Factors Influencing the Likelihood of Customer Defection: The Role of Consumer Knowledge
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JOURNAL OF THE ACADEMY
Capraro
10.1177/0092070302250900
etOF
al. MARKETING
/ CUSTOMER SCIENCE
DEFECTION ARTICLE SPRING 2003
Factors Influencing the Likelihood
of Customer Defection:
The Role of Consumer Knowledge
Anthony J. Capraro
University of North Carolina at Asheville
Susan Broniarczyk
University of Texas at Austin
Rajendra K. Srivastava
University of Texas at Austin
Customer satisfaction is the predominant metric firms use Today, most firms’programs to control customer defec-
for detecting and managing customers’ likelihood to de- tion center heavily on the management of customer satis-
fect. But while satisfaction and defection are related, satis- faction. There is, of course, good support for this
faction is only a weak predictor of whether a customer will approach. A substantial body of research suggests that
defect. This article suggests that for repurchase decisions both repurchase intent (e.g., Anderson and Sullivan 1993;
that involve an information-based evaluation of alterna- Coyne 1989; Cronin and Taylor 1992; LaBarbera and
tives to the incumbent, likelihood of defection will be influ- Mazursky 1983; Reichheld and Sasser 1990) and repur-
enced by “how much” customers know about those chase behavior (e.g., Bolton 1998; LaBarbera and
alternatives. The relationship between level of knowledge Mazursky 1983; Newman and Werbel 1973; Sambandam
about alternatives and defection is examined in the context and Lord 1995) are linked to customer satisfaction.
of actual health insurance choices. Results suggest that the However, a closer review raises the question as to how
level of objective and subjective knowledge about alterna- well defection can be controlled by focusing solely on
tives has a direct effect on likelihood of defection—above managing satisfaction. A number of academic studies
and beyond satisfaction level. The view of defection for- found that satisfaction explains a relatively small propor-
warded in this article suggests that managers may be able tion of the variance (less than 8%) in repurchasing behav-
to gain additional control over customer defection through iors (Bolton 1998; LaBarbera and Mazursky 1983;
actions aimed at influencing how much customers know Newman and Werbel 1973). Reichheld (1996) cited
(or come to know) about alternative vendors. research indicating that 65 to 85 percent of defecting cus-
tomers do so despite being “satisfied” or “highly satis-
Keywords: customer defection; knowledge about alterna- fied.” Thus, high satisfaction levels do not guarantee that
tives; satisfaction; missing information; evalu- customers will not defect. And dissatisfaction does not
ation of alternatives necessarily lead to defection—customers may continue to
purchase from a vendor that has been a source of dissatis-
faction (Hennig-Thurau and Klee 1997).
Some observe that the relationship between satisfaction
Journal of the Academy of Marketing Science. and defection is nonlinear—that satisfaction has a stronger
Volume 31, No. 2, pages 164-175.
DOI: 10.1177/0092070302250900 effect on defection when customers are “extremely satis-
Copyright © 2003 by Academy of Marketing Science. fied” (Coyne 1989; Jones and Sasser 1995; Mittal andCapraro et al. / CUSTOMER DEFECTION 165
Kamakura 2001). However, reaching uniform “extreme FIGURE 1
satisfaction” across a diverse customer base can be costly The Relationship Between Consumer
(Fornell 1992)—at some point, efforts to reduce defection Knowledge, Satisfaction Level,
by improving satisfaction may yield diminishing (or nega- and Likelihood of Defection—
tive) returns. Specifications of Competing Models
The above observations suggest value in broadening
our understanding of the factors that can influence a cus- Figure 1A. Main Effects of Knowledge (Model 1)
tomer’s likelihood to defect. To this end, Oliver (1999)
Objective Knowledge (+)
explored the notion of a psychological state of loyalty. At at Initiation of
the highest states of such loyalty, a customer will block out Prepurchase Search
communications from the incumbent’s competitors, mak- Satisfaction
(-) Likelihood of
Level Defection
ing him or her less likely to defect. Other authors have
(+)
explored switching barriers as a deterrent of defection Subjective Knowledge
at Time of Choice
(e.g., Fornell 1992; Heide and Weiss 1995; Jones,
Mothersbaugh, and Beatty 2000; Oliva, Oliver, and
MacMillan 1992). Figure 1B. Interaction Between Satisfaction and Knowledge (Model 2)
This article attempts to further our understanding of
defection, focusing on repurchase decisions that involve Objective Knowledge
at Initiation of
(+)
an information-based evaluation of alternatives to the Prepurchase Search (-)
(-) Likelihood of
incumbent (alternatives). It examines a factor previously Satisfaction
Level Defection
noted as a switching barrier (Klemperer 1987), but whose (+/-)
(+)
relationship to likelihood of defection has not been sys- Subjective Knowledge
at Time of Choice
tematically studied—customers’ level of knowledge about
alternatives. Controlling for satisfaction level, it explores
the relationship between likelihood of defection and the
Figure 1C. Knowledge as Mediator Between Satisfaction and Likelihood
level of objective knowledge (Hypothesis 1) and subjec- of Defection (Model 3)
tive knowledge (Hypothesis 2). Level of objective knowl-
(-) Objective Knowledge
edge about alternatives is defined as the number of at Initiation of
(+)
Prepurchase Search
instances of accurate information about alternatives (e.g.,
product features) stored in memory. Level of subjective Satisfaction (-) Likelihood of
Level Defection
knowledge (Brucks 1985; Park, Mothersbaugh, and Feick
Subjective Knowledge (+)
1994) is defined in terms of how much individuals per- at Time of Choice
(-)
ceive they know about alternatives. The article also tests
competing models of the interrelationship between knowl-
edge and satisfaction (Hypothesis 3), examining whether
they have independent effects on likelihood of defection customer knows about alternatives. For instance, informa-
(Figure 1A), whether the effect of knowledge is moderated tion-processing theories of choice (e.g., Bettman 1979;
by satisfaction level (Figure 1B), or whether knowledge Fishbein and Ajzen 1975) suggest that a consumer’s evalu-
mediates the effect of satisfaction level on likelihood of ation of an alternative will depend on the content of the
defection (Figure 1C). consumer’s knowledge (i.e., information pertaining to
how the alternative performs on decision-relevant
Objective Knowledge About Alternatives attributes).
and Likelihood of Defection Another stream suggests that simply having less
knowledge can influence evaluation. Consumers com-
Dick and Basu (1994) conceptualized a customer’s monly make decisions with incomplete knowledge about
decision to defect or not defect as depending on the relative alternatives (Kivetz and Simonson 2000). Most research
evaluation of the incumbent versus alternatives. This per- has examined the situation where consumers are missing
spective has been useful in explaining the linkage between information about a single attribute. Under these condi-
satisfaction and defection—reduced satisfaction dimin- tions, consumers may infer a value for the missing attrib-
ishes the evaluation of the incumbent, which in turn influ- ute based on (1) the average value of the attribute across
ences relative evaluation in favor of alternatives, making other competitors (Ross and Creyer 1992), (2) the overall
defection more likely (Oliver 1997). evaluation of the product based on known common attrib-
Research suggests that the relative evaluation of incum- utes (Kivetz and Simonson 2000), or (3) the value of a
bent and alternatives can also be influenced by what a known attribute of the alternative that is perceived to be166 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SPRING 2003
related to the missing attribute (Broniarczyk and Alba buyer will not initiate purchase behavior toward an alter-
1994). native until some requisite level of information about that
A number of authors report that the inferred value is alternative has been gathered. Translated to a defection
negatively discounted in evaluating the alternative context, this suggests that a customer will need to have
(Jaccard and Wood 1988; Johnson and Levin 1985; Meyer some requisite level of knowledge about an alternative at
1981). Others find that consumers bypass inferring a value time of choice in order for him or her to consider that alter-
for missing information and simply assign a negative value native a viable option to defect to. While a customer may
to the missing information (Simmons and Lynch 1991). have a positive evaluation of an alternative, a perception of
Presumably the likelihood of making inferences decreases insufficient knowledge about an alternative will preclude
as the amount of missing information increases, making switching to it.
the assignment of a negative value more likely as the Factors that result in customers perceiving that there are
amount of missing information increases. Furthermore, more alternatives about which they have sufficient knowl-
Johnson and Levin (1985) found that when inferences are edge to defect should increase the likelihood of defection.
made, the magnitude of negative discount increases with Customers with higher levels of subjective knowledge
the amount of “missing” information. Thus, as the level of about alternatives will perceive themselves as being more
missing information about alternatives increases, one knowledgeable about alternatives (Brucks 1985) and thus
would expect a lowering of their evaluation and conse- should perceive that there are more alternatives for which
quently a reduction in likelihood of defection.1 they have sufficient knowledge to defect to.
The amount of “missing” information about alterna-
tives at time of choice will be substantially influenced by Hypothesis 2: Level of subjective knowledge about alter-
how much the customer knows (objective knowledge) natives to the incumbent at the time of choice will be
positively associated with likelihood of defection.
about alternatives at initiation of prepurchase search. This
prior knowledge, which reflects the history of the cus-
The Relationship Between
tomer’s experience with, passive exposure to, and prior in-
Satisfaction and Knowledge
vestigation of alternatives, is a base on which prepurchase
search will build. The larger this base, the less there is to The above hypotheses implicitly treat knowledge and
learn about alternatives (Brucks 1985; Moorthy, satisfaction level as having independent effects on defec-
Ratchford, and Talukdar 1997; Punj and Staelin 1983). In tion (Model 1, Figure 1A). However, conceivably, the rela-
addition, customers with more prior knowledge more tionship of these variables to each other and to likelihood
tightly focus their search on gathering information that is of defection may be more complex.
decision relevant (Alba and Hutchinson 1987; Johnson
Model 2: Satisfaction as Moderator Between Level of
and Russo 1984). Having less to learn about alternatives,
Knowledge and Likelihood of Defection
and more strongly focused on gathering information rele-
vant to evaluating alternatives, customers with more objec- Another potential model is that satisfaction level might
tive knowledge about alternatives at initiation of prepurchase moderate the strength of the relationship between knowl-
search should be “missing” less decision-relevant infor- edge about alternatives and likelihood of defection (Figure
mation about alternatives at time of choice and therefore 1B). Dick and Basu (1994) pointed out that a customer’s
more likely to defect. attitude toward the incumbent may influence how compet-
itor information will be gathered (Alba and Hutchinson
Hypothesis 1: Level of objective knowledge about alter- 1987), interpreted (Fazio 1990), or evaluated (Cacioppo
natives at initiation of prepurchase search will be and Petty 1985). One implication of this is that customers
positively associated with likelihood of defection. with a higher satisfaction level (an influence on attitude)
might exhibit greater encoding and interpretation biases
Subjective Knowledge About with regard to information about the incumbent’s competi-
Alternatives and Likelihood of Defection tors, resulting in the development of knowledge about
alternatives that is more biased in favor of the incumbent.
We have argued that level of objective knowledge about If so, the relationship between objective knowledge about
alternatives can affect likelihood of defection by influenc- alternatives and defection might be weaker among cus-
ing evaluation of alternatives. We now turn to level of sub- tomers who are more satisfied—in other words, an interac-
jective knowledge about alternatives, suggesting that this tion effect.
type of knowledge will affect defection by influencing the An interesting further question is how subjective
set of alternatives that consumers will perceive as viable knowledge and satisfaction might interact in determining
candidates for purchase. likelihood of defection. Customers who are more dissatis-
Marketing’s seminal studies in buying behavior (e.g., fied with the incumbent might be willing to switch to
Engel, Kollat, and Blackwell 1973) have observed that a another vendor without having to feel as knowledgeableCapraro et al. / CUSTOMER DEFECTION 167
about alternatives—in other words, dissatisfied customers subjective knowledge about alternatives at time of choice.
might have a lower requisite level of knowledge needed to If the level of such knowledge is a predictor of likelihood
defect. Such a reduction in the requisite level of knowledge of defection, then subjective knowledge at time of choice
needed to defect would have little effect on likelihood of may mediate the relationship between satisfaction and
defection among customers with a high level of subjective likelihood to defect. Model 3 (Figure 1C) reflects these
knowledge. However, for customers with a low level of posited mediating roles of subjective and objective knowl-
subjective knowledge, such a reduction should increase edge about alternatives.
the likelihood to defect. This reasoning would lead one to We will test these three competing models of the rela-
posit that the relationship between subjective knowledge tionship between satisfaction, objective knowledge at the
about alternatives and likelihood to defect will be weaker initiation of prepurchase search on defection, and subjec-
among more dissatisfied customers. tive knowledge at the time of choice on likelihood of de-
Alternatively, dissatisfaction might make customers fection. The relationship between knowledge and
more aware of the potential for negative consequences that satisfaction on likelihood of defection is as follows:
could result from purchasing alternatives about which one
perceives having too little knowledge. Such a reaction Hypothesis 3a: Model 1: Knowledge and satisfaction
could make accuracy goals (Bettman, Luce, and Payne have independent effects on likelihood of defection.
1995) more salient and thus elevate the requisite level of Hypothesis 3b: Model 2: Satisfaction moderates the ef-
fect of knowledge on likelihood of defection.
knowledge that customers need in order to switch vendors.
Hypothesis 3c: Model 3: Knowledge mediates the rela-
In contrast to the previous reasoning, this would suggest a tionship between satisfaction and likelihood of
moderating effect in which customers with low levels of defection.
subjective knowledge would be less likely to defect when
they are less satisfied—in other words, the relationship
between subjective knowledge and likelihood to defect METHOD
would be stronger among the more dissatisfied. We inves-
tigate potential interactions between satisfaction and the Hypotheses were tested in the context of a choice of
level of both objective knowledge and subjective knowl- health insurance plan at a large university. This context
edge in Model 2 (see Figure 1B). provided an appropriate test environment for several rea-
sons. First, a choice of health plan is a decision for which
Model 3: Level of Knowledge as a Mediator Between Satis-
faction and Likelihood of Defection consumers have been observed to seek out information
about available alternatives (Gibbs, Sangl, and Burrus
Recent studies have begun to consider mediators in the 1996). Thus, it appears to be a decision that involves an
relationship between satisfactions and repurchase behav- information-based evaluation of alternatives.
ior (e.g., Nijssen, Singh, Sirdeshmukh, and Holzmüeller Second, the highly controlled health plan renewal pro-
2003; Oliver 1999). Here we consider the level of objective cess at this university offered an opportunity to study
knowledge at initiation of prepurchase search and level of defection in a context where respondents could be sur-
subjective knowledge at time of choice in such a mediating veyed at times close to their initiation of prepurchase
role. search and to their making a final choice. At this univer-
Bloch, Sherrell, and Ridgeway (1986) observed that sity, employees select health insurance plans annually dur-
consumers conduct ongoing search between purchases, ing a prescribed deliberation period that lasts for exactly 1
for instance, to build a store of knowledge for later use. month. Information about the next year’s plans is unveiled
Given that satisfaction level has been found to be nega- only at the beginning of this deliberation period—prior to
tively related to search effort (Newman and Staelin 1972), this, no official information is available. Given these con-
one would expect less satisfied customers to expend more ditions, the unveiling of information about the next year’s
effort in ongoing search. In turn, greater ongoing search plan seemed to provide a relatively well-defined popula-
should result in higher levels of objective knowledge about tion-level marker of the initiation of prepurchase search
alternatives at initiation of prepurchase search. If such and the closing of the deliberation period a well-defined
knowledge is a determinant of likelihood of defection, marker as to time of choice.
then some of the effect of satisfaction on likelihood of Third, the context was one in which consumers’ knowl-
defection may be mediated by the level of objective knowl- edge about alternatives prior to initiating prepurchase
edge about alternatives at initiation of prepurchase search. search would represent a base on which knowledge about
Greater prepurchase search effort has been associated the coming year’s plan could be built. For instance, an
with higher levels of subjective knowledge (Park et al. understanding of the previous year’s HMO coverage
1994). Given the above-described relationship between guidelines and/or procedures would facilitate understand-
satisfaction level and search effort, one would expect that ing for the next year. However, knowledge of past year’s
less satisfied customers should exhibit higher levels of plans would not be completely predictive for the following168 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SPRING 2003
year—benefits offered by a particular provider (e.g., Responses were coded correct if the answer was right
deductible levels, situations covered, etc.) changed from (e.g., answered “Y” when the correct answer was “Y”).
year to year. Incorrect, “?”, or blank responses were coded as not cor-
Data were collected in two stages. The first stage began rect. Objective knowledge about alternative health plans
about 1 month before the “unveiling” of the new health was operationalized as the total number of correct answers
plans for the upcoming year. Surveys were mailed to a ran- for the three plans to which the respondent was not cur-
dom sample of 1,000 university staff and faculty members, rently a subscriber. Raw scores were collapsed into high-
stratified to provide approximately equal numbers of sub- knowledge and low-knowledge groupings (split at the
scribers to each of the four health insurance providers. median).
This first stage survey measured respondents’ satisfaction
with their current plan, their perceptions about the general Subjective Knowledge About Alternatives
risk associated with switching health plans, and their level
of objective knowledge about alternative plans. A follow- Subjective knowledge was measured by a single item
up was mailed 1 week after the initial mailing. Only that asked respondents to assess “how well do you under-
responses returned before the unveiling of the new health stand the coverage provided” for each of the health plans
plan (385 responses) were used. (1 = well, 7 = not well). Level of subjective knowledge
The second stage consisted of recontacting these 385 about alternatives was operationalized as the sum of sub-
respondents immediately after the close of the health plan jective knowledge scores for the three plans to which the
deliberation period (a follow-up was sent 1 week later). In respondent was not a subscriber.
this second stage, respondents were asked how much they
felt they knew about the new plans that had been offered— Switching Risk
an indicator of their subjective knowledge about alterna-
tives at the time of choice. Again, a large majority of Burnham (1998) reported that perceptions of risk asso-
responses were received within 3 weeks of the initial mail- ciated with switching to a new vendor can act as a barrier to
ing. Two hundred thirty-five responses were received, switching. To control for this effect, we included a single
resulting in a total response rate of more than 23 percent. item to measure customers’ perceptions of the general
riskiness of switching to a different health plan. The
degree of this perceived risk was measured by agreement
MEASUREMENT
to a single item: “There is risk in changing health plans—
you never know what you forgot to ask” (1 = strongly
Measures were developed either by adapting scales agree, 7 = strongly disagree).
from the existing literature (e.g., satisfaction) or in concert
The measures used for each construct were evaluated
with university health plan administrators and existing
according to the procedure outlined by Gerbing and
theory. The items used to measure the following constructs
Anderson (1988). Results (see Appendix A) suggest that
are listed in Appendix B.
the measures were adequate in terms of unidimensionality,
convergent or discriminant validity, and reliability.
Satisfaction Response bias was analyzed from several perspectives
(Armstrong and Overton 1977). Defection rates in
Satisfaction with the current health insurance plan was returned responses (~4%) were similar to those in the sub-
measured using a four-item scale adapted from Oliver scriber population (~5%). Furthermore, the results suggest
(1980). Each item used a 7-point scale, ranging from little early or late response bias. Response rates varied
strongly agree to strongly disagree. A composite satisfac- somewhat across health plans—the PPO plan (all others
tion score was developed by summing the responses to the were HMO) had the lowest response rate (13%) and the
four items. lowest defection rate (~1%). Satisfaction levels across
plans were indistinguishable.
Objective Knowledge About Alternatives
Consistent with Brucks (1985) and Park et al. (1994), RESULTS3
level of objective knowledge about alternatives was mea-
sured as the number of correct responses to 13 questions There were 13 defections in the 222 responses used in
asked about features in the various health plans offered for the following analysis. Descriptive data for the variables in
the current year.2 this study are displayed in Appendix C. The data suggest
Respondents were asked to answer “Y,” “N,” or “?” to that most respondents were satisfied with their current
indicate whether each plan offered a particular feature. health plan (median score of 20 out of a possible 28) andCapraro et al. / CUSTOMER DEFECTION 169
TABLE 1
Relationship Between Level of Knowledge About Alternatives,
Satisfaction, and Likelihood of Defection (N = 222)
b p Model Fit
Baseline model
Satisfaction –.09 .09 –2 LL = 92.8, p = .045
2
Switching risk –.38 .03 R = .08
Model 1—main effects of knowledge
Satisfaction –.10 .07 –2 LL = 78.0, p = .000
2
Switching risk –.41 .02 R = .25
a
Objective knowledge 1.47 .04
b
Subjective knowledge .20 .008
Model 2—interaction between satisfaction and knowledge
Satisfaction –.22 .47 –2 LL = 76.7, p = .000
2
Switching risk –.39 .04 R = .27
a
Objective knowledge 3.45 .25
Subjective knowledgeb .52 .17
a
Objective Knowledge × Satisfaction –.10 .48
Subjective Knowledgeb × Satisfaction –.02 .19
Model 3—knowledge as mediator between satisfaction and likelihood of defection
Satisfaction → Likelihood of defection –.09 .09
a
Satisfaction → Objective knowledge –.002 .95
Satisfaction → Subjective knowledgeb .11 .16
c
Satisfaction → –.10 .07
Objective knowledgea → → Likelihood of defection 1.47 .04
b
Subjective knowledge → .20 .008
NOTE: LL = log likelihood.
a. At initiation of prepurchase search.
b. At time of choice.
c. Two-tailed test.
that respondents view the action of switching to a new TABLE 2
health plan as risky (median score of 6 out of 7). As Distribution of Defections Across Satisfaction
expected, respondents had less than complete objective Level and Level of Knowledge
knowledge about current alternatives (median score of 4 A. Frequency of Defection Across
out of a possible 39) but had greater objective knowledge Satisfaction and Objective Knowledge
about their own plan (median of 6.0 out of a possible score High Objective Low Objective
of 13). Consistent with Brucks (1985), subjective and Knowledge About Knowledge About
objective knowledge were positively correlated (r = .2, p = Alternatives at Alternatives at
.004). None of the other variables were significantly corre- Initiation of Initiation of
Prepurchase Search Prepurchase Search
lated with each other.
Not satisfied/neutral 4/23 (17%)a 0/26 (0%)
a
A hierarchical logistic regression procedure was used a a
Satisfied 6/81 (7%) 3/92 (3%)
to test Hypotheses 1 and 2. The first step estimated a base-
line model using satisfaction and switching risk as predic- B. Frequency of Defection Across
tors of defection. A second step (the main effects model or Satisfaction and Subjective Knowledge
Model 1) added level of objective knowledge at initiation High Subjective Low Subjective
of prepurchase search and level of subjective knowledge at Knowledge About Knowledge About
time of choice to the baseline model. Alternatives at Alternatives at
Time of Choice Time of Choice
As shown in Table 1, the baseline model4 adequately
fits the data (–2 log likelihood = 92.8, p = .045), explaining Not satisfied/neutral 4/29 (14%)a 0/20 (0%)
a
a a
Satisfied 9/99 (9%) 0/74 (0%)
8 percent of the variation in likelihood of defection
(Nagelkerke 1991). Consistent with past research, the a. Number of defections/number of respondents in cell (% defections).
defection rate (see Table 2, part A) is higher among the
unsatisfied (4 out of 49, or 8%) than the satisfied (9 out of
173, or 5%). Furthermore, as shown in Table 1, the coeffi- switching risk (recoded so that 7 represents high per-
cient for satisfaction (bsatisfaction = –.09) is negative and sig- ceived risk) is negative and significant (bswitching risk = –.38,
nificant at the p = .09 level. The coefficient for perceived p = .03). Thus, higher satisfaction and greater perceived170 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SPRING 2003
switching risk are associated with lower likelihood of likelihood of defection and as a moderator in the relation-
defection. ship between subjective knowledge at time of choice and
likelihood of defection.
Knowledge About Alternatives The results do not support Hypothesis 3b (see Model 2
and Likelihood of Defection in Table 1). Neither the interaction term between satisfac-
tion and objective knowledge (bobjective knowledge × satisfaction =
Model 1 tests (see Table 1) for main effects of satisfac- –.10, p = .48) nor the interaction term between satisfaction
tion, perceived switching risk, objective knowledge, and and subjective knowledge (bsubjective knowledge × satisfaction = –.02,
subjective knowledge. As with the baseline model, satis- p = .19) is significant. Furthermore, the fit of Model 2,
faction and perceived switching risk are negatively related while adequate (–2 LL = 76.7, p = .000), is not signifi-
to likelihood of defection (bsatisfaction = –.10, p = .07; bswitching cantly better than the main effects model (Ξ2difference = 1.3, 2
risk = –.41, p = .02). Supporting Hypothesis 1, the coeffi- df, p = .43). Based on moderated regression analysis crite-
cient for objective knowledge (bobjective knowledge = 1.47, p = ria (Aiken and West 1991), we conclude that Model 2
.04) is positive and significant. Thus, after controlling for (Hypothesis 3b) is not supported.
satisfaction and perceived switching risk, higher levels of Model 3 in Table 1 reports on the test of Hypothesis 3c
objective knowledge at initiation of prepurchase search (consumer knowledge mediates the effect of satisfaction
are associated with greater likelihood to defect. on likelihood of defection). Baron and Kenny’s (1986)
The distribution of defections with regard to satisfac- procedure, adapted for two mediators (Shapiro and Spence
tion level and level of objective knowledge about alterna- 2002) was used. First, satisfaction5 was regressed against
tives prior to prepurchase search is reported in Table 2, part likelihood to defect. As shown, satisfaction is a predictor
A. The defection rate among those with a high level of of likelihood of defection (bsatisfaction = –.09), significant at
objective knowledge about alternatives (10 out of 104, or the p = .09 level. In the next stage of the test, satisfaction
10%) is higher than among those with a low level (3 out of was regressed against objective knowledge at initiation of
118, or 3%). Among the unsatisfied, all defectors exhib- prepurchase search and subjective knowledge at time of
ited a high level of objective knowledge about alternatives. choice in separate analyses. As can be seen from the mid-
Supporting Hypothesis 2 (Model 1, Table 1), the coeffi- dle section of the results pertaining to Model 3, satisfac-
cient for subjective knowledge (bsubjective knowledge = .20, p = tion is not a significant predictor of either level of objective
.008) is positive and significant, indicating that after con- knowledge at initiation of prepurchase search (bobjective knowl-
trolling for satisfaction and perceived switching risk, edge = –.002, p = .95) or level of subjective knowledge at
higher levels of subjective knowledge at time of choice are time of choice (bsubjective knowledge = .11, p = .16). This finding
associated with greater likelihood to defect. Table 2, part B violates a necessary condition for mediation—that satis-
reports the distribution of defections with regard to satis- faction must be a predictor of the posited mediating
faction level and subjective knowledge at the time of variables.
choice (above or below the median). Note that only those Finally, comparing the top and bottom sections
with a high level of subjective knowledge defected. depicted in Model 3, the coefficient for satisfaction is
Furthermore, adding objective and subjective knowl- essentially the same whether both objective knowledge at
edge improves the model fit (–2LL = 78.0, p = .000) rela- initiation of prepurchase search and subjective knowledge
tive to the baseline model (Ξ2difference = 14.8, 2 df, p < .000) at time of choice are included as predictors of likelihood of
and raises the R2 from .08 to .25. Thus, going beyond con- defection (bsatisfaction = –.1, p = .07) or whether they are omit-
sumers’ satisfaction and perceived switching risk to con- ted (bsatisfaction = –.09, p = .09). This again violates the condi-
sider consumers’ objective and subjective knowledge sig- tions necessary for mediation. Thus, Model 3 (Hypothesis
nificantly enhances our ability to predict likelihood of 3c) is not supported.
defection. In summary, our competing model tests of the relation-
ship between knowledge, satisfaction, and likelihood of
Relationship Between Knowledge and defection support Model 1 (Hypothesis 3a)—objective
Satisfaction and Likelihood of Defection and subjective knowledge appear to have independent
direct effects on consumers’ likelihood of defecting,
The support for Hypotheses 1 and 2 provides evidence beyond effects associated with satisfaction level.
for independent effects of knowledge and satisfaction on
likelihood of defection, as suggested by Hypothesis 3a.
Competing models of the relationship between knowledge DISCUSSION
and satisfaction on likelihood of defection were also
tested. Model 2 tested Hypothesis 3b, adding satisfaction Recently, researchers have begun to consider factors
as a moderator in the relationship between objective beyond satisfaction that may influence whether a customer
knowledge at initiation of prepurchase search and will defect. This article contributes to this stream, focusingCapraro et al. / CUSTOMER DEFECTION 171 on “how much customers know about alternatives” as a satisfaction, these two customers would be equally likely determinant of likelihood of defection. Specifically, it sug- to defect. However, the first customer’s drop in satisfac- gests that for repurchase decisions where customers con- tion is likely to have motivated him or her to investigate sider alternatives and make information-based decisions, (search) alternatives during the period of dissatisfaction likelihood of defection will be influenced by the level of (Newman and Staelin 1972). That fluctuation will have two kinds of knowledge—subjective knowledge and left that customer with more knowledge about alternatives objective knowledge about alternatives. These two types as prepurchase search begins—a position that our findings of knowledge appear to have independent effects and suggest will make the customer more vulnerable at the together account for about twice as much variance in like- next time of choice. And even if the customer does not lihood of defection as satisfaction and perceived switching defect at that time, such knowledge about alternatives will risk. render customers more capable of learning about alterna- The mediation hypotheses were not supported. While a tives for future purchase opportunities (Alba and Hutchin- satisfaction-knowledge-likelihood of defection link is the- son 1987). oretically plausible, little of the effect of satisfaction on Previous authors have suggested that it may be possible likelihood of defection is mediated by knowledge about to influence customers’ buying behavior by influencing alternatives. Furthermore, no evidence was found for an the content of the information that customers attend to and interaction between satisfaction and level of knowledge perceive (e.g., Hoch and Deighton 1989; Kivetz and about alternatives. Of course, additional power may have Simonson 2000). We broaden this thinking to suggest that allowed us to detect the posited mediation and interaction even if it is not possible to influence the content of what effects. But from a practical perspective, our results sug- customers know about alternatives, defection may be gest that both level of objective knowledge at initiation of reduced if one can simply influence customers to know prepurchase search and level of subjective knowledge at less about alternatives. time of choice primarily operate on likelihood to defect as Interestingly, there may be potential to do so. Research main effects. suggests that the search effort that a consumer exerts Although we believe this study is the first to systemati- depends on the trade-off between the projected costs and cally investigate how a customer’s level of knowledge the benefits of search (Stigler 1961). To the extent that about alternatives influences likelihood of defection, pre- managers could induce customer perceptions that it will be vious studies have recognized that “level of knowledge difficult (and/or less rewarding) to gather information about competitors” plays a role in defection. Klemperer’s about alternatives, customers should reduce their investi- (1987) and Fornell’s (1992) characterizations of a lack of gation of alternatives, know less about alternatives, and knowledge about competitors as a switching barrier seem ultimately be less likely to defect. While specific manage- to recognize that level of knowledge about alternatives rial actions that could influence customers’ investigation plays a role in defection. Similarly, Oliver’s (1999) asser- of alternatives are a matter for future research, it seems that tion that highly loyal customers become less vulnerable, in actions such as designing one’s offering to make direct part because they “tune out” competitive overtures, also comparison with alternatives more difficult (increase the seems consistent with our view that customers who know cost of search) or actions to ensconce oneself as the cus- less about alternatives will be less likely to defect. tomer’s trusted and primary source of product class infor- Consideration of level of knowledge about alternatives mation (reduce the projected benefit of search) seem advances our understanding of defection. For instance, it promising. helps to explain why dissatisfied customers sometimes do not defect. Although dissatisfaction may tend to shift rela- tive evaluation in a way that disfavors the incumbent, if a LIMITATIONS AND FUTURE RESEARCH customer does not know enough about alternatives at the time of choice to defect or discounts alternatives due to There are some limitations that should be noted in inter- missing information, defection may not occur. preting this study. For instance, while our view of the role It also carries implications as to how managers might of knowledge about alternatives in defection is intended to assess and manage their customers’ vulnerability to defec- be generally relevant to repurchase decisions that involve tion. For instance, consideration of level of knowledge an information-based evaluation of alternatives, such about alternatives suggests that a customer’s vulnerability decisions are more likely to occur with purchases that con- will depend not only on satisfaction level but also on how a sumers consider important. Knowledge about alternatives, customer has reached that level of satisfaction. Consider for instance, would not be expected to play a role in con- two customers about to initiate prepurchase search—one texts where consumers use low-effort decision strategies whose satisfaction level has fluctuated from high to low to such as choice tactics (Hoyer 1984). high since the last purchase and a second whose satisfac- Furthermore, our results reflect only a single context. tion level has been consistently high. Looking only at Certainly that context is substantial, with more than $250
172 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SPRING 2003
billion paid in private health care insurance premiums in impact on likelihood of defection in a pure credence or
the United States in 1997 (National Committee for Qual- experience good than in health insurance—a hybrid of
ity Assurance 2001). Of course, not every health plan deci- experience and search qualities.
sion involves an evaluation of alternatives. However, a Three measurement limitations should be noted. First,
substantial proportion apparently does (Gibbs et al. 1996). subjective knowledge and switching risk were measured
Given the high customer acquisition costs in the health by a single item. While our single items seem to tap into
insurance industry ($200 to $400 per member), even if our the central construct sampled by other studies’ multi-item
findings could lead to only a small reduction in rate of scales (Campbell and Goodstein 2001; Park et al. 1994),
defection, that reduction would have a substantial profit multiple items would have been preferable. Second, it
impact (Wood 1999). would be valuable to reproduce this study in a context
Replication in other contexts is needed to assess the where there is a higher rate of defection. Although our
generalizability of our findings and conclusions. For 200+ sample size is adequate for logistic regression tech-
instance, our findings reflect an all-or-none defection/ niques, the small number of observed defections limits our
nondefection decision. While such decisions are common statistical power to detect effects. This lack of power, how-
(e.g., automobile purchases, choice of school), there are ever, seems to suggest robustness in the relationships we find.
other kinds of decisions for which customers can switch Finally, our design leaves open the possibility that satis-
vendors for only a portion of their purchases (e.g., opening faction level might have changed between the measure of
an account with a new stock broker). If customers view satisfaction and time of choice (~1.5 months) and that a
such a decision as less consequential, knowledge about satisfaction measure taken closer to time of choice would
alternatives may play a smaller role in defection. account for more of the variance in defection. However,
It would also be interesting to explore the role of knowl- given the historically low defection rates in health plans at
edge about alternatives in defection for more purely expe- this institution (~5%), it seems likely that satisfaction level
rience or credence goods. By definition, there is more would not have substantially changed in the 1 to 2 months
uncertainty about the performance of an experience or cre- between the measure of satisfaction and the choice of the
dence good than a search good. If customers are accus- next year’s health plan. Consequently, we believe that a
tomed to more uncertainty in choosing the former kinds of later measure satisfaction of level would have had little
goods, then a lack of subjective knowledge might have less effect on our results.
APPENDIX A
Measurement Model
Subjective Objective
Satisfaction Risk Knowledge
Knowledge
S1 S2 S3 S4 R1 SK OK
Unidimensionality: LISREL modification indexes suggested that one satisfaction indicator might also load on the subjective knowl-
edge construct as well. However, the standardized loading of this indicator on subjective knowledge was eight times weaker than on
satisfaction.
Reliability: Cronbach’s alpha for the satisfaction measure was .85—substantially above the recommended level of .7 (Nunnally 1978).
Reliability cannot be assessed for the single-item measures.
Discriminant validity: Examination of the Phi correlations indicated that all were significantly different from 1, with the largest corre-
lation plus twice the standard error being .35.
Convergent validity: All satisfaction items had a significant loading on the satisfaction construct, with the lowest t-value in the Lambda
matrix being 10.0—evidence of adequate convergent validity (Sujan, Weitz, and Kumar 1994) of the satisfaction items.
Overall fit: Error variances for satisfaction are calculated by LISREL. Estimates of error for the remaining constructs are set at 10 per-
2
cent of the sample variance (Hayduk 1987). Although the goodness-of-fit test is significant (Ξ = 20.0, 11 df, p = .045), other indica-
tors suggest adequate fit, with Goodness-of-Fit Index, Adjusted Goodness-of-Fit Index, Nonnormed Fit Index, Normed Fit Index,
and Comparative Fit Index at .9 or above.Capraro et al. / CUSTOMER DEFECTION 173
APPENDIX B
Items Used in Study
Satisfaction Items (α = .85)
Strongly Strongly
Agree Disagree
1 2 3 4 5 6 7
I am satisfied with my current health plan.
I am satisfied with the way my plan handles financial matters (e.g., billings, reimbursements).
I have been pleased with my health plan’s response when I have a question or complaint.
I am confident that my health plan will provide the care I need whenever I need it.
Objective-Knowledge Questions (predeliberation period)
Indicate whether each plan has the following features (circle “Y,” “N,” or “?”)
Annual Well Woman Exam
Prescription Coverage Through Caremark
Access to Emergency Room Treatment, but Must Notify Primary Care Physician Within 48 Hours
Access to Doctors Affiliated With Austin Regional Clinic (ARC)
24-Hour Access to Medical Personnel
Access to Doctors Affiliated With Austin Diagnostic Clinic (ADC)
Use of HMO Facilities or Choice of Any Doctor or Hospital
Obtain a 90-Day Supply of Prescription Medicine and Incur Only a Single Co-Pay
Ability to Use Austin Diagnostic Medical Center as a Hospital
Preexisting Condition Restrictions Waived During Enrollment Period
Use of Seton Medical Center as a Hospital
Counseling for Mental Health and Substance Abuse Problems
Use of St. David’s as a Hospital Facility
Subjective Knowledge About Alternatives (postpurchase)
Well Not Well
1 2 3 4 5 6 7
Please rate how well you understand the coverage provided by each of the following health plans.
Switching Risk
Strongly Strongly
Agree Disagree
1 2 3 4 5 6 7
There is risk in changing health plans—you never know what you forgot to ask.
APPENDIX C
Descriptive Statistics (N = 222)
Low High Response/ Median Mean Standard
Response Maximum Possible Response Response Deviation
Satisfaction level 4 28/28 20 19.6 5.0
Switching risk 1 7/7 6 5.8 1.4
Objective knowledge of alternatives 0 28/39 4 5.7 6.0
Subjective knowledge of alternatives 3 21/21 12 12.0 5.8
Objective knowledge of own plan 0 11/13 6 5.8 2.6
Subjective knowledge of own plan 1 7/7 6 5.8 1.4174 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SPRING 2003
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at Emory University. He earned his doctorate from the University
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School Press. ogy-intensive products and services.You can also read
