Does innovation capability improve SME performance in Malaysia? The mediating effect of technology adoption

Abstract

SMEs still need innovation to boost their performance in the age of globalisation and fierce market rivalry. Previous studies have identified that innovation capability is an essential driver in manufacturing industries for their survival. Yet, the featuring role of innovation capability has been considered theoretically in developed economies whereas empirical studies in emerging economies are still lacking. Therefore, the purpose of this study to examines the importance of innovation capability towards SME performance and the mediating role of technology-adoption. Structured questionnaires were used to collect the data from a sample size of 611 SMEs operating in the developing market of Malaysia. Derived hypotheses were verified through Structural Equation Modelling (SEM) using AMOS 21. The findings of the study indicated that innovation capability has a signiﬁcant positive impact on SME performance. Technology- adoption partially mediates the relationship between innovation capability and SME performance. SMEs are required to generate an operative innovation model to gain sustainable performance and competitive advantage in the Malaysian market.

Keywords

innovation capability small and medium enterprises performance technology adoption structure equation modelling malaysia

Introduction

In recent times, small enterprises performance is related to innovation capabilities by important determinants of the developing country's economy. Both growth and enterprises success are investigated through different determinants (Yadegaridehkordi et al., 2018). Several developed economies stay in communication with SMEs’ owners to provide them information, direction and expert opinions, therefore easy for them to implement innovation to attain the competitive advantages and substantial performance in the market (Ndubisi et al., 2020). Similarly like other developing countries, Malaysia also encourages stability for small enterprises sector, as shows their important role for growth and development of economy (Ratnawati, 2020), reduce poverty, source of generating the income for the poor and also the major source of creating employment opportunities (Shahbaz et al., 2020).

Recently in the SME sector around the world, rapid variations can be found and they are making progress by the adaptation of the innovative approaches. Implementation of Innovation can be realised as a vital feature for the successful performance of SMEs (Korauš et al., 2020). Therefore, the competency of SMEs is to implement the innovation can be considered as a major advantage in the market. In particular, SMEs required to implement innovative skills and knowledge to attain value in the market effectively (García-Sánchez et al., 2018).

In the entrepreneurial mechanism, innovative performance related with the production of innovative goods or services, and play a vital role for the adoption of environment in the market, competitions, and new technologies (Si et al., 2020). In addition, innovative capability and entrepreneurial performance have an optimistic relation, in that way healthy enterprises with adaptation of innovative capabilities like leadership, strategic planning, and knowledge achieves high performance and growth (Saunila, 2020). Ganguly et al. (2019) clarify that innovative capabilities is linked with leadership, strategic planning and knowledge. Similarly, the innovative capabilities are used to be defined as a knowledge of innovative practice, strategic planning need to introduce the novel product in the market or the right product for the right market, adoption of technology, and leadership management. Therefore, this study measuring the significant impact of innovation capability with the factors like leadership, strategic planning and knowledge to improve the entrepreneurial performance.

The study of Prasanna et al. (2019) explained the concept of innovation in small enterprises as adoption of new technology and as over time, these diversify technologies leading the markets. The conception of technology adoption recommends that the appealing technology is not superior or essential technology for the enterprises. A leading technology is designed over a process of negotiation with the stakeholders and then become a chosen one. Enterprises that they adopt the technologies first and later on it dominate the market and became a major reason for survival and growth, whereas those enterprises are sluggish in diversify their technologies are more probable to be unsuccessful (Si et al., 2020). Since the study (Bagheri et al., 2019) identified that, technology adoption and innovation are closely related with SMEs’ performance.

In developing economies, the relationship between innovation capabilities and SMEs’ performance is a hot concern (Saunila, 2020). Innovation considered a main aspect in the quick economic growth, specifically in Malaysia. Naala et al. (2017) highlighted that Malaysia is a developing economy using SMEs as its key instrument of reducing unemployment and growth. Theoretical and practical assessment of innovation capabilities for SMEs’ development is not only take place in developed economies but correspondingly in developing economies like Malaysia. This study aims to introduce related factors of innovation capability like leadership, strategic planning and knowledge to improve the performance of SMEs. Moreover, this study identified significant research methods that can build the relationship between academic research on SMEs and innovation capability, particularly in Malaysia. However, researchers have neglected to look into the mediating influence of technology adoption, and empirical studies of this sort in SMEs are rare. This research provides answers to the following questions: RQ1, “Does innovation capability improve SME performance in Malaysia?” And RQ2, “Does technology adoption mediate the relationship?” The findings are useful for SME owners and managers who want to get a competitive advantage and improve their performance.

Literature review and hypotheses development

Theoretical perspectives

The theoretical perspective of innovation capability has examined from different evolved stances. The initial view on describing innovation capabilities is constructed on three key factors. The first factor is the strategic planning for the enterprise model innovation, which puts efforts into building a new system for innovative activities and arranged these activities in an exceptional way as compared to the current SME models (Arnold et al., 1998; Grama-Vigouroux et al., 2020; Hanifah et al., 2019; Heikkilä et al., 2018). Thus, an innovation capability refined existing models and reshaped the importance of value creation. The second important factor of innovation capability is leadership management, which is important for innovation, as depends on a leader who is responsible to direct and support innovation and sustainability in the business (Akram et al., 2019; Obal et al., 2020; Waite, 2014). Leadership stands to develop a model and strategies that aims to understand the niche of the market that can be even totally different and conflict with the traditional adapted strategies of innovation. The third factor is termed knowledge management. This factor focuses on developing a more improved model for new products, processes, and services. Knowledge is important for businesses to understand the current value of the product and the current position of the market (Abbas et al., 2020; Lopes et al., 2017; Shahzad et al., 2020). Then the business is progressively improved to float into the market.

The second perspective of innovation capability is based on technology, therefore, technical and administrative innovations play an important role in improving and promoting an enterprises’ performance (Chege et al., 2020). Saeidi et al. (2019) found that adoption of technology has increased the enterprise's competitiveness and had a positive impact on performance. The innovation capability process is effective for the adaptation of new technology (Yousaf et al., 2020) therefore, it is seen as a source of competitive performance. It also affects the performance of enterprises in terms of quality and productivity. Hence, it is a positive relationship between technology and innovative performance of SMEs, practitioners need to understand the importance and manage innovation to increase operational performance.

Innovation is one of the most significant factors which definitively affects SME performance. Udriyah et al. (2019) emphasizes that SMEs need to have a strong innovative direction to gain a growth. In addition, the SMEs’ innovation refers to the practice of new ideas, concepts, and discovering new methods and creativity in the operational program in terms of quality and productivity of a new product in a market and these ideas had a positive impact on the SMEs’ performance. Anwar (2018) spotted the vital relation between SMEs’ performance and innovation, they argued that there is an encouraging correlation between both phenomena.

Gopalakrishnan (2000) identify the factors that affect the SMEs’ performance such as the innovation capability. Innovation capability is a sign of organizational resources and rapid adoption of the procedures or measures of the products the industry is interested in. It has a substantial impact on the overall performance of SMEs. Furthermore, innovation capability has a favourable impact on product quality at the organisational level. Most researchers (Guerrero-Villegas et al., 2018; Laforet, 2013; Simpson and Tamayo, 2020) believe that more innovative actions lead to greater innovation benefits, profits, and reduced organizational costs.

Strategic planning and innovation capability

Many researchers (AlQershi, 2021; Karbasi and Rahmanseresht, 2020; Kunzmann, 2013; Raymond et al., 2013) noted that innovation capability required proper strategic planning, therefore it plays an important role in SMEs to take innovative decisions according to the need of a market. Arend et al. (2017) found that strategic planning improves the pace of innovation method for adaptation and achieved the desire performance. Pollanen et al. (2017) collected data from 143 firms of different sectors and found a positive association among strategic planning and SMEs’ performance in Canada. Moreover, the study also explained that SMEs have an intention towards innovation and greatly depend on strategic planning to encourage them. Similarly, Suarez et al. (2016) who collected data of 225 Spanish firms, demonstrated that strategic planning was an important enabler towards SMEs performance. Therefore, this study hypothesized a positive relationship between innovation capabilities and strategic planning, the hypothesis can be formed as;

H1: Strategic planning has a positive relationship with innovation capability.

Leadership management and innovation capability

SMEs in Malaysia have failed several times due to a lack of strong leadership that brings innovative roles or encouragement to lead the businesses in an optimistic direction (Bagheri, 2017; Madanchian and Taherdoost, 2019; Nor-Aishah et al., 2020; Tajasom et al., 2015). Leadership management has the ability to support innovation capability and sustainability. The study of Cortes and Herrmann (2021) collected data from 194 managers from 97 SMEs and found a significant association among leadership and SMEs’ performance. Sawaean and Ali (2020) collected the data from a sample of 384 SME owners in Kuwait. The study assessed a positive association among leadership and the competitive performance of the firms. Furthermore, they also explained the importance of leadership that encourages and motivates to fetch a great level of innovation in the firm and improve the capabilities of a firm to lead the market. This study also focused on leadership management that enhances and supports the desire for SME innovation. Thus, the hypothesis can be formed as;

H2: Leadership management has a positive relationship with innovation capabilities.

Knowledge management and innovation capability

SMEs has a lack of connection between knowledge and innovation (Hassan and Raziq, 2019; Ibidunni et al., 2020; Kmieciak and Michna, 2018; Saqib et al., 2017; Shannak et al., 2017), therefore this study is going to identified the relationship between knowledge management and innovation. Knowledge management practices are playing an important role to develop an innovation capability within enterprises to adapt new technologies and as a result, they achieving higher performance (Usai et al., 2018). Inkinen et al., (2015) assessed the strong association between knowledge management and innovative capabilities of SMEs to produce healthier performance in market of Finland. The study more discussed the important role of knowledge management to help the SMEs’ owners in generating an innovative atmosphere within the organization and encourage them to achieve more than normal expected outcomes. Thus, the hypothesis is;

H3: Knowledge management has a positive relationship with innovation capability.

Innovation capability and SMEs’ performance

Innovation is one of the significant factors which decisively affects the organizational performance and fulfils the need to have a technological direction to gain a competitive advantage. Previous studies (such as, Falahat et al., 2020; Mustafa and Yaakub, 2018; Naala et al., 2017; Rosli and Sidek, 2013) identified the positive relationship between innovation capability and SMEs’ performance. In the same approach, Hajar (2015) revealed the positive relationship between innovation and SMEs’ performance of manufacturing industries in Indonesia and found that technological innovation and strategic planning are main teamsters of a firm's performance. Similarly, Sulistyo (2016) data collected from 120 SMEs owners in Troso Jepara found the positive relationship between innovation capabilities and enterprises’ performance and discover that entrepreneurial innovativeness is directly associated with the performance of a firm. Sahoo (2019) identified the positive impression of innovation and the firm's overall performance in the Indian manufacturing firms. The author collected the data from 134 SMEs proved that innovation is the adaptation of new technology and has a significant influence on a firm's overall performance. Najafi-Tavani et al. (2018) reported that in Iran, higher innovative capability displayed better performance in manufacturing industries. Therefore, the hypothesis can be formed as;

H4: Innovation capability has a positive relationship with SMEs’ performance.

Technology adoption, innovation capability and SMEs’ performance

Adoption of new technologies has been considered as a dominant path for SMEs to acquire new innovative directions (Bagheri et al., 2019; Davcik et al., 2021; Hervas-Oliver et al., 2021; Quintana-García and Benavides-Velasco, 2008). Nevertheless, little empirical evidence exists in the literature on the exact mediating relationship of technology adoption between innovation capability and SMEs’ performance. Huang and Chen (2019) highlighted the SMEs’ issues to the technologies’ adoption and discuss the growing importance of technologies to attain the competitive advantage. Lee et al. (2017) collected the data from 168 manufacturing firms suggested that the adoption of the technology can improve the performance of SMEs. The empirical findings of Wen and Zheng (2020) from United States manufacturing industries suggested that technology is a key factor for innovative performance of SMEs. As Muscio and Ciffolilli (2018) described the evolutionary role play by technological adoption in the SMEs’ performance in Europe and help them to improve innovation capabilities. Therefore, this study proposes the mediating impact of technology-adoption on innovation and SME performance. Thus, the following hypothesis is developed.

H5: Innovation capability has a positive relationship with technological diversity.

H6: Technological diversity has a positive relationship with SMEs’ performance.

H7: The mediating effect of technological diversity between innovation capabilities and SMEs’ performance.

Statement of intent and research framework

As a result of the above arguments, the aim of this study is to guide the SMEs’ to focus on the three major factors of innovation capabilities (strategic planning, leadership management, and knowledge management) to achieve sustainable performance. Moreover, the novelty of this study is the mediating effect of technology-adoption between innovation capability and SMEs’ performance in Malaysia. The conceptual model is presented in Figure 1, proposed in this research is best suited for SMEs, who are driven their businesses due to a lack of innovation capability in the emerging markets. Perceptive the importance of innovation capability in the performance of SMEs would help to settle the controversy about whether SMEs succeeds and in what circumstances.

Figure 1.

Conceptual framework.

Methodology

Research paradigm and design

The phrase "research paradigm" is used by Antwi and Hamza (2015) to refer to the theoretical structure of a methodical discipline, which is built on hypotheses, theories, purposes, assumptions, and methodologies that characterize a scientific investigation. For this study, a positivist approach was adopted as the study's paradigm, emphasizing quantitative methodologies. The positivist technique use inferential reasoning to define a theoretical confirmation of variables, which is then followed by empirical acceptance or rejection of the established hypothesized relationships (Kock et al., 2017). According to Venkatesh et al. (2016), the methods utilized to guide the research must be on the same track as the main goal. As a result, a quantitative method was used in this study to evaluate the hypotheses and attain the study's goal. According to Smith and Hasan (2020), scholars can use a quantitative technique to generate statistical proof of the depth of links between dependent and independent conceptions.

Measurement of variables

The selection of items to measure the innovation capabilities with strategic planning, leadership management, and knowledge management, SMEs’ performance, and the mediating effect of technology-adoption were chosen from the literature that is based on SMEs’ perception. This study adapted items from previous studies with reliable and valid measures of variables to measure the innovation capability (strategic planning, leadership management, and knowledge management) was adapted from Saunila (2014), Prajogo and Ahmed (2006), Charles (2014) and Lang et al. (2012). The construct of technology-adoption of the items was adapted from Lima et al. (2018) and Lang et al. (2012). Similarly for the construct of SME's performance the items adapted from Saunila (2014) and Lang et al. (2012).

Data collection: procedure and sample

According to Gupta and Bashir (2018) self-administered questionnaires are defined as “a data collection method in which the respondent reads the survey questions and reports his/her responses without the presence of a qualified interviewer”. In this study, a research methodology based on the self-administered questionnaires was used. Moreover, the form of the self-administered questionnaire used for this study is known as the “drop-off survey”. This technique requires the researcher or representative of the researcher (i.e. enumerators in this study) to move to the respondent's location and deliver questionnaires to respondents (Hair et al., 2011). After completion, these questionnaires were collected by the researcher or representative.

In a study based on Structural Equation Modelling (SEM), the sample size would be at least in the range of 100 to 200 (Kline, 2015). Others have recommended the lowest sample size of 200 cases (Biddle and Marlin, 1987; Tanaka, 1987). In this study, data collected from the survey cover the nine different states in Malaysia, according to the total number of SMEs registered in SMEs Crop Malaysia (see Table 1). SMEs were selected by using a stratified random sampling method, a total of 800 questionnaires distributed during the survey. After screening the data, only 611 (76.4%) questionnaires were completed in all respects and hence were initially fed in the computer and then imported for further analysis.

Table 1.

Survey population and sampling.

State	Population of SMEs	Percentage (%)	Questionnaires Distributed
Pulau Pinang	15,791	9%	72
Kedah	11,523	6.6%	53
Perak	17,712	10.3%	82
Selangor	42,252	24.2%	194
WP Kuala Lumpur	31,369	18%	144
Melaka	7469	4.2%	33
Johar	23,047	13.2%	106
Kelantan	10,883	6.24%	50
Sarawak	14,297	8.3%	66
Total	174,343	100%	800

Method of data analysis

Structural equation modelling (SEM) was employed in a two-stage technique to assess the structural and measurement models. In these two stages, the structural and measurement models’ interactional influence is decreased. In the first step, confirmatory factor analysis (CFA) was used to assess the measurement model's convergent validity and causal link between adapted items and variables (Byrne, 2013). The validity and reliability of the instrument were also evaluated, as recommended by Hair et al. (2011), for further study. In the second stage, the structural model was utilised to look into the relationship between the exogenous variables (innovation capabilities with strategic planning, leadership management, and knowledge management) and the endogenous variables (technology adoption and SMEs’ performance).

Data analysis

Respondent demographics

The demographic characteristics are personal and enterprise information of the respondents through survey questionnaires from different states in Malaysia. A total of 611 respondents’ profile has been classified based on their gender, age, marital status, education, ethnic group, religion, income, business activities, business started the year, and paid employees. Demographic analysis was carried out by using a descriptive statistic to perceive the background of the respondents based on the questions in the questionnaire.

According to Kheng and Minai (2016), the Chinese community is regarded to control and manage the majority of SMEs in Malaysia. Therefore, Chinese was categorized as the highest respondent rate compared with other ethnics. Furthermore, the services and manufacturing sectors have always dominated, accounting for 90.6% of Malaysian SMEs and contributing 83.3% in overall GDP (Chin and Lim, 2018). Thus, business activities were based on manufacturing and services sector is the study. Table 2 provides detailed information.

Table 2.

Respondent demographics.

Variables		Number	Percentage
Gender	Male	439	71.8%
Gender	Female	172	28.2%
Age	Less than 25	3	0.5%
	26-30	7	1.2%
	31-35	65	10.6%
	36-40	173	28.3%
	41-45	108	17.7%
	46-50	98	16.0%
	Above 51	157	25.7%
Marital Status	Single	105	17.2%
Marital Status	Married	506	82.8%
Education	High school or less	243	39.8%
	Diploma	210	34.4%
	Bachelor degree	127	20.8%
	Master	27	4.4%
	Doctorate	4	0.6%
Ethnic Group	Malay	223	36.5%
	Chinese	294	48.1%
	Indians	84	13.8%
	Others	10	1.6%
Religion	Muslim	249	40.8%
	Hindu	73	11.9%
	Christian	119	19.5%
	Buddhist	143	23.4%
	Others	27	4.4%
Income Level	Less than RM5000	274	44.8%
Income Level	RM5001 or above	337	55.2%
Business Activities	Service sector	225	36.8%
Business Activities	Manufacturing sector	386	63.2%
Busines Started	Before 2018	413	67.6%
	2018	103	16.9%
	2019	64	10.5%
	2020	31	5.0%
Paid Employees	Less than 5	94	15.4%
	6-10	176	28.8%
	11 or above	341	55.8%

Reliability of the variables

According to Tarhini et al. (2016), “internal consistency signifies the extent to which respondents are reliable across the items mentioned in the questionnaire as a measurement scale”. Further, Pallant (2020) explained that Cronbach's alpha more than 0.70 is considered as a good internal consistency. In this study, Cronbach's alpha used to measure the internal consistency for the variables: Strategic Planning, Leadership Management, Knowledge Management, Technology Adoption, and SME's Performance (α = 0.934, 0.945, 0.951, 0.869, and 0.918, respectively). According to Kline (2015), variables have reliability more than 0.8 considered as very good or excellent internal consistency.

Correlation analysis

The relationship between the constructs measured by correlation. In this study, Pearson correlation analysis is determined by figuring the correlation of all the variables used. The Pearson correlation coefficient (r), took the value range from + 1 to −1 (Cleophas and Zwinderman, 2018). If the value is indicated 0, it displays that there is no relationship among the two variables. A value is greater than 0 means that there is a positive relationship among the two variables. This means that when the value of one variable was increasing, hence, another variable value also increases. On the other hand, if a value is less than 0 which shows that there is a negative association. A value less than 0 is the value of one variable was increases while the value of another variable was decreasing. The correlation analysis result of all the variables used in this research is presented in Table 3.

Table 3.

Correlation analysis.

	1	2	3	4	5
Strategic Planning	1
Leadership Management	0.326	1
Knowledge Management	0.317	0.308	1
Technological Diversity	0.386	0.353	0.372	1
Performance	0.413	0.395	0.407	0.388	1

Confirmatory factor analysis

The result of confirmatory factor analysis showed that 6 items were used to measure the strategic planning, 6 items were used to measure the leadership management, and 6 items for knowledge management. Thus, 18 items were retained after CFA to measure the relationship of strategic planning, leadership management, and knowledge management with innovation capability. While, 4 items retained after CFA to measure the mediating variable technology adoption. Moreover, SMEs’ performance measured with 4 items and no item was removed after CFA. Marsh et al. (2020) revealed the least standard value that factor loadings of all indicators are more than 0.5. The following Table 4 showed the summary of CFA results.

Table 4.

Summarised CFA results.

Constructs	Chi-Square	CMIN/df	GFI	AGFI	CFI	RMESA	AVE	CR
Strategic Planning	25.864	2.223	0.957	0.928	0.967	0.072	0.616	0.906
Leadership Management	24.357	2.314	0.965	0.936	0.957	0.075	0.662	0.921
Knowledge Management	26.728	2.247	0.961	0.922	0.970	0.061	0.670	0.924
Technological Diversity	28.829	2.631	0.966	0.931	0.974	0.067	0.793	0.936
SMEs’ Performance	26.579	2.833	0.970	0.935	0.977	0.074	0.771	0.931

Measurement model

According to the Hair (2020), standardized estimation of all constructs were inside the threshold. The items used in this study had acceptable range values, and showed the good fitness of overall measurement model. The consequences of measurement model revealed (see Figure 2) that it was under the fitness threshold which generated an RMSEA of 0.035 and a chi-square value of 814.324 with 610 degrees of freedom (p < 0.005). The statistics for the test of fit were GFI = 0.931, AGFI = 0.912, CFI = 0.942 and CMIN/df = 1.693. Overall, the results identified that the measurement model is good.

Figure 2.

Stage 1; measurement model.

Construct reliability and average variance extracted

The specification of the model was due to the reason that the factors were highly correlated (> 0.85) which indicated the lack of discriminant validity. Items that were not highly loaded with hypothesized factor (Significance of standardized parameters of estimates), model is not adequately fit (Goodness of fit) and a large number of residuals and modification indices (Kirikkanat and Soyer, 2018; Sellbom and Tellegen, 2019). But this specification of the model should be in conjunction with theory. The resulting modified model was then analysed for acceptable goodness of fit to proceed to further analysis.

Further analysis was conducted to check the reliability and validity of each construct at the second step in the modified model. Internal consistency was evaluated through Cronbach, CR, and AVE. As mentioned, the identified values of these measures were above the recommended level (Cronbach alpha: 0.70, CR: 0.60, and AVE: 0.50). This indicated an acceptable level for constructs’ reliability. Moreover, the convergent validity was supported by the significance of all items (P < 0.001) and loadings on specified factors. The AVE was 0.50 and which also supported the convergent validity. Furthermore, the goodness of fit model has confirmed the construct validity.

In the measurement model (stage one), it is required to estimate the uni-dimensionality of the constructs through reliability and validity, before testing the hypothesised relationship in the structural model (stage two) (Hair, 2020). Therefore, Cronbach's Alpha, Construct Reliability (CR), and Average Variance Extracted (AVE) were estimated for all variables (see Table 5).

Table 5.

Cronbach's alpha, construct reliability (CR), and average variance extracted (AVE).

Strategic Planning
Items	Indicators	Factor Loading	Cronbach's alpha (α)	CR	AVE
			0.939	0.925	0.673
SP_1	Skill to recognise strengths and weakness.	0.83
SP_2	The new strategies contribute to enhancing performance.	0.79
SP_3	A clear plan with measurable milestones.	0.74
SP_4	Road map for future activities.	0.76
SP_5	Skill to identify external opportunities and threats.	0.88
SP_6	Progress towards achieving strategic objectives is regularly reviewed.	0.81
Leadership Management
Items	Indicators	Factor Loading	Cronbach's alpha (α)	CR	AVE
			0.948	0.924	0.672
LM_1	The managers encourage initiatives.	0.85
LM_2	The managers give positive feedback.	0.92
LM_3	Pass employees’ ideas to the management.	0.74
LM_4	The managers encourage idea generation process.	0.76
LM_5	Practices for transferring knowledge.	0.75
LM_6	Appreciation for their work.	0.88
Knowledge Management
Items	Indicators	Factor Loading	Cronbach's alpha (α)	CR	AVE
			0.953	0.928	0.682
KM_1	Possibility for education.	0.84
KM_2	Voluntary learning and development are supported.	0.87
KM_3	Learning is an investment, not an expense.	0.71
KM_4	Gaining knowledge through external contacts.	0.78
KM_5	Comparing our operations to other organizations.	0.82
KM_6	Develop actions together with stakeholders.	0.92
Technological Diversity
Items	Indicators	Factor Loading	Cronbach's alpha (α)	CR	AVE
			0.879	0.943	0.807
TD_1	Organization is considered by rapidly changing technology.	0.87
TD_2	Keep up with changes in technology, it will be difficult to remain competitive.	0.93
TD_3	The rate of process obsolescence is high.	0.85
TD_4	The technology changes regularly.	0.94
Performance
Items	Indicators	Factor Loading	Cronbach's alpha (α)	CR	AVE
			0.925	0.940	0.797
P_1	Cost-effectiveness	0.85
P_2	Enterprise image	0.88
P_3	Product performance	0.93
P_4	Stakeholders (customers etc.) satisfaction.	0.91

Structural model

Kline (2015) explained that all variables were validated and fitness was acceptable in stage one (measurement model), then a second stage (structural model) can be tested. Field (2013) defined the second stage (structural model) as “the portion of the model that specifies how the latent variables are related to each other”. Therefore, the structural model employed in this study to identify the direct and indirect relationship between the constructs. The structural model is showed below in Figure 3.

Figure 3.

Stage 2; structural model.

The consequences presented that the structure model was well fitted, which generated a RMSEA of 0.031 and chi-square value of 864.913 with 610 degrees of freedom (p < 0.001). The statistics for the test of fit were GFI = 0.939, CFI = 0.948, AGFI = 0.911 and CMIN/df = 1.698. In totality, the results specified that the structure model is good.

The path analysis results (see Table 6) showed the standardized estimation of respective variables with significance level and found significant relationship between all six hypotheses. The relationship between strategic planning and innovation capability (H1) has supported by beta = 0.37, t-value = 4.621, p-value = 0.000. Similarly, the relationship between leadership management and innovation capability (H2) has supported by beta = 0.34, t-value = 4.217, p-value = 0.000. H3 carried the relationship between knowledge management and innovation capability has been supported by beta = 0.32, t-value = 4.018, p-value = 0.000). H4 identified the relationship between innovation capability and performance of SMEs has supported by beta = 0.31, t-value = 3.909, p-value = 0.000. H5 assessed the relationship between the innovation capability and technology adoption has supported by beta = 0.64, t-value = 6.899, p-value = 0.000. Lastly, the findings of H6, where technology adoption significant relationship with performance of SMEs has been supported by beta = 0.53, t-value = 6.011, p-value = 0.000.

Table 6.

Testing hypotheses .

Hypothesized Path	Standardized Estimate	T-Value	P-value	Result
H1: SP-->IC	.37	4.621	***	Significant
H2: LM-->IC	.34	4.217	***	Significant
H3: KM-->IC	.32	4.018	***	Significant
H4: IC -->P	.31	3.909	***	Significant
H5: IC-->TD	.64	6.899	***	Significant
H6: TD-->P	.53	6.011	***	Significant

The mediation analysis

Hypothesis 7 (H7) tested the technology adoption mediates the association among innovation capability and SME performance. This study followed the Awang et al. (2015) identified technique to measure the mediation effect. According to Awang et al. (2015) the indirect effect between the constructs is more than the direct effect, once the direct effect is also significant then it is called partial mediation. In this study, the indirect effect is 0.35 (0.64 × 0.55 = 0.35), while the direct effect is 0.31. Hence, technology adoption partially mediates the relationship among innovation and SME performance.

One more method to deal with the sampling distribution of the constructs is to practice resampling, also known as “bootstrapping”. In this method, numerous samples were generated, from the estimations of constructs calculated from thousands of newly created samples, the features of the sampling distribution can be indirect without supposing normality.

In methodological assessments, the bootstrapping procedure to get the mediating effect executes very well (MacKinnon et al., 2004). According to Awang et al. (2015), bootstrapping was reliably the most influential. Similarly, Demming et al. (2017) stated that nowadays researchers believe bootstrap is the first choice to measure the mediation effect. The results obtained are shown in Table 7. Therefore, hypothesis 7 (H7) is accepted, once the consequence of the bootstrapping procedure confirmed the partial mediation occurred.

Table 7.

Bootstrapping results.

	Effect Results	P-Value
Indirect Effect	.37	.000 (Significant)
Direct Effect	.32	.001 (Significant)

Discussion

In the Malaysian context, there is no recognised procedure to measure the performance of SMEs. SME Crop Malaysia gave a clue to measure the performance of micro, small and medium enterprises on the basis of the number of employees, employed capital, and annual turnover. Whereas, the latest empirical studies (Ndiaye et al., 2018; Pollanen et al., 2017; Soto-Acosta et al., 2018; Suarez et al., 2016) from developed economies explained the importance of innovation and adoption of technologies for the performance and sustainability of the sector. Due to the absence of empirical evidence, the starring role of innovation capability in SME performance in developing economies stays mysterious. In this study, the role of innovation capability assesses empirically towards SME performance in Malaysian markets. Additionally, this study contributes theoretically by examine the technology adoption mediates the relationship between innovation capability and performance of SMEs in Malaysia. Furthermore, this study spread out the consequences of the findings that SMEs are more probable to accept innovative methods in the businesses because of strategic planning, leadership management, and knowledge management.

The first research question that has been developed is: “Does innovation capability improve SME performance in Malaysia?” The consequences of RQ1 were based on the H1, H2, H3, and H4 hypothesised relationships.

Hypothesis 1 (H1) the study examined the relationship between strategic planning and innovation capability of SMEs and found a positive relationship. H1 is supported is consistent with the previous studies, like Arend et al. (2017) noted that strategic planning for the development of innovation capability in firms plays a key role in encouraging their performance. According to Kunzmann (2013), strategic planning is important and significant to boast the innovation capability within the firm which lead the competitive advantages and sustainable performance of SMEs. The study of Pollanen et al. (2017) in Canada found a significant association among strategic planning and innovation capability. The study discussed that SMEs have a tendency to be innovative and depend on strategic planning for competitive advantages. Similarly, AlQershi (2021) revealed that strategic planning is a significant enabler of SMEs’ innovations.

The second hypothesis (H2) examined the relationship between leadership and innovation capability and found a positive relationship. In the same way with the previous studies, Sawaean and Ali (2020) revealed a significant association among leadership and innovation in SMEs. Similarly, the study of Cortes and Herrmann (2021) assessed that leadership improve the innovative performance of SMEs. Furthermore, Bagheri (2017), identified the optimistic influence of leadership on innovative capability which lead SMEs towards substantial performance.

The third hypothesis (H3) found a positive significant association between knowledge management and innovation capability. The findings supported by the study of Inkinen et al. (2015) emphasized that knowledge management give the impression as one of the strong impacts on the innovative capability of SMEs and play a vigorous part to develop an innovative surroundings in which SMEs capable to go more than standard potential performance. Shannak et al. (2017) found that knowledge management not simply consider as a part of innovative and creative performance but is essentially encouraging SMEs for competitive advantages.

The fourth hypothesis (H4) identify the relation between innovation capability and SME performance and found a positive relationship. In the way with previous studies, Hajar (2015) discussed that innovation helps SMEs to produce valuable goods with minimum cost and generate a good image in the market that makes values for enterprises. The study of Falahat et al. (2020) found that SMEs with improved innovation plans increase sustainable performance contrasting those enterprises who have old-style attitudes towards innovation. Furthermore, Najafi-Tavani et al. (2018) analysed that SMEs have to update their innovation strategies to increase performance and achieved a sustainable competitive advantage. Similarly, Mustafa and Yaakub (2018) resulted that innovation is the finest optimal path for SMEs to improve their performance, particularly in developing economies.

The second research question that has been developed is: “Does technology adoption mediate the relationship?” The consequences of RQ2 were based on the H5, H6, and H7 hypothesised relationships.

The hypotheses H5, H6, and H7 proposed in this study focus on the mediating role of technology adoption in the relationship between innovation capability, and SME performance. The findings revealed that technology adoption partially mediates the relationship between innovation capability and individual performance. Therefore, hypotheses (H5, H6, and H7) are supported. The positive relationship reported is consistent with the study of Huang and Chen (2019), technology enhancement is the vital key factor to mediate the relationship of innovation and SMEs performance. Wu et al. (2015) found a positive and significant association between innovation and the perception of technology. The study also found a positive connection between this perception and firms’ performance. The study of Muscio and Ciffolilli (2018), found technology adoption mediates the association among innovation and firm performance. Therefore, innovation is capable to improve the performance of enterprises once adoption of technologies are encouraged. In a similar way, Lee et al. (2017) revealed that the mediating effect of technology capable to improve the innovation process and performance of firm. In line with these findings, the study of Wen and Zheng (2020) found that the perception of new technology has a positive relationship with innovation and firms’ performance.

Conclusion

In this study, the seven hypotheses developed to test the influential relationship in two steps. Step 1: To identify the relationship between innovation capability (strategic planning, leadership management, and knowledge management), and SME performance (H1, H2, H3, and H4). Step 2: to determine the direct relationship of technology adoption towards innovation capability and SME performance (H5 and H6), and also assess the mediating effect of technology adoption (H7) in the developing market of Malaysia. Structured questionnaires were used to collect the data from 611 SMEs from different sectors and then statistically analysed through AMOS - 21. The findings of the study indicated that strategic planning, leadership management, and knowledge management are influential factors of innovation capabilities and found a positive relationship. The findings found positive relationship between innovation capability and SME performance in Malaysia. Furthermore, the study found partial mediating effect of technology adoption between innovation and SME performance. Precisely, the results show that innovation capability perform an imperative role in the performance of SMEs in developing markets.

Research implications

The vital reason of this research is to provide empirical results that may be theoretically and practically beneficial for the owners of SMEs and policymakers. For example, owners of SMEs of different sectors are looking for growth and sustainable performance using diverse strategies and resources. This study proposes that innovation capability (with factors strategic planning, leadership management, knowledge management) is a key driver that contributes signiﬁcantly to SME's performance. In a developing market like Malaysia, owners of SMEs are facing numerous challenges to survive in an unsettled market. In emerging markets, innovation capability is an important aspect that contributes signiﬁcantly to SME performance (Afriyie et al., 2019; Dabić, et al., 2019; Donkor et al., 2018). While owners of SMEs particularly from the small industries sector capitalise plenty of resources keen on tangible and uncertain ventures that could be a reason for large damage. Therefore, owners of SMEs are necessary to give substantial consideration to innovation in mandate to perform and survive in the emerging markets. Hence, the growth and performance of SMEs cannot improve with investment intangible resources, innovation capability gives substitute and easy method to perform and survive over the long term. The study results also explain that the manufacturing sector of SMEs should not disregard the adoption of technology as it contributes signiﬁcantly in developing markets like Malaysia.

Featuring the significance of the study for policymakers, this study suggests for SMIDEC to focus on innovation of SMEs and support them for adaptation of new technologies either than pay attention to training and seminars, through that SMEs may be capable to construct an operative innovation structure. As SMIDEC is in authority for all the created and implanted policies for the growth and survival of SMEs in Malaysia. Therefore, it would be more beneficial to organize exceptional provision plans to assist SMEs regarding innovation.

Limitations and directions for future research

As mentioned earlier, this research has signiﬁcant implications. Researchers (Anwar and Ali, 2020; Robison and Thomas, 2018) identified that study showing theoretical and practical implications has its limitations. According to Greener (2018), identifying study limitations is an ability and part of the strength of the research. Regarding this study, some limitations are classified and provide a chance for prospective scholars to address and create further productive understandings. For instance, due to the COVID-19 traveling restrictions and lockdown, this study has conducted only in the major cities of different states in Malaysia, however, innovation is not limited to SMEs in metropolitan cities but also with small cities. It is a direction for researchers, to conduct the study on the rural SMEs or comparatively analyse the rural and urban SMEs performance. Another limitation of this study is the adaptation of a cross-sectional design to collect the data means that data was collected at one specific point in time. Researchers can use longitudinal research in the future and collect data at different time frames may present diversified results. Finally, the empirical findings of this research found partial mediating effect of technology adoption between innovation capability and SME performance. While the researchers can include mediator(s) or moderator(s) in the established framework to get new findings.

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) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Muhammad Farhan Jalil

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