The Service Industries Journal
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How transformational leadership fuels employees’
service innovation behavior
Ping-Jen Kao, Peiyu Pai, Tingling Lin & Jun-Yu Zhong
To cite this article: Ping-Jen Kao, Peiyu Pai, Tingling Lin & Jun-Yu Zhong (2015) How
transformational leadership fuels employees’ service innovation behavior, The Service Industries
Journal, 35:7-8, 448-466, DOI: 10.1080/02642069.2015.1015519
To link to this article: https://doi.org/10.1080/02642069.2015.1015519
Published online: 26 Feb 2015.
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The Service Industries Journal, 2015
Vol. 35, Nos. 7–8, 448–466, http://dx.doi.org/10.1080/02642069.2015.1015519
How transformational leadership fuels employees’ service innovation
behavior
Ping-Jen Kaoa*, Peiyu Paib, Tingling Lina and Jun-Yu Zhonga
a
Department of Business Administration, National Taipei University, 151, University Rd., San Shia,
New Taipei City 237, Taiwan; bDepartment of Business Administration, National Chengchi
University, No. 64, Sec. 2, Zhi-Nan Rd., Taipei, Taiwan
(Received 1 April 2014; accepted 1 February 2015)
Although there is a consensus that transformational leadership (TFL) is critical to
successful service innovation behavior, the relationship between the two remains
inconclusive. This study adopts a dual perspective approach that considers both
motivational and social-political perspectives to further elicit the influence of TFL on
the service innovation behavior of frontline employees. Using multiphase and
multisource data from 269 employees and 1396 customers of hair salons, the results
show that the perceived organizational climate for innovation, creative self-efficacy,
and expected image gains fully mediate the relationship between TFL and employees’
service innovation behavior. TFL positively influences employees’ perceived
organizational climate for innovation, which in turn enhances the service innovation
behavior of employees through both motivational (i.e. creative self-efficacy) and socialpolitical (i.e. expected image gains) mediating mechanisms. Surprisingly, expected
image risks are found to have a non-significant relationship with service innovation
behavior. We discuss implications of these findings with respect to innovation literature
and management practice, as well as offer suggestions for further research.
Keywords: transformational leadership; psychological climate; motivational
mechanism; social-political mechanism; service innovation
Introduction
Frontline employees are essential to achieving innovation success in service industries
because of their pivotal role in delivering innovative service offerings to customers (Lin,
2013). Scholars in various disciplines have investigated the wide variety of factors that
potentially affect innovation behavior in employees (e.g. Scott & Bruce, 1994; Yuan &
Woodman, 2010; Zhang & Bartol, 2010). Transformational leadership (TFL) is one
factor widely acknowledged as being of particular importance (Bass, 1985; Gong,
Huang, & Farh, 2009; Gumusluoglu & Ilsev, 2009; Shin & Zhou, 2003; Sun, Zhang, Qi,
& Chen, 2011). However, the underlying influence processes for TFL remain vague and
lack systematic investigation (Yukl, 1999, p. 287).
This study attempts to address three research gaps in the extant research. The first gap
pertains to the relationship between TFL and the service innovation behavior of employees.
*Corresponding author. Email: pingjenkao@gmail.com
This article was originally published with errors. This version has been amended. Please see Corrigendum (DOI: http://dx.doi.org/10.1080/02642069.2015.1026203).
© 2015 Taylor & Francis
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Although studies have examined the direct and indirect effects of TFL on the innovation
performance of employees, empirical findings have been inconclusive (e.g. Gong et al.,
2009; Jaussi & Dionne, 2003; Jung, Chow, & Wu, 2003; Shin & Zhou, 2003). Avolio,
Walumbwa, and Weber (2009, p. 429) stated that ‘despite significant progress in understanding how and when charismatic and transformational leadership behaviors are more
effective, further research is needed that explores the process and boundary conditions
for charismatic and transformational leadership with beneficial work behaviors’.
The second gap involves the limitations that are inherent to the single theoretical perspective that is most commonly used in the innovation literature (Wolfe, 1994). These limitations preclude considerations of combinations of multiple theoretical perspectives, which
hold the potential to further elucidate the relationship between TFL and innovation behavior. Many studies have adopted a motivational perspective in their explorations of the influence of TFL on innovation behavior (e.g. Gong et al., 2009; Shin & Zhou, 2003; Sun et al.,
2011) while overlooking the pivotal role of the social-political (i.e. expected image outcomes) mediating mechanism (Farr & Ford, 1990; Yuan & Woodman, 2010). Considering
both motivational and social-political mediating mechanisms facilitates the assessment of
how these dual mediating processes simultaneously affect the relationship between TFL
and employees’ service innovation behavior.
The third gap addresses the vital role of organizational climate for innovation in the
TFL–dual mediating processes linkage. This climate has been shown to have a significant
mediating influence on the relationship between TFL and innovation behavior (Eisenbeiss,
van Knippenberg, & Boerner, 2008; Wang, Rode, Shi, Luo, & Chen, 2013), acting as a
psychological mechanism that directs employees’ attention toward innovation and shapes
their behavior in the workplace (Scott & Bruce, 1994). However, despite its importance,
the effect of organizational climate for innovation has not been previously linked to these
mediating processes. Anderson, Potočnik, and Zhou (2014) suggested that future research
should investigate the effects of leadership on climate for innovation, which stimulates
innovation behavior. Therefore, this study includes an in-depth examination of the mediating processes between TFL and service innovation behavior by combining the perceived
organizational climate for innovation with motivational and social-political perspectives.
To address these research gaps, the current study builds and tests a theoretical model
linking TFL and service innovation behavior via perceived organizational climate for innovation, creative self-efficacy, and expected image outcomes. This study contributes to scholarly knowledge in the field of service innovation in three important ways. First, we advance
prior research by resolving the controversy regarding the relationship between TFL and
service innovation behavior (Avolio et al., 2009). Specifically, this study develops a comprehensive framework to explain how the sequentially dual mediating processes influence
the relationship between TFL and service innovation behavior by employing a multiphase
research design and collecting data from multiple data sources. This research design
reduces the risk of observed correlations reflecting same-source or common method
biases (Rindfleisch, Malter, Ganesan, & Moorman, 2008). Second, this study extends
and deepens extant research by elucidating the simultaneous influences of motivational
and social-political variables on the relationship between TFL and service innovation behavior (Gong et al., 2009; Yuan & Woodman, 2010). Integrating motivational and
social-political perspectives into one research design facilitates investigation of how
these dual mediating mechanisms affect frontline employees’ service innovation behavior.
Third, this study extends extant research by investigating whether the perceived organizational climate for innovation fully channels the effects of TFL onto both motivational
and social-political mediating mechanisms. By exploring the sequentially mediating
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P.-J. Kao et al.
mechanisms, we can more deeply investigate the service innovation processes and better
understand the relationship between TFL and service innovation behavior.
Theoretical background and research hypotheses development
This section introduces service innovation behavior and develops hypotheses pertaining to
the effects of motivational and social-political mediating mechanisms on this behavior. This
is followed by explanations of the mechanisms by which TFL influences service innovation
behavior. Figure 1 depicts the research framework.
Service innovation behavior
Marketing research and practice reflect a strong consensus that innovation helps organizations succeed in a dynamic environment (Aas & Pedersen, 2011; Ordanini & Parasuraman,
2011). Research has traditionally classified innovation into two categories: manufacturing
innovation and service innovation (Carlborg, Kindström, & Kowalkowski, 2014). Innovation in manufacturing refers to product-centric orientation activities. This type of innovation requires R&D departments to invest substantial money and efforts into
developing new products (Sood & Tellis, 2005). In contrast, innovation in services does
not require new, quantifiable investments, but rather is ‘a process, a sequence of operations,
a formula, a protocol, a problem solution’ (Gallouj & Savona, 2009, p. 154). Although
some service innovation creation such as combined financial products might be created
by service providers themselves, most service innovations represent the cooperative
effort of service providers and customers, particularly in situations in which customer feedback is used to continuously improve the innovation. Therefore, customers are in general an
integral part of service innovation because they are active coproducers in the innovation
creation process (Ordanini & Parasuraman, 2011).
According to Carlborg et al. (2014, p. 373), ‘Service innovation introduces something
new into the way of life, organization, timing and placement of what can generally be
described as the individual and collective processes that relate to consumers.’ A service
innovation depends on the presence and cocreation of the customer, which means that
the value of service innovation is always uniquely and phenomenologically determined
by the customers (Vargo & Lusch, 2008). Drawing on these concepts, this paper defines
Figure 1. Conceptual framework.
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451
‘service innovation behavior’ as a frontline employee’s introduction or application of new
ideas, skills, technologies, processes, and procedures to his/her customers. As previously
stated, this behavior is an outcome of value cocreation contributed by the efforts of frontline
employees and their respective customers (Jaakkola & Alexander, 2014). Examples of such
behavior include developing new ideas to improve service performance, adopting new technologies, and using new processes to serve customers (Gallouj & Savona, 2009).
Creative self-efficacy → service innovation behavior
Creative self-efficacy refers to the belief that one has the ability to produce creative outcomes (Tierney & Farmer, 2002, 2011). In their work on self-efficacy theory, Bandura
and Locke (2003, p. 97) noted that ‘a resilient sense of efficacy provides the necessary
staying power in the arduous pursuit of innovation and excellence’. Empirical evidence
shows that this motivational variable is a preeminent factor stimulating creativity. For
example, Tierney and Farmer (2011) suggested that a creativity-focused sense of efficacy
facilitates creativity by offsetting obstacles inherent to creative engagement. Gong et al.
(2009) also show that employees with high levels of creative self-efficacy set higher creativity goals, which relates positively to actual creative performance. For these reasons, it is
important to understand the relationship between creative self-efficacy and innovation behavior in the context of service.
Creative self-efficacy is conducive to service innovation behavior in at least three ways.
First, creative self-efficacy facilitates the adoption of a mastery goal orientation, which contributes to more trial-and-error experimentation and a greater willingness to learn from these
efforts (Richter, Hirst, van Knippenberg, & Baer, 2012). Thus, frontline employees with
higher creative self-efficacy are more likely to engage in service innovation behavior.
Second, in the context of service, frontline employees face uncertain and unfamiliar situations and must come up with the best ideas to address customer needs. Employees with
highly creative self-efficacy behave in a self-starting manner rather than ‘do[ing] things
by the book’ (Raub & Liao, 2012, p. 654). They are more proactive in terms of implementing creative ideas and trying new methods (Chen, Farh, Campbell-Bush, Wu, & Wu, 2013;
Raub & Liao, 2012). Finally, Ryan and Deci (2000) indicated that employees engage intrinsically in activities when they feel efficacious with respect to those activities. Employees
with higher creative self-efficacy are more likely to set their own challenging goals,
focus on solving problems, and commit to those goals, due to higher task motivation.
Therefore, this study hypothesizes:
H1: Creative self-efficacy is positively related to service innovation behavior.
Expected image gains/risks → service innovation behavior
In addition to the motivational mechanism, we propose a social-political mechanism, identified as a pivotal proximal antecedent to individual innovation behavior (Farr & Ford, 1990;
Yuan & Woodman, 2010), as another important source stimulating frontline employees to
engage in service innovation behavior. Individuals’ behaviors are often limited to specific
situations and determined by impression management tactics (Ashford & Cummings, 1983;
Ashford, Rothbard, Piderit, & Dutton, 1998). These tactics help them create various desired
social images and elicit benefits from social interaction partners (Liu, Wang, & Wayne,
2014). A previous study has clearly distinguished between two approaches to impression
management: assertive and defensive (Schlenker, 1980). Assertive impression management
(i.e. expected image gains) helps employees improve their social image, whereas defensive
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impression management (i.e. expected image risks) helps employees protect an established
social image (Tetlock & Manstead, 1985; Yuan & Woodman, 2010). Both tactics are critical factors that affect service innovation behavior for two reasons.
First, from a social-political perspective, expected image gains push frontline employees to apply new ideas in their daily tasks, which can assist them to earn a positive social
image (e.g. competence and conscientiousness) from supervisors and coworkers
(Raghuram, 2013). Conversely, expected image risks deter employees from service innovation behavior because doing so may cause others to think unfavorably of them (e.g.
incompetence and show-off) (McDonnell & King, 2013). They choose to play it safe to
prevent negative social evaluations from other employees (Yuan & Woodman, 2010).
Second, from a feedback-seeking perspective, individuals search proactively for evaluative information about their performance due to the potential image gains and costs associated with their behavior (Wood & Hoeffler, 2013). If employees expect innovation behavior
to enhance their social image, they become more likely to seek feedback. Conversely,
employees who believe that the risks associated with feedback-seeking may harm their
social image will be less likely to engage in this type of behavior (Ashford & Cummings,
1983). In their study on work team performance, De Stobbeleir, Ashford, and Buyens
(2011) demonstrated that direct verbal feedback from leaders and coworkers enhances
employees’ understanding regarding how others perceive their work and ideas, and thus
facilitates subsequent adjustments and improvements. In addition, employees examine
their environment for indirect feedback cues to diversify their thinking and enrich their
innovation performance. Therefore, this study hypothesizes:
H2a: Expected image gains are positively related to service innovation behavior.
H2b: Expected image risks are negatively related to service innovation behavior.
Transformational leadership → perceived organizational climate for innovation
Bass and Avolio (1995) characterize TFL as comprising four unique but interrelated behavioral components. These components include (1) idealized influence (including idealized
attribution and idealized behavior), in which a leader influences followers by inspiring
strong admiration, respect, and loyalty and by fostering a collective sense of mission; (2)
inspirational motivation, in which a leader communicates high expectations, articulates a
compelling vision for the future, shows followers how to achieve stated goals, and
expresses confidence in their ability to accomplish these goals; (3) intellectual stimulation,
in which a leader heightens the attention of followers to problems and encourages them to
view old problems from new perspectives; and (4) individualized consideration, in which a
leader supports, mentors, and develops followers.
Service research has shown that transformational leaders are central to the creation and
maintenance of a climate in service contexts (Bowen & Schneider, 2014; Liao & Chuang,
2007). Leaders who utilize TFL influence employees’ perceptions of the organizational
climate for innovation, which refers to the degree of support and encouragement an organization provides to employees to increase their willingness to take initiative and explore
innovative approaches (Sarros, Cooper, & Santora, 2008; Scott & Bruce, 1994). This
climate comprises two important signals during the service innovation process at the individual level: the organizational expectations with regard to innovation behavior and the
potential outcomes of that behavior (Amabile, 1988; Scott & Bruce, 1994). These
signals influence employees’ psychological mechanisms, which in turn enhance their individual innovation behavior. Previous studies have demonstrated that leaders who possess
TFL behaviors influence the climate for innovation at the organizational level (e.g.
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Eisenbeiss et al., 2008; Jung et al., 2003). Following Sarros et al.’s (2008) suggestion to
directly measure innovation behavior at the individual level, we posit that TFL has a positive impact on frontline employees’ perceived organizational climate for innovation.
Several TFL behaviors influence the perceptions of frontline employees with regard to
the organizational climate for innovation. As idealized influencers and inspirational motivators, transformational leaders serve as influential models that articulate a compelling
vision throughout the organization. These leaders establish an organizational culture that
underscores the central role of service innovation in business success (Wang, Oh, Courtright, & Colbert, 2011). As intellectual stimulators, transformational leaders help foster
an organizational climate that challenges and motivates employees to seek innovative
approaches at work (Sarros et al., 2008). As mentors, transformational leaders provide individualized support and personalized development guidance to help employees understand
the importance of service innovation (Chen et al., 2013). Given these arguments, this study
hypothesizes:
H3: TFL is positively related to perceived organizational climate for innovation.
Perceived organizational climate for innovation → creative self-efficacy
Previous research has conceptualized psychological climate as a construct comprising an
individual’s psychologically meaningful representations of proximal organizational structures, processes, and events (James, Hater, Gent, & Bruni, 1978). Scott and Bruce
(1994) suggested that this climate represents the signals that individuals receive regarding
organizational expectations for behavior and potential outcomes of behavior. In the service
innovation context, the process whereby frontline employees interpret the meaning of
environmental cues is critical to the innovation process (Tierney & Farmer, 2004).
Bandura (1982) proposed four main sources for the formation of self-efficacy beliefs: (1)
social persuasion, (2) vicarious experience, (3) mastery experience, and (4) psychological
state. We expect that employees who perceive the existence of an organizational climate
for innovation will elevate their creative self-efficacy through these four mechanisms. First,
employees who strongly perceive that an organizational climate for innovation exists tend
to focus on information that confirms their capabilities for innovation, and ignore contradictory information (Tierney & Farmer, 2011), thereby enhancing the positive influence of social
persuasion. Second, these employees will actively exchange information and hence learn
vicariously from their coworkers (Richter et al., 2012). Third, these employees tend to interpret this climate as being connected to the expectations of their supervisors, and engage in the
innovation process. They feel free to propose new ideas without fear of being criticized
(Anderson et al., 2014), making them more likely to accumulate mastery experience over
time (Gong et al., 2009). Fourth, these employees tend to believe that they will receive
support when implementing innovation (Chen et al., 2013). Therefore, they are more likely
to focus on the positive side of innovation, which helps reduce aversive states of psychological
arousal related to fear, anxiety, and stress. In summary, the perceived organizational climate
for innovation is an important precursor to enhancing creative self-efficacy.
H4: Perceived organizational climate for innovation is positively related to creative selfefficacy.
Perceived organizational climate for innovation → expected image outcomes
The perceived organizational climate for innovation not only influences how employees see
themselves in terms of creative self-efficaciousness, but also influences their expectations of
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potential image outcomes. According to Jones and James (1979), psychological climate
perceptions affect how an individual interprets events, predicts possible outcomes, and
ascertains the appropriateness of subsequent actions.
In the context of service innovation, employees use these signals regarding perceived
organizational climate for innovation to predict potential image gains and risks. Specifically, employees with a higher perception of organizational climate for innovation are
more likely to experience a sense of psychological safety due to the belief that their supervisors favor innovation behavior in their employees. These employees are also more likely
to believe that engaging in service innovation behavior is valuable (Chen et al., 2013),
which enhances expected image gains and reduces expected image risks (Ashford et al.,
1998). Likewise, employees who perceive the existence of an organizational climate for
innovation are more inclined to interact with their colleagues and perceive a greater
sense of context favorability, which legitimates their desire for innovation behavior (Tse,
Ashkanasy, & Dasborough, 2012). In other words, this type of climate indicates that
with respect to organizational norms and values, the organization favors innovation
rather than tradition; therefore, frontline employees will be more likely to consider innovativeness as a desirable image, which strengthens expected image gains and reduces
expected image risks.
H5a: Perceived organizational climate for innovation is positively related to expected image
gains.
H5b: Perceived organizational climate for innovation is negatively related to expected image
risks.
Methodology
Research context
This study was conducted in hair salons in urban areas of northern and central Taiwan. A
hair salon is a setting in which services are marked by a high level of interaction between
frontline employees and customers, and in which the participation of customers is critical to
successful service innovation (Liao & Chuang, 2007). Therefore, hair salons offer a good
setting for researching service innovation issues and achieving our study objectives.
This study uses the standard term ‘hairstylist’ to refer to the frontline service providers
working at hair salons. Hairstylists offer innovative services such as hair design, hair
cutting, and hair coloring to satisfy customer expectations. Examples of service innovation
behavior in this industry include implementing new hair service concepts, developing new
hair design methods, and using new technologies in providing hair services.
Because customers are codevelopers in the service process (Jaakkola & Alexander,
2014), their opinions provide a strong and valid measure of hairstylists’ service innovation
behaviors. Therefore, this study collected dyadic data (i.e. a combination of information
offered by hairstylists and their respective customers) in three phases (see Table 1). This
temporal separation (i.e. multiphase) design is recognized as a key marker of causality
by assessing the influence of a predictor at a time subsequent to its cause (Rindfleisch
et al., 2008). Moreover, this design enables the investigation of individual service innovation behavior over time and reduces common method bias (Podsakoff, MacKenzie,
Lee, & Podsakoff, 2003). The questionnaire used in this study was translated from
English into Chinese by a Taiwanese marketing professor. Two doctoral students then independently translated the questionnaire back into English to confirm equivalency of
meaning.
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Table 1. Measurement design.
Variable
Transformational Leadership
Perceived Organizational Climate for Innovation
Creative Self-Efficacy
Expected Image Outcomes
Customer Participation
Service Innovation Behavior
Time 1
Time 2
Time 3
X
X
X
X
X
X
Data source
Service
Service
Service
Service
Service
Service
providers
providers
providers
providers
receivers
receivers
Notes: We collected data at three time periods: Time 1 (initial), Time 2 (one month after Time 1), and Time 3 (one
month after Time 2).
Data collection
In the first phase (Time 1), eight trained graduate-level research assistants administered the
hairstylist surveys at each of the targeted salons. The hairstylist participants were asked to
evaluate the TFL behaviors of the store manager and their perceptions of the organizational
climate for innovation. Hairstylist surveys and the corresponding envelopes were coded
using random identification numbers interpretable only by the researchers to facilitate later
matching of the data from the hairstylist participants to the data from the second survey. A
central collection box was set up in the staff room for participants to submit their completed
surveys in confidence. Additionally, participants were informed that they could mail their
surveys directly to the researchers using a prepaid return envelope if they preferred.
We administered a second survey to the same hairstylist participants one month after the
first survey (Time 2) to assess their creative self-efficacy and expected image outcomes. To
motivate participation, those who completed both surveys were offered a shopping voucher
worth NTD 200 (approximately US$7). In the next phase (Time 3), research assistants
approached customers randomly to fill out a brief survey after these customers had received
their hair service. This random approach avoided any potential selection bias. Customers
answered questions about the service innovation performance of their hairstylist. All
respondents were guaranteed confidentiality for their responses.
Sample characteristics
A total of 331 hairstylist participants completed the first wave of the survey, while 309 completed both waves. We deleted 40 incomplete surveys, leaving 269 valid survey responses
available for analysis (valid response: 81.2%). The average number of employees in each
salon was approximately seven. The average age of the participants was 27 years. Most
(221; 82%) were women, and the average tenure was seven years. In terms of education,
10 (4%) held a junior high school degree, 213 (79%) held a senior high (or vocational)
school degree, 26 (10%) held a two-year college degree, and 20 (7%) held a university
or higher degree. In addition, 1630 customers were asked to participate in the third
wave, and 1396 (85%) completed and returned the study questionnaire. On average, five
customers for each participant completed the questionnaire. These returned surveys
enabled us to identify 269 usable matched data from hairstylists and customers.
Measures
Table 2 provides a detailed summary of the multiple-item scales, composite reliabilities
(CRs), and average variances extracted (AVEs). As in the original English-language versions, the questionnaires used seven-point Likert scales to record responses.
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P.-J. Kao et al.
Table 2. Summary of measures.
Construct
TFL (CR = .89,
AVE = .67)
Perceived
Organizational
Climate for
Innovation
(CR = .85,
AVE = .66)
Creative
Self-Efficacy
(CR = .82,
AVE = .61)
Expected Image
Gains
(CR = .87,
AVE = .62)
Expected
Image Risks
(CR = .95,
AVE = .85)
Service
Innovation
Behavior
(CR = .84,
AVE = .63)
Conceptual definition/measuresa
Standardized
factor
loadingb
TFL is defined as four unique but interrelated behaviors enacted by a leader (Bass
& Avolio, 1995)
(1) Idealized influence (Attributes)
.70
(2) Inspirational motivation
.86
(3) Intellectual stimulation
.89
(4) Individual consideration
.82
Organizational climate for innovation refers to the degree of support and
encouragement an organization provides employees to take initiative and
explore innovative approaches (Sarros, Cooper, & Santora, 2008; Scott &
Bruce, 1994).
(1) I perceive that people in this store are always searching for
.85
new ways of looking at problems
(2) I perceive that we take time to develop ideas in this store
.81
(3) I perceive that people in this store cooperate in order to
.77
develop and apply ideas
Creative self-efficacy refers to the belief that one has the ability to produce
creative outcomes (Tierney & Farmer, 2002, 2011)
(1) I have confidence in my ability to solve problems creatively
.80
(2) I feel that I am good at generating novel ideas
.73
(3) I have a knack for further developing the ideas of others
.81
Expected image gains help improve an individual’s social image (Tetlock &
Manstead, 1985; Yuan & Woodman, 2010)
(1) If I were to do something innovative, my image in the hair
.80
salon would be enhanced
(2) Searching out new technologies or techniques for my
.86
company will make me look good
(3) Participating in the implementation of new ideas will
.81
improve my images in the hair salon
(4) Suggesting new ways to achieve goals will improve my
.67
supervisor’s evaluation of me
Expected image risks helps protect an individual’s established social image
(Tetlock & Manstead, 1985; Yuan & Woodman, 2010)
(1) My coworkers will think I am showing off if I come up with
.93
new approaches in my work
(2) People will think I am crazy if I come up with new ways of
.95
doing my job
(3) Other people will think worse of me if I try to change the way
.89
things operate within the hair salon
Service innovation has been defined as introducing something new into the way
of life, organization, timing, and placement of what can generally be described
as the individual and collective processes that relate to consumers (Carlborg
et al., 2014)
(1) I think this hairstylist often comes up with new and practical
.78
ideas to improve hair service performance
(2) I think this hairstylist often develops new methods for hair
.79
design
(3) I think this hairstylist often uses new technologies, processes,
.82
and techniques in hair services
Notes: CR = composite reliability and AVE = average variance extracted.
a
All items were assessed on seven-point scales, anchored at 1 = Strongly disagree, 4 = Neutral, and 7 = Strongly
agree.
b
All factor loadings are significant at p < .001.
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Service innovation behavior. Drawing on the work of Gong et al. (2009) and Zhang and
Bartol (2010), this study used a three-item service innovation measure. Customers provided
information about service innovation behavior by answering the three items. These items
are as follows: ‘I think this hairstylist often comes up with new and practical ideas to
improve hair service performance’; ‘I think this hairstylist often develops new methods
for hair design’; and ‘I think this hairstylist often uses new technologies, processes, and
techniques in hair services’. These items are consistent with Ordanini and Parasuraman’s
(2011) conceptualization of service innovation. This adaptation approach is also consistent
with Farh, Canella, and Lee’s (2006) recommendation for developing valid research instruments in a Chinese context. This study further aggregated evaluations of the same stylist
from multiple customers at the stylist level to measure the hairstylists’ overall service innovation performance (Liao & Chuang, 2007).
TFL. This study adopted the multifactor leadership questionnaire form 5X-Short to scale
TFL (Bass & Avolio, 1995). Respondents were asked to respond to each item based on
how appropriately it described their manager. Sample items included ‘My manager displays
self-confidence and authority in dealing with matters’ (idealized influence category); ‘My
manager often expresses his/her confidence that we will achieve our goals’ (inspiration
motivation category); ‘My manager often encourages us to face problems with diverse perspectives’ (intellectual stimulation category); and ‘My manager spends time teaching and
coaching me’ (individualized consideration category).
Perceived organizational climate for innovation. This study measured perceived organizational climate for innovation with three items that were adapted from the support for innovation scale in the Team Climate Inventory developed by Kivimaki and Elovainio (1999).
A sample item is: ‘I perceive that people in this store are always searching for new ways of
looking at problems.’ Participants indicated the degree to which they perceived an organizational climate for innovation.
Creative self-efficacy. This study used the three-item Creative Self-Efficacy Instrument
(Tierney & Farmer, 2002) to assess this measure. Participants responded to items such as ‘I
have confidence in my ability to solve problems creatively’ to evaluate their creative ability.
Expected image gains. This study measured expected image gains using four items adapted
from Ashford et al.’s (1998) measures of image outcomes associated with issue selling (e.g.
‘If I were to do something innovative, my image in the hair salon would be enhanced’). The
participants indicated their anticipated degree of image gain.
Expected image risks. This study asked participants to indicate their anticipated degree
of image risk using three items adapted from Ashford’s (1986) measure of image risk (e.g.
‘My coworkers will think I am showing off if I come up with new approaches in my work’).
Results
Measurement model evaluation
Item reliability. The standard used to measure items reflects research suggestions
by Bagozzi and Yi (1988) that factor loadings for each observed item of every latent
construct should exceed 0.50. All results obtained in the model were significantly higher
than 0.70 (p < .001).
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P.-J. Kao et al.
Internal consistency. We used the CR coefficient (Bagozzi & Yi, 1988; Fornell & Larcker,
1981) and AVE (Fornell & Larcker, 1981) to assess the internal consistency of the construct
measures. Though identical to Cronbach’s alpha, the CR coefficient uses the corresponding
estimated factor loading to weigh each measure. CR coefficient and AVE estimates above
0.60 and 0.50, respectively, are deemed to demonstrate adequate internal consistency. CR
and AVE values for all constructs in the model were significantly higher (0.82∼0.95 and
0.61∼0.85, respectively) than the stipulated criteria, indicating good internal consistency.
Discriminant validity. Three different approaches were used to evaluate the discriminant
validity of model constructs. The results of a confirmatory factor analysis model, built
with 6 latent constructs and a total of 20 measures, showed good fit with the data. The goodness-of-fit statistics for the model were as follows: χ2(169) = 319.09, non-normed fit index
(NNFI) = 0.96, confirmatory fit index (CFI) = 0.97, and root mean squared error of approximation (RMSEA) = 0.056. As a first test of discriminant validity, we checked whether correlations among latent constructs were significantly less than one. Lack of the value of 1
appearing in any of the confidence intervals for the φ values (φ values ± two standard
errors) suggests good discriminant√validity
(Bagozzi & Yi, 1988). Next, Fornell and
Larcker (1981) suggested that the AVE value of each latent construct should exceed
its vertical and horizontal correlation values (i.e. the correlation value between the latent
construct and other constructs).
√ All correlation values for each latent construct in this
study were less than the AVE value (see Table 3). Finally, for each pair of factors, the
chi-square value obtained using a measurement model that constrained their correlation
to one was compared against a baseline measurement model without this constraint. We
Table 3. Correlation matrix and summary statistics.
Correlationa
Variable
1. Service Innovation
Behavior
2. Creative Self-Efficacy
3. Expected Image Gains
4. Expected Image Risks
5. Perceived Organizational
Climate for Innovation
6. Transformational
Leadership
7. Customer Participation
Response range of
multiple-item scales
Mean
Standard Deviation
a
1
2
3
4
5
6
7
b
0.80
0.27
(0.06)c
0.27
(0.06)
−0.01
(0.06)
0.13
(0.06)
0.09
(0.05)
0.67
(0.07)
2–7
0.72
(0.06)
−0.10
(0.05)
0.46
(0.06)
0.19
(0.04)
0.14
(0.05)
1–7
−0.15
(0.05)
0.39
(0.06)
0.26
(0.04)
0.13
(0.05)
2–7
−0.11
(0.06)
−0.08
(0.05)
0.02
(0.05)
1–7
0.54
(0.05)
0.06
(0.05)
3–7
0.02
(0.04)
1–7
0.76
5.83
0.55
5.75
1.01
5.78
1.01
2.75
1.62
4.24
0.67
5.67
1.06
5.35
0.90
0.78
0.79
0.92
0.81
0.82
1–7
All correlations are significantly less than 1.00.
The figures on the diagonal are the square roots of the average variance extracted score for each construct.
c
Standard errors appear in parentheses.
b
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459
then performed a chi-square difference test on each (see Appendix). When, for example, we
tested whether construct 1 (Service Innovation Behavior) and construct 2 (Creative SelfEfficacy) were distinct, the measurement model with the correlation constrained to one
yielded a chi-square value of 386.78 (df = 70), and the difference between the baseline
model and the constrained model was x2d (1) = 67.69 (p < 0.001). This result identified
the two factors as distinct. These tests revealed factor pairs as distinct in all cases, again
offering evidence of discriminant validity for all construct measures.
Structural model estimation
Structural equation models were built separately for the full sample to test H1–H5.
Mediation tests were then conducted on the full sample to determine robustness. The fit statistics for the full sample model (χ2(181) = 421.08, p ≅ 0.00; NNFI = 0.94; CFI = 0.95;
RMSEA = 0.066) demonstrate the significance of chi-square (p < .001), likely a reflection
of the large sample size. All other statistics were also within acceptable ranges, indicating
a good fit to the data.
We found significant support for the paths from creative self-efficacy and from expected
image gains to service innovation behavior (see Figure 2), with beta coefficients of 0.12 (p
< .05) and 0.13 (p < .01), respectively, providing support for H1 and H2a. We found that
expected image risks (β = 0.01) was not a significant predictor of service innovation behavior; thus, H2b was not supported. These antecedents explained nearly half (46%) of all
service innovation behaviors. In terms of the perceived organizational climate for innovation, we found that TFL (γ = 0.53) was a significant predictor of perceived organizational
climate for innovation, which supported H3. In terms of creative self-efficacy, we found
that the path from perceived organizational climate for innovation to creative self-efficacy
was significant (β = 0.49), supporting H4. Finally, for the path from perceived organizational climate for innovation to expected image outcomes, we found that the expected
image gain was positive and significant (β = 0.45) and that the expected image risk was
negative but not significant (β = –0.12), which supported H5a but not H5b.
Test of mediation
To obtain further support for the model’s validity, rather than using a saturated model in
which ‘everything is related to everything else’ as the baseline, we performed formal
Figure 2. Standardized path coefficients for the model.
Note: *p < .05, **p < .01.
460
P.-J. Kao et al.
tests of mediation for all possible paths in the model. We did this to check for the potential
significance of additional direct paths not specified in the model. Four of these tests were
conducted. To check the significance of the direct path from the perceived organizational
climate for innovation to service innovation behavior, M1 (see Table 4) was compared
with a model in which an additional path from perceived organizational climate for innovation to service innovation behavior was added. We used the difference in chi-square
values between the two models (M0 and M1) (x2d (1) = 0.02) with a single degree of
freedom to test the significance of the added path. We found a lack of significance (p
> .88), which indicates the non-significance of this direct path and that creative self-efficacy
and expected image outcomes mediate all the effects of the perceived innovation climate on
innovation behavior, as hypothesized. Furthermore, we tested the direct paths from TFL to,
respectively, creative self-efficacy, expected image gains, and risks (M2, M3, and M4), with
chi-square differences from the baseline model (M0) calculated as x2d (1) = 2.19, x2d (1) =
0.19, and x2d (1) = 0.10, respectively, indicating clear non-significance. Therefore, the
hypotheses stating that perceived organizational climate for innovation mediates the
effects of TFL on creative self-efficacy, expected image gains, and expected image risks
were supported.
Discussion and conclusions
This study developed a comprehensive framework for investigating the effect of dual mediating processes on the relationship between TFL and the service innovation behavior of
frontline employees. Using a multiphase design and multiple measurement sources, we
found that the perceived organizational climate for innovation, creative self-efficacy, and
expected image gains fully mediated the relationship between TFL and service innovation
behavior. We also found that the perceived organizational climate for innovation channeled
the entire effect of TFL onto creative self-efficacy and expected image gains. Motivational
mechanisms and social-political mechanisms each uniquely affect service innovation behavior when considered simultaneously.
Theoretical implications
We extend and deepen the existing research in this area by resolving the controversy over
the relationship between TFL and service innovation behavior (Avolio et al., 2009). Jaussi
and Dionne (2003) found that TFL had no influence on individual creativity, whereas Shin
and Zhou (2003) found a positive relationship between these two constructs. Early innovation research relied predominantly on the motivational perspective to investigate the
association between TFL and service innovation behavior (e.g. Gong et al., 2009; Gumusluoglu & Ilsev, 2009; Jaussi & Dionne, 2003; Shin & Zhou, 2003), which neglected the
potential effects of other mediating mechanisms. The present study combined motivational
and social-political perspectives to build a framework that effectively captures a more comprehensive picture of the relationship between TFL and service innovation behavior. The
findings reveal that three factors fully mediate this relationship: perceived organizational
climate for innovation, creative self-efficacy, and expected image gains. TFL is an integral
part of successful service innovation. Supervisors may use four TFL behaviors (i.e. idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration) when seeking to establish an organizational climate that enhances the pivotal
role of innovation in corporate success and creates a favorable working environment that
encourages employees to seek innovative solutions, which should lead to increased
The Service Industries Journal
461
Table 4. Mediation tests.
Model
Added Path
M0
Baseline model: hypothesized paths
M1
Perceived organizational climate for
innovation →Service innovation
behavior
Transformational leadership
→Creative self-efficacy
Transformational leadership
→Expected image gains
Transformational leadership
→Expected image risks
M2
M3
M4
Goodness of Fit
x2 (181) = 421.08, p .00,
RMSEA = 0.066, NNFI =
0.94, CFI = 0.95.
x2 (180) = 421.06
x2 (180) = 418.89
x2 (180) = 420.89
x2 (180) = 420.98
Tests of
Hypotheses
–
M0−M1:x2d (1)
= .02, p > .88
M0−M2:x2d (1) =
2.19, p > .13
M0−M3:x2d (1) =
0.19, p > .66
M0−M4:x2d (1) =
0.10, p > .75
Notes: RMSEA = root mean squared error of approximation; NNFI = non-normed fit index; and CFI =
confirmatory fit index.
levels of service innovation behavior due to strengthened creative self-efficacy and
expected image gains.
Furthermore, the present study empirically incorporates both motivational and socialpolitical mediating mechanisms into one research design, which improves on Gong
et al.’s work (2009). Our results reveal that the dual mediating processes simultaneously
shape the way TFL influences service innovation behavior. Although extant research has
focused on creative self-efficacy as the important antecedent of innovation behavior, our
results highlight that, in addition to creative self-efficacy, the desire to enhance one’s
own image serves as another important psychological mechanism that underpins service
innovation behavior. Specifically, our results indicate that creative self-efficacy and
expected image gains are positively related to service innovation behavior when considered
simultaneously.
In addition, the present study extends the work of previous studies by demonstrating
that the perceived organizational climate for innovation fully channels the effects of TFL
onto both motivational and social-political mediating mechanisms. Somech and DrachZahavy (2013) found that team creativity enhances innovation implementation only
when the climate for innovation is high. In this study, we use perceived organizational
climate for innovation to connect TFL and dual mediating mechanisms. That is, our
finding extends Somech and Drach-Zahavy’s work (2013) by considering this critical variable as an important precursor of both motivational and social-political mediating processes. We demonstrate that this type of climate indirectly influences service innovation
behavior through creative self-efficacy and expected image gains. This finding is important
because prior innovation research has directly linked this climate to innovation behavior.
The current results suggest that the perceived organizational climate for innovation may
also have an indirect association with service innovation behavior through both motivational and social-political mechanisms.
Finally, this study extends prior research by addressing the call to examine the impact of
expected image outcomes on innovation behavior in different cultures. Yuan and Woodman
(2010) found that in a Western cultural context, expected image gains had a significant and
negative effect on innovative behavior (β = –0.22) and expected image risks had a negative
effect on innovative behavior (β = –0.19). In contrast with their findings, the results of the
462
P.-J. Kao et al.
present study suggest a positive association between service innovation behavior and
expected image gains (β = 0.13) and no significant association between service innovation
behavior and expected image risks (β = 0.01). Differences in the cultural settings of these
studies may explain these contradictory findings. This study used data obtained in
Taiwan, a country with different cultural values from Western countries (Hofstede & Hofstede, 2005). Chinese culture emphasizes the importance of the social ‘face’ (i.e. mianzi)
and the moral ‘face’ (i.e. lian) (Hwang, 1987). ‘Mianzi represents the social reputation of
a person … that has been deliberately accumulated through effort and achievement and
with pride over the course of life’ (Hwang, 2012, p. 267). ‘Lian is the social respect
offered by a group to an individual with high morality. A person with lian would do
what is proper no matter what difficulties were encountered and behave honestly in
every situation’ (Hwang, 2012, p. 268). Specifically, the effect of expected image gains
is stronger in Chinese culture because Chinese emphasize mianzi more than Westerners,
while the effect of expected image risks on service innovation behavior is weaker in
Chinese culture because Chinese also emphasize lian more than Westerners. For these
reasons, it is worth considering the extent to which our findings are culturally specific.
Managerial implications
The results of the present study suggest that service firms should focus greater effort on
building training programs that enhance their managers’ TFL style. In addition, firms
should hire and promote employees who have personalities marked by extraversion and
emotional stability, as these individuals are more likely to become transformational
leaders (Wang et al., 2011).
In terms of recommendations to managers of service businesses, the present study highlights the importance of TFL in developing the service innovation behavior of frontline
employees. The results show the potential benefit of using several TFL behaviors to
strengthen the service innovation behavior of frontline employees. First, managers can
serve as influential models and use verbal persuasion to encourage employees to be innovative (Wang et al., 2011). Second, to stimulate creative thinking, transformational leaders
can create hands-on opportunities for subordinates to apply new skills to resolve difficult
problems (Sarros et al., 2008). Third, transformational managers can provide individualized
support and personalized development guidance to help alleviate potential feelings of
anxiety or fear that might arise in employees due to the inherent uncertainties of innovative
endeavors (Chen et al., 2013). These behaviors may facilitate service innovation behavior
by creating an organizational climate that favors service innovation.
For frontline employees, the results of the present study show a positive relationship
between creative self-efficacy and service innovation behavior. Employees with highly
creative self-efficacy behave in a self-starting manner and are more proactive in tackling
unclear/unfamiliar situations. Furthermore, our findings reveal that expected image gains
affect service innovation behavior positively. This image-enhancing motive may encourage
frontline employees to use service innovation behavior to create a favorable impression on
their managers and coworkers. The finding is consistent with Baer’s (2012) research that
creative ideas are realized when employees expect positive outcomes to be associated
with their creative implementation efforts.
In conclusion, our results suggest that service firms should include effective TFL training in their regular managerial training programs. In addition, firms should create an
environment that encourages service innovation behavior. Having managers with a TFL
style and an organizational climate that is favorable toward innovation will enhance the
The Service Industries Journal
463
creative self-efficacy of frontline employees and help them achieve desired image gains –
two factors that have been shown to foster service innovation behavior.
Research limitations and further research
This study has several limitations that suggest potential avenues for additional research.
First, the focus of this study on one service-sector business area limits the observed variability, and thus decreases the external validity of results. However, the single-industry focus
facilitated the control of potential industry-level confounding variables. Future research
may selectively widen the scope of consideration to other service businesses of a similar
nature (e.g. medical and financial) as well as to other professional settings in order to
increase the generalizability of the findings. Second, this study examined two psychological
mechanisms that mediated the relationship between the perceived organizational climate for
innovation and service innovation behavior. Other variables such as intrinsic motivation,
prosocial motivation, and mood may be examined to further expand our knowledge of
service innovation processes. Third, the present study was conducted in Taiwan. Further
research may replicate this study in other cultural contexts and compare findings. Fourth,
participants in the present study had a relatively low level of formal education. Further
research may examine the effect of education by comparing the results of respondents in
groups segmented by education level. Fifth, because business size and number of employees per salon are largely similar across the hair salon sector in Taiwan, the present study
included customer participation as the only control variable. Future research may add
other variables as controls. Moreover, it may be fruitful to collect data from service managers in order to analyze data from three sources, rather than two. Finally, the methodology
of future studies may be supplemented with qualitative case studies. The present investigation provides a clear direction and purpose for additional research and adds meaningfully
to the current knowledge in this area.
Acknowledgements
The authors are indebted to participants of International Conference on Innovation and Management
for their helpful and insightful comments on versions of this article.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This research was supported in part by the grant [NSC 101-2410-H-004 -211 -MY2] from the
National Science Council, Taiwan.
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Appendix 1. χ2 Statistics regarding discriminant validity of factor pairs
Construct
1. Service
Innovation
Behavior
2. Creative
Self-Efficacy
3. Expected Image
Gains
4. Expected Image
Risks
5. Perceived
Organizational
Climate for
Service
Innovation
6. Transformational
Leadership
7. Customer
Participation
a
1a
2
3
4
x2d (1) =
67.69
x2d (1) =
68.51
x2d (1) =
261.11
x2d (1) =
86.66
x2d (1) =
31.29
x2d (1) =
138.78
x2d (1) =
51.52
x2d (1) =
157.09
x2d (1) =
61.68
x2d (1) =
137.76
x2d (1) =
112.39
x2d (1) =
19.19
x2d (1) =
106.98
x2d (1) =
99.79
x2d (1) =
95.97
x2d (1) =
102.22
x2d (1) =
153.75
x2d (1) =
113.28
5
6
x2d (1) =
55.05
x2d (1) =
110.01
x2d (1) =
135.16
7
The difference in the chi-square values of the two models (i.e. the baseline and the constrained model), with one
degree of freedom.