Keywords

Knowledge Management (KM), KM Practices, HRM Practices for KM, Retail Firm Performances, PLS-SEM.

Introduction

In the current arena of knowledge age, knowledge management is considered to be driver of business performance and innovation of the firms. It is an increasingly popular concept that many firms have put into practice in their organisational activities, management philosophies, and technological methods, based on the underlying assumption that KM contributes significantly to their bottom-line (Cohen & Olsen, 2015; Wang & Wang, 2012). KM refers to the processes as well as practices applied in a firm to unleash its intellectual potential by enhancing the efficiency and effectiveness in managing the firm’s knowledge resources (Andreeva & Kianto, 2012). The two avenues and components of KM in research (KM processes and KM practices) have been discussed in empirical studies while associating the KM and firm’s performances. First Avenue deals with the impact of knowledge processes on innovation and firm performance (Chen et al., 2010). The second stream of studies have discussed conscious firm’s and managerial practices or knowledge management practices to achieve firm’s goals by managing the knowledge resources in an efficient and effective way (Andreeva & Kianto, 2012; Foss & Michailova, 2009).

The present literature on KM practices particularly knowledge-based HRM practices is very scant. To the best of authors’ knowledge, there are very few studies that address the knowledge-based HRM practices/HRM practices for knowledge management explicitly (Kianto et al., 2017; Andreeva et al., 2017). The studies on HRM on KM practices have considered its impact on innovation performance of firms (Kianto et al., 2017; Andreeva et al., 2017), but its impact on organizational business performance is relatively ignored. Using the knowledge-based view of HRM, it can be maintained that HRM practices for KM in particular and KM practices in general can drive the business performance (Andreeva et al., 2017). To bridge all these gaps, the purpose of this study is to test the impact of HRM practices for KM on the business performance. The data was collected from 200 Malaysian retails firm (100 Malay and 100 Chinese owners). The partial least square-structural equation modelling is applied with Multi-Group analysis.

Theoretical Background

Knowledge-based HRM practices and HRM practices for KM are interchangeable terms. HRM knowledge-based practices can be referred as a system to obtain valuable as well as unique knowledge (Lopez-Cabrales et al., 2009) through specific selection, development, training, appraisal, and compensation practices. HRM practices play an important part in KM and are the greatest support of staff effort with knowledge process of organisation. Furthermore, they are the main background of KM (Foss & Minbaeva, 2009). Rewards and performance appraisal are the two HR practices that have been used mostly to align the firm’s goal and people’s behaviours. These practices set expectations, encourage desired behaviours, and provide feedback as well as evaluations (DeNisi & Pritchard, 2006). From the perspective of knowledge, the rewards for knowledge behaviours aim in encouraging as well as guiding the knowledge behaviours along with recognising the achievements to facilitate the firm’s innovation (Cabrera et al., 2006). Such rewards usually include the bonuses for new ideas or for the practical application new acquired knowledge (Andreeva et al., 2017). Thus, rewards and performance appraisal may encourage and foster knowledge behaviours that may result into superior financial performance, non-financial performance, business growth, as well as better performance relative to competitors. KM has significant meaning for HRM, especially for the improvement of knowledge sharing (Scarbrough, 2003). Numerous theoretical studies examining the relationships among KM, HRM, and financial performance have been conducted, but there is a lack of empirical studies in this area (Andreeva & Kianto, 2012). In fact, empirical studies on significant aspects of HRM for KM have largely been based on case studies, which create a need for more quantitative research (Andreeva & Kianto, 2012). HRM practices are influential in motivating knowledge performance in employees, which in turn would have a positive impact on the four measures of business performance. Therefore, this study hypothesised that:

HI: HRM practices for KM positively impact the perceived financial performance of Malay and Chinese firms in Malaysia.

H2: HRM practices for KM positively impact the perceived non-financial performance of Malay and Chinese firms in Malaysia.

H3: HRM practices for KM positively impact the perceived business growth of Malay and Chinese firms in Malaysia.

H4: HRM practices for KM positively impact the perceived business performance of Malay and Chinese firms in Malaysia relative to their competitors.

In Malaysia, many researchers found the values and ethics of the Malays and Chinese to have a huge influence over business practices in Malaysia (Ahmad et al., 2012; Mohamed Yunos et al., 2012). These ethnic groups play a vital role in the economic progress of the country (Alam et al., 2015). However, there are differences between them. The businesses of Malay entrepreneurs are reportedly less capable of surviving and growing because Malay entrepreneurs have less business exposure and are less creative and innovative as compared to Chinese entrepreneurs (Alam et al., 2015). From the existing literature, it is visible that there are differences between the business practices of the Malays and Chinese, which might influence business performance. Thus, it is hypothesised that:

H5: There is a significant difference between Malay and Chinese firms in Malaysia in relation to the impact of HRM practices for KM on business performance.

Research Method

Data Collection and Construct Measures

Data were collected through face-to-face survey with Malay and Chinese firms’ owners operating in the retail sector of Klang Valley, Malaysia. A total of 200 retailers comprising 100 Malay retailers and 100 Chinese retailers, participated and responded to the survey through convenience sampling including 63% females and 37% males from each group. The scale for HRM practices for KM is adopted from the Andreeva & Kianto (2012). This scale was compiled and developed based on a combination of concepts from Foss & Michailova (2009), Storey (2005); Scarbrough (2003). The measurement used in this study to assess perceived business performance consists of four items measuring FP, three items measuring NFP, four items measuring BG, and four items measuring CP. These items were adopted from various past study of Ahmad (2007). Items in this measurement were rated on a five-point Likert scale ranging from 1 (very dissatisfied) to 5 (very satisfied).

Data Analysis

The current study employed a PLS-SEM approach using SmartPLS version 3.2.7 (Ringle et al., 2017) to analyse collected data. This study adopted the two-step approach as recommended by Chin (2010) which includes assessment of outer or measurement models and examination of the inner model. Multi-group analysis was used to examine the structural model across the Malay and Chinese retail firms (Henseler et al., 2009).

Results

Outer Model Analysis

The internal reliability of all constructs was established; composite reliability values were above the lower limit of 0.60 (Hair et al., 2017). Likewise, the constructs’ convergent validity with AVE values was found above 0.50. Additionally, the reliability of the indicators was also established as all outer loadings were above 0.70. Discriminant validity of constructs was established using Heterotrait-Monotrait ratio (HTMT) criterion. For the HTMT criterion, the confidence intervals of the correlations between constructs were lower than 0.85 (Hair et al., 2017).

Inner Model Analysis

As presented in Table 1, HRM practices for KM were found to be positively and significantly related to the FP, NFP, and BG among Malay and Chinese retail firms (H1, H2, and H3). On the other hand, HRM and CP were found to be negatively related in the Malay sample but positively related in the Chinese sample (H4). Thus, these findings support H1, H2, and H3 across the two samples but not H4.

Table 1: Significance Of The Path Coefficients (Bootstrapped)
Relationships	Malay Sample (n=100)				Chinese Sample (n=100)
	Std beta	SE	t-value	Decision	Std beta	SE	t-value	Decision
H1: HRM practices for KM→ FP	0.694	0.055	***12.61	Accepted	0.288	0.114	**2.529	Accepted
H2: HRM practices for KM→ NFP	0.174	0.088	**1.97	Accepted	0.393	0.106	***3.699	Accepted
H3: HRM practices for KM→ BG	0.252	0.148	*1.704	Accepted	0.440	0.113	***3.910	Accepted
H4: HRM practices for KM→ CP	-0.263	0.076	***3.481	Not Accepted	0.058	0.133	0.441	Not Accepted

Multi-Group Analysis

The partial measurement invariance was established between the Malay and Chinese samples using MICOM, fulfilling a basic requirement to compare and interpret the results of PLS-SEM for determining group specific differences in MGA (Henseler et al., 2016). The results of the assessment of the structural models and MGA using nonparametric method namely Henseler’s MGA (Henseler et al., 2009) is shown in Table 2. Henseler’s MGA compares groupspecific bootstrapped estimates from each bootstrapped sample. In Henseler’s MGA, if the pvalue of the differences in path coefficients is higher than 0.95 or lower than 0.05, it indicates a 5% level of significant difference between the specific path coefficients of both groups (Henseler et al., 2009). Using Henseler’s MGA, the results showed significant differences for the impact of HRM practices for KM on FP and CP between the Malay and Chinese samples. Thus, the results partially support H5 as differences only exist for FP and CP among Malay and Chinese retail firms.

Table 2: Results Of Hypothesis Testing And Differences Among Malay And Chinese Samples
	Malay Sample		Chinese Sample		Difference in Path Coefficients	Difference in p-value (one-tailed)	Supported
	Path Coefficients	Confidence Interval (95%)	Path Coefficients	Confidence Interval (95%)		Henseler’s MGA
H1: HRM Practices for KM →FP	0.252	(0.550, 0.787)	0.440	(0.212, 0.599)	-0.406	0.999*	Yes
H2: HRM Practices for KM → NFP	-0.263	(-0.226, 0.296)	0.058	(-0.246, 0.219)	0.219	0.051	No
H3: HRM Practices for KM→ BG	0.694	(-0.439, 0.368)	0.288	(-0.099, 0.485)	0.188	0.088	No
H4: HRM Practices for KM→ CP	0.174	(-0.391, -0.075)	0.393	(0.188, 0.598)	0.322	0.026*	Yes
* Note: In Henseler’s MGA method, the p-value lower than 0.05 or higher than 0.95 indicates a 5% level of significant difference between the specific path coefficients of both samples. *p < 0.05.

Discussion And Conclusion

The current study examined the influence of HRM practices for KM on four subjective measures of business performance- perceived financial performance, perceived non-financial performance, perceived business growth, and perceived performance relative to competitors. The HRM practices assessed in this study were whether firms reward knowledge sharing with nonmonetary incentives, reward knowledge creation with monetary incentives, and if knowledge sharing is part of employee performance evaluation. The results revealed these HRM practices for KM to have a statistically significant positive impact on perceived financial performance, perceived non-financial performance, and perceived business growth of both Malay and Chinese retail firms in Malaysia. This means that the three HRM practices for KM positively predict these three measures of business performance. This study did not find the significant influence of HRM practices on perceived firms’ performance relative to competitors. This suggests that KM, specifically HRM practices for KM, does not predict business performance that is measured based on comparisons with competing firms. It seems that HRM practices for KM only matter when it comes to perceived financial performance, perceived non-financial performance, and perceived business growth measures of subjective business performance. Thus, the future studies are recommended to explore the impact of HRM practices for KM on firms’ performances under different contexts.

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