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Complainers vs. Non-Complainers: A Multi-national Investigation of Individual and Situational Influences on Customer Complaint Behaviour

Piyush Sharma1 Roger Marshall2

Peter Alan Reday3 Woonbong Na4

1 Corresponding Author: Dr. Piyush Sharma, Assistant Professor Department of Management and Marketing, Faculty of Business The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Phone: +852-2766-7367, Fax: +852-2765-0611, Email: mspiyush@polyu.edu.hk 2 Professor of Marketing, Department of Marketing and Advertising, Faculty of Business Auckland University of Technology, Auckland, New Zealand Phone: +649-921-9999 ext 5478, Fax: +649-921-9975, Email: roger.marshall@aut.ac.nz 3 Assistant Professor, Department of Marketing Williamson College of Business, Youngstown State University, Ohio, USA Phone: + 1 (330) 941-3078, Fax: + 1 (330) 742-1459, Email: pareday@ysu.edu 4 Professor, College of Business Administration, Kyunghee University Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, South Korea Phone: +81 31 201 2319, Fax: +81 31 204 8113, Email: nawoonbong@yahoo.co.kr

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ABSTRACT

One of the least understood areas in customer complaint behaviour (CCB) research is why some customers complain and others do not in similar dissatisfaction situations. Prior research has explored differences in customer characteristics between complainers and non-complainers, but not in association with relevant situational factors. This gap is addressed with a new conceptual framework incorporating two situational variables– customer dissatisfaction and involvement, and two consumer traits – impulsivity and self-monitoring. Several hypotheses about their main and interaction effects are tested in two different contexts, using a survey-based study in three countries (Singapore, South Korea and the US). Specifically, it is shown that CCB is positively associated with involvement and impulsivity, and negatively with self-monitoring. Involvement and impulsivity are shown to moderate the association between dissatisfaction and CCB positively, and self-monitoring moderates it negatively. Some implications and directions for future research are also discussed. Keywords: Customer complaint behaviour, customer satisfaction, dissatisfaction, impulsivity, involvement, self-monitoring

Author Bios: Piyush Sharma is an Assistant Professor in the Department of Management and Marketing, Faculty of Business at The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (mspiyush@polyu.edu.hk). His research interests include exploratory purchase Behaviours, cross-cultural differences in consumer behaviour and services marketing. He has published his research in the Journal of Services Marketing and Journal of Marketing Management. Roger Marshall is a Professor and Chair of Marketing at Auckland University of Technology Business School, Auckland, New Zealand (roger.marshall@aut.ac.nz). He has published extensively in advertising, family decision-making, personality in marketing, brand equity and retail pricing practice areas. His current research interests are in marketing of high technology, relationship marketing and industrial buying centre dynamics. Peter Alan Reday is an Assistant Professor in the Department of Marketing at Williamson College of Business, Youngstown State University, Ohio (pareday@ysu.edu). He is a specialist in Sales, Marketing, and Management Development, with expertise in developing new markets and products, identifying and managing key accounts, establishing promotional strategies, developing appropriate channels as well as hiring, training and managing sales staff. Woonbong Na is Associate Professor of Marketing at Kyunghee University, Seoul, South Korea (nawoonbong@yahoo.co.kr). His research is focused on brand management and marketing communications. He teaches marketing management, brand management and IMC.

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INTRODUCTION

The study of customer complaint behaviour (CCB) has attracted significant attention

from consumer researchers in the last few decades although mostly in Western countries (Day

and Landon Jr., 1977, Onyeaso, 2007). However, in recent years researchers have also studied

this behaviour in Asian and other non-Western markets such as China (Hui and Au, 2001), Hong

Kong (Heung and Lam, 2003), Indonesia (Phau and Sari, 2004), Malaysia (Ndubisi and Ling,

2006), Singapore (Kau and Loh, 2006), South Korea (Kim et al., 2003, Liu and McClure, 2001)

and Turkey (Atalik, 2007). However, most of this research has primarily addressed the

antecedents and outcomes of CCB along with some broad taxonomical and typological issues

(Onyeaso, 2007). As a result we still know little about why individual customers react to similar

dissatisfaction situations in different ways, and how do they decide whether to complain or not.

Some researcher have explored differences in customer characteristics between

complainers and non-complainers such as attitude towards complaining (Kim et al., 2003),

perceived control and self-efficacy (Bodey and Grace, 2006), consumer politeness (Lerman,

2006), and seeking redress propensity (Chebat et al., 2005). Others have looked for answers in

situational influences such as service recovery (Kau and Loh, 2006). However, there are not

many conceptual frameworks in extant literature that combine relevant situational and individual

variables to help us understand the differences between complainers and non-complainers.

This gap is addressed with a new conceptual framework incorporating four relevant

constructs – two situational variables (customer dissatisfaction and involvement) and two

consumer traits (impulsivity and self-monitoring). Based on this framework, several hypotheses

are developed and tested in a survey-based study conducted in three countries (Singapore, South

Korea and the US), in two different contexts – one involving dissatisfaction with a product and

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another with a service. Finally, the results are discussed along with some limitations of this

research, its implications and possible directions for future research.

LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

Defining Customer Complaint Behaviour

CCB includes a wide range of behavioural as well as non-behavioural responses ranging

from taking no action in most situations to prolonged legal action in some cases (Crié, 2003).

The other common responses include efforts to seek redress from the company and negative

word-of-mouth (WOM). There have been many definitions of CCB over the last few decades.

Some define it as an action begun by an individual including a communication of something

negative about a product (service), targeted either towards the company of a third entity (Jacoby

and Jaccard, 1981). Others call it a consequence of a given act of consumption, following which

the customer is confronted with an experience generating a high dissatisfaction, of sufficient

impact so that it is, neither liked psychologically, nor quickly forgotten (Day, 1980).

On the other hand, Fornell and Wernerfelt (1987) define it merely as an attempt to change

an unsatisfactory situation while Singh (1988) suggests that activated at an emotional or

sentimental level by a perceived dissatisfaction, CCB may manifest itself as behavioural as well

as non-behavioural responses and hence constitute a subset of all possible responses to perceived

dissatisfaction around a purchase episode, during consumption or possession of the good (or

service). Hirschman (1970) classified CCB responses into exit, voice and loyalty. Exit is an

active and destructive response to dissatisfaction, exhibited by a break of relationship with the

object (brand, product, retailer, supplier or service provider). Voice is a verbal and constructive

response with an expectation of change in the organization’s practices, policies and responses; it

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is commonly addressed towards friends, customer associations and relevant organizations. The

third type of response, loyalty has two aspects - constructive and passive, with the individual

hoping that things would evolve in a positive way.

Typology of Dissatisfied Customers

Several researchers have tried to categorise groups of customers with regard to the type

of response adopted by them within the wider framework of dissatisfaction such as complainers

versus non-complainers (Stephens and Gwinner, 1998, Bodey and Grace, 2006, Chebat et al.,

2005). Some other typologies focus on a few specific Behaviours such as irritated actives

(Warland et al., 1975, Masson and Himes, 1973), activists (Pfaff and Blivice, 1977), irates

(Singh, 1990b), voicers (Dart and Freeman, 1994) and the champions (Weiser, 1995). Generally

speaking these typologies may not help companies understand the complexity and variety of

CCB in order to respond to complaints in an effective manner, because some types of behaviour

may be more common under certain situations compared to others. For example, one would tend

to observe a greater proportion of “irritated actives” or “irates” among complainers in durable

goods category, possibly because of the high price and involvement level (Singh and Wilkes,

1996). Similarly, in a weakly competitive market many complainers are likely to be “activists”

with a sense of public good rather than benefit to oneself (Crié, 2003). Hence, there is a need for

a research framework combing the individual and situational factors for a more realistic

representation of real-life customer complaint behaviour.

Prior research shows that CCB is generally the outcome of a process of preliminary

evaluations under the influence of several initiating and modulating factors. Others have argued

that it is a result of deliberate reflection by the customer (Conlon and Murray, 1996). It is thus

relevant to analyze how this intention to complain arises and gets acted upon over time. After a

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period of initiation represented by the initial level of dissatisfaction, there appears to be a phase

of reflection necessary for some analysis of its causes and evaluation of the possible responses.

This is followed by a phase of decision and action, which would probably reflect only the

residual dissatisfaction at that stage (Singh, 1990a).

Customer Dissatisfaction (CD) and CCB

It is fairly well-established in extant literature that dissatisfaction with a product or a

service encounter often triggers the process of complaining although the strength of this

association appears to be low to moderate because of the presence of several mediating or

moderating variables (Singh and Pandya, 1991, Keng et al., 1995, Jacoby and Jaccard, 1981).

Using time-series panel data, recent studies have shown that dissatisfaction and complaints have

a stable long-term equilibrium relationship which permits them to influence each other positively

(Onyeaso, 2007). Therefore, this main effect is hypothesised in this research to confirm the

findings in prior research and establish the strength of this association:

H1: CD is positively associated with CCB i.e. Consumers are likely to complain more under

high dissatisfaction compared to low dissatisfaction, in general.

Involvement and CCB

Prior research has shown that several situational factors may influence CCB such as,

perceived costs and benefits (Singh and Wilkes, 1996), price and product importance (Richins,

1985), seriousness of the problem (Bearden and Teel, 1983), convenience of the complaint

mechanism (Mattila and Wirtz, 2004), and reputation of the retailer for responsiveness (Richins

and Verhage, 1985). It is also shown that the more involved a customer is with a product, service

or a consumption situation, the more likely is s/he to be willing to commit resources such as

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time, effort and money to complain or redress a dissatisfactory experience (Lau and Ng, 2001).

On the other hand, under low involvement situations customers in general are unlikely to be

bothered too much about a dissatisfactory experience with a product or service (Chebat et al.,

2005). Hence, the following hypothesis:

H2: Involvement is positively associated with CCB i.e. Consumers are likely to complain

more under high involvement compared to low involvement.

Impulsivity and CCB

Researchers have also attempted to explore the association of several demographic and

psychographic variables with CCB such as neuroticism (Mooradian and Olver, 1994),

assertiveness, self-confidence and individualism (Keng et al., 1995), group versus self-

orientation (Keng and Liu, 1997), consumer alienation, prior complaining experience and

controllability (Kim et al., 2003). Keng et al. (1995) also found that male, older, better educated

and high income earners were more likely to use public actions. However, the association of

consumer traits such as impulsivity and self-monitoring with CCB has not been explored.

Impulsivity is defined as a relatively stable tendency of customers to act in a spontaneous

and unreflective without much deliberation about various available alternatives or possible

outcomes (Rook and Fisher, 1995, Sharma and Sivakumaran, 2004a). One of the common

behavioural manifestations of this tendency is impulse buying, which is usually associated with

post-purchase guilt and regret (Trocchia and Janda, 2002). Trait impulsivity is also positively

associated with neuroticism, and hence a tendency to give in to strong impulses when

accompanied by negative emotions such as depression, anger and anxiety (Whiteside and

Lynam, 2001). Neuroticism is also directly and positively related with a propensity to complain

more frequently (Mooradian and Olver, 1994).

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Therefore, highly impulsive customers are likely to complain more often because they are

likely to be more reactive under negative affective states. On the other hand, low impulsives are

less likely to be affected immediately by feelings of anger or dissatisfaction and hence they may

complain less frequently and to a less extent. Therefore, the following hypothesis:

H3: Trait Impulsivity is positively associated with CCB i.e. High impulsives are likely to

complain more than low impulsives.

Self-monitoring and CCB

Self-monitoring is defined as the tendency to modify or adapt one’s behaviour in

response to others’ presence or behaviour (Becherer and Richard, 1978), and it is one of the

important variables that moderate the relative influence of traits and/or situations (Darley and

Lim, 1992). High self-monitors are willing to adapt their behaviour to enact clearly defined roles

appropriate to different situations, low self-monitors are less willing to put on a show to please

those around them, preferring instead to be true to their own attitudes and values across different

situations. For example, high self-monitors have been shown to seek more variety in public to

depict themselves as more interesting and creative people (Ratner and Kahn, 2002).

Complaint behaviour represents a particularly observable social activity compared to

information search, alternative evaluation and decision making (Goodwin and Spiggle, 1989).

Hence, orientation towards complaining is related to the attributions the complainer perceives

others to make of their complaining behaviour. High self-monitors are likely to be more

concerned about the feelings and reactions of the parties to which they would complain,

especially in public (Bodey and Grace, 2006). They may also be more self-conscious about

complaining, compared to low self-monitors. Hence, they are likely to deliberate over all the

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possible alternatives and outcome scenarios before complaining. On the other hand, low self-

monitors may not be affected by any such concerns and hence, the following hypothesis:

H4: Self-monitoring is negatively associated with CCB i.e. High self-monitors are likely to

complain less than low self-monitors.

Moderating Role of Involvement

As argued earlier, customers are less likely to complain in general under low involvement

situations compared to those which they consider more important in terms of cost, money or

effort involved. However, customers may tend to complain even under low involvement

situations if there perceived dissatisfaction is very high whereas under high involvement

situations even a small amount of dissatisfaction may be enough to trigger CCB. Hence,

H5: Under low involvement consumers are more likely to complain only when CD is very

high whereas under high involvement they would complain even when CD is low

Moderating Role of Impulsivity

Highly impulsive customers are likely to spend little time deliberating or analyzing a

dissatisfactory experience and as a result, even a small dissatisfaction may trigger a complaint

response from them. On the other hand, low impulsives are more likely to think carefully and

consider all options before deciding to complain or not. Hence, the level of impulsivity appears

to moderate the influence of customer dissatisfaction on CCB. Therefore, the following

hypothesis:

H6: Low impulsives are more likely to complain only when CD is very high whereas high

impulsives would complain even when CD is low.

Moderating Role of Self-monitoring

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Dissatisfaction Complaints

Impulsivity

Self-monitoring

Involvement

H4 (-)

H1 (+)

H2 (+)

H3 (+)

H7 (-)

H6 (+)

H5 (+)

As explained earlier, high self-monitors are very careful about their behaviour in public

situations because they like to create a good impression on others and are concerned about what

others may think about them (Snyder, 1987). Therefore, they may ignore small dissatisfaction

experiences to avoid getting into any kind of confrontation with the service provider,

manufacturer or their representatives. On the other hand, low self-monitors do not have any such

concerns and they are likely to complain irrespective of the level of dissatisfaction. Hence,

H7: High self-monitors are more likely to complain only when CD is very high whereas low

self-monitors would complain even when CD is low.

The conceptual framework and all the hypotheses are summarised in Figure 1.

Figure 1: Conceptual Framework and Hypotheses

Note: The (+) and (-) signs denote the direction (valence) of influence.

METHODOLOGY

Critical-Incident-Technique (CIT) is one of the popular methodological approaches used

in the research on service encounters and customer satisfaction areas (see Gremler, 2004 for a

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detailed review). In this approach, participants are asked to recall a recent critical incident related

to the phenomenon being studied and then asked questions about their attitudes, perceptions and

actual behaviour related to these incidents. Although considered as more realistic than using

imaginary scenarios, the CIT method has been criticised on issues of reliability and validity

(Chell, 1998). In particular, respondent stories reported in incidents can be misinterpreted or

misunderstood (Edvardsson, 1992, Gabbott and Hogg, 1996). Moreover, in the context of

complaint behaviour respondents may recall incidents across multiple product or service

categories, thus resulting in some ambiguity associated with category labels and coding rules

within a particular study (Weber, 1985).

CIT and other similar retrospective research method rely on the participant’s memory for

the correct recall of incidents that may have happened quite some time ago. Hence, such methods

may also suffer from a recall bias (Michel, 2001), a lack of consistency and memory lapses

(Singh and Wilkes, 1996), and reinterpretation of the incident (Johnston, 1995). Such methods

also require respondents to provide a detailed description of what they consider to be critical

incidents. However, respondents may not be accustomed to or willing to take the time to tell (or

write) a complete story when describing a critical incident (Edvardsson and Roos, 2001).

Because the technique requires respondents to take time and effort to describe situations in

sufficient detail, a low response rate is also likely (Johnston 1995).

In view of the above concerns, we used a survey-based research design in this research to

test all the hypotheses, wherein student participants were exposed to imaginary dissatisfaction

situations and then their behavioural intentions to indulge in CCB under these situations were

measured (adapted from Tax and Brown, 1998). Using imaginary scenarios instead of asking

respondents to recall actual incidents helps in controlling various extraneous factors such as the

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product or service category, timing, branding etc. and hence, is considered a more reliable

approach. This approach has been used by several researchers and shown to be appropriate for

this kind of research methodology (Singh and Wilkes, 1996, Kim et al., 2003). Such use of

undergraduate students responding to written scenarios has been documented as a valid practice

in many previous studies especially if the products and services used in these scenarios are

relevant to the student population, as in this study (Bendapudi and Leone, 2003, Swanson and

Kelley, 2001, Tucci and Talaga, 1997, Hocutt et al., 2006, Magnini et al., 2007).

Pre-test

Two different dissatisfaction situations were developed, one for a product (malfunction in

a laptop computer) and the other for a service (over-billing by a mobile phone service provider).

Both these were selected after short-listing and pre-testing 20 options among 25 students with a

similar profile to our sample, with highest scores on personal relevance, ownership and regular

usage. In fact, the penetration of both these (laptop and mobile phone) is almost 100% in the

target population of undergraduate students. Even in terms of usage, undergraduate students are

one of the most important user categories for both laptop computers and mobile phones

(Learning Technology Services, 2005).

Undergraduate students use their laptops most often during class for taking notes (56%)

and internet/research (42%), and for schoolwork (79%), e-mail/keeping in touch (70%) and

research tool/access web (54%) outside of class. Similarly, most college students report using

mobile phones for voice calls, text messaging, taking pictures and for multi-media applications.

Hence, they are clearly an important target segment for these products and services.

Moreover, defective product (68%) and over-billing (35%) are found to be among the

leading sources of consumer complaints (Estelami, 2003). Even in our pretest, students ranked

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laptop breakdown due to some defective part (58%) and wrong billing by mobile service

provider (27%) as the two top-most sources of complaints for these product categories. Hence,

this study combines two very common products/services used by undergraduate students i.e., a

product (laptop) and a service (mobile phone), with two most common complaints i.e., defective

product and over-billing experienced by customers. Therefore, the use of a student sample is

justified to test our conceptual framework with these products/services. However, it would be

prudent to test it with other diverse consumer samples to further generalise our findings.

Sample

A total of 270 undergraduate business students participated at large universities in three

countries, Singapore (n1 = 92), South Korea (n2 = 85) and the US (n3 = 93). They all filled in the

questionnaires individually during one of their lectures. They were told that the purpose of this

study was to study customer satisfaction and that the data collected would be used in their

tutorials. This was done to prevent them from guessing the real purpose or the hypotheses of the

study and to make them participate seriously.

Procedure

A questionnaire was developed in English, which was translated into Korean and then

back-translated to make sure it carried the same meaning. The questionnaire consisted of two

parts. In the first part, a hypothetical dissatisfaction situation was described briefly and

participants were asked to imagine themselves in that situation. Two different dissatisfaction

situations were developed, one for a product (malfunction in a laptop computer) and the other for

a service (over-billing by a mobile phone service provider). Both these were selected after short-

listing and pre-testing 20 options among 25 students with a similar profile to our sample, with

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highest scores on personal relevance, ownership and regular usage. Moreover, defective product

(68%) and over-billing (35%) are found to be among the leading sources of consumer complaints

(Estelami, 2003). Therefore, the following dissatisfaction situations were chosen:

Product: “You recently bought a lap-top computer. Suddenly, one day your lap-tops

screen goes blank and it starts making a strange sound.”

Service: “You recently subscribed to a 2-year plan with a mobile phone company.

Suddenly, one month you receive a bill showing an amount that is more

than twice your regular monthly bill.”

Using these two scenarios, two different versions of the final questionnaire were

developed, one for the product and the other for the service. Each participant was given one of

these versions. Overall 270 completed questionnaires were collected, 132 for the service context

and 138 for the product. Table 1 shows the situation- and country-wise break-up of the sample.

Table 1: Sample Composition

Dissatisfaction Situation

Country Service Product Total

Singapore 49 43 92

United States 42 51 93

South Korea 41 44 85

Total 132 138 270

The second part of the questionnaire consisted of abridged versions of several well-

known scales with three items each and 7-point Likert-type response format, for study realism

(Feick and Higie, 1992), customer complaint behaviour (Singh, 1988), impulsivity (Sharma and

Sivakumaran, 2004b), and self-monitoring (Lennox and Wolfe, 1984), and 7-point Semantic

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differential scale for involvement (Zaichkowsky, 1985), and satisfaction (Crosby and Stephens,

1987). All the scale items were counterbalanced to minimise any order effect.

DATA ANALYSIS

Exploratory factor analysis (EFA) was conducted on the pooled data using Principal

Component method with Varimax rotation. Using Kaiser Criterion, nine clear factors emerged

explaining 76% of the variance in the data. All the items loaded on the relevant factors as

expected with high factor loadings (> 0.80) and no significant cross-factor loadings for all the

scales. All the scale items, factor loadings and reliabilities are reported in table 2.

Table 2: Scale-items, Factor loadings and Reliabilities (N = 270)

Items Factor Loadings

Variance Explained

Reliability (α)

Mean (SD)

Study Realism 1. The situation seems realistic .869

20% .761 5.9

(0.8) 2. It is easy to imagine being in such a situation .830 3. Something like this can happen to me .775 Involvement 1. Unimportant – Important .863

14% .814 5.7

(1.0) 2. Irrelevant – Relevant .853 3. Insignificant - Significant .851 Dissatisfaction 1. Satisfied – Dissatisfied .934

13% .914 5.8

(1.2) 2. Happy – Angry .919 3. Pleasant – Unpleasant .917 Customer Complaint Behaviour 1. Discuss the problem with the company .851

11% .823 3.8

(1.4) 2. Ask the company to take care of the problem .773 3. Inform the company about the problem .758 Impulsivity 1. I am often restless .809

10% .798 3.7

(1.3) 2. Sometimes I say things without thinking .791 3. I lose my self-control easily .757 Self-monitoring 1. I am concerned about what others think of me .788

8% .762 4.1

(1.5) 2. I can adjust my behaviour to suit any situation .742 3. I am careful about how I present myself .717

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All the scales also showed high reliability in terms of Cronbach's alpha values; study

realism (0.76), involvement (0.81), satisfaction (0.91), CCB (0.82), impulsivity (0.79) and self-

monitoring (0.76). Next, the average scores for these scales were compared across the two types

of situations using ANOVA and some interesting results were found. First, there was a

significant difference in the scores between the products and services for the overall sample, with

participants reporting significantly greater levels of realism (4.1 vs. 3.8, p < 0.01) as well as

dissatisfaction (6.0 vs. 5.6, p < 0.01) for the product compared to the service situation, although

the differences do not appear to be very high. There was no significant difference in the level of

involvement between these (5.8 and 5.7, p > 0.50). However, more interestingly there were

significant differences across the three sub-samples with Singaporean and South Korean samples

reporting greater realism, dissatisfaction as well as involvement for the product compared to the

service situation, whereas there was no significant difference on either of these for the US

sample as shown in table 3.

Table 3: Country and Situation-wise Mean Scores Comparison

Singapore (n1 = 92)

United States (n2 = 93)

South Korea (n3 = 85)

Overall (N = 270)

Study Realism 0.4 *** 0.1, ns 0.5 *** 0.3 ** Product 4.7 4.1 3.4 4.1 Service 4.3 4.0 2.9 3.8 Involvement 0.4 * 0.2, ns - 0.4, * 0.1, ns Product 6.4 5.8 5.0 5.8 Service 6.0 5.6 5.4 5.7 Dissatisfaction 0.3 * 0.2, ns 0.8 *** 0.4 ** Product 6.3 6.1 5.7 6.0 Service 6.0 5.9 4.9 5.6

* p < 0.05, ** p < 0.01, *** p < 0.001

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From these results it seems that the participants from different countries reacted

differently to the two types of situations hence, each sub-sample was tested separately and six

sets of moderated multiple regression models were tested, one for service and another for

product situation, for each sub-sample from the three countries (Baron and Kenny, 1986). The

average scores for involvement, impulsivity and self-monitoring were mean-cantered and

multiplied with the mean-cantered dissatisfaction score, to calculate the product terms for use in

the moderated multiple regression models (involvement x dissatisfaction, impulsivity x

dissatisfaction and self-monitoring x dissatisfaction). All the results are summarised in table 4.

Table 4: Consumer Complaint Behaviour - Regression Output

Singapore United States South Korea Service Product Service Product Service Product Adj R2 value 0.566 *** 0.442 *** 0.384 ** 0.372 *** 0.462 *** 0.336 ***

Dissatisfaction 0.303 ** 0.283 ** 0.322 ** 0.320 ** 0.339 ** 0.368 ***

Involvement 0.288 ** 0.437 *** 0.447 *** 0.279 ** 0.404 *** 0.262 **

Impulsivity 0.374 *** 0.291 ** 0.422 *** 0.402 *** 0.241 ** 0.142 *

Self-monitoring - 0.117 - 0.108 0.183 * 0.159 * - 0.168 * - 0.177 *

Involvement x

Dissatisfaction

- 0.088 0.367 *** 0.274 ** 0.261 ** 0.175 * 0.222 *

Impulsivity x

Dissatisfaction

0.484 *** -0.105 0.191 * 0.316 ** 0.209 * 0.331 **

Self-monitoring

x Dissatisfaction

0.300 ** -0.097 0.297 ** - 0.119 0.256 ** - 0.194 *

* p < 0.05, ** p < 0.01, *** p < 0.001

RESULTS AND DISCUSSION

As seen from table 4, all the regression models provided a good fit to the data and

explained reasonably large variance in both the dissatisfaction situations (product as well as

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service) for all the three sub-samples, with adj-R2 values ranging from 0.336 to 0.566, p < 0.001.

Specifically, dissatisfaction (β = 0.283 to 0.368), involvement (β = 0.262 to 0.447) and

impulsivity (β = 0.142 to 0.422) were found positively associated with CCB in all the six models,

providing a strong support to the first three hypotheses. In other words, greater levels of

dissatisfaction, involvement and impulsivity led to greater consumer complaints. However,

hypothesis 4 had mixed support, wherein self-monitoring was found negatively associated with

CCB for Singapore and South Korean sub-samples (β = – 0.108 to – 0.177) but positively with

the US sub-sample (β = 0.159 to 0.183).

This finding suggests that there could be some cross-cultural differences in the influence

of self-monitoring on consumers’ complaint behaviour i.e. the high self-monitors among

American consumers may consider complaining to be seen as a positive reflection on them and

show them as either knowledgeable or strong rebellious individuals (Goodwin and Spiggle,

1989). Recent research has recognised cross-cultural differences in CCB and showed that

consumer in South Korea (collectivistic culture) were less likely to engage in voice (public)

behaviour and more likely to use a private response compared to the American (individualistic)

consumers (Liu and McClure, 2001).

Prior research has found similar effects in case of variety seeking, wherein American

consumers sought more variety in public versus private, because it showed that they had varying

tastes and hence put them in a better light (Ratner and Kahn, 2002). On the other hand, Asian

consumers are shown to be more collectivistic and concerned about the impression they create on

others, as reflected in a lower incidence of impulse buying, a common consumer behaviour with

negative normative associations (Kacen and Lee, 2002). Hence, the unexpected finding in this

research seems to be supported by existing cross-cultural research and merits further exploration.

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Hypotheses five and six were also supported. Specifically, involvement was found to

have a positive moderating effect under all situations (β = 0.175 to 0.367, p < 0.05) except for

service situation in Singapore (β = - 0.088, ns). Hence, consumers are likely to complain more,

even under low dissatisfaction if their involvement level is high. Similarly, impulsivity had a

positive moderating effect under all situations (β = 0.191 to 0.484, p < 0.05) except for service

situation in Singapore (β = - 0.105, ns). Hence, highly impulsive consumers are likely to

complain more compared to low impulsives, even under low dissatisfaction.

The unexpected findings in the Singaporean sample can be explained because of the

unique nature of this market. First, a mobile phone service was used in this study and this service

has a very high penetration in Singapore. Hence, the inherent involvement levels in Singapore

are significantly higher than in the other two countries in this study (6.0 in Singapore compared

to 5.6 in the US and 5.4 in South Korea, p < 0.01). Hence, for our Singaporean participants,

involvement level may not have influenced the relationship between dissatisfaction and CCB.

Similarly, in case of the lap-top example in this study, it was found that most of the Singaporean

student participants had bought their lap-tops under a subsidy scheme from their university

covered by a long-term service contract. Under this contract, there is a prescribed procedure for

addressing complaints of this nature and it is usually very prompt and efficient. Therefore, the

level of impulsivity may not influence the relationship between dissatisfaction and complaints.

Finally, hypothesis 7 about the negative moderating influence of self-monitoring also

found mixed support, with a strong positive influence in the service situation (β = 0.256 to 0.300,

p < 0.01) for all the three sub-samples but a weak negative influence in the product situations (β

= – 0.097 to – 0.194, I < 0.05). These results suggest that consumers are likely to respond

differently in dissatisfaction situations involving services versus products. Specifically, it appears

20

that for services (e.g. mobile phone) high self-monitors may tend to complain more compared to

low self-monitors, even under low dissatisfaction whereas for products (e.g. lap-top) this

influence may be negative or even not significant.

These findings could be due to the types of service and product chosen in this study. For

example, pointing a billing error to a mobile phone service provider does not need the consumer

to be very knowledgeable or confident. It is an obvious mistake and hence, high self-monitors

are likely to be encouraged to complain more because this would give them an opportunity to

show that they are right and hence, create a good impression on others. On the other hand, a

problem in the lap-top could well be due to mishandling by the consumer and it would require

him/her to be much more knowledgeable to logically argue and convince the company otherwise.

Therefore, high self-monitors in such a situation may not be encouraged to complain more, for

the fear of being exposed or making a bad impression.

Overall, most hypotheses were supported strongly but some found only mixed support

probably due to differences in the two dissatisfaction scenarios and the samples from the three

different countries. However, despite this limitation, this research extends the existing literature

on customer complaint behaviour. By combining two individual characteristics with two

situational factors, it was shown that complainers and non-complainers differ not only in their

individual characteristics but also in the way they behave in different situations.

Specifically, it was shown that high impulsives complain more than low impulsives,

whereas high self-monitors complain less than low self-monitors. However, these influences are

stronger when the level of dissatisfaction is high and not so strong under low dissatisfaction

level. Similarly, it was shown that involvement level is also positively associated with CCB but

this effect is also stronger under high level of dissatisfaction. In other words, complainers are

21

likely to be more impulsive and less self-monitoring compared to non-complainers. Moreover,

their tendency to complain would be greater under situations of high dissatisfaction level.

This research makes several important contributions. First, it combined individual traits

with situational factors in a common conceptual framework. Specifically, this research

contributes to this growing field by bringing in two new customer traits (impulsivity and self-

monitoring) into a common conceptual framework with two relevant situational factors

(involvement and dissatisfaction) and using these to explain differences in CCB. Second, the

insights gained from this research can help the managers to understand the importance of

individual differences among their customers and incorporate this knowledge in the training

programs for their front-line customer service representatives. This should help the service reps

to understand why different customers behave differently in similar situations and be prepared to

handle them appropriately. Using samples from three different countries and two different

dissatisfaction situations, this study demonstrates significant differences in how customers

respond to any given dissatisfaction situation. Indeed, future research may even find important

cross-cultural differences in CCB as suggested by some researchers (Liu and McClure, 2001).

LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH

While this study makes some useful conceptual and managerial contributions, it also has

some limitations. First, the scenario-based approach is a technique with strong precedence, but it

may be argued that it is limited by the capability of respondents to project their behaviour and to

respond as they really would in an actual situation. Since this study used paper-and-pencil

scenarios, feelings and emotions surrounding an actual service failure may not have been fully

experienced by the participants. However, this limitation on the external validity is offset by

22

controlling the possible confounding influence of various other factors, such as different product

categories, brand names or some situational factors, by using the scenario-based approach.

Specifically, using customers’ real experience of making complaints may introduce a lot

of heterogeneity in the data because their experiences may vary significantly across product

categories, the nature of problem, the degree of involvement and the extent to which they

actually made efforts to complain or get the problem resolved, besides inconsistencies in their

ability to recall past incidents accurately. Using the same standard scenarios with all the

participants helps us control all these extraneous factors and allow a more reliable and accurate

testing of our conceptual framework. Moreover, the reasons for complaints used in this study

(defective laptop and wrong mobile billing) are quite common among consumers in general and

were also considered as the two top-most concerns by the undergraduate population in our pre-

test. Hence, this study helped us tap into two very genuine problems faced by real customers.

Second, the conclusions of this study are based upon findings from a single sample

(undergraduate students) for one product and service category each, and therefore caution must

be exercised in generalizing the results to other products, services and populations. Prior research

argues that findings from research studies employing student samples may not generalise to

nonstudent groups (Sears, 1986). For example, compared with older adults, college students are

likely to have less-crystallised attitudes, less-formulated senses of self, stronger cognitive skills,

stronger tendencies to comply with authority, and more unstable peer-group relationships. The

laboratory setting of many studies is likely to exaggerate all these differences.

However, the context of this study was the problems faced with usage of a laptop and

mobile phone service; situations that the undergraduate student population identifies with, and

hence is less likely to be affected by their peer-group relationships or compliance motivations.

23

On the other hand, it may be argued that the penetration and usage of a laptop and mobile phone

service is not as high among the general consumer population and therefore, the findings of this

study may not be applicable to other product categories such as toilet papers, furniture or a

washing machine. Moreover, different consumer segments may vary in their scores on the

consumer traits used in this research (impulsivity and self-monitoring) as well as the degree to

which these may influence their behaviour. Hence, it would be useful to test our hypotheses for

other products and services with diverse groups of customers possessing unique characteristics

such as teenagers, adult shoppers, housewives, the elderly etc.

We also included involvement as a situational factor in this study to assess its moderating

influence on the relationship between dissatisfaction and complaint behaviour. However, there

may be other important characteristics of different product categories that may affect the

relationships among the various constructs included in our study. Therefore, it would be useful to

identify those important dimensions which distinguish these product categories from each other

(e.g., hedonic vs. utilitarian) to explain the reasons for their departure from our general

conceptual framework. Moreover, different customer segments may have varying involvement

levels with the same product category. Hence, future research may address this issue as well.

Third, the study was conducted in three countries (Singapore, South Korea and the US)

but the sample sizes were relatively small in each sub-sample considering the number of items in

the questionnaire, which may account for some of the non-significant results. In fact, the focus of

this study was to explore individual differences in complaint behaviour rather than cross-cultural

differences as such. Hence, the small size of sub-samples may not be pertinent as long as we

could pool the data and test our conceptual framework with the combined dataset. However, we

did find some significant differences among our sub-samples from the three countries, although

24

it would be difficult to interpret them because of the small size of these sub-samples. However, it

is likely that besides individual differences there may be some important cross-cultural

differences in complaint behaviour. Hence, future research may use larger sample sizes from

different countries and include relevant dimensions of culture such as uncertainty avoidance,

power distance and future time orientation to better understand these cross-cultural differences.

Fourth, this research addresses only simple cases of a complaint process that may in

reality stretch to many more future interactions. For example, the first phone call or email may

have to be followed up with a series of emails, phone calls or even a couple of letters. Hence, this

research may not fully capture the complexities of real-world complaints especially under high-

involvement situations. However, by focusing on a single step of the complaint process in this

research, we are able to provide reliable results by controlling other confounding variables.

Finally, the conceptual framework used in this study included only two consumer traits

(impulsivity and self-monitoring) and two situational factors (levels of dissatisfaction and

involvement), but many more variables such as culture and attitude towards complaining (Kim et

al., 2003) may also influence CCB in real-life situations. Future research may also employ other

methodologies (e.g., experiments) for a better control over variables such as involvement and

satisfaction, and manipulate other situational variables such as time pressure.

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