Customer Service Understanding: Gender Differences ofFrontline Employees

Abstract

Purpose: Despite widespread acknowledgement of the importanceof employees to the success of service firms, research into how well frontline service staff understand service remains scarce. This study investigates what constitutes good customerservice from the viewpoint of frontline service employees and explores gender differences in particular.

Design/methodology/approach: The data were collected from 876frontline employees in 20 different service industries. An automated text analysis using Leximancer explored general and gender-specific patterns in employees’ customer service understanding.

Findings: Irrespective of gender, frontline service staffshare the perception that the pillars of good customer serviceare listening skills, making the customer happy, and offering service. Males have a more functional, outcome-oriented interpretation of customer service; females focus more on the actual service interaction and emotional outcomes.

Practical implications: A greater understanding ofBy acknowledging gender-based nuances dissimilarities in the customer service understanding of frontline service employees can increase , the efficiency of recruitment and training efficiency by ensuring these processes acknowledge attitude dissimilarities across genderswill be enhanced.

Originality/value: This study contributes to limited work on service models of frontline staff and showshows that gender can explain some differences. This study also adds another dimension to the understanding of gender effects in services, beyond its influence on customers’ quality perceptions and behaviours. The results are important for services marketing research and for managers in charge of recruiting and trainingfrontline service staff.

Keywords: frontline service employees, good customer service, customer orientation, gender, Leximancer

1 Introduction

Some service industries have been dominated by either male or

female frontline service staff. For example, flight attendants

remain predominantly female, a legacy from the 1930s when

nurses replaced young boys in that position. Women were

considered better able to care for customers and promote a

female presence that increased the perceived safety of

flying . Frontline service positions overall tend to be filled

by women, often based on their stereotypical roles as

emotionally expressive nurturers . Men instead typically gain

recognition for their technical competence and practical task

orientation .

Three basic arguments suggest why gender stereotypes in

service roles still prevail; these arguments also imply that

male and female service staff may have different

understandings of good customer service. First, gender

differences mark customers’ perceptions of various aspects of

customer service and service quality . These perceptions

partly reflect gender stereotypes applied to both staff

members and customers during service consumption , as well as

customers’ reactions to service failure and recovery and

loyalty toward employees .

Second, customers may expect, and respond better to, frontline

service staff of the “appropriate” gender. Existing research

thus has investigated the influence of the gender of the

server and the gender dyad between customer and server on

perceived service quality and customer satisfaction .

Third, the firm’s service climate in general influences

customer-oriented behaviour , but male and female frontline

service employees also react differently to work environments

and job characteristics. For example, women tend to experience

higher levels of role ambiguity and conflict, as well as more

mental stress and emotional labour, than men (Babin and Boles,

1998). They are also expected to manage their emotional

displays better than men. Despite this greater challenge,

women are generally more satisfied with their work, perhaps

due to their gender-specific work expectations and

understanding of their work .

In light of this existing research, we propose that male and

female frontline service employees differ in their beliefs

about what determines good customer service. A gender-based

interpretation of work could explain why men or women appear

more suitable for certain frontline service roles. Extant

literature on customer service perceptions in general, and

potential gender differences in particular, mostly adopts the

customers’ perspective, such that we know little about the

employee’s perception of good customer service. One notable

exception is Di Mascio (2010), who finds that retail staff

express different interpretations of customer service. that

guide their work behaviours. Her typology of cognitive

interpretations or schemas of their work environment, which

she terms customer service models, offers important insights,

but more research is needed to investigate theirthe underlying

determinants and antecedentsdriving these service models.

Noting the significant impact of gender on customers’ service

perceptions, we are particularly interested in the effect of

the server’s gender on his or her interpretation of the

meaning of customer service. That is, do gender stereotypes

translate into the service understanding of service staff?

staff?

Accordingly, we add to the scarce research that addresses how

frontline service employees understand customer service and

whether differences in their models of service understanding

might result from gender differences. Our two main research

questions thus ask: (1) What doare the key facets of the

customer service models (schemas) of frontline service

employees consider important for providingthat guide their

efforts to provide good customer service? And (2) are there

any differences in the customer service models understandings

(schemas) of men and women?

2 Literature Review

3 The service understanding of frontline service employees

Our first research question concerns the building blocks of

the customer service understandings of frontline service

employees. Conceptual and empirical work on frontline service

employees’ interpretation of customer service is sparse. Di

Mascio (2010, p. 63) identifies three generic interpretations,

or schemas, of customer service by retail service employees,

namely, as “(1) the act of giving customers what they ask for,

efficiently and courteously, (2) a means to accomplishing

immediate objectives, such as sales quota, and (3) the

formation of mutually beneficial relationships with customers

through problem-solving”. However, these three interpretations

—referred to as service models—may not be exhaustive, because

according to Di Mascio (2010) both contextual effects and

individual characteristics are likely to affect perceptions of

good service and its delivery. Our research expands on her

work by extending the exploration of service models to a broad

range of services industries, and by investigating gender as

one individual characteristic that shapes the service models

of frontline service employees.

Existing research on customer service from the employee’s

perspective also focuses mainly on customer orientation, and

the dimensions of service quality as perceived by their

customers. It therefore defines employees’ Employees’ customer

service orientation is defined as their willingness and

ability to deliver excellent customer service and adjust their

service delivery to meet the customer’s needs and

preferences . Thus, an individual employee’s customer

orientation (which is distinct from the firm’s service

orientation) constitutes his or her service-related attitudes

and behaviours, which directly shape the type of customer

service provided . This customer orientation can then can

influence customer satisfaction and perceptions of service

quality and employee performance . Intuitively though, the

customer orientation of a frontline employee should be closely

linked to his or her interpretation of good customer service.

Vella et al. offer some indirect support for this notion with

their finding that customer service orientation influences an

employee’s perception of the service quality provided for

their customers. However, a customer orientation cannot

addresscapture the employee’s underlying beliefs about what

constitutes good customer service.

Furthermore, whenAnother area of service research that we can

draw on is the understanding of good service from the

customers’ perspective. When research adopts the customer’s

point of view, it usually measures the quality of the service

received using five dimensions of service quality established

by Parasuraman et al. : tangible evidence of the service; the

reliability of the service outcome; and the responsiveness;

assurance; and empathy of the service delivery. These features

all relate closely to the attitudes and behaviours of the

service staff. That is, the way in which service staff carry

out their tasks and interact with customers likely mirrors

their own interpretation of good customer service.

In summary, frontline service employees’ beliefs about good

customer service form the guidelines for their service

interpretation and service behaviours. Services researchers

and managers alike will benefit from a greater understanding

of the facets of frontline service employees’ service models.

4 Gender effects in service research

Gender research has a long history in social psychology,

organisational research, consumer behaviour, and marketing. We

briefly outline what is currently known about gender effects

in services research, shedding some light on possible

differences in the service models embraced by male versus

female frontline service employees as posited in our second

research question.

Babin and Boles research gender differences in service

employee behaviours and cite some important underlying factors

from organisational literature that might explain gender

effects. For women, service work is more emotionally

exhausting, their work roles are more ambiguous, and the

separation of work and non-work tasks is more difficult,

because they take on greater responsibilities. An employee’s

work roles often dominate gender roles, but frontline service

jobs offers an exception; gender roles and associated

stereotypes continue to reign in this context. Despite these

added complexities, women express persistently higher levels

of job satisfaction than men. Differences also emerge in the

quitting intentions of men and women in services and sales

roles, depending on their job satisfaction and job

performance. For example, men are more likely to leave an

unsatisfactory job , but high-performing women are more loyal

to their employers than are high-performing men .

Here again though, gender effects have been studied far more

widely in services research from the customer perspective.

Research focuses on three main topics: quality and

satisfaction judgments, and loyalty; preference for servers of

certain genders; and perceived differences in dealing with

service failure and recovery.

First, male and female customers offer different quality and

satisfaction judgments. Men put more emphasis on the provision

of the core service, whereas women value the relationship with

the service staff more . Men generally rate service quality

higher than women, and the relative importance attributed to

different dimensions of service quality depends on the

customer’s gender . Mattila et al. (2003) investigate gender

effects in service encounters and discover that the effect of

the server’s emotional display on customer satisfaction varies

with the customers’ gender. In particular, men are more

outcome focussed, and negative affective displays do not

influence their satisfaction with a successful service

encounter. However, the satisfaction of female customers drops

in response to negative emotional displays, even if the

service encounter succeeds. Female customers thus seem more

focussed on the service process, whereas men place more

emphasis on the service outcome.

Second, research on service perceptions confirms the

prevalence of gender stereotypes; Fischer et al. , Snipes et al. ,

and Mohr and Henson all show that the gender of the server

influences customer satisfaction ratings. In particular, the

(dis)confirmation of expected gender stereotypes by the server

has a (negative) positive influence on customers’ service

perception. Customers expect a man to work in a hardware store

or automotive repair shop, and they picture an aerobics

instructor or a nurse as a woman; they react more negatively

to service failures if these gender expectations have not been

met . In addition, female customers tend to form stronger

relationships with same-sex servers , though Bove and Smith

(2006) cannot confirm this claim. We postulate that the

different perceptions of women and men in service roles might

reflect the employees’ underlying service models.

Third, gender differences manifest themselves in service

failure perceptions and thus in reactions to service recovery

attempts. The effectiveness of service recovery varies with

customers’ gender, the service employee’s gender, and the

customer–employee gender dyad . For example, concern shown by

a male employee weighs more than female concern, possibly

because customers are less likely to expect a male server to

be concerned. Female servers prompt more critical assessments

of the level of compensation they offer during service

recovery. With regard to the customer’s gender, women appear

to place more emphasis on obtaining voice during the recovery

process. Butcher and Kayani also find that women react more

favourably to waiting time in a service interaction if they

have information about the duration of the wait, but this

effect is absent for men.

In summary, we presume that similar gender stereotypes and

effects prevail in frontline service staff’s own

interpretations of what entails good customer service. In

particular, we expect to find that female employees place

higher importance on emotional displays and expression of

genuine concern. Furthermore, the focus on service outcomes

versus service processes likely differs between male and

female service employees.

5 Methodology

The sample for our exploratory study consists of 876 usable

responses from frontline service employees who work in more

than 20 different service industries in Australia, mostly in

non–sales-oriented roles. We used the Australian and New

Zealand Standard Industrial Classification (ANZSIC) to

identify respondents’ industries. Respondents were recruited

through an online panel over a one week period in February

2009 and screened, such that the final participants spend at

least 40% of their working time interacting with customers. Of

the surveyed service staff, 52.1% indicated that they spent

more than 80% of their working time in face-to-face

interactions with customers, and the average duration per

interaction was 21.2 minutes. In a reflection of the

overrepresentation of women in frontline service roles, 55.4%

of the respondents were women and 44.6% were men. We also

measured the service orientation of frontline service staff

using Brown et al.’s 12 item scale, and noted that the

service orientation of females (4.01) was higher than that of

males (4.18, p<0.001) . The participants responded to the

following open-ended question: “What do you think is good

customer service?” Response statements typically comprised

between one and three sentences, with an average length of

10.2 words.

We applied automated text analysis using Leximancer version

2.25 to examine underlying concepts (common text elements

identified in this paper by their ‘concept name’) and themes

(representing groupings of concepts) in the descriptions of

the customer service understanding of frontline service

employees, as well as to explore any gender differences in

these common text elements. Leximancer has been used

effectively for both conceptual and relational analyses of

textual data Leximancer is particularly well suited to

exploratory research, because it facilitates the extraction of

concepts and thematic clusters. Leximancer concept

identification has been found to have close agreement with

expert judgement (face validity) and an ability to handle

“short and ungrammatical comments” . Leximancer is

particularly well suited to exploratory research of these

comprehensive mental models, because it facilitates reliable

and reproducible extraction of concepts and thematic clusters,

based on Bayesian probability, without inducing the

expectation biases common to manually coded text analyses .

The analysis process comprised three steps. An initial

exploratory analysis examined the data for word clusters,

called entities or concepts. The concepts were then further

explored by tagging the text with the respondents’ gender,

which revealed concepts that occurred to a greater or lesser

extent in each gender group’s responses, and also highlighted

those that occurred in only one gender group . The final step

consisted of an analysis of the pattern of grouping of

concepts into themes, to reveal in greater detail the thematic

clusters underlying employees’ customer service

understanding . Leximancer generates themes by aggregating

concepts, with the aggregation levels being varied to gain

greater insight into how concepts inter-connect. We report the

results as the frequencies of occurrence of common word

clusters and show, using mapsa map, how these entities relate

to the respondents’ gender and other identified concepts and

the respondents’ gender (see Figure 1: concepts shown as dots

and themes as circles).

As with all exploratory studies, this Leximancer analysis of

open-ended responses to frontline employees understanding of

customer service has some limitations. As noted above, there

was a range both in the nature and length of responses.

Furthermore, whilst there is a richness in the various

Leximancer outputs, there is still researcher interpretation

of the data required. However, overall this methodological

approach has proven to be robust and insightful.

6 Results and Discussion

To address our two research questions, we report the outcomes

of the first step of our analysis, in which we identified the

key concepts or word groupings for the overall sample. We also

list the relative results pertaining to concepts that emerged

when we linked these responses to the gender of the frontline

service employee, as we summarise in Table 1. However, we

omitted occurrences with a very low frequency from this table,

to highlight the key differences.

Table 1: Overall and gender-specific key concepts (%) and rankorder

Concepts Overall Female Male Rank Absolu

tecount

Percent

Absolute

count

Percent(Rank)

Absolute

count

Percent(Rank)

(1)Customer 359 81.7% 210 47.8%(1)

149 45.1%(1)

(2)Customers 181 41.2% 105 23.9%(2)

76 23.0%(2)

(3)Listening 87 19.8% 58 13.2%(3)

29 8.7% (4)

(4)Happy 81 18.4% 51 11.6%(4)

30 9.0% (3)

(5)Service 66 15.0% 47 10.7%(5)

19 5.7% (5)

(6)Friendly 58 13.2% 47 10.7%(6)

(7)Smile 50 11.3% 35 7.9% (7) 15 4.5% (9)(8)Polite 38 8.6% 28 6.3% (8)(9)Product 37 8.4% 22 5.0%

(11)15 4.5%

(10)(10) Satisf 36 8.2% 20 4.5% 16 4.8% (7)

ied (14)(11) Time 36 8.2% 19 4.3%

(16)17 5.1% (6)

(12) Help 35 7.9% 27 6.1% (9)(13) Manner 32 7.2% 20 4.5%

(13)12 3.6%

(12)(14) Helpfu

l32 7.2% 26 5.9%

(10)(15) Abilit

y31 7.0% 21 4.7%

(12)(16) Making 29 6.6% 17 3.8%

(17)12 3.6%

(11)(17) Expect

ations29 6.6% 16 4.8% (8)

(18) Meeting

29 6.6% 20 4.5%(15)

(19) Giving 24 5.4%(20) Provid

ing23 5.2% 16 3.6%

(18)

Notes: Absolute count refers to the number of text segments inwhich the concept was identified, according to Leximancer.

As might be expected, the two most common concepts or ideas

were customer and customers, which might be variations on the

same theme. However, we did not merge them into one concept

during our analysis, because their degrees of connectivity

with other concepts, as well as the lexical words associated

with each focal word, show that they actually represent

different underlying sentiments. For example, the concepts

satisfied, making, smile, product, manner, feel, and time all

are unique to the term ‘customer’, whereas meeting, happy,

help, and ability are associated exclusively with the term

‘customers’. Furthermore, the text word “needs” links more

closely to the concept of customers, such as in the phrase

“customers’ needs”. In other sections of text though, both

these concept labels are likely to co-occur. Thus frontline

service employees use different terminology to distinguish a

general approach toward customers and their needs from

attending to an individual customer (e.g., “I believe the

customer is always right. I am always attentive to all our

customers” [respondent 658, female]).

The next most frequent concepts related to good customer

service are listening, happy, and service—regardless of the

frontline service employees’ gender. To clarify the basic

service models emerging from these data, we also investigated

the connectivity scores of the ten most connected concepts

(see Table 2). This score offers an additional indicator of

the prevalence of a concept and its association with other

concepts extracted by the Leximancer analysis. It reveals how

often two concepts are mentioned together. The results

reconfirm the basic definition of service as a subjective

experience of value by the customer . Listening provides the

foundation for understanding and meeting customers’ needs,

which is a prerequisite for value creation. Frontline service

employees also meet customer needs by providing a particular

value-creating service, which has a product or tangible

service outcome at its core; the service delivery interaction

is an auxiliary value creator. The end result is to make

customers happy by satisfying their needs in a friendly and

timely manner. This basic interpretation of good customer

service is also a common denominator underlying Di Mascio’s

(2010) efficiency and win-win service models, though it seems

unrelated to her sales-oriented means service model, possibly

because most of our respondents did not represent an industry

with a sales focus.

Table 2: Degree of concept connectivity in overall sample

Concept*

Connectivity

Relevance

Associated Concepts (co-occurrence in %, most to least common)**

Listening

23.2% customer (48.2), customers (26.4), meeting (9.1), giving (6.8), manner (5.7), service (5.7)

Happy 21.9% customer (67), customers (14.6), satisfied (12.1), service (12.1), making (8.5), time (7.3), feel (7.3), friendly (6.0), smile (6.0)

Service 20.0% customer (54.6), customers (28.0), product (18.6), happy (13.3), friendly (9.3), smile (9.3), feel (9.3), providing (9.3), making (6.6), expectations (6.6) listening (6.6), satisfied (5.3), polite (5.3)

Friendly

16.0% customer (40.0), customers (26.6), manner (20.0), helpful (18.3), polite (15.0), service

(11.6), help (10.0), happy (8.3), product ( 8.3), listening (6.6), smile (6.6), expectations (5.0), providing (5.0)

Smile 13.3% customer (48.0), polite (20.0), service (14.0),help (12.0), customers (12.0), happy (10.0), friendly (8.0), helpful (8.0), ability (6.0), providing ( 6.0)

Product 11.4% customer (53.4), service (32.5), customers (18.6), making (13.9), friendly (11.6), helpful(9.3), happy (6.9), time (6.9)

Polite 10.4% customer (28.2), smile (25.6), friendly (23.0),customers (20.5), helpful (17.9), service (10.2), help (10.2), time (5.1), manner (5.1), listening (5.1)

Time 10.1% customer (50.0), customers (18.4), happy (15.7), manner (7.8), product (7.8), satisfied (5.2), outcome (5.2), listening (5.2), friendly(5.2), helpful (5.2), polite (5.2), service (5.2)

Satisfied 9.8% customer (89.1), happy (27.0), making (24.3), feel (10.8), service (10.8), customers (10.8), smile (5.4), time (5.4). outcome (5.4)

Help 9.3% customer (34.2), customers (28.5), friendly (17.1), smile (17.1), ability (14.2), polite (11.4), listening (11.4), service (8.5), feel (5.7), product (5.7), happy (5.7)

*Concepts customer and customers not included.** Included if >5%.

Thematic convergence analysis identified three individual,

though interrelated, thematic concept groupings of Service,

Manner, and Customers. We distinguish such thematic clusters

from similarly named individual concepts by using

capitalization and italics to aid interpretation of the

results, such that Smile is a theme, whereas ‘smile’ indicates

a concept. These three overarching thematic groupings are

shown with other theme circles on the Leximancer map (Figure

1). They combine to reflect a general service model

effectively illustrated by this respondent’s description:

“Knowing your customers’ needs and wants, making sure they

leave satisfied..., a smiling face and a happy customer who

you will see again and again” [respondent 672, female].

Beyond this common core, we find notable differences between

men and women, as highlighted in Table 1 when a concept is

proportionally more represented, or non-existent, for one or

other gender. In Figure 1 Leximancer places concepts and

thematic clusters more closely associated with one particular

gender group visually closer to that gender tag (e.g.,

TG_MALE_GS_TG). Common elements are more centrally positioned and

appear highlighted with a frame to facilitate their

recognition. To explore potential gender differences in

employees’ customer service understanding, we relied on gender

concept relativities from the entire data set. By

investigating the tagged concepts and overarching themes, we

can reveal the similarities and differences between genders;

the common central themes remain the same.

Specifically, the concepts of friendly, polite, smile, help,

helpful, and meeting were more commonly linked to female

frontline service personnel. For women, concepts such as smile

and polite epitomise good customer service, which means

“Serving with a smile and being polite” [respondent 324,

female]. For male respondents, interactional facets such as

smiling and politeness are more means to an end: “Smile, be

polite, being able to answer questions effectively and reach

the customers goal as that is my goal” [respondent 202, male].

The concepts most closely associated with male responses also

included satisfied, making, time, and expectationexpectations.

These findings are supported by the thematic analysis in

Figure 1, where the proximity of a theme (circle) to a gender

tag is a direct reflection of the closeness of the

relationship. Smile, Friendly, and Polite emerge again as mainly

female themes; (upper half); Expectations, Provider and Job are the

themes most clearly linked to male frontline service

employees. (lower half).

Take in Figure (1)

These gender differences reflect our findings regarding the

gender differences in customer service orientation, and seem

to correspond with the distinction between functional and

emotional outcomes of customer service, as proposed by

Sandström et al. . According to their study, services provide a

means to reach end states and thus fulfil both basic

functional qualities and the need for emotional end states.

The emotional and functional components contribute equally to

the overall service experience. Grönroos also distinguishes

technical and functional qualities of services but uses this

terminology differently; he refers to the technical dimension

as what is being done (outcome), whereas the functional

dimension addresses how the service gets delivered (process).

Our findings thus suggest that male frontline service

employees tend to focus on the service outcome, and

particularly the functional outcome, such as “good product

knowledge and ability to listen and evaluate correctly”

[respondent 36, male]. Their product-driven view of customer

service emphasises the core product or service, rather than

auxiliary services. This attitude relates most closely to Di

Mascio’s (2010) efficiency service model, which involves

giving customers what they ask for, quickly and efficiently.

The focus on the efficient provision of a desired outcome is

clearly reflected in the following quotes from male

respondents:

“Prompt service, on time for appointments, a happy

customer at the end” [respondent 97].

“Being available quickly to service client needs; ensuring

client's requirements are satisfied” [respondent 238].

“Making sure your customers expectations are met with the

product or service you are providing, if they are not met

then clearly explain the benefits your goods or service

will deliver to them” [respondent 386].

In contrast, female frontline service employees reveal a focus

on the emotional value of the service encounter and the manner

of service interaction. This service understanding is somewhat

similar to the relationship aspect of the mutually beneficial

win-win service model , but it does not explicitly highlight a

problem-solving focus. The following quotes from female

respondents illustrate how women acknowledge the functional

service outcome as a prerequisite for satisfactory service but

focus on the delivery of this outcome:

“To help customers to the best of my ability, to answer

their needs and to provide enthusiastic, helpful, and

friendly advice and/or results” [respondent 267].

“Being friendly, helpful and listening to a customer’s

needs; asking questions to be sure they get the correct

product or service” [respondent 596].

“Being greeted with a genuine, connected smile and a

polite, friendly hello; someone who is interested in you

as a customer and providing you with whatever you require

in a warm but professional manner” [respondent 663].

These findings correspond with Grönroos’s (2000) process-

oriented functional quality dimension. For female frontline

service staff members, it is equally important to achieve an

emotional outcome, such as warm service. Women’s focus on

processes rather than outcomes has been well documented . In a

similar vein, Mohr and Henson report that industrial buyers

rate female sales personnel more favourably on process-related

aspects, such as understanding of other people and

friendliness, but male sales personnel score higher ratings on

outcome-oriented aspects, such as product knowledge and

technical assistance. Our results indicate that this prevalent

process-versus-outcome pattern also emerges in the customer

service understanding of women versus men.

7 Managerial Relevance and Conclusion

Our exploratory study adds to sparse literate on the

interpretation of what constitutes good customer service, as

described by those who deliver it. Regardless of the

individual employee’s service model or gender-specific

predispositions, listening to the customer to offer service in

a way that makes the customer happy constitutes the core of

frontline employees’ service understanding. However, the

interpretation of good customer service also is influenced by

the gender of the employee. The service models of women and

men, though similar in their core elements, reveal obvious

differences. For female service staff, the quality of the

interaction and service processes dominate their understanding

of good customer service; their male counterparts instead are

more outcome focussed and consider good customer service

mainly as a result of efficient problem solving.

An appreciation of gender-specific differences in the customer

service understanding of frontline service employees has

important implications for managing service staff. Certain

service roles lend themselves to either a “male” or “female”

service understanding, and service staff recruiters should

consider the best fit between service types and service

models. We imply no judgement about the efficacy of the

service models of men and women; both have equal merit, though

perhaps in different service situations. Our results also

provide important insights for the members of service delivery

teams, because team efficacy can benefit from a greater

awareness of team members’ different service understandings,

linked to their gender.

Although our findings provide some valuable insights, into

gender differences of service models, we acknowledge the

limitations of our research. Our exploratory study is based

on short written statements rather than a face-to-face

interview which would afford the opportunity to explore in

depth their service models. However this is partially offset

by the large and diverse sample. Furthermore, gender is just

one – albeit important – of the underlying determinants of

frontline service employees’ service models. Human beings are

complex, thus further research is needed to. Such studies

could determine the effects of other defining employee

characteristics of frontline service employees, as well as and

the influence of contextual issues, such as the type of

service delivered, onas well as their interplay, on their

underlying service models. For example, the employee’s degree

of work experience in a boundary-spanning service role, their

customer orientation, display of emotional labour, age,

cultural background, and overall job satisfaction are likely

to play important rolesalso impact on service models. Some of

these factors may even be confounded with the server’s gender,

and additional research is required to distinguish their

effects clearly. Also of great interest would be a

longitudinal study tracking how service models develop with

increasing work experience in a boundary-spanning service

role, or changes in the work environment. Differences also

might exist among various service industries, and for high

versus low contact services. What we demonstrate clearly with

our exploratory study is that gender, in itself, is an

important factor that influences the formation of service

models held by frontline service employees.

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Figure 1: Gender concept relativities, allrespondents.Leximancer map – thematic analysis tagged forgender

Concepts common to both genders (added rectangle), themecircles enclose related concepts.

Notes: 1000 iterations; themes at 33% aggregation level, frameadded.