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Organizational learningPerception of external environment and

innovation performanceYu-Lin Wang

Department of Business Administration, National Cheng Kung University,Taiwan, and

Andrea D. Ellinger Department of Human Resource Development and Technology,

The University of Texas at Tyler, Tyler, Texas, USA

Abstract

Purpose The purpose of this study was to examine the antecedent, perception of the externalenvironment, and its relationship to organizational learning, as well as explore the relationshipsbetween organizational learning and innovation performance at two levels, including individual andorganizational-level innovation performance.Design/methodology/approach Questionnaire data were collected from 268 senior R&D projectteam members who reported their perception about the external environment and organizationallearning along with 83 R&D managers who evaluated their employees innovative behaviors.Findings The results indicated that the antecedent of organizational learning, perception of external environment, was signicant to organizational learning, and organizational learning wassignicant to both individual and organization-level innovation performance and contributed more tothe individual-level than organizational innovation performance.Originality/value The value of the study lies in its contributions to the scholarly literature onorganizational learning and innovation because examining the antecedent perception of the external

environment and the relationships between organizational learning and innovation performance aswell as the relationship between individual and organizational-level innovation performance have notreceived considerable empirical attention.

Keywords Organizational learning, Workplace training, Innovation, Organizational performance,Individual development, Information dissemination, Competitive advantage,Human resource managementPaper type Research paper

IntroductionGlobalization of economy, the diverse workforce environment, and use of informationtechnology have made organizations become more aware of competitive environmentand pursue competitive advantage that lies in learning and knowledge. Learning hasbeen acknowledged as a key process that contributes to successful innovation, whichdetermines and supports an organizations success (Kang et al., 2007; Voronov, 2008).Organizational learning is dened as the process of acquiring, distributing,integrating, and creating information and knowledge among organizationalmembers (Dixon, 1992; Huber, 1991). The processes of organizational learninginvolve key components that support knowledge productivity processes, which includesearching for information, assimilating, developing and creating new knowledge onproducts, processes, and services. The essence of organizational learning in generating

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organizational knowledge not only sustains competitive advantage but also leads tonew markets and niches creation (Hult et al., 2003). In other words, an organizationsknowledge is an asset that can be managed to contribute to the rms innovationperformance (Pham and Swierczek, 2006). In addition, the literature has also connectedorganizational learning to the principle means of achieving strategic renewal in anorganization (Crossan and Berdrow, 2003). Therefore, organizational learning has beenequated with innovative efciency in the innovation literature (Crossan and Berdrow,2003; McKee, 1992; Lopez et al., 2005).

Strategic human resource management (HRM) practice is concerned withoptimizing learning, development, and performance improvement at the individual,group, team, and organization levels that enables an organization to keep pace with thechanging environment. Saru (2007) has acknowledged that organizational-levellearning and development can be facilitated under a clear linkage between corporatestrategy and HRM practices. It is because organizations require competent people tolearn and interpret new information and technology changes from the externalenvironment in order to create new knowledge faster than other competitors(Birdthistle and Fleming, 2005; Casey, 2005). In other words, organizational learningmust be coherent with an organizations design, strategy, structure, and strategic HRMpractices and context. In the past, the HRM function has tended to be preoccupied withindividual performance and training-dominated activities. In more recent years,strategic HRM practices have focused on learning and knowledge creation to enhanceindividuals competencies and collaboration within organizations (Lo pez et al., 2006).Lopez et al.s (2006) empirical study has demonstrated that HRM practice has a positiveinuence on the organizational learning processes. Also Cano and Canos (2006)empirical study has demonstrated that it is HRM practices that impact anorganizations learning and innovation performance. As a result, HRM professionalsoften serve as facilitators in cultivating an organizations structure and culture to

encourage learning at every level within an organization.

Problem statementAlthough the concept of organizational learning has been examined since the 1950sand the base of literature has expanded conceptually, theoretically, and somewhatempirically during the past decades (Dodgson, 1993; Easterby-Smith, 1997; Lipshitzet al., 2002), few studies have investigated the relationship between organizationallearning, its antecedent, and specic performance outcome variables empirically. Mostresearchers have focused on the theoretical side of explaining organizational learning(Saru, 2007). Easterby-Smith and Araujo (1999) have pointed out that learning is acomplex process with many potential levels to investigate empirically. Therelationship between organizational learning and innovation performance has beenmentioned and assumed conceptually in organizational learning and innovationliterature, but little empirical evidence has supported this perspective and the nature of this phenomenon has not been fully explicated empirically (Bontis et al., 2002; Lopezet al., 2005). Moreover, there has also been limited research focused on howorganizational learning processes affect performance (Bapuji and Crossan, 2004;Easterby-Smith and Araujo, 1999).

In addition, the innovation literature has overly focused on organizational-levelinnovation performance (Simsek, 2002; Zahra, 1996). Most prior organizational

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learning and innovation research has predominantly adopted the rm as the unit of innovation performance analysis and has overly focused on organizational-levelinnovation performance (A mo and Kolvereid, 2005). In fact, organization learning andinnovation is based on individual-level efforts that contribute to organizational-levellearning and performance (Dixon, 1992; Huber, 1991). Further, the organizationallearning literature has conceptually and theoretically acknowledged thatindividual-level learning should be embedded and transferred intoorganizational-level learning, but the fundamental question of the connectionbetween individual learning and organizational learning still lacks empiricalinvestigation in this eld (Antonacopoulou, 2006; Ron et al., 2006; Saru, 2007).

As a result, the overall purpose of this study was to examine the antecedents of andthe relationships between organizational learning and innovation performance at twolevels, including individual-level and organizational-level innovation performance.Also, the relationship between individual-level innovation performance andorganizational-level innovation performance was explored.

Literature review and research hypothesesOrganizational learning The construct of organizational learning has been articulated for more than 40 years,and the eld of organizational learning has grown rapidly in the 1990s (Casey, 2005;Dodgson, 1993; Easterby-Smith et al., 1998). The concept of organizational learning hasnot only attracted the attention of scholars from disparate disciplines but alsoconsultants and managers in the business world (Chiva and Alegre, 2005). It is becausethe concept of learning provides insights to rms that face uncertain and changingcircumstances (Dodgson, 1993). However, with the emerging importance of organizational learning, there seems to be little agreement on the denitions,processes, and models in this eld (Lundberg, 1995). A consequence of this is that

diverse disciplinary perspectives are presented in the literature on organizationallearning (Easterby-Smith, 1997). Therefore, Dodgson (1993) has emphasized that it isimportant to use a multi-disciplinary approach to fully understand the complexity andvariety of organizational learning literature.

Easterby-Smith (1997) has identied various disciplines that contribute toorganizational learning. One noticeable debate in the literature is whether scholarsshould try to move toward a single integrated framework or acknowledge that diversedisciplinary perspectives exist (Easterby-Smith and Araujo, 1999). Since a number of scholars have recognized that there is more than a single framework or model inunderstanding organizational learning process, researchers have tended to map manyfacets of organizational learning and developed integrative conceptual frameworks.Lipshitz et al. (2002) have stressed that organizational learning should be explicatedmore than the cognitive perspective, which has been a dominant focus in the literature.It is because organizational learning produces and changes the learning in culture,structures, policy, and norms aspects. Therefore, Lipshitz et al. (2002) have integratedve facets: structural, cultural, psychological, policy, and contextual, to build theirorganizational learning conceptual framework.

In addition, Gnyawali and Stewart (2003) have also argued that the cognitiveperspective has been widely recognized in organizational learning models, but littleresearch has examined organizational learning by using a contingency approach.

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Hence, Gnyawali and Stewart (2003) have adopted the contingency approach inproposing an integrative model of organizational learning, which emphasizes howenvironmental conditions affect organizational learning processes and result indifferent types of learning. This conceptual model contains antecedents of organizational learning, modes of learning, and interaction of the modes andresulting types of learning. The antecedents of organizational learning are onesperception of environmental conditions, which include perception of uncertainty andequivocality. This antecedent of organizational learning, perception of the externalenvironment, is similar to Lipshitz et al.s (2002) contextual facet, which focuses onexogenous factors, such as environmental uncertainty. Gnyawali and Stewart (2003)have suggested that the characteristics of external environment, uncertainty andequivocality, interact with the way an organization learns. In other words, howorganizational members perceive the external environment inuences organizationallearning.

Organizational learning and its antecedents. From the resource-based perspective,

an organization learns to develop organizational structures and systems to transformitself to become more adaptive and responsive to changes and jolts in the externalenvironment (Dodgson, 1993; Meyers, 1982). Meyers (1982) has argued that suchenvironmental jolts are a good opportunity for an organization to learn to deal withcrisis. In other words, an organization improves performance and readjusts itself tothe dynamic environment through the learning process. Also Gnyawali and Stewarts(2003) contingency perspective delineates how ones mental model towardsenvironmental change and the organizations information processing are keyconcepts which have been elaborated in the early organizational learning literature,such as Argyris (1977) and Daft and Weick (1984). These early organizationallearning contributions have viewed an organization as an interpretation system of itsenvironment. In other words, organizations, as open systems, rely on theinterpretation formulated by organization and individuals, especially the topmanagers. The organizational interpretation is analogous to an individuals learning.An organizations overall interpretation functions as the information processingsystem with three stages: scanning, interpretation, and learning. Researchers havesuggested that how individuals interpret the external environment inuencesorganizational learning (Daft and Huber, 1987; Daft and Weick, 1984; Gnyawali andStewart, 2003; Garca-Morales et al., 2006). Moreover, when an organization perceivesand recognizes the disconnection between external environment and its internaldesign component, it is critical that organization members have the capability toquestion underlying assumptions and create learning strategies for higher-levelorganizational learning (Dodgson, 1993). In other words, organizational learning is

generated by the organization members reaction and perception about the externalenvironment. However, such conceptual propositions in how members within anorganization perceive the external environment and its inuence on organizationallearning have not been investigated empirically. Therefore, when the externalenvironment is perceived as an uncertain and complex system, organizationallearning is generated:

H1-1. R&D employees perception of the external environment (as uncertain andcomplex) will be positively associated with organizational learning.

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Innovation performanceInnovation is often characterized as a kind of capital for the organization and hasbeen broadly dened as an idea, a product, or process, system or device that isperceived to be new to an individual, a group of people or rms, an industrial sector ora society as a whole (Rogers, 1995, p. 276). Innovation has also been viewed as amethod to sustain competitive advantage since the beginning of the industrialrevolution (Heffner, 2006). March (1991) has indicated that product innovation havetwo modes, explorative and exploitive, which mean innovation may be achieved byexploiting existing products or exploring new ones. Although different denitions of exploration and exploitation exist, the connotation of these two terms is focused onlearning, and new knowledge acquisition and recognition (Gupta et al., 2006; Irelandand Webb, 2007). Kanter (1984) has stressed that innovation is not only merely denedas technological innovation but also organizational learning and change processes insupporting and stimulating innovations. Recently, one major stream of innovationstudies focus on human aspect that lead to innovation (Prajogo and Ahmed, 2006).Based on the resource-based view, an organizations innovation performance is rootedin the human capital embedded in it that cannot be replicated and transferred (Irelandand Webb, 2007). In other words, an organization with the most advanced technologybut one that is lacking talented employees still cannot perform and conduct innovativeprojects. The concept of organizational learning stresses that organizations aresystems that support learning and performance improvement at multiple levels of anorganization. Therefore, the term innovation performance is connected withorganizational learning practices.

In the organizational learning literature, current empirical research has tried to useorganizational learning as a moderator variable to explain organizational performance(Bapuji and Crossan, 2004). Empirical studies have tended to adopt an organizationsnancial and marketing performance, either subjective measure or objective measure,as the organizational learning performance outcome variable. Recently, some empiricalstudies have started to demonstrate that an organization learning capability has apositive effect on the organizational innovation performance (Garc a-Morales et al.,2007; Lopez et al., 2005) Yet, despite some of the limited empirical evidence, there is acompelling need to continue building the base of empirical research on organizationallearning and performance outcomes that extend beyond marketing and nancialperformance to include other performance outcomes such as innovation performance,at both the individual and organizational-levels. This is because innovationperformance is considered to be a critical competitive advantage and has beenrecognized as an important current trend in the strategic HRM eld. Therefore, theresearchers hypothesized that:

H1-2a. Organizational learning will be positively associated with individual-levelinnovation performance.

H1-2b. Organizat ional learning will be posi tively associated withorganizational-level innovation performance.

Individual and organizational-level innovation performance. The organizationallearning literature is primarily underpinned by the concept that individual-levelefforts may contribute to organizational-level performance (Dixon, 1992; Huber, 1991;Ron et al., 2006). Also, researchers have applied individual learning theories, such as

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cognitive psychology theory or behavior theory as the fundamental basis to build theorganizational learning constructs. Without the individuals focus and intentiontowards the new opportunity and innovative information, an organization would havedifculty to achieve and initiate entrepreneurial activities. Although the sum of theindividuals innovative behavior might not equal organization-level innovationperformance, organizational-level innovation performance is based on its membersinnovative behaviors. That is, organizational innovation occurs under the conditionsthat organizational members innovation is able to be transferred to theorganizational-level (Glynn, 1996). In addition, an innovative organization mayfacilitate its employees innovation performance. Hence, the researchers hypothesizedthat:

H3a. Individual-level innovation performance will be positively associated withorganizational-level innovation performance.

H3b. Organizational-level innovation performance will be positively associatedwith individual-level innovation performance.

Conceptual frameworkBased on the reviews of organizational learning and innovation performanceliteratures, the researchers have proposed Figure 1 to represent the conceptualframework from which the above research hypotheses have been developed. Groundedupon and informed by Huber (1991), Dixon (1992), and Gnyawali and Stewarts (2003)organizational conceptual frameworks, the researchers have developed the frameworkto included organizational learning, its antecedent, perception of external environment,and individual and organizational-level innovation performance as the outcomevariables. The organizational learning process contains four sub-processes:information acquisition, information distribution, information interpretation, andorganization memory.

Research sampleThe research sites for this study included organizations in an industry that pursuesand puts an emphasis on innovation performance. In addition, Yang et al .s (2007)empirical study has demonstrated that in Taiwan, only high technology rmsemphasize supporting and applying the organizational learning practice through theirorganizations to improve their organization performance and effectiveness.Consequently, high technology rms located within one science park in Taiwanbecame the target sample selection sites for this study.

Figure 1.Conceptual framework

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Each organizations R&D manager and three to ve senior R&D project team members,randomly selected by the R&D manager, in each high technology rm listed in theDirectory of 2006 Association of Industries in the specic Science Park Directory of Taiwan were invited to participate in this study. The criterion for each organizationsR&D manager to identify three to ve senior R&D project team members in each hightechnology rm was to randomly select individuals who have been working for morethan three years in the R&D project teams of each rm. Thesepotential participants maybe product developers, designers, engineers, or marketing personnel. Individuals inthese positions tend to pay more attention to knowledge and information towardsinnovation performance. The main study resulted in 83 (30.29 percent response rate)high technology rms with 83 R&D managers evaluating their respective senior R&Dproject team members innovative behaviors and 268 (64.58 percent response rate) validreturned questionnaires from senior R&D project team members. The descriptiveinformation on the participants organizational demographics and the participantsindividual demographics are presented in Table I and Table II, respectively.

Non-participants biasTo address the non-participants bias issue, the T test comparisons of the participantrms and non-participants rms on number of employees t 1:23; p 0:21 andannual revenue t 1:18; p 0:24 did not reveal signicant differences between thetwo groups. The researcher concluded that participant rms did not differ signicantlyfrom the non-participants rms.

Frequency ( n ) %

Number of employees51 100 9 10.8101 200 15 18.1201 250 18 21.7251 300 28 33.7301 400 2 2.4401 500 6 7.2More than 501 5 6.0

Annual revenueLess than 1 billion NTD 13 15.71 4 billion NTD 17 20.54 7 billion NTD 14 16.97 10 billion NTD 19 22.9More than 10 billion NTD 20 24.1

Main product of the organizationComputer and peripherals 15 18.1Integrated circuits 11 13.3Opto-electronics 14 16.9Precision machinery 15 18.1Telecommunications 13 15.7Others 15 18.1

Note: N 83

Table I.Characteristics of organizations

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InstrumentationThe researcher developed two sets of survey instruments from measures drawn fromthe existing literature: one instrument was designed for senior R&D project teammembers and the other was designed for R&D managers in each participating rmrespectively. The instrument for the senior R&D project team members included twosections: section 1 contained questions using Garc a-Morales et al.s (2006) perceptionof external environment measure, Lo pez et al .s (2006) organizational learning measure,and Simseks (2002) organizational-level innovation measure. Responses wereve-point Likert-type scales. Section 2 contained demographic items. The instrumentfor R&D managers included six items derived from Scott and Bruces (1994)individual-level innovation performance measure. The R&D managers were asked torate every senior R&D project team members innovative behavior on a ve-pointLikert-type scale.

Perception of external environment The items measuring perception of external environment were adopted fromGarc a-Morales et al.s (2006) environment instrument (Cronbachs alpha was 0.864).All four items measure the degree of dynamism, complexity, diversity, and

Frequency (n) Percentage (%)

Gender Female 25 9.3Male 243 90.7

Length of employment 3 5 years 77 28.76 8 years 146 54.59 11 years 37 13.812 2 15 years 8 3.9

Work experience in industry3 5 years 65 24.76 8 years 136 50.712 15 years 54 20.116 20 years 13 4.9Level of education

Associate degree 4 1.5Bachelors degree 85 31.7Masters degree 172 64.2PhD 7 2.6

Hours dedicated to work related learning 0 hours per month 10 3.71 10 hours per month 162 60.411 20 hours per month 74 27.621 35 hours per month 15 5.6More than 36 hours per month 7 2.6

Note: N 268

Table II.Characteristics of

participants

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heterogeneity of external environment were summed to derive the measure of perception of external environment.

Organizational learning The items measuring organizational learning were adopted from Lo pez et al.s (2006)measure of organizational learning, which was based on Huber (1991) and Dixons(1992) organizational learning conceptual frameworks. A total of 25 items with fourdimensions, information acquisition (seven items), information distribution (veitems), information interpretation (ve items), and organizational memory (eightitems), were summed to derive the measure of organizational learning. The Cronbachsalphas on Lo pez et al.s (2006) information acquisition, information distribution,information interpretation, and organizational memory measure were 0.796, 0.772,0.822, and 0.844, respectively. Respondents were asked to use a ve-point rating scaleto rate extent of agreement at items describing the learning practices and activities intheir organizations.

Individual-level innovation performanceThe items measuring individual-level innovation performance were adopted from Scottand Bruces (1994) six-item innovative behavior measure (Cronbachs alpha was 0.89),which asks supervisors to rate their subordinates innovative behaviors in workplace.In addition, Scott and Bruces (1994) measure was designed for employees who work intechnology related areas, such as R&D personnel. All six items were summed to derivethe measure of each senior R&D project team members individual-level innovationperformance.

Organizational-level innovation performance

In collecting sensitive performance data, prior literature has tended to adopt an indirectapproach (Lo pez et al., 2005). That is, scholars have tended to ask participants to reporthow well their rm innovatively performed in terms of the extent of new product,process, and service development rather than ask participants directly to reportnancial statement data. Although objective data has been argued to provide greatervalidity in theory, Homburg et al.s (1999) empirical study has demonstrated that bothobjective and subjective data are all valid in measuring an organizations performance.In this study, the researchers primarily adopted the subjective self-report approach anduse subjective secondary sources as supplementary data. Simseks (2002) ve-iteminnovation performance was adopted (Cronbachs alpha was 0.87).

A unique method in revising the self-report approach was used to resolve thecommon method bias issue. Instead of adopting the participants own score on theorganizational-level innovation performance, the researcher used the mean score fromthe rest of the participants in the same rm. Therefore, the participants own rating onthe organizational-level innovation performance was excluded in the measure.

In addition, the researcher also compared the participants evaluation of theorganizational-level innovation performance with the organizations actual nancialperformance in the market, such as return on investment (ROI), which was accessedfrom the 2006 Association of Industrial Directory to ensure credibility. However, theDirectory is not an ofcial directory, and some rms choose not to release their

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nancial reports and statements. Therefore, this method was used as a supplementarytool to increase the validity of the measure.

Control variablesOn the basis of a review of the literature, the researchers identied the followingvariables that should be controlled in the data analyses. These variables include anorganizations annual revenue and number of employees, individuals off ofce timespent on work-related learning and lengths of employment in the high technologyindustry (Zahra, 1996).

Results Psychometric properties of the instrument An exploratory factor analysis with varimax rotation method was conducted on allitems of the survey. The results of the factor analyses indicated that the groupings of factors were exactly the same as the instrument factor analyses reported in the pastresearch, and no items were deleted in this stage. Next, the researchers conductedconrmatory factor analysis to evaluate the factor structure, and Cronbachs alpha wasused to rate the reliability of the instrument. The overall t of the 7-constructconrmatory factor model to the data suggested a good t of the measurement scales( x 2 996.896, df 710, p , 0.01; Comparative Fit Index (CFI) 0.956, theTucker-Lewis Index (TLI) 0.952; the Incremental Fit Index (IFI) 0.957, the rootmean square error of approximation RMSEA 0:039: In addition, each of thestandardized factor loadings was signicant ( p , 0.001) and quite high (Table III). Asindicated in Table III, the Cronbach alpha internal consistency reliability estimateswere all above Nunally and Bernsteins (1994) recommended level of 0.70.

Further, to conrm the dimensionality of the organizational learning, second order

conrmatory factor was conducted, following Lo pez et al. (2006). The second ordermodel was estimated to test whether information acquisition, information distribution,information interpretation, and organizational memory are affected by the higher-orderconstruct, organizational learning. In other words, the second-order conrmatoryfactor analysis of the organizational learning was based upon the hierarchical structurethat the dimensions obtained from the rst-order analysis (information acquisition,information distribution, information interpretation, organizational memory) areviewed as factors that presumed to be caused by organizational learning. In Table IV,standardized loadings of second order factors were all high and signicant at p ,0.001, and the CFI exceeded the recommended norm of 0.90. Therefore, the results as awhole conrm the dimensionality of the organizational learning measure.

Test of the model A series of multiple regressions with hierarchical method were used to examine theresearch hypotheses. That is, relevant control variables were entered into theregression rst, followed by the respective independent variables, organizationallearning, entrepreneurial opportunity recognition, to estimate the additionalcontribution of the organizational learning variable to the dependent variables,individual-level and organizational-level innovation performance. Table V presentedan overview of the means and standardized deviation of all the variables.

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ItemStandardized

loadingAverage variance

extractedCronbachs

alphaCompositereliability

Perception of external environment 0.731 0.870 0.8701. There are few changes in the

industry that could affect theorganization 0.900

2. The changes in the industryhave been easily predictable 0.882

3. The industrys evaluationdepended on multiple factors 0.680

4. The factors that affect theindustrys evolution differgreatly from each other 0.671

Information acquisition (IA) 0.701 0.919 0.9191. Makes co-operation agreement

with other companies,universities, technical colleges,etc 0.607

2. Is in touch with professionalsand expert technicians 0.658

3. Encourages employees to joinformal or informal nets made upby people outside theorganization 0.665

4. Asks employees to attend fairsand exhibitions regularly 0.663

5. Has a consolidated andresourceful R&D policy 0.706

6. New ideas and approaches on

work performance areexperimented continuously 0.7207. Has the organizational systems

and procedures that supportinnovation 0.708

Information distribution (ID) 0.720 0.869 0.8691. Informs all members about the

aim of the company 0.6792. Holds meetings periodically to

inform all the employees aboutthe latest innovations in thecompany 0.680

3. Has formal mechanisms toguarantee the sharing of the bestpractices among the differentelds of the activity 0.792

4. There are within theorganization individuals whotake part in several teams ordivisions and who also act aslinks between them 0.764

( continued )

Table III.Construct measurementsummary: conrmatoryfactor analysis andreliability

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ItemStandardized

loadingAverage variance

extractedCronbachs

alphaCompositereliability

5. Has individuals responsible forcollecting, assembling, anddistributing internallyemployees suggestions 0.769

Information interpretation (IT) 0.714 0.854 0.8541. All the members of the

organization share the same aimto which they feel committed 0.780

2. Employees share knowledgeand experience by talking toeach other 0.660

3. Teamwork is a very commonpractice 0.708

4. The organization developsinternal rotation programs so asto facilitate the shifts of theemployees from one departmentor function to another 0.722

5. The organization offers otheropportunities to learn (visits toother parts of the organization,internal training programs, etc.)so as to make individuals awareof other people or departmentsduties 0.692

Organizational memory (OM) 0.707 0.846 0.8461. Has databases to stock its

experiences and knowledge soas to be able to use them later on 0.816

2. Has directories or emails eldaccording to the eld theybelong to, so as to nd an experton a concrete issue at any time 0.843

3. Has up-to-date databases of itsclients 0.789

4. Has access to the organizationsdata basis and documentsthrough some kind of network(Lotus Notes, Intranet, etc.) 0.824

5. Keeps databases up-to-date 0.6566. Allows all the employees have

access to the organizationsdatabases 0.702

7. Employees consult thedatabases 0.707

8. The codication and knowledgeadministration system makework easier for the employees 0.800

( continued ) Table III.

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Organizational learning and its antecedent. To examine the contribution of senior R&Dproject team members perception of the external environment to organizationallearning, organizational learning was entered into the regression model. In Table VI,R&D project team members perception of the external environment (beta 0.40)variable was signicant to the organizational learning. Hence, H1-1 was supported.

Organizational learning and innovation performance. To examine the contributionof organizational learning to individual-level innovation performance, organizationallearning was entered into the regression model after the four control variables, annualrevenue, number of employees, out of ofce time spent on work-related learning, andwork experience in high technology industry. There was a 24 percent increment in thetotal variance explained when organizational learning variable was added to theregression model (Model 2 of Table VII). The total variance explained, including the 8percent by the four control variables, was 32 percent F 5=262 24:62; p , 0.001).

ItemStandardized

loadingAverage variance

extractedCronbachs

alphaCompositereliability

Individual-level innovation performance 0.709 0.918 0.9181. Searches out new technologies,

processes, techniques, and/orproduct ideas 0.786

2. Generate creative ideas 0.8053. Promotes and champions ideas

to others 0.7904. Investigates and secures funds

needed to implement new ideas 0.7905. Develops adequate plans and

schedules for theimplementation of new ideas 0.828

6. Is innovative in workperformance 0.843

Organizational-level innovation performance 0.713 0.899 0.8991. Has spent heavily (well above

the industry average) on productdevelopment 0.750

2. Has introduced a large numberof new products to the market 0.812

3. Has pioneered the developmentof breakthrough innovations inits industry 0.840

4. Has spent on new productdevelopment initiatives 0.805

5. Has acquired signicantly morepatents than its majorcompetitors 0.799

Note: All estimates were signicant at p , 0.0, N 268; CFI 0.956; TLI 0.952; IFI 0.957;RMSEA 0.039; and x 2 996.896 with 710 degrees of freedom ( p , 0.01)Table III.

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Annual revenue beta 0:15; employee work experiences in industry (beta 0.14),and organizational learning beta 0:50 variables were signicant to theindividual-level innovation performance. Therefore, H1-2a : organizational learningactivities will be positively associated with individual-level innovation performance,was supported.

Next, to examine the contribution of organizational learning to organizational-levelinnovation performance, organizational learning was entered into the regression modelafter four control variables, annual revenue, number of employees, out of ofce time

Item Standardized loading

First order model Information acquisition (IA)

IA1 0.601IA2 0.660IA3 0.660IA4 0.658IA5 0.713IA6 0.719IA7 0.713

Information distribution (ID)ID1 0.681ID2 0.676ID3 0.788ID4 0.767ID5 0.773

Information interpretation (IT)IT1 0.782IT2 0.659IT3 0.706IT4 0.722IT5 0.691

Organizational memory (OM)OM1 0.816OM2 0.843OM3 0.790OM4 0.825OM5 0.656OM6 0.701OM7 0.705

OM8 0.801Second order model Information acquisition (IA) Organizational learning 0.723Information distribution (ID) Organizational learning 0.874Information interpretation (IT) Organizational learning 0.918Organizational memory (OM) Organizational learning 0.795

Note: First order model: All estimates signicant at p , 0.01; N 268; CFI 0.954; TLI 0.948;IFI 0.955; RMSEA 0.05; and x 2 438.292 with 263 degrees of freedom ( p , 0.01)Second order model: All estimates signicant at p , 0.01; N 268; CFI 0.954; TLI 0.948;IFI 0.954; RMSEA 0.05; and x 2 441.559 with 265 degrees of freedom ( p , 0.01)

Table IV.First order and second

order conrmatory factoranalysis of the

organizational learningmeasurement

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V a r i a b l e

M e a n

S D

1

2

3

4

5

6

7

8

1 . A n n u a l r e v e n u e

3 . 0 8

1 . 4 2

2 . N u m b e r o f e m p l o y e e s

3 . 4 4

1 . 5 5

0 . 0 7

3 . O u t o f o f c e t i m e s p e n t i n w o r k - r e l a t e d l e a r n i n g

2 . 0 6

0 . 7 7

0 . 0 6

0 . 0 4

4 . W o r k e x p e r i e n c e i n i n d u s t r y

2 . 4 3

0 . 8 0

2 0 . 0 8

0 . 0 9

0 . 1 0

5 . P e r c e p t i o n o f e x t e r n a l e n v i r o n m e n t

3 . 3 7

0 . 6 0

0 . 1 3 *

0 . 1 1

0 . 0 4

0 . 1 0

6 . O r g a n i z a t i o n a l l e a r n i n g

3 . 3 9

1 . 5 4

0 . 1 3 *

0 . 1 0

0 . 0 7

0 . 1 1

0 . 4 0 * *

7 . I n d i v i d u a l - l e v e l i n n o v a t i o n p e r f o r m a n c e

2 . 9 1

1 . 6 8

0 . 1 9 * *

0 . 0 6

2 0 . 0 1

0 . 1 6 * *

0 . 4 3 * *

0 . 5 3 * *

8 . O r g a n i z a t i o n a l - l e v e l i n n o v a t i o n p e r f o r m a n c e

3 . 3 8

1 . 7 9

0 . 2 3 * *

0 . 1 6 * *

0 . 1 7 * *

0 . 1 5 *

0 . 4 1 * *

0 . 4 8 * *

0 . 4 6 * *

N o t e :

* p ,

0 . 0 5

, * * p ,

0 . 0 1 ; N 2 6 8

Table V.Means, standarddeviations, andcorrelations for allvariables

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spent on work-related learning, and employee work experience in high technologyindustry. Table VII Model 2 indicated that the total variance explained, including the12 percent by the four control variables, was 29 percent ( F 5=262 21:80; p , 0.001).That is, organizational learning added another 17 percent of variance explained. InTable VII Model 2, annual revenue beta 0:18; employee off ofce time spent inlearning beta 0:11; employee work experiences in industry beta 0:12; andorganizational learning beta 0:42 were signicant to organizational-levelinnovation performance. Therefore, H1-2b: organizational learning activities will bepositively associated with organizational-level innovation performance, was alsosupported. Apparently, organizational learning plays a critical role to bothindividual-level and organizational-level innovation performance.

Organizational learning subprocesses and innovation performance. In organizationallearning subprocesses and innovation performance correlation table (Table VIII), theorganizational learning and its subprocesses, information acquisition r 0:46;information distribution r 0:47; information interpretation r 0:38; andorganizational memory r 0:45 were all positively correlated to individual-levelinnovation performance. Also, among the four subprocesses, it was informationdistribution activities that correlated most to an individuals innovative behaviors. Inaddition, these four organizational learning subprocesses, information acquisition r 0:41; information distribution r 0:43; information interpretation r 0:31; andorganizational memory r 0:41 were also all positively correlated to

Predictors Organizational learningb

Perception of external environment 0.40 * R 2 0.18

F 49.82*Note: *p , 0.001; N 268

Table VI.Senior R&D project team

members perception of

external environment aspredictor of organizational learning

PredictorsIndividual level/

organizational levelIndividual level/

organizational levelModel l Model 2

b b

Annual revenue 0.23 * * * 0.24 * * * 0.15 * * 0.18 * *Number of employees 0.03 0.12 * 2 0.01 0.09Off ofce time spent in learning 2 0.04 0.13* 2 0.07 0.11*

Work experience in industry 0.20 * * * 0.17 * * 0.14 * * 0.12 *Organizational learning 0.50 * * * 0.42 * * * R 2 0.08 0.12 0.32 0.29Adjusted R 2 0.06 0.11 0.31 0.28D R 2 0.24 0.17 F 5.59* * * 9.30 * * * 24.62* * * 21.80 * * * F Change 92.93 * * * 63.02 * * *

Note: * p , 0.05; * * p , 0.01; * * * p , 0.001; N 268

Table VII.Organizational learning

as predictor of individual-level andorganizational-level

innovation performance

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organizational-level innovation performance. Like the result in individual-levelinnovation performance, it was information distribution activities that played the mostcritical role in affecting an organizations innovation performance.

To further examine the impact of these four subprocesses on individual-levelinnovation performance and organizational-level innovation performance, a structuralequation modeling was conducted with AMOS 5.0 version. The indicatorsdemonstrated that the model-t indexes have an acceptable overall disposition( x 2 880.785, df 573, p, 0.01; CFI 0 .948; TLI 0 .942; IFI 0.948;RMSEA 0.045). The analytical results (Table IX) showed that individual-levelinnovation performance is positively inuenced by information acquisition(beta 0.402, t 5.56, p, 0.01), information distribution beta 0:412; t 6:09; p, 0.001), information interpretation beta 0:385; t 4:08; p, 0.05), andorganizational memory beta 0:391; t 4:46; p , 0.05). Additionally, these foursubprocesses, information acquisition beta 0:398; t 4:68; p, 0.05), informationdistribution beta 0:406; t 5:74; p, 0.01), information interpretation beta

Variables 1 2 3 4 5 6

1. Information acquisition 2. Information distribution 0.54 * 3. Information interpretation 0.54 * 0.67 * 4. Organizational memory 0.48 * 0.63 * 0.67 * 5.Individual-level innovation performance 0.46 * 0.47 * 0.38 * 0.45 * 6. Organizational-level innovation performance 0.41 * 0.43 * 0.31 * 0.41 * 0.46 *

Note: * p , 0.01; N 268

Table VIII.Organizational learningsubprocesses andinnovation performancecorrelation

Standardized estimate t -value

Information acquisition ! individual-levelinnovation performance 0.402 * * 5.56 * *Information distribution ! individual-levelinnovation performance 0.412 * * * 6.09 * * *Information interpretation ! individual-levelinnovation performance 0.385 * 4.08 *Organizational memory ! individual-levelinnovation performance 0.391 * 4.46 *Information acquisition ! organizational-levelinnovation performance 0.398 * 4.68 *Information distribution ! organizational -level

innovation performance 0.406 * * 5.74 * *Information interpretation ! organizational -levelinnovation performance 0.379 * 3.67 *Organizational memory ! organizational -levelinnovation performance 0.390 * 3.90 *

Note: * p , 0 .05; * * p , 0.01; * * * p , 0 .001; N 268: All estimates were signicant at p , 0.01.The model t index x2 880.785 with 573 degrees of freedom (\hskip 1.5pt p , 0.01). CFI 0.948;TLI 0.942; IFI 0.948; RMSEA 0.045

Table IX.Structural equationmodeling onorganizational learningsubprocesses andinnovation performance

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0:379; t 3:67; p , 0.05), and organizational memory beta 0:390; t 3:90; p ,0.05) were also positively associated with organizational-level innovation performance.As a result, information distribution had the most critical role in both individual andorganizational-level innovation performance.

Individual-level innovation performance and organizational-level innovation performance. Innovation performance, individual-level innovation performance wasentered into the regression model after annul revenue, number of employees, out of ofce time spent in work-related learning, and work experience in industry. There wasa 16 percent increment in the total variance explained when the individual-levelinnovation performance variable was added to the regression model (Model 2 of Table X). The total variance explained, including the 12 percent by four controlvariables, was 28 percent F 3=264 20:43; p , 0.001). Work experience in industry wasnot signicant to the organizational-level innovation performance in regression Model2 of Table X. Only annual revenue beta 0:15; number of employees beta 0:15;out of ofce time spent in work-related learning beta 0:11; and individual-levelinnovation performance beta 0

:41 were signicant to organizational-level

innovation performance. Hence, H3a : individual-level innovation performance willbe positively associated with organizational-level innovation performance, wassupported.

On the other hand, to examine the contribution organizational-level innovationperformance to individual-level innovation performance, organizational-levelinnovation was entered into the regression model after the annual revenue and workexperience in industry. Because number of employees and out of ofce time spent onwork-related learning r 0:04; r 2 0:01 did not correlate with individual-levelinnovation performance, they were excluded from the Model 1 of Table XI in theregression analysis. Inclusion of two control variables, Table XI pointed out that thetotal variance explained, including the 8 percent by the two control variables, was 23

percent F3=264 26:65; p , 0.001). Annual revenue beta 0:12; work experiencein industry beta 0:12; and organizational-level innovation performance beta 0:42variables were signicant to individual-level innovation performance. As a result,

Predictors Organizational levelModel 1 Model 2

b b

Annual revenue 0.24 * * * 0.15 * *Number of employees 0.13 * 0.15 * *Off ofce time spent in learning 0.12 * 0.11 *

Work experience in industry 0.17 * * 0.09Individual-level innovation performance 0.41 * * * R 2 0.12 0.28Adjusted R 2 0.11 0.27D R 2 0.16 F 9.30* * * 20.43 * * * F change 57.02 * * *

Note: * p , 0.05; * * p , 0.01; * * * p , 0.001; N 268

Table X.Individual-level

innovation performanceas a predictor of

organizational-levelinnovation performance

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H3b: organizational-level innovation performance will be positively associated withindividual-level innovation performance was supported.

Discussion Antecedent of organizational learning The results indicated that the senior R&D project team members perception of external environment was positively correlated with organizational learning. Thendings empirically supported early organizational learning conceptual studies, whichargued that an organization as an interpretation system of its environment andindividuals mental model in scanning and interpreting the external environment andinformation have been very important for organizations to learn to prepare for externalenvironmental jolts (Daft and Huber, 1987, Daft and Weick, 1984; Gnyawali andStewart, 2003; Meyers, 1982). Recently, Garc a-Morales et al.s (2006) empirical studyhas demonstrated that managers perceptions of the environment are positively relatedto organizational learning. Unlike their study, the researcher used senior R&D projectteam members as participants in this study. In the high technology industry, seniorR&D project team members were deemed to be the new information and knowledgegatekeepers of their organizations, and are rst-line employees engaging in learningand innovation activities rather than the managers.

Subprocesses of organizational learning One unique aspect of this study was that the researcher further investigated therelative importance of the four organizational learning subprocesses towardinnovation performance. The ndings indicated that information distribution wasthe most important predictor to both individual and organizational-level innovationperformance. Although information distribution follows after information acquisitionin the organizational learning process, the practices and activities that informationdistribution contain do help organization members to contribute and transfer theirindividual-level learning and knowledge acquisition to the organizational level. Thatis, based upon this study, information distribution can be viewed as the most criticalfunction in the organizational learning process.

Predictors Individual levelModel 1 Model 2

b b

Annual revenue 0.23 * * 0.12 *

Work experience in industry 0.20 * * 0.12 *Organizational-level innovation performance 0.42 * * R 2 0.08 0.23Adjusted R 2 0.07 0.22D R 2 0.15 F 10.87* * 26.65 * * F Change 53.89 * *

Note: * p , 0.05; * * p , 0.001; N 268

Table XI.Organizational levelinnovation performanceas a predictor of individual levelinnovation performance

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Organizational learning and innovation performanceThe current study showed a clear division between individual and rm-levelinnovation performance and their relative importance to the organizational learning.The ndings indicated that organizational learning contributed more to individuallevel than organizational-level innovation performance. The results may challenge thegeneral thought that organizational learning should contribute more toorganizational-level innovation performance than individual-level innovationperformance. However, organizational learning relies on its membersindividual-level learning performance (Easterby-Smith and Araujo, 1999). Also, howan individual perceives his or her organizations learning systems and practices mayaffect the individuals attitudes towards learning (Gnyawali and Stewart, 2003). Thatis, an organization members learning behaviors and performance are shaped andinuenced by the organizations learning mechanisms they perceived. Therefore,organizational learning has a much more direct impact on an individuals performancethan on organizational-level performance.

Individual and organizational-level innovation performanceUnder control variables related to the rm-level innovation performance (Table IX), theindividual-level innovation performance was signicant to rm-level innovationperformance. The ndings conrmed that organization learning is primarilyunderpinned by the concept that individual-level efforts may contribute toorganizational-level performance (Dixon, 1992; Huber, 1991; Ron et al., 2006). Thisstudy further extended the limitation of existing literature in which individual-levellearning and innovation measures are usually neglected and restricted toorganizational-level performance (Antonacopoulou, 2006). Also the empiricalndings supported Glynns (1996) and Antonacopoulous (2006) conceptualframework that organizational innovation occurs under the condition that an

organization members innovation performance is able to transferred to be theorganization.Moreover, the ndings in Table X indicated that rm-level innovation performance

had signicant impact on individual-level innovation performance. It contributed tothe understanding of innovation performance literature from another perspective,which not only emphasized how individuals contribute to the whole organization butalso how an innovative organization facilitates and encourages its members to pursueinnovation. In addition, this empirical study supported Antonacopoulous (2006)conceptual argument that the organizations learning and innovation environmentinuences its members learning and innovation performance.

Implications for practiceAs HRM professionals roles have shifted from traditional operational functions to anorganizational consultancy role, HRM professionals have become more proactive asstrategic advisors of the organization (Ulrich, 1997). A range of environmentalpressures contributed to the emergence of HRM strategic activities and practice.Learning at the individual, group, and organizational-levels has become a criticalimperative in organizations, and HRM professionals are being challenged to becomestrategic business partners that can impact learning and development to improveinnovation performance and enhance organizational functioning in a competitive

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global business environment. Although scholars have articulated the applications andpractice of organizational learning from the management perspective and literature(Bapuji and Crossan, 2004), the ndings from this study have specic applications andutility for HRM professionals and managers.

The ndings of this study indicated that organizational learning processes enhanceboth individual-level and organizational-level innovation performance. Among theorganizational learning subprocesses, the nding suggested that it is informationdistribution that contributes most to both individual-level and organizational-levelinnovation performance. In other words, information distribution is the most importantsubprocess within organizational learning. From an organization developmentperspective, improving upon systems and structures may strengthen informationdistribution thus augmenting organizational learning. Therefore, HRM professionalsand managers may be positioned to better assist the organization with informationdistribution by creating the environment with technology system and informal culturethat fosters employees to share, exchange, and disseminate information andknowledge within the organization. It is also important that the organizationstructure or system does not limit the information distribution activities. In addition,from the employee development practice perspective, job rotations can broaden theemployees information distribution scope, especially in the high technology industry.For example, research and development division employees often need to communicatewith manufacturing division employees and rotating employees may enhance thissubprocess.

Moreover, the results also empirically reect the importance of informationacquisition to individual-level innovation performance is the next most importantsubprocess next to information distribution. This suggests that informationacquisition strategies should be strengthened as one critical subprocess within theorganizational learning infrastructure. HRM professionals should help their

organizations and employees to identify and assess diverse or informationacquisition channels and opportunities either internal or external the organization.In addition, grafting new employees and merging with other rms areorganizational-level information acquisition strategies. For example, managers mayconsider initiating collaborative relationships with other rms and recruiting talentedemployees from other rms, which are concepts of the construct of informationacquisition. Further, an organizations managers and HRM professionals may considerstrategic organization development interventions at the organizational-level, such asmergers, acquisitions, networks, or alliances.

Findings also suggest that senior R&D employees perception of externalenvironment was positively correlated to organizational learning. If R&D employeesare taught to be more sensitive in scanning and searching the external general

environment and industrial environment, they may become more proactive in fosteringlearning. Hence, HRM professionals may be positioned to encourage R&D employees,especially those junior R&D employees, to perceive and interpret the jolts and changeson the external environment.

Limitations and recommendations for future researchThere were some limitations which need to be acknowledged in this study. First,although the researcher assumed that each R&D manager did randomly select the

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senior R&D project team members in the organization, the potential differencesbetween employees might exist. Moreover, the survey data were mostly based onself-reports, which may become a research bias. To resolve the challenges associatedwith common method bias, the researcher assumed that having R&D managers ratetheir employees individual-level innovation performance would address this issue.Also, the researcher assumed that excluding the participants own rating on therm-level innovation, and along with using partial secondary sources of nancial dataon rm-level innovation performance as a supplementary tool would address thecommon method bias and enhance the validity of rm-level innovation performancemeasure. Furthermore, one limitation is that the instrumentation on individual-levelinnovation performance may underestimate tacit knowledge creation.

Given the research limitations addressed and the ndings reported aforementioned,the following recommendations are made for future research. First, this studyinvestigated individual-level and organizational-level innovation performance. Futureresearch may involve team or group-level participants and investigate their innovationperformance since there is an increasing interest in understanding organizationallearning processes at the group-level, especially virtual teams (Vince et al., 2002).Hence, understanding how team or group-level innovation performance impacts uponindividual and organizational-level innovation performance may provide HRMprofessionals with signicant practice knowledge on developing team or group-levelinterventions to facilitate their learning and performance.

In addition, Lipshitz et al. (2002) have integrated ve facets: structural, cultural,psychological, policy, and contextual, to build their organizational learning conceptualframework. As a result, future research may include other facets or variables related toorganizational learning, such as organizational culture or leadership to have acomprehensive understanding of how these variables interactively or independentlyimpact on learning and innovation and analyze their relative importance towards

innovation performance and other outcome variables. While the organizationallearning instrument used in this study appeared to be robust in this Asian context,future research may be focused on developing an indigenous organizational learninginstrument that captures organizational learning practices and other indigenous facetsthat may be unique to Taiwan. Finally, a longitudinal research design may providemore detailed data in further understanding the change of an individuals innovativebehaviors and an organizations innovation performance over more substantial periodsof time.

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About the authorsYu-Lin Wang is Assistant Professor in the Department of Business Administration at NationalCheng Kung University, Taiwan. Her research interests focus on organizational learning andentrepreneurship in small and medium enterprises. Yu-Lin Wang is the corresponding authorand can be contacted at: ywang@mail.ncku.edu.tw

Andrea D. Ellinger is Human Resource Development Professor in the Department of HumanResource Development and Technology at The University of Texas at Tyler, USA. Her researchinterests focus on managerial coaching, informal workplace learning, organizational learning,and the learning organization concept.

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