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Domain-Relevant Commitment and Individual Technical
Innovation Performance*Lance A. Bettencourt, Edward U. Bond III, Michael S. Cole, and Mark B. Houston
Individual innovators play a critical organizational role in that they generate and often champion technology
and product ideas. Amidst an ongoing stream of organizational and team innovation research, few empirical
studies focus on differences in individual innovation performance despite the importance of the individual inno-
vator to a firm’s innovation efforts. Based on goal commitment theory, we introduce a new domain-relevant
commitment construct and develop a conceptualization of conditional indirect effects. Our model suggests that
relevant individual abilities enhance commitment to technical innovation and innovation performance while
also insulating against the impact of situational variables, making employees’ commitment to innovation perfor-
mance less dependent upon context. Hypotheses are tested using two sources of data and a sample of 339 R&D
professionals from a Fortune 100 industrial firm. Results suggest that commitment to innovation is a key moti-
vational factor in explaining individual technical innovation performance. Situational characteristics impact
motivation differently for individuals with lower vs. higher ability levels, even in this context in which truly
low-ability individuals, in the absolute sense, have been screened out by the employment selection process. The
relationship between commitment and innovation performance is strengthened by higher levels of individual
ability.
Practitioner Points
� Because lower-ability employees are more strongly
affected by leader behaviors, climate, and innovation
constraints, the “hands-off” management style pre-
ferred by many innovation managers is not optimal
to maximize the performance of lower-ability
employees.
� A reward structure that recognizes individual perfor-
mance is even more critical to high-ability employees.
� Employers should measure personality predispositions
toward innovativeness during their R&D employment
screening. Tailored continuing education/training pro-
grams also might help enhance creative-thinking and
technical skills among R&D specialists.
Introduction
The first thing I want to focus on is ruthlesslyremoving any obstacles to allow every individualin our organization to innovate. Then focus all ofthat innovation on what Microsoft can uniquelydo.
—Satya Nadella, on his first day as the new CEO
of Microsoft
In a survey of 114 innovation leaders, nearly one
in three cite “getting more innovation from employ-
ees” as their top human resource concern (Kochan-
ski, Mastropolo, and Ledford, 2003). Nearly 70% of
responding R&D leaders said their work culture sup-
ports innovation, but only 25% believed they had
enough employees with cutting-edge skills. In short,
the beliefs and behaviors of R&D leaders suggest
that they see individual contributions to innovation
as critical. The practical significance of individual
innovation performance is underscored by the num-
ber of leading innovation firms (e.g., GE, 3M,
Microsoft) that rate each innovator’s individual per-
formance and use those ratings for retention and pro-
motion decisions (Greenwald, 2001). Scholars agree
that firms need employees who are more innovative
to meet goals (Birdi, Leach, and Magadley, 2016). A
Address correspondence to: Mark B. Houston, Department of Mar-keting, Mays Business School, TAMU 4112, Texas A&M University,College Station, Texas 77843. E-mail: [email protected]. Tel:979-845-7257.
*The authors are listed alphabetically to reflect equal contributions.The authors thank the firm that provided access to employees for thisstudy, as well as Dan Smith and Robert Smith of Indiana University;Ajith Kumar of Arizona State University; Rob Palmatier of Universityof Washington; and Marsha Richins, Christopher Robert, Lisa Scheer,and Dan Turban of University of Missouri-Columbia, and seminar par-ticipants at Texas Christian University for their comments on previousdrafts of this paper.
J PROD INNOV MANAG 2016;00(00):00–00VC 2016 Product Development & Management AssociationDOI: 10.1111/jpim.12339
central question for leaders of innovation appears to
be, “What can we do to enhance the performance of
the individuals hired to create technical innovation?”
Given the importance of this central question, crea-
tivity and innovation scholars have sought to explain
individual innovation behavior, but tests of comprehen-
sive models of individual innovation are rare and schol-
ars have found inconsistent empirical support for them.
For example, Birdi et al. (2016) found that several pre-
dictors of idea generation and implementation did not
behave as theory suggested (see Shalley, Zhou, and Old-
ham, 2004, for a review). Further theoretical develop-
ment would fill important gaps in our understanding of
individual innovation performance. First, extant findings
can be hard to reconcile because of variations in both
domain (innovation or creativity) and level of analysis
(Anderson, Potocnik, and Zhou, 2014). Second,
although scholars have broadly agreed on the impor-
tance of a motivational element in creativity, more work
is needed regarding the conceptualization and measure-
ment of motivational features in innovation (Shalley
et al., 2004). Third, some seminal works in innovation
and creativity (e.g., Amabile, 1988) draw on novel theo-
ry bases, but perhaps useful insights into drivers of indi-
vidual innovation performance can be gained by
leveraging rich theories that have strong empirical sup-
port from decades of industrial and organizational (I-O)
psychology research into human behavior. We examine
each of these three potential gaps and show how we
designed individual-level research in an R&D setting to
make contributions.
Regarding variations in domain across studies, the
domain in individual innovation articles is often unclear
because the terms “innovation” and “creativity” are fre-
quently interchanged. Although the underlying constructs
are related, they are subtly distinct. Both creativity and
innovation rely on individual abilities (i.e., innovative
cognitive ability; Amabile, 1996), but innovation is more
closely linked to organizational goals and competitive
advantage because of its emphasis on implementation
(Varadarajan and Jayachandran, 1999). Recognizing the
close relationship between the concepts (see Anderson
et al., 2014, for a review), this study draws key insights
from the creativity literature, but focuses on the domain
of innovation, specifically, technical innovation perfor-
mance—generating and championing technology and
product innovations by individual innovation employees
(Scott and Bruce, 1994).
Regarding levels of analysis, few studies have
examined the drivers of innovation performance at the
individual level (Bammens, 2015). Research streams
investigate organization-level (e.g., Han, Kim, and Sri-
vastava, 1998) and team-level (e.g., Sethi, Smith, and
Park, 2001) innovation, but research that centers on
BIOGRAPHICAL SKETCHES
Dr. Lance A. Bettencourt is associate professor of professional prac-
tice, Neeley School of Business, Texas Christian University, and a
partner with Service 360 Partners, an innovation consultancy. Dr. Bet-
tencourt was formerly a strategy adviser with Strategyn, Inc., the pio-
neer of Outcome-Driven InnovationTM. In his consulting, he has
worked with many of the world’s leading companies to uncover prod-
uct and service innovation opportunities, including Allstate, Hewlett-
Packard Company, Microsoft Corporation, Morningstar, and TD Bank
Financial Group. His research on services and innovation is published
in Harvard Business Review, MIT Sloan Management Review, Califor-
nia Management Review, Journal of Applied Psychology, Journal of
Retailing, Journal of Personal Selling and Sales Management, and
Journal of the Academy of Marketing Science, among others. He is
author of Service Innovation: How to Go from Customer Needs to
Breakthrough Services (McGraw-Hill 2010).
Dr. Edward Bond is chair of the Department of Marketing in the Foster
College at Bradley University. He has served as a visiting professor at
the University of Cambridge (UK, 2004) and as the Flint Hills Resour-
ces Visiting Professor of Marketing at the University of Alaska, Fair-
banks (2009). Dr. Bond teaches in Bradley University’s MBA and
EMBA programs and has advised businesses and trade organizations
in the United States and Europe. He has worked extensively with prod-
uct development teams to enhance product value to customers and pro-
vides training and coaching for technical experts to align their
activities more effectively with firm-level business objectives. Dr.
Bond’s research addresses drivers of innovation performance within
established corporations, customer satisfaction in medical care, and
the role that marketing assumes in strategy formulation by top-
management teams. Dr. Bond’s research has been published in Journal
of Product Innovation Management, Journal of Strategic Marketing,
Industrial Marketing Management, Journal of the American Medical
Association, and others.
Dr. Michael S. Cole is an associate professor of management at Texas
Christian University. His professional interests focus on multilevel
theories, research, and methodologies as they relate to behavior in
organizations. His research has appeared in Academy of Management
Journal, Journal of Applied Psychology, Journal of Management,
Organizational Research Methods, among others. Michael is also a
member of the editorial review boards for Journal of Applied Psychol-
ogy and Journal of Organizational Behavior and is currently senior
associate editor for The Leadership Quarterly.
Dr. Mark B. Houston (Ph.D. Arizona State, MBA University of Mis-
souri, B.S. Southwest Baptist University) is department head, profes-
sor of marketing, and Foreman R. and Ruby S. Bennett Chair in
Business Administration at Texas A&M University. Mark is also affil-
iated with Arizona State University’s Center for Services Leadership
and the University of M€unster. His research has been published in
Marketing Science, Journal of Marketing, Journal of Marketing
Research, Journal of Consumer Research, and Journal of Financial
and Quantitative Analysis, among others. He is AE of Journal of Ser-
vice Research, AE of Journal of the Academy of Marketing Science,
and a member of the Review Board of Journal of Marketing. Mark has
served as President of the AMA Academic Council, and has co-
chaired the AMA Summer Educators’ Conference and the AMA/Sheth
Foundation Doctoral Consortium.
2 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
individual contributions to innovation is rare (Wei,
O’Neill, Lee, and Zhou, 2013). Notable exceptions are
studies of “product champions” and innovation by
front-line employees (e.g., Markham and Griffin,
1998), as are works seeking to integrate approaches to
individual innovation behavior (Bammens, 2015; Scott
and Bruce, 1994) or to identify individual capabilities
and their impact on innovation behaviors (Birdi et al.,
2016). The relative under-representation of individual-
level innovation research prompts concern because
organizational and team innovation require inputs from
individual innovators who generate and/or search out
technology and product ideas, champion those ideas,
and seek to implement them (Bharadwaj and Menon,
2000). Although scholars have theorized about
individual-level innovation (cf., Bammens, 2015), our
approach is differentiated because it follows a well-
established practice in organizational behavior studies
by measuring individual-level perceptions of the work
environment (“psychological climate,” James and
James, 1989; Parker et al., 2003); we conceptualize
and test a model that is entirely at the individual level.
Given the vital role of motivation in performance,
in general, scholars working from both innovation and
creativity perspectives recognize the importance of
motivation as a necessary precursor to performance
(Amabile, 1988; Anderson et al., 2014). In a recent
review, Shalley et al. (2004) argue that intrinsic moti-
vation plays a mediating role between contextual ele-
ments and creativity, but note that empirical work on
the topic is limited and has not uniformly found sup-
port for an intervening effect. They call for further
work to explore the measurement and specification of
motivation and other variables that may impact the
relationship between context and creativity. In answer
to that call, a second contribution of this research is to
specify a domain-relevant form of commitment, com-mitment to technical innovation (CTI), and clarify its
motivational role for individual innovators (cf. Meyer,
Becker, and Vandenberghe, 2004).
At the highest level, we follow Amabile’s (1988)
componential theory of creativity, which was devel-
oped using a process view to capture core ideas of
skills and motivation, but has generated only limited
empirical investigation and support (Anderson et al.,
2014). In this vein, a third contribution to the individu-
al innovation literature is to integrate a closely
interrelated family of goal-related theories that have
successfully modeled human behavior. Although goal
commitment theory (Hollenbeck and Klein, 1987;
Locke, Latham, and Erez, 1988) has been sparsely
used in the innovation literature (e.g., to study reward
systems; Wei, Frankwick, and Nguyen, 2012; Xie, Song,
and Stringfellow, 2003), it holds promise to guide more
comprehensive models that can describe the interaction
of the ability, trait, situation, and motivation elements
that affect individual innovation. The expectancy ratio-
nale of goal commitment theory is used to specify the
relationships among the drivers of individual innovation
performance. By doing so, we are able to more precisely
model motivation as CTI than is possible using the
broader formulations of intrinsic motivation based on
novel innovation theories alone.
Also within the goal-commitment literature, path-
goal theory (House and Dessler, 1974) and behavioral
plasticity theory (Brockner, 1988; LePine and Van
Dyne, 1998) are used to predict the effects of situa-
tional factors on individual innovation performance.
Path-goal theory has been used to guide inquiry as to
how leader behaviors influence product development
team dynamics (Sarin and O’Connor, 2009), but is
generally under-researched in innovation settings. We
deploy these closely related theories to propose and
test a nuanced theoretical framework featuring condi-tional indirect effects that models the complex inter-
play between innate employee abilities (which an
employer can manage only through selection and hir-
ing) and situational characteristics (some of which are
open to significant influence by employers) as they
contribute to the technical innovation performance of
individual innovators.
The remainder of this paper is organized as follows.
First, it reviews relevant literature and presents a con-
ceptual model in which CTI, individual abilities, and
situational factors combine to drive technical innova-
tion performance. Next, it describes the research meth-
od and, using multisource data for 339 R&D
employees from a high-tech Fortune 100 industrial
firm, presents tests of hypotheses. It closes with dis-
cussion of the findings, their implications for theory
and practice, and the limitations of the study with rec-
ommendations for future research.
Theoretical Background and Hypothesis
Development
As is illustrated in Figure 1, individual performance is
best understood by three key sets of factors: (1) indi-
vidual context-relevant abilities including technicalskills and innovativeness, (2) domain-specific goal
commitment that serves as a motivating force (i.e., a
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
3
new construct we introduce called commitment to tech-nical innovation—CTI), and (3) individual perceptions
of situational factors that help or hinder goal pursuit.
It is expected that a higher level of each ability will
(1) weaken the influence of situational characteristics
on goal commitment (CTI) and (2) enhance the
hypothesized direct effects of CTI on individual inno-
vation performance. Two control variables are includ-
ed. Extra-role innovation is akin to organizational
citizenship behavior (Organ, 1990) and may be associ-
ated with so-called soft skills that may enhance indi-
vidual performance. Patents should be driven by many
of the same variables as our focal outcome. Including
these two controls accounts for important covariates to
avoid estimation bias in the tests of hypotheses.
The central themes of this paper, reflected in Figure
1 and in the hypotheses, are (1) goal commitment
(CTI) mediates the influence of context-relevant abili-
ties on individual innovation performance, (2) CTI is
influenced by perceptions of situational factors, contin-
gent on levels of the context-relevant abilities, and (3)
the influence of CTI on individual innovation perfor-
mance is also conditional based on levels of context-
relevant abilities. These conditional indirect effects
provide useful insights and implications (i.e., how,
why, and when) regarding the mechanisms that shape
the performance of individual innovation workers.
Individual Antecedents to Innovation Performance
Our theoretical framework is based on the expectancy
rationale that is at the heart of goal commitment theo-
ry. In short, situational characteristics and a person’s
abilities influence his or her commitment to relevant
goals by increasing the expectancy or attractiveness of
goal achievement (cf. Hollenbeck, Williams, and
Klein, 1989; Locke et al., 1988). The individual and
situational factors in our model directly affect expec-
tancy because they influence individuals’ beliefs that
they can accomplish specific tasks by altering confi-
dence, providing goal clarity, and facilitating perfor-
mance (White, Varadarajan, and Dacin, 2003). As an
energizing force “that contributes to motivated (inten-
tional) behavior” (Meyer et al., 2004, p. 991), commit-
ment motivates behavior, but it also interacts with
individual characteristics to drive performance (Tett
and Burnett, 2003).
Commitment to technical innovation. Empirical
tests of domain-specific commitment measures have
shown them to associate with role performance (e.g.,
students, Hollenbeck et al., 1989; employees, Noble
and Mokwa, 1999). Locke, Shaw, Saari, and Latham
(1981) noted that “Goal commitment implies a deter-
mination to try for a goal (or to keep trying for a
goal)” (p. 143). That is, goal commitment includes a
determination to expend effort over time in the pursuit
of a goal (Hollenbeck et al., 1989). Following the
goal-commitment literature, commitment to technicalinnovation (CTI) is defined as an employee’s determi-
nation to fulfill his or her individual technical innova-
tion job responsibilities. CTI centers attention on the
domain or task to which the individual is committed
(i.e., innovation). Thus, CTI should enhance technical
innovation performance by enhancing persistence in
the face of obstacles (West and Anderson, 1996).
Motivation is important; however, both innate abili-
ties and individual skills relevant to a specific domain
Figure 1. Conceptual Model of Antecedents of Individual Technical Innovation Performance
4 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
can explain additional variance in creativity-related
behaviors such as innovation (Amabile and Meuller,
2008; Andrews and Smith, 1996). Individual character-
istics may play a key role in understanding the relation-
ships predicted by path-goal theory, but they have not
been thoroughly investigated in R&D settings (Elkins
and Keller, 2003). For guidance, we turn to Amabile’s
(1988) domain-relevant and creativity-relevant skills
and propose that technical skills and innovativeness are
distal antecedents of technical innovation perfor-
mance—they influence CTI by enhancing the expectan-
cy and attractiveness of innovation performance.
Technical skills. Domain-relevant skills include
“factual knowledge, technical skills, and special talents
in the domain in question” (Amabile, 1988, p. 130). In
our model, technical skills refer to a person’s relative
level of technology knowledge, skills, experience, and
abilities. A person with relatively higher technical
skills possesses relevant competencies and should per-
form better in technical innovation (Taggar, 2002).
These skills provide the raw material to generate and
synthesize new ideas (Amabile, 1996; Andrews and
Smith, 1996); further, they allow identification and
exploration of alternatives that may solve complex
technological challenges.
Innovativeness. Following Amabile (1996), Kirton
(1976), and Taggar (2002), a person’s cognitive ability
to combine information in novel ways or conceive of
solutions that deviate from accepted conventions is
referred to as innovativeness, an orientation toward
innovative problem solving (Tierney, Farmer, and
Graen, 1999), and we expect it to be a distal anteced-
ent of technical innovation performance. Innovative
cognitive abilities provide the mental capacity to dis-
cover novel linkages among diverse ideas and enhance
creativity (Amabile, 1988), without which innovation
would be impossible (Taggar, 2002).
Mediating role of commitment to technical innova-tion. Goal commitment theory (Hollenbeck and Klein,
1987; Locke et al., 1981, 1988) suggests that domain-
relevant skills relate positively to task-focused motiva-
tion (e.g., CTI), such that high (versus low) skilled
employees have high expectations of fulfilling their
responsibilities (Sackett, Gruys, and Ellingson, 1998).
Citing this expectancy rationale, a meta-analysis by
Klein, Wesson, Hollenbeck, and Alge (1999) provides
evidence that employees high in domain-relevant skills
are more committed to goals within that domain.
Innovativeness should also relate positively to CTI
because those with greater innovativeness likely pos-
sess greater self-confidence about their innovation
duties (Hollenbeck and Klein, 1987) and greater
expectancy of achieving innovation goals. As these
individual abilities thus enhance CTI, enhanced CTI
provides persistence that subsequently, all else equal,
improves task performance.
H1: CTI mediates the relationships (a) betweentechnical skills and technical innovation perfor-mance, and (b) between innovativeness and techni-cal innovation performance.
Ability 3 motivation. A conditional relationship
should also exist between CTI and technical innova-
tion performance. The notion that workplace perfor-
mance is a consequence of the interaction between
ability and motivation has been accepted in the applied
psychology literature for decades (Lawler, 1966) and
has been substantiated empirically in several contexts.
Interestingly, despite a large body of work that high-
lights a critical role for ability as a driver of innova-
tion performance (see Anderson, de Dreu, and Nijstad,
2004), little empirical evidence of the contingent
nature of this relationship has been generated in inno-
vation or creativity contexts.
Goal commitment theory suggests an ability 3
motivation interaction (Lawler, 1966). Sackett et al.
(1998, p. 545) state that “[w]hen ability is low, incre-
mental increases in motivation will result in smaller
increases in performance than when ability is high.
Furthermore, when motivation is low, incremental
increases in ability will result in smaller increases in
performance than when motivation is high.” That is,
ability affects whether an employee can perform a
task but provides little insight into whether he or she
will (Sackett et al., 1998); motivation influences will-
ingness but provides little insight into whether a per-
son can perform. Stock and Hoyer (2005) found a
similar ability by motivation interaction in a sales con-
text, where the impact of a customer-oriented attitude
on behavior was stronger for high-expertise salespeo-
ple. Amabile (1998, p. 79) argues that expertise and
imagination are raw materials for creativity, but “a
third factor—motivation—determines what people will
actually do.” Thus, a positive interaction should exist
between ability and motivation.
H2: The relationship between CTI and technicalinnovation performance is moderated by individual
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
5
abilities, such that CTI will have a stronger rela-tionship with technical innovation performance athigher versus lower levels of (a) technical skillsand (b) innovativeness.
Situational Antecedents of Commitment to
Technical Innovation
Amabile (1988) highlights the criticality of the organi-
zational environment to employee motivation in creativ-
ity contexts. Similarly, the expectancy rationale in goal
commitment theory suggests that situational characteris-
tics are important antecedents of individual commitment
to work-related goals (Hollenbeck and Klein, 1987;
Locke et al., 1988). We draw on an established tradition
that considers the cognitive representations that individ-
uals hold of their work environment (James and James,
1989; see Parker et al., 2003, for a meta-analysis of
individual-level perceptions and their impacts on work
attitudes and performance). We center attention on
worker perceptions of their situations because those per-
ceptions may enhance or detract from the attractiveness
or expectancy of goal attainment.
Our focus is on individual representations of leader
behaviors, climate, rewards, and constraints. These spe-
cific representations of situations are clearly suggested by
theory, span a range from general to specific perceptions,
and can be directly influenced by managers. Leader
behaviors are critical to worker performance in
creativity-related tasks (Amabile, Schatzel, Moneta, and
Kramer, 2004). A supportive climate is essential for crea-
tivity (Amabile, 1988) and is also reflected in explana-
tions of individual innovation (Birdi et al., 2016; Scott
and Bruce, 1994). The “presence or absence of salient
extrinsic constraints in the work environment” also influ-
ences creativity (Amabile, 1988, p. 134). Finally, rewards
are a means of enhancing individual motivation, includ-
ing in innovation settings (Burroughs, Dahl, Moreau,
Chattopadhyay, and Gorn, 2011). Together, these four
categories represent a wide range of the situational forces
that may be represented in employee thought processes.
Moreover, path-goal theory and behavioral plasticity the-
ory suggest that their impact on commitment and perfor-
mance may be shaped by individual skills or traits and
show better promise for finding conditional effects than
the job characteristics commonly explored in innovation
studies (Elkins and Keller, 2003).
Situational characteristics. Although Scott and
Bruce (1994) consider situational characteristics to be
direct antecedents of innovation performance, studies
of goal commitment (Hollenbeck and Klein, 1987),
individual creativity (Amabile, 1998), and path-goal
leadership behaviors (House and Dessler, 1974) sug-
gest that those factors influence performance primarily
through their effects on employee psychological states.
Following this reasoning, situational characteristics
(perceptions of innovation leadership, supportive inno-
vation climate, innovation constraints, and rewards)
are modeled as antecedents to CTI.
Path-goal theory argues that leader behaviors are
critical as leaders coach, guide, and encourage employ-
ees toward performance (House and Dessler, 1974;
Sarin and O’Connor, 2009). In the domain of creativity,
leadership has been described as a wide array of behav-
iors including emotional support, support for ideas,
resource support, and provision of structure (Tierney
et al., 1999). Because innovation is differentiated from
creativity by a greater focus on implementation, we
conceptualize innovation leadership as instrumental in
nature, reflecting perceived guidance, monitoring, and
feedback related to innovation capabilities (cf. Kohli,
Shervani, and Challagalla, 1998). Path-goal (House and
Dessler, 1974) and goal commitment (Challagalla and
Shervani, 1996; Klein and Kim, 1998) theories suggest
that innovation leadership behaviors will positively
influence technical innovation performance through
their impact on CTI because they clarify expectations
and enhance competencies (perceived or actual), which
leads to the increased attractiveness and expectancy of
fulfilling role responsibilities.
Leader behaviors focus on specific guidance provid-
ed by supervisors, but an individual’s perception of a
supportive innovation climate exists when that individu-
al perceives the broader organizational setting as condu-
cive to and actively encouraging of innovation (James,
Hater, Gent, and Bruni, 1978; Scott and Bruce, 1994).
Climate is specific to a referent, such as “a climate that
supports innovation in the R&D division” (Schneider,
Gunnarson, and Niles-Jolly, 1994, p. 18). Individual
cognitive climate representations “serve as the major
source of situational information for the formulation of
[motivational] expectancies and instrumentalities”
(Tyagi, 1982, p. 242). Following the expectancy ratio-
nale in goal theory, when a person perceives that the
organizational climate supports innovation, this should
lead to higher CTI.
Innovation constraints refer to a perceived lack of
resources, including time, budget, personnel, equip-
ment, assistance, education, training, or experience
(Peters and O’Connor, 1980; Scott and Bruce, 1994).
6 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
Although creative ideas can be generated without
funds or materials, innovation requires resources
(Amabile, 1988). In turn, perceived innovation con-
straints should relate negatively to CTI, because they
reduce an employee’s expectancy of being able to ful-
fill technical innovation responsibilities (Hollenbeck
and Klein, 1987; Klein and Kim, 1998).
Finally, although there is some evidence in pure
creativity contexts that rewards can actually reduce
intrinsic motivation (e.g., Amabile, 1996), in a com-
mercial innovation context, an expectancy rationale
suggests that the innovation rewards that an employee
earns for individual innovation performance will influ-
ence the attractiveness of expending effort and engag-
ing in behaviors relevant to that goal. Burroughs et al.
(2011) provide evidence from lab experiments that
rewards, in certain circumstances, can enhance motiva-
tion. If an individual receives the same rewards regard-
less of individual performance, the relationship
between effort and reward is diminished and the
attractiveness of expending effort to achieve the goal
declines.
Situation 3 individual factors. Although studies of
innovation often focus on the main effects of situation-
al factors on performance (cf., Birdi et al., 2016), situ-
ational factors may also alter the impact of individual-
level factors on performance. Logic in path-goal and
behavioral plasticity theories predicts a stronger rela-
tionship between these situational factors and CTI at
lower levels of individual technical skills or innova-
tiveness. According to path-goal theory, leaders help
employees travel the “path” to personal rewards. How-
ever, a leader’s impact on employee motivation
depends on the personal characteristics of employees
and the degree to which the leader helps the employee
to identify appropriate paths (House and Dessler,
1974). House and Mitchell (1974) argue that low-
ability employees are more responsive to external
guidance because they are less confident about suitable
actions and goals. Consistent with that expectation,
Kohli (1989) found evidence that the positive influence
of leader role clarification behaviors is stronger for
salespersons with less work experience (i.e., less
expertise).
Behavioral plasticity theory makes similar predic-
tions, but it extends the moderator argument beyond
leadership. Brockner (1988) suggests that persons with
low (versus high) self-esteem are more responsive to
situational cues because they are uncertain of their
beliefs and depend on others for feedback and
approval; LePine and Van Dyne (1998) also apply a
plasticity rationale to individual differences that reflect
low confidence or greater responsiveness to external
cues. Similarly, technical skills and innovativeness are
individual difference variables that may relate to confi-
dence in an R&D context.
Although behavioral plasticity theory has been
applied to the relationship between situation and behav-
ior, the logic applies more directly to goal-related moti-
vation. First, because many forces beyond the
individual affect performance, motivation should be
more malleable and responsive to situational cues than
performance. In fact, researchers have found that situa-
tional factors explain greater variance in motivation
than in either behavior or performance (Oliver and
Anderson, 1994). Second, a meta-analysis (Wofford and
Liska, 1993) found that individual ability moderates the
link between leader behaviors and employee satisfaction
but not the leader behavior-employee performance con-
nection. Thus, technical skills and innovativeness should
be expected to moderate the relationships between situ-
ational factors and CTI.
H3: The relationship between innovation leader-ship and CTI is moderated by individual context-relevant factors, such that innovation leadershiphas a stronger positive relationship with CTI atlower versus higher levels of (a) technical skillsand (b) innovativeness.
H4: The relationship between a supportive innova-tion climate and CTI is moderated by individualcontext-relevant factors, such that a supportiveinnovation climate has a stronger positive rela-tionship with CTI at lower versus higher levels of(a) technical skills and (b) innovativeness.
H5: The relationship between innovation con-straints and CTI is moderated by individualcontext-relevant abilities, such that innovationconstraints have a stronger negative relationshipwith CTI at lower versus higher levels of (a) tech-nical skills and (b) innovativeness.
To conceptualize the conditional relationship between
rewards and CTI, we follow the more basic expectancy
logic in goal commitment and predict that in the presence
of attractive rewards for technical innovation perfor-
mance, high (versus low) ability individuals will be more
highly motivated. Because of their higher abilities, these
individuals should have a greater expectation that they
will achieve the goals and receive the rewards.
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
7
H6: The relationship between innovation rewards andCTI is moderated by individual context-relevant abili-ties, such that innovation rewards have a strongerpositive relationship with CTI at higher versus lowerlevels of (a) technical skills and (b) innovativeness.
Method
Hypotheses are tested in the field using a sample of
R&D employees from a Fortune 100 high-tech industri-
al goods and services firm. Studying employees within
a single firm allows measures to be tailored to the set-
ting and to reduce extraneous variation introduced by
multiple settings that could reduce a researcher’s ability
to isolate the relationships of interest. The firm’s R&D
unit is tasked with developing basic technologies and
product applications for the firm’s market units, as well
as for generating improvements in existing products.
The sample comprises salaried R&D personnel who
create and develop new technologies and applications.
The leaders of the R&D unit agreed to participate
and we sent survey packets to 454 R&D employees; to
avoid same-source biases, the supervisors of employees
who responded were also surveyed. The employee sur-
vey measured perceived situational factors, individual
ability factors, and CTI; the supervisor survey assessed
an employee’s technical innovation performance.
Respondents were assured of confidentiality by (1) e-
mails guaranteeing confidentiality prior to survey distri-
bution, (2) a letter personally signed by the researchers
describing data security procedures, and (3) having
respondents return surveys directly to the researchers
via a post-paid return envelope. E-mail and voice mail
were used to follow-up and maximize returns.
Sample Characteristics
We received 347 surveys (a 76.4% response rate).
Median tenure of these respondents with the R&D unit
was five years, with two years in their present position.
More than half (56%) had spent their entire careers
with the firm in the R&D unit, and another 33% had
spent two or more years in one of the firm’s market
units. Most (93%) had management responsibilities,
and the average respondent had produced .82 patents.
Supervisors provided data for all 347 responding
employees, but due to missing responses in either the
employee or supervisor surveys, the final sample size
for analysis was 339 (74.7% of distributed surveys).
Nonresponse bias was assessed via a time-trend
extrapolation test (Armstrong and Overton, 1977) and by
ruling out differences between early and late respondents
in terms of demographic characteristics (age, patents, ten-
ure, all ns) and self- and supervisor-rated construct mea-
sures (all ns). We also accessed archival data and
compared our respondents with a sample of nonrespond-
ents (n 5 22) whose firm ratings of overall job perfor-
mance were available. Across two years of data, there are
no significant differences between respondents and non-
respondents. Together, these analyses suggest that nonre-
sponse bias is not a concern.
The 339 respondents were nested within 53 supervi-
sors. We therefore explored whether the lack of complete
independence in supervisor groupings would result in a
loss of statistical power or a Type II error (see Bliese and
Hanges, 2004, for a detailed discussion). Following Nete-
meyer, Maxham, and Pullig (2005) and Kreft and de
Leeuw (1998), a series of mixed-effects models was first
conducted to assess the impact of nonindependence with-
in supervisor groups. Results demonstrated that the model
accounting for nonindependence did slightly improve
model fit, but did not alter the significance of relation-
ships. The simbias function included in the NLME statis-
tical package for R (Bliese, 2006) was then used to
conduct a series of simulations that allowed us to deter-
mine the relative power of our ordinary least squares
(OLS) models to alternative models that accounted for
supervisors’ nested performance ratings. On average,
estimates demonstrate that our OLS models have about
1.3% less power than the multilevel regression (i.e., ran-
dom coefficient modeling) approach. Although noninde-
pendence due to nested supervisory ratings is present in
the data, it does not have a sufficient impact on power to
have a meaningful effect on the results. However, to
address any remaining nonindependence concerns, we
accounted for clusters of dependent supervisory ratings
and any potential violations of homoscedasticity in tests
of hypotheses. To do so, heteroscedasticity-consistent
standard errors (HCSEs) were employed to obtain robust
and unbiased standard-error estimates (White, 1980; see
also Sch€oler, Skiera, and Tellis, 2014). The HCSE esti-
mator known widely as HC3 was calculated because sim-
ulation results suggest it is superior to other HCSE
estimators (Hayes and Cai, 2007; Long and Ervin, 2000).
Measures
Construct indicators are listed in Table 1, with item means,
standard deviations, and standardized measurement
8 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
loadings. Innovation leadership was assessed with eight
items adapted from the capabilities orientation (four) and
end-results orientation (four) subscales of Kohli et al.’s
(1998) study of sales supervisors. These items reflect the
degree to which an employee perceives that his or her
supervisor provides guidance, monitoring, and feedback
related to innovation performance capabilities and stand-
ards. Supportive innovation climate was measured with
four items from Scott and Bruce (1994) that assess
employee perceptions of the degree to which innovation is
supported by the organization’s public recognition of inno-
vation and encouragement of creativity. We measure inno-
vation constraints with an index based on the situational
resources framework developed by Peters and O’Connor
(1980). As an index, the items are viewed as forming rath-
er than reflecting the latent variable (Diamantopoulos and
Winklhofer, 2001). To ensure that the range of situational
resources that might facilitate or constrain employee inno-
vation performance are represented, we include six items
adapted from Scott and Bruce’s (1994) resource supply
scale and three additional items inspired by the Peters and
O’Connor (1980) framework. For innovation rewards, two
items from Sarin and Mahajan (2001) were used that tap
the degree to which respondents perceive innovation
rewards to be given based on individual performance ver-
sus all group members receiving equal rewards.
To measure CTI, five items were adapted from
Noble and Mokwa’s (1999) role commitment scale.
The items of our measure refer to employee commit-
ment, determination, and persistent effort to fulfill
technical innovation performance responsibilities.
Technical skills were measured with a new, four-item
scale that follows the structure of Singh’s (1998) over-
all performance measure. Respondents rated their tech-
nology skills, abilities, knowledge, and experience
relative to those of their colleagues. To assess innova-
tiveness, we follow Im, Bayus, and Mason (2003) and
use five items inspired by the originality subdimension
of the Kirton Adaption-Innovation Inventory (Kirton,
1976), a reliable measure of innovative cognitive abili-
ty (Goldsmith, 1986).1 Finally, technical innovation
performance was measured using five items suggested
by Scott and Bruce (1994). These items focus on the
generation and implementation of innovative ideas.
Results
The first step of our analysis involved verifying mea-
surement relationships and evaluating measure reliabil-
ity and validity. A confirmatory factor model was
estimated with 40 reflective indicators loading on the
latent constructs of Figure 1 using covariances as input
(innovation leadership was initially modeled as two
distinct dimensions). Because innovation constraints
are conceptualized as a latent variable with formative
indicators, it is represented in the model as a single
indicator—i.e., the mean of the individual items. We
cannot assess the reliability of this construct using
coefficient alpha, so we set a reliability of .90 and set
the error term to its variance multiplied by one minus
the assumed scale reliability.
Analyses indicated a lack of discriminant validity
between the capabilities and end-results orientation com-
ponents of innovation leadership (correlation 5 .92), so
they are modeled as a single latent construct. The mea-
surement model provides a good fit to the observed data
(v2 [df 5 630] 5 1923.94, p< .001; confirmatory fit
index [CFI] 5 .92; Tucker–Lewis index [TLI] 5 .91; root
mean square error of approximation [RMSEA] 5 .05).
Table 2 provides construct reliability estimates, square
roots of the average variance extracted (AVE) estimates
(to facilitate the application of Fornell and Larcker’s
[1981] criteria), and construct intercorrelations.
There is evidence of within-measure convergent
validity; all standardized measurement loadings (Table
1) exceed .5, and each indicator t-value exceeds 10.0
(p<.001). In addition, the AVE estimate exceeds .50
for six of the eight construct measures. Coefficient
alphas (Table 2) all exceed .70, providing further evi-
dence of internal consistency. Discriminant validity is
also demonstrated. The shared variance between all
latent construct pairs is lower than their respective
AVE with the single exception of innovativeness in its
relationship with CTI (innovativeness AVE5.45,
squared correlation5.504, cf. Fornell and Larcker,
1981).
Focal Data Analyses
Our conceptual model identifies a set of conditional
indirect effects in shaping individual technical innova-
tion performance. Thus, an analysis method is chosen
that is capable of accounting for these patterns
(Preacher, Rucker, and Hayes, 2007; i.e., moderated
mediation), while accounting for nonindependence.
1Kirton’s (1976) full questionnaire is a copyrighted instrument that uses 32 ques-
tions and specific instructions and procedures to measure adaption and innovative-
ness. Because the inventory includes constructs that are not relevant to our study,
and to achieve parsimony in survey length, we follow published American Psy-
chological Association guidelines and create a limited number of items inspired
by Kirton rather than employing the copyrighted scale.
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
9
Table 1. Means, Standard Deviations, and Standardized Loadings for the Measures
Item M SD STD
Innovation Leadership (self-rated)b
My supervisor provides assistance by suggesting why a particular technology
development approach or technique may be useful.
3.71 1.63 .735
My supervisor provides guidance on ways to improve technology development
and innovation skills.
3.37 1.45 .780
My supervisor has standards by which my technology development skills are
evaluated.
3.52 1.37 .739
My supervisor evaluates how I approach challenging technology development
tasks.
3.79 1.46 .697
My supervisor ensures that I am aware of the extent to which I attain innovation
and technology development goals.
3.56 1.46 .769
My supervisor tells me about the level of achievement expected on innovation
and technology development goals.
3.27 1.54 .751
I receive feedback on whether I am meeting expectations on innovation and
technology development goals.
3.08 1.54 .786
My supervisor monitors my progress on achieving innovation and technology
development goals.
3.26 1.52 .833
Supportive Innovation Climate (self-rated)b
This organization publicly recognizes those who are innovative. 2.98 1.45 .675
Around here, people are allowed to try to solve the same problems in different
ways.
3.93 1.49 .584
Our ability to function creatively is respected by the leadership. 4.40 1.51 .516
Creativity is encouraged around here. 4.44 1.62 .755
Innovation Constraints (self-rated)a,b 4.30 .85 .947
Innovation Rewards (self-rated)b
The best performers in our group receive extra rewards. 4.19 1.62 .801
The rewards that group members receive for working in this group are proportional
to their contribution to the group’s performance.
4.65 1.41 .772
Commitment to Technical Innovation (CTI) (self-rated)c
I take tremendous pride in my responsibilities regarding technical innovation. 5.15 1.3 .722
In creating or refining technical innovations, I try to work as hard as possible. 5.41 1.18 .718
I intentionally expend a great deal of effort in carrying out my responsibilities
regarding technical innovation.
4.69 1.28 .790
I give tremendous effort in creating and/or refining technical innovations. 4.68 1.26 .840
I am committed to my role in creating and/or refining technical innovations. 4.95 1.19 .779
Technical Skills (self-rated)d
How would you rate your technological skills relative to your colleagues in
XYZ?
5.02 1.15 .854
Compared to your work colleagues, how would you rate your ability to address
tough technology development and application questions?
4.96 1.18 .834
How does your amount of R&D experience compare with that of your work
peers?
4.71 1.59 .607
In terms of sheer technology knowledge, how do you stack up to others within
XYZ?
4.66 1.24 .832
Innovativeness (self-rated)c
I will always think of something when stuck. 5.80 1.1 .540
I have original ideas. 5.47 1.07 .664
I often risk doing things differently. 5.12 1.2 .720
I can cope with several ideas at the same time. 4.99 1.28 .531
I can stand out in disagreement against a group. 5.11 1.44 .631
Technical Innovation Performance (individual employee, rated by supervisor)e
This employee generates creative technology and/or product ideas. 4.06 1.49 .913
This employee promotes and champions technology-related ideas to others. 4.06 1.61 .869
This employee searches out or develops new technologies and/or product-related
ideas.
3.98 1.64 .910
This employee is innovative. 4.23 1.43 .869
This employee investigates and secures funds needed to develop and/or implement
new technology or product ideas.
2.80 1.81 .674
Extra-role Innovation Performance (individual employee, rated by supervisor)c
This employee often tries to implement solutions to pressing XYZ problems. 4.52 1.05 .814
10 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
This analysis method has been employed in studies in
top journals in organizational behavior (e.g., Goodman,
Wood, and Chen, 2011), leadership (e.g., Collins, Bur-
rus, and Meyer, 2014), and psychology (e.g., Wang,
Galinsky, and Murnighan, 2009). We choose this data
analytic approach while recognizing important trade-
offs between its limitations and those of other possible
methods. For example, a possible downside is that all
interactions cannot be tested in a single model.
All predictors were standardized prior to entering
into the regression equations. Given that no empirical
model is truly comprehensive, it was important to con-
trol for effects of omitted variables that might alter CTI
and technical innovation performance in tests of hypoth-
eses. Thus, both the number of patents held by the indi-
vidual and extra-role innovation performance were
included to capture variance explained by more general
ability and effort. Extra-role performance was measured
using the six highest-loading items from the “taking
charge” measure developed by Morrison and Phelps
(1999). This measure is a list of prototypical change-
oriented employee behaviors with respect to identifying
and implementing work process improvements.
Tests of mediation. Our conceptualization suggests an
indirect effects model (H1), wherein the relationships
between individuals’ abilities (i.e., technical skills,
innovativeness) and technical innovation performance are
transmitted by individuals’ CTI. Mediational analyses
should be based on formal significance tests of the indirect
effect ab—of which the Sobel (1982) test is the best
known. Preacher and Hayes (2004) have shown that a for-
mal test of the indirect effect is more powerful than the
Baron and Kenny (1986) stepwise procedure. Nevertheless,
Sobel assumes the indirect effect ab to be normally distrib-
uted, a tenuous assumption because the distribution of abis known to be nonnormal (Shrout and Bolger, 2002). Con-
sequently, Shrout and Bolger (2002) have recommended
resampling (i.e., bootstrapping) be used to generate a confi-
dence interval around the ab. Thus, confidence intervals
were derived for the population value of the indirect effect
using bias corrected and accelerated (BCa) bootstrapping
methods; mediation is supported when the confidence
interval does not include zero (Efron and Tibshirani,
1993). Through bootstrapped confidence intervals, it is
possible to avoid power problems introduced by asymmet-
ric and other nonnormal sampling distributions of an indi-
rect effect (MacKinnon, Lockwood, and Williams, 2004).
Statistical methods and SPSS syntax presented in Preacher
and Hayes (2004, 2008) were used to estimate our indirect
effects models.
Tests of moderated mediation. H2–6 predicted that
individuals’ abilities and behavior would moderate a
Table 1. Continued
Item M SD STD
This employee often tries to change how my job is executed in order to be
more effective.
4.66 .91 .838
This employee often tries to bring about improved procedures for my work
unit.
4.66 .98 .884
This employee often tries to set up new work methods that are more effective
for XYZ.
4.38 1.02 .909
This employee often tries to correct faulty procedures or practices within XYZ. 4.36 1.04 .858
This employee often tries to introduce new structures, technologies, or
approaches to improve efficiency within XYZ.
4.27 1.07 .880
Notes: M, mean; SD, standard deviation; STD, standardized measurement loading.
All loadings are significant at p< .001. v2 (df 5 630) 5 1923.41, p< .001; CFI 5 .92; TLI 5 .91; RMSEA 5 .05.aThe innovation constraints scale is formative and represented in the confirmatory measurement model as a single-item, reflective indicator determined
by the mean of the nine items. The reliability of the scale was set by fixing the error term for the single indicator to the variance of the indicator mul-
tiplied by one minus an assumed scale reliability of .90. The innovation constraint items are as follows: “There is not adequate time available to pur-
sue creative ideas in XYZ,” “Assistance in developing new ideas is not readily available in XYZ,” “This organization does not provide the necessary
education, training, and/or experience that people need in order to pursue innovative technical ideas,” “XYZ doesn’t give me enough free time to pur-
sue creative ideas during the workday,” “In XYZ, innovation is often hindered by personnel shortages,” “I have the financial resources and budget
needed to effectively do my job (reverse-coded),” “XYZ does not devote adequate resources to innovation,” “Sufficient help from others within XYZ
is available to do my job properly (reverse-coded),” and “The physical environment of my workplace (layout, lighting, noise levels, etc.) interferes
with my ability to be innovative.”bMeasured on a 7-point scale, 1 5 very inaccurate and 7 5 very accurate.cMeasured on a 7-point scale, 1 5 strongly disagree and 7 5 strongly agree.dMeasured on a 7-point scale, 1 5 significantly below average and 7 5 significantly above average.eMeasured on a 7-point scale, 1 5 not at all and 7 5 to an exceptional degree.
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
11
number of relationships proposed as part of our concep-
tual model. Assuming these moderation hypotheses are
supported, it is plausible that the strength of the above-
mentioned indirect (mediation) effects are conditional
on the hypothesized moderators—or what has been
termed conditional indirect effects (Preacher et al.,
2007; i.e., moderated mediation). As such, procedures
used to test the moderation hypotheses were integrated
such that we fully considered the possibility of a statis-
tically significant indirect effect (ab) being contingent
on the value of the proposed moderator. More specifi-
cally, statistical methods and SPSS syntax presented in
Preacher et al. (2007) were employed to test our condi-
tional indirect effects hypotheses. This approach simi-
larly facilitates the implementation of the recommended
bootstrapping (BCa) methods.
Test of H1
H1 predicted that CTI would mediate the relationships
between (a) technical skills and technical innovation
performance, and (b) innovativeness and technical
innovation performance. In computing each indirect
effect, the remaining focal variable was included as a
study covariate. For example, in testing H1a, innova-
tiveness was treated as an additional control when
examining the indirect effect of individuals’ technical
skills on technical innovation performance (via CTI).
As shown in Table 3(a), an individual’s technical
skill has a positive relationship with CTI (B 5 .28,
p< .01) and, in turn, CTI was positively related to
technical innovation performance (B 5 .14, p< .01).
The indirect effect (ab) of technical skill via CTI on
technical innovation performance was also significant
as the bias corrected and accelerated (BCa) confidence
interval did not include zero (see Table 3(b); as we
hypothesized (H1a), this indirect effect was positive
(ab 5.04) with a BCa 95% confidence interval 5 .01
to .08. As also shown in Table 3(a), innovativeness
related positively to CTI (B 5 .55, p< .01) and the
indirect effect of innovativeness on technical innova-
tion performance via CTI was significant and positive
(ab 5.07) with a BCa 95% confidence interval 5 .02
to .14, supporting H1b.
Tests of H2
H2 stated that the relationship between CTI and tech-
nical innovation performance will be moderated by
individuals’ technical skills (H2a) and innovativeness
(H2b). This moderated mediation hypothesis posits
individuals’ abilities as influencing their CTI and also
moderating the relationship between CTI and technical
innovation performance. As shown in Table 4(a), the
cross-product between CTI 3 technical skills was
related to technical innovation performance (B5.15,
p<.01). Consistent with expectations, the relationship
between CTI and technical innovation performance
was relatively strong (and positive) for individuals
with high technical skill, but not for individuals with
low technical skill.
We further examined the conditional indirect
effect—the value of the indirect effect conditioned on
values of the moderator—of technical skills on technical
Table 2. Construct Descriptive Statistics and Latent Construct Intercorrelations
Reliabilities and Correlations
Construct M SD AVE5 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
1. Innovation Leadership 3.44 1.19 .76 .92
2. Supportive Innovation Climate 3.97 1.16 .68 .55* .77
3. Innovation Constraints 4.29 .85 .90 2.41* 2.48* .90
4. Innovation Rewards 3.97 1.32 .80 2.41* 2.50* .27* .79
5. Commitment to Technical
Innovation (CTI)
4.99 1.02 .77 .24* .12 2.09 .01 .88
6. Technical Skills 4.86 1.08 .79 .03 .02 .08 .04 .48* .85
7. Innovativeness 5.37 .89 .67 .11 .05 2.06 .07 .56* .41* .71
8. Technical Innovation Perf. 3.83 1.08 .85 2.05 .08 2.02 .20* .32* .37* .30* .92
9. Patents .82 2.27 2.04 .21* 2.06 .21* .10 .17 .14 .25* na
10. Extra-role Innovation Perf. 4.48 .89 .86 .07 .16 .11 2.09 .19* .17 .25* .75* .11 .94
Notes: M, mean; SD, standard deviation; AVE.5, square root of average variance extracted. Means and standard deviations are computed for scale aver-
ages. Coefficient alphas are reported along the diagonal. Reliability (and by default AVE) of the innovation constraints scale was set to .90. Latent
construct intercorrelations are reported below the diagonal. p<.05 (Critical Value 5.11); p < .01 (Critical Value5.14). *p < .001 (Critical Value 5.18)
(two tailed); n 5 339.
12 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
innovation performance (through CTI) at three values of
individuals’ technical skill: the mean, one standard devia-
tion below the mean, and one standard deviation above
the mean. As shown in Table 4(b), the bootstrapped BCa
confidence intervals indicated the indirect and positive
effect of technical skills on technical innovation perfor-
mance through CTI was observed when levels of techni-
cal skill were moderate to high, but not low. These
results support H2a.
The cross-product of CTI 3 innovativeness related
to technical innovation performance (B 5 .12, p<.01)
and the form of this interaction is in line with expecta-
tions; the relationship between CTI and technical inno-
vation performance was strong and positive for
individuals with high innovativeness, whereas it was
positive but not as strong in magnitude for individuals
with low innovativeness. Likewise, the conditional indi-
rect effect of innovativeness on technical innovation
performance (through CTI) was also examined. Table
4(b) shows that the bootstrapped BCa confidence
interval for the conditional indirect effect was observed
when levels of innovativeness were moderate to high,
but not low. These results support H2b.
Tests of H3–6
H3 predicted that any relationship between innovation
leadership and CTI will be moderated by individual
abilities, such that innovation leadership will have a
stronger positive relationship with CTI at lower versus
higher levels of technical skills (H3a) and innovative-
ness (H3b). Reported in Table 5(a), the cross-product
term of innovation leadership 3 technical skills
(B 5 2.08, p <. 05) was related to CTI. Further, CTI
was positively associated with technical innovation
performance when controlling for innovation leader-
ship and individuals’ abilities. Collectively, these
results provide support for H3a. Conversely, H3b was
not supported because innovation leadership 3 innova-
tiveness (B 5 2.05, p 5ns) was not related to CTI.
Table 3(a). Mediating Role of Commitment to Technical Innovation (CTI) (H1)
H1a B SE p-value
CTI regressed on Technical Skills .28 .05 .000
Technical Innovation Performance regressed on CTI .14 .05 .010
Technical Innovation Performance regressed on Technical Skills, controlling for CTI .17 .06 .001
Partial Effect of Controls on Technical Innovation PerformanceInnovativeness 2.01 .08 .945
Patents .09 .02 .000
Extra-role Innovation Performance .75 .03 .000
H1b B SE p-value
CTI regressed on Innovativeness .55 .07 .000
Technical Innovation Performance regressed on CTI .14 .05 .010
Technical Innovation Performance regressed on Innovativeness, controlling for CTI 2.01 .08 .945
Partial Effect of Controls on Technical Innovation PerformanceTechnical Skills .17 .05 .000
Patents .09 .02 .000
Extra-role Innovation Performance .75 .03 .000
Overall Model Summary valueOverall F 144.58
Adjusted R2 .63
Table 3(b). H1 Bootstrapping Results
Technical innovation performance
Indirect effect model Boot Indirect Effect Boot SE BCaL95 BCaU95
H1a: Technical skills via CTI on Technical Innovation Performancea .04 .016 .012 .076
H1b: Innovativeness via CTI on Technical Innovation Performanceb .07 .030 .024 .143
Note. n 5 339 individuals. Bootstrap N 5 1000. BCaL95 5 95% confidence interval lower limit. BCaU95 5 95% confidence interval upper limit. Bias
corrected and accelerated confidence intervals are reported. CTI 5 commitment to technical innovation.aControlling for innovativeness, patents, and extra-role innovation performance.bControlling for technical skills, patents, and extra-role innovation performance.
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
13
Although these results support moderated mediation
for H3a, bootstrapping results were examined for further
verification. First, the conditional indirect effect of inno-
vation leadership on technical innovation performance
(through CTI) was examined at three values of techni-
cal skill: the mean, one standard deviation above the
mean, and one standard deviation below. As shown in
Table 5(b), bootstrap confidence intervals (bias cor-
rected and accelerated) indicated the indirect and posi-
tive effect of innovation leadership on technical
innovation performance through CTI was observed
when levels of technical skill were low and moderate,
but not high; in fact, the conditional indirect effect at
low levels of technical skill (ab 5.061) was over three
times the magnitude of the conditional indirect effect at
high levels of technical skill (ab 5.018).
H4 predicted that the relationship between a support-
ive innovation climate and CTI will be moderated by
individual abilities, such that a supportive climate will
have a stronger positive relationship with CTI at lower
rather than higher levels of technical skills (H4a) and
innovativeness (H4b). Shown in Table 6(a), the cross-
product term between supportive innovation climate 3
technical skills (B 5 2.08, p< .09) is marginally related
to CTI. Also consistent with moderated mediation, CTI
was positively associated with technical innovation per-
formance when controlling for supportive innovation
climate and individuals’ abilities and behavior. More-
over, bootstrap confidence intervals (bias corrected and
accelerated) provide further support for H4a; the indi-
rect and positive effect of supportive innovation climate
on technical innovation performance through CTI was
observed when technical skill levels were low, but not
moderate or high (Table 6(b)). In contrast, supportive
innovation climate 3 innovativeness (B 5 2.07, p 5 ns)
was not related to CTI (Table 6(a)); this latter finding
does not support H4b.
H5 predicted that individuals’ abilities will moder-
ate the inverse relationship between innovation con-
straints and CTI, such that when technical skills (H5a)
or innovativeness (H5b) is low (high), the relation-
ship between innovation constraints and CTI will be
Table 4(a). Individual Factor 3 Motivation Interactions on Technical Innovation Performance (H2)
Mediator: CTI
DV: Technical innovation
Performance
Variable H2a Step 1 H2b Step 1 H2a Step 2 H2bStep 2
Patents 2.01(.02) 2.01(.02) .08(.02)** .09(.02)**
Extra-role Innovation Performance .03(.03) .03(.03) .76(.03)** .75(.03)**
Technical Skills (TS) .28(.05)** .28(.05)** .18(.05)** .17(.05)**
Innovativeness (I) .55(.07)** .55(.07)** 2.00(.07) .02(.08)
CTI .15(.05)** .14(.05)**
CTI 3 TS .15(.03)**
CTI 3 I .12(.04)**
Note. n 5 339 individuals. Unstandardized regression coefficients are reported. Standard errors are reported in parentheses. CTI, commitment to tech-
nical innovation.
**p< .01; *p< .05; 1p< .09.
Table 4(b). H2 Bootstrapping Results
Technical Innovation Performance
Conditional indirect effects model Value of Moderator
Boot Indirect
Effect Boot SE BCaL95 BCaU95
H2a: Indirect effect of Technical Skills via CTI on
Technical Innovation Performance is further
moderated by Technical Skillsa
Low, 21 SD (21.08) 2.01 .018 2.040 .030
Average (.01) .04 .017 .014 .083
High, 11 SD (1.08) .09 .024 .045 .143
H2b: Indirect effect of Innovativeness via CTI on
Technical Innovation Performance is further
moderated by Innovativenessb
Low, 21 SD (2.79) .03 .029 2.030 .084
Average (.01) .08 .030 .026 .144
High, 11 SD (.79) .13 .041 .056 .219
Note. n 5 339 individuals. Bootstrap N 5 1000. BCaL95 5 95% confidence interval lower limit. BCaU95 5 95% confidence interval upper limit. Bias
corrected and accelerated confidence intervals are reported. CTI, commitment to technical innovation.aControlling for innovativeness, patents, and extra-role innovation performance.bControlling for technical skills, patents, and extra-role innovation performance.
14 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
strengthened (weakened). As shown in Table 7(a),
each of the proposed cross-product terms was signifi-
cantly related to CTI, supporting H5a and H5b. These
results are supportive of moderated mediation pro-
cesses. Bootstrap confidence intervals (bias corrected
and accelerated) further support the conditional indi-
rect effects models (low and moderate, but not high
for both technical skills and innovativeness) (see
Table 7(b).
Finally, H6 predicted that abilities will moderate
the relationship between innovation rewards and CTI,
such that when technical skills (H6a) and innovative-
ness (H6b) are high (low), the relationship between
innovation rewards and CTI will be strengthened
(weakened). Support was found for the moderating
role of innovativeness, supporting H6b, but not for
technical skills; thus, H6a was not supported. Table
8(a), shows the cross-product terms, while Table 8(b)
reports the bootstrap confidence intervals that demon-
strate significance at high levels of innovativeness, but
not low or moderate levels.
Discussion
Theoretical Implications
This paper contributes to an underrepresented body of
research within innovation (Wei et al., 2013) with a
model that focuses on individual innovation perfor-
mance—the development and championing of technolo-
gy and product innovations by individuals (Scott and
Bruce, 1994). A second contribution of this paper is the
Table 5(a). Individual Factor 3 Innovation Leadership Interactions on Technical Innovation Performance (H3)
Mediator: CTI Technical Innovation Performance
H3a Step 1 H3b Step 1 H3a Step 2 H3b Step 2
Patents 2.01(.02) 2.01(.02) .09(.02)** .08(.02)**
Extra-role Innovation Performance .01(.03) .02(.03) .76(.03)** .75(.03)**
Innovation Leadership (IL) .17(.04)** .16(.04)** 2.11(.04)** 2.10(.04)*
Technical Skills (TS) .30(.05)** .29(.05)** .16(.05)**
Innovativeness (I) .53(.06)** .54(.07)** 2.02(.08)
CTI .23(.06)** .16(.05)**
IL 3 TS 2.08(.04)*
IL 3 I 2.05(.05)
Note. n 5 339 individuals. Unstandardized regression coefficients are reported. Standard errors are reported in parentheses. CTI, commitment to tech-
nical innovation.
**p< .01.
*p< .05.
1p< .09.
Table 5(b). H3 Bootstrapping Results
Value of Moderator Boot Indirect Effect Boot SE BCaL95 BCaU95
H3a: Indirect effect of
Innovation Leader-
ship via CTI on
Technical Innovation
Performance is mod-
erated by Technical
Skillsa
Low, 21 SD (21.08) .06 .020 .027 .102
Average (2.01) .04 .013 .017 .067
High, 11 SD (1.08) .02 .013 2.005 .045
H3b: Indirect effect of
innovation leader-
ship via CTI on
Technical Innovation
Performance is mod-
erated by
Innovativenessb
Low, 21 SD (21.01) – – – –
Average (2.01) – – – –
High, 11 SD (.99) – – – –
Note. n 5 339 individuals. Bootstrap N 5 1000. BCaL95 5 95% confidence interval lower limit. BCaU95 5 95% confidence interval upper limit. Bias
corrected and accelerated confidence intervals are reported.aControlling for innovativeness, patents, and extra-role innovation performance.bControlling for technical skills, patents, and extra-role innovation performance.
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
15
specification and measurement of a new domain-specific
form of commitment, commitment to innovation. Theo-
rists have long seen motivation as playing a critical role
in individual performance, but a lack of consistency in
findings from empirical work has made enhancing the
conceptualization and measurement of motivational con-
cepts a priority (cf. Shalley et al., 2004). We drew on the
work of scholars outside of innovation who had explored
domain-specific forms of goal commitment (Hollenbeck
et al., 1989; Noble and Mokwa, 1999) to guide develop-
ment of CTI.
A third contribution of this paper is drawing from a
set of closely related theories from I-O psychology to
clarify the role of motivation in shaping innovation
performance (Shalley et al., 2004). We use a set of highly
interrelated goal commitment theories to precisely spec-
ify the indirect effects of individual ability and perceived
situational factors through CTI on individual innovation
performance. Goal-commitment theory (Hollenbeck and
Klein, 1987) shaped expectations regarding CTI as a
mediator for distal antecedents to individual innovation
behavior. Predictions guided by path-goal theory (House
and Dessler, 1974) and behavioral plasticity theory
(Brockner, 1988) pointed to individual characteristics
(consistent with Amabile, 1988) rather than job character-
istics (cf. Elkins and Keller, 2003) as driving conditional
effects. Guided by these predictions, we were able to
specify and test the conditional relationships between
Table 6(a). Individual Factor 3 Supportive Innovation Climate Interactions on Technical Innovation Perfor-
mance (H4)
Mediator: CTI
DV: Technical Innovation
Performance
H4a Step 1 H4b Step 1 H4a Step 2 H4b Step 2
Patents 2.01(.02) 2.01(.02) .09(.03)** .09(.02)**
Extra-role Innovation Performance .02(.03) .03(.03) .76(.03)** .75(.03)**
Supportive Innovation Climate (SIC) .08(.05)* .09(.05)1 2.02(.04) 2.01(.04)
Technical Skills (TS) .29(.05)** .28(.05)** .17(.05)**
Innovativeness (I) .54(.07)** .54(.07)** .03(.08)
CTI .20(.05)** .13(.05)**
SIC 3 TS 2.08(.05)1
SIC 3 I 2.07(.06)
Note. n 5 339 individuals. Unstandardized regression coefficients are reported. Standard errors are reported in parentheses. CTI, commitment to tech-
nical innovation.
**p< .01.
*p< .05.
1p< .09.
Table 6(b). H4 Bootstrapping Results
Value of Moderator Boot Indirect Effect Boot SE BCaL95 BCaU95
H4a: Indirect effect of
supportive innova-
tion climate via CTI
on Technical Inno-
vation Performance
is moderated by
Technical Skillsa
Low, 21 SD (21.09) .03 .018 .007 .082
Average (2.01) .02 .011 2.001 .046
High, 11 SD (1.09) 2.00 .011 2.026 .019
H4b: Indirect effect of
supportive innova-
tion climate via CTI
on Technical Inno-
vation Performance
is moderated by
Innovativenessb
Low, 21 SD (21.01) – – – –
Average (2.01) – – – –
High, 11 SD (.99) – – – –
Note. n 5 339 individuals. Bootstrap N 5 1000. BCaL95 5 95% confidence interval lower limit. BCaU95 5 95% confidence interval upper limit. Bias
corrected and accelerated confidence intervals are reported.aControlling for innovativeness, patents, and extra-role innovation performance.bControlling for technical skills, patents, and extra-role innovation performance.
16 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
situation characteristics and CTI and, in turn, the condi-
tional relationship between CTI and individual innova-
tion performance. This perspective also led us to
highlight a key organizational resource—human innova-
tion skills—and thus has potential ramifications for alter-
ing a firm’s advantage in a market, consistent with a
resource-based view of the firm.
Managerial Implications
Kochanski et al. (2003) found that leaders of R&D
organizations rate “getting more innovation from
employees” as a top priority and are concerned about
skill levels of employees. Our results suggest that this
concern is well founded. First, individual technical
skills and innovativeness play vital roles in driving
innovation performance. Second, because significant
interactions reveal that employees with relatively low-
er ability levels respond differently to situational fac-
tors, a major challenge to innovation leaders is to
adapt their own behaviors as they deal with employees
whose abilities vary. The results also reveal that a
reward structure that recognizes individual perfor-
mance is even more critical to high-ability employees.
Developing CTI among R&D employees is crucial
because it serves as an immediate driver of technical
innovation performance. Our results suggest that R&D
employees’ CTI and, in turn, performance, may be
Table 7(a). Individual Factor 3 Innovation Constraint Interactions on Technical Innovation Performance (H5)
Mediator: CTI
DV: Technical innovation
Performance
H5a Step 1 H5b Step 1 H5a Step 2 H5b Step 2
Patents 2.01(.02) 2.01(.02) .10(.03)** .09(.02)**
Extra-role Innovation Performance .02(.03) .03(.03) .76(.03)** .75(.03)**
Innovation Constraints (IC) 2.13(.06)* 2.14(.06)* .09(.05) 1 .06(.06)
Technical Skills (TS) .31(.05)** .30(.05)** .16(.05)**
Innovativeness (I) .52(.06)** .53(.07)** .04(.08)
CTI .21(.05)** .14(.05)**
IC 3 TS .11(.05)*
IC 3 I .16(.07)*
Note. n 5 339 individuals. Unstandardized regression coefficients are reported. Standard errors are reported in parentheses. CTI, commitment to tech-
nical innovation.
**p< .01.
*p< .05.
1p< .09.
Table 7(b). H5 Bootstrapping Results
Value of moderator
Boot indirect
effect Boot SE BCaL95 BCaU95
H5a: Indirect effect of
innovation con-
straints via CTI on
Technical Innovation
Performance is mod-
erated by Technical
Skillsa
Low, 21 SD (2.97) 2.05 .021 2.1034 2.018
Average (.02) 2.03 .014 2.062 2.006
High, 11 SD (1.00) 2.01 .015 2.038 .023
H5b: Indirect effect of
innovation con-
straints via CTI on
Technical Innovation
Performance is mod-
erated by
Innovativenessb
Low, 21 SD (2.79) 2.04 .016 2.075 2.012
Average (.02) 2.02 .010 2.044 2.004
High, 11 SD (.79) 2.00 .011 2.026 .018
Note. n 5 339 individuals. Bootstrap N 5 1000. BCaL95 5 95% confidence interval lower limit. BCaU95 5 95% confidence interval upper limit. Bias
corrected and accelerated confidence intervals are reported.aControlling for innovativeness, patents, and extra-role innovation performance.bControlling for technical skills, patents, and extra-role innovation performance.
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
17
enhanced by meaningful interactions with managers.
However, preliminary in-depth interviews suggested that
R&D managers are often drawn from top R&D perform-
ers and often use a hands-off managerial style. If this
approach is systematic, it can create a bias toward hiring
“qualified” people and then leaving them alone to do
their work. In contrast, our survey results suggest that the
hands-off approach will not help employees with relative-
ly lower skills maximize their performance. Given that
many of R&D managers’ biggest problems center on
leadership (balancing scientific and management respon-
sibilities, motivating scientists, providing appropriate
feedback; Sapienza, 2005), firms should be prepared to
provide extra preparation and guidance for technical spe-
cialists as they become leaders.
Those in R&D leadership should employ a contin-
gency approach to manage an employee according to
that person’s level of technical skills and innovative-
ness. Our results reveal that this implication is relevant
even for innovation contexts in which individuals with
low ability are screened out and the absolute ability
level of “lower ability” employees is high. Thus, there
does not appear to be a fixed threshold below which
managers’ influence begins to matter. Instead, such
Table 8(a). Individual Factor 3 Reward Interactions on Technical Innovation Performance (H6)
Mediator: CTI
DV: Technical innovation
performance
H6a Step 1 H6b Step 1 H6a Step 2 H6b Step 2
Patents 2.01(.02) 2.01(.02) .09(.03)** .08(.02)**
Extra-role Innovation Performance .02(.03) .02(.03) .76(.03)** .75(.03)**
Rewards (R) .03(.04) .02(.06) 2.02(.04) 2.02(.03)
Technical Skills (TS) .28(.05)** .28(.05)** .17(.05)**
Innovativeness (I) .56(.07)** .56(.07)** .02(.08)
CTI .21(.05)** .14(.05)**
R 3 TS 2.02(.03)
R 3 I .11(.05)*
Note. n 5 339 individuals. Unstandardized regression coefficients are reported. Standard errors are reported in parentheses. CTI, commitment to tech-
nical innovation.
**p< .01.
*p< .05.
1p< .09.
Table 8(b). H6 Bootstrapping Results
Value of Moderator Boot Indirect Effect Boot SE BCaL95 BCaU95
H6a: Indirect effect of
innovation rewards
via CTI on Techni-
cal Innovation Per-
formance is
moderated by Tech-
nical Skillsa
Low, 21 SD (2.97) – – – –
Average (.02) – – – –
High, 11 SD (1.00) – – – –
H6b: Indirect effect of
innovation rewards
via CTI on Techni-
cal Innovation Per-
formance is
moderated by
Innovativenessb
Low, 21 SD (2.79) 2.01 .009 2.031 .004
Average (.02) .00 .005 2.007 .014
High, 11 SD (.79) .01 .007 .003 .033
Note. n 5 339 individuals. Bootstrap N 5 1000. BCaL95 5 95% confidence interval lower limit. BCaU95 5 95% confidence interval upper limit. Bias
corrected and accelerated confidence intervals are reported.aControlling for innovativeness, patents, and extra-role innovation performance.bControlling for technical skills, patents, and extra-role innovation performance.
18 J PROD INNOV MANAG2016;00(00):00–00
L. A. BETTENCOURT ET AL.
managerial interventions are necessary even among
relatively high-ability employees.
Where are the leverage points? The motivation of
employees with relatively lower technical skills or
innovativeness is more strongly influenced by situa-
tional characteristics, so special care should be taken
to provide them with extra guidance, monitoring, and
feedback. Employees with low to moderate technical
skills and innovativeness are more greatly influenced
by perceptions of innovation constraints; thus, coach-
ing about why constraints exist and how to deal with
them is critical. Perceptions of leader behaviors and a
supportive innovation climate have greater influence
on those employees with lower relative technical
skills. Thus, leaders must manage both the actual level
of guidance (leader behaviors) and the climate for
innovation as well as provide coaching for lower-
relative-skill employees so they develop accurate men-
tal models of these situational variables. Interestingly,
the influence of perceptions that innovation behavior is
rewarded has its greatest influence on highly innova-
tive employees, suggesting potential targets for the
most fruitful coaching about rewards for innovation
performance.
Our results also have implications for selection sys-
tems. Employers should measure personality predispo-
sitions toward innovativeness during their R&D
employment screening. Tailored continuing education/
training programs also might help enhance creative-
thinking and technical skills among R&D specialists.
Finally, the results reveal the central importance of
addressing innovation resource constraints. Because
innovation resources are not substitutable, R&D man-
agers should assess the range of resource levels to
ensure the availability of the most critical ones. At the
same time, managers should work to influence R&D
employees’ perceptions of innovation constraints
through candid discussions of resource allocation
choices. Thus, management tools, such as the kill
option in stage-gate reviews, might be reinforced to
focus the firm’s limited resources on the very best
(i.e., highest sales, profitability) market offerings.
Limitations and Recommendations for Future
Research
Limitations. First, because our design is cross-
sectional, we can conclude only that our model is a
feasible explanation of the observed relationships.
Nevertheless, both theory and empirical evidence
provide support for the relationships in the model.
Second, our model is consistent with goal commit-
ment theory, but other variables (e.g., training, social
skills, risk attitudes, other traits) could be chosen.
Control variables were included to reduce the impact
of potentially omitted variables, but further research
with other factors is needed. Third, we rely in part on
self-reports for the constructs; however, it is unlikely
that common method variance introduces serious bias
into our results because a second source of data was
used for our dependent variable. Further, complex
moderation effects suggest that common method bias
is not a strong alternative explanation for our results.
Fourth, we use data from employees of a single firm
to reduce cross-context variability (i.e., sample het-
erogeneity). Further studies should investigate the
generalizability of our findings across a broad cross-
section of firms and innovation contexts. Fifth, a limi-
tation of our method for testing moderated mediation
is that it does not fully alleviate the potential for cor-
related residual terms across equations. Preacher et al.
(2007) suggest that any impact (biased or inconsistent
results) is likely inconsequential to the pattern and
magnitude of results for tests of reasonable power,
but this is a limitation of the method. Further, given
the number of moderators we hypothesize, all are not
tested in a single model. Finally, note that the AVE
values for supportive innovation climate and innova-
tiveness did not exceed the desired level, raising
potential concerns about construct and discriminant
validity.
Future research. If individual-level variables com-
bine to influence individual innovation performance
and these contributions are critical to team and firm
innovation outcomes (cf. Smith and Reinertsen, 1991),
then studies of innovation at the team and firm levels
should recognize and control for individual-level fac-
tors. Carefully designed studies may be able to incor-
porate key variables at individual, team, and/or firm
levels to isolate the variance explained at each level,
along with cross-level interactions (Kreft and de
Leeuw, 1998).
This paper approached the issue of motivation by
exploring it from the perspective of goal commitment
theory (Locke et al., 1981; Noble and Mokwa, 1999).
That foundation allowed us to specify and measure
CTI, a new construct for the innovation literature.
Because research that focuses on motivation as a psy-
chological phenomenon that interacts with other fac-
tors to influence innovation is rare (cf. Anderson et al.,
DOMAIN-RELEVANT COMMITMENT J PROD INNOV MANAG2016;00(00):00–00
19
2014), we believe that CTI can be profitably used by
researchers to study the motivational aspects of indi-
vidual innovation behavior. This goal commitment
construct should be of value as researchers seek to per-
form multi-level studies of innovation. Moreover, the
goal commitment foundation of CTI suggests a rich
heritage of I-O psychology research that innovation
scholars could profitably apply to generate theory-
driven models of motivation’s role in individual inno-
vation performance.
Relatively few studies have sought to understand
the impact of individual traits or abilities on innova-
tive behavior (Anderson et al., 2014). Nevertheless,
scholars who rely on path-goal theory to specify mod-
erator relationships have been more successful when
focusing on individual differences than when explor-
ing job characteristics (Elkins and Keller, 2003). Fol-
lowing Amabile (1988), we sought to examine the
impact of domain-relevant (technical skills) and
creativity-relevant (innovativeness) factors on innova-
tion performance. In addition, we found that the abili-
ty constructs moderate the relationships among
situational characteristics and CTI, consistent with
plasticity notions and expectancy theory. Further
research should consider other situational characteris-
tics and their interactions with individual differences,
including the consideration of varying situational
strength (cf. Tett and Burnett, 2003). Future research
could seek to find the most important tools for innova-
tion managers to impact individual innovation by
examining the relative impacts of different individual
abilities.
Finally, studies that address individual innovation
behavior have tended to focus attention toward direct
effects on innovation (capabilities and environmental
support; Birdi et al., 2016) or direct effects with mod-
eration (thinking style moderated by innovativeness;
Froehlich, Hoegl, and Weiss, 2015), and a few have
sought to explain mediating effects (leader-member
exchange mediating the impact of goal orientation;
Janssen and Van Yperen, 2004). By doing so, they
have identified important drivers of innovation behav-
ior, but leave room for further exploration of contin-
gent relationships mediated by motivation that may
provide more specific guidance to managers as they
seek to enhance individual innovation performance.
Drawing on a closely related set of goal commitment
theories, we believe that scholars can move ever closer
to capturing the complex forces that drive individual
innovation performance.
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