboration of attribute

information, and (b) more reliance on peripheral cues available

in the message (Celsi & Olson, 1988; Batra & Ray, 1985; Petty,

Fig. 1. Accuracy, confidence, and calibration matrix.




Cacioppo, & Schumann, 1983). Starting with Ray et al. (1973),large numbers of studies have reported that consumers involved

in a situation or product are more active processors of cognitive

information about it. Celsi and Olson (1988) tested several

hypotheses concerning the effects of felt involvement on the

amount of attention and comprehension effort, the focus of

attention and comprehension processes, and the extent of

cognitive elaboration during comprehension. These authors

found that involved consumers attend to and comprehend more

information about shopping situations and products and

produce more elaborate meanings and inferences about them

(Celsi & Olson, 1988).

Research in memory-based product judgments has shownthat individuals are cognitive misers who attempt to minimize

search effort (Wyer & Srull, 1986), and involvement during a

memory-based judgment influences this processing objective

and affects search intensity (Park & Hastak, 1994). Compared to

uninvolved consumers, involved consumers are more motivated

to form a relatively accurate judgment and hence they search

more intensely for judgment-relevant information.

The above discussion highlights the role of involvement in

enhancing effort by consumers. Involvement can also be

expected to increase familiarity with the domain, and practice

in dealing with specific issues concerning the domain. Effort

and practice are cues associated with performance, which refers

to knowledge accuracy and correct assessment of confidence in

a testing task where accuracy and confidence are measured, asin the case of this study. More involved consumers are likely to

collect more information about the product, and will be more

motivated to form realistic and correct notions regarding their

knowledge of the domain. Hence,

H1. Higher the involvement with the world wide web, higher

the calibration of knowledge of the web.

2.3.2. Cumulative online experience

Consumers' ability to understand and represent web-based

information is structured and constrained according to their

existing domain experience (Moreau, Lehmann, & Markman,2001), which is driven by their tenure online, or duration of time

they have used the web. Consumer experience may include both

direct experiences such as information searching, evaluation,

purchase, and consumption of interactive products, and indirect

experiences such as advertising exposure and the observation of

others' consumption (Alba & Hutchinson, 1987).

Considerable evidence suggests that cumulative consumer

experience increases knowledge (e.g., Park, Mothersbaugh, &

Feick, 1994), and thus some researchers measured experience as

a proxy of consumer knowledge (e.g., Bettman & Park, 1980).

Alba and Hutchinson (1987) proposed that consumer knowl-

edge consists of two dimensions — familiarity and expertise.

The existing literature indicates that domain experience is

Fig. 2. Model of antecedents of calibration of knowledge of the web. Dashed arrows indicate that no formal hypotheses are developed, but the relationships are tested as

important empirical issues.




closely related to both familiarity and expertise. Consumer

experience is considered as a direct antecedent to familiarity

(Alba & Hutchinson, 1987), and research has shown that

experience is related to expertise as well (Mitchell & Dacin,

1996). We argue that the effect of experience on familiarity

will be stronger than the effect on expertise, leading to the

feeling of knowing. Of course expertise should increase as afunction of cumulative time logged online, but not nearly as

fast as their sense of familiarity. Research on the concept of

confidence that has examined practice – a variable closely

related to experience – supports this argument. This research

suggests that practice produces dissociation between confidence

and performance because of elevations in confidence that are

unaccompanied by corresponding increases in accuracy (Paese

& Sniezek, 1991). Similar themes were echoed by Fischoff and

Slovic (1980), who argued that even minimal familiarity with

a task or tentative rules of performance in the task can produce

surprising amounts of conviction, and by Heath and Tversky

(1991), who noted that familiarity and knowledge are accompanied by feelings of competence. In the information systems

field, research has recorded the role of experience in increasing

users' confidence in their ability to master and use computers

supporting their task (DeLone, 1988). Based on these, we

reason that experience will be positively related to confidence

in knowledge, but will not be related to calibration, since it

produces a dissociation between confidence and accuracy.

H2. Higher the experience, higher the level of confidence in

knowledge.

2.3.3. Weekly usage

Here, usage refers to the extent of current weekly use of theWorld Wide Web. The dominant paradigm that informs us of

adoption of the web is the technology acceptance model (TAM).

Recent research has employed TAM to explain continued usage

of the web (Kim & Malhotra, 2005). In the context of web

usage, TAM suggests that there exists a direct and positive

effect between attitude towards web usage, usage intention, and

actual usage. Perceived usefulness and ease of use determine the

attitudes toward using the web. In turn, usage intentions are

determined by these attitudes and perceived usefulness. Finally,

usage intentions lead to usage. These relationships have found

strong empirical support (Davis, 1993; Venkatesh & Davis,

2000). However, a significant body of theoretical evidenceunderscores the importance of intrinsic motivators in web

usage. Researchers have become increasingly aware of the

relevance of the non-extrinsic motives of use such as intrin-

sically enjoyable experiences (i.e., flow) in understanding

attitudes and behaviors. Flow is defined as the holistic sensation

that people feel when they act with total involvement

(Csikszentmihalyi, 1977). Flow is a positive, highly enjoyable

state of consciousness that occurs when our perceived skills

match the perceived challenges we are undertaking. When this

occurs, an individual derives intrinsic enjoyment from the ac-

tivity and tends to continue with it. If the task is too easy, the

person becomes bored. If the work demands skills beyond the

capabilities of the individual, anxiety is created. When goals are

clear, and the individual's abilities are up to the challenge, then

he or she becomes involved in the activity and is intrinsically

motivated.

Thus, it can be seen that TAM posits a goal-directed,

extrinsically motivated route to usage of the web, while the flow

theory argues the case of an intrinsically motivated path. We

argue that the extrinsically motivated route is likely to operate ina manner similar to experience. That is, higher usage will lead

to people developing a feeling of knowing, more than what

is warranted by an actual increase in knowledge. While the

accuracy increases, calibration does not. But for the intrinsically

motivated people, higher usage will lead to, as well as follow

from, higher involvement, and this is likely to lead to more

realistic assessments of their knowledge of the web; i.e., better

calibration. Examining the disparate effects of different types of

motivation is beyond the scope of this research. Previous

research has noted that some people are motivated by extrinsic

factors while others by intrinsic factors (Darley, 1999), and

often both coexist in individuals (Roberts, Hann, & Slaughter,2006). Given the null effect from extrinsically motivated indi-

viduals and the positive effect from intrinsically motivated

individuals, we expect a weak positive effect of usage on

calibration of consumer knowledge.

H3. Higher the usage, higher the calibration of knowledge of

the web.

2.3.4. Gender

Research has recorded the role of gender on confidence.

Males have been found to have higher confidence than females

in various domains of knowledge (Estes & Hosseini, 1988;

Goldsmith & Goldsmith, 1997; Kalaian & Freeman, 1994).Estes and Hosseini (1988) examined confidence in investment

decisions in the stock market and found that men are more

confident than women. Sutton's (1987) research on the illusion

of control in insurance also corroborates these findings. Sutton

(1987) found that women are better prospects than men for auto

insurance because of a more realistic perception of their risk of

auto accidents. We generalize from these findings and expect

males to have higher confidence in their knowledge of the web.

Also, previous research has noted a gender gap in computer

literacy in which males are more literate than females (Chen,

1986; Sachs & Bellisimo, 1993). It has also been found that, in

general, men tend to have more favorable attitudes toward IT(Schumacher & Morahan-Martin, 2001; Simon, 2001). Some

research has also noted that women tend to be less skilled in IT

(Harrison & Rainer, 1992). In the light of this substantial body

of finding, we expect males to have higher accuracy than

females in their knowledge of web. Higher accuracy along with

higher confidence results in no difference in calibration between

genders. Hence,

H4. Males will have higher confidence in their knowledge of

the web, compared to females.

Following the examination of individual-specific factors, we

examine the antecedent effects of knowledge-specific factors

(refer Fig. 2).




2.3.5. Knowledge type — procedural versus declarative

Based on the literature from cognitive sciences and mar-

keting, a distinction can be drawn between procedural and

declarative knowledge. Declarative knowledge is defined as the

‘factual information that is somewhat static in nature which is

usually describable' (Best, 1989, p. 7); for instance, information

about the attributes, terminology, evaluative criteria, facts, andusage situations of the domain of interest (Brucks, 1986).

Procedural knowledge, by contrast, refers to the dynamic infor-

mation underlying skillful actions (Best, 1989; p 7); that is, the

knowledge of rules or procedures for taking action believed to

be stored and organized into production systems (Brucks, 1986).

Anderson (1983) discussed the impact of these different

types of knowledge content on behavior, suggesting that

although both types can guide behavior, procedural knowledge

content is considered to be of greater influence on actual

behavior. For example, to send an e-mail, knowing how to send

an e-mail (procedural) will have more influence on the final

behavior than knowing what an e-mail is (declarative).Research on procedural and declarative information in soft-

ware manuals have found that users prefer procedural to declar-

ative information (Ummelen, 1997). Such preference for

procedural information is likely to result in greater familiarity

with procedural information and better realism in perceptions

regarding procedural knowledge, compared to declarative

knowledge.

Procedural knowledge has been recognized as a link between

experience and performance (e.g., Bonner & Walker, 1994).

Anderson's (1983) ACT⁎ theory (ACT is not an acronym,

though it has been translated as Adaptive Control of Thought),

a general theory of cognition that focuses on memory processes,

offers a view that practice of a cognitive task results in pro-cedural knowledge, which enhances performance. In the con-

text of knowledge of the web, practice will also help verify the

correctness or incorrectness of the knowledge, since incorrect

knowledge will hinder effective navigation of the web. That is,

web users operate in an environment where immediate and real-

time feedback on their procedural knowledge is available. Such

a feedback is not available in the case of declarative knowledge

of the web. Research has recorded the role of feedback in

improving calibration (e.g., Lichtenstein et al., 1982). Higher

calibration has been observed in certain professions, such as

weather forecasting, where immediate feedback is available.

Given the availability of feedback for procedural knowledge of the web, we posit that web users will be better calibrated in their

procedural knowledge compared to declarative knowledge.

Therefore,

H5. Calibration will be higher for procedural knowledge of the

web, compared to declarative knowledge.

2.3.6. Knowledge type — common versus specialized

Knowledge literature also distinguishes between experts

and novices, and the familiar and the unfamiliar (Alba &

Hutchinson, 1987; Page & Uncles, 2004). This distinction leads

to two types of knowledge — common and specialized.

Common knowledge can be defined as general and/or publicly

known information of the domain of interest required to perform

general and common domain related tasks successfully. Con-

versely, specialized knowledge is defined as skilled and/or

extraordinary information about facts, terms, attributes, and

dynamic information underlying skillful actions about a domain

required to perform skilled domain related tasks successfully

(Page & Uncles, 2004). The definition of common knowledge isa non-technical one and should be distinguished from the

technical definition of the term in economics and game theory,

where it denotes a piece of information that is available to

agents A and B, and both A and B know that the other has the

information, and both A and B know that the other knows that

the other has the information, and so on ad infinitum (Lewis,

1969).

Given the widespread usage of the web, consumers are likely

to possess a high degree of common knowledge. They are

also likely to have higher familiarity with common knowledge,

enabling realistic assessments of confidence. Specialized knowl-

edge questions are more technical in nature and consumersare likely to be less familiar with these. Also, given the wide-

spread use of computers and the web, consumers could have

been exposed to some of these technical aspects during formal

training or in their own usage of the web. We posit that the

modicum of familiarity arising from such exposure leads to a

perception that they know these topics, even when they do not.

Research on the feeling of knowing has recorded how such an

intuition often produces dissociation between accuracy and

confidence (Koriat, 1998; Nelson et al., 1986). Such dissocia-

tions will lead to a miscalibration of knowledge. It is argued that

this phenomenon is more likely in the case of specialized

knowledge than in the case of common knowledge, leading to

higher calibration in the latter, compared to the former. Hence,

H6. Calibration will be higher for common knowledge,

compared to specialized knowledge.

3. Method

3.1. Sample

We used two samples to test the hypotheses. Sample 1

comprised 151 students enrolled in undergraduate business

courses in a large university in the southeastern United States,

who participated in the study for course credit. Sample 2comprised 153 adults in a British city. The adults included

(a) community members (40%) who were contacted in libraries,

parks, residential apartments, and retail stores, (b) nonacademic

staff members (25%) and faculty members (5%) in a leading

British university and (c) postgraduate and undergraduate stu-

dents in the same university (30%). All respondents were

contacted individually and were paid 2 pounds for participation.

The respondents filled out a structured questionnaire that

measured consumer knowledge, involvement with the web,

experience, and usage. In order to control for order effects,

roughly half of the sample answered questionnaires which

had procedural knowledge questions first, followed by declar-

ative knowledge questions, while the other half answered




questionnaires which had declarative knowledge questions first,

followed by procedural knowledge questions. Common and

specialized knowledge questions were distributed within these.

We conducted t -tests to assess differences between the

samples. No difference was observed in mean calibration

( p = .58) while a difference was observed in confidence

( p =.00). The latter could be attributed to the difference in age,in that the first sample comprised only undergraduate students,

whereas the second comprised a distribution of adults of varying

age groups. A simple regression model in the second sample

showed a significant effect (beta=− .21; p =.01) of age on

confidence. Moreover, regression models between confidence

and the independent variables showed comparable beta param-

eter estimates and significances across the two samples. Con-

sequently, we decided to combine the two samples, with sample

included as a covariate. Results are reported for the combined

sample as well as the separate samples. Given the similarity

between American and British cultures, and the technical nature

of the questions, cross cultural issues are discounted.Information on gender was missing from 28 questionnaires

in the first sample and hence the sample size for multivariate

tests was correspondingly lower. In addition, in some cases web

involvement could not be computed due to missing data, which

also resulted in a few dropouts. When calibration was computed

for subscales, there were a few instances where correlation

could not be computed because one variable was constant,

which resulted in slight reductions in sample sizes for the

specific analyses using the subscales.

3.2. Measures

3.2.1. Consumer knowledge calibration

As mentioned earlier, consumer knowledge of the web was

measured using a 38-item scale developed by Page and Uncles

(2004). Based on qualitative and quantitative analyses, these

authors developed four scales that measure common declarative

knowledge, specialized declarative knowledge, common pro-

cedural knowledge, and specialized procedural knowledge. The

number of items for each of the aforementioned scales was 10,

11, 6, and 11. Most of the items employed a true/false format to

elicit responses, while a few had multiple choice responses.

A combined scale, comprising all 38 items (21 declarative

knowledge items and 17 procedural knowledge items) was used

to measure consumer knowledge of the web in this study. Thefew multiple choice items (3 items) were converted to dichoto-

mous (true/false) format to ensure correspondence. Respon-

dents indicated their choice of answer for each item, and then

indicated their confidence that the answer is correct, on a scale

ranging from 50% to 100%. We chose a restricted scale ranging

from 50% to 100%, as against a scale ranging from 0% to 100%,

because of the dichotomous response format (true or false)

which provides a 50% chance of success even in a random

choice.

Knowledge calibration was measured through point biserial

correlation between accuracy and confidence (e.g., Lin, Moore,

& Zabrucky, 2001). Calibration was measured at the within

subjects level. Correlation between accuracy (either 0 or 1)

and confidence rating for each item provided the measure

of calibration at the respondent level. In keeping with the

definition of calibration as ranging from 0 (nil calibration) to 1

(perfect calibration), we restricted the lower bound of the

correlation measure to zero. That is, negative correlations

observed in a few cases (which were not statistically significant)

were restricted to zero.

3.2.2. Involvement

Involvement was measured using the ten-item scale pro-

posed by Zaichkowsky (1994). Respondents were asked to

indicate their perceptions of the web on a 7-point bipolar scale

anchored by adjectives such as important –unimportant, fasci-

nating–mundane, relevant –irrelevant, etc. Involvement was

measured as the mean of the ten items, after recoding the

negatively framed items.

3.2.3. Experience

Experience was measured by a single item that askedrespondents to specify the number of years they have been using

World Wide Web.

3.2.4. Usage

Usage was measured by a single item that asked respondents

to specify the number of hours per week they spend browsing or

using the web. It should be noted here that online researchers

have employed single-item measures to measure experience/

usage (e.g., Rodgers, Negash, & Suk, 2005). Also, the use of

self-reported usage measures have been validated by research

that suggests that self-reported usage measures correlate well

with actual usage measures (Venkatesh & Davis, 2000).

4. Results

4.1. Scale reliability

The ten-item involvement scale had acceptable reliability

(alpha=.875). The reliability of the knowledge of the web scale,

computed using KR 20, fell below the threshold of .7 (overall

.52; specialized=.83; common=.13; procedural=.35; declara-

tive=.39). This is not deemed to be a cause for concern due to

the following reasons: (a) the construct of knowledge of the web

is necessarily multidimensional and a scale that attempts to tap

into the various dimensions will have to compromise onreliability indices and (b) the focal construct is calibration of

knowledge of the web and not knowledge of the web per se. As

long as the scale has face and content validity, it serves to be an

acceptable instrument to measure calibration. It should also be

noted that previous research on consumer knowledge has used a

set of items chosen judgmentally by researchers, for which

reliabilities have not been reported (e.g., Capraro, Broniarczyk,

& Srivastava, 2003).

4.2. Descriptive statistics

Table 1 shows the descriptive statistics. Calibration ranged

from 0 to .72, with a mean of .34. Mean level of involvement




with the web was 6.15, indicating a high level of involvement

with the web. Experience with the web ranged from 1 to

15 years, with a mean of 8.3 years. Usage ranged from 1 to 40 h

per week, with a mean of 11.26 h. Accuracy ranged from 55% to

97%, with an average of 80%. Hence, the sample exhibited high

levels of accuracy. This finding leads to the discounting of

instances of calibration arising from low accuracy and low

confidence.

4.3. Tests of hypotheses

We employed a general linear model procedure to test the

hypotheses, with calibration and confidence as the dependent

variables and web-involvement, experience, usage, and gender

as the independents. Sample was a covariate, when the overall

sample was tested. Comparison of means was employed to test

hypotheses 5 and 6.

The first hypothesis stated that higher involvement will lead

to higher calibration of knowledge. This hypothesis was sup-

ported by the overall sample (Wilks' lambda= .96; F =5.11;

p =01; b =.03; p = .03) and the American sample (Wilks'lambda=.93; F =4.13; p =.02; b =.05; p =.03), but not by the

British sample (Wilks' lambda=.97; F =1.94; p =.15; b =.01;

p =.33). Table 2a shows the multivariate test results (Wilks'

lambda, F , and p values), while Table 2b shows the parameter

estimates and their p values.

The second hypothesis stated a positive relationship between

experience and confidence. Though no hypothesis was offered,

we examined the relationship between experience and calibra-

tion. The overall sample supported H2 (Wilks' lambda=.96;

F =6.16; p =.00; b =.69; p = .00), while no relationship was

found between experience and calibration (Wilks' lambda = .96;

F =6.16; p =.00; b =.00; p = .69). Similarly, American andBritish samples also supported H2 (American — Wilks'

lambda=.94; F =3.98; p =. 02; b =.78; p = .02; British —

Wilks' lambda= .96; F =3.25; p =.04; b =.62; p =.02) and

found no relationship between experience and calibration

(American — Wilks' lambda= .94; F =3.98; p =.02; b =.01;

p = .16; British — Wilks' lambda= .96; F =3.25; p =.04;

b =.00; p =.65).

The third hypothesis stated that usage will have a positive

effect on calibration. The overall sample did not support thishypothesis (Wilks' lambda= .92; F =10.92; p =.00; b =.00;

p =.17). The American sample also did not support the

hypothesis (Wilks' lambda = .98; F =1.36; p =.26; b =.00;

p = .95). The British sample showed an effect, albeit in the

negative direction (Wilks' lambda=.84; F =12.73; p =.00; b =

− .004; p =.04).

Hypothesis 4 stated that males will have higher confidence in

their knowledge of the web, compared to females. We also

examined the relationship between gender and calibration as

an empirical issue. The overall sample supported H4 (Wilks'

lambda=.84; F =24.71; p =.00; b =−6.65; p =.00) and found

no relationship between gender and calibration (Wilks'

lambda=.84; F =24.71; p =.00; b =.02; p = .29). Similar effectswere obtained in American (H4 — Wilks' lambda= .90;

F =6.65; p =.00; b =−4.73; p = .00; gender –calibration —

Wilks' lambda = .90; F =6.65; p =.00; b =.00 p = .96) and British

samples (H4 — Wilks' lambda= .79; F =18.42; p =.00; b =

−8.12; p = .00; gender –calibration — Wilks' lambda= .79;

F =18.42; p =.00; b =.03; p =.24). It is to be noted that gender

was entered as a dummy variable in the general linear model

with values of 1 for males and 2 for females.

The fifth hypothesis stated that calibration will be higher for

procedural knowledge compared to declarative knowledge. We

computed calibration scores for declarative and procedural

subscales for each respondent. This was done by measuring thecorrelations between accuracy and confidence in the subscales

Table 1

Descriptive statistics — means and standard deviations

Overall N American N British N Overall mean (S.D.) American mean (S.D.) British mean (S.D.)

Calibration 304 151 153 .34 (.16) .34 (.16) .33 (.15)

Web involvement 295 149 146 6.15 (.84) 6.32 (.71) 5.97 (.93)

Experience 302 150 152 8.31 (2.52) 8.66 (2.15) 7.97 (2.80)

Usage 301 150 151 11.26 (7.85) 12.45 (8.04) 10.07 (7.51)Accuracy 304 151 153 80% (8%) 81% (7%) 79% (9%)

Confidence 304 151 153 81% (9%) 83% (8%) 79% (10%)

Table 2a

Multivariate tests for H1, H2, H3, and H4

Effect Overall sample American sample British sample

Value F Error df Sig. Value F Error df Sig. Value F Error df Sig

Intercept Wilks' Lambda .43 172.68 259 .00 .44 73.37 116 .00 .40 101.98 138 .00

Experience Wilks' Lambda .96 6.16 259 .00 .94 3.98 116 .02 .96 3.25 138 .04

Usage Wilks' Lambda .92 10.92 259 .00 .98 1.36 116 .26 .84 12.73 138 .00

Gender Wilks' Lambda .84 24.71 259 .00 .90 6.65 116 .00 .79 18.42 138 .00

Web involvement Wilks' Lambda .96 5.11 259 .01 .93 4.13 116 .02 .97 1.94 138 .15

Sample Wilks' Lambda .96 5.44 259 .01




for each respondent. The hypothesis was tested by comparing

the mean calibration of procedural knowledge (.38) among all

respondents with the mean calibration of declarative knowledge

(.31). A paired sample t -test indicates that the difference is

significant ( p =.00). Table 3 shows the test results. These showthat respondents are better calibrated in their procedural

knowledge compared to their declarative knowledge. The

American sample also supported the hypothesis ( p =.00),

while the British sample fell marginally short of significance

( p =.055).

The sixth hypothesis stated that calibration will be higher

for common knowledge compared to specialized knowledge.

Similar to the procedure mentioned above, paired sample t -tests

were used to test the hypothesis. The overall sample supported

the hypothesis (mean calibration common= .39; special-

ized=.28; p =.00). The American as well as the British samples

provided similar effects (American p = .00; British p =.01)

(Table 4).To throw further light on the gender differences, we also

examined the differences in accuracy. It was found that the

accuracy levels are higher for males compared to females

( p = .00). Hence, although no difference was observed in

calibration, the results lend support to previous findings regard-

ing gender differences in accuracy and confidence.

We also examined whether the hypothesized relationships

between individual-specific factors and calibration and confi-

dence differ between the various types of knowledge. Four

models, where calibration and confidence were computed for

the subscales (procedural knowledge, declarative knowledge,

common knowledge, and specialized knowledge), were exam-ined for the entire sample (including sample as a covariate).

Results were similar to those for the entire scale for hypotheses

2–4, as well as the empirical relationships between (a) expe-

rience and calibration and (b) gender and calibration. Regarding

hypothesis 1, it was found that web involvement led to high

calibration in the cases of declarative (b =.03; p =.04) andspecialized knowledge (b =.04; p =.01), but not in the cases of

procedural (b =.02; p =.33) and common knowledge (b =.00;

p =.89).

Since it could be argued that the constructs of experience

(number of years the person has been using the web) and usage

(number of hours spent browsing the web) are inherently

related, we ran the model with (a) calibration and (b) confidence

as dependents, and (a) web involvement, (b) gender and (c) the

product of experience and usage as independents, with sample

as the covariate. The results remained the same. Only web

involvement was related to calibration ( p =.04).

5. Discussion

We organize the discussion section into two parts. In the first

part, we continue the discussion of the model, examining some

potential antecedents and, more importantly, the consequences

of calibration of consumer knowledge of the web. In the second

part, we examine the implications of the study and outline future

research directions.

5.1. Extension of the model-antecedent factors

Several other antecedents of consumer knowledge of the web

can be identified. Two potential antecedents are distortedmemory and counterfactual thinking. Notions of “availability,”

Table 2b

Parameter estimates

Overall sample American sample British sample

Dependent variable Parameter B Standard error t Sig. B Standard error t Sig. B Standard error t Sig.

Calibration Intercept .16 .08 1.94 .05 − .04 .14 − .26 .79 .26 .09 2.79 .01

Experience .00 .00 .40 .69 .01 .01 1.41 .16 .00 .01 − .46 .65

Usage−

.00 .00−

1.39 .17 .00 .00−

.07 .95 .00 .00−

2.11 .04Gender .02 .02 1.05 .29 .00 .03 .05 .96 .03 .03 1.17 .24

Web involvement .03 .01 2.15 .03 .05 .02 2.20 .03 .01 .01 .98 .33

Sample − .00 .02 − .114 .91

Confidence Intercept 75.02 4.04 18.57 .00 72.45 5.96 12.16 .00 73.21 5.12 14.29 .00

Experience .69 .19 3.51 .00 .78 .33 2.39 .02 .62 .25 2.44 .02

Gender −6.65 .96 −6.93 .00 −4.73 1.29 −3.66 .00 −8.12 1.40 −5.79 .00

Sample −3.22 .98 −3.30 .00

Table 3

Comparison between calibration of procedural and declarative knowledge results of t -test

Mean (S.D.) N Paired differences t df Sig.

Mean S.D. s.e. (mean)

Calibration — procedural O — .38 (.24) O — 286

A — .41(.27) A — 149 O .07 .29 .08 4.44 285 .00

B — .35(.21) B — 137 A .11 .32 .03 4.14 148 .00

Calibration—declarative O — .31 (.19) O —286 B .04 .26 .02 1.94 136 .055

A — .30 (.19) A — 149

B — .31 (.18) B — 137

O — Overall; A — American; B — British.




“dominance of the given,” “salience effect ” and so forth

formalize the idea that memory and judgment have a simple

direct relationship. Empirical studies support the expectation of

a direct relationship between memory and judgment. Beyth-

Marom and Fischoff (1977) provided strong evidence on the

strength of memory– judgment relationship, and Gabrielcik and

Fazio (1984) demonstrated that ease of retrieval exerts a causal(not just correlational) influence on frequency estimates. Based

on the evidence, we reason that distorted and incomplete

memory diminishes calibration of knowledge of the web. Con-

versely, undistorted and complete memory promotes calibra-

tion. Another potential antecedent factor is counterfactual

thinking. It has been suggested that individuals who are

required to consider contradictory evidence have lower over-

confidence than individuals who are not required to do so

(Hoch, 1985). Experimental studies have demonstrated that

individuals automatically generate supporting reasons as a

byproduct of the question–answering process, but must be

explicitly instructed to consider the counterfactual (Koriat,

Lichenstein, & Fischoff, 1980). Reviewing the relevant psychology literature, Faust (1986) identifies a series of rules

or necessary conditions that help improve calibration. Among

the rules suggested are to (1) present evidence that contradicts,

disconfirms, and refutes one's position and (2) generate com-

peting alternative hypotheses. It follows that those who are

given to considering opposite and alternative perspectives

regarding their knowledge of the web are likely to be more

calibrated in their knowledge than those who do not consider

such counterfactual perspectives (Pillai & Goldsmith, 2006).

Hence, counterfactual thinking could lower overconfidence

and enhance calibration of knowledge of the web. Several

other antecedent variables could be identified. The interestedreader is referred to the review article by Alba and Hutchinson

(2000).

5.2. Consequence of calibration of knowledge of the web

We discuss two potential consequences of calibration of web

knowledge — frustration with web and flow experience.

5.2.1. Frustration with web

A lack of calibration could contribute to the frustrations

ordinary people experience when dealing with the paradoxes of

web technology (Mick & Fournier, 1998). Frustration is the

natural outcome when the technology challenge is too great

(Hoffman & Novak, 1996), or to restate slightly, when a firm's

web interface is not sufficiently easy to use. In addition to the

key role that ease of use has with the TAM described previously,

numerous other studies (e. g., Collier & Bienstock, 2006; Yoo &

Donthu, 2001; Yang & Jun, 2002) have uncovered a critical role

for ease of use in perceptions of online service quality. Over-

confidence could make the consumer more likely to concludethat the site is hard to use and to attribute e-service failure to

the site, not to him or herself, with potentially negative con-

sequences for the retention of that customer.

Several firms plan to move customer service to the web in

order to lower costs and provide faster service. Frustration

arising from poor calibration could lead to the disconfirmation

of expected service quality. This in turn will lead to lower levels

of exclusive loyalty and repeat-purchase rates. The frustration

could be attributed to self (“I am not as good as I thought ”) or to

the firm (“the website is not good”). Neither of these augurs

well for the firm. Thus, calibration of web knowledge could be

a mediator between firm's e-service offering and customer

satisfaction/loyalty.While calibration of knowledge leads to optimal informa-

tion search, overconfident people (cell 1) might act presump-

tuously and commit errors in their web use. Underconfident

people (cell 4) might spend considerable time searching for

the relevant information, even while they are in the know.

Both of these are likely to result in frustration with web use.

Calibrated people who possess the right amount of knowledge

(cell 2) spend little time searching for such information, while

calibrated people who don't possess the right kind of knowl-

edge spend appropriate and optimal amount of time searching

for the relevant information. Some research has examined

specific antecedents of user frustration, including lack of navigation standards ( Neerincx, Lindenberg, & Pemberton,

2001), disorientation in hyperspace and associated cognitive

overload ( Nielsen, 2000; Patel, Drury, & Shalin, 1998), and

speed, accuracy, and preference of navigational systems

(Kiger, 1984). Apart from the direct effect of miscalibration

on frustration as argued above, miscalibrated individuals

are likely to feel disorientated, which also contributes to

frustration.

5.2.2. Flow

Flow is the process of optimal experience (Csikszentmihalyi,

1977). Flow experience in a computer mediated environment

has been defined as the state occurring during network

Table 4

Comparison between calibration of common and specialized knowledge results of t -test

Paired differences t df Sig.

Mean (S.D.) N Mean S.D. s.e. (mean)

Calibration — common O — .39 (.27) O — 279

A — .41 (.28) A — 134 O .10 .32 .02 5.38 278 .00

B — .36 (.25) B — 145 A .13 .32 .03 4.83 133 .00O —.28 (.18) O — 279 B .07 .31 .03 2.78 144 .01

Calibration—specialized A — .28 (.19) A — 134

B — .29 (.18) B — 145

O — Overall; A — American; B — British.




navigation which is (1) characterized by a seamless sequence of

response facilitated by machine interactivity, (2) intrinsically

enjoyable, (3) accompanied by a loss of self-consciousness,

and (4) self-reinforcing (Hoffman & Novak, 1996). In the

flow experience, consumers are so acutely involved in the act

of network navigation in the hypermedia environment that

“nothing else seems to matter ” (Csikszentmihalyi, 1990, p.4).Flow is assumed to have a number of positive outcomes for the

firm (Hoffman & Novak, 1996).

Calibration of knowledge of the web will facilitate the flow

experience. As noted earlier, flow occurs when perceived skills

match perceived challenges. A realistic assessment of the skill is

required to achieve flow. Since knowledge of the web is a skill

in the context of navigation, better calibration leads to better

flow. Overconfident individuals will face bottlenecks in their

navigation and wouldn't know how to remedy the situation

since they are unaware of their ignorance. They will also

undertake tasks that are too challenging, which results in a

reduction in flow experience. Underconfident individuals willdither, which inhibits their navigation. They are likely to engage

in navigation that is too simple to be enjoyable. Calibrated

individuals are likely to experience flow in the strict sense of the

term.

5.3. Implications of the findings

The results of the study point to the role of involvement

with the web in promoting calibration of knowledge of the

web. Higher calibration implies greater realism of judgment

in the level of one's knowledge. Greater realism enables one

to judge correctly what one is capable of doing. It also facili-

tates optimal information search, which in the context of knowledge of the web could include attending training pro-

grams, reading manuals, or seeking information from collea-

gues. And, as argued above, calibration lowers frustration with

web usage and enhances flow experience. Hence, calibration

of knowledge of the web will have performance implications

for the web user.

The study suggests that getting involved with the web is one

way to achieve better performance while using the web. Apart

from the possible direct effect of involvement on performance,

calibration will have a mediating role on the involvement –

performance relationship. In a dynamic environment such as the

web, technological changes and innovations by companiesresult in the creation of new knowledge, which in turn requires

that users continuously update their knowledge. In such an

environment, the mediating role of calibration can be partic-

ularly relevant. Knowing what they know and what they don't

know and being realistic in their perceptions enables web users

to (a) devote the optimal amount of effort in adding to their

knowledge of the web and (b) anticipate potential usability

problems arising from their level of knowledge and take appro-

priate steps to counter them, thus minimizing frustration and

dissatisfaction.

The study found that neither experience nor usage has an

effect on calibration of knowledge of the web. This underscores

the need to focus on involvement to enhance calibration

of knowledge of the web. It was reasoned that intrinsically

motivated people might be more calibrated in their knowledge

of the web than extrinsically motivated people. This is an area

that merits further investigation.

It is also shown that calibration is higher for procedural

knowledge, compared to declarative knowledge. This finding

can add to the research on designing computer and softwaremanuals. In particular it adds a new dimension to the debate on

the optimum contents of software manuals. Some researchers

(Carroll, 1990; Van der Maij & Carroll, 1995) have asserted that

manuals should be reduced to contain only procedural, task-

supporting information. The finding regarding the differences

in calibration of procedural and declarative knowledge of the

web can inform this debate. If people are more likely to be

miscalibrated in their declarative knowledge, there is a case to

include such knowledge in manuals. Similar reasoning would

lead to the argument that, apart from common knowledge,

manuals should focus on specialized knowledge as well. These

findings will also contribute to the design of contents for training modules on the World Wide Web.

The study found no difference in calibration between the

genders. However, the previously reported finding that males

are more confident than females was supported. Though we did

not offer formal hypotheses, data on age and education was

collected in the British sample. A simple regression model

indicated that neither age ( p =.17) nor education ( p =.65) had

any effect on calibration. Education, however, was found to be

positively correlated with confidence (.22).

Generalizing from the above findings, it can be stated that

calibration of knowledge is more a function of type of knowl-

edge and less a function of demographic variables. This is an

important finding to the program of research on consumer knowledge calibration.

This study contributes to the development of a program of

research on knowledge calibration in consumer sciences. A

large body of work on knowledge and confidence calibration,

its antecedents and consequences informs the dialogue in

several areas of enquiry such as psychology, education, etc.

Surprisingly, little research on knowledge calibration exists in

consumer research. It is hoped that this study acts as a catalyst

for enquiry in this important area.

Future research needs to extend the proposed model by

examining other antecedents and consequents. As mentioned

earlier, the mediating role of calibration between e-serviceoffering and satisfaction/loyalty is of considerable importance

and needs to be examined. Research also needs to investigate

the roles of other antecedent factors in influencing calibration of

knowledge of the web. Further research can also distinguish

between the different types of high and low calibration (cells 1

to 4) and examine the differential effects of antecedent factors

and consequences arising from these different instances. In

particular, the two types of calibration will have different effects

on consequences such as information search. As discussed

before, high accuracy and high confidence were observed in this

study, rendering such distinctions redundant.

At a general level, the model can be applied to the domain of

specific knowledge of consumers and their use of the web.




Considerable research has examined online information search.

Researchers have suggested several antecedents to online

information search, such as income, experience with the web,

and education (Klein & Ford, 2003). Models of online search

that combine the benefit –cost perspective and the ability–

motivation paradigm have been proposed (Kulviwat, Guo, &

Engchanil, 2004). Calibration of knowledge of the domain(product category) could be an antecedent to online search

(those in cells 1 and 2 perform lower search compared to those

in cells 3 and 4). More importantly, calibration could be a

determinant of the effectiveness of search, whereby calibrated

individuals will perform more effective search compared to

miscalibrated individuals.

Acknowledgments

The authors thank Professor Hubert Gatignon and three

anonymous reviewers for their valuable comments throughout

the review process.

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