Unit 4 Essay LDRSHP
How Leaders’ Motivation Transfers to Customer Service Representatives
Jan Wieseke 1 , Florian Kraus
1 , Sascha H. Alavi
1 , and
Tino Kessler-Thönes 2
Abstract Motivating customer service representatives (CSRs) to their highest performance levels is a major task of service unit managers. However, previous studies focused on the impact of leader behavior on follower motivation, while the influence of leader motivation on follower motivation has not been investigated yet. Thus, the authors develop and test a multilevel framework for the motivation spillover principle, which holds that the three components of Vroom’s motivation theory transfer from managers to CSRs. The authors apply this framework to the context of service technology adoption and test it with a matched multilevel sample of 387 service unit managers, 1,018 CSRs, and objective company records. The results support the notion of a motivation spillover from managers to CSRs, which exists incrementally beyond the direct effect of manager’s adop- tion behavior on CSR’s adoption. However, not all motivation components transfer unconditionally but are contingent on char- ismatic leadership and manager-CSR similarity––a finding that implies for researchers that an undifferentiated view of motivation in multilevel settings might not suffice. For organizations, the findings suggest that managers are important multi- pliers of motivation and thus organizations should direct their motivation efforts toward middle-level managers, as they might turn into serious roadblocks to CSR motivation.
Keywords motivation, motivation transfer, leader-follower dyad
What you wish to kindle in others must burn within yourself.
Augustine of Hippo
As organizations increasingly experience the deleterious
effects of employee disengagement, employee motivation has
become a topic of paramount importance. In 2009, in the
U.S. economy, an estimated 18% of all workers (24.7 million) were disengaged, reducing employee performance and costing
the U.S. economy a total of $300 billion annually (Gallup
2010). These figures make the investigation of levers for
employee motivation a high priority.
For service companies, motivation of customer service
representatives (CSRs) is critical because CSRs work at the
boundary between organizations and their customers, where
oversight and supervision are difficult. Since ‘‘customer con-
tact employees are the first and only representatives of a service
firm’’ (Hartline, Maxham III, and McKee 2000, p. 35), service
providers constantly face the challenge of finding ways to
improve the performance of service personnel (de Jong, de
Ruyter, and Lemmink 2003). As it impels action, motivation
is a major factor in achieving CSR performance (Locke and
Latham 2004).
Consider a typical CSR in a service unit, who experiences
his or her service unit manager as highly motivated to adopt a
new service technology. Would the CSR’s motivation to
adopt the service technology be lower if she or he worked
with a manager who is not at all motivated to adopt the new
technology, all else being equal? And what are the implica-
tions for the CSR’s motivation in that scenario, when she/he
is very similar to his or her service unit manager or when the
service unit manager is very charismatic? The transfer of
motivation from service unit managers to CSRs and the fac-
tors on which this transfer depends are not yet well understood
in leadership research.
Motivating CSRs to their highest performance levels is a
major task of service unit managers, and ‘‘motivation is a core
competency of leadership’’ (Latham 2007, p. 4). Previous stud-
ies emphasize the importance of leaders’ behavior for their fol-
lowers’ behavior (Berry and Parasuraman 1992; Hartline and
Ferrell 1996), since ‘‘leader behaviors result in follower heigh-
tened motivation to attain designated outcome(s) which then
leads to performance’’ (Ilies, Judge, and Wagner 2006, p. 1).
1 Ruhr-Universität Bochum, Universitätsstraße, Bochum, Germany
2 Sana Kliniken AG, Kaiser-Wilhelm-Straße, Bad Kreuznach, Germany
Corresponding Author:
Jan Wieseke, Ruhr-Universität Bochum, Universitätsstraße 150, 44780
Bochum, Germany
Email: jan.wieseke@rub.de
Journal of Service Research 14(2) 214-233 ª The Author(s) 2011 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1094670510397177 http://jsr.sagepub.com
Given these strong links between leader behavior, follower
motivation, and performance, quite unsurprisingly a plethora
of research exists that investigates the effect of leader behavior
on follower motivation (Lord and Brown 2001; Lord and
Maher 1991). The impact of leader behavior on follower moti-
vation has been focus of several leadership and work
motivation theories, including the path-goal theory of leader-
ship (House 1971), self-concept based leadership (Shamir,
House, and Arthur 1993), transformational and transactional
leadership (Bass 1985), organizational behavior modification
(Luthans and Kreitner 1975), goal-setting theory (Locke
1968), self-determination theory (Deci and Ryan 1990), and
expectancy theory (Vroom 1964).
However, while a number of investigations have explored
the impact of leader behavior on follower motivation, no study
yet has analyzed the impact of leader motivation on follower
motivation. Therefore, this paper investigates the transfer of
motivation from leader to follower. We explore the potentially
powerful motivation spillover between service unit manager
and CSR, focusing on task-specific motivation of managers and
CSRs to adopt a new service technology.
Drawing on the literature of charismatic leadership, social
learning theory, and expectancy theory, we derive a multilevel
framework of motivation spillover. We define motivation spil-
lover as the transfer of different components of motivation from
the service unit manager to the CSR. Specifically, we argue that
the three components of Vroom’s well-known motivation theory
transfer from service unit managers to CSRs. To investigate con-
tingency factors of motivation spillover, we analyze whether the
motivation transfer from managers to CSRs is contingent on
charismatic leadership style and manager-CSR similarity.
A key asset of our research is the testing of motivation spil-
lover and its consequences for the adoption of a completely
new service technology. We employ a large-scale multilevel
data set of 387 managers and 1,018 subordinates, which links
data from three levels (service unit managers, CSRs, and objec-
tive company data on service technology use). This investiga-
tion thus answers the call in leadership research to consider
multiple levels of analysis (Avolio et al. 2009), especially as
‘‘relatively few studies in any of the areas of leadership
research have addressed levels-of-analysis issues appropriately
in theory, measurement, data analysis, and inference drawing’’
(Yammarino et al. 2005, p. 879). By developing a multilevel
conceptual model, collecting data from different organizational
levels, and applying multilevel data analysis techniques,
we hope to provide a more complete understanding of CSR
motivation. This effort is particularly important because, ‘‘even
though [leader-follower] dyads are ubiquitous to organiza-
tional settings, they are the least studied level of analysis rela-
tive to individuals, groups, or organizations’’ (Gooty and
Yammarino 2011, p. 1). To account for level-of-analysis
issues, such as the nesting of CSRs in managers, we explicitly
include an effect of coworker motivation on CSR motivation
as a control to account for motivational spillover effects of
coworkers and thus isolate the motivation spillover effect
between leader and employees.
Our study contributes to the leadership literature in several
ways. Primarily, our examination broadens current understand-
ing of employee motivation by identifying an effect that
describes how leaders motivate employees. Further, motivation
spillover is moderated by manager-CSR similarity and
charismatic leadership style. In fact, we find that the transfer
of one motivation component (valence) requires either charis-
matic leadership or high manager-CSR similarity.
Our results thus have far-reaching implications for service
firms. For example, when implementing a new service technol-
ogy, organizations should direct their motivation efforts toward
middle-level managers instead of targeting primarily CSRs.
Middle managers may be important multipliers of motivation,
and in the absence of this attention, they may become serious
roadblocks to the service technology implementation.
The remainder of the article proceeds as follows. We begin
by developing a conceptual model of motivation transfer from
manager to CSR. We then present an empirical study that tests
the hypotheses proposed in the conceptual model. We conclude
with a detailed discussion of the research findings, implications
for service marketing practices, and future research directions.
Theoretical Framework and Hypotheses Development
The core of our research framework is motivation spillover
between service managers and CSRs, which proposes a vertical
cascade of manager motivation to CSR motivation. As stated
above, the model considers motivation with respect to the
adoption of service technology. Specifically, the framework
encompasses three categories of constructs: (a) motivation to
adopt service technology on both the manager and CSR levels,
(b) service technology adoption behavior on the manager and
CSR levels to account for the behavioral consequences of moti-
vation, and (c) charismatic leadership style and manager-CSR
similarity as contingency factors for the motivation transfer.
Figure 1 presents our conceptual framework.
To conceptualize motivation, we rely on expectancy theory
(Vroom 1964). In brief, expectancy theory postulates that an
individual’s motivation depends on his or her expectancy,
instrumentality, and valence estimates, and that higher motiva-
tion leads to increased effort (Vroom 1964). According to
Vroom (1964), motivation is a multiplicative function of an
individual’s expectancy that a certain effort will lead to the
intended performance, the instrumentality of this performance
to achieving a certain result, and the desirability of this result
for the individual, that is, valence.
The theory posits that motivation increases to the extent that
an individual experiences enhanced expectancy and instrumental-
ity estimates, along with valence for job-related outcomes.
Expectancy refers to an individual’s estimate of an action or effort
leading to an outcome or performance (Vroom 1964). Instrumen-
tality is an individual’s belief about the degree to which a specific
performance level will result in favored job-related outcomes,
such as pay or the success of one’s company, or to the blocking
of undesirable outcomes, such as stress or extra work (Sanchez,
Wieseke et al. 215
Truxillo, and Bauer 2000). Valence refers to an affective orienta-
tion toward outcomes and is interpreted as desirability or antici-
pated satisfaction with outcomes (Vroom 1964).
Expectancy theory is appropriate for measurement of moti-
vation for three reasons. First, expectancy theory has achieved
wide acknowledgment by a broad array of researchers (Ambrose
and Kulik 1999; Bartol and Locke 2000; Miner 2005; Van Eerde
and Thierry 1996). Second, expectancy theory produces valid
results within a context that establishes performance-reward
contingencies in an unambiguous, concrete manner (Graen
1969; Wanous, Keon, and Latack 1983). The service technology
adoption context of our research should meet this boundary con-
dition, since the relationship between performance and rewards
is distinct and unambiguous. Third, previous studies show that
with sufficient attention to the selection and initial conceptuali-
zation of the constructs, expectancy theory fulfills measurement
requirements (Ilgen, Nebeker, and Pritchard 1981; Klein 1991;
Mitchell 1974). For our study, we put extensive effort into item
generation, as we explain below in the Measures section.
The theoretical framework described above provides a basis
for our hypotheses development. We start by introducing our
theoretical reasoning for the transfer of managers’ expectan-
cies, instrumentalities, and valences to CSRs, and subsequently
postulate hypotheses regarding the moderating effect of charis-
matic leadership and manager-CSR similarity on motivation
transfer. Finally, we develop hypotheses on the consequences
of motivation.
Motivation Transfer from Service Unit Manager to CSR
Transfer of expectancies. We argue that a manager’s expec- tancies with respect to service technology adoption will trans-
fer to the CSR through social learning. Social learning theory
posits that individuals learn from significant others by obser-
ving their behavior (Bandura 1977). In particular, seeing or
even visualizing significant others performing successfully
can raise perceptions of efficacy, because observers infer that
they may be able to master comparable tasks (Bandura et al.
1980). In organizational contexts, expectancies and instru-
mentalities are particularly susceptible to adoption through
social learning as they are important for workers’ orientation
in the workplace. Moreover, owing to their status and
Service Technology Adoption
Manager-Focal CSR • Similarity
Manager
Level 2 – Manager
Level 1 – CSR
Motivation
Motivation
• Expectancy, Valence, Instrumentality Service Unit • Motivational Climate
Expectancy
Instrumentality
Valence
Expectancy
Instrumentality
Valence
Focal CSR
Manager
Focal CSR
Service Technology Adoption
Coworkers of Focal CSR
Controls • Organizational Training and Support • Job Satisfaction • Commitment • Service Experience
Controls • Organizational Training and Support • Job Satisfaction • Commitment • Service ExperienceManager
• Charismatic Leadership
Validation with
Company Records
Contingency Factors
Controls
Validation with
Company Records
M ot
iv at
io n
Sp ill
ov er H1a-c
H4b
H4a
H2a-c
H3a-c
Figure 1. Conceptual framework: multilevel motivation and its effects on service technology adoption.
216 Journal of Service Research 14(2)
competence, leaders are usually highly potent role models
(Manz and Sims 1981).
Researchers have applied social learning theory to the orga-
nizational context and found that a manager can be a powerful
behavior model for subordinates (Davis and Luthans 1980;
Luthans and Kreitner 1984; Rich 1997; Sims and Manz 1982;
Weiss 1977). Davis and Luthans’s (1980) social learning
framework for organizational behavior explicitly incorporates
cognitions that are acquired by social learning (also Luthans
and Kreitner 1984). Therefore, managers might represent role
models to subordinates, who adopt managers’ cognitions in
addition to their behavior. For example, work values transfer
from leaders to subordinates (Weiss 1978).
This notion is supported by researchers who embrace the
cognitive interpretation of leadership, which posits that cogni-
tions in organizations transfer by means of behavioral scripts
that individuals observe and then model (Gioia and Manz
1985; Wofford and Goodwin 1994; Wofford, Goodwin, and
Whittington 1998). Thus, we argue that CSRs should follow the
example of their managers and take on the manager’s expectan-
cies as cognitive representations of confidence in the service
technology adoption.
When managers express their expectancies through verbal
or nonverbal behavior, CSRs can emulate the manager’s
expectancies through social learning. Therefore, CSRs should
be aware of their manager’s expectancies, which are reflected
in self-confident problem solving, and evidence them through
confidence in their own abilities to solve a given task or
through high expressed expectations for success (De Cremer
and Van Knippenberg 2004; Riggs and Knight 1994) in task-
solving situations, task feedback situations (Shea and Howell
1999), situations of crisis (Mumford et al. 2007), or situations
of self-disclosure (Gardner et al. 2005). For example, manag-
ers might exhibit confidence in using the new service technol-
ogy in a coaching session with their CSRs.
We thus argue that in striving to equal their managers, CSRs
will emulate expectancies through social learning, since man-
agers ‘‘engage in behaviors designed to create impressions of
competence and effectiveness’’ (Sosik and Dworakivsky
1998, p. 504).
Hypothesis 1a: Managers’ expectancies have a positive effect
on CSRs’ expectancies.
Transfer of instrumentalities. In line with our argument that expectancies spill over, we contend that social learning will like-
wise transfer the manager’s instrumentalities concerning the ser-
vice technology adoption to the CSR. In accordance with social
learning theory, we argue that CSRs should adopt their manager’s
instrumentalities with respect to service technology adoption.
Managers often employ symbolic behaviors to express their
inherent beliefs (Shamir et al. 1998) and engage in self-
presentation and self-disclosure behaviors that CSRs perceive
(Gardner and Avolio 1998). In particular, managers should
express their instrumentality beliefs in social interactions with
their CSRs, informing them about connections between work
performance and contingent rewards. Typical situations would
include providing incentives or encouragement to CSRs toward
goal achievement or articulating a future vision (House 1996).
For example, a service unit manager might indicate to a CSR
that success in adopting a new service standard commonly
results in job promotions, thus expressing an inherent belief
that goal achievement (a first-order outcome) leads to career
advances (a second-order outcome).
To summarize, we argue that in striving to model their man-
ager, CSRs will observe their manager’s expressions of instru-
mentality beliefs. Therefore,
Hypothesis 1b: Managers’ instrumentalities have a positive
effect on CSRs’ instrumentalities.
Transfer of valences. Social learning theory proposes that, apart from observational learning, people actively adopt their
behavior as a consequence of rewards and punishments,
which they experience either directly or vicariously (Bandura
1977; Manz and Sims 1981). We suggest that in addition to
observational learning, reinforcement by rewards and punish-
ments plays a role in the transfer of manager’s valences to
CSR’s valences.
First, in line with Hypothesis 1a and b, we suggest that
CSRs adopt their superior’s valences through social learning,
as CSRs strive to imitate the manager (Luthans and Kreitner
1984; Rich 1997; Weiss 1997). Typically, managers engage
in verbal behavior when conveying their assessment of task-
specific outcomes (i.e., their valences). Communicating these
assessments to CSRs is an essential function of the manager,
since it serves an important guiding purpose for the CSR
(House 1996).
Second, we contend that CSRs’ valences concerning a ser-
vice technology adoption align with their superior’s valences
as a result of conditioning through reward and punishment.
Fundamental to this reasoning is the assumption that manag-
ers are hierarchically superior to their CSRs and thus may
legitimately reward or punish them. If CSRs express a
valence that deviates from their manager’s valence, the man-
ager should sanction the CSR to produce conformity with the
manager’s valence. For example, if a CSR states that the
increase in customer satisfaction entailing the implementation
of a new service technology in a service unit is unimportant
(low valence), while the superior considers the implementa-
tion to be crucial for the success of the service unit (high
valence), the manager might give the CSR negative feedback
(punishment) to achieve compliance with the manager’s
valence. In contrast, if the expressed valence is congruent
with the leader’s valence, the manager should reinforce the
CSR’s behavior (Mawhinney and Ford 1977; Stajkovic and
Luthans 2003).
To summarize, we propose that managers express their
valences, which CSRs adopt through social learning. Reinfor-
cement and punishment further enhance the adoption of man-
agers’ valences by CSRs, providing incentives to CSRs to
conform to their superior’s valences. Thus:
Wieseke et al. 217
Hypothesis 1c: Managers’ valences have a positive effect on
CSRs’ valences.
The Influence of Charismatic Leadership and Manager–CSR Similarity on Motivation Transfer from Manager to CSR
Charismatic leadership. We contend that charismatic leader- ship reinforces the transfer of expectancies, instrumentalities,
and valences from service unit manager to CSR for two rea-
sons: (a) charismatic managers engage in symbolic behaviors
that foster strong follower identification, resulting in a higher
likelihood of imitating the manager and (2) charismatic manag-
ers are more prone to self-expression.
Charismatic leadership is an attribution resulting from
CSRs’ perceptions of their manager’s behavior. Therefore, sub-
ordinates do not commit themselves to their managers because
of their legitimate authority, but ‘‘out of perceptions of their
leader’s extraordinary character’’ (Conger, Kanungo, and
Menon 2000, p. 748). Charismatic managers exhibit a plurality
of behaviors that foster strong follower identification, such as
individual consideration and empowerment of employees, cre-
ating a compelling vision of the future and exemplary acts
involving personal risk and self-sacrifice (Bass 1985; Conger,
Kanungo, and Menon 2000; De Cremer and Van Knippenberg
2004). Prior investigations show that charismatic leadership
results in a personal identification with the manager (Kark,
Shamir, and Chen 2003). When followers admire and identify
with a manager, they are more likely to emulate the manager’s
beliefs and values (Yukl 1994).
Second, prior studies show that charismatic managers are
particularly expressive of feelings, aesthetic values, and self-
concepts (Shamir, House, and Arthur 1993). Specifically, char-
ismatic managers display self-confidence (Gardner et al. 2005;
Shamir, House, and Arthur 1993; Shea and Howell 1999; Sosik
and Dworakivsky 1998); project beliefs such as hope, faith, and
optimism (Gardner et al. 2005; Sosik and Dworakivsky 1998);
and express their values by creating a ‘‘value laden vision of the
future’’ (Sosik 2005, p. 224).
In sum, charismatic managers are more likely to be the
object of strong personal identification for CSRs and are more
inclined to reveal their expectancies, instrumentalities, and
valences. Thus, CSRs are more likely to model charismatic
managers and are more likely to adopt charismatic managers’
expectancies, instrumentalities, and valences through social
learning. Therefore:
Hypothesis 2a: Charismatic leadership enhances the positive
relationship between managers’ and CSRs’ expectancies.
Hypothesis 2b: Charismatic leadership enhances the posi-
tive relationship between managers’ and CSRs’
instrumentalities.
Hypothesis 2c: Charismatic leadership enhances the positive
relationship between managers’ and CSRs’ valences.
Manager-CSR similarity. Research on charismatic leadership has regarded similarity between leader and follower as an
important antecedent of interaction outcomes in the leader-
follower dyad (Ehrhart and Klein 2001). We suggest that
manager-CSR similarity enhances the transfer of expectancies,
instrumentalities, and valences from service unit manager to
CSR for two reasons. The more similar a manager is to his or her
CSR, (a) the more likely the CSR is to regard the manager as a
role model and (b) the more likely the manager is to express his
or her expectancies, instrumentalities, and valences to the CSR.
Similarity-attraction theory suggests that individuals have
self-based schemata that lead to a positively biased evaluation
of others who are similar to themselves (Byrne 1971). Investi-
gators have broadly applied this notion to the organizational
context and verified it in the leader-follower dyad (Ashkanasy
and O’Connor 1997; Ehrhart and Klein 2001; Felfe and Schyns
2006; Keller 1999; Schyns and Sanders 2007). In line with
similarity-attraction theory, the more similar a CSR is to the
manager, the more attractive the CSR will find the manager.
‘‘When individuals perceive themselves to be similar to their
leaders, they are more attracted to the leaders than are those
who do not feel similar to their leaders’’ (Schyns and Sanders
2007, p. 2346). A higher attraction to the manager increases the
probability that the CSR will develop a strong personal identi-
fication with the manager and regard the manager as a role
model (Gardner and Avolio 1998).
Further, increased interpersonal attraction resulting from
high similarity leads to a closer, more confidential relationship
between manager and CSR (Ashkanasy and O’Connor 1997;
Boyd and Taylor 1998) and an increase in the quality and fre-
quency of the dyadic interaction (Engle and Lord 1997; Phillips
and Bedeian 1994). For example, to elevate the CSR’s career
prospects, the manager might engage in coaching and mentor-
ing the CSR. Not only does this social interaction enhance the
manager’s opportunity to express expectancies, instrumental-
ities, and valences, but the greater exposure to the manager
raises the likelihood that the CSR will adopt the manager’s
motivational components through social learning.
To summarize, the more similar a CSR is to the manager,
the stronger the probability that the CSR will regard the
manager as a role model and the greater the propensity of the
manager to express expectancies, instrumentalities, and valences
to the CSR. Thus:
Hypothesis 3a: Manager-CSR similarity enhances the posi-
tive relationship between managers’ and CSRs’
expectancies.
Hypothesis 3b: Manager-CSR similarity enhances the posi-
tive relationship between managers’ and CSRs’
instrumentalities.
Hypothesis 3c: Manager-CSR similarity enhances the posi-
tive relationship between managers’ and CSRs’ valences.
The Influence of Motivation on Service Technology Adoption
The three components of expectancy theory can explain the
cognitive process by which individuals initiate, direct, and
218 Journal of Service Research 14(2)
sustain behavior (Campbell et al. 1970), especially as expec-
tancy theory ‘‘was developed to explain virtually all work-
related behavior ranging from occupational choice to perfor-
mance on the job’’ (Latham 2007, p. 45). Thus, CSRs’ motiva-
tion, as well as managers’ motivation, should have a positive
effect on their service technology adoption:
Hypothesis 4a: The higher the CSR’s motivation, the higher
the CSR’s service technology adoption.
Hypothesis 4b: The higher the manager’s motivation, the
higher the manager’s service technology adoption.
Method
We tested the hypotheses in a service context, in cooperation
with a large-scale travel agency franchise organization. The
firm consists of a large number of homogeneous service units
with a low span of control and close interaction between ser-
vice unit managers and CSRs (on average each manager leads
three customer-contact employees). We chose a franchise con-
text because it presents a typical service organization structure,
exhibiting a sales-laden service environment that spans a
sizeable geographic area.
At the outset of our study, the travel agency franchise system
introduced a completely new service technology tool that facil-
itates customer contact by creating custom-tailored travel offers,
providing travel information aligned to the individual customer’s
needs, and proposing additional services based on the customer’s
account history. In this travel agency organization, both the
CSRs and the service unit managers have direct customer con-
tact and therefore also use the new service technology. 1
Collection of Multilevel Data
We distributed questionnaires to the manager of each agency
(N ¼ 1,080) and all CSRs (N ¼ 3,410), providing separate return envelopes for each respondent, and questionnaires came
back to the researchers via mail. We collected data on manager
motivation and CSR motivation at two different points in time,
distributing the manager survey first and the CSR survey
2 months later. The use of such a time lag in data collection
is consistent with expectancy theory, and the temporal order
should provide a first test of our theory of a motivation spil-
lover from managers to followers.
We received usable questionnaires from 552 managers
(response rate: 51.1%) and 1,598 CSRs (response rate: 45.7%). To construct a three-level data set, we used data from 387 managers and 1,018 CSRs that were connectable via code
numbers. In this data set, 64.5% of the managers were female, with a mean age of 41 years (SD ¼ 9.1 years). On the CSR level, 86% were female, with a mean age of 31.9 years (SD ¼ 9.6 years). While a high proportion of female employees is normal in the travel industry, we controlled for gender, oper-
ationalized as a dummy variable, and results show that gender
did not exert any significant impact on the relationships we
examined. Additionally, 6 months after the collection of the
CSRs’ self-reported technology adoption data, the travel com-
pany recorded information on objective service technology use
(generated sales with new service technology) over a 6-month
period.
We assessed nonresponse bias using time-trend extrapola-
tion (Armstrong and Overton 1977), and we detected no differ-
ences between early and late responders on any of the
constructs of interest or demographic variables within the two
samples. To control for multicollinearity, we inspected the var-
iance inflation factors of the variables. The variables yielded
values between 1.0 and 1.9, indicating that no problems exist
with multicollinearity (Kleinbaum et al. 1998).
Measurement Measurement sources. We measured the constructs in this
study with items we adapted from well-established operationaliza-
tions, making modifications on the basis of an extensive qualitative
pre-study as needed to fit the study’s context. We took several steps
to acquire a thorough understanding of the CSRs’ and service
managers’ motivational components with respect to the adoption
of the new service technology and thus ensure the validity and
reliability of our motivation measurements. Drawing on a review
of the relevant literature that addresses conceptualization issues
in expectancy theory (Ilgen, Nebeker, and Pritchard 1981; Klein
1991; Mitchell 1974; Van Eerde and Thierry 1996), we followed
the approach of Sanchez, Truxillo, and Bauer (2000) and Teas
(1981) in measuring valence, instrumentality, and expectancy.
We assessed the CSRs’ and managers’ perceived importance of
job-related outcomes, their perceived probability of mastering the
system after putting some effort into using it, and their belief that
using the system would lead to obtaining the desired outcomes.
Further, we conducted an extensive qualitative study with
in-depth interviews to validate a pool of items that measure the
motivation of CSRs and managers regarding the adoption of a
new service technology. Focus group discussions among CSRs,
service unit managers, and marketing faculty prior to the
survey provided an intimate understanding of CSRs’ and man-
agers’ confidence in using the technology and favorable and
unfavorable work outcomes and goals related to the service
technology adoption. We conceptualized the survey items on
the basis of the insights we gained in this qualitative study.
Finally, we conducted interviews with five marketing scholars
to validate and supplement the items previously developed. We
are thus confident that our scales have high validity and
reliability.
Validity and reliability of the measurement. All variables used in our study are based on well-established scales. Appendix
A provides a complete list of all items we used to measure the
constructs in the study, and Table 1 presents descriptive statis-
tics, internal consistency reliabilities, and intercorrelations of
all study variables. As Table 1 shows, all the measurement
scales have reliability indexes that exceed the 0.70 threshold
(Nunnally 1978) and an average variance extracted that is
greater than 0.50 (Fornell and Larcker 1981).
Wieseke et al. 219
T a b
le 1 .
M e an
s, S ta
n d ar
d D
e vi
at io
n s,
an d
In te
rc o rr
e la
ti o n
M at
ri x
V ar
ia b le
s 1
2 3
4 5
6 7
8 9
1 0
1 1
1 2
1 3
1 4
1 5
1 6
1 7
1 8
1 9
L e ve
l 2 : M
an ag
e rs
1 . E x p e ct
an cy
(. 8 6 )
2 . In
st ru
m e n ta
lit y
.3 4
(. 7 5 )
3 . V
al e n ce
.2 9
.2 7
(. 7 8 )
4 . S e rv
ic e
T e ch
n o lo
gy A
d o p ti o n
.2 9
.3 5
.2 6
(. 8 9 )
5 . C
h ar
is m
at ic
L e ad
e rs
h ip
.3 3
.4 1
.2 1
.3 7
(. 9 0 )
6 . O
rg . T
ra in
in g
an d
S u p p o rt
.0 9
2 5
.3 1
.2 8
.1 2
(. 8 7 )
7 . Jo
b S at
is fa
ct io
n .1
3 .1
4 .1
2 .2
6 .3
1 .2
1 (. 8 4 )
8 . C
o m
m it m
e n t
.2 3
.3 0
.2 6
.2 8
.5 2
.3 1
.4 2
(. 7 2 )
9 . S e rv
ic e
E x p e ri
e n ce
.0 1
.0 2
.0 5
.0 2
.0 6
.0 7
.0 2
.0 5
_ a
L e ve
l 1 : C
S R
s
1 0 . E x p e ct
an cy
.2 5
.0 9
.0 8
.0 5
.1 0
.0 8
.0 7
.1 5 �
.0 9
(. 8 2 )
1 1 . In
st ru
m e n ta
lit y
.0 5
.2 1
.0 6
.1 1
.0 6
.0 7
.0 3
.0 7
.1 2 �
.0 7
(. 8 1 )
1 2 . V
al e n ce
.0 5
.0 2
.1 1
.0 8
.0 9
.0 3
.0 2
.0 7
.0 2
.0 6 �
.0 5
(. 7 2 )
1 3 . S e rv
ic e
T e ch
n o lo
gy A
d o p ti o n
.0 3
.0 4
.0 5
.0 3
.0 6
.0 3
.0 3
.0 4 �
.0 3
.4 1
.3 2
.2 9
(. 8 7 )
1 4 . M
gr -C
S R
A ge
S im
ila ri
ty .0
3 .0
1 .0
3 .0
4 .0
2 .0
2 .0
5 .0
6 .0
4 .0
5 .0
6 .0
8 .0
2 _
a
1 5 . M
gr -C
S R
G e n d e r
S im
ila ri
ty .0
1 .0
2 .0
1 .0
2 .0
2 .0
2 .0
2 .0
3 .0
3 .0
4 .0
4 .0
5 .0
3 ,0
5 _
a
1 6 . O
rg an
iz at
io n al
C o m
m it m
e n t
.0 6
.0 5
.0 7
.0 4
.0 4
.0 1
.0 3
.0 0
.0 4
.3 9
.1 2
.3 1
.3 5
.0 4
.0 4
(. 8 3 )
1 7 . Jo
b S at
is fa
ct io
n .0
7 .0
6 .0
8 �
.0 1
.0 1
.0 3 �
.0 1 �
.0 3 �
.0 4
.2 7
.1 6
.2 3
.3 4
.0 5
.0 2
.5 9
(. 7 4 )
1 8 . O
rg . T
ra in
in g
an d
S u p p o rt
.0 5
.0 4
.0 6 �
.0 1
.0 9
.0 3
.0 5
.1 2 �
.0 4
.1 5
.2 1
.1 8
.2 3
.0 2
.0 3
.1 2
.1 6
(. 7 6 )
1 9 . S e rv
ic e
E x p e ri
e n ce
� .0
6 �
.0 2 �
.0 4 �
.0 7 �
.1 1 �
.0 3
.1 1 �
.0 5
.0 4
.0 9
.0 8
.0 7
.1 6
.0 4
.0 5
.2 4
.0 5
.0 2 �
.0 2
M 4 .6
2 4 .6
7 4 .6
4 5 .3
7 5 .3
9 3 .5
7 5 .0
4 5 .2
6 1 2 .9
4 .1
2 4 .2
3 4 .2
1 5 .8
2 1 8 .1
0 .4
5 5 .4
8 4 .9
7 4 .0
6 5 .3
6 S D
1 .2
4 1 .0
3 1 .0
9 1 .3
2 .8
5 1 .2
0 .9
9 .9
4 8 .5
1 1 .4
9 1 .1
2 1 .2
6 1 .0
7 1 1 .2
1 .4
9 1 .3
1 1 .1
9 .5
8 2 .8
9 A
ve ra
ge va
ri an
ce e x tr
ac te
d .6
1 .6
7 .5
3 .5
9 .5
2 .5
9 .6
6 .5
1 _
.6 7
.6 0
.5 9
.6 5
_ _
.7 0
.5 5
.5 6
_
N o te
: C
o rr
e la
ti o n s
b as
e d
o n
sc o re
s d is
ag gr
e ga
te d
p e r
C S R
ar e
b e lo
w th
e d ia
go n al
(C S R
s: N ¼
1 ,0
1 8 ),
an d
C ro
n b ac
h ’s
(1 9 5 1 )
in te
rn al
co n si
st e n cy
re lia
b ili
ty co
e ff ic
ie n ts
ap p e ar
o n
th e
d ia
go n al
. W
e m
e as
u re
d se
rv ic
e e x p e ri
e n ce
in ye
ar s.
a C
o n st
ru ct
s ar
e m
e as
u re
d b y
a si
n gl
e it e m
. |r
| �
.0 7
si gn
if ic
an t
at p
< .0
5 (t
w o -t
ai le
d ).
|r |�
.0 9
si gn
if ic
an t
at p
< .0
1 (t
w o -t
ai le
d ).
220
We assessed the discriminant validity of all construct mea-
sures using the criterion proposed by Fornell and Larcker
(1981), which suggests that discriminant validity is present if
the average variance extracted exceeds the squared correlations
between all pairs of constructs. All constructs passed this test.
Validation of service technology adoption. Furthermore, we validated the CSRs’ self-reported responses concerning their
service technology adoption with objective use data (i.e., sales
generated with the new service technology). To do so, we
aggregated the self-reported data on CSR service technology
adoption per travel agency before correlating these scores with
the objective service technology use data from the company
database. Both measures show a high correlation (r ¼ .59; p < .01), indicating the CSRs’ self-reported service technology
adoption evaluations had a significant validity in that they were
not potentially influenced by answers to other questions in the
survey or by social desirability. Thus, in our analysis, we use
the self-reported service technology adoption to assess Hypoth-
esis 4a and b.
Motivation construct. To test the dimensionality of the moti- vation construct, we conducted both an exploratory and a con-
firmatory factor analysis. We identified three distinct
dimensions with a good data fit. Following established
approaches in the literature, we multiplied the scores of each
of the three components to compute a global score of motiva-
tion, which we used to test Hypothesis 4a and b (Ingram et al.
1989; Kohli 1985; Le Bon and Merunka 2006; Tyagi 1985).
Contingency factors. To capture charismatic leadership, we use the measure of Conger and Kanungo (1998). To measure
manager-CSR age similarity, we calculated the absolute age dif-
ference between each manager and CSR and then recoded this
variable to simplify the interpretation (i.e., a higher value reflects
greater similarity). The second aspect of similarity, manager-CSR
gender similarity, is a dummy variable, coded ‘‘1’’ if the manager
and CSR have the same gender and otherwise as ‘‘0.’’
Control variables. In addition, we calculated coworkers’ influence by the average expectancy, instrumentality, and
valence of the other CSRs within a travel agency, that is, as an
average of all members’ expectancy, instrumentality, and
valence in the service unit, excluding the focal employee’s
motivational components. Thus, because we exclude the expec-
tancy, instrumentality, and valence score of each focal employee
in our calculation, coworkers’ expectancy, instrumentality, and
valence are three individual-level constructs varying with each
focal employee of our sample.
Further, as all CSRs work under the same manager, to take
the average CSR’s motivation into account we added the mean
level of CSRs’ motivational components per service unit led by
a manager (varying between the agencies in our sample) as a
control for the motivational climate because it might also influ-
ence the spillover effects. In other words, we averaged each of
the three motivational components per service unit, and then
included this aggregated variable as a Level 2 (the service unit
level) control.
The measurement of the control variables for the service
technology adoption, namely, organizational training and sup-
port, job satisfaction, commitment, and service experience, is
based on well-established scales (see Appendix A for sources
and specific items).
Model Analytical approach. Because the CSRs were nested in man-
agers, we used hierarchical linear modeling (HLM). In contrast
to the ordinary least squares approach, HLM accounts for the
fact that, in our hierarchically nested data design, the measure-
ments at the CSR level are not independent but are nested in
service units supervised by a business unit manager. HLM
allows the simultaneous processing of data from the two levels
without losing important information. At the same time, HLM
provides the opportunity to model cross-level effects such as
the transfer of managers’ motivation components to CSRs.
Finally, to analyze the single-level effects of CSRs’ and man-
agers’ motivation on their service technology adoption (Hypoth-
esis 4a and b), we employed ordinary least squares regression.
For the HLM, we conducted three steps. First, we estimated
null models (with no predictors at Level 1 and an intercept only
at Level 2) to test whether significant variations occurred
across service units with respect to the dependent variables
(CSRs’ motivation components). The results of those null mod-
els showed that CSRs who worked under different managers
exhibited significant between-group variance in their expec-
tancy, instrumentality, and valence. The null model also pro-
vides information for computing the intraclass correlation
coefficient (ICC[1]), which indicates the proportion of
between-groups variance relative to the total variance exhibited
by a variable. This statistic represents the maximum amount of
variance in a Level 1 variable that can potentially be explained
by a Level 2 predictor variable. Our calculations show that
28–36% of the variances in CSRs’ expectancy, instrumentality, and valence (i.e., their ICC[1]) resides between managers
(Raudenbush and Bryk 2002). In addition, we calculated the
ICC[2] values, which were slightly higher than their
corresponding ICC[1] values, ranging between 38 and 46% (Schneider et al. 1998). The values for the ICC[1] and ICC[2]
indicate that HLM is required.
Second, we then added the focal predictors and motivational
climate of the service unit and coworkers’ motivation as con-
trol variables. Third, to estimate whether the inclusion of inter-
action effects is empirically meaningful, we followed
Ganzach’s (1997) hierarchical procedure. Ganzach’s simula-
tion study shows that misleading effects are obtained when
interaction effects are present but not modeled. We therefore
entered the interaction terms (i.e., manager’s expectancy,
instrumentality, and valence each with (a) charismatic leader-
ship, (b) manager-CSR age similarity, and (c) manager-CSR
gender similarity) after the other predictors and controls in our
models. The inclusion of the interaction terms yields
Wieseke et al. 221
T a b
le 2 .
R e su
lt s
o f A
n al
ys e s
In d e p e n d e n t
V ar
ia b le
s
D e p e n d e n t
V ar
ia b le
s
C S R
’s E x p e ct
an cy
C S R
’s In
st ru
m e n ta
lit y
C S R
’s V
al e n ce
C S R
’s S e rv
ic e
T e ch
. A
d p tn
M gr
’s S e rv
ic e
T e ch
n . A
d p tn
g (S
E )
g (S
E )
g (S
E )
b (S
E )
b (S
E )
S im
p le
E ff e ct
s M
gr ’s
E x p e ct
an cy
[H 1 a]
.1 6 1 **
(. 0 2 6 )
.0 5 1
(. 0 4 6 )
.0 1 3
(. 0 3 7 )
M gr
’s In
st ru
m e n ta
lit y
[H 1 b ]
.0 1 2
(. 0 3 7 )
.2 4 7 **
(. 0 4 2 )
.0 3 4
(. 0 2 7 )
M gr
’s V
al e n ce
[H 1 c]
.0 1 1
(. 0 3 7 )
.0 3 5
(. 0 3 6 )
.0 2 3
(. 0 2 7 )
M gr
’s C
h ar
is m
at ic
L e ad
e rs
h ip
.1 0 4 **
(. 0 3 1 )
.1 3 0 **
(. 0 3 0 )
.0 7 8 **
(. 0 2 9 )
M gr
-C S R
A ge
S im
ila ri
ty .0
8 2
(. 0 6 6 )
.0 1 3
(. 0 2 7 )
.0 5 6
(. 0 7 8 )
M gr
-C S R
G e n d e r
S im
ila ri
ty .0
5 0
(. 0 4 3 )
.0 4 1
(. 0 2 9 )
.0 5 2
(. 0 4 8 )
C S R
’s M
o ti va
ti o n
[H 4 a]
.1 9 5 **
(. 0 2 3 )
M gr
’s M
o ti va
ti o n
[H 4 b ]
.2 6 3 **
(. 0 1 9 )
In te
ra ct
io n
E ff e ct
s M
gr ’s
E x p e ct
an cy �
M gr
’s C
h ar
is m
at ic
L e ad
e rs
h ip
[H 2 a]
.1 3 4 **
(. 0 4 3 )
.0 7 3
(. 0 8 9 )
.0 2 4
(. 0 1 9 )
M gr
’s In
st ru
m e n ta
lit y �
M gr
’s C
h ar
is m
at ic
L e ad
e rs
h ip
[H 2 b ]
.0 8 1
(. 0 7 0 )
.1 8 1 **
(. 0 6 1 )
.0 1 9
(. 0 2 3 )
M gr
’s V
al e n ce �
M gr
’s C
h ar
is m
at ic
L e ad
e rs
h ip
[H 2 c]
.0 5 5
(. 0 5 7 )
.1 0 4
(. 9 3 )
.1 2 2 **
(. 0 4 5 )
M gr
’s E x p e ct
an cy �
M gr
-C S R
A ge
S im
ila ri
ty [H
3 a]
.1 2 1 **
(. 0 5 1 )
.0 3 2
(. 0 2 9 )
.0 5 8
(. 0 6 7 )
M gr
’s In
st ru
m e n ta
lit y �
M gr
-C S R
A ge
S im
ila ri
ty [H
3 b ]
.0 7 0
(. 0 6 9 )
.1 4 2 **
(. 0 3 2 )
.0 2 4
(. 0 1 9 )
M gr
’s V
al e n ce �
M gr
-C S R
A ge
S im
ila ri
ty [H
3 c]
.0 9 1
(. 0 7 9 )
.0 1 7
(. 0 2 1 )
.1 3 0 **
(. 0 3 9 )
M gr
’s E x p e ct
an cy �
M gr
-C S R
G e n d e r
S im
ila ri
ty [H
3 a]
.0 7 1
(. 0 6 8 )
.0 5 9
(. 0 4 7 )
.0 8 0
(. 0 9 2 )
M gr
’s In
st ru
m e n ta
lit y �
M gr
-C S R
G e n d e r
S im
ila ri
ty [H
3 b ]
.0 4 1
(. 0 5 7 )
.1 0 3
(. 9 9 )
.0 7 3
(. 0 9 2 )
M gr
’s V
al e n ce �
M gr
-C S R
G e n d e r
S im
ila ri
ty [H
3 c]
.0 8 9
(. 0 9 4 )
.0 2 3
(. 0 3 7 )
.0 9 9
(. 0 9 4 )
C o n tr
o ls
O rg
an iz
at io
n al
T ra
in in
g an
d S u p p o rt
(C S R
an d
M gr
) .0
9 1 **
(. 0 1 2 )
.0 7 8 **
(. 0 3 7 )
Jo b
S at
is fa
ct io
n (C
S R
an d
M gr
) .1
3 5 **
(. 0 5 0 )
.0 9 7
(. 0 5 9 )
C o m
m it m
e n t
(C S R
an d
M gr
) .0
7 8 **
(. 0 2 6 )
.0 5 7 *
(. 0 2 9 )
S e rv
ic e
E x p e ri
e n ce
(C S R
an d
M gr
) �
.0 8 3
(. 0 4 1 )
.0 0 2
(. 0 5 7 )
M e an
o f in
d iv
id u al
-l e ve
l C
S R
’s E x p e ct
an cy
p e r
se rv
ic e
u n it
.2 0 1 **
(. 0 6 0 )
.0 9 4
(. 0 7 8 )
.0 2 3
(. 0 3 6 )
M e an
o f in
d iv
id u al
-l e ve
l C
S R
’s In
st ru
m e n ta
lit y
p e r
se rv
ic e
u n it
.0 5 6
(. 0 4 2 )
.2 5 1 **
(. 0 6 9 )
.0 7 2
(. 0 7 0 )
M e an
o f in
d iv
id u al
-l e ve
l C
S R
’s V
al e n ce
p e r
se rv
ic e
u n it
.0 5 4
(. 0 5 0 )
.0 6 7
(. 0 6 2 )
.1 0 1 *
(. 0 5 1 )
C o -w
o rk
e r’
s E x p e ct
an cy
.2 0 6 **
(. 0 6 3 )
.0 7 1
(. 0 7 0 )
.0 3 9
(. 0 4 6 )
C o -w
o rk
e r’
s In
st ru
m e n ta
lit y
.0 4 2
(. 0 3 7 )
.2 6 0 **
(. 0 7 8 )
.0 8 5
(. 0 7 9 )
C o -w
o rk
e r’
s V
al e n ce
.0 6 1
(. 0 5 2 )
.0 8 5
(. 0 7 9 )
.1 2 3 *
(. 0 6 2 )
P se
u d o
R 2
.2 3 3
.2 7 1
.2 0 8
A d j.
R 2
.1 9 7
.1 7 6
F V
al u e
5 3 .6
0 4 7 .8
2
N o te
: C
S R ¼
C u st
o m
e r
S e rv
ic e
R e p re
se n ta
ti ve
, M
gr ¼
M an
ag e r,
S e rv
ic e
T e ch
. A
d p tn
. ¼
S e rv
ic e
T e ch
n o lo
gy A
d o p ti o n . S ig
n if ic
an ce
is b as
e d
o n
o n e -t
ai le
d te
st s
fo r
p ro
p o se
d d ir
e ct
io n al
re la
ti o n sh
ip s.
p <
.0 5
** p
< .0
1 .
222
significant model improvements (all Dw2 were above 630, with df ¼ 6, p < .01). Thus, hierarchical linear models along with the interaction terms appear to be appropriate. Moreover, the
pseudo-R 2
(Snijders and Bosker 1999) in Table 2 show that the
variances explained in our models were equal to or above 20%, which indicates a sufficient goodness of fit of the model.
Model description. To test the transfer of the three different motivational facets, we ran three separate two-level models in
which, at Level 1 (the CSR level), the CSRs’ expectancy, instru-
mentality, or valence were the dependent variables. The inde-
pendent variables at level 1 are manager-CSR age and gender
similarity (see line (1) in the following model specification).
We added coworkers’ expectancy, instrumentality, and
valence as controls in the Level 1 equation. The intercept
(i.e., b0j, see also line (2) in the following model specification) is a function of managers’ expectancy, instrumentality,
valence, and charismatic leadership as well as the interactions
between those motivational facets and charismatic leadership
at Level 2 (the manager level). Moreover, the intercept is deter-
mined by the aggregated CSRs’ expectancy, instrumentality,
and valence per service unit, which were added as controls for
the motivational climate at Level 2. To test the proposed cross-
level interactions, the slopes of manager-CSR age and gender
similarity at Level 1 were functions of the manager’s expec-
tancy, instrumentality, and valence at Level 2 (see lines 3 and
4 in the following model specification). The final multilevel
models were as follows:
Level 1 (CSR level)
DVij ¼ b0j þb1j MCASij � �
þb2j MCGSij � �
þb3j CEXPij � �
þb4j CINSij � �
þb5j CVALij � �
þ rij ð1Þ
Level 2 (Manager level)
b0j ¼ g00 þg01 MEXPj � �
þg02 MINSj � �
þg03 MVALj � �
þg04 MCHARj � �
þg05 MEXPj � MCHARj � �
þg06 MINSj � MCHARj � �
þg07 MVALj � MCHARj � �
þg08 MIEXPj � �
þg09 MIINSj � �
þg10 MIVALj � �
þ u0j ð2Þ
b1j ¼ g10 þ g11 MEXPj � �
þ g11 MINSj � �
þg11 MVALj � �
ð3Þ
b2j¼g20 þ g21 MEXPj � �
þ g21 MINSj � �
þg21 MVALj � �
ð4Þ
b3j ¼ g30 ð5Þ
b4j ¼ g40 ð6Þ
b5j ¼ g50 ð7Þ
where
DV ¼ CSR’s expectancy, CSR’s instrumentality, or CSR’s valence
MCAS ¼ manager-CSR age similarity MCGS ¼ manager-CSR gender similarity CEXP ¼ coworkers’ expectancy CINS ¼ coworkers’ instrumentality CVAL ¼ coworkers’ valence MEXP ¼ manager’s expectancy MINS ¼ manager’s instrumentality MVAL ¼ manager’s valence MCHAR ¼ manager’s charismatic leadership MIEXP ¼ mean of individual-level CSR’s expectancy per
service unit
MIINS ¼ mean of individual-level CSR’s instrumentality per service unit
MIVAL ¼ mean of individual-level CSR’s valence per service unit, rij *0,s
2 )
Results
We start by presenting the results for the main effects and con-
tingency factors of the motivation transfer and follow with the
results for the control variables, thereby addressing the model’s
robustness. Eventually, in an additional analysis, we provide
evidence for the external validity of our model and test the
validity of the motivation spillover effect. Table 2 shows the
estimation results for the multilevel regression model.
Results for Main Effects of Motivation and Motivation Transfer
We found support for spillover effects of the motivational
components suggested by Hypothesis 1a and b but not for
Hypothesis 1c. Specifically, results show a significant effect
of managers’ expectancy on their subordinates’ expectancy
(Hypothesis 1a: g ¼ .161, p < .01). We also found a significant positive effect of managers’ instrumentality on their subordi-
nate’s instrumentality (Hypothesis 1b: g ¼ .247, p < .01). Sur- prisingly, managers’ valence had no impact on their CSRs’
valence (Hypothesis 1c: g ¼ .023, n.s.). Hypothesis 4a and b predicted a direct effect of CSRs’
(Hypothesis 4a) and managers’ (Hypothesis 4b) motivation
on the respective self-reported service technology adoption.
In line with Hypothesis 4a and b, we found support for the
direct effects of CSRs’ motivation (Hypothesis 4a: b ¼ .195, p < .01) and manager’s motivation (Hypothesis 4b: b ¼ .263, p < .01) on their self-reported service technology adoption.
Results for Contingency Factors of Motivation Transfer
In Hypotheses 2 and 3, we predicted various interaction effects
between managers’ charismatic leadership and manager-CSR
similarity and the spillover of the motivational factors. We illus-
trate the patterns of the moderating effects of managers’ charis-
matic leadership and manager-CSR similarity in Figures 2 and 3.
Wieseke et al. 223
Charismatic leadership. The results show that managers’ charismatic leadership positively moderates the spillover effect
of managers’ expectancy on their employees’ expectancy, as
Hypothesis 2a proposes (Hypothesis 2a: g ¼ .134, p < .01). Managers’ charismatic leadership also amplifies the instrumen-
tality spillover from managers to their subordinates, as is evi-
dent from its positive coefficient (Hypothesis 2b: g ¼ .181, p < .01). Finally, we found support for the moderating effect
of managers’ charismatic leadership on the valence spillover
(Hypothesis 2c: g ¼ .122, p < .01).
Manager-CSR similarity. The analyses of the cross-level inter- action effect between managers’ expectancy, instrumentality, and
valence and manager-CSR similarity showed split results.
Manager-CSR age similarity strengthened the spillover of all
three motivational components from managers to their
followers, whereas manager-CSR gender similarity had no
moderating effects. The coefficients of the interaction effects
of manager-CSR age similarity with manager’s expectancy
(g ¼ .121, p < .01; Figure 3A), manager’s instrumentality (g ¼ .142, p < .01; Figure 3B), and manager’s valence (g ¼ .130, p < .01; Figure 3C) were all positive and significant.
Furthermore, we found interesting moderating effects of char-
ismatic leadership and age similarity for the valence spillover.
We observed significant downward-sloping patterns for the
valence transfer of managers to their CSRs (i.e., the manager’s
valence negatively influences the CSRs’ valence) in cases where
the managers are uncharismatic (see Figure 2C) or for low age
similarity (Figure 3C). We discuss this very interesting ‘‘backfir-
ing effect’’ for the valence spillover in the discussion section.
Results for Controls
In the multilevel models, we also controlled for the influ-
ence of coworkers’ expectancy, instrumentality, and valence
on an individual CSR’s expectancy, instrumentality, and
valence. Our results show that coworkers’ motivational
components positively affect the focal CSR motivational
counterparts. We found a similar pattern of results for the
cross-level influences of the motivational climate (i.e., mean
level of CSRs’ motivational components per travel agency)
in the service unit.
In the ordinary least squares regressions to test
Hypothesis 4a and b, the within-level control variables,
A. Expectancy Spillover
3.8
4.3
4.8
Low Mgr's Expectancy High Mgr's Expectancy
C SR
's E
xp ec
ta nc
y
Low Mgr's Charismatic Leadership
High Mgr's Charismatic Leadership
B. Instrumentality Spillover
4.2
4.7
5.2
Low Mgr's Instrumentality
High Mgr's Instrumentality
C SR
's I
ns tr
um en
ta lit
y
Low Mgr's Charismatic Leadership
High Mgr's Charismatic Leadership
C. Valence Spillover
4.5
5
Low Mgr's Valence High Mgr's Valence
C SR
's V
al en
ce
Low Mgr's Charismatic Leadership
High Mgr's Charismatic Leadership
Figure 2. Managers’ charismatic leadership as moderator of motivation spillover. Note: CSR ¼ customer service representative, Mgr ¼ manager.
224 Journal of Service Research 14(2)
training and support provided by the organization as well as
commitment on both levels had positive effects on service
technology adoption. These findings are largely consistent
with results of previous studies in the service technology
literature. However, the mere effect sizes of motivation
on service technology adoption behavior on both levels
compared to the effect sizes of the control variables under-
line the incremental predictive power of the motivation
construct as evidenced by the standardized regression coef-
ficient of CSR motivation (bCSR ¼ 0.20) in comparison to the standardized regression coefficients of the respective
controls (bOrganizational Support & Training ¼ 0.09; bJobSatisfaction ¼ 0.14; bCommitment ¼ 0.08; bServiceExperience ¼ �0.08).
Model Robustness Checks
To test the robustness of our results, we repeated the multilevel
regression analyses for less complex base models without the
above-mentioned control variables to validate our results
regarding the hypothesized effects (Cohen et al. 2003). The
results are stable, regardless of whether control variables are
included and thus confirm the robustness of our findings.
Mean centering. We assessed whether the type of mean centering (group or grand mean centering) influenced our results.
For cross-level interactions, group mean centering of Level 1 is
recommended (Raudenbush and Bryk 2002). Our conceptual
framework included both within-level interactions and cross-
level interactions. We therefore used grand mean centering to
standardize the predictors within their respective level (Chen, Bli-
ese, and Mathieu 2005) and conducted additional tests with group
mean centering. All of the cross-level interactions remained sig-
nificant. Those results show that our results are robust and that the
way we centered our variables did not change our findings.
Additional Analysis External validity. To add additional external validity to our
model, we show that managers’ and CSRs’ motivation is posi-
tively related to the objectively observed service technology
adoption data (measured as sales generated with the new
service technology). 2
Employing ordinary least squares regres-
sions, we show that manager’s and CSRs’ motivation influ-
ences actual use behavior.
For the service unit level, we operationalized CSRs’ motiva-
tion as the average of employees’ motivation in the respective
service unit. The indexes of within-group agreement (ICC[1]
A. Expectancy Spillover
4
4.5
Low Mgr's Expectancy High Mgr's Expectancy
C SR
's E
xp ec
ta nc
y
Low Mgr-CSR Age Similarity
High Mgr-CSR Age Similarity
B. Instrumentality Spillover
4
4.5
5
Low Mgr's Instrumentality
High Mgr's Instrumentality
C SR
's I
ns tr
um en
ta lit
y
Low Mgr-CSR Age Similarity
High Mgr-CSR Age Similarity
C. Valence Spillover
4.5
5
Low Mgr's Valence High Mgr's Valence
C SR
's V
al en
ce
Low Mgr-CSR Age Similarity High Mgr-CSR Age Similarity
Figure 3. Managers-CSR age similarity as moderator of motivation spillover. Note: CSR ¼ customer service representative, Mgr ¼ manager.
Wieseke et al. 225
and ICC[2]) and median within-group agreement (rwg) justi-
fied this aggregation (Bliese 2000; James, Demaree, and Wolf
1984). We first entered the aggregated CSR motivation and
manager motivation. In the next step, we added the interaction
term of managers’ and CSRs’ motivation. The manager’s moti-
vation (b ¼ .22, p < .01), CSR’s motivation (b ¼ .20, p < .01), and their interaction term (b¼ .15, p < .01) had a strong impact on the business unit’s objective service technology adoption.
These results add additional external validity to our model, as
managers’ and CSRs’ motivation significantly impacts actual
use behavior. Table 3 reports these results.
Indirect effect of motivational spillover. We found that under certain conditions, the manager’s motivational components
transfer to CSRs’ motivational components, and these in turn
influence CSRs’ service technology adoption. However,
Homburg, Wieseke, and Kuehnl (2009) show that a manager’s
technology adoption might have a direct influence on the
CSR’s technology adoption. Therefore, in an additional analy-
sis, to explore the validity of the motivation spillover effect, we
test whether the motivation spillover effect exists beyond the
direct effect of a manager’s technology adoption on CSRs’
technology adoption. To do that, we test whether the CSRs’
motivation mediates the relationship between the manager’s
motivation and the CSRs’ technology adoption, while, impor-
tantly, controlling for the direct effect of the manager’s tech-
nology adoption on CSRs’ technology adoption.
We used a mediational model that combines single-level
and multilevel modeling. The model is characterized as a
2 !1 ! 1 multilevel mediation model (Krull and MacKinnon 2001), in which the initial variable (managers’ motivation) is
measured at the macro level and both the mediator (CSR’s
motivation) and the outcome (CSRs’ service technology
adoption) are individual-level variables. We apply the para-
metric bootstrap method, which involves the use of parameter
estimates between the independent variable and the mediator as
well as the mediator and the dependent variable, while control-
ling for manager’s service technology adoption and the covari-
ates in our framework. 3
Following recommendations in the
literature, we used 20,000 repetitions and the percentile method
to create a 95% interval of the hypothesized indirect effect, relying on an SPSS macro (Hayes 2005).
Bootstrapping demonstrated that zero did indeed fall
outside the confidence interval of the hypothesized effect
(95% CI ¼ [.17,.90]. Thus, managers’ motivation has a pos- itive and significant effect on CSRs’ service technology
adoption, which runs indirectly through CSRs’ motivation
while controlling for managers’ service technology adoption
(p < .05). By and large, the results of this mediation analysis
confirm the existence of the significant indirect motivation
effect on CSR technology adoption that extends beyond the
direct effect of managers’ technology adoption on CSR tech-
nology adoption. Table 4 provides an overview of the results
of the study.
Discussion
Research Issues
The aim of this paper was to explore the motivation dissemina-
tion in the manager-CSR dyad. Despite the undisputed impor-
tance of motivating CSRs as the first representatives of a
company, the effect of manager motivation on CSR motivation
has not been previously investigated, especially in the area of
service technology implementation.
In response to this neglect, we developed a conceptual
framework based on the concept of a motivation spillover prin-
ciple from manager to CSR and its consequence for service
technology adoption. Using a linked multilevel sample of
387 service unit managers, 1,018 CSRs, and objective firm
data, we tested our motivation spillover framework. Both the
findings from the empirical analyses and the multilevel design
of our study have a number of important academic and practical
implications, particularly in terms of gaining a broader under-
standing of the means by which managers can influence
employee motivation. To the best of our knowledge, this study
is the first to investigate motivation dissemination through the
different hierarchical levels of an organization.
Our study makes several contributions to research on motiva-
tion and leadership. First, drawing on expectancy theory, social
learning theory, and charismatic leadership, we find support for
our hypothesis that a motivation spillover from manager to CSR
exists. Our results reveal the occurrence of an indirect multilevel
motivation spillover of managers’ motivation to CSRs’ motiva-
tion, which then leads to CSRs’ service technology adoption.
Importantly, this effect exists incrementally beyond the direct
effect of manager service technology adoption behavior on
CSRs’ adoption behavior. Thus, we discover an alternative moti-
vational effect on CSRs’ task-specific behavior.
Table 3. Hierarchical Regression Results for Objective Service Technology Adoption
Predictor
OSTA ¼ b0 þ b1(MMOT) þ b2(CMOT) þ b3(MMOT � CMOT) þ ri
Step 1 Step 2 Standardized b (t Value)
Standardized b (t Value)
Step 1 �Manager Motivation (b1) .23** (7.88) 0.22** (7.46) �CSR Motivation (b2) .18** (3.29) 0.20** (3.41) Step 2 �Manager Motivation � CSR Motivation (b3)
0.15** (5.21)
F value 19.89** 20.47** R2 .09 .11 Adjusted R2 .086 .105 DR2 .02**
Notes: OSTA ¼ objective service technology adoption, CSR ¼ customer service representative, MMOT ¼ manager’s motivation, CMOT ¼ CSR’s motivation. * p < .05 ** p < .01.
226 Journal of Service Research 14(2)
Specifically, the main effects in our hierarchical regression
model show that the motivational components derived from
expectancy theory, namely, expectancies and instrumentalities,
transfer directly from managers to CSRs, while valences do not
transfer. Further, our moderation analysis shows that the spil-
lover of expectancies, instrumentalities, and valences strongly
depends on charismatic leadership and manager-CSR similar-
ity. Under low charismatic leadership or low manager-CSR
similarity, expectancies and instrumentalities do not transfer
from service unit manager to CSR. However, under high char-
ismatic leadership or high manager-CSR similarity, we observe
an enhanced transfer of expectancies and instrumentalities
from manager to CSR.
Concerning the spillover of valences from manager to
CSR, this transfer requires either charismatic leadership or age
similarity of managers with their CSRs. However, in contrast to
the transfer of expectancies and instrumentalities, under the
condition of low charisma or low manager-CSR similarity,
we observe a ‘‘backfiring effect,’’ in that an increase in the
manager’s valence reduces the CSR’s valence. This reaction
of the CSR indicates that the CSR will adopt the manager’s
valences only under the very specific condition of a high level
of identification between manager and CSR. This phenomenon
can be attributed to the notion that valences, which are concep-
tually close to personalized values and thus integrated into the
self-concept, are more resistant to social pressure than expec-
tancies and instrumentalities.
On a methodological level, another contribution of our study
is to address the inadequate examination of levels-of-analysis
issues by leadership research (Yammarino et al. 2005).
Although the value of such work is not in question, a single
level of analysis may not appropriately account for the multile-
vel nature of the motivation construct in the leader-follower
dyad. Our use of a hierarchical study design, which accounts
for CSRs being nested within managers, addresses the call in
leadership research to consider multiple levels of analysis
(Avolio et al. 2009).
Managerial Implications
In view of the critical role of CSRs as ‘‘first representatives’’ of
a service firm and the relationship of this role to the high costs
of employee disengagement, motivating CSRs to achieve their
highest performance levels is a major challenge for service
firms. Our study provides several important implications for
organizations and managers.
First, we find that managers are critical multipliers of
task-specific motivation for CSRs. Consequently, to motivate
CSRs to perform a certain task, such as adopting a service tech-
nology, large-scale organizations must concentrate their moti-
vation efforts to a greater extent on middle-level managers.
When middle-level managers are truly convinced and moti-
vated to perform the task, their motivation will spread quickly
to their frontline employees. As middle-level managers usually
have a certain span of control, directing motivation efforts at
them is an efficient and resourceful way of stimulating work-
force motivation concerning a given task. In contrast, if compa-
nies neglect middle-level managers and fail to involve them
when aiming to motivate CSRs, middle-level managers may
become serious roadblocks to employee motivation, as their
disengagement spills over to their employees. This implication
appears to be particularly significant, since organizations pri-
marily target frontline employees rather than extending their
focus to their managers.
Second, we find that the spillover of task-specific motiva-
tion from managers to CSRs is enhanced when leaders are char-
ismatic and the age similarity with their employees is high.
Thus, to successfully implement a new service technology,
organizations should identify service units with either charis-
matic managers or managers that are of an age similar to their
Table 4. Overview of the Results
Main Effects of Motivation Transfer
Independent Variable Dependent Variable Moderator Hypothesized
Effect Results
H1a Manager’s Expectancy CSR’s Expectancy þ P H1b Manager’s Instrumentality CSR’s Instrumentality þ P H1c Manager’s Valence CSR’s Valence þ X H4a/b Manager’s/CSR’s Motivation Manager’s/CSR’s Service
Technology Adoption þ P
Contingency Factors of Motivation Transfer
H2a Manager’s Expectancy CSR’s Expectancy Charismatic Leadership þ (P) H2b Manager’s Instrumentality CSR’s Instrumentality Charismatic Leadership þ (P) H2c Manager’s Valence CSR’s Valence Charismatic Leadership þa (P)a H3a Manager’s Expectancy CSR’s Expectancy Manager-CSR Similarity þ (P) H3b Manager’s Instrumentality CSR’s Instrumentality Manager-CSR Similarity þ (P) H3c Manager’s Valence CSR’s Valence Manager-CSR Similarity þa (P)a
Note: a Backfiring effect for uncharismatic managers or low Manager-CSR Similarity: Manager’s valence has negative effect on CSR’s valence.
(P) Supported for Age Similarity; not supported for Gender Similarity.
Wieseke et al. 227
employees and use them as starting points for the service tech-
nology introduction. Identifying charismatic managers should
be possible, as charismatic leaders are found to ‘‘stand out in
a crowd’’ (De Vries, Roe, and Taillieu 1999, p. 110). In those
service units selected, the manager’s motivation should spill
over rapidly to the employees, making those service units
examples of best practices that facilitate the diffusion of the
new technology in the company.
Third, manager training should sensitize managers that
employees must discern their motivation concerning a given
undertaking. As motivating employees is an essential manage-
rial task, making use of motivation spillover to engender
employee motivation is not only the manager’s responsibility
but also strongly in the manager’s interest. Managers must be
profoundly aware of motivation spillover and thus be moti-
vated themselves concerning a specific task. Providing manag-
ers with that knowledge endows them with a powerful lever to
influence their employees. To make optimal use of motivation
spillover, managers should distinctly exhibit their own motiva-
tion to their employees. They should (a) display confidence in
solving the task and show their conviction that effort leads to
the desired performance (expectancy) and (b) display confi-
dence that achieving the desired level of performance leads
to certain outcomes (instrumentality) and that those outcomes
are cherished and important (valence).
Limitations, Conclusions, and Directions for Future Research
As with all research, our study has some limitations that restrict
its interpretation and generalizability. An ideal design to clarify
a causal direction would be one in which causation across time
helps to reduce the likelihood of reversed causality. Therefore,
in our study, we compiled data on motivation at two separate
points in time, collecting data on the CSRs’ level 2 months
after the service unit managers’ survey. Although this temporal
order is an indicator for a causal direction of the effects from
managers’ variables on service personnel’s variables, analyz-
ing longitudinal data would be the ideal way to control for
reverse causality. For example, highly motivated CSRs who are
adopting new technologies fast might have an influence on
leaders’ motivation and adoption behaviors as well, although
this bottom-up influence of emerging leaders among followers
might be an exception rather than the rule.
Running of a profound test for causation requires a fully
cross-lagged model. As recommended by de Jonge et al.
(2001), for a systematic evaluation, a baseline model including
only stability paths must be compared to more complex models
incorporating cross-lagged paths. Since we surveyed the ser-
vice unit managers and their CSRs at two different points in
time, we could not test the model for cross-lagged effects. Fur-
ther research using longitudinal data or an experimental design
could address these limitations. In this regard, an examination
of the stability of motivational effects might also be interesting.
Moreover, we are limited to providing a conceptual explana-
tion for the motivation spillover mechanism based on social
learning theory but cannot offer a measurement for the
mechanism. In this, we are in line with several recent works
in leadership research that draw on social learning theory as
a theoretical foundation without operationalizing it (Brown,
Trevino, and Harrison 2005; Chen et al. 2007; Mayer et al.
2009; Tucker et al. 2010). However, future research should
investigate the underlying mechanisms of motivation spillover
by measuring social learning and test it as a mediator of the
relationship between manager and CSR motivation.
Finally, additional research is necessary to identify other
potential moderators and mediators of the relationship
between managers and CSRs, such as organizational and
Appendix A. Measurement Scales
Scales
Motivation (Managers and CSRs) Source: adapted from Sanchez, Truxillo, and Bauer 2000 (1 ¼ strongly disagree and 7 ¼ strongly agree)
Valence: What is important to you concerning your work? The success of our travel agency is important to me A sustainable customer satisfaction is important to me A high customer retention is important to me Making greater use of my skills and abilities on my job is important to me An interesting and diversified work is important to me Avoiding stress at work is important to me Avoiding extra work in my job is important to me
Instrumentality: By using the new system . . . our travel agency is more successful our customers are sustainable satisfied customer retention can be generated I can make greater use of my skills and abilities on my job my responsibilities are more diversified I have a lot more stress at work. (reverse coded) it requires a lot of extra time. (reverse coded)
Expectany If I try, I succeed in using the new service technology system for all my service activities If I put all my efforts in it, I can use the new service technology system Concentrating on the new service technology tool’s usage, it is no problem for me to use it
Service technology adoption (Managers and CSRs) Source: adapted from Jelinek et. al. 2006 (1 ¼ strongly disagree and 7 ¼ strongly agree) I consider myself a frequent user of the new service automation tool I fully use the capabilities of the new service automation system I have completely integrated the new service automation system into my service process I utilize the new service automation tool as often as I can The new service automation tool is the most frequently used sys- tem, when I make flight arrangements (CSRs)
Charismatic leadership (Managers) Source: adapted from Conger and Kanungo 1998 (1 ¼ strongly disagree and 7 ¼ strongly agree I am very successful in inspiring my employees for a shared vision
(continued)
228 Journal of Service Research 14(2)
relational identification (Hogg 2001; Kelman 1958; Sluss and
Ashforth 2007) and compensation (Anderson 1985). Further-
more, additional variables should be examined to rule
out effects that might result from factors related to the
organizational context of the respondents, such as organiza-
tional climate (Schneider et al. 2005).
In conclusion, the model and results presented here clearly
constitute an important first step in understanding the transfer
of motivation in the manager-CSR dyad. The motivation spil-
lover from manager to CSR is of high importance for service
companies and leadership researchers alike, and we make an
unprecedented discovery of an effect that describes how man-
ager motivation transfers to CSRs. A deeper understanding of
the process and drivers of motivation spillover from manager
to CSR is certainly valuable.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
Notes
1. This aspect appears to be common for many service firms. Also, as
is commonly the case, both managers and service representatives
were free to choose whether to adopt or reject the new service
technology.
2. For the calculation of motivation we relied on Vroom’s multiplica-
tive approach: Motivation ¼ Expectancy � Instrumentality � Valence.
3. We diverge from Baron and Kenny’s (1986) approach and instead
use the bootstrapping method, for reasons outlined by Shrout and
Bolger (2002). Multiple authors in marketing (e.g., De Luca and
Atuahene-Gima 2007; Rucker and Galinsky 2008; Ye, Marinova,
and Singh 2007) and management (e.g., Eisenbeiss, Boerner, and
van Knippenberg 2008; Giessner and van Knippenberg 2008;
Smith, Collins, and Clark 2005) have applied the bootstrapping
method to test for mediation. Pituch and Stapleton (2008), after
comparing different methods, explicitly recommend bootstrapping
over other methods for mediation analysis, and in particular for
2 !1! 1 indirect multilevel effects like ours.
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I can inspire my employees even on bad days In difficult times I find it easy to convey a sound optimism to my employees I have a vision that I try achieve with creative ideas I provide inspiring strategic and organizational goals I permanently create new ideas to make my travel agency ready for the future I am an entrepreneurial person and readily take opportunities I recognize new opportunities in the market that may facilitate our achievement of organizational objectives I am able to motivate my employees by articulating effectively the importance of what they are doing I am a convincing representative to the external public
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Bios
Jan Wieseke (1974) is a professor of Marketing and Chair of Business
Administration and Marketing, a University of Bochum, Germany. He
obtained his PhD in 2004 at the University of Marburg, Germany. His
research interests center on internal marketing, services marketing, the
employee-customer interface and the application of social identity the-
ory in marketing settings. Jan serves as associate editor of the British
Journal of Management. His work has been published in outlets includ-
ing the Journal of Marketing, Journal of Marketing Research, Journal
of the Academy of Marketing Science, Marketing Letters, Journal of
Marketing Theory and Practice, and Journal of Vocational Behavior.
Florian Kraus (Ph.D., University of Marburg, Germany) is Assistant
Professor of Marketing at the Department of Marketing,
Ruhr-University Bochum, Germany and Research Fellow at the C.T.
Bauer College of Business, University of Houston. His current
research focuses on salesperson behavior and performance. Florian
also conducts research on house brands, motivation and organizational
identification in the context of sales management and services market-
ing. His work appeared in the Journal of Marketing.
Sascha H. Alavi (1984) is a doctoral student at the Chair of Business
Administration and Marketing, at the University of Bochum,
Germany. Sascha’s research focuses on leadership, sales and price
management. His work has been published in Schmalenbachs
Business Review.
Tino Kessler-Thönes (1978) received his PhD at the University of
Marburg, Germany in 2008. He works as marketing manager at Sana
Kliniken AG in Düsseldorf, Germany.
Wieseke et al. 233
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