Closing the gap between perceived and

actual waiting times in a call center:

results from a field study
Anita Whiting
Department of Marketing, School of Business, Clayton State University, Georgia, USA, and
Naveen Donthu
Department of Marketing, Georgia State University, Atlanta, Georgia, USA

Abstract
Purpose – The purpose of this paper is to investigate what factors influence the gap between caller’s perception of how long they think they waited
and how long they actually waited on hold and to determine what call managers can do to reduce this gap called estimation error.
Design/methodology/approach – A field experiment was conducted with a corporation’s call center.
Findings – The findings were: the higher the estimation error of callers, the less satisfied they are; music increases estimation error, unless callers can
choose the music; waiting information reduces estimation error; callers with urgent issues have more estimation error and they overestimate more; and
females have higher estimation error and they overestimate more than males.
Research limitations/implications – Limitations are one call center in one context. Implications are identification of antecedents of overestimation.
Practical implications – The paper provides guidelines for call center managers for reducing estimation error and increasing caller satisfaction. It
discusses the need for understanding callers and measuring items that are important to them.
Originality/value – The study investigates an under researched variable called estimation error. Study also provides information about some of the
causes for why consumers overestimate or underestimate their waiting time. Study provides guidelines from an actual call center and discusses
variables that managers can easily use to decrease estimation error and overestimation.

Keywords Call centres, Operating times, Estimation, Individual perception

Paper type Research paper

An executive summary for managers and executive factors that influence caller satisfaction. Despite the
readers can be found at the end of this issue. contributions of research on service quality and call centers,
there is still a strong need for research in this area (Jack et al.,
2006). Organizations with call centers have been criticized for
Introduction focusing on what is easy to measure (e.g., number of callers
Call centers have become the dominant form of contact with served per hour) instead of what is important to measure
customers (Micak and Desmarais, 2001). Over 70 percent of (e.g., perceived wait time) and for focusing on quantity of
customer contact occurs through call centers (Feinberg et al., calls instead of quality of calls (Robinson and Morley, 2006).
2002). Because call centers handle a diverse array of issues Academic literature also lacks knowledge about what is
ranging from complaint resolution to order taking, call important to caller satisfaction (Feinberg et al., 2002). Most
centers have become a critical touch point for managing and academic studies on call centers have focused on employee
increasing customer satisfaction (Anton, 1997; Dawson, issues such as staff dissatisfaction and emotional labor rather
1998). According to Bennington et al. (2000, p. 162), call than on caller satisfaction (Bennington et al., 2000). Feinberg
centers have the potential to become the “hub of successful et al. (2002, p. 179) claim that uncovering the significant
customer relationship management (CRM) strategies and the variables that influence caller satisfaction is “crucial if we are
fulcrum of organizations”. Call centers will only continue to to provide guidance for call center managers”. Thus, both
grow in importance as more and more companies focus on managers and academics are very concerned about the lack of
CRM (Burgers et al., 2000). knowledge about what influences and drives caller
With call centers becoming a critical touch point for most satisfaction.
organizations, it is important to investigate and understand Within the few studies that have been conducted on caller
satisfaction, there is one important variable that has been
The current issue and full text archive of this journal is available at shown to influence callers and that variable is waiting time.
www.emeraldinsight.com/0887-6045.htm Millions of customers wait on hold in telephone queues to
speak to a call center representative (Knott et al., 2004). This

Journal of Services Marketing

23/5 (2009) 279– 288 Received: October 2007
q Emerald Group Publishing Limited [ISSN 0887-6045] Revised: May 2008, September 2008
[DOI 10.1108/08876040910973396] Accepted: September 2008

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waiting on hold experience has been shown to directly impact The article first begins by summarizing the literature on
satisfaction (Whiting and Donthu, 2006; Antonides et al., actual waiting times, perceived waiting times, and estimation
2002; Unzicker, 1999). There are two important variables in error. Next, the model is presented and discussed. Third, the
a waiting on hold experience. The first variable is the actual article describes the methodology and data collection. Fourth,
(objective) waiting time which is defined as how long the the article describes the findings from the study and, finally,
customer actually waited on hold (Hornik, 1984). The second the article discusses the implications and conclusions from the
variable is perceived (subjective) waiting time which is defined study and future research opportunities.
as how long the customer thinks they waited on hold (Hornik,
1984).
For most consumers, there is usually a gap or discrepancy Literature review
between actual and perceived waiting time with most More and more businesses are adding call centers to their
consumers overestimating how long they have waited organization. According to the Center for Customer Driven
(Hornik, 1984; Katz et al., 1991; Chebat et al., 1991; Knott Quality (CCDQ) at Purdue University, the number of call
et al., 2003). This discrepancy between perceived and actual centers has grown from 75,000 in 2001 to an estimated
wait times is defined as an estimation error (Knott et al., 115,000 in 2005. Approximately 98 percent of Fortune 500
2003). Some researchers refer to estimation error as companies have call centers (Feinberg et al., 2002). Many
overestimation but some consumers may underestimate organizations are adding call centers because their customers
their waiting time too. Estimation error is a very important
are demanding and expecting telephone access to companies
variable because it has been show to influence customer
(Cowles and Crosby, 1990).
satisfaction (Jones and Peppiatt, 1996).
As the number of call centers continues to grow, businesses
Because estimation error has been shown to have a
must begin to investigate and focus more on managing the on
significant impact on customer satisfaction, it is important
hold telephone wait experience. Waiting on hold to speak to
to investigate variables that influence the discrepancy or gap
an employee may not be a pleasant experience for some
between perceived and actual waiting times within a call
consumers. Many consumers are very conscious of their time
center context. Call center managers need to know what
costs when waiting (Berry, 1979) and most consumers resent
variables are causing estimation error and what factors are
having to wait (Unzicker, 1999). Consumers who have a
causing it to increase or decrease. In particular, are there
negative wait experience may even retaliate against businesses
some variables that are causing callers in a call center to
by switching to competitors and spreading negative word of
overestimate their waiting time while other variables are
helping callers to be more accurate in their perceptions of mouth (Tom et al., 1997). In order to keep customers happy
their on hold waiting time? Answering these questions and and satisfied, businesses must be concerned about their
helping call center managers to decrease estimation error customer’s waiting on hold telephone experiences.
(especially overestimation) is the goal of this research project. As discussed previously there are two important variables
In particular, this paper will develop and empirically test a within an on hold telephone experience. These two variables
conceptual model that examines determinants of estimation are actual waiting time and perceived waiting time. Studies on
error and its impact on caller satisfaction in a call center. The these variables have shown that both influence customer
model contends that real time, expectations, individual satisfaction (for a review of waiting time literature see
differences during the wait, and situational factors during Durrande-Moreau (1999)). Estimation error is the difference
the wait will influence estimation error and satisfaction within in perceived and actual wait times.
a call center context. Estimation error is a very important variable because it
This article seeks to make many contributions to the occurs very frequently among many consumers. Most
marketing and call center literature. First, this article focuses estimation error studies have focused on overestimation but
on perceived wait times, actual wait times, and estimation consumers may also underestimate their actual waiting time.
error within a call center. Most of the research on wait times Research on overestimation error has shown that many
has focused on either perceived wait times or actual wait times consumers greatly overestimate how long they have waited.
but rarely the discrepancy between the two. Second, this According to Jones and Peppiatt (1996, p. 47), it is commonly
research extends the waiting time literature by investigating assumed that “the average customer’s perception of waiting
the neglected variable called estimation error. Third, this time is different from reality” with most customers thinking
study seeks to explain what factors cause estimation error and that they have waiting longer than they actually have. Other
why some consumers overestimate their waiting time while studies have also found that most consumers overestimate
others underestimate their waiting times. This research also their waiting time. Hornik (1984) conducted a field study on
seeks to add to the literature by investigating waiting times in waiting times within the retail industry and found that
a new context that is a call center. Most services literature has consumers in a shopping context overestimated their waiting
focused on waiting times in physical settings such as banks, time by 36 percent. Katz et al. (1991) found that bank
hospitals, and fast food restaurants. However, according to customers overestimated their waiting times by twenty five
Maister (1985), people will perceive waits differently under percent. Jones and Peppiatt (1996) found that their
different circumstances and therefore, waiting on the respondents overestimated their wait times by 40 percent.
telephone may be very different than waiting in an actual Feinberg and Smith (1989) found that 77 percent of its
service environment. Thus, the findings in a call center may respondents overestimated their waiting times. Thus,
be very different from previous studies in physical service estimation error is occurring in many consumers and the
environments. Last, this article provides managerial error or discrepancy between actual and perceived waiting
implications and guidelines to help call center managers time is rather large with most consumer overestimating how
decrease estimation error and overestimation. long they have waited.

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With so many consumers experiencing estimation error and estimation error (Davis and Volman, 1990; Jones and
by such a large percentage, it is important to investigate what Peppiatt, 1996). Evangelist et al. (2002) found that
drives the discrepancy between perceived and actual waiting customers with waits of less than three minutes were more
times especially within a call center context. As previously likely to overestimate their waiting time while customers with
discussed there are only a few studies that have investigated waits greater than five minutes were more likely to
estimation error. However, these studies did not investigate underestimate their waiting time.
the causes of estimation error and they did not investigate The inverse effect of real time on estimation error can be
estimation error within a call center context. Most waiting explained by Zakay’s (1989) Resource Allocation model.
time studies have focused on the customer’s perception of the Zakay’s model proposes that time perception is a function of
wait (and not actual wait time) and most studies have focused the number of time units recorded by a cognitive timer. This
on waiting within a service setting (e.g., bank or store) and cognitive timer is activated when people pay attention to the
not on the telephone. Most wait studies collected perceived passage of time. At the beginning of the waiting experience,
wait times but they did not measure actual wait times; and consumers are occupied with the passage of time and they
thus did not investigate estimation error (Jones and Peppiatt, actively engage in time estimations. However, as the wait
1996). The lack of literature on estimation error may be due continues, consumers become distracted by stimuli and they
to the challenges of collecting actual wait times from begin to make fewer time estimations. These fewer wait
consumers. This article seeks to address this gap in the estimations lead to more accurate perceptions of the wait time
literature by developing a model of determinants that or even under evaluations of the wait time. Based on Zakay’s
influence estimation error and caller satisfaction within a model and on the findings in physical service settings, the
call center context. following hypothesis is proposed:
H1. The longer the actual waiting time in a call center, the
Model development lower the estimation error.
In order to investigate the determinants of estimation error, Estimation error has also been shown to influence consumer
we chose to rely on Durrande-Moreau’s (1999) review of the satisfaction. Jones and Peppiatt (1996) investigated the gap
waiting literature. She reviewed over 30 papers on wait between actual and perceived waiting times and found that
management between the years of 1984 through 1997 and she estimation error had an impact on satisfaction. In particular,
concluded that there are six factors that influence consumers they found that higher estimation error leads to less
while waiting. These six factors are: satisfaction. This inverse relationship between estimation
1 real time; error and satisfaction can be explained by Parasuraman et al.’s
2 personal expectations; (1985) widely accepted service quality model. According to
3 individual factors before the wait; the model, there is a gap between actual delivery of service
4 situational factors before the wait; (actual wait time) and customer’s perception of the service
5 individual factors during the wait; and (perceived wait time). This gap along with the other gaps has
6 situational factors during the wait. a negative influence on customer satisfaction and service
quality. Therefore, the following hypothesis is proposed:
Because factors before the wait cannot be easily controlled by
call center managers, we chose to focus on four variables that H2. The higher the estimation error of a caller, the lower
can be managed and their impact on estimation error and the caller’s satisfaction.
caller satisfaction. The four variables are:
1 real time; Personal expectations
2 personal expectations; According to Durrande-Moreau’s (1999) review, personal
3 individual factors during the wait; and expectations strongly influence outcome variables. In her
4 situational factors during the wait. review of 18 articles on expectations and waiting time, she
The effects of these four variables will be explained by observed a “classical comparative mechanism between
applying Zakay’s (1989) Resource Allocation model (see expectation and reality that exemplifies the confirmation-
Figure 1). disconfirmation paradigm” (Durrande-Moreau, 1999, p. 175).
It is important to note that Durrande-Moreau’s review did She also reported that customers who expect a short wait will
not find estimation error to be a frequently investigated react more negatively than others. We predict that
variable. Most of the studies reviewed by Durrande-Moreau expectations of a short wait will have a negative impact on
were focused on perceived wait times and satisfaction. This estimation error and satisfaction. This prediction is based on
study builds upon and extends Durrande-Moreau’s review by the discrepancy theory (Michalos, 1985) and the expectancy
investigating four of her six variables and their impact on disconfirmation paradigm. These theories suggest that
estimation error. consumers establish expectations, observe the performance,
compare the performance to expectations, and then form
Real time disconfirmation perceptions (Yan and Lotz, 2006). When the
Real time has been shown to have a negative impact on the disconfirmation between expectations of the wait and the
waiting experience. According to Durrande-Moreau’s (1999) actual wait time is large, the estimation error will be large and
review, real time was the central stimulus for reactions to the the consumer will be less satisfied. The following hypotheses
wait and that the longer the duration, the more negative the are therefore proposed:
reaction to the wait. In addition to satisfaction, real time may H3. Individuals with expectations of a short wait will have
also have an impact on estimation error. Studies on estimation higher estimation error than those with expectations of
error have found that shorter the wait, the greater the a longer wait.

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Figure 1 Model of estimation error in a call center

H4. Individuals with expectations of a short wait will have “observes the environment, processes, evaluates and retrieves
lower customer satisfaction scores than those with information, and makes judgments”. Women look at the
expectations of a longer wait. details and process lots of information when making decisions
while men use heuristics and process less information
(Sunden and Surette, 1998). It has also been shown that
Individual factors during the wait females experience higher levels of stress (Nelson and Quick,
Maister (1985) proposed that people will perceive waits 1985). Based on these findings, we predict that gender will
differently. There have been many individual factors reported influence estimation error. The following hypothesis is
to influence waiting times such as type of customers proposed:
(experienced vs novice), value of purchase, and time
pressure. Based on Durrande-Moreau’s (1999) review of H6. Females will have higher estimation error than males.
individual factors, we chose to look at music preference, Despite the previous findings on gender, we acknowledge that
gender, and experience and their impact on estimation error other studies have found contradictory results showing that
and satisfaction. there are no differences between men and women and their
Music played during the wait has been shown to influence waiting experiences. Both Davis and Volman (1990) and
waiting times. In particular, music has been shown to Jones and Peppiatt (1996) did not find any gender differences
influence perceived waiting duration and behavior (Hui et al., in their waiting studies. However, there were many other
1997). Music adds to the service environment and helps variables investigated in their studies which may have caused
create a more positive evaluation (Baker et al., 1992). Both noise in the data and thus caused the gender differences not to
Kellaris and Kent (1992) and Katz et al. (1991) found that come through.
playing music reduces the negative effects of waiting. North Experience may also play a role in estimation error.
et al. (1999) investigated the effects of liking and fit of music Customer’s prior experience has been shown to influence
on the amount of time callers would stay on hold. He found both perceived wait and satisfaction (Davis and Volman,
that callers would wait on hold longer when music they liked 1990). Jones and Peppiatt (1996) found that new or
was played. The beneficial effects of liked music can be infrequent users had significantly higher perceived wait
explained by Zakay’s (1989) Resource Allocation model. times than frequent users. Customers’ prior experience can
According to the model, consumers are occupied with the also by explained by Zakay’s Resource Allocation model. New
passage of time and they actively engage in time estimations. and inexperienced users may focus on the passage of time and
However, when liked music is played, consumers become actively engage in time estimations. Experienced users may
distracted by the music and they begin to make fewer time not engage in as many time estimations because they are
estimations. These fewer wait estimations lead to more familiar with the waiting situation. We therefore predict that
accurate perceptions of the wait time or even under lack of experience will have a negative impact on estimation
evaluations of the wait time. We therefore predict that liked error. The following hypothesis is proposed:
music will have a positive influence on estimation error. The H7. Novice callers will have higher estimation error than
following hypothesis is proposed: experienced callers.
H5. Callers who like the music played will have lower
estimation error than callers who don’t like the music
Situational factors during the wait
played.
Durrande-Moreau’s (1999) found that situational factors
Gender is also another individual factor that may influence were the most examined factor and that many of them have an
estimation error. Gender has been shown to influence many influence on consumers. Some of the situational variables
outcome variables. According to Karatepe et al. (2006, investigated have been type of queue, television, and
p. 1088) there is a distinction between how each gender information displays. For this study, we chose to focus on

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presence of music, waiting information given, and urgency of urgency of the call will have a negative impact on estimation
the call. error. The following hypothesis is proposed:
The presence of music has been shown to impact many H10. Callers with urgent issues will have higher estimation
important dependent variables such as length of time in store, error than callers with nonurgent issues.
amount purchased, and likelihood of returning (Oakes,
2000). Presence of music differs from the previously
mentioned variable about liking the music that is played. Methodology
Liking the music played is an individual factor while presence
of music is a situational factor that deals only with the Overview
presence or absence of music. Research on the presence of A national corporation agreed to let us use their call center to
background music has been shown that it has positive effects collect data. Their call center supports franchisees with their
on consumers by decreasing stress and increasing relaxation point of sales systems and their back office systems. The
(Tansik and Routhieaux, 1999). Research on music has also respondents in this study were independent franchise owners
shown that there is a significant relationship between waiting who paid monthly fees for the services provided by the call
and music (Chebat et al., 1993). Music has been shown to center. The call center was currently using background music
reduce perceived wait times in restaurants and supermarkets and two information cues and they wanted to see how these
(Milliman, 1982, 1986). These positive effects can be variables were affecting their callers. Therefore, we conducted
explained by Zakay’s Resource Allocation model. The an experiment to investigate these situational factors while
presence of music may distract individuals from the passage also gathering data on other variables.
of time and it may cause them not to engage in as many time For the experiment, we manipulated music, estimated wait
estimations. These fewer time estimations may lead to more time given, and number in the queue given. The experiment
accurate perceptions of the wait time or even under consisted of eight different treatments. The treatments were:
evaluations of the wait time. We therefore predict that the 1 no music, no information;
presence of music will decrease estimation error. The 2 with music, no information;
following hypothesis is proposed: 3 no music, estimated wait time given;
4 with music, estimated wait time given;
H8. Callers with background music will have lower 5 no music, number in queue given;
estimation error than callers without background 6 with music, number in the queue given;
music. 7 no music, both estimated wait time and number in queue
In addition to music, waiting information has also been shown given; and
to influence consumer’s perception of the wait (Ahmadi, 8 with music, both estimated wait time and number in
1984; Katz et al., 1991). There are two types of waiting queue given.
information: estimated wait time and number in the queue. The experiment was conducted over an eight-week period
Estimated wait time is information about the expected length with each week being a different treatment.
of the wait and queuing information is the consumer’s
position in the queue (Hui and Tse, 1996). According to Procedure
Maister (1985) uncertain waits are longer than known waits. Franchisees would call into the call center for assistance with
Zakay and Hornik (1994) suggest that information about the issues about their point of sales systems or their back office
wait reduces consumers from thinking about how long they systems. While they were on hold, the callers were exposed to
have been waiting and thus reduces their perception of the the treatment for that week. The experiment was conducted
waiting time. Based on these findings, we predict that waiting over eight weeks and measures were taken to ensure that
information will have a positive impact on estimation error. survey respondents were only questioned once about their
The following hypothesis is proposed: waiting hold experience. Call center employees were
H9. Callers with waiting information will have lower instructed to write down the actual waiting time of each
estimation error than callers without waiting caller and the store’s number (both of which were on the
information. computer screen) as they answered each call. The employees
also wrote down the caller’s name. Later that evening, an
Urgency of the call may influence estimation error. Criticality e-mail survey was sent to the franchisee at the store’s e-mail
of time to the customer has been shown to influence address. The e-mails were sent out from a university
perception of the wait and satisfaction (Davis and Volman, e-mail address so that callers could be more candid with their
1990). Davis and Heineke (1998) found that satisfaction with responses. Reminder e-mails were also sent out a day after the
the wait depends on the differences in the needs of the initial e-mail. A total of 211 completed e-mail surveys were
consumer. According to Maister (1985), people perceive returned. The response rate was approximately 18 percent.
waits differently under different situations such as an urgent
situation. Maister also proposes that uncomfortable waits Measures
(such as an urgent call) seem longer than comfortable waits. The survey consisted of 14 questions. Participants were asked
The relationship between urgency and estimation error can be to state the reason for their call and they were asked how long
explained by Zakay’s Resource Allocation model. Individuals they think they waited on the phone before an agent answered
with urgent issues are very focused on the passage of time and the call. Participants were also surveyed about the presence of
they are constantly making time estimations. These frequent music and their feelings about the music. Additional
time estimations may lead to very inaccurate accurate questions on the survey were about expectations about the
perceptions of the wait time with most urgent callers greatly wait and satisfaction with the wait. Actual wait time data and
overestimating their wait time. We therefore predict that perceived wait time data were matched up for each caller and

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Anita Whiting and Naveen Donthu Volume 23 · Number 5 · 2009 · 279 –288

thus estimation error was calculated. The company also correlation between the two (20.243 correlation which is
provided additional information to help with the data analysis. significant at 0.05 level). Thus, H1 was supported and the
Gender and years of experience as franchisee (based on finding was that as the actual wait increases, estimation error
company records) were provided by the organization. A declines. Next, we investigated the relationship between
supervisor in the call center categorized the data from the estimation error and caller satisfaction.
reason for the call question into two categories: urgent and As predicted by H2, estimation error had a significant
nonurgent calls. negative correlation with satisfaction (2 0.280 which is
significant at 0.01 level). Thus, the more estimation error,
the less satisfied they are.
Data analysis and results
We first began our data analysis by analyzing the average wait Expectations
times for each treatment group. We did not find actual wait Next, we investigated how expectations about the wait would
times for each treatment group to be significantly different; so, impact estimation error and satisfaction. Findings from an
we were able to move forward with analyzing the data. For ANOVA tests showed that there we no significant differences
each treatment, we calculated estimation error by subtracting in estimation error among those whose waits were more than
the actual wait times from the perceived wait times. We expected, about what expected, and less than expected. Thus,
conducted an ANOVA on estimation error and found that H3 was not supported. We also investigated how expectations
estimation error approached significance at the 0.08 level impact satisfaction using ANOVA. We found there to be a
among all eight treatments. There were also significant significant difference in satisfaction scores among consumers
differences among some of the treatments but this will be with difference expectations (0.001 sign). Thus, H4 was
discussed later under situational factors. We also conducted supported. Overall, we found expectations about the wait did
an ANOVA on perceived waiting times and found that there not impact estimation error but it did greatly impact
were no significant differences in perceived wait times among satisfaction.
the treatments. Thus estimation error was impacted more
than perceived wait times by the experimental variables of Individual differences during the wait
music and information cues. The three individual differences that we investigated were
The average wait time for the entire sample was 1 minute feelings about the music, gender, and experience (novice vs
experienced user). An ANOVA test on feelings about music
and 38 seconds with actual wait times ranging from 12
and estimation error was significant at 0.027. The estimation
seconds to 8 minutes. The average perceived wait time for the
error scores were 1.88 for no music, 0.275 for liked music,
entire sample was 3 minutes and 23 seconds with perceived
and 2.18 for does not like music. Pair wise comparison tests
wait times ranging from 20 seconds to 15 minutes. The
showed a 0.021 significant difference between no music and
average estimation error for the entire sample was 1 minute
liked music and a 0.011 significant difference between liked
and 39 seconds with estimation error ranging from less than 4
music and does not like music. Thus, H5 was supported and
minutes to 10 minutes. Approximately 79 percent of the
the major finding was that estimation error is significantly less
sample overestimated their waiting time. The average
for consumers who like the music that is played. For gender,
estimation error for each of the eight treatment groups is
we conducted another ANOVA on estimation error. From this
shown in Table I.
analysis, we found gender to be significant at 0.016. The
Next we analyzed the data from our hypotheses on real
average estimation error score was 0.559 for males was and
time, personal expectations, individual factors during the
2.27 for females. Thus H6 was supported and the major
wait, and situational factors during the wait. Our empirical
finding was that females have higher estimation error scores
findings of these four variables on estimation error are
than males and they significantly overestimate wait times
discussed below (see Table II).
more than males. For experience, we categorized our
respondents into two categories: novice (0-2 years of
Real time experience) and experienced (three to 30 years). Despite
We first investigated the actual length of the wait and its correlation and ANOVA tests, we did not find experience to
impact on estimation error and found a significant negative influence estimation error or satisfaction. Thus, H7 was not
supported.
Table I Average estimation error in experimental treatments
Situational factors during the wait
Average The three situational variables that we investigated were:
Treatment estimation error 1 presence of music;
(1) No music, no information 2.25 2 waiting information given; and
(2) With music, no information 2.34 3 urgency of call.
(3) No music, estimated wait time given 2 0.34 Presence of music was manipulated in the experiment with
(4) With music, estimated wait time given 2.07 callers with four of the eight treatments having music played
(5) No music, number in queue given 0.36 in the background while waiting on hold. An ANOVA was
(6) With music, number in the queue given 2.59 conducted on estimation error with treatments as the
(7) No music, both estimated wait time and independent variable. Treatments approached significance at
number in queue given 0.37 the 0.08 level. Pair wise comparisons of the different
(8) With music, both estimated wait time and treatment groups yielded significant findings. Treatment 3
number in queue given 1.70 (no music and estimated wait time given) and treatment 4
(with music and estimated wait time given) were significantly

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Table II Summary of hypotheses and findings

Hypotheses Findings
Real time
1 The longer the actual waiting time in a call center, the less the estimation error Supported at .05 level
2 The higher the estimation error of a caller, the lower the caller’s satisfaction
Expectations
3 Individuals with expectations of a short wait will have higher estimation error than those with expectations of a Not supported
longer wait
4 Individuals with expectations of a short wait will have lower customer satisfaction than those with expectations of a Supported at 0.01 level
longer wait
Individual factors
5 Callers who like the music played will have lower estimation error than callers who do not like the music played Supported at 0.05 level
6 Females will have higher estimation error than male Supported at 0.05 level
7 Novice callers will have higher estimation error than experienced callers Not supported
Situational factors
8 Callers with background music will have lower estimation error than callers without background music Supported at 0.05 level
9 Callers with waiting information will have lower estimation error than callers without waiting information Supported at 0.05 level
10 Callers with urgent issues will have higher estimation error than callers with non urgent issues Supported at 0.05 level

different at the 0.05 level. The estimation error values for treatment 7 (no music and both estimated wait time and
treatments 3 and 4 are 2 0.34 and 2.07 respectively. When number in queue given). The estimation error values for
treatment 5 (no music and number in queue information treatments 1 and 7 are 2.25 and 0.37. Thus, both estimated
given) was compared to treatment 6 (with music and number wait time and number in queue information greatly reduced
in queue information given), a 0.26 significant difference was estimation error among callers. Thus, H9 was supported and
found. The estimation error values for treatment 5 and 6 are the overall finding from these three analyses was that waiting
0.36 and 2.59 respectively. These findings demonstrate that information does in fact greatly reduce estimation error
the presence of music actually increases estimation error. among callers.
Thus, H8 was not supported. The overall finding from this
analysis is that the presence of music greatly increases Urgency of call
estimation error among callers. An ANOVA was conducted on estimation error with urgency
of call as the independent variable. Urgency of the call was
Waiting information found to be significant at the 0.013 level. The average
It was also manipulated in the experiment. The four estimation error for nonurgent calls was 1.11 while average
categories were: estimation error for urgent calls was 2.60. Thus, H10 was
1 no information given; supported and the overall finding was that callers with more
2 estimated wait time given; urgent issues are more likely to have more estimation error
3 number in queue information given; and and are also more likely to inflate how long they have waited
4 both estimated wait time and number in queue on hold.
information given.
An ANOVA was conducted on overestimation with Discussion and implications
treatments being the independent variable. Treatments An experiment was conducted at a call center to understand
approached significance at the 0.08 level. Pair wise what drives the gap between perceived and actual waiting
comparisons of the different treatment groups against the times in a call center. This was done in order to help call
control group yielded three significant findings. Treatment 1 center managers understand the huge impact that estimation
(no music and no information) and treatment 3 (no music error has on caller satisfaction and to provide guidelines for
and wait information given) were significantly different at the reducing estimation error and increasing satisfaction. Call
0.003 level. The estimation error values were 2.25 and 2 0.34 centers are a critical customer touch point that must be
respectively. Thus, estimated wait time information managed. Managers of call centers have to not only focus on
significantly reduced estimation error among callers. The reducing the actual waiting time for callers put on hold, but
second pair wise analysis showed significant differences at the they must also manage and align the perception of wait time
0.014 level between treatment 1 (no music and no more closely with the actual waiting time, and thus decrease
information) and treatment 5 (no music and number in estimation error. Managers must focus on closing the gap
queue information given). The estimation error values for between perception and reality.
treatments 1 and 5 are 2.25 and 0.36 respectively. Thus, the From our study, we found eight major findings. These
second finding is that number in queue information greatly findings are very actionable and they have major implications
reduces estimation error among callers. The third pair wise for call center managers. The major findings are:
analysis showed significant differences at the 0.046 level .
The larger the gap between perceived and actual waiting
among treatment 1(no music and no information) and times, the less satisfied consumers are.

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Anita Whiting and Naveen Donthu Volume 23 · Number 5 · 2009 · 279 –288

.
As actual wait increases, estimation error declines. Fourth, organizations should consider providing
.
Expectations about the wait do not impact estimation information cues such as estimated wait times. Information
error, but it does greatly impact customer satisfaction. cues reduce estimation error and it helps callers be more
.
Presence of music greatly increases estimation error realistic about how long they actually waited. Providing
among callers. waiting information also helps reduce uncertainty and it helps
.
However, estimation error of waiting times is significantly consumers decide whether or not to wait on hold. Consumers
less for consumers who like the music that is played. who hear an estimated wait time of 25 minutes may quickly
. Compared to males, females have significantly more decide to call back later. Estimated wait times also set
estimation error and they overestimate their wait time consumers’ expectations for how long they will have to wait.
more. With telephone holds, consumers can not see the virtual line
.
Waiting information does greatly reduce estimation error of callers in front of them and they have no way of knowing
and overestimation among callers. how long the wait may be. Managing customer’s expectations
.
Callers with more urgent issues have more estimation is critical for providing good customer service.
error are more likely to inflate how long they have waited Fifth, urgent callers should be given special attention and if
on hold. possible not be put on hold. One way to address this is by
letting callers specify why they are calling via a menu
selection. An example of this would be press 1 for computer
Implications problems, 2 for payroll problems, and 3 for e-mail problems.
First, call centers are a major customer touch point that must More urgent calls should be answered first. Organizations
be managed and it must be managed well. A majority of may also use different telephone numbers for different types
customer contact occurs through them and the image of the of calls.
company from the customer’s eyes can either be demolished Sixth, organizations need to understand their callers. This
or enhanced from interactions with a call center. There are study and a study by Jones and Peppiatt (1996) found that
major consequences for companies that do not focus on caller females had more estimation error and they overestimated
satisfaction. Dissatisfied customers are more likely to spend more than males. Thus, organizations need to know the
less, switch to competitors, and spread negative word of gender of their target audience. Retailers whose customer
mouth communication. Organizations that do not focus base is mostly women need to focus heavily on perception
heavily on caller satisfaction may want to evaluate how much techniques. The waiting strategies of organizations must line
and how important the business activities are that occur up with the target market that it serves.
through their call centers. For organizations that conduct Overall, the theme that emerges from these implications is
critical and/or numerous activities through their call center, that organizations must understand who the caller is.
the stakes are much higher for increasing caller satisfaction. Organizations need to know their gender, music preference,
Second, organizations must focus on estimation error. and urgency of the call. By using customer profiling, the call
Estimation error occurs frequently with most consumers center can do every thing possible to reduce estimation error
thinking that they have waited significantly longer than they and overestimation of waiting times. Reducing estimation
actually have. Decreasing estimation error and overestimation error and overestimation of waiting times is key to increasing
should be an initiative for organizations because of its direct caller satisfaction. From CRM initiatives, organizations are
impact on customer satisfaction. Organizations must also moving toward understanding and creating relationships with
collect data on perception of the wait and estimation error its customers. Call centers are a crucial touch point where
(and not just collect actual wait time data because of its relationships can be created and grown.
convenience). Information on perceptions of the wait and
estimation error is important because consumers may feel like
a one-minute wait lasted ten minutes. Organizations may also
Limitations and future research
want to rethink the data (e.g., number of calls handled per Even though this study was conducted in a corporate call
employee) that they are collecting to see if that data are really center, it still has some limitations. First, the current study
important to customers. From the study we found that actual findings are based on one experiment that was conducted in
wait times did not influence customer satisfaction, but one call center. Different types of call centers may produce
perception of the wait and most significantly estimation error different results. Another limitation is that waits of different
of the wait did. However, not many organizations are lengths may produce different responses. The average wait
currently collecting this data. time in this study was approximately two minutes. Future
Third, just playing music while callers are put on hold is not work should increase the generalizability of the findings by
adequate. Music should be liked by the callers for it to reduce testing in other contexts with different samples and with
the estimation error of waiting time. Organizations should different waiting times. Despite these limitations, this field
research their customers and match the music to the study demonstrated that organizations can close the gap
customer’s preferences. For organizations with many between perception and reality and decrease overestimation.
different types of consumers, they may want to use a system Because estimation error occurs with almost all consumers,
that would give callers a choice of music. Organizations may it is an area that must be further investigated. Businesses need
also want to consider the option of silence or the news. to understand the causes for people inflating their waiting
Silence may be soothing for stressed customers and it may be time. This study only looked at a few causes of estimation
better than listening to something that is not liked. News may error but there are lots more to investigate. In particular,
also be a good option for consumers who want to be informed future studies could look at additional individual and
and educated while waiting on hold instead of just wasting situational factors. For individual factors, researchers could
time. look at different types of callers, frequency of the caller,

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Anita Whiting and Naveen Donthu Volume 23 · Number 5 · 2009 · 279 –288

reason for call, and age. Additional individual factors to operation”, Journal of Service Marketing, Vol. 4 No. 1,
explore are callers that balk and do not even wait on hold and pp. 61-9.
callers that renege and hang up before their call is answered. Dawson, K. (1998), The Call Center Handbook: The Complete
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Young-Helou, S. and Metters, R. (2002), “Linking
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No. 7, pp. 539-56. About the authors
Parasuraman, A., Zeithaml, V. and Berry, L. (1985), “A
conceptual model of service quality and its implications for Anita Whiting is an Assistant Professor of Marketing at
Clayton State University. She received her PhD in Marketing
future research”, Journal of Marketing, Vol. 49 No. 4,
from Georgia State University. Dr Whiting also holds an
pp. 41-50.
MBA from the Georgia Institute of Technology. She has
Robinson, G. and Morley, C. (2006), “Call centre
published in Journal of Service Research, Journal of Services
management: responsibilities and performance”,
Marketing, and International Business: Research, Teaching, and
International Journal of Service Industry Management, Practice. Services marketing, retailing, frontline service
Vol. 17 No. 3, pp. 284-300. employees, and atmospherics are her major research
Sunden, A. and Surette, B. (1998), “Gender differences in interests. Anita Whiting is the corresponding author and can
the allocation of assets in retirement savings plans”, be contacted at: AWhiting@clayton.edu
American Economic Review, Vol. 88 No. 2, pp. 207-12. Naveen Donthu (PhD University of Texas at Austin) is the
Tansik, D. and Routhieaux, R. (1999), “Customer stress Katherine S. Bernhardt Research Professor of Marketing at
relaxation: the impact of music in a hospital waiting room”, Georgia State University. His expertise is in the areas of
International Journal of Service Industry Management, Vol. 10 marketing research methodology, consumer research,
No. 1, pp. 68-81. advertising, electronic business and services marketing. He
Tom, G., Burns, M. and Zeng, T. (1997), “Your life on hold: has published over 65 articles in peer reviewed journals,
the effect of telephone waiting time on consumer including Journal of Marketing, Journal of Marketing Research,
perception”, Journal of Direct Marketing, Vol. 11 No. 3, Journal of Consumer Research, Marketing Science and
pp. 25-31. Management Science.

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