BusVII

O R I G I N A L A R T I C L E

Building knowledge stock and facilitating knowledge flow through human resource management practices toward firm innovation

Sun Young Sung1 | Jin Nam Choi2

1School of Business, Nanjing University,

Nanjing, P.R. China

2College of Business Administration, Seoul

National University, Seoul, South Korea

Correspondence

Jin Nam Choi, College of Business

Administration, Seoul National University,

1 Gwanak-ro, Gwanak-gu, Seoul 151-742,

South Korea.

Email: jnchoi@snu.kr

Funding information

This work was supported by the National

Natural Science Foundation of China (NSFC

71472092) and the Ministry of Education of

the Republic of Korea and the National

Research Foundation of Korea

(NRF-2015S1A5A2A03048150).

The present study theoretically identifies the meaningful human resource management (HRM)

practices that explain the emergence of two distinct dimensions of firm-level knowledge man-

agement, namely, firm knowledge stock and flow, which are critical drivers of firm innovation.

We also propose that these knowledge dimensions interact synergistically and that their effects

on firm innovation are accentuated in firms with strong innovation strategy. The proposed theo-

retical framework was tested using multisource, large-scale data collected from 203 manufactur-

ing companies at three time points over a 5-year period. Analysis confirmed that stock-building

and flow-facilitating HRM practices increase firm-level knowledge stock and flow, respectively.

Firm knowledge flow (but not knowledge stock) exhibited a significant positive main effect on

firm innovation. Firm knowledge stock was not related significantly to firm innovation, but it

became a significant predictor of firm innovation when existing along a high level of firm knowl-

edge flow, as well as in firms adopting innovation strategies. The present study provides signifi-

cant insights for researchers and practitioners by offering comprehensive understanding of the

nomological network of firm-level knowledge management enhanced by effective HRM

practices.

KEYWORDS

HR and technology, innovation, knowledge management, strategic HR

“Knowledge management has become ubiquitous in organizational

research and has served as a foundation for both theoretical and

empirical advances in major management fields” (Minbaeva, Foss, &

Snell, 2009, p. 477). Scholars taking the knowledge-based view (KBV)

particularly emphasize the implications of knowledge on innovation,

which enables firms to adapt flexibly to changing environmental

demands by developing or frequently adopting new products, ser-

vices, programs, or innovative ideas (Donate & Guadamillas, 2011;

Mabey & Zhao, 2017; Özba�g, Esen, & Esen, 2013). This emphasis can

be attributed to knowledge resources and effective knowledge utiliza-

tion being fundamental sources of higher-order forms of thinking and

creative solutions (C. J. Chen & Huang, 2009; Jiménez-Jiménez &

Sanz-Valle, 2011; Mabey & Zhao, 2017). Drawing on the KBV of inno-

vation (Grant, 1996; Lopez-Cabrales, Perez-Luno, & Cabrera, 2009),

the present study extends the literature by specifying critical human

resource management (HRM) practices that promote firm-level knowl-

edge management, which in turn enhances firm innovation.

Various attempts have been made to understand the inter-

section and cross fertilization between HRM practices and knowledge

management (Chang, Gong, Way, & Jia, 2013; Chen & Huang, 2009;

Swart & Kinnie, 2013). This is not surprising, considering the prevailing

emphasis on the role of people as core carrier and processor of knowl-

edge (Felin & Hesterly, 2007; Mahoney & Kor, 2015; Wright, Dun-

ford, & Snell, 2001). Nonetheless, due to the lack of consensus on the

conceptualization and operationalization of the HRM practices as well

as on knowledge management constructs, previous studies suffer

from a fragmented and limited understanding of how HRM practices

explain the emergence and development of firm-level knowledge-

related processes.

The present study responds to the call for theoretically identifying

HRM practices pertinent to knowledge management at the firm level

(Mabey & Zhao, 2017). Such efforts should be driven by a systematic

theory of knowledge management that informs core dimensions of

knowledge management that need to be shaped by appropriate

DOI: 10.1002/hrm.21915

Hum Resour Manage. 2018;57:1429–1442. wileyonlinelibrary.com/journal/hrm © 2018 Wiley Periodicals, Inc. 1429

bundles of HRM practices. To this end, we draw on the content and

process, interactive perspectives of knowledge management to spec-

ify the core dimensions that need to be considered for a systematic

identification of knowledge-related HRM practices (Gardner, Gino, &

Staats, 2012). The content perspective focuses on the quantity and

quality of knowledge stock or repositories in the overall pool of infor-

mation, skills, and abilities of organizational members (Griffith &

Sawyer, 2010). The process perspective focuses on knowledge flow or

redeployment process that enables the transfer, application, and utili-

zation of knowledge among members (Al-Tit, 2016; Basadur & Gelade,

2006; Mahoney & Kor, 2015). Thus, previous studies differentiate

knowledge stock and flow and demonstrate their distinct functions

toward team and organizational performance (Chang et al., 2013;

Gardner et al., 2012; Mabey & Zhao, 2017). On the basis of this dis-

tinction, we propose two main bundles of HRM practices to promote

firm knowledge management and ultimately firm innovation: (a) stock-

building practices and (b) flow-facilitating practices. Stock-building

practices (e.g., training, on the job [OJT], and external education)

enable firms to develop and expand the pool of knowledge resource

of their employees, which enlarges firm knowledge stock. In contrast,

flow-facilitating practices (e.g., group-based incentive system and

intranet knowledge sharing system) promote the efficient exchange,

activation, and exploitation of information and knowledge, which

encourages firm knowledge flow.

These two distinct bundles of HRM practices may affect firm

innovation indirectly by shaping the stock and flow of firm-level

knowledge. In predicting firm innovation, we further propose a syner-

gistic interaction between knowledge stock and flow. Knowledge

stock is the repository of resources required to generate innovation.

However, merely possessing knowledge resource per se may “remain

undiscovered, underleveraged, and trapped in the minds of individ-

uals” (Minbaeva, 2013, p. 383); thus it may not be translated automat-

ically into innovation unless it is applied and utilized effectively across

members (Sung & Choi, 2012). Therefore, a critical step in advancing

the literature is understanding the manner by which the knowledge

stock and knowledge flow interact and complement each other and

how the copresence of these two reinforces their positive effects on

firm innovation beyond their independent main effects.

In addition to the interactive dynamics involving knowledge stock

and flow, we also identify innovation strategy of the firm as a critical

organizational contingency that strengthens the firm-level connection

between knowledge management and innovation (Terziovski, 2010).

The elaboration of the boundary condition is important because any

organizational phenomenon is constrained or strengthened by their

embedding context (Johns, 2006). HRM scholars suggest that combi-

nations of HRM practices maximize organizational performance when

the HRM system is designed appropriately in accordance with the

organizational strategy (Delery & Doty, 1996; Zhang & Li, 2009).

Firms with a strategy that actively encourages innovative approaches

in response to task and environmental demands can amplify the inno-

vation potential of firm knowledge stock and flow shaped by HRM

practices.

In summary, the present study advances the HRM literature by

developing a systematic framework that identifies two core bundles

of HRM practices, which promote knowledge stock and flow, and

ultimately firm innovation. Scholars pointed out that knowledge itself

is not a main barrier for the successful implementation of knowledge

management; the main problem occurs in knowledge transfer, espe-

cially when employees are not willing to share their information and

knowledge with others (Cabrera & Cabrera, 2005). In explaining firm

innovation, we elaborate further on the potential synergetic interac-

tions between knowledge stock and flow as well as their further inter-

actions with the firm's innovation strategy. These considerations are

of significant practical importance in that these mechanisms reveal

how firms can exploit knowledge that might otherwise remain

untapped, thus benefiting the organization.

Apart from these theoretical and practical insights, the present

study makes considerable empirical contributions through its rigorous

research design and analysis. Our theoretical model was validated

empirically using three-wave time-lagged data collected from 2,407

managers and 3,946 employees of 203 Korean firms that span over

5 years. This longitudinal research design addresses common method-

ological limitations, such as “postpredictive” (i.e., predicting past per-

formance) or “retrospective” (i.e., asking respondents to recall HR

practices that existed prior to the performance period) that cause

severe causality issues in existing studies on HRM (Wright, Gardner,

Moynihan, & Allen, 2005).

1 | THEORETICAL FRAMEWORK AND HYPOTHESES

Increasingly uncertain and competitive business environments have

bestowed firm innovation a definite status related to value creation

and sustainable competitive advantage (N. Anderson, Poto�cnik, &

Zhou, 2014). KBV identified the knowledge embodied in organiza-

tional members as the main and inimitable resource responsible for

innovation differences across firms (Donate & Guadamillas, 2011;

Grant, 1996; Mabey & Zhao, 2017; Mahoney & Kor, 2015; Özba�g

et al., 2013). HRM practices comprise the deliberate architecture of a

firm used to channel the processes of knowledge acquisition and utili-

zation (Kang, Morris, & Snell, 2007; Swart & Kinnie, 2013; Wright

et al., 2001). With increasing appreciation for the inextricable connec-

tion between people and knowledge management in predicting inno-

vation, studies linking HRM practices to innovation based on KBV

have also increased. Nevertheless, the lack of consensus on the con-

ceptualization and operationalization of HRM practices related to

knowledge management toward firm innovation results in fragmented

and limited understanding of the given phenomenon.

We draw on previous studies of knowledge and innovation at the

individual, team, and organizational levels to develop a conceptual

framework of HRM practices for knowledge management in the con-

text of innovation. Research on individual creativity has emphasized

domain-specific knowledge and creative processes for recombining

knowledge toward creative solutions (Amabile, 1996). At the team

level, studies on knowledge management (Gino, Todorova, Miron-

Spektor, & Argote, 2009; Sung & Choi, 2012; Tallman & Chacar, 2011)

identified two distinct dimensions of knowledge management, namely,

knowledge stock and knowledge flow, to explain team performance

and creativity. Similarly, organizational learning literature differentiated

1430 SUNG AND CHOI

between learning stocks possessed by individuals, groups, and organiza-

tions, from learning flows that involve feedforward (bottom-up) and

feedback (top-down) sharing of those stocks (Bontis, Crossan, & Hul-

land, 2002).

These conceptual and empirical developments at multiple levels

inform a systematic identification of bundles of HRM practices for

knowledge management. Based on the distinction between the stock

and flow of knowledge, we specify two bundles of HRM practices,

each targeted at building knowledge stock and facilitating knowledge

flow, that will ultimately generate firm innovation (see Figure 1). In an

effort to integrate strategic HRM discipline and knowledge manage-

ment, HRM researchers have appreciated the theoretical distinction

between knowledge stock or intellectual capital embedded in people

and knowledge flow, which enables knowledge transfer and applica-

tion (Lepak & Snell, 2002; Wright et al., 2001). For example, Lopez-

Cabrales et al. (2009) identified knowledge-based practices (e.g., job

training) and collaborative practices (e.g., team-based task arrange-

ment) that predict the value and uniqueness of employee knowledge.

Recently, Chang et al. (2013) differentiated between practices for

gaining resource flexibility (e.g., skill training and job rotation) and

those for achieving coordination flexibility (e.g., suggestion system

and team-based pay). Drawing on these studies, we propose specific

practices included in the two HRM bundles for knowledge stock

and flow.

1.1 | HRM practices and knowledge management

Given that people and their interactions form the foundation of

knowledge-related processes in organizations, knowledge manage-

ment can be shaped by the “people management practices” of an

organization (Özba�g et al., 2013; Swart & Kinnie, 2013; Wright et al.,

2001). Scholars have established knowledge stock and flow as the

“linchpin” between people management practices and core compe-

tence of firms, such as innovation capability (Mäkela & Brewster,

2009; Tallman & Chacar, 2011). Responding to the call for research on

HRM practices that shape knowledge stock and flow (Mabey & Zhao,

2017; Minbaeva et al., 2009) and addressing the need for a systematic

approach to HRM practices related to knowledge management, the

present study advances two distinct dimensions of HRM practices,

namely, stock-building practices and flow-facilitating practices.

1.1.1 | Stock-building HRM practices

One of the foundational roles of HRM is to improve the skills and abil-

ities of employees (Boxall, 1998; Swart & Kinnie, 2013; Tallman &

Chacar, 2011), which expands and strengthens the unique knowledge

repository of a firm (Griffith & Sawyer, 2010). Drawing on prior stud-

ies, we isolate HRM practices particularly relevant to knowledge-spe-

cific, resource-generating functions, which should affect the

development of cognitive resources directly and enrich firm knowl-

edge stock. These stock-building practices include corporate training,

external education, OJT, and task rotation (Cabrera & Cabrera, 2005;

Chang et al., 2013; Lepak & Snell, 2002; Zhang & Li, 2009). For exam-

ple, firms can expose their employees to new information and skills

that can expand their cognitive assets by providing in-house corporate

training (Donate & Guadamillas, 2011; Pastor, Santana, & Sierra,

2010). Moreover, firms can diversify and broaden their knowledge

reservoir by supporting external educational and degree programs

(Sung & Choi, 2014). OJT effectively equips employees with task-

specific knowledge catered to immediate task goals (Hatch & Dyer,

2004). Regular task rotations also encourage employees to assume

Time 1 Time 2 Time 3

- Stock-building HRM Practices

- Flow-facilitating HRM Practices

- Firm Innovation

Knowledge Management- enhancing HRM Practices

Firm Performance

- Firm Knowledge Stock

- Firm Knowledge Flow

- Interaction of Firm Knowledge Stock and Knowledge Flow

Firm-level Knowledge Management

- Innovation Strategy

Firm Strategy

FIGURE 1 Theoretical framework predicting firm innovation

SUNG AND CHOI 1431

different roles and tasks, which diversify their knowledge base,

thereby resulting in multiskilling (Beugelsdijk, 2008; Mäkela & Brew-

ster, 2009). These stock-building practices help expand the knowledge

reservoir collectively, which in turn increases firm knowledge stock.

Hypothesis 1: Stock-building HRM practices are posi-

tively related to firm knowledge stock.

1.1.2 | Flow-facilitating HRM practices

Another critical bundle of HRM practices is geared toward the pro-

motion of knowledge flow through which knowledge distributed

across individuals and work units exchanged, activated, and utilized

effectively (Basadur & Gelade, 2006; Boxall, 1998; Chang et al.,

2013). In activating and utilizing knowledge possessed by a firm,

employees' willingness to solve collective problems and the sense

of “we-ness” are crucial (Beugelsdijk, 2008). Social dilemma theory

attends to the motivational dilemma or the mixed motivation situa-

tion involving knowledge sharing and provides an influential expla-

nation regarding the challenges of knowledge exchange (Wang &

Noe, 2010). Individuals contribute to public goods when they attri-

bute great value to collective goals because such contribution

incurs personal costs (Cabrera & Cabrera, 2005). Therefore,

employees are likely to engage in knowledge sharing and utilization

when rewards are contingent on collective performance instead of

or in addition to individual performance. In this regard, group- and

firm-based reward systems (i.e., incentives contingent on team and

firm performance) are important in promoting horizontal and verti-

cal flows of knowledge by aligning the personal or work unit inter-

ests with collective or organizational goals (Lopez-Cabrales

et al., 2009).

In addition, considering that knowledge exchange and applica-

tion occur in the context of social interactions (Kang et al., 2007;

Mabey, Wong, & Hsieh, 2015; Pastor et al., 2010; Wei, Zheng, &

Zhang, 2011), HRM practices involving social and technological infra-

structure (i.e., suggestion system, quality circle, and intranet system

for knowledge sharing) activate the flow of knowledge in organiza-

tions. Suggestion systems and quality circles cater to knowledge

exchanges by encouraging and legitimizing inputs from employees

and increasing the frequency and opportunity for institutional inter-

actions that generate social ties or channels for contributing knowl-

edge (Donate & Guadamillas, 2011; Griffith & Sawyer, 2010). An

intranet facilitates the contribution, retrieval, and usage of knowl-

edge among employees and reduces communication barriers

(Mäkela & Brewster, 2009). These practices enhance the social

expectation and legitimacy of knowledge flow and offer venues for

employees to contribute to the knowledge pool. Flow-facilitating

HRM practices may contribute to firm knowledge flow by increasing

motivation toward knowledge exchange behavior and providing

social and technical infrastructure for effective interactions among

employees.

Hypothesis 2: Flow-facilitating HRM practices are posi-

tively related to firm knowledge flow.

1.2 | Firm-level knowledge management and firm innovation

1.2.1 | Firm knowledge stock

Innovative solutions are driven by new combinations of existing ideas

and knowledge (Felin & Hesterly, 2007; Jiménez-Jiménez &

Sanz-Valle, 2011; Özba�g et al., 2013). The presence of a greater

knowledge stock is conducive to firm innovation because employees

gain access to more and diverse experiences, skills, and information

reservoir. These factors increase the likelihood of identifying creative

breakthroughs by promoting opportunities to recombine and reconfi-

gure existing knowledge (Griffith & Sawyer, 2010; Mahoney & Kor,

2015; Sung & Choi, 2012; Swart & Kinnie, 2013). Moreover, firms

with a large reservoir of cognitive resources can recognize the value

of new ideas and opportunities accurately and absorb the necessary

information from external sources given that the absorptive capacity

of a firm relies heavily on its possession of relevant prior knowledge

(Tallman & Chacar, 2011; Zahra & George, 2002). Therefore, substan-

tial knowledge repositories can be a conducive condition for firms to

appreciate and respond properly to external challenges. Such a pro-

cess enables them to introduce new and differentiated products or

services (Damanpour, Walker, & Avellaneda, 2009; Donate &

Guadamillas, 2011; Mabey & Zhao, 2017). Thus, we advance the fol-

lowing hypothesis.

Hypothesis 3: Firm knowledge stock is positively related

to firm innovation.

1.2.2 | Firm knowledge flow

Activating the exchange and utilization of collective knowledge asset

is another critical element of knowledge management. Innovation is

driven by the effective exchange, redeployment, and application of

existing knowledge and resources (Özba�g et al., 2013; C. L. Wang,

Rodan, Fruin, & Xu, 2014). Knowledge flow reflects “the process by

which knowledge held by an individual is converted into a form that

can be understood, absorbed, and used by other individuals” (Ipe,

2003, p. 341). Knowledge flow activates the elaboration and refine-

ment of knowledge among individuals and stimulates in-depth applica-

tion to current problems (Mabey et al., 2015; Wei et al., 2011), which

may result in product or service innovations. Therefore, unconstrained

horizontal and vertical knowledge flows refine collective thinking and

spur the application and utilization of knowledge, thereby enhancing

the ability of a firm to develop innovative responses to environmental

challenges.

Hypothesis 4: Firm knowledge flow is positively related

to firm innovation.

1.2.3 | Synergistic interaction between knowledge stock and flow

Extending the previous investigations of the main effects of knowl-

edge stock and flow on firm performance in separate studies

(Beugelsdijk, 2008; Liao, Rice, & Martin, 2011; Shipton, West,

Dawson, Birdi, & Patterson, 2006), we examine the effects of the two

1432 SUNG AND CHOI

components simultaneously and focus on their interaction. The avail-

ability of information and knowledge does not translate automatically

to innovation unless employees share and utilize those cognitive

resources effectively (Basadur & Gelade, 2006). Likewise, knowledge

stock serves as a platform for innovation (Damanpour et al., 2009).

However, unconstrained flows of knowledge across individuals and

teams are essential for its value to be appropriated (Kang et al., 2007).

Bontis et al. (2002) demonstrated that the alignment between learning

stocks and flows is critical for business performance because the lack

of an adequate flow causes accumulated learning stocks to create bot-

tlenecks that impede rather than promote performance. Similarly,

increased knowledge stock may not lead to firm innovation (if not hin-

der it) unless this knowledge is redistributed proficiently and flows

across individuals and work units (Mabey & Zhao, 2017; Sung & Choi,

2012). Therefore, the two dimensions of firm-level knowledge man-

agement complement each other, and their copresence can

strengthen (i.e., synergistic interaction) or is even required for

(i.e., strong complementarity) their positive effects on firm innovation

(Johns, 2006).

Hypothesis 5: The relationship between firm knowledge

stock and firm innovation is moderated by firm knowl-

edge flow in such a way that the relationship is more pos-

itive when firm knowledge flow is high than when it

is low.

1.3 | Innovation strategy as a moderator

In addition to the potential interaction between knowledge stock and

flow, we identify further a boundary condition of the knowledge-

innovation relationship at the firm level to offer a more contextualized

explanation of the phenomenon in question (Johns, 2006). HRM

scholars underscore the importance of the fit between managerial sys-

tems and organizational strategy (Delery & Doty, 1996; Zhang & Li,

2009). This underscoring is because a firm's overall strategic position

provides legitimacy and momentum to a series of managerial decisions

and employee actions in support of the strategy. Strategy researchers

also highlight that managerial actions and internal processes are

affected considerably by the overall strategic direction of a firm

(Terziovski, 2010). In the present study, we isolate firm innovation

strategy as a core contingency that strengthens the effects of firm

knowledge management on firm innovation.

Strategy literature often compares innovation strategy (also

referred to as differentiation strategy) with cost leadership strategy.

While the latter focuses on the provision of products and services at

the most competitive prices, the former emphasizes the creation of

customer value by offering novel and differentiated products (Santos-

Vijande, López-Sánchez, & Trespalacios, 2012). Firms with innovation

strategy require a significantly broader range of knowledge and skills

to produce innovative ideas and are more likely to benefit from the

expanded knowledge asset compared with those pursuing cost leader-

ship strategies. Moreover, these firms encourage knowledge diffusion

and transfer processes that help them generate innovation and differ-

entiation in their products and services (Argote, McEvily, & Reagans,

2003). Thus, the roles of knowledge stock and flow toward innovation

are likely to be more pronounced in firms with a strong innovation ori-

entation, which shapes an organizational context that urges the

exploitation of knowledge resources and utilization for developing

innovation.

Hypothesis 6: The relationship between firm-level

knowledge management (stock and flow) and firm inno-

vation is moderated by innovation strategy in such a way

that the relationship is more positive when innovation

strategy is high than when it is low.

2 | METHOD

2.1 | The dataset

Investigating the current research framework that proposes the

effects of the two different dimensions of HRM practices on firm

innovation through firm-level knowledge stock and flow imposes vari-

ous empirical challenges. Specifically, it requires a decent firm-level

sample with time-lagged, multisource data to avoid methodological

confounding and enable causal explanations of the unfolding firm-

level processes. To address these challenges, we employed the

Human Capital Corporate Panel (HCCP) data archived by Korea

Research Institute for Vocational Education and Training (KRIVET) in

support of the Ministry of Labor of the Korean government. To pro-

vide primary statistics and information that support national policies

on HR development, a stratified random sample was drawn from com-

panies listed in the database of the Korea Investors Service. Specifi-

cally, KRIVET created a 3 × 4 × 2 matrix based on the industry

(i.e., manufacturing, banking, and service), four categories of firm size

(i.e., 100–299, 300–999, and 1,000–2,999 and more than 3,000

employees), and two ownership types (i.e., publicly vs. privately

owned). The initial sample of 1,851 organizations was classified into

each cell depending on the above-mentioned organizational charac-

teristics. Approximately 25% of the companies were selected ran-

domly from each cell of the matrix to avoid the potential problems of

over- or under-sampling of specific cells.

Before the corporate survey, considerable efforts were exerted to

ensure content validity by establishing relevance with existing HRM

practices and eliminating wording problems (such as biased, ambigu-

ous, inappropriate, or double-barreled items). Most scales employed in

HCCP were adopted from discussions with academics and directors

of the companies and from in-depth case studies during the 1-year

pretesting phase of questionnaire development. Before the first wave

of data collection, the research instrument of HCCP was pretested in

three companies, and the scale items were modified further to

increase their clarity. In this pretest phase, the most appropriate and

qualified key respondents for different items were also confirmed.

2.2 | Analysis sample for the present study

In the present study, we used firm-level data collected at the following

three time points: 2007 (T1, N = 467), 2009 (T2, N = 473), and 2011

(T3, N = 500). In the HCCP dataset, different scales were used across

SUNG AND CHOI 1433

manufacturing, banking, and service industry companies in assessing

their knowledge stock and innovation. Thus, analyzing the companies

in different industrial categories together was not possible. Given that

HRM practices have strong implications for functional operations and

performance particularly for manufacturing companies (Combs, Liu,

Hall, & Kitchen, 2006) and manufacturing companies comprise more

than half of the current sample, we focused on the manufacturing

industry and excluded companies in the banking and service indus-

tries. Consequently, the sample size was reduced to 314, 336, and

369 companies at T1, T2, and T3, respectively. Among this initial sam-

ple, 203 companies participated in all three waves of data collection

and provided usable data for the present analysis. These companies

constitute 10 manufacturing industries, such as food, fiber, chemical

products, plastic, metal, machinery, computer, electronics, electric

appliance, and automobile. This study used the complete survey data

from these firms and identified matching firm-level archival data, such

as patent registration.

The three-wave research design corresponded with the causal

flow of the present conceptual framework: (a) Two dimensions of

knowledge management–enhancing HRM practices were reported by

HRM directors at T1. (b) Firm-level knowledge stock and flow were

rated by HRM directors, production managers, and employees at T2.

(c) Firm innovation was reported by department managers and indi-

cated by patent registration data, as archived by the Korean Intellec-

tual Property Office (KIPO) at T3. The 2-year time-lagged design is

consistent with existing innovation literature, which usually puts a 1-

to 2-year temporal gap between predictors and firm innovation

because organizational innovation process unfolds over a long period,

and it takes a minimum of 1 year before positive organizational prac-

tices or employee behaviors lead to organizational innovation (Ahuja,

2000; Hagedoorn & Cloodt, 2003).

Different groups of organizational members participated in corpo-

rate surveys during the three waves of data collection. The HRM and

the strategy directors of the 203 companies completed the survey for

T1 data. The T2 sample was composed of 1,082 production managers

and 3,946 employees, which included managers, engineers, office

workers, and factory workers. Thus, the T2 sample included an average

of 30.79 members (SD = 11.16) from each company. Males constituted

85.1% of the sample. The mean age was 43.6 years (SD = 8.20), and

average organizational tenure was 16.1 years (SD = 7.85). For the T3

data, 919 department managers participated in the survey, including on

average 4.53 (SD = 2.39) managers per company. Males constituted

97.4% of the T3 sample. The average age was 46.6 years (SD = 5.66),

and the average tenure was 18.1 years (SD = 7.07).

2.3 | Measures

All study variables were assessed using multi-item measures with a

5-point Likert-type scale ranging from 1 (strongly disagree) to

5 (strongly agree). Individual responses were aggregated to the organi-

zation level for analysis. All aggregated scales exhibited acceptable

within-organization agreement (rwg(j)) and intraclass correlations (ICC

[1] and ICC[2]), which suggests that employees and managers of the

same organization held shared perceptions of the present constructs

(Chen, Mathieu, & Bliese, 2004).

2.3.1 | Knowledge management-enhancing HRM practices (HRM director, T1)

The development of the measures of knowledge management-

enhancing HRM practices is generally lacking because the integrative

work between HRM practices and knowledge management is in its

initial stage. Given the lack of existing measures, we reviewed extant

studies on HRM practices thoroughly to identify the pool of HRM

practices relevant conceptually to the domain of knowledge manage-

ment (Beugelsdijk, 2008; Chang et al., 2013; Chen & Huang, 2009;

Liao et al., 2011; Lopez-Cabrales et al., 2009; Shipton et al., 2006).

Our review identified 18 potential items, such as internal training,

external training/tuition assistance, OJT, flexible work hours, job secu-

rity, participation in decision-making/empowerment, performance

appraisal, pay/incentive for performance, group/organization-based

reward/pay, employee suggestion system, job design, quality circles,

information technology/intranet knowledge sharing system, quality

circles, performance assessment-based on collaboration, selection/

recruitment, task rotation, and cross-functional teams. Among the

18 items, 5 items (job security, job design, performance assessment

based on collaboration, selection/recruitment, and cross-functional

teams) were excluded because they were not available or measured

inadequately for the current purpose in the HCCP data

(e.g., selection/recruitment: HRM directors of each company marked

the three most important practices that their companies administer

for hiring). The remaining 13 items were subjected to an assessment

of content validity.

We employed the Q-sort procedure, which is used widely in vari-

ous behavioral fields of science by offering a powerful quantitative

tool for examining opinions and assessments (Brown, 1986). To this

end, 10 experts, including 5 professors and 5 doctoral students of

strategic management and HR disciplines, participated in Q-sort using

those 13 items. Based on the descriptions of the stock-building and

flow-facilitating practices, these experts were asked to classify the

13 items into two practices or the category of “NA” (not applicable).

Among the 13 items, only 8 achieved unanimity from all members of

the expert group (4 items each for stock-building and flow-facilitating

HRM practices).

Stock-building HRM practices were assessed by examining both

formal and informal approaches. The formal approach in building

knowledge stock is measured by assessing corporate resource invest-

ment, which is devoted mostly to the collective formal training for

employee skills development (Lopez-Cabrales et al., 2009; Sung &

Choi, 2014). Each HRM director reported the total expenditure on

employee training based on the company's financial record. The total

amount of training-related expenses was divided by the size of the

firm to obtain per capita spending on training and development. The

informal aspect of stock-building practices was assessed using the fol-

lowing three items rated by HRM directors (α = .85). “Our company

actively utilizes the following practices: (a) tuition assistance for exter-

nal education (e.g., private education programs, colleges, and graduate

schools), (b) OJT, and (c) task rotation” (1 = not at all, 5 = a great deal;

Beugelsdijk, 2008; Chang et al., 2013; Liao et al., 2011). The HRM

directors' ratings of the three items were averaged to create an index

of informal practices of stock building. The overall score for stock-

1434 SUNG AND CHOI

building practices was computed by averaging the formal and informal

aspects of stock-building practices. Considering the different metrics

of these indicators, we transformed them into z-scores prior to

averaging them.

Flow-facilitating HRM practices were indicated by motivation- and

system-related practices drawn from previous studies (Cabrera &

Cabrera, 2005; Chang et al., 2013; Donate & Guadamillas, 2011;

Lepak & Snell, 2002). For motivation-related practices, we used the

proportion of collective incentives or bonuses paid in the total amount

of incentive compensation. Collective incentive compensation

includes all forms of team- and firm-level bonuses and extra remuner-

ation disbursed to employees. HRM directors reported the proportion

of the amount of collective incentive in the total employee incentive

based on the financial records of their firm. For system-related prac-

tice that involves social and technical infrastructure for knowledge

flow, HRM directors rated three items (α = .73). “Our company

actively utilizes the following practices: (a) suggestion system,

(b) quality circle, and (c) intranet knowledge-sharing system” (1 = not

at all, 5 = a great deal; Beugelsdijk, 2008; Chang et al., 2013). The

three items were averaged to create a system-related measure of

flow-facilitating practices. The score for flow-facilitating practices of

each company was calculated by averaging the z-scores of

motivation- and system-related practices.

2.3.2 | Firm knowledge stock (HRM director and production managers, T2)

In accordance with previous studies (Boxall, 1998; Sung & Choi, 2012),

firm knowledge stock was operationalized as the knowledge and skills

possessed by employees. Firm knowledge stock was measured by com-

bining two aspects of knowledge stock: (a) the task-related skills and

abilities of employees in general and (b) the task-specific knowledge

and skills of production workers. The HRM directors reported the level

of overall ability of employees by rating six items (α = .85): “The

employees of our company possess adequate levels of skills and abili-

ties in (a) task coordination, (b) problem solving, (c) learning,

(d) resource utilization, (e) interpersonal relationship, and (f ) informa-

tion processing.” Given that the core competence of manufacturing

companies lies in production procedures and technology (Sung & Choi,

2014), production managers were asked to report the level of their pro-

duction workers' task-specific knowledge and skills (1 = conducting sim-

ple labor and routine tasks, 5 = performing multiple tasks proficiently).

The ratings of production managers were aggregated to organization

level (ICC [1] = .31, ICC[2] = .74, F = 3.87, p < .001). Firm knowledge

stock was calculated by averaging the ratings from the HRM director

and those from production managers.

2.3.3 | Firm knowledge flow (Employees, T2)

Consistent with existing measures (Cabrera & Cabrera, 2005), a three-

item scale (α = .72) was used to assess firm-level knowledge flow: “In

our company, (a) employees actively share their ideas and knowledge

with one another; (b) employees freely communicate their ideas and

opinions to their supervisors; and (c) employees actively share their

ideas and know-how with members of other departments.” This scale

exhibited acceptable inter-rater agreement (rwg(3))= .92) and

acceptable intraclass correlations (ICC [1] = .09, ICC[2] = .73,

F = 3.69, p < .001) for firm-level aggregation.

2.3.4 | Innovation strategy (Strategy director, T2)

Based on existing innovation strategy items that underscore innova-

tive activities in organizations (Li & Atuahene-Gima, 2001), firm inno-

vation strategy was assessed using two items (α = .69): (a) “The

market strategy of our company is oriented toward leading the

changes in the customer and the market by developing new products

and services ahead of competitors.” (b) “In our company, developing

new products and services is the first priority.”

2.3.5 | Firm innovation (Department managers and KIPO, T3)

Complementing prior studies that rely solely on either a subjective

measure of innovative performance or an objective one, the present

study assessed multiple dimensions of innovation, such as new prod-

uct development (NPD), product and service differentiation, and pat-

ent registration. The department managers rated two items (α = .70,

rwg(2) = .83, ICC [1] = .28, ICC[2] = .68, F = 3.17, p < .001). “Our com-

pany holds a competitive advantage over other companies in terms of

(a) developing and introducing new products and (b) introducing dif-

ferentiation in the products and/or services offered” (1 = not at all,

5 = a great deal; Beugelsdijk, 2008; Shipton et al., 2006). The ratings

of the department managers on the two items were averaged to cre-

ate a subjective measure of innovation. The number of patents regis-

tered by the company, as archived by KIPO, was also used as an

objective measure of innovation given the significance of patents that

provide strong protection for proprietary knowledge of firms and the

direct relationship of patents with inventiveness and technological

novelty (Ahuja, 2000; Cohen, Goto, Nagta, Nelson, & Walsh, 2002;

Kwan & Chiu, 2015). Consistent with a recent study (Sung & Choi,

2014), the overall innovation score for each firm was calculated by

averaging the subjective (i.e., NPD and product and service differenti-

ation) and objective measures (i.e., number of patents) using their z-

scores.

2.3.6 | Control variables (Strategy director and HRM director, T1)

The present analysis included industry type and firm size as controls.

Industry type has often been considered a critical determinant of firm

innovation (Jiménez-Jiménez & Sanz-Valle, 2011). Thus, the effect of

industry type was controlled using nine dummies created for 10 indus-

try categories. Firm size is another critical firm-specific determinant of

various firm outcomes (Zhang & Li, 2009). Firm size was indicated by

the number of employees. Firm size was transformed using the loga-

rithm function to reduce the undue effects of very large firms.

3 | RESULTS

Before testing the hypotheses, we examined the empirical validity of

the scales using confirmative factor analysis (CFA; Anderson & Gerb-

ing, 1988) and conducted CFA on the 11 items that comprise five

study variables. This measurement model included covariances among

SUNG AND CHOI 1435

all study variables. The five-factor model exhibited good fit with the

data, χ2 (df = 32)= 39.74, p = .16; comparative fit index, CFI = .98;

root-mean-square error of approximation, RMSEA = .03, and per-

formed better than any of the alternative factor models (all p < .001).

All indicators loaded significantly on the corresponding latent factors

(p < .01) and the covariances remained low to moderate (all below

.25), indicating the convergent and discriminant validity of the mea-

sures used. The descriptive statistics and correlations among variables

are presented in Table 1.

3.1 | Hypothesized model and alternative models

After confirming the empirical validity of the measures with CFA, we

tested structural relations among constructs by conducting a struc-

tural path analysis using statistical software AMOS. The present

model included 11 indicators of five study variables aside from 10 con-

trol variables (i.e., nine dummies for 10 industries and firm sizes) that

resulted in 210 parameters to be estimated [21(21–1)/2 = 210],

which were larger than the size of the present sample of 203 firms.

The sample size is not sufficient to attain reliable estimates of the

parameters. Thus, we used a structural path analysis that employs the

scale means of each variable instead of item-level indicators

(Bandalos & Finney, 2001). The results of the structural models based

on all item-level indicators were identical with the results based on

the scale means reported below, although the former was character-

ized by lower levels of model fit.

We fit the hypothesized structural model as shown in Figure 1,

which produced good fit to the data (Hu & Bentler, 1999): χ2 (df = 56)

= 66.05, p = .17; CFI = .97; RMSEA = .03; Akaike information crite-

rion, AIC = 194.05. The possibility that theoretically plausible alterna-

tive models could offer a better explanation of observed patterns in

the data was verified (Anderson & Gerbing, 1988). First, we examined

the possibility of cross effects of the two knowledge management–

enhancing HRM practices, that is, stock-building and flow-facilitating

practices predicting both firm knowledge stock and flow. This model

exhibited a good fit as reported in Table 2, but failed to improve the

model fit, Δχ2 (df = 2) = 4.76, p > .05, significantly, and the two

added paths were statistically insignificant. Second, the mediating

roles of knowledge stock and flow were only partial rather than full,

although the present model suggested full mediation. Thus, we tested

the possibility of partial mediation by adding the direct paths from

knowledge management–enhancing HRM practices to firm innova-

tion. This partial mediation model produced excellent model fit, χ2 (df

= 54) = 57.68, p = .34; CFI = .99; RMSEA = .02; AIC = 189.68, which

was better than that of the hypothesized model, Δχ2 (Δdf = 2)

= 8.37, p < .05. In the third alternative model, we tested the possibil-

ity that HRM practices and firm-level knowledge management have

parallel or independent effects on firm innovation instead of having a

mediated relationship. The fit of this model was worse than that of

TABLE 1 Means, standard deviations, and correlations among study variables (N = 203)

Variables M SD 1 2 3 4 5 6 7 8

1. Firm size 6.01 1.05 —

2. Food industry .09 .30 .29**

3. Fiber industry .04 .20 .28** —

4. Chemical industry .13 .34 .35** −.08 —

5. Plastic industry .05 .22 .26** −.05 −.09 —

6. Metal industry .19 .39 .29** −.10 −.19** −.11 —

7. Machinery industry .09 .28 .28** −.07 −.12 −.07 −.15* —

8. Computer industry .02 .14 .35** −.03 −.05 −.03 −.07 −.04 —

9. Electronics industry .08 .27 .26** −.06 −.11 −.06 −.14* −.09 −.04 —

10. Electric appliance industry .16 .37 .15* −.09 −.17* −.10 −.21** −.14 −.06 −.13

11. Stock-building HRM practices −.12 .64 .29** −.09 .09 −.15* −.02 .02 .06 .03

12. Flow-facilitating HRM practices −.03 .69 .28** −.01 −.02 −.05 .05 .04 .05 −.16*

13. Firm knowledge stock −.02 .79 .35** −.02 .15* .06 .01 −.02 .10 −.10

14. Firm knowledge flow 3.70 .26 .26** −.12 .16* −.02 .04 −.15* .02 −.02

15. Innovation strategy 1.98 .62 .15* −.03 −.01 −.14* .02 .02 −.03 .02

16. Firm innovation .05 .70 .28** −.01 −.06 −.08 .07 −.02 .11 .08

Variables M SD 9 10 11 12 13 14 15 16

9. Electronics industry .08 .27 —

10. Electric appliance industry .16 .37 −.01 —

11. Stock-building HRM practices −.12 .64 .01 .29** —

12. Flow-facilitating HRM practices −.03 .69 .04 .28** .31** —

13. Firm knowledge stock −.02 .79 −.03 .35** .25** .16* —

14. Firm knowledge flow 3.70 .26 −.04 .26** .16* .19** .29** —

15. Innovation strategy 1.98 .62 .01 .15* .20** .16* .11 .13 —

16. Firm innovation .05 .70 −.07 .28** .19** .27** .20** .26** .34** —

Note: Unit of analysis is firm (N = 203). *p < .05; **p < .01.

1436 SUNG AND CHOI

the hypothesized model (see Table 2). Thus, the partial mediation

model was selected as the final best-fitting model, which also pro-

vided a plausible theoretical account of the observed pattern (see

Figure 2).

3.2 | Hypothesis testing

3.2.1 | HRM practices and knowledge management

As reported in Figure 2, the present structural path analysis revealed

that stock-building and flow-facilitating HRM practices were associ-

ated positively with firm knowledge stock and flow (β = .22, p < .001

and β = .15, p < .05, respectively). Therefore, Hypotheses 1 and 2 are

confirmed. As part of the test of alternative structural relationships,

the cross effects of the two HRM practices on knowledge stock and

flow were insignificant, thereby demonstrating that stock-building and

flow-facilitating practices have different effects on firm knowledge

stock and flow.1

3.2.2 | Knowledge management and firm innovation

Structural path analysis (Figure 2) showed that firm knowledge stock

is not related to firm innovation (β = .09, ns.), which disproves Hypoth-

esis 3. However, the results supported Hypothesis 4 in that firm

knowledge flow was a significant predictor of firm innovation (β = .14,

p < .05). Thus, only firm knowledge flow was a meaningful predictor

of firm innovation in the two components of knowledge management.

3.2.3 | Interaction between firm knowledge stock and flow

Hypothesis 5 posited a positive interaction between knowledge stock

and flow in predicting firm innovation. The moderation hypothesis

was tested by employing hierarchical regression analyses that

included all control variables for firm innovation to avoid potentially

biased estimates of interaction terms in the structural path analysis

(Little, Card, Bovaird, Preacher, & Crandall, 2007). As expected, the

interaction between knowledge stock and flow was positive for inno-

vation (β =.17, p < .01). This significant interaction was examined fur-

ther using a simple slope analysis (Aiken & West, 1991). Plot A of

Figure 3 shows that knowledge stock is a positive predictor of firm

innovation when knowledge flow is high or one SD above the mean

(b = .77, p < .01), but not when it is low or one SD below the mean

(b = −.08, ns.), which is a pattern that supports Hypothesis 5.

3.2.4 | Moderating effects of innovation strategy

In Hypothesis 6, we proposed that the relationships between firm

knowledge management and innovation are moderated by innovation

strategy. The results based on hierarchical regression equations

revealed significant positive interaction between firm knowledge

stock and innovation strategy (β = .18, p < .01) in predicting firm inno-

vation. The two regression lines depicted in Plot B of Figure 3 confirm

that knowledge stock contributes to innovation when firms have high

innovation strategy (b = .92, p < .001), but not when they have low

innovation strategy (b = −.07, ns.). However, the interaction between

firm knowledge flow and innovation strategy was not significant.

Therefore, the moderating effect of innovative strategy is confirmed

only for knowledge stock, which provides partial support for

Hypothesis 6.

4 | DISCUSSION

The role of people and people management has long occupied a central

position in knowledge management (Al-Tit, 2016; Kang et al., 2007;

Kwan & Chiu, 2015; Mabey & Zhao, 2017; Özba�g et al., 2013). In this

regard, understanding how people management practices develop

effective knowledge management is important because the accumula-

tion and utilization of knowledge are crucial sources of innovation and

competitive advantage of firms (Chang et al., 2013; Donate &

Guadamillas, 2011; S. Wang & Noe, 2010). This study elaborated on

the two dimensions of HRM practices that explain the emergence of

firm-level knowledge stock and flow that contribute to firm innova-

tion through their interactive effects. The proposed framework was

validated through a systematic empirical investigation using multi-

source firm-level data collected over a 5-year period. The implications

of this study are highlighted in the following section along with the

limitations and directions for future research.

4.1 | Implications for theory and research

Drawing on KBV literature, we identified two dimensions of HRM

practices that exert disparate values toward firm-level knowledge

management. On one hand, stock-building HRM practices develop

and expand the firm-level knowledge reservoir by providing many

opportunities to improve task-related skills and competence (Chang

et al., 2013; Hatch & Dyer, 2004; Pastor et al., 2010). On the other

hand, flow-facilitating HRM practices enable firms to apply and utilize

effectively the knowledge resources they possess by legitimizing and

TABLE 2 Comparison of model fit of alternative models

Model χ2 (df) p CFI RMSEA AIC

Hypothesized model 66.05 (56) .17 .97 .03 194.05

Alternative models:

1. Stock-building and flow-facilitating HRM practices predicting both firm knowledge stock and firm knowledge flow

61.29 (54) .23 .98 .03 193.29

2. Direct effects of stock-building and flow-facilitating HRM practices on firm innovation (partial mediation model)

57.68 (54) .34 .99 .02 189.68

3. Parallel effects of stock-building and flow-facilitating HRM practices and firm knowledge stock and firm knowledge flow on firm innovation

72.32 (56) .07 .95 .04 200.23

CFI = comparative fit index; RMSEA = root-mean-square error of approxi- mation; AIC = Akaike information criterion.

SUNG AND CHOI 1437

alleviating the cost incurred by knowledge exchange and activation

among employees and motivating them to engage in such activities

(Al-Tit, 2016; Mäkela & Brewster, 2009). Considering the distinct pro-

cesses of firm-level knowledge management spurred by different

dimensions of HRM practices, undifferentiated approaches to these

processes appear to oversimplify the complex firm-level dynamics

involving HRM, knowledge, and innovation.

The acquisition and exchange of knowledge are core processes of

knowledge management (Sung & Choi, 2012). Nevertheless, organiza-

tions often face difficulties in encouraging their employees to involve

themselves in these core knowledge processes, because knowledge

management processes within organizations represent a particular

case of a paradigmatic social situation known as a “social dilemma”

(Cabrera & Cabrera, 2005). This phenomenon is a collective action

Firm Knowledge Stock

Low High

0

1

2

-1

F ir m

I n n o v a ti o n

F ir m

I n n o v a ti o n

Firm Knowledge Stock

Low High

0

1

2

-1

High Firm Knowledge Flow

Low Firm Knowledge Flow

High Innovation Strategy

Low Innovation Strategy

(A) (B)

FIGURE 3 Interaction between firm

knowledge stock and firm knowledge flow, and between firm knowledge stock and innovation strategy in predicting firm innovation

(T1) Stock-building HRM Practices

(T1) Flow-facilitating HRM Practices

.22*** (T2) Firm Knowledge Stock

(T2) Firm Knowledge Flow

(T3) Firm Innovation

.15*

.14*

.18**

.13*

Fiber Industry

Chemical Industry

Plastic Industry

Metal Industry

Machinery Industry

Electronics Industry

Control variables

Computer Industry

Firm Size

Food Industry

Electric Appliance Industry

.16*

.09

.05

FIGURE 2 Final structural model predicting firm innovation

Note. Thicker lines represent statistically more significant results. Insignificant paths are depicted as dotted lines in the diagram. Insignificant paths from control variables are not presented in the diagram. *p < .05; **p < .01; ***p < .001

1438 SUNG AND CHOI

situation, in which there exists conflict between individual and collec-

tive interests; thus, when individuals try to maximize their pay off, this

can lead to a collective disadvantage or damage. Under such a situa-

tion, organizations can mitigate this dilemma by increasing the

employees' belief that their information can certainly increase shared

good (information self-efficacy) by training and by convincing individ-

ual gains achieved through the increase of collective gain (Cabrera,

2002; Tallman & Chacar, 2011).

The present analysis also reveals directions to advance further

the literature on KBV at the firm level. With regard to the two knowl-

edge management dimensions investigated, knowledge flow increased

firm innovation, but knowledge stock did not. Empirical studies that

examine the effects of knowledge stock and flow simultaneously are

rare, particularly at the firm level, and hence, accepting the present

findings single-mindedly and judging that only knowledge flow is

important for firm innovation would be premature. Nevertheless, the

present empirical findings based on a multiyear longitudinal design

suggest the potential “primacy” of knowledge transfer and integration

over the presence of accumulated knowledge reservoir (Al-Tit, 2016;

Mahoney & Kor, 2015). From the KBV of firms, the current findings

suggest that knowledge creation through collective knowledge trans-

fer and exchange (i.e., knowledge flow) may supersede knowledge cre-

ation based on individual knowledge retention (i.e., knowledge stock;

Argote et al., 2003; Grant, 1996; Swart & Kinnie, 2013; Tallman &

Chacar, 2011). A balanced consideration of both knowledge dimen-

sions is still warranted because knowledge stock fuels the entire

knowledge management process, and the flow and exploitation of a

flimsy body of knowledge may stall in the middle. In this respect, fur-

ther studies may explore at what level knowledge stock can operate

as a “constraint” that causes the malfunction of knowledge flow

(Bontis et al., 2002).

Knowledge stock exhibited a significant positive effect on innova-

tion in firms characterized by the free flow of knowledge and innova-

tion strategy despite its nonsignificant main effect. Unlike knowledge

flow, which is dynamic and fluid, knowledge stock per se is static. The

mere possession of knowledge is insufficient to produce innovative

outcomes because unless it is exploited, such knowledge only repre-

sents potential (Kwan & Chiu, 2015; Mabey & Zhao, 2017; Sung &

Choi, 2012). Potential remains a potential without application and

exploitation. Knowledge stock can only benefit firms that have the

appropriate tools and strategy to mobilize such potential resource

effectively (Delery & Doty, 1996; Donate & Guadamillas, 2011;

Zhang & Li, 2009). Moreover, increased knowledge stock can hamper

innovation when such resource fails to flow freely to the parties who

need it, which creates indigestion or stockpiling of knowledge that

overloads or blocks the organizational system (cf. alignment between

learning stock and flow; Bontis et al., 2002). The HRM literature high-

lights the synergetic effects that occur collectively in various HRM

practices. However, implementing all HRM practices may not be the

best way to enhance innovation. Some practices are likely to be more

appropriate for building knowledge reservoir while others are likely to

be more suitable for facilitating knowledge application and utilization,

and vice versa. Future studies should consider the selective implemen-

tation of HRM practices instead of combining all possible HRM prac-

tices that a firm can provide.

Future studies may also consider multilevel issues of HRM prac-

tices. The present study maintained the theoretical focus on individ-

uals and their interactions that drive firm-level processes. This

orientation is in line with the emphasis on the microfoundation under-

lying macroprocesses and firm outcomes (Felin & Hesterly, 2007).

Concerning HRM practices that lead to firm-level knowledge pro-

cesses, researchers may investigate their multilevel implications for

knowledge management by observing simultaneously intended prac-

tices at the firm level, implemented practices at the unit level, and per-

ceived practices at the individual level (Minbaeva, 2013). Further work

should address and overcome the conceptual and empirical challenges

involving multilevel and micro- versus macrodynamics to help firms

fully utilize accumulated knowledge resources and transform them

into innovative outcomes.

4.2 | Implications for practice

The present analysis offers various practical implications for business

leaders. Our results suggest that the free flow of knowledge across

individuals and groups unleashes the potential value of knowledge

repository for innovation (Kang et al., 2007; Mabey et al., 2015; Wei

et al., 2011). Knowledge sharing elicits mixed motives among

employees because of the social dilemma between personal and col-

lective interests (Wang & Noe, 2010). Therefore, managers must

resolve the motivational dilemma with regards to employees' knowl-

edge sharing within organizations. Cabrera (2002) suggested restruc-

turing the payoff function (e.g., reward based on the combined efforts

of individual and bonus based on the success of the knowledge-

sharing program) and reducing the time necessary to distribute indi-

vidual ideas (e.g., making it easier for employees to share information)

as important potential interventions for overcoming those dilemmas

and effectively managing organizational knowledge. In support of

these practical recommendations, the present study demonstrated the

significant association between flow-facilitating practices (collective

incentive and knowledge infrastructure) and knowledge flow.

Group- and firm-level collective incentives may promote knowl-

edge sharing and utilization by aligning the employees' self-interest

with the collective, organizational interest, thereby overcoming the

motivational dilemma inherent in sharing one's own knowledge. As

another important managerial intervention for knowledge sharing and

utilization, managers may strengthen knowledge-facilitating organiza-

tional infrastructure and supportive systems, such as suggestion sys-

tems, quality circles, and intranet information systems, which

effectively facilitate the flow of knowledge among employees and

work units. These social and technical support for knowledge sharing

may reduce the psychological burden and physical costs associated

with offering knowledge, which in turn can increase employees' abili-

ties and opportunities of knowledge exchanges and activation

(cf. AMO framework, Kehoe & Wright, 2013). To overcome social

dilemma and increase employee motivation toward knowledge sharing

and utilization, managers could further nurture social capital among

employees, such as strong social ties, shared language and narratives,

and trust and group identification (Cabrera & Cabrera, 2005).

Our analysis also confirmed the importance of the fit between

managerial systems and firm strategy by demonstrating the significant

SUNG AND CHOI 1439

interaction between firm knowledge stock and innovation strategy.

Contemporary organizations exert considerable efforts in building

greater knowledge reservoir, which enables them to achieve advan-

tage through increased innovation. However, even though organiza-

tions implement diverse HRM practices to build knowledge stock, if

such systems are not congruent with overall firm strategy, such efforts

may not lead to the intended outcomes. In this regard, practitioners

should draw a clear blueprint for their organization, set a strategic

direction, and develop appropriate HRM practices in line with such

strategy.

4.3 | Study limitations and future research directions

The present analysis is based on a longitudinal multisource research

design and offers robust empirical support for most of the theoretical

hypotheses proposed. Nonetheless, the current findings should be

interpreted with caution in light of several limitations. First, because

of the practical survey limitation, other practices that could bear dis-

tinct implications for knowledge management (e.g., selection/hiring,

performance appraisal, and job design) were not included. Although

the current HRM practice measures addressed the most representa-

tive practices identified in previous studies (Chang et al., 2013;

Kehoe & Wright, 2013; Liao et al., 2011), future studies should

employ a broader operationalization of HRM practices that capture

various managerial policies, interventions, and activities.

Second, the present measures of HRM practices were not fine-

grained to detect delicate and nuanced effects of organizational con-

text on firm knowledge stock and flow. For example, this study

focused on the corporate monetary expenditure on formal training of

any kind. However, the effects of training are contingent on the con-

tent or type of training (Noe, Tews, & Dachner, 2010). A more elabo-

rate understanding could be gained by exploring the specific

characteristics of training (i.e., content, types, instructional designs, or

delivery formats) with regard to their distinct contributions toward

knowledge management. Some practices (e.g., OJT, job rotation) can

also contribute to the building and flow of knowledge in organizations.

Researchers should use finer-grained measures of HRM practices and

consider their boundary conditions.

Third, firm knowledge stock and flow were operationalized by

aggregating individual-level ratings offered by managers and

employees. Such an aggregation is a common practice and justified by

various statistics such as rwg(j), ICC[1], and ICC[2]. However, Coleman

(1966) argued that aggregate-level outcomes are more than a mere

sum of individual-level outcomes. By contrast, global, organization-

level ratings offered by CEOs or executives suffer from another prob-

lem: “If one were able to assess some form of ‘organizational human

capital’ through a measure at the organization level, then the measure

ignores the emergence process and misses important individual level

variance” (Wright & McMahan, 2011, p. 101). Likewise, Minbaeva

(2013) called for deeper understanding of explanatory mechanisms

that involve individual heterogeneity and interaction in explaining the

relationship between HRM practices and knowledge management.

The issue of emergence and the challenge of multilevel dynamics have

been neglected in empirical literature on HRM and knowledge

management, which must be addressed in a more elaborate manner in

future studies.

Finally, although we used items pertinent to innovation strategy

(i.e., strong tendency/orientation toward and emphasis on innovative-

ness) drawn from existing literature (Li & Atuahene-Gima, 2001), only

two items were used, which could pose a problem in terms of asses-

sing the firm-level context. Future research should replicate the cur-

rent framework using a comprehensive scale that assesses broader

aspects of innovation strategy, such as the pursuit of novel products

and services, exploration of various markets, and radical versus incre-

mental product innovations (Zhang & Li, 2009).

Despite the limitations, the present study enriches literature on

HRM and knowledge management by clarifying the often arbitrary

conceptualization and operationalization of HRM practices and knowl-

edge management and proposing two distinct dimensions of HRM

practices, each predicting firm knowledge stock and flow. This sys-

tematic scheme offers firms valuable insights into effective implemen-

tation of HRM practices toward firm innovation. Thus, this study

provides a foundation for the further development of a fine-grained

theory of HRM practices and knowledge management at the firm

level.

ENDNOTE

1Given that each componential HRM practice may have distinct impli- cations that can differ from their combined effects as a system of practices (Combs et al., 2006; Wright et al., 2001), we performed a follow-up analysis to examine the individual effects of each constitut- ing subdimension of the two practices. This post hoc analysis showed that the formal dimension of stock-building HRM practices (i.e., the total expenditure on corporate training) exerted a more significant effect on firm knowledge stock (β = .23, p < .01) than the informal dimension (i.e., external education, OJT, and task rotation; β = .13, p < .10). The results also revealed that motivation-related flow-facilitating practices (i.e., collective incentives) had a more significant effect on firm knowledge flow (β = .18, p < .01) than infrastructure-related prac- tices (i.e., suggestion system, quality circle, and intranet; β = .11, p < .10). Each constituting dimension of the two HRM practices exhibited different magnitudes of effects on their corresponding aspects of knowledge management. However, the overall patterns of individual effects were consistent with their combined effects.

ORCID

Jin Nam Choi https://orcid.org/0000-0001-7890-3195

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AUTHOR'S BIOGRAPHIES

SUN YOUNG SUNG is an Associate Professor at Nanjing Uni-

versity, China. She earned her PhD in International Business from

Seoul National University, Korea. Her research interests include

knowledge management in teams and organizations, organiza-

tional demography, and innovative performance at multiple levels

of analysis.

JIN NAM CHOI (jnchoi@snu.kr) is a Professor of Management

at Seoul National University, South Korea. He earned his PhD in

Organizational Psychology from the University of Michigan. His

research interests include innovation implementation, organiza-

tional creativity, and multilevel processes of human behavior in

organizations.

How to cite this article: Sung SY, Choi JN. Building knowl-

edge stock and facilitating knowledge flow through human

resource management practices toward firm innovation. Hum

Resour Manage. 2018;57:1429–1442. https://doi.org/10.

1002/hrm.21915

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