human resource development quarterly volume 22 issue 3 2011 [doi 10.1002%2fhrdq.20078] woojae choi;...

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A R T I C L E S

Influences of Formal Learning,Personal Learning Orientation,and Supportive LearningEnvironment on InformalLearning

Woojae Choi, Ronald L. Jacobs

While workplace learning includes formal and informal learning, the rela-tionship between the two has been overlooked, because they have been viewedas separate entities. This study investigated the effects of formal learning, per-sonal learning orientation, and supportive learning environment on informallearning among 203 middle managers in Korean commercial banks. To con-trol the common method biases, the predictor and criterion variables weremeasured at different points in time with two separate sets of questionnaires.The results from using structural equation modeling showed that the proposedmodel indicated a better fit to the data than alternative models. It was foundthat formal learning and personal learning orientation have significant and

positive impacts on informal learning. Although a supportive learning envi-ronment did not have a direct effect on informal learning, it had a modest but

significant indirect effect on informal learning through formal learning. Impli-cations for future research and practices are also discussed.

Workplace learning is usually considered to be comprised of formal learning,which is planned or supported by the organization, and informal learning, whichis mainly initiated by individuals on their own (Marsick & Volpe, 1999;Marsick & Watkins, 1990). Formal and informal learning tend to address dif-ferent learning goals that may be viewed as being complementary in nature

(Jacobs & Park, 2009). Several conceptual models have been proposed toattempt to integrate formal and informal learning (e.g., Malcolm, Hodkinson, &Colley, 2003; Matthews, 1999; Sambrook, 2005; Svensson, Ellstrom, & Aberg,2004). It is suggested that competence is not dependent on either formal or infor-mal learning but must be accomplished through an integration of knowledge

HUMAN R ESOURCE DEVELOPMENT QUARTERLY, vol. 22, no. 3, Fall 2011 © Wiley Periodicals, Inc.Published online in Wiley Online Library (wileyonlinelibrary.com) • DOI: 10.1002/hrdq.20078 239



240 Choi, Jacobs

gained through formal learning and practical knowledge obtained throughinformal learning (Burns, Schaefer, & Hayden, 2005; Berg & Chyung, 2008;Enos, Kehrhahn, & Bell, 2003; Svensson, Ellstrom, & Aberg, 2004).

Furthermore, formal learning may stimulate informal learning, because thetools or methods learned by attending formal learning programs may help indi-viduals improve their ability to assimilate informal learning in the workplace(Svensson et al., 2004). Previous studies have shown that one type of work-place learning relates to the occurrence of another type (Rowden, 2002;Rowden & Conine, 2005). Formal learning provides the critical thinking andindependent learning skills that individuals need to perform well in demandingwork situations (Brockman & Dirk, 2006; Burns et al., 2005) and also enhancesthe ability and desire of individuals to learn informally (Lohman, 2003).

However, few studies have sought to investigate how experiences with for-mal learning influence informal learning. Recent research has made importantprogress in understanding the personal and work environment variables nec-essary to promote or inhibit engagement in informal learning as a distinct entity(Kwakman, 2003; Lohman, 2005; van Woerkom, Nijhof, & Nieuwenhuis,2002). Discussion of how these variables might be influenced in turn by for-mal learning has been lacking. For instance, it is generally agreed that work-place learning is enhanced by the development of a favorable workenvironment (Sambrook, 2005; Svensson et al., 2004). Unlike the importance

of the workplace as a learning environment (Billett, 2001; Hargreaves, 1992),Berg and Chyung (2008) contend that an individual might find a way to learnwhen he or she wants to gain new knowledge, regardless of whether the orga-nization has an effective structure for encouraging informal learning. Kwakman(2003) also suggests that the effect of the work environment may be dimin-ished if personal factors are included simultaneously in the analysis.

Therefore, if workplace learning comprises formal and informal learning,and if information is lacking in the literature about the relationship betweenthese two forms of workplace learning, then more needs to be known about

whether one form of workplace learning influences the other. In addition, if informal learning is influenced by personal and work environment factors,and if the research findings are still insufficient and inconsistent to support theimpact of the factors, then more needs to be understood about how personaland work environment factors, as well as formal learning, simultaneously affectinformal learning.

Review of the Literature

Workplace Learning. Workplace learning is defined as either a means foraddressing employee development that is consequently designed to enhancethe likelihood of achieving individual and/or organizational performance, oras an individual process designed to achieve learning toward the attainment of personal and professional goals ( Jacobs & Park, 2009). Workplace learning

HUMAN R ESOURCE DEVELOPMENT QUARTERLY • DOI: 10.1002/hrdq



includes both formal and informal learning in this study. Marsick and Watkins(1990) identify the different forms of workplace learning as formal, informal,and incidental learning. Formal learning comprises planned events or experi-

ences that are designed to prepare individuals to attain a specific set of knowl-edge and skills. Learners are separated from their day-to-day work toparticipate in lectures, discussions, and other instructional activities that areplanned and structured. Informal learning occurs as the result of individuals’making sense of the experiences they encounter during their daily work. Infor-mal learning is not intentionally structured; the control of learning is in thehands of the learners. Incidental learning takes place as an unintended by-product derived from other activities and is often regarded as a subset of infor-mal learning (Garavan, Morley, Gunnigle, & McGuire, 2002).

Traditionally, most organizational resources for employee developmenthave been allocated to formal learning (Tannenbaum, 2002). However, recentempirical research reveals that the majority of what employees need to knowto perform their work requirements may be acquired through informal learn-ing (Burns et al., 2005; Enos et al., 2003; Kwakman, 2003). These findings donot claim that formal learning is useless; rather, they suggest that both formsof learning need to be integrated to maximize the benefits of employee devel-opment. Individual learning in the workplace takes place through the dynamicinteraction between formal and informal learning, so it may be difficult to con-

ceive of one of the two forms being successful without adding another form’scomponents to it (Sambrook, 2005; Svensson et al., 2004).

The Relationships Between Formal and Informal Learning. Participationin formal learning may be viewed as promoting employees’ engagement in infor-mal learning and as stimulating informal learning in the workplace (Rowden,2002; Rowden & Conine, 2005). All types of workplace learning likely includethe attributes of both formality and informality (Colley, Hodkinson, &Malcolm, 2002; Lee et al., 2004; Malcolm et al., 2003), although the interre-lationships between formal and informal attributes vary according to the situ-

ation in which the learning occurs (Billett, 2001; Lave, 1990). Woodall and Winstanley (1998) and Doyle and Young (2004) have addressed the blurringof the boundaries between formal and informal development and education,arguing that formal and informal learning occur concurrently. Therefore, it maybe impossible for a learning activity to be regarded as either entirely formal orentirely informal. In addition, Rowden (2002) found that individuals withmore experience in formal learning tend to engage in more informal learningactivities, and Westbrook and Veale (2001) concluded that workers with moreformal education and training devote more time to self-directed learning activ-ities. With this in mind, it can be assumed that informal learning can be bet-ter enhanced and initiated by individuals who have well-developed formalknowledge (Leslie, Aring, & Brand,1998).

Individual competence is increased by both forms of workplace learning,although they each cultivate different kinds of knowledge and skills (Svensson

Factors That Influence Informal Learning 241




242 Choi, Jacobs

et al., 2004). The two forms of workplace learning share the common area of individual competence. Therefore, the knowledge and skills learned throughengagement in one type of workplace learning interact with those from another

type at the same time. In particular, formal learning programs give employeesexperimental learning tools that make it possible to perform self-initiated learn-ing through self-reflection and improve the capability of learners to assimilateinformal learning at the workplace (Svensson et al., 2004). Planned and sup-plied learning programs precede informal learning. Burns et al. (2005) arguethat “although informal learning occurs for new teachers, it is stimulated andaugmented through formal learning techniques” (p. 69). Given this discussion,we offer:

H YPOTHESIS 1: Formal learning will be positively related to informal learning.

Personal Learning Orientation as an Influence on Informal Learning.Personal learning orientation is here defined as ability, personality, and inter-ests related to learning and development activities in the workplace. In thisstudy, personal learning orientation includes a person’s motivation to learn,self-efficacy, and learning goal orientation, all of which have been addressed inmany places in the workplace learning literature (e.g., Baldwin & Magjuka,1991; Dweck & Leggett, 1988; Lohman, 2005; Noe & Schmitt, 1986).

Motivation to learn is defined as the specific desire of a learner to learn thecontent of a program (Noe & Schmitt, 1986). Motivation to learn is relevantto how much a learner learns during learning events (Noe & Wilk, 1993). Pre-vious studies have shown that motivational attitudes, such as commitment tolearning, interest in content area, and a love of learning, have an impact onengagement in informal learning activities (Lohman, 2005). Baldwin andMagjuka (1991) found that pretraining motivation was associated with actuallearning in a workplace learning program designed to improve skills for con-ducting performance appraisals and providing feedback.

Self-efficacy is defined as a person’s judgment of their capabilities to orga-nize and execute courses of action required to achieve designated types of per-formance (Bandura, 1986). Self-efficacy has been found to be positively relatedto intentions and participation in self-development activities (Maurer & Palmer,1999; Noe & Wilk, 1993) and to many aspects of human competencies, includ-ing training, goal-setting, and performance appraisals (Gist, 1987). Researchhas consistently shown that self-efficacy predicts trainee learning and perfor-mance (Lohman, 2005; Tannenbaum, Mathieu, Salas, & Cannon-Bowers, 1991).In particular, van Woerkom, Nijhof, and Nieuwenhuis (2002) found that themost important predictor of critical reflective working behavior is self-efficacy.

Learning goal orientation refers to one’s intention to engage in challengingactivities, an eagerness to improve oneself, and a tendency to use one’s past per-formance as a standard for evaluating current performance (Button, Mathieu, &Zajac, 1996). Individuals who have a learning goal orientation seek to understand




something new or to enhance their competence through learning; addition-ally, they are less concerned with others’ evaluations of them (Dweck &Leggett, 1988). Brett and Vandewalle (1999) found that individuals who hold

a higher learning goal orientation are more likely to participate in learningand are more able to apply what they have learned than those with lowerlearning goal orientations. A learning goal orientation can have a strong effecton learning and the allocation of effort during learning (Klein, Noe, & Wang,2006).

These learning orientation variables have in common a likelihood toencourage informal learning share the common area that is likely to encour-age informal learning, and they have been regarded as an integrated personalconstruct on some theoretical model (e.g., Sambrook, 2005) and empirical

research (e.g., Lohman, 2005). Given the discussion, we offer:

H YPOTHESIS 2: Personal learning orientation will be positively related to formal andinformal learning.

Supportive Learning Environment as an Influence on Informal Learning. An important consideration for fostering engagement in informal learning isthe extent to which an organization provides an environment that facilitatescontinuous learning (Senge, 1990). Organizations can provide a working envi-

ronment that promotes and encourages opportunities for informal learning(Marsick & Volpe, 1999). This study examines three key aspects of the per-ceived environmental condition in the workplace: organizational support,supervisor support, and job characteristics (Tracey & Tews, 2005).

Organizational support is the perceived level of support from the organiza-tion for workplace learning activities. Organizational support includes suchthings as HRD policy; the value placed on learning and development; rewards;and materials and supplies allocated for learning. Contextual factors, such asorganizational culture and incentive systems, play an enormous role in informal

leaning (Ellinger, 2005; Lee et al., 2004; Leslie et al., 1998), while a weak, non-supportive internal culture and lack of work tools and resources are majorinhibitors of informal learning. Lohman (2005) found that an unsupportive orga-nizational culture, a lack of time, and a lack of proximity to colleagues’ workareas inhibit engagement in HRD professionals’ informal learning activities.

Supervisor support has been clearly established as a major work environ-ment characteristic that influences workplace learning processes and outcomes(Clarke, 2004; Russ-Eft, 2002). Russ-Eft defines supervisory support as thereinforcement provided by a supervisor to encourage learning on the job. Sev-eral researchers have discussed the importance of a social context that is favor-able and supportive of training and learning (Noe & Wilk, 1993; Maurer &Tarulli, 1994; Xiao, 1996). Cohen (1990) found that individuals with support-ive supervisors perceived workplace learning activities as more useful than didindividuals with less supportive supervisors.





244 Choi, Jacobs

Job characteristics are the features embedded in a job that are likely tofacilitate the workplace learning activities required to perform it. Learning inthe workplace is influenced by the jobs that individuals perform, because the

workplace itself is highly structured for work processes, business objectives,and job assignments (Berg & Chyung, 2008; Billett, 2001). Kozlowski and Farr(1988) reviewed research on technical updating that illustrates that the degreeof job challenge or complexity is a key factor in fostering updating, as these job characteristics may promote or inhibit informal learning activities. Lohman(2003) found that a new task, a new leadership role, and/or adherence to poli-cies and procedures for teachers are work situations that trigger participationin informal learning. A supportive learning environment is likely to facilitatelearning at the workplace. Given the preceding discussion, we offer:

H YPOTHESIS 3: Supportive learning environment will be positively related to formaland informal learning.

Design of the Study

Correlational research was used to examine the relationships between formallearning, personal learning orientation, supportive work environment, andinformal learning among middle managers in the Korean banking sector.

Sample and Data Collection. Middle managers in the Korean bankingsector were the target population for the present study. The data was collectedthrough the Korea Banking Institute (KBI), which was established by a consor-tium of 11 Korean domestic banks and the Bank of Korea in 1976. Most majorbanks in Korea work in close partnership with KBI, sending employees to KBIand jointly developing diverse professional training programs. The Koreanbanking sector has undergone significant changes since 1997 as a result of thedeterioration of the financial health of the Korean corporate sector, and the sec-tor has been characterized as one of the fastest-changing industries in Korea

(Ashton, Green, Sung, & James, 2002). This perception has led each bank torequire its employees to learn more by themselves than they ever have before.

Among 299 sets of questionnaires distributed, 203 sets were finally usedfor this study, after deleting 14 incomplete or insincere responses (responserate 72.2%). The combined sample (N 203) consisted of 109 (53.7%)general managers and 94 (46.3%) deputy general managers. Many middlemanagers (n 157, 77.4%) had a 4-year university diploma (n 139, 68.5%)or graduate degree (n 18, 8.9%). The median and mean values of respon-dent tenure were 15 (SD 5.70) and 15.88 years, respectively.

The instrument was designed and developed to minimize the potentialsources of common method variance “that is attributable to the measurementmethod rather than to the construct of interest” (Podsakoff, MacKenzie, &Podsakoff, 2003, p. 879). To control for common method biases, the predictor vari-ables, including formal learning, personal learning orientation, and supportive




learning environment and informal learning pertaining to criterion variable,were measured at different points in time with two separate sets of question-naires. The first questionnaire, with items for the predictor variables, was dis-

tributed and collected at the beginning of each program that respondentsattended; the second questionnaire, which included items for informal learn-ing, was implemented at the end of each program. Respondents were partici-pating in different programs, and the average period of elapsed time betweenthe former and latter questionnaires was five days. Separating the measurementof the predictor and criterion variables has been recommended when it isimpossible to obtain data from different raters or sources (Podsakoff et al.,2003). To match the first questionnaire to the second, participants wereprompted to “Please fill out the last four digits of your cell-phone number in

the box below” on the second page of each questionnaire.Measures. All variables were assessed using a 5-point Likert scale. For-

mal and informal learning were measured on the scale representing effective-ness of each learning activities (1 very ineffective to 5 very effective).Personal learning orientation and supportive learning environment were mea-sured by the scale of agreement (1 strongly disagree to 5 strongly agree).

Formal Learning. Formal learning was measured using the 10-item scaledeveloped by the researchers. Formal learning was measured by two observedvariables, which were distinguished by the location in which the formal learn-

ing occurs (Jacobs & Park, 2009; KRIVET, 2008). In this study, the effective-ness of each type of formal learning activity was calculated as an average scorecomprised of five items for formal on-the-job learning (an alpha of 0.89) andfive items for formal off-the-job learning (an alpha of 0.88). An example of for-mal on-the-job learning is “I received formal coaching from a peer or supervi-sor to help me improve on some aspect of my job.” An example of formaloff-the-job learning is “I attended a company-sponsored training program inthe training center to improve some job-specific competence.”

Personal Learning Orientation. Personal learning orientation was measured

by a combination of three observed variables: motivation to learn, self-efficacy,and learning goal orientation. First, motivation to learn was assessed using an8-item scale developed by Tharenou (2001). He conducted a principal com-ponent factor analysis and a confirmatory factor analysis with 17-item scalethat was originally developed by Noe and Schmitt (1986). The study yieldedan alpha of 0.82. Second, self-efficacy was measured using ascale modified byBosscher and Smit (1998), which was originally developed by Sherer et al.(1982) and consisted of three subscales (i.e., initiative, effort, and persistence).This study employed the subscale of effort (number of items 4, 5, 6, 7, 8),which yielded an alpha of 0.81. Third, learning goal orientation was measuredusing the 8-item scale developed by Button et al. (1996).The study yielded analpha of 0.79.

Supportive Learning Environment. Supportive learning environment wasassessed using a 12-item scale based on a measure by Tracey and Tews (2005),





246 Choi, Jacobs

which yielded alphas of 0.87 for organizational support, 0.87 for managerialsupport, and 0.85 for job characteristics. An example of organization supportis: “My company makes it possible for employees to participate in a wide range

of learning activities.” An example of supervisor support is: “My supervisorencourages me to participate in as many learning activities as possible.” Anexample of job characteristics is: “My job requires me to seek better ways todeal with changes in the work.”

Informal Learning. Informal learning was measured using a 12-item scalebased on Lohman’s (2005) 8-item measure. Lohman’s instrument consisted of three types of informal learning: knowledge exchange, experimentation, andenvironmental scanning, which yielded an alpha of 0.63 for the eight items.The three types of informal learning were revised and extended to align with

the context of this study by the researchers. Consequently, the measureincluded three subtypes of informal learning: learning with others, self-exper-imentation, and external scanning. Each type had four items and was measuredby the effectiveness of the learning activity. An example of learning with oth-ers is “I collaborate with others who shared the need to solve a particular prob-lem.” An example of self-experimentation is “I spend time to reflect on how Idealt with a challenging work situation.” An example of external scanning is“I attend nonmandatory professional conferences or seminars that might pro-vide useful information.”

Translation. The instruments were developed in English first, and thencross-translated into Korean as suggested by McGorry (2000). Six doctoral stu-dents and two HRD professionals who are bilingual served as subject matterexperts, and a doctoral student majoring in linguistics verified the final trans-lated version. Based on forward and backward translation, the instrument wasfinally modified and confirmed by the researchers.

Data Analysis. Prior to proceeding with subsequent analyses,exploratory factor analyses (EFAs) were conducted. Factor loadings variableyielded 85.08 percent for formal learning, 61.19 percent for personal learn-

ing orientation, 63.06 percent for supportive learning environment, and 56.77percent for informal learning. Next, confirmatory factor analyses were con-ducted first to estimate the quality of the structural reliabilities and designatedfactor loadings by testing the model fit between the proposed measurementmodels and the collected data. Third, descriptive statistics were computed forall variables. Fourth, internal consistency reliability estimates and intercorre-lation were calculated. Finally, the fit of the proposed model was tested. Col-lected data were analyzed using SPSS 17.0 and AMO 17.0. Structuralequation modeling (SEM) was selected to investigate the research questionsbecause SEM is a statistical technique for testing a set of relationships repre-senting multiple equations by using a combination of statistical data and qual-itative causal assumptions (Hair, Black, Babin, Anderson, & Tatham, 2006;Kline, 2005).




Results

Results of data analysis on this study are presented in the next section.Measurement Model Assessment and Descriptive Statistics. To validate

the proposed factor structures for each latent variable, such as formal learning,personal learning orientation, supportive learning environment, and informallearning, four second-order factor analyses were conducted. The results indi-cated that four second-order model structures were indicative of a moderatefit of the model to the data (RMR 0.045 to 0.026; GFI 0.899 to 0.954;CFI 0.889 to 0.959; RMSEA 0.068 to 0.078). After second-order factoranalyses, the four factors model yielded moderate model fit to the data repre-senting x

2 98.465 (df 38), NC 2.591, RMR 0.020, GFI 0.919,CFI 0.907, and RMSEA 0.089. Although the chi-square statistic was sta-tistically significant, and RMSEA did not meet a rigorous fit guideline (less than0.08), satisfying the criteria of NC, RMR, GFI, and CFI indices indicated thatthe hypothesized model of the CFA could be accepted as a reasonable fit to thedata. Thus, factor loadings were investigated to assess the convergent validityof each measure. To verify convergent validity among item measures, all factorloadings should be statistically significant, and standardized factor loading esti-mates should be 0.5 or higher, even though results greater than 0.7 are desir-able (Hair et al., 2006). The results showed that all factor loadings were

statistically significant at p

0.01, and factor loadings ranged from 0.898(learning goal orientation to personal characteristics) to 0.525 (external scan-ning to informal learning). These results indicated that the hypothesized mea-surement model could be considered as reasonable, thereby confirming theexistence of reflection of the underlying latent variables.

Harman’s single factor test was conducted to diagnose whether separatingthe measures was appropriate (Podsakoff et al., 2003). A worse fit for the sin-gle factor model than for the hypothesized measurement model indicates thatcommon method variance does not occur. The single factor model yielded a

x2

199.98 with df

44. All fit indices were worse (NC

4.454; RMR

0.029; GFI 0.847; CFI 0.764; and RMSEA 0.132) than those of thehypothesized measurement model, thus indicating that common method vari-ance did not pose a problem in this study. Means, standard deviations, inter-nal consistency reliability estimates, and intercorrelations among all measuresof observed variables rather than latent variables are reported in Table 1. Although all measures demonstrate adequate levels of reliability, the relativelylow reliabilities of informal learning and job characteristics indicate that oneshould proceed with caution.

Structural Equation Model Assessment and Hypotheses Testing. Thestructural equation method is used to test the research model and verify thehypotheses.





T a b l e 1 . M e a n s , S t a n d a

r d D e v i a t i o n s , I n t e r n a l

C o n s i s t e n c y R e l i a b i l i t i e

s , a n d C o r r e l a t i o n s

A m o n g A l l O b s e r

v e d V a r i a b l e s

M e a n

S D

a

1

2

3

4

5

6

7

8

9

1 0

1 .

F o r m a l o n - t h e - j o b l e a r n i n g

3 . 7

7

0

. 6 1

0 . 8

9

2 .

F o r m a l o f f - t h e - j o b l e a r n i n g

3 . 8

2

0

. 5 7

0 . 8

8

0 . 4

5

3 .

S e l f - e f fi c a c y

3 . 8

1

0

. 5 7

0 . 8

1

0 . 1

6

0 . 2 3

4 .

L e a r n i n g g o a l o r i e n t a t i o n

4 . 1

2

0

. 5 0

0 . 8

8

0 . 2

9

0 . 2 8

0 . 5

7

5 .

M o t i v a t i o n t

o l e a r n

4 . 2

3

0

. 5 1

0 . 8

8

0 . 2

1

0 . 3 0

0 . 3

6

0 . 6

9

6 .

O r g a n i z a t i o n

a l s u p p o r t

3 . 8

0

0

. 5 7

0 . 7

7

0 . 1

9

0 . 3 5

0 . 3

0

0 . 2

7

0 . 2

6

7 .

S u p e r v i s o r s

u p p o r t

3 . 4

4

0

. 6 7

0 . 7

3

0 . 2

6

0 . 2 7

0 . 2

5

0 . 2

4

0 . 2

1

0 . 4

1

8 .

J o b c h a r a c t e

r i s t i c s

3 . 7

6

0

. 5 5

0 . 6

4

0 . 2

1

0 . 2 4

0 . 3

0

0 . 4

0

0 . 3

7

0 . 3

0

0 . 4

8

9 .

L e a r n i n g w i t h o t h e r s

3 . 8

9

0

. 4 9

0 . 6

9

0 . 3

7

0 . 2 3

0 . 2

8

0 . 4

9

0 . 3

4

0 . 2

4

0 . 2

8

0 . 3

0

1 0 .

S e l f - e x p e r i m

e n t a t i o n

3 . 8

3

0

. 5 8

0 . 7

6

0 . 2

6

0 . 4 1

0 . 2

8

0 . 4

4

0 . 4

0

0 . 1

5

0 . 1

1

0 . 1

4

0 . 3

5

1 1 .

E x t e r n a l s c a n n i n g

3 . 6

0

0

. 6 9

0 . 6

8

0 . 1

8

0 . 2 4

0 . 2

3

0 . 3

1

0 . 2

3

0 . 1

2

0 . 0

6

0 . 2

1

0 . 3

5

0 . 4

6

N o t e .

F o r c o r r e l a t i

o n s 0 . 1

4 a n d a b o v e , p 0 . 0

5 .



Structural Equation Model Assessment. Model fit was evaluated using thesample variance-covariance matrix and a maximum likelihood method. Thehypothesized model was compared with some theoretically alternative mod-

els and modified to produce the most appropriate model for the data. A structural equation model was assessed, representing multiple relation-ships among the variables of interest. The hypothesized model yielded x2

98.465 (df 38), NC 2.591, RMR 0.020, GFI 0.919, CFI 0.907,and RMSEA 0.089, the same results as those obtained for the hypothesizedmeasurement model. Although these results indicated a reasonable fit to thedata, the hypothesized structural equation model should be compared with alimited number of theoretically different alternative models (Schumacker &Lomax, 2004).

The alternative approach used a chi-square difference test to compare eachof the alternative models. The first alternative model was the model that infor-mal learning influences formal learning, because most research that has inves-tigated the relationship between formal and informal learning supported thepositive association between the two learning forms, though they have not des-ignated any specific direction for the association. The first alternative modelyielded an overall x2 value of 114.866 (df 39, p 0.000), with NC

2.945, RMR 0.041, GFI 0.911, CFI 0.878, and RMSEA 0.098.Compared with the results of the hypothesized model presented in Table 2,

the first alternative model fit the data poorly. Thus, the x2 difference betweenthe hypothesized model and alternative model 2 was statistically significant(x2

(hypothesized-alternative 2) 16.401), so these results indicated that thehypothesized model was a better fit for the data than alternative model 1.

The second alternative model was the model that addressed only the rela-tionship between formal learning and informal learning, excluding any otherrelationships among latent variables because the relationship between twoforms of workplace learning was of particular interest in this study. In compar-ison, the chi-square difference test was not appropriate, because the hypothe-

sized model and alternative model 1 were not nested, so parsimonious fitindices were used to compare the models (Byrne, 2001; Schumacker &Lomax, 2004). The hypothesized model yielded the PGFI of 0.529, the PNFIof 0.594, the PCFI of 0.626, and the AIC of 154.465, while alternative model2 produced the PGFI of 0.552, the PNFI of 0.547, the PCFI of 0.581, and the AIC of 249.049. Although the PGFI of alternative model 2 was lower than thatof the hypothesized model, the hypothesized model yielded lower values of fitindices for PNFI and PCFI than did alternative model 2. In addition, the AICof the hypothesized model was lower than that of alternative model 2. Theseresults indicated that the hypothesized model was a better fit for data thanalternative model 2. Taken together, these results suggested that the hypothe-sized model was a better fit to the data than were the two alternative models.The results of the comparisons are presented in Table 2.





250 Choi, Jacobs

Given the results of the comparisons of the hypothesized model and alter-

native models, the next procedure in the model evaluation process was to iden-tify any area of misfit in the hypothesized model (Joreskog, 1993). There aretwo types of information—the standardized residuals and the modificationindices—that can be helpful in detecting model misspecification (Byrne,2001). On the one hand, the standardized residuals are fitted residuals dividedby their asymptotical standard errors, which are analogous to Z scores. Valuesgreater than 2.58 are consideredlarge. In examining the standardized residu-als, no large value appeared. All values were less than 1.81 (job characteristicsand self-efficacy).

On the other hand, the modification indices are the expected values thatthe chi-square would decrease if such a parameter were to be included. A seriesof modifications was conducted to produce the most appropriate model byusing the modification indices produced in AMOS outputs. However, the mod-ifications were conducted by reflecting existing knowledge in the fields of research, HRD, and workplace learning (Byrne, 2001). Based on the hypoth-esized model, four covariances between error terms were added to produce afinally modified model, as presented in Table 3. The modified model presentedthe best fit to the data and no further consideration was made on the modifi-

cations.Consequently, the results of SEM show that the structural equation model,

representing formal learning, personal learning orientation, supportive learn-ing environment, and informal learning, fit the data from middle managers.The arrows depicted in Figure 1 represent dependent relationships that showthe impact of one construct on another construct or variable. The significanceof the estimated parameters between the proposed latent variables was consid-ered because “statistically significant estimated parameters in the structuralequation model provide evidence that covariation is present” (Hair et al., 2006,

p. 721).Hypotheses Testing. Standardized path coefficients were examined to test

research hypotheses. Table 4 shows that formal learning had a positive and sig-nificant effect (SPC 0.392, p 0.01) on informal learning, while supporting


Table 2. Model Fit Indices for the Hypothesized Modeland Alternative Models

Fit Index x 2(df ) p PGFI PNFI PCFI AIC

Hypothesized Model 98.465(38) 0.000 0.529 0.594 0.626 154.465

Alternative Model 1 114.866(39) 0.000 0.539 0.589 0.623 168.866

Alternative Model 2 203.049(43) 0.000 0.552 0.547 0.581 249.049

Comparison Hypothesized model Hypothesized model andand Alternative 1 Alternative 1





Table 3. Model Fit Indices for the Hypothesized Modeland Modified Model

Model x 2 (df ) p NC RMR GFI CFI RMSEA

Hypothesized Model 98.465(38) 0.000 2.591 0.020 0.919 0.907 0.089

Modified Model 64.670(34) 0.001 1.902 0.017 0.949 0.953 0.067

→ Significant path;

PersonalCharacteristics

WorkEnvironment

FormalLearning

InformalLearning

0.383**

0.619**

0.285*

0.024

0.392**

Nonsignificant path

Figure 1. Results From a Finally Modified Model

Note: * p 0.05, ** p 0.01

Table 4. Results of Standardized Path Coefficients Representing theInfluences of Formal Learning, Personal Characteristics, and Work

Environment on Informal Learning

Total Direct IndirectPath Coefficient Effect Effect Effect p-Value

Formal Learning → Informal Learning 0.392 0.392 – 0.006

Personal Characteristics → Informal Learning 0.731 0.619 0.112 0.000 Work Environment → Informal Learning 0.175 0.024 0.151 0.849

Personal Characteristics → Formal Learning 0.285 0.285 – 0.014

Work Environment → Formal Learning 0.386 0.386 – 0.004



252 Choi, Jacobs

Hypothesis 1. Personal characteristics had a positive and significant effect onformal learning (SPC 0.285, p 0.05) and on informal learning (SPC

0.619, p 0.01), so Hypothesis 2 was supported. Work environment did not

significantly affect informal learning (SPC

0.024, p

0.849), although it hada positive and significant effect on formal learning (SPC 0.383, p 0.01) soHypothesis 3 was partially supported.

Discussion and Conclusion

The study tested the research model representing the influences of formal learn-ing, personal learning orientation, and supportive work environment on infor-mal learning. The proposed model was shown to be better than alternative

models. The results indicated that both forms of workplace learning can beviewed as complementary (Rowden, 2002; Leslie et al., 1998; Westbrook & Veale, 2001). In particular, the results of the present study are consistent withthe findings of Rowden and Conine (2005), in which formal learning was deter-mined to be related to the occurrence of informal learning in the U.S. bankingsector. These results also supported the notion that all types of workplace learn-ing have the attributes of both formality and informality (Colley et al., 2002;Malcolm et al., 2003), although the relationship between formal learning andinformal learning has only rarely been examined in the research of workplace

learning.It has been found that personal learning orientation should be viewed as

a significant influencing factor on informal learning, and this is consistent withprevious research (e.g., Kwakman, 2003; Lohman, 2005; van Woerkom et al.,2002). Although no significant relationships were found between work envi-ronment and informal learning, work environment was shown to indirectlyaffect informal learning through formal learning in the hypothesized model. Although work environment may not be directly influential on informal learn-ing activities, it may still be regarded as an important factor for informal learn-

ing and would benefit from further investigation (Kwakman, 2003). However,strict HRD policies, which are only favorable for formal learning, may inhibitemployees from engaging in informal learning, because employees need toallocate their working time, learning resources, and learning opportunities.These results also can be interpreted through cultural and industrial condi-tions. The work environment influences informal learning differently in differ-ent cultural contexts.

Three limitations should be noted about the study. First, inferences of causality should be interpreted cautiously, given the correlational design of thisstudy. However, there is some support for the predictive relationship betweenpredictor variables and consequent variables, because the data were collectedat two different times. It should also be noted that the relationships among for-mal learning, work environment, and informal learning may be somewhatinflated due to single-source bias (Podsakoff et al., 2003). Second, because the




respondents were employees of a single industry, the generalizability of theresults may be limited to middle managers in the Korean banking sector. Thefindings should be replicated in other job levels of employees, across indus-

tries, and in different countries. Also the relatively small number of the sam-ple should be mentioned as a limitation. Finally, although the measures of formal learning and informal learning were reliable, formal and informal learn-ing should be reexamined with different attributes in future research.

Implications for Practice. There are two major implications for HRD prac-tices. First, when planning any formal training program, planners should rec-ognize the likelihood of informal learning and seek ways to accommodate itsoccurrence (Brockman & Dirk, 2006; Svensson et al., 2004). Related to this isthe intentional selection of formal training approaches that encourage learn-

ing in the context of doing, which in turn may promote informal learning. Itis suggested that structured on-the-job training (Jacobs, 2003), a formal train-ing approach that occurs in the work setting, may be well-suited to helptrainees make this transition. As Marsick and Volpe (1999) noted, learning canbe maximized through daily work, and individuals’ competence in perform-ing the work can be enhanced by accumulating theoretical knowledge andskills provided by formal learning programs. The two types of workplace learn-ing can be quite complementary in terms of individual learners, because theymay not separate their learning into categories of formal or informal. When

effective formal learning occurs, the investment in formal learning becomes areliable way to encourage managers’ informal learning and, consequently,results in the development of managerial competence.

Another implication is that, although the work environment was notshown to influence informal learning, the work environment plays an impor-tant role in encouraging managers to attend formal learning programs or activ-ities. However, organizations and HRD professionals must remember that themajority of what managers need to know for their work requirements is estab-lished by informal learning (Enos et al., 2003; Marsick & Watkins, 1990).

These findings suggest that formal and informal learning in the workplacemust be integrated to maximize the benefits of organizational investment onemployee development. It is recommended that organizations and HRD pro-fessionals consider creating a learning environment in which employees con-tinuously learn both informally and formally (Senge, 1990). Thus, organizationalpolicies for HRD should be formulated in a way that empowers managers toplan, design, take action, and evaluate their learning, because excessively strictHRD policies and practices that focus exclusively on formal learning programsmay inhibit managers’ engagement in informal learning.

Implications for Future Research. Future research needs to be conductedby considering the potential to help understand and explain learning in theworkplace and by overcoming some limitations in this study. First, the exami-nation of the impact of formal learning on informal learning extends our under-standing of how these two types of workplace learning interact and influence





254 Choi, Jacobs

each other. However, future research should pay attention to which aspects of workplace learning are related to each other and which are not. In this study,formal learning was characterized according to the location where the learn-

ing takes place, while informal learning was categorized by the process andsources of learning. Although the aspects of workplace learning may be appro-priate for investigating the relationship between formal and informal learning,various aspects can be used to better understand workplace learning ( Jacobs &Park, 2009). If different aspects are used for identifying workplace learning, theresults may vary according to these aspects. It is therefore important that var-ious attributes of workplace learning, such as process and content, are inves-tigated to understand the nature of workplace learning beyond the relationshipbetween formal and informal leaning (Malcolm et al., 2003). Also, future stud-

ies should replicate this study in various research settings with appropriatesamples.

Second, a notable finding is the weak, indirect impact of work environ-ment on informal learning through formal learning. Although the work envi-ronment has been regarded as an important factor for informal learning, thework environment itself may not be powerful in practice (Kwakman, 2003).The impact of the work environment tends to occur through other factors, suchas formal learning. It could be that managers are seasoned, with high levels of competency, thus enabling them to depend on their own learning mechanisms

under even unsupportive conditions (Enos et al., 2003). These results requirea more careful interpretation because different types or activities of workplacelearning can be encouraged or discouraged differently by culture and industry.Future research should be conducted to examine which factors have a greaterimpact on one form of workplace learning than on another form.

Finally, other variables likely to influence informal learning need to beidentified. This study included a number of influencing variables that havebeen considered as major variables. Nevertheless, the limited set of variablesrestricts our understanding of what determines engagement in informal learn-

ing activities and the effectiveness that results from this engagement. A non-significant impact of work environment on informal learning may contributeto the exclusion of influential variables. Contextual or situational characteris-tics have been examined from various perspectives in the workplace and HRDliterature (Rouiller & Goldstein, 1993). From this perspective, it is worthwhileto consider other variables, such as learning culture, work pressure, task auton-omy (Sambrook, 2005), rewards for learning, and learning resources (Lohman,2003) in future research.

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Woojae Choi is a full-time lecturer at Cheongju University.

Ronald L. Jacobs is a professor in the Ohio State University, Ltd.



human resource development quarterly volume 22 issue 3 2011 [doi 10.1002%2fhrdq.20078] woojae choi;...

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