The influence of investment in workplace learning on learning outcomes and organizational performance

The Influence of Investment inWorkplace Learning on Learning Outcomes andOrganizational Performance

Yoonhee Park, Ronald L. Jacobs

Although the importance of workplace learning has been recognized in researchand practice, there is little empirical support that describes how workplacelearning, including both formal and informal learning, is linked to organiza-tional performance. This study investigated the influence of investment in work-place learning on learning outcomes and organizational performance using the2005 and 2007 Human Capital Corporate Panel survey of South Korean com-panies. The data were analyzed using structural equation modeling. The studyfound that investment in workplace learning influences organizational perfor-mance through the outcomes of workplace learning. Implications for humanresource development research and practice are discussed.

Addressing the learning needs of employees has become an essential issuefor organization managers (Rowden, 2007; Shipton, Dawson, West, & Patterson,2002). As a result, the workplace is now considered to be appropriate for awide range of learning activities, both formal and informal (Clarke, 2005;Fuller & Unwin, 2004). Workplace learning helps individuals and organizationsrespond to changes in job responsibilities, work processes, and any otherissue that might provide obstacles to meeting organizational expectations(Ellström, 2001; McCauley, Ruderman, Ohlott, & Morrow, 1994; Poell, VanDam, & Van Den Berg, 2004).

Workplace learning is defined as the process of acquiring job-related knowl-edge and skills, through both formal training programs and informal socialinteractions among employees (Rowden, 2007). In this sense, workplace learn-ing may be the most appropriate term to describe all the various ways thatemployees acquire new job-related information. Consider that the term train-ing has a relatively more limited meaning as it usually refers to a planned effort

HUMAN RESOURCE DEVELOPMENT QUARTERLY, vol. 22, no. 4, Winter 2011 © Wiley Periodicals, Inc.Published online in Wiley Online Library (wileyonlinelibrary.com) • DOI: 10.1002/hrdq.20085 437

438 Park, Jacobs

to facilitate the acquisition of job-related competencies through formal means( Jacobs, 2003). And the term employee development typically refers to a broaderset of activities that have both learning and career development goals, such astalent development, job rotation, degree programs, special courses, and men-toring relationships, to achieve individual and organizational goals (Noe, 2008).

However, regardless of the term used, the point remains that organiza-tional managers have come to realize the importance of employee learning asa means for improving organizational performance (Clarke, 2005; Doornbos,Simons, & Denessen, 2008; Enos, Kehrhahn, & Bell, 2003; Fuller & Unwin,2004). Consequently, organizations have invested extensive financial resourcesin their employees’ learning activities, believing that the investment in thelearning will result in useful outcomes (Aragón-Sánchez, Barba-Aragón, &Sanz-Valle, 2003; Delaney & Huselid, 1996; Delery & Doty, 1996; Pate, Martin, Beaumont, & McGoldrick, 2000; Tzafrir, 2005). While the relation-ship between workplace learning and organizational performance is commonlyaccepted in practice, few studies have investigated how specific variables relatedto workplace learning, including both formal and informal learning, influenceorganizational performance (Jacobs & Washington, 2003; Tharenou, Saks, &Moore, 2007).

Numerous studies have shown how single training programs, as a formalapproach to learning, are related to organizational performance (Aragón-Sánchez et al., 2003, García, 2005; Russell, Terborg, & Powers, 1985). Otherstudies have shown that offering training programs, in a broad sense, are asso-ciated with successful firm performance (Delaney & Huselid, 1996; Faems,Sels, DeWinne, & Maes, 2005; Paul & Anantharaman, 2003; Tzafrir, 2005;Vlachos, 2008). However, no studies have been found that examine the rela-tionships of both formal and informal forms of workplace learning and orga-nizational performance. Given that employees engage in a range of learning toacquire job-related knowledge and skills (Leslie, Aring, & Brand, 1997; Rowden, 2007; Yoo, 2002), understanding both types of learning would seeman important contribution to the human resource development (HRD) litera-ture. Furthermore, although some conceptual frameworks (García, 2005;Guest, 1997; Ostroff & Bowen, 2000; Tharenou et al., 2007) suggest thatworkplace learning outcomes mediate the relationship between workplacelearning and organizational performance, there is little evidence supportingthis view. The purpose of this study was to investigate the influence of invest-ment in workplace learning on organizational outcomes of workplace learningand organizational performance in Korean companies.

Literature Review

To address the research purpose, this review of literature focuses onworkplace learning and the outcomes of workplace learning. It concludeswith an introduction to the conceptual framework of the study.

HUMAN RESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq

Workplace Learning. Workplace learning has been defined differently byvarious authors, with no one common definition being recognized by all(Antonacopoulou, Jarvis, Anderse, Elkjaer, & Høyrup, 2006; McCormack,2000). Watkins and Marsick (1992) suggested that there are different forms ofworkplace learning: formal, informal, and incidental. Other researchers (Barnett, 1999; Enos et al., 2003; Hodkinson & Hodkinson, 2004; Sambrook,2005) have categorized workplace learning as formal and informal learningonly. While various perspectives on workplace learning have been proposed,the literature suggests that formal learning or training and informal learningare the most common components ( Jacobs & Park, 2009).

Formal learning is generally recognized as using a planned approach toachieve employee learning, such as a program in a training classroom (Mocker &Spear, 1982). Formal learning is typically composed of planned learning activ-ities that are intended to help the trainees to achieve specific training outcomes( Jacobs & Park, 2009). It mostly involves institutionally sponsored andendorsed programs, which might include almost all training and developmentprograms that organizations offer.

Informal learning refers to the learning activities that employees initiate inthe workplace mainly through social interactions and relationships with oth-ers (Lohman, 2005; Rowden, 2007). Informal learning is based on learning asprocess or learning as participation (Sfard, 1998). The approach has helpedunderstanding of how people learn during work and address issues of learn-ing transfer by emphasizing engagement in the diverse and ongoing learningsettings (Fuller, Ashton, Felstead, Unwin, Walters, & Quinn, 2003). From theperspective of informal learning, learning is embedded in work activities andoccurs in the workplace context. Furthermore, informal learning is viewed assituated and contextualized within the activity in which it takes place (Blaaka &Cathrine, 2005/2006; Lave & Wenger, 1991), and social, contextual, and cul-tural aspects of learning are emphasized (Hager, 2004).

Informal learning may be planned or unplanned, structured or unstruc-tured (Lohman, 2005; Watkins & Marsick, 1992). Similarly, Doornbos et al.(2008) stated that employee learning may be spontaneous, unintended,unplanned, or deliberate, planned and sought out by workers. Enos et al.(2003) and Koopmans, Doornbos, and Van Eekelen (2006) proposed thatinformal learning can be intentional and planned learning activities throughinteractions with others as social aspects of learning within the workplace.Examples of informal learning activities include talking and sharing resourceswith others, unstructured on-the-job training (OJT), collaborations, job rota-tion, observing others, networking, coaching, and mentoring (Leslie et al.,1997; Lohman, 2005; Rowden, 2007; Watkins & Marsick, 1992). Embracingthe definitions of formal and informal workplace learning is meaningful since theintegration of formal and informal learning into workplace learning reflects the reality of most employees’ experiences (Leslie et al., 1997; Rowden, 2007;Yoo, 2002).

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440 Park, Jacobs

Outcomes of Workplace Learning. Numerous studies have shown thatvarious outcomes result from workplace learning: knowledge, skills and abil-ities, motivation, organizational commitment, job performance, organizationalperformance, transfer of learning, and motivation to transfer learning (Aragón-Sánchez et al., 2003; Becker, Huselid, Pickus, & Spratt, 1997; Clarke, 2005;Delaney & Huselid, 1996; Enos et al., 2003; Jacobs & Washington, 2003;Lankau & Scandura, 2002; Paul & Anantharaman, 2003; Velada & Caetano,2007; Vlachos, 2008). The theoretical models reviewed (Becker et al., 1997;García, 2005; Guest, 1997; Ostroff & Bowen, 2000; Tharenou et al., 2007)suggest that workplace learning outcomes can be categorized into three types:(1) workplace learning outcomes from training, such as employees’ satisfac-tion, commitment, motivation, behavior and skills, and individual or groupperformance; (2) organizational performance outcomes, such as productivity,quality, innovation, absence, turnover, conflict, and quality and service; and(3) organizational financial outcomes, such as profits, return on investment(ROI), return on assets (ROA), return on equity (ROE), and market value orstock-market performance for publicly held firms.

Organizational performance outcomes include indicators of productivity,quality, innovation, absence, turnover, conflict, and quality and service. In con-trast, organizational financial outcomes, such as profits, ROI, ROA, ROE, andstock market performance for publicly held firms, are focused on the economicprofitability of firms. The underlying premise is that workplace learning activ-ities lead to certain learning outcomes, which in turn lead to some organiza-tional performance outcomes.

In explaining the relationship between workplace learning and organiza-tional performance, Delery and Doty (1996) considered three perspectives: (1) universalistic, (2) contingency, and (3) configurational. The universalisticperspective views workplace learning outcomes or human resource outcomesas mediating in the relationship between workplace learning and organizationalperformance. In addition, while the contingency perspective addresses that therelationship between workplace learning and organizational performancemight be moderated by organizational factors such as firm strategy, the config-urational perspective poses that the relationship might be moderated by othercongruent workplace learning practices or human resource practices.

However, few studies have empirically investigated the relationshipbetween workplace learning and organizational performance. A majority ofstudies have linked some form of training and measures of organizational per-formance. In general, training was found to be the most frequently used typeof workplace learning and was found to result in positive organizational per-formance. Several researchers have investigated the influence of training onorganizational outcomes (Aragón-Sánchez et al., 2003; García, 2005; Russellet al., 1985), whereas others have examined the impact of human resourcepractices on organizational performance (Delaney & Huselid, 1996; Delery &Doty, 1996; Faems et al., 2005; Paul & Anantharaman, 2003; Vlachos, 2008).


Aragón-Sánchez et al. (2003) found that training activities are significantlyrelated to effectiveness. Their study also demonstrated that companies makinghigher investments in employees’ training could obtain better results in termsof effectiveness than would those making lower expenditures in training. Sim-ilarly, García (2005) found that training policy had a positive influence on orga-nizational performance. Russell et al. (1985) examined the impact of storewidetraining support on retail store performance and found a positive relationshipbetween training and performance. Furthermore, Faems et al. (2005) foundthat training had a positive and significant impact on productivity but did nothave a significant relationship with voluntary turnover and financial perfor-mance in terms of profitability, liquidity, and solvency. Additionally, using theNational Organizations Survey, Delaney and Huselid (1996) found a positiverelationship between HRM practices, such as training, staffing selectivity, andincentive compensation, and perceived organizational performance.

Moreover, several researchers (Becker et al., 1997; Paul & Anantharaman,2003) have proposed the intervening variables, such as employee skills,employee motivation, employee retention, employee productivity, productquality, speed of delivery, and operating cost, between human resource systemand financial profits. Indeed, Paul and Anantharaman found that trainingenhances employee productivity, which in turn improves financial perfor-mance. They also argued that mere linkage between human resource practicesand organizational performance is unlikely to uncover the whole process.

Conceptual Framework

Figure 1 presents the conceptual framework of the study. The conceptualmodel consisted largely of four components: (1) investment in workplacelearning as an independent variable, (2) organizational outcomes of work-place learning as a mediating variable, (3) organizational perspective on HRDas a moderating variable, and (4) organizational performance as a dependentvariable. This conceptual framework was based on the theoretical relationshipspresenting that workplace learning affects organizational performance throughworkplace learning outcomes (García, 2005; Guest, 1997; Ostroff & Bowen,2000; Tharenou et al., 2007). Additionally, the theoretical ground supportedthe conceptual framework: The research investigating the association betweenworkplace learning and firm performance should have an integrated approachby including the relationships of workplace learning practices, mediatingvariables such as workplace learning outcomes or operational performanceindicators, and financial performance (Paul & Anantharaman, 2003).

Moreover, in the relationship between investment in workplace learningand its outcomes, the organizational perspective on HRD was considered. As thecontingency perspective suggests (Delery & Doty, 1996), organizations that placegreater value on HRD were believed to have superior workplace learning out-comes and subsequently better firm performance. Therefore, the organizational



442 Park, Jacobs

perspective on HRD was expected to play a moderating role in the relationshipbetween investment in workplace learning and its outcomes. Accordingly,investment in workplace learning was expected to affect organizational out-comes of workplace learning by interacting with organizational perspective onHRD. Also, in turn, organizational outcomes of workplace learning were pre-sumed to influence organizational financial performance. Therefore, workplacelearning was assumed ultimately to affect organizational financial performanceby being mediated through organizational outcomes of workplace learning.

In addition, the literature remains unclear whether any differences existacross industrial sectors. The industries might not have the same organizationalculture and priorities for employee learning, which can make a difference inthe relationship between workplace learning and organizational performance.Research showed that employees in the service sector, such as banking,finance, insurance, and business consultation, were more likely to participatein workplace learning (education and training in their study) than were thosefrom the manufacturing sector (Xiao & Tsang, 2004). Based on these findings,the current study expected employees’ workplace learning and its outcomes tobe different across the industrial sectors. This study compared subgroups ofmanufacturing and nonmanufacturing industry by examining whether the fitof the measurement model and the structural equation model differed acrossthe two subgroups.

Method

This study employed correlational research, which seeks to investigate therelationship among variables and suggests the cause-and-effect direction ofthe relationships (Ary, Jacobs, Razavieh, & Sorensen, 2006; Fraenkel &Wallen, 2008).

Data and Sample

The study used extant data from the 2005 and 2007 Human CapitalCorporate Panel (HCCP) surveys, which were conducted in South Korea


Investment inWorkplace Learning

OrganizationalPerformance

Organizational Outcomesof Workplace Learning

OrganizationalPerspective on HRD

Figure 1. Conceptual Framework for the Study

with support from the Korea Research Institute for Vocational Education andTraining (KRIVET). KRIVET was established in 1997 as a national researchinstitute under the jurisdiction of the prime minister, with the mandate ofestablishing an integrated system that interconnects education, employment,and social welfare by providing comprehensive policy plans that fuse schoolsand the labor market.

The HCCP survey is based on a national probability sample of companiesin South Korea and consists of: (1) corporate data, (2) employee data, (3) com-pany financial information from the Korea Information Service (KIS), and (4) patent information provided by Korean Intellectual Property Office(KRIVET, 2008). A sample was drawn from the KIS data using a stratified-random sampling method by industry, size, and type of organization. KRIVETcarried out the sampling, which was first conducted by industry based on largeand medium-size categories. In the process of sampling, some industries inwhich the representative numbers of companies available were relatively smallwere excluded, such as agriculture, forestry, fishing, mining, wholesale andretail trade, and hotels and restaurants areas. Then companies with fewer than100 employees were excluded before sampling them based on size. Finally,companies were sampled in terms of corporate types, such as (1) listing, (2) Korea Securities Dealers Automated Quotations (KOSDAQ), and (3) regis-tered in financial supervisory services, independent auditor, and unlisted companies. As a result, 454 companies were included in the final sample.

Measures

The KIS data in the 2007 HCCP data set were used for organizationalfinancial performance, while organizational data in the 2005 HCCP data setwere used for the remaining variables. Table 1 summarizes the respondentand measures of each variable.

Investment in Workplace Learning. Investment in workplace learning wasdefined as the extent to which an organization invests its financial resources inboth formal learning and informal learning. Formal learning was defined as anarray of workplace learning activities previously planned and structured as theorganization’s procedure in its course of action ( Jacobs & Park, 2009; Mocker &Spear, 1982). Investment in formal workplace learning was measured by HRDmanagers’ perceived amount of expenditure in each type of formal workplacelearning, such as (1) group-based off-the-job classroom training, (2) group-based on-the-job classroom training, (3) e-learning, and (4) distance trainingby mail. In the instrument, HRD managers were asked to determine the extentof financial investment in each learning activity (1 � no investment to 5 � agreat deal of investment) when only implementing each activity in the organi-zation. In addition, investment in informal workplace learning was defined as theextent to which an organization invests its financial resources in an array ofinformal learning, which are intentionally arranged by the organization but



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mainly occurred through relationships or interactions with others. Informallearning in this study referred to planned learning in the types of informal learn-ing; that is, it is intentional interactions with others as social aspects of learning (Enos et al., 2003; Koopmans et al., 2006). In this respect, inciden-tal learning, which is referred to as an unintended by-product of some otheractivity (Watkins & Marsick, 1992), was not included in the boundary ofworkplace learning in this study because it is not regarded as intentional(Rowden, 2007).

Furthermore, nonformal learning was excluded from the scope of work-place learning because nonformal learning seems similar to informal learningin practice for describing the contrast to formal learning (Eraut, 2000; Malcolm, Hodkinson, & Colley, 2003). Moreover, the contents of nonformallearning may be planned, but the learning is provided outside of organizations(Mocker & Spear, 1982). Investment in informal workplace learning was mea-sured by HRD managers’ perceived amount of expenditure in each type ofinformal workplace learning, such as (1) mentoring or coaching, (2) OJT, (3) task force team (TFT) project, and (4) action learning, which were imple-mented in 2004.

After conducting the data editing, four measures—e-learning, distancetraining by mail, mentoring or coaching, and action learning—were removedsince they had high level of missing data, as specifically described in next dataediting section. Thus, the reliability and validity of the constructs for group-based on-the-job classroom training, group-based off-the-job classroom train-ing, OJT, and task force team project were calculated. Regarding the internalconsistency reliability, the Cronbach’s alpha was 0.76, indicating a relativelyreliable scale. Construct validity was established through exploratory factoranalysis, indicating that those three items formed a single factor, eigenvalue �2.32 with 57.9% of variance explained. Factor loadings were 0.81 for group-based on-the-job classroom training, 0.71 for group-based off-the-job class-room training, 0.76 for OJT, and 0.76 for task force team project.

Organizational Outcomes of Workplace Learning. Organizational out-comes of workplace learning were measured by the HRD managers’ perceiveddegree of organizational level improvement as derived from employees’ work-place learning participation or engagement estimated during 2004. A total ofthree items was used to measure this construct: employees’ (1) job compe-tence, (2) labor productivity, and (3) enthusiasm, indicating 1 (neverimproved) to 5 (very much improved). The Cronbach’s alpha for organizationaloutcomes of workplace learning was 0.85, which showed a relatively high levelof internal consistency reliability.

In addition, the exploratory factor analysis established that those threeitems formed a single factor, indicating an eigenvalue � 2.33 with 77.5% ofvariance explained. Factor loadings were 0.89 for employees’ job competence,0.88 for labor productivity, and 0.86 for employees’ enthusiasm.



446 Park, Jacobs


Organizational Perspective on HRD. Organizational perspective on HRDwas measured by three items to assess top management’s attitude toward HRD in an organization. Strategic planning directors were asked to indicatetheir level of agreement on a 5-point Likert-type scale (1 � strongly disagreeto 5 � strongly agree). The measurement items included: (1) “The top man-agement in my company has a clear vision toward HRD”; (2) “This companyvalues talented people”; and (3) “The top management in my company oftenemphasizes the importance of talented people.” The Cronbach’s alpha of orga-nizational perspective on HRD was 0.86, representing a fairly reliable set ofitems. The exploratory factor analysis yielded an eigenvalue of 2.35 with78.4% of the variance explained and factor loadings of 0.89 for top manage-ment’s clear vision toward HRD, 0.87 for company’s value for talented people,and 0.90 for top management’s emphasis on talented people.

Organizational Performance. Organizational performance was measuredby financial indicators such as (1) sales per employee, (2) net profit peremployee, (3) gross margin, and (4) ROA. For the measurement of organiza-tional performance, organizational financial data were used for the year of 2006(on December 31, 2006) from the KIS in the 2007 HCCP data set, which wastwo years after the implementation of employee learning programs in 2004.

The measures of organizational performance appear to address limitationsthat have been mentioned in the literature. That is, first, while prior researchwith cross-sectional estimates is deemed to have a simultaneity bias (Delaney &Huselid, 1996), this study avoided potential problems of measurement errorbecause it allowed a two-year time lag between workplace learning practicesand manifestation of organizational performance (Wright, Gardner, Moynihan,Park, Gerhart, & Delery, 2001).

Second, the use of objective indicators for the financial performanceavoids potential problems of common method variance because the sources ofdata were different from the data of workplace learning outcomes and organi-zational financial performance (Podsakoff & Organ, 1986). The commonmethod variance is likely to occur when the respondents report their percep-tions regarding both independent and dependent variables, possibly inflatingresponses on the dependent variables (Delaney & Huselid, 1996; Podsakoff,MacKenzie, Lee, & Podsakoff, 2003).

Regarding the reliability of organizational performance, the Cronbach’salpha was 0.65. Since 0.70 of Cronbach’s alpha is generally agreed on the lowerlimit for the reliability (Hair et al., 2005), ROA was excluded to obtain a morereliable scale, resulting in Cronbach’s alpha of 0.77. Then three items wereexamined using exploratory factor analysis. The exploratory factor analysisshowed that those three items formed a single factor, indicating an eigenvalueof 2.18 with 72.7% of the variance explained. The factor loadings were 0.87for sales per employee, 0.89 for net profit per employee, and 0.80 for grossmargin.



Contextual Variable. The industrial sectors, the manufacturing industry,and nonmanufacturing industry were considered for investigating any differ-ences across industrial sectors in the relationships among variables. The man-ufacturing industries consist of manufacturers of rubber and plastic products,basic metals, other nonmetallic mineral products, coke and refined petroleumproducts, textiles and wearing apparel, food products and beverages, motorvehicles and trailers, electrical machinery and apparatuses, electronic compo-nents and communication equipment, and computers and office machinery.The nonmanufacturing industry includes the financial industry, such as financeand insurance, and the nonfinancial service industry, including computer andrelated activities, recreation and sports, education, research and development,and post and telecommunications.

Data Editing

This section provides the process of identifying final measures through datascreening process. Data editing is critical in structural equation modeling(SEM) because the measurement scale, missing data, outliers, nonlinearity,and nonnormality of data may affect the SEM analysis by influencing thevariance-covariance among variables (Schumaker & Lomax, 2004). Therefore,before presenting the data for the SEM analysis, it should be carefully screenedin terms of (1) missing data, (2) outliers, (3) linearity, and (4) normality(Schumaker & Lomax).

As stated, there were two data sets to be edited for the data analysis: the2005 HCCP organizational data and the 2007 HCCP organizational data.When the 2005 HCCP organization-level data were screened, the missing datawere found in the observed variables of investment in workplace learning.Even though there is no clear guideline of an appropriate level of missing datato delete, Hair et al. (2005) recommend that variables which have 50% ormore missing data should be deleted. Based on the cutoff of 45% in missingdata, four observed variables in workplace learning remained: (1) group-basedon-the-job classroom training, (2) group-based off-the-job classroom training,(3) OJT, and (4) task force team project.

Next, outliers across all variables were examined. Outliers were detectedwith descriptive statistics and graphical methods, such as examining box plotsand scatter plots. The outliers were small organizations, and those small com-panies seemed to invest less in employee learning. So the outliers in the vari-ables across the investment in workplace learning were retained to representthe population of small companies.

By handling missing data with the matching response-pattern approachusing a LISREL Pre-processor program (LISREL-PRELIS) and pooling twodata sets of the 2005 HCCP organizational data and the KIS data from the2007 HCCP data set, 399 cases (n � 280 cases in the manufacturing indus-try; n � 119 cases in the nonmanufacturing industry) remained as usable

448 Park, Jacobs

data for the total sample. The linearity and normality were acceptable acrossall variables.

Results

Table 2 presents the means, standard deviations, and correlations for allvariables. Table 3 presents the descriptive statistics and correlations for thesubgroups of the manufacturing industry and the nonmanufacturingindustry.

Model Testing

SEM (LISREL 8.8; Jöreskog & Sörbom, 1993) was used to assess the relations among the variables presented in the conceptual model. Thepurpose of model testing is to fit the sample data to the specified theoreticalmodel (Schumaker & Lomax, 2004). The conceptual model was assessed forthe overall sample and for each industrial group.

Confirmatory Factor Models. As shown in Table 4, in terms of confirma-tory factor analysis (CFA) models, all of the parameters were significantly differ-ent from zero (p � 0.05). More specifically, all of the observed variables werefound to have significant factor loadings on the appropriate latent variables, p �0.05. In addition, all of the fit indices indicated an acceptable level of fit. Thus,the measurement models for the total sample, the manufacturing industry, andthe nonmanufacturing industry were statistically supported, indicating that thesample data in the observed models adequately fit the theoretical models.

Structural Equation Models. Structural equation models for total sampleand each industrial group were analyzed, as shown in Figure 2. It should benoted that organizational perspective on HRD was not included in the analy-sis of the structural equation model, even though it is depicted in the figure.Rather, organizational perspective on HRD was analyzed as a latent moderat-ing variable between investment in workplace learning and organizational out-comes of workplace learning.

For the total sample, the chi-square statistic was not significant (x225 �

25.74, p � 0.42), which indicated that the observed model and the impliedmodel were similar. Other model fit indices also revealed an acceptable levelof fit (the root-mean-square error of approximation [RMSEA] � 0.01, the stan-dardized root mean square residual [SRMR] � 0.02, the goodness-of-fit index[GFI] � 0.99, the adjusted goodness-of-fit index [AGFI] � 0.97, normed fitindex [NFI] � 0.99). In addition, the manufacturing industry showed anacceptable level of fit (x2

25 � 34.75 (p � 0.09), RMSEA � 0.04, SRMR � 0.03,GFI � 0.98, AGFI � 0.95, NFI � 0.98). Also, the overall goodness-of-fitindices for the nonmanufacturing model suggested an acceptable model fit(x2

31 � 31.19 (p � 0.46), RMSEA � 0.01, SRMR � 0.07, GFI � 0.95, AGFI �0.91, NFI � 0.96).


Tab

le 2

.M

ean

s, S

tan

dar

d D

evia

tion

s, a

nd

Cor

rela

tion

s fo

r th

e To

tal

Sam

ple

Vari

able

s1

23

45

67

89

1011

1213

1. C

lear

vis

ion

tow

ard

HR

D1

2. V

alui

ng t

alen

ts0.

64**

13.

Em

phas

is o

n ta

lent

ed

0.72

**0.

67**

1pe

ople

4. O

n-th

e-jo

b cl

assr

oom

0.

30**

0.19

**0.

26**

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aini

ng5.

Off-

the-

job

clas

sroo

m0.

26**

0.21

**0.

22**

0.57

**1

trai

ning

6. O

JT0.

28**

0.27

**0.

27**

0.46

**0.

28**

17.

Tas

k fo

rce

team

pro

ject

0.19

**0.

16**

0.31

**0.

41**

0.34

**0.

57**

18.

Em

ploy

ee c

ompe

tenc

e0.

34**

0.30

**0.

35**

0.53

**0.

41**

0.40

**0.

32**

19.

Lab

or p

rodu

ctiv

ity

0.39

**0.

23**

0.31

**0.

43**

0.33

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37**

0.31

**0.

70**

110

. Em

ploy

ee e

nthu

sias

m0.

40**

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111

. Sal

es p

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mpl

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0.06

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. Gro

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1

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493.

763.

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0.84

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0.84

0.80

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0.38

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Tab

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.M

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s, S

tan

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evia

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s, a

nd

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s fo

r th

e M

anu

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Multiple-Group Models for Industrial Sectors. Multiple-group modelswere tested to determine the extent to which there was group invariance orgroup equality of parameter estimates across the two industrial groups (themanufacturing and nonmanufacturing industries). In CFA, a chi-square differ-ence test between Model A (all parameters are free) and Model B (factor load-ings invariant across groups) was statistically significant, showing that x2

difference of 95.62 with 9 degrees of freedom was greater than 16.92 of x2 value at p � 0.05.

The results showed that the measurement models in those two industrieswere statistically different, indicating that the structural model should be differ-ent as well. That is, the fit of the measurement model and the structural equa-tion model differed across the two groups. Then separate analyses forconfirmatory factor models and structural equation models were justified for themanufacturing industry group and the nonmanufacturing industry group. Addi-tionally, as shown in Figure 2, when comparing the two industries separately, themanufacturing industry showed greater magnitudes in all paths. However, the magnitudes between the two groups were not considerably different.

Moderation Effect Test. To examine whether organizational perspectiveon HRD moderates between investment in workplace learning and organiza-tional outcomes of workplace learning, multiple-group model analysis in SEMwas conducted. The data were first divided into two groups: (1) low level oforganizational perspective on HRD and (2) high level of organizational per-spective on HRD based on the median of values of organizational perspectiveon HRD. Then, those two groups were tested to determine their measurementmodels were different.

The multiple-group model analysis focused on CFA for testing the moder-ation effect of organizational perspective on HRD showed that two models werestatistically identical. The result of a chi-square difference test between ModelA (all parameters are free) and Model B (factor loadings invariant across groups)was not statistically significant since x2 difference of 4.45 with 9 degrees of free-dom was less than 16.92 of x2 value at p � 0.05. This indicated that factorloadings between the groups were invariant. Thus, the invariance of structurecoefficients in the structural model was examined for the two groups.

The testing of whether the structural coefficients of structural models in two groups showed that the structural coefficients between investment inworkplace learning and organizational outcomes of workplace learning were

Table 4. Confirmatory Factor Model Results by Groups

Model �2 df p RMSEA SRMR GFI AGFI NFI

Total 44.86 44 0.44 0.01 0.03 0.98 0.96 0.99Manufacturing 51.13 47 0.32 0.02 0.03 0.97 0.95 0.98Nonmanufacturing 48.00 52 0.63 0.00 0.06 0.94 0.90 0.96

452 Park, Jacobs


statistically invariant across two groups. The result of a chi-square differencetest between Model A (all parameters are free in structural paths) and ModelB (structural coefficient of investment in workplace learning and organizationaloutcomes of workplace learning is invariant) was not statistically significant,showing that x2 difference of 0.20 with 1 degree of freedom was less than 3.84of x2 value at p � 0.05. Thus, organizational perspective on HRD was foundto have no moderating function between investment in workplace learning andorganizational outcomes of workplace learning.

Mediation Effect Test. To assess the mediating role of employee outcomesof workplace learning, effect decomposition was performed by analyzing thetotal effect, direct effect, and indirect effect between variables. For the pathbetween investment in workplace learning and organizational performance,the direct effect was not found. Therefore, the investment in workplace learn-ing influences organizational performance through organizational outcomes ofworkplace learning.

Discussion

The results of this study mostly support previous research on therelationship between workplace learning and organizational performance. As

OJT

OrganizationalPerformance

Vision Valuing Importance

HRDPerspective

OrganizationalOutcomes ofWP Learning

EmployeeEnthusiasm

LaborProductivity

EmployeeCompetence

GrossMargin

Net Profit perEmployee

Sales perEmployee

Investmentin Workplace

Learning

On-the-JobClassroomTraining

Off-the-JobClassroomTraining

TaskforceTeam Project

0.74 (0.76, 0.72)

0.38 (0.36, 0.22)

Figure 2. Structural Equation Model Results by Groups

Note. The values outside the parentheses are for the total sample, while those inside the parenthesesare for industrial sectors (the first one is the manufacturing industry and the latter is thenonmanufacturing industry). The parameter estimates in this figure are standardized values. Allestimates are significant (p � 0.05).



predicted, the findings of the current research confirmed that investment inboth formal and informal learning can influence organizational financialperformance through outcomes of workplace learning. The current studyconfirmed the results from prior studies, which investigated the relationshipbetween training and organizational performance (d’Arcimoles, 1997; Faemset al., 2005; Huselid, 1995; Paul & Anantharaman, 2003: Vlachos, 2008).Based on the results, an important point can be made in that increasedinvestment in HRD programs does not necessarily improve financialoutcomes in organizations. Rather than the issue of the degree of investmentin workplace learning, the results of this study reinforce the notion that HRDprograms can result in improved financial performance when those programscan meet the expectations of organizational needs. Moreover, the findingssupported the argument that employee learning can be meaningful if it resultsin an expected level of competence or anticipated job performance, which byextension could lead to positive financial outcomes in organizations ( Jacobs,2006).

In addition, the investment in workplace learning was found to be indi-rectly associated with organizational financial performance through learningoutcomes such as employee competence, labor productivity, and employeeenthusiasm. In this vein, this finding clearly supports the mediating process ofworkplace learning outcomes between workplace learning and organizationalperformance, which were proposed by previous researchers (Becker et al., 1997;García, 2005; Guest, 1997; Ostroff & Bowen, 2000; Paul & Anantharaman,2003; Tharenou et al., 2007). Thus, this finding suggests that workplace learn-ing outcomes should be ensured to achieve better financial performance frominvestment in employee learning.

In contrast to previous research, this study did not identify the moderat-ing effect of organizational perspective on HRD. Organizational value for HRDdid not make a difference in the relationship between investment in employeelearning and learning outcomes. This leads to the possible interpretation thatorganizational perspective on HRD may be an antecedent of organizationaldevotion in financial resources to employee learning. Additionally, the resultsof the current study may be inconsistent with the previous research (Xiao &Tsang, 2004), which showed that employees in the financial industry are morelikely to actively participate in workplace learning than those from the manu-facturing sector. Even though the manufacturing industry showed slightlygreater magnitude in the relationship between variables depicted in the con-ceptual model compared to the nonmanufacturing industry, the difference inmagnitude was minimal between the two groups. Therefore, it is difficult tosay that the relationships specified in the conceptual model are different in thetwo groups.

There are several limitations related to this research. The current researchused existing data derived from the 2005 and 2007 HCCP surveys of SouthKorean companies. These data were collected from a number of companies

454 Park, Jacobs

across the nation, which is difficult for an individual researcher to do. How-ever, there was a high level of missing data in investment in employees’ learn-ing activities, which restricted the researcher from including a wide range oflearning programs as measures for workplace learning. That is, the conceptionof workplace learning in this study is limited since it was only measured usingfour variables. Moreover, the assessment of informal learning as OJT and taskforce team project is not enough to measure various informal learning activi-ties. Additionally, it is possible that those surveyed might not have been theoptimal participants. Thus, this research is limited in its use of the existing dataset such as instrument design.

Conclusions and Implications for Future Research and HRD Practice

Insight into the potential influence of various aspects of workplace learning onorganizational performance is crucial for practice and research. In spite ofrecent advances in workplace learning research, limited empirical research hasexamined the impact of workplace learning, including both formal and infor-mal learning, on its outcomes and organizational performance. This studyinvestigated investment in workplace learning and its influence on organiza-tional outcomes of workplace learning and organizational performance in theKorean context. Considering that few studies have investigated how specificvariables related to workplace learning influence organizational performance,the findings of this study explicate how and by which process workplace learn-ing is linked to organizational financial performance. The research findings alsoshow that it is not just general belief but true that employee learning canimprove organizational financial performance. The results of this research pro-vide implications for future HRD research and practice.

Implications for Future HRD Research. First, levels (individual, group orteam, or organizational) can be considered for future research, examining thelinkage of employee learning and organizational performance. As Klein,Dansereau, and Hall (1994) argued, organizations are multilevel in nature sinceemployees work in groups and teams within organizations. Thus, it isinevitable to have level issues in organizational studies. Garavan, McGuire, andO’Donnell (2004) also maintained that HRD researchers must pay attention tothe distinction between the level of theory and the level of measurement; whilethe level of theory is related to the targets, the level of measurement focuseson the sources of data.

Second, future research may consider the organizational perspective onHRD as an antecedent of organizational decisions for investing its financialresources. This study did not find the moderating effect of the organizationalperspective on HRD in the relationship between investment in workplace learn-ing and organizational outcomes of workplace learning. Therefore, since theimpact of management roles and organizational culture on workplace learning


is considerable (Poell et al., 2004), an investigation of the organizational per-spective on HRD as an antecedent of workplace learning can provide moreunderstanding of the linkage with organizational performance.

Finally, this study used objective financial indicators, such as sales peremployee, net profit per employee, and gross margin, to measure organiza-tional performance. There has not been any agreement on which are morevalid indicators between objective measures, such as firms’ financial data, andsubjective measures responded through individuals’ self-report (Colakoglu,Lepak, & Hong, 2006). In this sense, an interesting avenue for future researchis, as Vlachos (2008) noted, to examine the extent to which individual percep-tions about financial performance as self-report measures are consistent withobjective organizational measures.

Implications for HRD Practice. There are two implications for HRD prac-tice. First, HRD practitioners should consider developing more effective learn-ing ways by combining both formal and informal learning activities formaximizing the effectiveness of various workplace learning. Learning mecha-nisms as blended types can be developed by mixing formal programs, such astraining, with informal processes, such as OJT or task force team projects. AsVan der Sluis, Williams, and Hoeksema (2002) noted, one type of learningbehaviors can occur in combination with another style of learning behaviors.Additionally, researchers (Leslie et al., 1997; Shipton et al., 2002; Svensson,Ellström, & Åberg, 2004; Van der Heijden, Boon, Van der Klink, & Meijs, 2009;Yoo, 2002) suggested that learning could be improved by linking formal andinformal learning because workplace learning occurs through a dynamic inter-action between formal and informal learning. Moreover, one learning methodmay facilitate another learning approach by interacting synergistically.

Second, as the current research findings suggest that high investment inworkplace learning alone cannot guarantee high levels of profitability, HRDpractitioners need to reinforce positive learning outcomes to achieve greaterfinancial performance. In doing so, they should be careful in creating a learn-ing culture since work environment conditions influence workplace learning(Clarke, 2005; Ellinger, 2005; Lohman, 2005; Poell, et al., 2004).

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Yoonhee Park is a research fellow at the Korea Research Institute for Vocational Education &Training.

Ronald L. Jacobs is a professor of human resource development at University of Illinois.

Documents

The influence of investment in workplace learning on learning outcomes and organizational performance