Waiting in Vain: Managing Time and Customer Satisfaction at Call Centers

doi:10.4236/psych.2012.32030

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Psychology

2012. Vol.3, No.2, 213-216

Published Online February 2012 in SciRes (http://www.SciRP.org/journal/psych) http://dx.doi.org/10.4236/psych.2012.32030

Waiting in Vain: Managing Time and Customer Satisfaction

at Call Centers

Danilo Garcia1,2, Trevor Archer2, Saleh Moradi2, Bibinaz Ghiabi3

1Institute of Neuroscience and Physiology, Psychiatry and Neurochemistry, Forensic Psychiatry,

The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

2Department of Psychology, University of Gothenburg, Gothenburg, Sweden

3Institute for Psychology and Educational Sciences, University of Tehran, Tehran, Iran

Email: [email protected]

Received December 1st, 2011; revised January 2nd, 2012; accepted January 29th, 2012

The aim was to investigate customers’ satisfaction with telephone waiting time using data collected

among 3013 customers who were asked for their waiting time satisfaction, information satisfaction, and

service satisfaction. The actual queue time was also measured and played a significant but small role on

time satisfaction. In order to keep customers satisfied with waiting time, a successful model is an infor-

mative satisfactory answer and top of the line service, even when queue times are large. Nevertheless, the

model was less useful to predict non satisfied customers. This specific information needs to be integrated

when organizations assess customers’ time satisfaction.

Keywords: Call Center; Customer Satisfaction; Information Intelligence; Queue Time; Time

Introduction

Information intelligent organizations use their rapid in-

creasing information assets successfully (Evgeniou & Cart-

wright, 2005). Nevertheless, managers and decision makers

tend to only look for information that simply confirms exist-

ing beliefs and often disregard all other information (for other

barriers to information management see Evgeniou & Cartwright,

2005). For instance, the aspect of time as part of customer sat-

isfaction is often regarded as important in many service situa-

tions (for a review see Durrande-Moreau, 1999). Most research

has shown that as waiting time increases, satisfaction de-

creases (Davis & Volmann, 1990), that customers tend to over-

estimate waiting time (Katz, Larson, & Larson, 1991; Pruyn &

Smidts, 1998), and these recalled wait durations have an equal,

if not greater, effect on satisfaction than objective waiting time

(Katz et al., 1991; Pruyn & Smidts, 1998). At a general level, as

perceived or recalled wait duration increases the wait becomes

less acceptable (e.g., Antonides, Verhoef, & van Aalst, 2002).

Time management is important because of the increase of

service activities in our economic society and because of the

increasing value of time for customers (Durrande-Moreau,

1999). The notion of rapid service often guides managers when

deciding the work strategy of the organization. Work at call

centers, for instance, is often designed around technical solu-

tions that imply some type of work schedule—every second

that an agent is not on the phone amounts to precious queue

time that must be managed. Nevertheless, these managerial

decisions are based on actual time and not recalled time. Man-

agers seem to believe that there is actually a “magic actual

time”—crossing over it might lead to customer dissatisfaction.

It is plausible to suggest that managers and decision makers

might need to know which variables might influence recollect-

tions of satisfaction with queue time. Retrospective measures of

experience are better predictors, compared to actual experience

(e.g., the exact recollection of waiting time and on-line emo-

tional experience), of future behavior (Kahneman, 2000). Hence,

when considering calling again or buying services from the

same company customers base their decisions, at least in part,

on their previous experiences.

As explained by Kahneman and Kruger (2006: p. 3): “a large

literature from behavioral economics and psychology finds that

people often make inconsistent choices, fail to learn from ex-

perience, exhibit reluctance to trade, base their own satisfaction

on how their situation compares with the satisfaction of others

and depart from the standard model of the rational economic

agent in other ways”. In other words, global and episodic self-

reports might not be based on real numbers, such as the actual

queue time. For instance, Redelmeier, Katz, and Kahneman

(2003) have demonstrated that extreme experiences such as a

colonoscopy are more pleasant when it ends less abruptly and

less painful, even when such end means a longer procedure.

Related to this issue, researchers have also found that people

are more willing to wait in line depending on how much they

value the service provided by the company (Maister, 1985). In

other words, time satisfaction might be influenced by how sat-

isfying the whole service experience is apprehended by the

customer.

The Present Study

In this line of thinking, the present article investigates which

variables influence customers’ satisfaction with the waiting

time. Moreover, in order to point out the need of information

intelligence, the data used in the present paper is “real life data”

(i.e., data that was collected for organizational purposes). Spe-

cifically, the present study operationalized customer satisfac-

tion using data collected by a call center in which 3013 cus-

tomers where asked (automatic survey) if they were satisfied

with the service they received, if they were satisfied with the

D. GARCIA ET AL.

waiting time, and if they got the information they needed. The

actual queue time was also measured.

Actual time was expected to predict whether a customer was

or was not satisfied with the waiting time. Nevertheless, the

other two measures of satisfaction (i.e., service and information)

where expected to play a major role in the prediction of cus-

tomer satisfaction with waiting time. As stated in the introduc-

tion, customers that put a high value on the service provided are

prone to wait more. Hence, the influence of actual time was

expected to be less important for satisfaction with the waiting

time the longer the customer waited. In this issue, the whole

sample is divided in four waiting groups, based on actual wait-

ing time, in order to investigate if information satisfaction,

service satisfaction, and actual time predicted time satisfaction

differently among groups.

Method

The data used in the present study was collected between

2010 and 2011, at three different times, on a three-month

interval, and for a period of three weeks each time. A total of

5000 customers that came in contact with a call center (a mo-

bile company in Sweden) were asked to take part in an auto-

matic survey on customer satisfaction. Customers were cho-

sen at random and asked for their participation before they

were put on queue at the beginning of their call. The survey

was conducted directly after they received help from the

agents. A total of 3013 agreed to participate. No demographic

variables were collected. The actual waiting time for each

customer was also logged by the same computerized system

handling the calls.

Instrument

Customer Satisfaction Survey. Participants were asked the

following recorded questions: “Are you satisfied with the ser-

vice you have received?”, “Are you satisfied with the waiting

time?”, and “Did you get the information you needed?”. After

each question participants were instructed to press “1” for “yes”

and “0” for “no”. This type of survey is pretty common when

call centers assess customer satisfaction. The reliability of the

survey was relatively high (Cronbach’s α = .83).

Results and Discussion

A discriminant analysis was performed, using the whole

sample of 3013 customers, with time satisfaction as the de-

pendent variable and service satisfaction, information satisfac-

tion, and actual time as predictor variables. Univariate Analysis

of Variance (ANOVA) revealed that customers that were satis-

fied with the waiting time and those not satisfied with the wait-

ing time differed significantly on each of the three predictor

variables (service satisfaction: F(1,3011) = 253.11, p < .001;

information satisfaction: F(1,3011) = 483.27, p < .001; actual

waiting time: F(1,3011) = 144.57, p < .001). The value of this

function was significantly different for time satisfied and time

non-satisfied customers (chi-square = 697.25, df = 3, p < .001).

The correlations between predictor variables and the discrimi-

nant function showed that actual time was negatively correlated

(–.43). Hence, suggesting that customers that had waited the

longer were more likely to be dissatisfied with the waiting time.

Nevertheless, information satisfaction (.79) and service satis-

faction (.57) were the best predictors for time satisfaction. In

other words, customers were more prone to be satisfied with the

waiting time if they also answered being satisfied with both the

information and the service received. Overall the discriminant

function successfully predicted outcome for 78.40% of cases,

with accurate predictions being made for 50.40% of customers

that were not satisfied with the waiting time and 85.90% of the

customers who were satisfied with the waiting time (see Table

1).

In order to tests if the influence of service satisfaction, in-

formation satisfaction, and actual time was different as the

waiting time increased, four waiting groups (low waiting group,

medium low waiting group, medium high waiting group, and

high waiting group) were created using the whole sample. A

waiting group (low vs medium low vs medium high vs high)

between-subjects ANOVA was conducted in order to test dif-

ferences in actual waiting time. The main effect of waiting

group was significant (F(3,3008) = 5075.36, p < .001). A Bon-

ferroni correction to the alpha level showed that the waiting

groups differed as expected, that is, each waiting group had

higher actual waiting time than the group under, but lesser ac-

tual waiting time than the group above. Hence, validating the

median split division. See Table 2 for means and differences in

waiting time between groups.

A discriminant analysis was performed for each group with

time satisfaction as the dependent variable and service satisfac-

tion, information satisfaction, and actual time as predictor vari-

ables. The results mapped on the results for the whole sample.

In all four groups, customers that were satisfied with the wait-

ing time and those not satisfied with the waiting time differed

significantly on each of the three predictor variables (see Table

3 for details). The value of this function was significantly dif-

ferent for time satisfied and time non-satisfied customers for all

four groups (see chi-square column in Table 3). The correla-

tions between predictor variables and the discriminant function

showed that actual time was negatively correlated for medium

Table 1.

Classification results by the descriminant function.

Predicted group membership

Are you satisfied

with the waiting time? NO YES Total

NO 321 316 637

Count

YES 336 2040 2376

NO 50.40 49.60 100

YES 14.10 85.90 100

Table 2.

Means in actual waiting time (minutes) for the four waiting groups.

N Range MeanSD

Low waiting group 754 0.00 - 1.52 .28* .41

Medium low waiting group 752 1.53 - 7.37 4.69*●1.64

Medium high waiting group 753 7.38 - 13.40 10.31*●1.69

High waiting group 754 13.42 - 52.17 19.99●6.10

*p < .001 vs all the waiting groups with higher actual waiting time. ●p < .001 vs

ll the waiting groups with lower actual waiting time. a

214

D. GARCIA ET AL.

Table 3.

Descriminant analysis results for the four waiting groups.

Waiting Group Information Satisfaction Service Satisfaction Actual Waiting Time Chi-Square Predictive Outcome

Low

“No”

“Yes”

F(1,752) = 518.00***

.94

F(1,752) = 133.53***

.47

F(1,752) = .74 ns

.03

436.39, df = 3***

92%

81%

93%

Medium Low

“No”

“Yes”

F(1,750) = 120.35***

.80

F(1,750) = 98.30***

.73

F(1,750) = 9.28**

–.22

166.02, df = 3***

84%

49%

90%

Medium High

“No”

“Yes”

F(1,751) = 67.94***

.90

F(1,751) = 30.69***

.61

F(1,751) = 7.01**

–.29

78.76, df = 3***

77%

31%

93%

High Waiting

“No”

“Yes”

F(1,752) = 86.72***

.88

F(1,752) = 56.70***

.71

F(1,752) = 1.85 ns

–.13

104.14, df = 3***

73%

32%

93%

Note: ns = non-significant; **p < .01; ***p < .001.

low, medium high, and high waiting groups. Hence, suggesting

that customers that had waited the longer, in these three groups,

were more likely to be dissatisfied with the waiting time. Ac-

tual time was not related to time satisfaction for low waiting

customers. Indicating that with waiting times being so low,

time loosed its value and that satisfaction with information and

service were more important. Indeed, information satisfaction

and service satisfaction were the best predictors for time satis-

faction for all groups. In other words, customers were more

prone to be satisfied with the waiting time if they also answered

being satisfied with both the information and the service re-

ceived. Overall the discriminant function successfully predicted

which customers were satisfied with the waiting time (see Ta-

ble 3). Nevertheless, as Table 3 shows the model was less ac-

curate for customers that were not satisfied with the waiting

time; perhaps other variables, such as uncertainty, increases as

actual time increases. Uncertain waits seem longer than certain

waits (Maister, 1985), which in turn might lead to feelings of

anxiety and dissatisfaction.

Nevertheless, one limitation in the present study is that only

few variables were controlled for. That being said, it is impor-

tant to acknowledge that the present study indicates that quality

service and excellent information is more valuable when cus-

tomers report recollections of waiting time satisfaction. The

specific order in which the variables in the present study (i.e.,

information satisfaction and service satisfaction) seem to influ-

ence time satisfaction might be noteworthy. When customers

are asked if they are satisfied with the waiting time, they are

assumed to objectively review the waiting time and to integrate

it into a mental representation of their whole waiting experience.

However, as explained by Schwarz and Strack (1999: p. 63):

“individuals rarely retrieve all information that may be relevant

to a judgment. Instead, they truncate the search process as soon

as enough information has come to mind to form a judgment

with sufficient subjective certainty” (see also Schwarz, Kahne-

man, & Xu, 2009). In other words, the judgment of time satis-

faction might be based on the information that is more accessi-

ble at that point in time. Strack, Martin, and Schwarz (1988),

for example, found that dating frequency was unrelated to life

satisfaction when the question about life satisfaction preceded

the dating frequency question, whereas reversing the order of

the questions increased the correlation significantly. One possi-

ble scenario would be changing the order in which questions

are presented to the customers that complete the survey, thus

making information accessible accordingly to the model. It is

plausible to suggest that, assuming that information and service

are good, asking customers for their information satisfaction,

service satisfaction, and then asking for their waiting time sat-

isfaction would lead to more customers to feel satisfied with the

queue time. Nevertheless, an experimental approach is needed

to test this specific suggestion.

Conclusion

The actual time played a significant but small role in cus-

tomer time satisfaction. The findings suggest that in order to

keep customers satisfied with the time, managers and decision

makers should concentrate on empowering agents to give an

informative satisfactory answer and top of the line service,

contradictory to “common sense” this is even more important

as queue time increases. In other words, customer satisfaction

with time can be achieved by just giving the customer more

quality time. However, the present study also suggests that

more information is needed in order to influence customers that

not feel satisfied with the waiting time in a positive direction.

Indeed, almost half of the non satisfied customers were not

predicted by the model. Although order effects might improve

the prediction power of the model, specific information about

non satisfied customers needs to be incorporated in data col-

lected by the organizations. Otherwise, managers and decision

makers risk letting customers wait in vain.

“I don’t wanna wait in vain for your love. From the very first

time I rest my eyes on you, girl, my heart says follow through.

But I know, now, that I’m way down on your line, But the wai-

tin’ feel is fine: So don’t treat me like a puppet on a string,

Cause I know I have to do my thing”Bob Marley.

Acknowledgements

As the first author I would like to thank The Royal Society of

Arts and Sciences in Gothenburg for their support in the devel-

D. GARCIA ET AL.

opment of this article. I would also like to thank Anders Biel

and Anver Siddiqui at the department of psychology for their

support. The authors direct their gratitude to the managers at

the call center for their openness and to the reviewers who

helped improve the article.

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