Impact of External Knowledge Inflow on Product and Process Innovation of Korean SMEs: Absorptive Capacity as a Mediator

Abstract

We examine the role of external knowledge inflow in improving the product and process innovation of Korean exporting small- and medium-sized enterprises (SMEs) in the textiles, apparel, and footwear industries. Building on the resource-based view (RBV), we investigate how five sources of knowledge inflow (suppliers, customers, competitors, universities, and government agencies) contribute to product and process innovation through absorptive capacity (AC) as a mechanism to explain the link between external knowledge inflow and innovation. Conducting a path analysis, we found that knowledge inflow from customers and government agencies had a positive effect on AC, subsequently enhancing product and process innovation. Additionally, knowledge inflow from universities and government agencies also affected process innovation and product innovation, respectively. We also demonstrated the mediating role of AC in the relationship between external knowledge inflow and innovation. Korean SMEs should thus invest in developing the knowledge sources of customers, universities, and government agencies to enhance AC and innovation.

Keywords

external knowledge inflow product innovation process innovation absorptive capacity SME

Innovation is a key factor that allows small- and medium-sized enterprises (SMEs) to achieve a competitive advantage, improve performance, and survive in international markets (Kostopoulos, Papalexandris, Papachroni, & Ioannou, 2011; Santamaría, Nieto, & Barge-Gil, 2009). However, the liability of smallness and the lack of resources and capabilities frequently hinder the innovation of SMEs (Valentim, Lisboa, & Franco, 2016). Researchers argue that external sources can facilitate the innovation process and benefit SMEs that suffer from these challenges (Brunswicker & Vanhaverbeke, 2015; Parida, Westerberg, & Frishammar, 2012).

Though scholars have recognized external knowledge inflow as a critical factor to compensate for lack of resources and to facilitate internal innovation (Kostopoulos et al., 2011; Parida et al., 2012), we found a research gap in the examination of various external knowledge sources in the context of SME innovation. The extant work provides little empirical evidence of how differently each source contributes to SME innovation through absorptive capacity (AC) as a means of integrating and applying external knowledge (Brunswicker & Vanhaverbeke, 2015; Kostopoulos et al., 2011). Thus, the results of this study could benefit SMEs by providing insights about which external knowledge sources contribute to innovation and thus which sources should be emphasized.

In addition, scholars of innovation studies have mostly focused on product innovation, while other factors such as organizational capabilities, management skills, processes, and knowledge contributing to firm innovation have been less studied (Ali & Park, 2016; Moilanen, Østbye, & Woll, 2014; Parida et al., 2012). Innovation, however, refers to the implementation of a new or significant improvement in various areas, such as products, processes, marketing methods, or organizational configurations (Atalay, Anafarta, & Sarvan, 2013). For example, SMEs in the textiles and clothing industries can achieve innovation by developing new products as well as optimizing time and resources (process) by utilizing external sources (Curwen, Park, & Sarkar, 2013; Ha-Brookshire & Lee, 2010). Thus, by investigating each type of innovation (product and process) in an industry-specific context (i.e., textiles, apparel, and footwear), this study will significantly contribute to SME innovation research.

Despite the considerable contributions of low-technology (LT) SMEs to employment in the manufacturing sector, research on these enterprises has remained scarce (Hirsch-Kreinsen, 2008; Kirner, Kinkel, & Jaeger, 2009). According to Organization for Economic Cooperation and Development (OECD, 2003), industries are classified into four categories based on the R&D intensity of each industry sector: LT; below 3%; textiles, food products, tobacco, wood, paper products, etc.); medium-low technology (MLT; below 3%; rubber and plastic products, refined petroleum products, basic metals, etc.); medium-high technology (MHT; between 3% and 5%; electrical machinery, motor vehicles, chemicals excluding pharmaceuticals, machinery, etc.); and high technology (HT; more than 5%; aircraft, spacecraft, pharmaceuticals, computing machinery, medical, etc.). Similar to HT sectors, innovative technologies and processes for updating products are also crucial for the success of LT firms (Santamaría et al., 2009). However, LT SMEs are characterized as being less innovative and investing a smaller percentage of their revenue than HT firms (Kirner et al., 2009). For LT industries, process innovation can be more critical than product innovation, and non-R&D activities and the use of external sources play a critical role in their innovation (Santamaría et al., 2009). Therefore, there is a clear need to deepen our understanding of the innovation process of LT SMEs.

To fill these research gaps, using the resource-based view (RBV), we aimed to examine the role of external knowledge inflow in improving innovation. Specifically, we investigated how different sources of knowledge inflow (suppliers, customers, competitors, universities, and government agencies) contribute to product and process innovation through AC as a mechanism to explain the link between knowledge inflow and innovation. In particular, we examined Korean (LT) SMEs because they are active exporters and are driven by severe competition (Moon, Rugman, & Verbeke, 1998), resulting in a need to actively exploit external knowledge sources and implement both product and process innovation. Following Santamaría, Nieto, and Barge-Gil’s (2009) approach, we focused on a specific sector of textiles, apparel, and footwear, frequently classified as a traditional LT sector (Kirner et al., 2009; OECD, 2003). While some scholars have used R&D intensity to classify an industry’s technology base, it can also be argued that this indicator cannot sufficiently explain the innovation of LT firms (Santamaría et al., 2009). LT SMEs are highly heterogeneous in terms of their technology base, industry classification, and resources (Alvarez & Iske, 2015), generating a need to investigate an industry-specific sector. In addition, in order to remain competitive, these industries consistently need to develop new products and practices in response to market demands by utilizing external sources (Ha-Brookshire & Lee, 2010; Jacob, 2007; Lu & Karpova, 2012), making them appropriate for the current study.

Literature Review

External Knowledge Inflow

According to the RBV, the sustained competitive advantage of a firm emanates from its valuable, inimitable, and nonsubstitutable resources and capabilities (Barney, 1991). However, firms cannot achieve a competitive advantage with the mere possession of resources; to deploy the resources, firms need to leverage their capabilities, suggesting a resource–capability–output link (Nath, Nachiappan, & Ramanathan, 2010). Accordingly, researchers identified knowledge as a critical resource of firm innovation and growth (Fey & Birkinshaw, 2005; Nath et al., 2010). Based on RBV, thus, we posit that a firm utilizes external knowledge, leveraging its internal capabilities (e.g., AC), to achieve its desired ends (e.g., innovation; Figure 1).

Figure 1.

Research framework.

Firms with little information rely on knowledge from external sources (Santamaría et al., 2009). In particular, LT SMEs tend to have fewer capacities and resources for in-house R&D than their counterparts, resulting in heavy reliance on external sources to access complementary knowledge relevant to technology, products, processes, and markets for innovation. While scholars provide only a limited investigation of various knowledge sources in the context of LT SMEs, a few researchers have identified the following types of external knowledge sources. For example, Fey and Birkinshaw (2005) identified knowledge inflow according to three sources of knowledge gained by university partnering, partnering through alliances, and contracting. Kostopoulos, Papalexandris, Papachroni, and Ioannou (2011) examined the external knowledge inflow from different sources (e.g., suppliers, customers, competitors, universities, research institutions, specialized journals, conferences, and meetings); however, they only employed one combined dimension of external knowledge inflow to test their research model. Moilanen, Østbye, and Woll (2014) further investigated the role of diverse external knowledge sources (e.g., trade organizations, institutions, competitors, personal networks, and customers) in enhancing product innovation. Other than Moilanen et al. (2014), however, the majority of researchers have focused on HT industries. Accordingly, to examine the role of external knowledge transfer in the context of LT SMEs, we focused on knowledge inflow from five external sources: suppliers, customers, competitors, universities, and government agencies.

AC

To utilize and transform accessed knowledge into tangible benefits (e.g., innovation), a firm needs the internal capability to identify, process, and exploit transferred knowledge from external sources (Cohen & Levinthal, 1990; Kostopoulos et al., 2011). This capability is AC, which has been identified as a critical antecedent of firm innovation (Liao, Fei, & Chen, 2007; Zahra & George, 2002). Cohen and Levinthal (1990) assert that AC is a function of prior knowledge or basic skills in relevant areas. AC enables a firm to recognize and utilize new external knowledge alongside existing knowledge stocks to generate tangible output, such as firm innovation (Todorova & Durisin, 2007). Prior knowledge underlies a firm’s AC, which develops cumulatively (Cohen & Levinthal, 1990); thus, it is a firm’s intrinsic capability that cannot be emulated by competitors in the short term.

Researchers contend that exposure to external knowledge assists a firm with exploring relevant knowledge and that knowledge acquisition from diverse sources enhances a firm’s capability to acquire, assimilate, and utilize this knowledge (Ferreras-Méndez, Newell, Fernández-Mesa, & Alegre, 2015; Kostopoulos et al., 2011). In studying Spanish biotechnology firms, Ferreras-Méndez, Newell, Fernández-Mesa, and Alegre (2015) found that the depth of the external knowledge search significantly influenced AC, whereas the breadth of the search did not have any effect. In the context of SMEs, Moilanen et al. (2014) confirmed the positive influence of external knowledge sources on AC, enhancing product innovation. Thus, exposure to external knowledge inflow from five sources—suppliers, customers, competitors, universities, and government agencies—will facilitate the AC of SMEs. Accordingly, we proposed the following hypotheses:

Hypothesis 1 (H1): External knowledge inflow from suppliers will positively affect AC.

Hypothesis 2 (H2): External knowledge inflow from customers will positively affect AC.

Hypothesis 3 (H3): External knowledge inflow from competitors will positively affect AC.

Hypothesis 4 (H4): External knowledge inflow from universities will positively affect AC.

Hypothesis 5 (H5): External knowledge inflow from government agencies will positively affect AC.

Product and Process Innovation

Researchers have previously recognized the various facets of innovation based on market and organization (Varis & Littunen, 2010), or product and process (Liao et al., 2007). While various typologies have been proposed, product and process innovation is most frequently employed (Wang & Ahmed, 2004). In the context of SMEs, however, other than a few examples, scholars have primarily focused on product innovation. Research on the role of external knowledge inflow in the product and process innovation of LT SMEs remains underexplored. Accordingly, in this study, we focus on two types of innovation—product and process—which are categorized according to the specific area of innovation. Product innovation is conceptualized as “a new product or service introduced to meet an external user or market need” (Damanpour, 1991, p. 561), while process innovation is defined as “a new element introduced into an organization’s production or service operations—input materials, task specifications, work and information flow mechanisms, and equipment used to produce a product or render a service”.

Previous researchers have suggested AC to be a major determinant of firm innovation (Tsai, 2001; Zahra & George, 2002). Tsai (2001) examined the role of intraorganizational knowledge transfer and AC on the innovation and performance of business units, eventually finding a positive association between AC and unit innovation. Moilanen et al. (2014) further confirmed that AC positively affected product innovation in Norwegian SMEs. In line with previous studies, we posited that SMEs in the textiles, apparel, and footwear industries with AC are likely to have a better understanding and utilization of new information and knowledge, resulting in superior product and process innovation.

Hypothesis 6 (H6): AC will positively affect product innovation.

Hypothesis 7 (H7): AC will positively affect process innovation.

The Mediating Role of AC

We also assert that AC mediates the relationship between external knowledge inflow and innovation. Intangible resources such as knowledge can be transferred from external sources and utilized by a firm to achieve innovation via a processing mechanism (i.e., AC) by leveraging the accessed knowledge (Barney, 1991; Tsai, 2001). The mediating role of AC between knowledge inflow and firms’ innovation has been well-established (Kostopoulos et al., 2011; Todorova & Durisin, 2007). Researchers empirically examined the role of AC as a mechanism that translates firms’ external knowledge inflows into the tangible benefits of superior innovation using a Greek sample (Kostopoulos et al., 2011) and a Spanish sample (Ferreras-Méndez et al., 2015). We postulated that AC enables Korean SMEs to generate value (innovation) from external knowledge sources.

Hypothesis 8 (H8): AC will mediate the relationship between external knowledge inflow (suppliers, customers, competitors, universities, and government agencies), and product and process innovation.

Research Methods

Research Design and Data Collection

To investigate the impact of external knowledge inflow on AC and innovation of Korean exporting SMEs in the textiles, apparel, and footwear industries, we employed a quantitative survey method using a structured questionnaire asking about external knowledge inflow sources, AC, and product and process innovation. The research design consisted of a pretest and main survey. The questionnaire was first developed in English, based on the literature, translated into Korean, and then retranslated into English by two different bilingual individuals who were not involved with the study. The Korean-translated survey was pretested and slightly modified by 12 managers of new product development, who provided feedback on its clarity and validity.

For the main survey, we gathered data by distributing a questionnaire to the managers of each firm between June 17 and December 15, 2016. To ensure data reliability, trained research assistants contacted senior managers and managers who were knowledgeable about new product development at each company via phone to confirm each firm’s innovation activities and to request the completion of the questionnaires. We obtained the approval of the Institutional Review Board prior to the data collection process.

For data collection, we selected the cases based on the following process. Data from the Korean Chamber of Commerce and Industry were made available. First, we selected firms satisfying the Korean SME law, which defines SMEs as companies that have fewer than 300 employees and generate less than 131 million US dollars (150 billion Korean won) from average annual sales across a 3-year period. Furthermore, in order to account for manufacturing firms with significant exports and to eliminate those too small in size, it was necessary to include companies with over 30 permanent employees and whose overseas sales comprised more than 10% of total sales. Based on these criteria, we selected 30,000 exporting manufacturing SMEs. Among them, we identified 3,240 firms as manufacturers of textiles, apparel, and footwear. From the list of 3,240 firms, we randomly selected 750 firms, 5 times the target sample size of 150, based on a conservative estimate of 20% (as a response rate of 20–30% is typical for a top management or executive survey; Baruch, 1999; Cycyota & Harrison, 2006). We determined the target sample size as 150 SMEs for path analysis based on the suggestion of Anderson and Gerbing (1988); they identified this number as the lowest possible to obtain parameter estimates with standard errors small enough to be of practical use in structural equation modeling analysis. Researchers have also suggested a ratio as low as 10 cases per variable (Nunnally, 1967), or 5–10 cases per estimated parameter (Bentler & Chou, 1987; Bollen, 1989; Kline, 2011). Therefore, the target sample size of 150 was deemed appropriate for the proposed model.

We faxed or e-mailed the questionnaire to 205 SMEs that agreed to participate in this study; 172 questionnaires were returned (24.6%). After deleting 16 questionnaires due to missing values or lack of credibility, 156 cases were used for the analysis. The characteristics of sample SMEs are presented in Table 1. Nonresponse bias was addressed by adopting the approach of Weiss and Heide (1993) for defining early responses as the first 75% of returned questionnaires, with the final 25% being treated as late responses, representing firms that did not respond to the survey. Late and early responses were compared on all variables using a t test, with the results revealing no significant differences (at p > .05) between early and late responses. Thus, nonresponse bias did not appear to be a significant issue in this study.

Table 1.

Sample Characteristics.

Category	Frequency	Percentage
Industry
Textiles & apparel	71	45.5
Shoes	85	54.5
Number of employees
<50	57	36.5
51–100	54	34.7
101–150	26	16.6
151–200	13	8.4
201–250	6	3.8
Sales volume in 2015 (million U.S. dollars)
<20	79	50.5
20–30	22	14.2
30–40	33	21.2
40–50	8	5.1
>50	14	9.0
R&D investment %
<5%	100	64.1
5–10%	37	23.7
10–15%	10	6.4
15–20	4	2.6
>20%	5	3.2

Note. 1 USD (dollar) = 1,151.97 KRW (Korea won).

Measurements

We measured all the constructs in the present study using scales established in previous studies. The measurement items were external knowledge inflow source, AC, and product and process innovation, as shown in Table 2.

Table 2.

Final Measurement Items.

Constructs/indicators	Loadings
External knowledge inflow (Kostopoulos et al., 2011)^a How important has each of the following sources of external knowledge been in terms of developing new products and establishing new manufacturing processes over the past 3 years? Suppliers Clients Competitors Universities Government agencies
Absorptive Capacity (Flatten, Greve, & Brettel, 2011; CR^b = .77; AVE^b = .68)
Acquisition (α = .87; CR = .84; AVE = .79)		.81
1. The search for relevant information concerning our industry is daily business.	.83
2. Our management motivates employees to use information sources within our industry.	.83
3. Our management expects that employees deal with information beyond our industry.	.89
Assimilation (α = .95; CR = .87; AVE = .85)		.84
1. In our company, ideas and concepts are communicated across departments.	.93
2. Our management emphasizes cross-departmental support when solving problems.^c
3. There is quick information flow, for example, if a business unit obtains important information, it communicates this information promptly to all other business units or departments.	.83
4. Our management demands periodic cross-departmental meetings to exchange information regarding new developments, problems, and achievements.	.88
Transformation (α = .95; CR = .88; AVE = .82)		.91
1. Our employees have the ability to structure and use collected knowledge.	.90
2. Our employees are used to absorbing new knowledge as well as preparing it for further purposes and making it available.	.81
3. Our employees successfully link existing knowledge with new insight.	.89
4. Our employees are able to apply new knowledge to their practical work.	.90
Exploitation (α = .96; CR = .86; AVE = .84)		.87
1. Our management supports the development of prototypes.	.81
2. Our company regularly reconsiders technologies and adapts them in accordance with new knowledge.	.81
3. Our company has the ability to work more effectively by adopting new technologies.	.82
Product innovation (Liao et al., 2007; α = .85; CR^b = .82; AVE^b = .75)
1. Our company often develops new products and services that are well accepted by the market.	.83
2. A great majority of our company’s profits are generated by the new products and services developed.^c
3. The new products or services developed by our company always engender imitation from competitors.	.90
4. Our company can often launch new products or services faster than our competitors.	.87
5. Our company has superior capabilities in R&D of new products or services as compared to our competitors.^c
6. Our company always develops novel skills for transforming existing products into new ones for the market.^c
Process innovation (Liao et al., 2007; α = .91; CR^b = .86; AVE^b = .75)
1. Our company often experiments with different operation procedures to facilitate the realization of its goals.^c
2. Our company constantly acquires new skills or equipment to improve manufacturing operations or service processes.	.84
3. Our company can develop more efficient manufacturing processes or operation procedures.	.91
4. Our company can flexibly provide products and services according to the demands of customers.	.84
5. The new manufacturing processes or operation procedures employed by our company always engender imitation from competitors.	.80

^aFormative scale not included in the CFA.

^bCR and AVE values at the final CFA model.

^cDeleted items due to low item-total correlations and cross-loadings.

Note. CR = composite reliability; AVE = average variance extracted; CFA = confirmatory factor analysis.

Following Kostopoulos et al. (2011), the external knowledge inflows were measured on a 7-point Likert-type scale in which firms rated the importance (1 = not at all to 7 = highly important) of the five different sources of external knowledge: suppliers, customers, competitors, universities, and government agencies. AC has usually been measured by R&D proxies, ignoring its dynamic nature, such as with R&D intensity (Cohen & Levinthal, 1990; Stock, Greis, & Fischer, 2001) and R&D activities (Chun & Mun, 2012). AC, however, reaches beyond simple R&D investment; it enables companies to effectively acquire and utilize external and internal knowledge that affects their ability to innovate and adapt to changing environments (Chen, Lin, & Chang, 2009; Daghfous, 2004). In this study, based on the previous discussion, we measured AC with the four dimensions of acquisition, assimilation, transformation, and exploitation adapted from Flatten, Greve, and Brettel (2011); we then combined these measurements into one latent construct based on its second-order nature (Saenz, Revilla, & Knoppen, 2014). Respondents were asked to specify the extent to which they agreed with statements regarding their firms’ information acquisition, assimilation, knowledge transformation, and commercial exploitation (1 = strongly disagree to 7 = strongly agree). Innovation was the dependent variable in the study, defined as a firm’s performance in the overall improvement of its capability regarding two aspects: the product and manufacturing processes (Liao et al., 2007). We employed 11 items developed by Liao, Fei, and Chen (2007) to measure product innovation (6 items) and process innovation (5 items; 1 = strongly disagree to 7 = strongly agree). We included firm size (in terms of the number of employees) and firm age as control variables for AC and innovation due to their known impact on AC and innovation (Laforet, 2013; Wu & Liu, 2009).

Results

Measurement Validation

We confirmed the three reflective latent constructs (i.e., AC, product innovation, and process innovation) regarding (a) content validity, (b) an analysis of item intercorrelations, (c) an analysis of item-total correlations, and (d) a confirmatory factor analysis (CFA). Researchers evaluated the contents of the items in terms of the repetition across items and the theoretical definition of the constructs (Raubenheimer, 2004), confirming the content validity. We used our evaluation as a reference for item deletion. The final measurement items with Cronbach’s α values, composite reliability (CR), and average variance extracted (AVE) are shown in Table 2. The reliability test results showed that the item-total correlations of 1 item in product innovation were below .4. After we deleted this poorly performing item, the Cronbach’s α values for all constructs exceeded .85, indicating good reliability.

CFA using EQS 6.3 was performed to examine the measurement model, and the parameters were estimated using maximum likelihood. The analysis was based on the covariance model. Because AC has four subdimensions, the unidimensionality of each subdimension was confirmed using CFA. Then, we took the average of the items under each dimension after deleting one cross-loaded item in assimilation. We used these averages as indicators of AC in the following measurement model. All reflective constructs (AC, product innovation, and process innovation) were subjected to measurement model assessment using CFA. The Lagrange multiplier or LM test procedure is designed to test whether fixed parameters that are set to zero in the model are, in fact, nonzero in the population, and hence would be better treated as free parameters to be estimated in a future run (Bentler, 1992). The results of the LM tests showed the following items to be cross-loaded: 2 items in product innovation and 1 item in process innovation. After deleting these items, the fit statistics of the measurement model indicated an acceptable fit (χ² = 95.22, df = 41, p < .001, comparative fit index (CFI) = .97, root mean square error of approximation [RMSEA] = .08). The convergent and discriminant validities were examined during the assessments of the measurement model. All of the item-factor loadings were found to be significant and greater than .60, providing evidence of convergent validity for the latent constructs. The AVEs supported the convergent validity of the constructs because all were above .50. The CR ranged from .77 to .86, indicating strong reliability. Discriminant validity was tested by fixing the covariance at 1.00 and then employing a χ² difference test on the values in the unconstrained versus constrained models. Through the results, we determined the discriminant validity of the constructs (Δχ²(1) > 3.84, p < .01). The AVEs of all three latent constructs were greater than the squared correlation estimates between them, exhibiting the discriminant validity of the constructs.

Model Respecification

We performed path analysis using EQS 6.3 to test hypotheses H1–H7. A correlation matrix with means and standard deviations is provided in Table 3. The results of the LM tests suggested that the following two paths that were set to zero in the proposed model should be estimated: knowledge inflow from universities to process innovation (Δχ² = 17.13, df = 1, p < .001) and knowledge inflow from government to product innovation (Δχ² = 6.81, df = 1, p < .001). In these additional findings, we may discover important implications for policy makers and SMEs, as they reflect the significance of external knowledge inflow from universities and government in process and product innovation. Thus, we released these two paths from the revised model. The results of the revised model showed a good fit of the model to the data (χ² = 35.22, df = 21, p < .05, CFI = .97, RMSEA = .06). The results of LM testing suggested no further releasing of paths. The amount of variance explained for each endogenous construct was as follows: 19.8% for AC, 22.9% for product innovation, and 44.3% for process innovation.

Table 3.

Descriptive Statistics and Correlations.

Construct	M	SD	1	2	3	4	5	6	7	8	9	10
Suppliers	3.00	2.16	1.00
Customers	3.74	2.37	0.78	1.00
Competitors	2.90	2.26	0.30	0.64	1.00
Universities	2.06	1.70	0.25	−0.05	−0.08	1.00
Government	1.77	1.48	0.35	0.14	0.06	0.50	1.00
AbsCap	4.32	1.04	0.22	0.28	0.19	0.07	0.30	1.00
ProdInno	4.26	0.90	0.30	0.14	0.11	0.18	0.36	0.44	1.00
ProcInno	4.25	0.94	0.29	0.24	0.12	0.28	0.39	0.59	0.72	1.00
LnSize	3.96	1.09	0.01	−0.03	−0.01	0.11	−0.02	0.15	0.16	0.14	1.00
LnAge	3.04	0.51	0.02	0.11	0.06	0.10	0.03	0.03	0.01	0.06	0.35	1.00

Note. AbsCap = Absorptive capacity, ProdInno = Product innovation, ProcInno = Process innovation, LnSize = Ln # of employees, LnAge = Ln firm age.

Hypotheses Testing

The standardized path coefficients and the t values from path analysis testing H1–H7 are presented in Table 4. Knowledge inflow from suppliers had no effect on AC (β = .06, [n.s.]), rejecting H1. However, knowledge inflow from customers had a positive effect on AC (β = .24, p < .05), supporting H2. Knowledge inflow from competitors and universities had no influence on Korean SME AC (β = .03, [n.s.]; β = −.09, [n.s.]). Thus, both H3 and H4 were rejected. Knowledge inflow from government, on the other hand, had a positive influence on AC (β = .33, p < .01), supporting H5. Supporting H6 and H7, AC had strong effects on both product and process innovation (β = .38, p < .001; β = .59, p < .001). Although not hypothesized, two additional paths were found to be significant: knowledge inflow from universities had a positive effect on process innovation (β = .20, p < .01), and knowledge inflow from government also had a positive effect on product innovation (β = .17, p < .05). These relationships are reasonable for the following reasons. Due to the lack of R&D resources, SMEs often collaborate with universities to solve manufacturing problems, devise more efficient manufacturing processes or operational procedures, and develop new techniques or equipment (Freitas & Maria, 2012). In addition, the Korean government provides extensive financial and nonfinancial support programs to enhance SME innovation (Doh & Kim, 2014). Thus, knowledge inflows from universities and government could become significant technical resources for SMEs and thereby directly affect their process and product innovation. Regarding control variables, firm size significantly affected AC (β = .20, p < .05), and firm age had a negative influence on process innovation (β = −.14, p = .055).

Table 4.

Results of Path Analysis Using EQS.

Hypotheses	ML estimates	t values	Results
H1: Suppliers → AbsCap	.06	0.65	Rejected
H2: Customers → AbsCap	.24*	2.29*	Supported
H3: Competitors → AbsCap	.03	0.03	Rejected
H4: Universities → AbsCap	−.09	−1.04	Rejected
H5: Government → AbsCap	.33**	3.80**	Supported
H6: AbsCap → ProdInno	.38***	5.07***	Supported
H7: AbsCap → ProcInno	.59***	9.63***	Supported
Additional paths
Universities → ProcInno	.20**	4.04**
Government → ProdInno	.17*	2.73*
Control variables
LnSize → AbsCap	.20*	2.58*
LnAge → AbsCap	−.09	−1.10
LnSize → ProdInno	.10	1.24
LnAge → ProdInno	−.03	−0.42
LnSize → ProcInno	.07	1.01
LnAge → ProcInno	−.14*	−2.11*

Note. AbsCap = Absorptive capacity; ProdInno = Product innovation; ProcInno = Process innovation; LnSize = Ln # of employees; LnAge = Ln firm age; ML= Maximum likelihood.

*p < .05. **p < .01. ***p < .001.

To test the mediation effects of AC (H8), we used bootstrapping methods to construct 95% confidence intervals (CIs) based on 5,000 random samples (Preacher & Hayes, 2008) with the SPSS “Process” macro (Hayes, 2013). Because the results of the path analysis indicate that only external knowledge inflows from customers and government had a significant influence on AC, we tested the following four models (i.e., two for product innovation and two for process innovation). We included the control variables (firm size and age) as covariates in each model. We first examined the mediation effect of AC on the relationship between knowledge inflow from customers (X1) and product innovation. The mediation effect is significant (mediation effect = .05, SE = .02, 95% CI = [.02, .09]), but knowledge inflow from customers has no direct effect on product innovation (direct effect of X1 = .02, SE = .03, p = [n.s.]). Thus, AC fully mediates the relationship. Likewise, AC also fully mediates the relationship between knowledge inflow from customers and process innovation (mediation effect = .04, SE = .02, 95% CI = [.01, .09]), as knowledge inflow from customers has no direct effect on process innovation (direct effect of X1 = .05, SE = .04, p = [n.s.]). Next, we examined the mediation effect of AC on the relationship between knowledge inflow from government (X2) and product innovation. The mediation effect is significant (mediation effect = .07, SE = .02, 95% CI = [.02, –.11]) and knowledge inflow from government also directly influences product innovation (direct effect of X2 = .15, SE = .05, p = .001); AC partially mediates the relationship. AC fully mediates the relationship between knowledge inflow from government and process innovation (mediation effect = .04, SE = .02, 95% CI = [.01, .08]), as knowledge inflow from government had no direct effect on process innovation (direct effect of X2 = .03, SE = .04, p = [n.s.]). Overall, H8 is partially supported by these results, suggesting significant mediation effects on the relationships between knowledge inflow from customers and government, and innovation.

Discussion

Building on the RBV approach, we investigated how five sources of external knowledge inflow (suppliers, customers, competitors, universities, and government agencies) influenced AC and two types of innovation (product and process). For the purpose of this study, we tested the theoretical framework in the context of Korean exporting SMEs from traditional LT industries, the textiles, apparel, and footwear sectors. We demonstrated the differential effects of each external knowledge inflow on AC and innovation. We found knowledge inflow from customers (H2) and government agencies (H5) to be a significant antecedent of AC. AC was found to significantly enhance product (H6) and process innovation (H7). Through model respecification, we additionally discovered the significant effect of knowledge inflow from universities on process innovation and the significant effect of knowledge inflow from government agencies on product innovation. These findings provide us with valuable insights.

Knowledge inflow from customers and government agencies was discovered to be a key factor in enhancing the AC of Korean LT SMEs. These findings are consistent with those of previous researchers, supporting a positive relationship between external knowledge and AC (Kostopoulos et al., 2011; Moilanen et al., 2014; Zahra & George, 2002). Customers served as a key source of knowledge and information for directing a firm’s business activities; thus, knowledge accessed through such networks would help SMEs develop their own capacity to utilize and transfer knowledge into tangible benefits such as innovation. Government sources were also effective. In particular, Korean government agencies actively provide a variety of resources and opportunities (e.g., marketing resources, financial programs, and networks) to support the exporting activities of Korean SMEs (Jeong, Jin, Chung, & Yang, 2017). However, knowledge inflow from suppliers, competitors, and universities did not have any direct effect on enhancing AC. To Korean LT SMEs with poor resources, horizontal (i.e., competitors) or indirect knowledge (i.e., universities) sources may not be effective in fostering AC. Rather, other organizational antecedents can have direct impacts on AC, such as technological capability (Tzokas, Kim, Akbar, & Al-Dajani, 2015), slack resources, and external openness to outside network partners (de Araújo Burcharth, Lettl, & Ulhøi, 2015).

The AC of Korean SMEs further predicted both product and process innovation, in line with previous findings (Cohen & Levinthal, 1990; Kostopoulos et al., 2011; Tsai, 2001). Similar to Kostopoulos et al.’s (2011) results, we demonstrated the role of AC as a conduit of acquired knowledge transfer to innovation and found a significant mediation effect on the relationships between external knowledge inflows from customers and government, and product and process innovation. Building on RBV, we confirmed the link of external knowledge inflow–AC–innovation, with AC as a mediator. We therefore provide new insight into the SME innovation literature. In addition, we found the direct effect of knowledge inflow from universities on process innovation and that of knowledge inflow from government on product innovation. Interestingly, knowledge inflow from universities directly contributed to process innovation. This implies that, without SME internal capabilities (e.g., AC), knowledge acquired from universities, for example, via collaborative projects, cannot contribute to developing new elements of the production operation. Also, the importance of government sources was reconfirmed, as knowledge inflow from government was found to facilitate new product development. This result may stem from the active role of government agencies in providing information to exporting Korean SMEs about foreign markets and new technologies.

With this study, we make theoretical implications, contributing to the SME innovation literature by extending the RBV approach. First, we confirmed the differing roles of external knowledge sources in enhancing AC and innovation, suggesting the need to examine each knowledge inflow source separately. Second, we offer theoretical implications by investigating how external knowledge sources are leveraged via AC in enhancing different types of innovation for LT SMEs in the textiles, apparel, and footwear industries, which has rarely been examined in the innovation literature. Given that the innovation process of LT SMEs is different from that of HT SMEs, investigation of innovation and its antecedents in the context of LT SMEs in a specific industry sector is worthy of study to expand our understanding of LT innovation.

Along with the findings of the study, we also provide managerial implications for practitioners. Because SMEs frequently encounter difficulty with accessing and managing a variety of external knowledge sources, it may be helpful for LT SMEs to provide insights about which external knowledge sources are effective at enhancing firm capability and contributing to innovation so that they can focus on developing and managing effective sources. Based on the study’s results, we recommend that Korean SMEs access the knowledge sources of customers, universities, and government agencies for enhanced AC and innovation. The important role of acquired knowledge from government agencies is particularly emphasized to achieve superior capacity and product innovation. For Korean textiles, apparel, and footwear SMEs providing customized product innovation for customers, externally generated knowledge is critical for firms’ competitive advantage and survival (Alvarez & Iske, 2015). Thus, in this study, we provide significant insights into where these companies can source market- and technology-relevant knowledge for innovation when they do not have sufficient internal knowledge.

While offering valuable implications, there are some limitations in this study, which may be addressed in the future. First, we employed two types of innovation as outcome variables to focus on their antecedents. Researchers in a future study could extend these approaches by investigating the outcomes of innovation, such as performance. In addition, they could examine additional factors affecting AC, such as intellectual capital (e.g., human, social, and organizational capital) to enhance the explanatory power of the model proposed in this study. Next, we retrieved data from Korean SMEs in LT industries. Given that innovation in the LT sector shows different patterns from that in the HT sector, researchers could conduct a study testing and comparing the relationships of the two types of innovation and their antecedents.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A3A2046811).

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