THE MEDIATING EFFECT OF SOCIAL MEDIA MARKETING ADOPTION BETWEEN COMPETITIVE INTELLIGENCE AND SME PERFORMANCE

Muhammad Faizal Samat¹⁺ --- Mohd Nor Hakimin Yusoff² --- Mohammad Ismail³

¹Faculty of Business Management, Universiti Teknologi Mara (UiTM) Kelantan, Malaysia

^2,3Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Malaysia

Keywords:Social media marketing Competitive intelligence SME performance

ARTICLE HISTORY: Received:20 March 2018. Revised:30 April 2018. Accepted:7 May 2018. Published:16 May 2018.

1. INTRODUCTION

Small and medium enterprises (SMEs) collectively have been recognized as the engine of economic activity and a generator of employment and growth. Moreover, SMEs play an important role and have been major contributors to the socio-economic development of Malaysia. In this country, SMEs are an important component of economic growth and they have been identified as one of the key engines to drive the transformation of Malaysia into a fully-developed high-income nation by 2020. In the national economy, the services sectors as a whole were the largest contributor to the GDP in the period from 2005 to 2014 while the construction, manufacturing, mining and quarrying sectors are the others sectors that are further supported to contribute to the total GDP in Malaysia.

There are three different categories of SMEs in Malaysia, namely, micro, small, and medium enterprises. SME categories are defined based on two criteria, which are the total number of full-time workers and annual sales turnover. The definition covers the manufacturing, primary agriculture and services sectors with minimum annual sales threshold of RM 300, 000 or less than 5 full-time employees. In Malaysia, SMEs can be defined as businesses that have sales turnovers below RM 50 million with maximum of 200 employees for the manufacturing sector and sales turnover below RM 20 million with a maximum of 75 employees for services and other sectors.

1.1. Problem Statement

Due to the increase in global economic competition, public sector organizations are becoming more susceptible to dwindling budgets and are realizing the need to invest in or divest capabilities to meet marketplace demand. Companies in the private sector are also confronted with an intensifying competitive environment where sustained competitive advantage is becoming more and more difficult to sustain. Thus, many organizations, either in the public or private sector, are instigating their own competitive intelligence (CI) services to guide their decision makers. Bose (2008) hypothesized CI as a practice of monitoring the competitive environment aimed at providing actionable intelligence that will give organizations a competitive edge. Most companies still do not establish a formal department of CI even though it is apparent that CI is becoming increasingly vital to a company’s survival in today’s dynamic economies (McGonagle and Vella, 2004). Typically, this could be due to lack of formal education in CI (Fleisher, 2004). In a study on CI practices of 44 small US enterprises, Groom and David (2001) found that 50 percent of the sample reported that their companies do not have formal CI practices which resulted in a breakdown in the flow and exchange of information. According to Dai et al. (2011) and He et al. (2013) CI is considered as a new factor and most companies are unfamiliar with the concept of CI to adopt social media marketing in increasing their performance. Thus, the aim of this study is to close this gap.

SMEs are expected to enhance business performance and competitiveness by using Internet-based applications (Shang, 2014). Overall, SMEs are expected to be significant users of the Internet and social media. According to Obiri-Yeboah et al. (2013) social media has turned into a main impetus for advancement and development in numerous nations with a large number of organisations associated with it around the world. This statement is upheld by a study done in 2008 which reports that 68 percent of large organisations in Albania make use of social media (Obiri-Yeboah et al., 2013). Prior insights by Dixon et al. (2002) additionally show that 63 percent of all UK SMEs were associated with social media in 2001 with 540,000 SMEs trading on the web. They discovered that more than half (57 percent) of SMEs in the UK credit broadband with enhanced benefits and 66 percent to reducing the cost base.

However, in studies of internet adoption by Malaysian SMEs in a developing country, the situation differs from that of developed countries. Although access to social media has improved, engagement and use of the Internet is still at low levels in small companies (Shang, 2014). Many companies have leveraged social media in conducting their marketing efforts, however, SMEs are known to have limitations and acceptance barriers in adopting a social media marketing approach (Dahnil et al., 2014). Social media marketing adoption among SMEs is companyly associated with the ability of owner-managers to develop knowhow (Martin, 2004). The adoption of social media marketing relies on the process of information collection and analysis by the owner-managers of SMEs.

1.2. Objectives

The objective that guided this study was to investigate the relationship between competitive intelligence and SME performance. Other than that, this study also investigates the mediation effect of social media marketing adoption between competitive intelligence and SME performance.

2. LITERATURE REVIEW

2.1. Competitive Intelligence

Competitive intelligence is a crucial segment of an organization's key management and administration process. It gathers information and data from an extensive and key perspective, permitting an organization to anticipate or gauge what will be happening in its market place. The very idea of competitive intelligence and its terminology have proved its role as a must-do practice by companies. The overview about intelligence practices has been adapted and it was depicted by a timeline of historical events in the subsequent sections.

Bose (2008) sees competitive intelligence as a method to investigate the competitive environment, aiming to provide noteworthy knowledge that will give a focused edge to the company (Bose, 2008). According to He et al. (2013), CI is characterised by the assembly and examination of knowledge about competitor's items, advancements, deals and so forth from external sources. Meanwhile, Calof and Skinner (1999) defined competitive intelligence as “…actionable recommendations arising from a systematic process involving planning, gathering, analyzing and disseminating information on the external environment for opportunities, or developments that have the potential to affect a company’s or country’s competitive situation”. No matter which definitions are chosen, the aim is to create knowledge from information that is widely accessible by means of a systematic method which is effective in reducing uncertainties in managerial decisions. The purpose of competitive intelligence is to forecast changes in customers, regulators and competitors so that the company is in a better position to take advantage of it. According to Agus et al. (2015) competitive intelligence can be measured through level of education, working capital and level of innovation.

Competitive intelligence is essential in enabling companies to make forward-looking decisions. Since every strategic decision is usually formed based on particular assumptions, competitive intelligence can facilitate a company to examine and validate the assumptions it makes which is the principle of strategic business analysis. Thus, there are several advantages of competitive intelligence: filling in gaps by covering areas that a company overlooked in its assumptions; it can facilitate the formulation of company strategies; it can help identify which areas to improve, and to recognize risks and opportunities; and it can provide information that helps a company to recognize the strengths, weaknesses, strategies, objectives, market positioning and reaction patterns of its competitors. As a result, the company can successfully identify, classify and track its competitors by looking at the points of comparison in relation to its strengths and weaknesses as opposed to its competitors. Consequently, the value of the intelligence can be determined through the following traits such as relevance, accuracy, usability, objectivity, readiness, and timeliness (Bose, 2008).

2.2. SME Performance

Performance measurement is described as “the process of quantifying action, where measurement is the process of quantification and action leads to performance” (Maurya et al., 2015). According to Daft et al. (1988) organizational performance is the capability of the organization to fulfill its goal through efficient and effective utilization of resources. Organizational performance is the real results of an organization compared to the planned outcomes (Ho, 2011).

Bernhardt (1996) suggests that although there is a growing interest amongst academics and practitioners in the area of intelligence, there are also doubts concerning its capability to effectively support business performance. According to Gbosbal and Kim (1986) intelligence units must directly support business performance in order to obtain legitimacy. Another study done by Subramanian and Ishak (1998) on 85 companies discovered that companies which have advanced systems in place to monitor the activities of their competitors gained larger profits when compared to companies that did not come up with such systems. This is supported by Fuld (1999) who found that competitive intelligence has positive effects on a company’s performance whereby corporate success is a result of well-designed products and services, hard-won marketing campaigns, and the strategic handling of intelligence, whereas most failures occur due to poor judgment, bad timing and misuse or inadequate use of competitive intelligence.

According to Hawking and Sellitto (2015) companies collect information about their competitors through the process of competitive intelligence and apply it in their decision making. Fan and Gordon (2014) in their study, found that competitive intelligence helps companies in making a decision by making business understand what they need to be competitive in the market. Conclusively, empirical studies show a strong correlation between competitive intelligence as a source of competitive advantage and its contribution to company’s performance. Therefore, the hypothesis below was developed:

H1: There is a significant relationship between competitive intelligence and SME performance.

2.3. Social Media Marketing Adoption

Blackshaw and Nazzaro (2004) described social media as an assortment of the latest means of online interaction that are created, initiated, circulated and can be used inform about products, brands, services, personalities, and issues. On the other hand, Kaplan and Haenlein (2010) describe social media as a group of Internet-based applications that was constructed from the concept and technology of web 2.0, allowing the construction and exchange of User Generated Content. Therefore, social media provides a platform for people to interact by creating, sharing, exchanging and commenting on topics posted in virtual communities and networks.

According to Vuori (2011) social media marketing allows for an improved distribution of competitive intelligence within a company. Social media marketing also was seen as possibly making a contribution to facilitating the competitive intelligence. Therefore, it can be hypothesized as:

H2: Social media marketing mediates the relationship between competitive intelligence and SME performance.

2.4. Linking Social Media Marketing Adoption between Competitive Intelligence and SME Performance

The research framework for the study at hand was derived from Technology Acceptance Model (TAM). TAM is a technology acceptance model that examined user perceptions of technological attributes presumed to influence intention to use technology and consequent behaviour. In this study, TAM has been used to explain IT users’ intention and behaviour regarding IT usage. Competitive intelligence act as source of perceive usefulness in this study to adopt social media marketing.

Figure-1.1. Proposed Research Framework

3. METHODOLOGY

In order to examine the relationship between competitive intelligence and SME performance, a total of 1920 owner/managers SMEs were randomly selected from the SME Corp Malaysia and sent with the questionnaires. Owner/managers were targeted because they are the key informants of the business and usually they are involved in the overall running of the firms. 405 responses were returned and 339 found useable for the final analysis using the structural equation model partial least square (SEM-PLS). For social media marketing adoption, the questions was adapted from Zolkepli and Kamarulzaman (2015). Meanwhile, in term of SME performance, the questionnaire was adapted from Maurya et al. (2015) who suggested that performance measures should include growth and market value. All items for competitive intelligence and SME performance were measured on five-point Likert scales ranged from 1=Strongly Disagree to 5=Strongly Agree.

4. DATA ANALYSIS AND RESULTS

4.1. Normality

Normality test was conducted and measured using skewness and kurtosis. Normality test was used to determine if a data significantly deviate from a normal distribution.

Table-1.1.Normality Result

	SME Performance	Competitive Intelligence	Social Media Marketing Adoption
Skewness Kurtosis	-0.810 1.220	-0.681 1.038	-0.576 0.498

Based on the above table, the result of normality test range from -0.810 to 1.220, considered that all value is acceptable. According to George and Mallery (2016) the value between -2 and +2 are acceptable and consider as a normal. It means that all variables that were used in this study are normal. Hence, the researcher can proceed for further analysis.

4.2. Multicollinearity

Multicollinearity occurs when there are correlations among variables. According to Tabachnick and Fidell (1996) this happens if a correlation between two or more variables is 0.9 or greater and in order to overcome this problem, one of the variables is removed from further analysis.

Table-1.2. Correlations among Variables

	SME Performance	Competitive Intelligence
SME Perforamnce Competitive Intelligence	1   0.629**	0.629**   1

**. Correlation is significant at the 0.01 level (2-tailed).

As all of the measures of correlation for the variables are below 0.9, it can be concluded that there is no multicollinearity problem

4.3. Respondents' Demographic Profiles

Table-1.3. Frequency and percentage distribution by company profile

Demographic		N	%
Type of Industry
	Manufacturing	144	42.5
	Service	158	46.6
	Others	37	10.9
	Total	339	100.0
Age of Business
	Less than 3 years	83	19.4
	4-8 years	130	40.3
	9-15 years	96	21.6
	16-20 years	17	17.9
	Above 20 years	13	0.7
	Total	339	100.0
Number of Full-time Employees
	Less than 5 employees	167	49.3
	5-50 employees	149	44.0
	51-150 employees	21	6.2
	150-200 employees	2	0.6
	Total	339	100.0

Source: Survey

A total of 339 respondents completed the questionnaire and summarized in Table 1.3 as above. For the type of industry category, 46.6 percent of the total numbers of respondents were in service, 42.5 percent were in manufacturing and 10.9 percent were others. In terms of type of company ownership category, majority of the respondents were sole proprietor which indicated the percentage of 75.2 percent, 17.7 percent were partnership business and 7.1 percent were limited company. When respondents were asked about their age of business, 38.3 percent reported to be operating for 4-8 years, followed by 28.3 percent were 9-15 years, while 24.5 percent were less than 3 years of operation. Only 5.0 percent were 16-20 years while 3.8 percent are above 16 to 20 years. Finally, when the respondents were asked about number of full-time employees, 49.3 percent were less than 5 employees, followed by 44.0 percent were 5-50 employees. Only 6.2 percents were 51-150 employees while 0.6 percent were 150-200 employees.

4.4. Construct Validity

Table-1.4. Construct validity and Reliability

Constructs	Items	Loadings	AVE	CR
SME Performance	SF1	0.904	0.808	0.962
	SF2	0.896
	SF3	0.906
	SF4	0.893
	SF5	0.907
	SF6	0.888
Competitive Intelligence	SD1	0.867	0.806	0.954
	SD2	0.907
	SD3	0.886
	SD4	0.907
	SD5	0.922
Social Media Marketing Adoption	SE1	0.911	0.800	0.952
	SE2	0.913
	SE3	0.879
	SE4	0.848
	SE5	0.884

AVE: Average Variance extracted; CR: Composite Reliability

The above table shown that, results show that indicator loadings for all items exceeded the recommended value of 0.5 (Hair et al., 2010). AVE were in the range of 0.800 to 0.808, which is above the recommended value of 0.5, and CR ranged from 0.952 to 0.962 which exceeded  recommended value of 0.7 (Hair et al., 2010).

4.5. Discriminant Validity

Table-1.5. Discriminant validity

	SMEP	Competitive Intelligence	Social Media Marketing Adoption (SMM)
SME Performance (SMEP)	0.899
Competitive Intelligence	0.629	0.898
SMM	0.632	0.587	0.894

The utmost common method of validating the discriminant validity as suggested by Hair et al. (2010) and Fornell and Larcker (1981) the square root of the average variance extracted (AVE) for each construct is greater than the correlation between the constructs. The above table indicate that there is adequate discriminant validity since the diagonal elements are significantly greater than the off-diagonal elements in the corresponding rows and columns.

4.6. Heterotrait-Monotrait Ratio

Table-1.6. Heterotrait-Monotrait Ratio

	SMEP	Competitive Intelligence	Social Media Marketing Adoption (SMM)
SME Performance (SMEP)
Competitive Intelligence	0.665		0.625

However, Fornell and Larcker (1981) was recently criticized by Henseler et al. (2015) that in most cases the above methods fails to fully scrutinize and validate the issue of discriminant validity. They, however, suggested the multitrait-multimethod matrix, to assess discriminant validity, the heterotrait-monotrait (HTMT) ratio of correlations as a more rigorous method of achieving discriminant validity. HTMT as a criterion involves comparing it to a predefined threshold. Clark and Watson (1995); Kline (2011) suggest a threshold of 0.85, whereas propose a value of 0.90 can claimed that there is a lack of discriminant validity. Hence, the result show that discriminant validity is fulfilled the threshold (table 4.3)

4.7. Hypotheses

Path analysis was performed to evaluate the structural model. The primary evaluation criteria for structural model are R2 values and the level of significance of the path coefficients.

Based on Cohen (1988) for a good model, the value of R2 of endogenous latent variable should be more than 0.26. The R² was found for this path model is 0.417, indicating that competitive intelligence can account for 41.7 percent of the variance in SME performance, which represent a substantial range.

Table-1.7. Hypotheses Result

Hypothesis	beta	t values	p-values	Result
H1: Competitive Intelligence – SME performance	0.241	2.163	0.000	Supported
H2: Competitive Intelligence – SMM – SME performance	0.114	4.197	0.000	Supported

SMM: Social Media Marketing Adoption

For H1 which stated competitive intelligence has a significant influence on SME performance, the result indicates (β = 0.241, t-values=2.163) has a significant relationship found between SME performance. Hence H1 was supported.

Furthermore, the above table shows result of an indirect effect between competitive intelligence and SME performance. The result revealed social media marketing adoption play role as a mediator between competitive intelligence (β = 0.114, t-values=4.197). Thus, the study can conclude that social media marketing adoption mediates the relationship between competitive intelligence and SME performance. Hence, H2 was supported.

5. CONCLUSION

The study was to identify the mediation effect of social media marketing adoption between competitive intelligence and SME performance in East Coast Region of Malaysia. For the first objective, competitive intelligence found and concluded to be significantly linked to SME performance in this study. Thus, it is proven that competitive intelligence plays a vital part in a company’s decision making process in order for it to improve performance and be competitive (Fan and Gordon, 2014).

For second objective, it is also found that social media marketing adoption mediates the relationship between competitive intelligence and SME performance. Basically, an effective strategy helps a company to successfully position itself in a marketplace. To achieve a position of high performance in today’s global competitive environment becomes a difficult task especially without competitive intelligence to provide valuable information on emerging opportunities and threats in formulating technological innovation such as social media marketing (Zhu et al., 2006; Aboelmaged, 2014).

As a conclusion, the findings are consistent and validate past research, as well as providing evidence for the mediating role of social media marketing adoption on the relationship between competitive intelligence and SME performance.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Contributors/Acknowledgement: All authors contributed equally to the conception and design of the study.

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