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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



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


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).



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.


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



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.


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



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.


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.



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.


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.


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


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.



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)**


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)


nical innovation.

**p< .01.

*p< .05.

1p< .09.


Value of Moderator Boot Indirect Effect Boot SE BCaL95 BCaU95

H3a: Indirect effect of

Innovation Leader-

ship via CTI on


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


Performance is mod-

erated by

Innovativenessb

Low, 21 SD (21.01) – – – –

Average (2.01) – – – –

High, 11 SD (.99) – – – –


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.


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


Patents 2.01(.02) 2.01(.02) .09(.03)** .09(.02)**


Supportive Innovation Climate (SIC) .08(.05)* .09(.05)1 2.02(.04) 2.01(.04)


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)


nical innovation.

**p< .01.

*p< .05.

1p< .09.




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


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) – – – –





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


Performance


Patents 2.01(.02) 2.01(.02) .10(.03)** .09(.02)**


Innovation Constraints (IC) 2.13(.06)* 2.14(.06)* .09(.05) 1 .06(.06)



CTI .21(.05)** .14(.05)**

IC 3 TS .11(.05)*

IC 3 I .16(.07)*


nical innovation.

**p< .01.

*p< .05.

1p< .09.


Value of moderator

Boot indirect

effect Boot SE BCaL95 BCaU95


innovation con-

straints via CTI on


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


innovation con-

straints via CTI on


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




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


performance


Patents 2.01(.02) 2.01(.02) .09(.03)** .08(.02)**


Rewards (R) .03(.04) .02(.06) 2.02(.04) 2.02(.03)



CTI .21(.05)** .14(.05)**

R 3 TS 2.02(.03)

R 3 I .11(.05)*


nical innovation.

**p< .01.

*p< .05.

1p< .09.




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) – – – –


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





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.,


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.

REFERENCES

Amabile, T. M. 1988. A model of creativity and innovation in organiza-tions. In Research in organizational behavior, vol. 10, ed. B. M.Staw and L. L. Cummings, 123–67. Greenwich, CT: JAI Press.

Amabile, T. M. 1996. Creativity in context. Boulder, CO: Westview Press.

Amabile, T. M. 1998. How to kill creativity. Harvard Business Review76: 76–87.

Amabile, T. M., and J. S. Meuller. 2008. Studying creativity, its pro-cesses and its antecedents. In Handbook of organizational creativity,ed. J. Zhou and C. E. Shalley, 33–64. New York: Lawrence Erl-baum Associates.

Amabile, T. M., E. A. Schatzel, G. B. Moneta, and S. J. Kramer. 2004.Leader behaviors and the work environment for creativity: Per-ceived leader support. The Leadership Quarterly 15 (1): 5–32.

Anderson, N., C. K. W. de Dreu, and B. A. Nijstad. 2004. The routiniza-tion of innovation research: A constructively critical review of thestate-of-the-science. Journal of Organizational Behavior 25: 147–73.

Anderson, N., K. Potocnik, and J. Zhou. 2014. Innovation and creativity inorganizations a state-of-the-science review, prospective commentary,and guiding framework. Journal of Management 40 (5): 1297–333.

Andrews, J., and D. C. Smith. 1996. In search of the marketing imagi-nation: Factors affecting the creativity of marketing programs formature products. Journal of Marketing Research 33: 174–87.

Armstrong, J. S., and T. S. Overton. 1977. Estimating non-response biasin mail surveys. Journal of Marketing Research 14: 396–402.

Bammens, Y. P. 2015. Employees’ innovative behavior in social con-text: A closer examination of the role of organizational care. Jour-nal of Product Innovation Management 33 (3): 244–59.

Baron, R. M., and D. A. Kenny. 1986. The moderator-mediator variabledistinction in social psychological research: Conceptual, strategic,and statistical considerations. Journal of Personality and Social Psy-chology 51: 1173–82.

Bharadwaj, S., and A. Menon. 2000. Making innovation happen inorganizations: Individual creativity mechanisms, organizational crea-tivity mechanisms or both? Journal of Product Innovation Manage-ment 17: 424–34.

Birdi, K., D. Leach, and W. Magadley. 2016. The relationship of indi-vidual capabilities and environmental support with different facetsof designers’ innovative behavior. Journal of Product InnovationManagement 33 (1): 19–35.

Bliese, P. D. 2006. Multilevel modeling in R (2.2): A brief introductionto R, the multilevel package and the NLME package. Available athttp://cran.r-project.org/.

Bliese, P. D., and P. J. Hanges. 2004. Being both too liberal and tooconservative: The perils of treating grouped data as though theywere independent. Organizational Research Methods 7: 400–17.

Brockner, J. 1988. Self-esteem at work: Research, theory, and practice.Lexington, MA: Lexington Books.

Burroughs, J., D. Dahl, C. Moreau, A. Chattopadhyay, and G. Gorn.2011. Facilitating and rewarding creativity during new productdevelopment. Journal of Marketing 75 (4): 53–67.

Challagalla, G. N., and T. A. Shervani. 1996. Dimensions and types ofsupervisory control: Effects on salesperson performance and satis-faction. Journal of Marketing 60: 89–105.

Collins, B., C. Burrus, and R. Meyer. 2014. Gender differences in theimpact of leadership styles on subordinate embeddedness and jobsatisfaction. Leadership Quarterly 25: 660–71.

Diamantopoulos, A., and H. M. Winklhofer. 2001. Index constructionwith formative indicators: An alternative to scale development.Journal of Marketing Research 38 (2): 269–77.

Efron, B., and R. J. Tibshirani. 1993. An introduction to the bootstrap.London: Chapman & Hall.



http://cran.r-project.org/

Elkins, T., and R. T. Keller. 2003. Leadership in research and develop-ment organizations: A literature review and conceptual framework.The Leadership Quarterly 14 (4): 587–606.

Fornell, C., and D. F. Larcker. 1981. Structural equation models withunobservable variables and measurement error: Algebra and statis-tics. Journal of Marketing Research 18: 382–88.

Froehlich, J. K., M. Hoegl, and M. Weiss. 2015. Thematic thinking andindividual performance in research and development. Journal ofProduct Innovation Management 32 (6): 939–53.

Goldsmith, R. E. 1986. Convergent validity of four innovativenessscales. Educational and Psychological Measurement 46: 81–87.

Goodman, J., R. Wood, and Z. Chen. 2011. Feedback specificity, infor-mation processing, and transfer of training. Organizational Behaviorand Human Decision Processes 115: 253–67.

Greenwald, J. 2001. Rank and fire. Time. June 11. Available at http://www.time.com/time/business/article/0,8599,1299.88,00.html.

Han, J. K., N. Kim, and R. K. Srivastava. 1998. Market orientation andorganizational performance: Is innovation the missing link? Journalof Marketing 62: 30–45.

Hayes, A. F., and L. Cai. 2007. Using heteroscedasticity-consistent stan-dard error estimators in OLS regression: An introduction and soft-ware implementation. Behavior Research Methods 39: 709–22.

Hollenbeck, J., and H. Klein. 1987. Goal commitment and the goal set-ting process: Problems, prospects and proposals for future research.Journal of Applied Psychology 72: 212–20.

Hollenbeck, J. R., C. R. Williams, and H. J. Klein. 1989. An empiricalinvestigation of the antecedents of commitment to difficult goals.Journal of Applied Psychology 74: 18–23.

House, R. J., and G. Dessler. 1974. The path-goal theory of leadership:Some post hoc and a priori tests. In Contingency approaches toleadership, ed. J. G. Hunt and L. L. Larson, 29–55. Carbondale, IL:Southern Illinois University Press.

House, R. J., and T. R. Mitchell. 1974. Path-goal theory of leadership.Journal of Contemporary Business 3: 81–97.

Im, S., B. L. Bayus, and C. Mason. 2003. An empirical study of innateconsumer innovativeness, personal characteristics and new productadoption behavior. Journal of the Academy of Marketing Science31: 61–74.

James, L. A., and L. R. James. 1989. Integrating work environment per-ceptions: Explorations into the measurement of meaning. Journal ofApplied Psychology 74 (5): 739.

James, L. R., J. J. Hater, M. J. Gent, and J. R. Bruni. 1978. Psychologi-cal climate: Implications from cognitive social learning theory andinteractional psychology. Personnel Psychology 31: 783–813.

Janssen, O., and N. W. Van Yperen. 2004. Employees’ goal orienta-tions, the quality of leader-member exchange, and the outcomes ofjob performance and job satisfaction. Academy of ManagementJournal 47 (3): 368–84.

Kirton, M. J. 1976. Adaptors and innovators: A description and mea-sure. Journal of Applied Psychology 61: 622–29.

Klein, H. J., and J. S. Kim. 1998. A field study of the influence of sit-uational constraints, leader-member exchange, and goal commit-ment on performance. Academy of Management Journal 41: 88–95.

Klein, H. J., M. J. Wesson, J. R. Hollenbeck, and B. J. Alge. 1999.Goal commitment and the goal-setting process: Conceptual clarifica-tion and empirical synthesis. Journal of Applied Psychology 84:885–96.

Kochanski, J., P. Mastropolo, and G. Ledford. 2003. People solutionsfor R&D. Research Technology Management 46: 59–61.

Kohli, A. K. 1989. Effects of supervisory behavior: The role of indi-vidual differences among salespeople. Journal of Marketing 53:40–50.

Kohli, A. K., T. A. Shervani, and G. N. Challagalla. 1998. Learningand performance orientation of salespeople: The role of supervisors.Journal of Marketing Research 35: 263–74.

Kreft, I., and J. de Leeuw. 1998. Introducing multilevel modeling. Thou-sand Oaks, CA: Sage Publications.

Lawler, E. E. 1966. Ability as a moderator of the relationship betweenjob attitudes and job performance. Personnel Psychology 19: 153–64.

LePine, J. A., and L. Van Dyne. 1998. Predicting voice behavior inwork groups. Journal of Applied Psychology 83: 853–68.

Locke, E. A., G. P. Latham, and M. Erez. 1988. The determinants ofgoal commitment. Academy of Management Review 13: 23–39.

Locke, E. A., K. N. Shaw, L. M. Saari, and G. P. Latham. 1981. Goalsetting and task performance: 1969-1980. Psychological Bulletin 90(1): 123–52.

Long, J. S., and L. H. Ervin. 2000. Using heteroscedasticity-consistentstandard errors in the linear regression model. American Statistician54: 217–24.

MacKinnon, D. P., C. M. Lockwood, and J. Williams. 2004. Confidencelimits for the indirect effect: Distribution of the produce and resam-pling methods. Multivariate Behavioral Research 39: 99–128.

Markham, S. K., and A. Griffin. 1998. The breakfast of champions:Associations between champions and product development environ-ments, practices, and performance. Journal of Product InnovationManagement 15: 436–54.

Meyer, J. P., T. E. Becker, and C. Vandenberghe. 2004. Employee com-mitment and motivation: A conceptual analysis and integrative mod-el. Journal of Applied Psychology 89: 991–1007.

Morrison, E. W., and C. C. Phelps. 1999. Taking charge at work: Extra-role efforts to initiate workplace change. Academy of ManagementJournal 42: 403–19.

Netemeyer, R. G., J. G. Maxham III, and C. Pullig. 2005. Conflicts inthe work-family interface: Links to job stress, customer serviceemployee performance, and customer purchase intent. Journal ofMarketing 69: 130–43.

Noble, C. H., and M. P. Mokwa. 1999. Implementing marketing strate-gies: Developing and testing a managerial theory. Journal of Mar-keting 63: 57–73.

Oliver, R. L., and E. Anderson. 1994. An empirical test of the conse-quences of behavior- and outcome-based sales control systems.Journal of Marketing 58: 53–67.

Organ, D. W. 1990. The motivational basis of organizational citizenshipbehavior. In Research in organizational behavior, ed. B. M. Stawand L. L. Cummings, 4372. Greenwich, CT: JAI Press.

Parker, C. P., B. B. Baltes, S. A. Young, J. W. Huff, R. A. Altmann,H. A. Lacost, and J. E. Roberts. 2003. Relationships between psy-chological climate perceptions and work outcomes: A meta-analytic review. Journal of Organizational Behavior 24 (4): 389–416.

Peters, L. H., and E. J. O’Connor. 1980. Situational constraints andwork outcomes: The influences of a frequently overlooked con-struct. Academy of Management Review 5: 391–7.

Preacher, K. J., and A. F. Hayes. 2004. SPSS and SAS procedures forestimating indirect effects in simple mediation models. BehaviorResearch Methods, Instruments, and Computers 36: 717–31.

Preacher, K. J., and A. F. Hayes. 2008. Asymptotic and resamplingstrategies for assessing and comparing indirect effects in multiplemediator models. Behavior Research Methods 40: 879–91.

Preacher, K. J., D. D. Rucker, and A. F. Hayes. 2007. Addressing mod-erated mediation hypotheses: Theory, methods, and prescriptions.Multivariate Behavioral Research 42: 185–227.

Sackett, P. R., M. L. Gruys, and J. E. Ellingson. 1998. Ability-personal-ity interactions when predicting job performance. Journal of AppliedPsychology 83: 545–56.


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http://www.time.com/time/business/article/0,8599,1299.88,00.html

Sapienza, A. M. 2005. From the inside: Scientists’ own experience ofgood (and bad) management. R&D Management 35 (5): 473–82.

Sarin, S., and V. Mahajan. 2001. The effect of reward structures on theperformance of cross-functional product development teams. Jour-nal of Marketing 65 (2): 35–53.

Sarin, S., and G. C. O’Connor. 2009. First among equals: The effect ofteam leader characteristics on the internal dynamics of cross-functional product development teams. Journal of Product Innova-tion Management 26 (2): 188–205.

Schneider, B., S. K. Gunnarson, and K. Niles-Jolly. 1994. Creating theclimate and culture of success. Organizational Dynamics 23: 17–29.

Sch€oler, L., B. Skiera, and G. Tellis. 2014. Stock market returns tofinancial innovations before and during the financial crisis in theUnited States and Europe. Journal of Product Innovation Manage-ment 31 (5): 974–86.

Scott, S. G., and R. A. Bruce. 1994. Determinants of innovative behav-ior: A path model of individual innovation in the workplace. Acade-my of Management Journal 37: 580–607.

Sethi, R., D. C. Smith, and C. W. Park. 2001. Cross-functional productdevelopment teams, creativity, and the innovativeness of new con-sumer products. Journal of Marketing Research 38: 73–85.

Shalley, C. E., J. Zhou, and G. R. Oldham. 2004. The effects of person-al and contextual characteristics on creativity: Where should we gofrom here? Journal of Management 30 (6): 933–58.

Shrout, P. E., and N. Bolger. 2002. Mediation in experimental and non-experimental studies: New procedures and recommendations. Psy-chological Methods 7: 422–45.

Singh, J. 1998. Striking a balance in boundary-spanning positions: Aninvestigation of some unconventional influences of role stressorsand job characteristics on job outcomes of salespeople. Journal ofMarketing 62: 69–86.

Smith, P. G., and D. G. Reinertsen. 1991. Developing products in halfthe time. New York: Van Nostrand Reinhold, Inc.

Sobel, M. E. 1982. Asymptotic intervals for indirect effects in structuralequations models. In Sociological methodology, ed. S. Leinhart,290–312. San Francisco: Jossey–Bass.

Stock, R. M., and W. D. Hoyer. 2005. An attitude-behavior model ofsalespeople’s customer orientation. Journal of the Academy of Mar-keting Science 33: 536–52.

Taggar, S. 2002. Individual creativity and group ability to utilize indi-vidual creative resources: A multilevel model. Academy of Manage-ment Journal 45: 315–30.

Tett, R. P., and D. D. Burnett. 2003. A personality trait-based interac-tionist model of job performance. Journal of Applied Psychology88: 500–17.

Tierney, P., S. M. Farmer, and G. B. Graen. 1999. An examination ofleadership and employee creativity: The relevance of traits and rela-tionships. Personnel Psychology 52: 591–620.

Tyagi, P. K. 1982. Perceived organizational climate and the process ofsalesperson motivation. Journal of Marketing Research 19: 240–54.

Varadarajan, P. R., and S. Jayachandran. 1999. Marketing strategy: Anassessment of the state of the field and outlook. Journal of theAcademy of Marketing Science 27: 120–43.

Wang, S., A. Galinsky, and K. Murnighan. 2009. Bad drives psychologicalreactions, but good propels behavior. Psychological Science 20: 634–45.

Wei, Y. S., G. L. Frankwick, and B. H. Nguyen. 2012. Should firmsconsider employee input in reward system design? The effect ofparticipation on market orientation and new product performance.Journal of Product Innovation Management 29 (4): 546–58.

Wei, Y. S., H. O’Neill, R. P. Lee, and N. Zhou. 2013. The impact ofinnovative culture on individual employees: The moderating role ofmarket information sharing. Journal of Product Innovation Manage-ment 30 (5): 1027–41.

West, M. A., and N. R. Anderson. 1996. Innovation in top managementteams. Journal of Applied Psychology 81: 680–93.

White, H. 1980. A heteroscedasticity-consistent covariance matrix esti-mator and a direct test for heteroscedasticity. Econometrica 48:817–38.

White, J. C., P. R. Varadarajan, and P. A. Dacin. 2003. Market situationinterpretation and response: The role of cognitive style, organizationalculture, and information use. Journal of Marketing 67: 63–79.

Wofford, J. C., and L. Z. Liska. 1993. Path-goal theories of leadership:A meta-analysis. Journal of Management 19: 857–76.

Xie, J., M. Song, and A. Stringfellow. 2003. Antecedents and conse-quences of goal incongruity on new product development in fivecountries: A marketing view. Journal of Product Innovation Man-agement 20 (3): 233–50.



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