International Journal of Entrepreneurship (Print ISSN: 1099-9264; Online ISSN: 1939-4675)

Research Article: 2019 Vol: 23 Issue: 2

Factors Affecting SMEs Owners to Use Social Media for Online Advertisement in Kuwait

Dr. Rashed Alhaimer, Arab Open University

Abstract

This study examines the factors that affect small and medium enterprises in Kuwait to use social media for online advertisement of their products. The study adopted to use the Unified Theory of Acceptance and Use of Technology, (UTAUT), model to check the acceptance level of social media applications as a platform for online advertisement and correlation analysis to establish how each factor affects behavioural intention to use social media for online advertisement. The study adopts six factors, i.e., Performance expectancy, perceived price advantage, social influence, hedonic value, facilitating conditions, as independent variables and behavioural intention as dependent variable. This research used questionnaire for the purpose of collecting primary numeric data from participants. Each questionnaire contained items, including location and biological information of the respondent and experience in social media advertisement. The sample size used in this study was 206 participants. After collecting the data, the analysis was done using SPSS software to do a one sample t-test and correlation analysis. The findings indicated that the independent variables, performance expectancy, facilitating conditions, social influence and hedonic value, positively affect behavioural intention to use social media for online advertisement. Perceived price advantage has a negative effect.

Keywords

SMEs, Social Media, Online Advertisement.

Introduction

Using social media for advertising businesses has gained popularity in recent years. Facebook has approximately more than 5 billion users worldwide. This implies that when you advertise your product on Facebook, it would likely reach a large population in short time. With Facebook becoming a many-many platform rather than a one-one platform, it proves to be the best way of promoting business growth and development (Derham, 2011). SMEs can now utilize Facebook functions, e.g. sharing, tagging, messaging, notifying and commenting, for selling, advertising and marketing their goods and services at a cheaper cost.

Connecting a business with social media e.g. Facebook can be done easily by just creating a page having the business name. Therefore, Small and Medium Enterprises can advertise daily using it since it is cheaper and doesn’t require computer skills (Alrawi & Sabry, 2009). The only cost is internet access while one only needs to know how to post their product so as to advertise it (Derham, 2011). According to a study in Sweden by Schubert & Leimstoll (2007) on social media platforms and SMEs, SMEs extensively use social media platforms in their day to day advertisements and that there is a high rate of inter-organizational social media usage and also, social media platforms successfully supports competitive strategies (Schubert & Leimstoll, 2007). Social media networks have grown rapidly to be the leading marketing and advertising channel in Kuwait (Rouibah et al., 2015). Tan & Macaulay (2007) claim that social media marketing is a key instrument to SMEs because it helps in increasing the efficiency of business operations. SMEs also play a major role in the overall economy by creating job opportunities for the local communities (Barba-Sanchez, 2007).

Intense social media usage is not a guarantee for faster growth of a Small and Medium Enterprise. In some cases, business growth by itself creates some operational requirements that best suit social media (Locke, 2004).

Literature Review

Adopting Social Media Platforms

The internet is used in almost all workplaces in todays’ business world. Internet communication has eased communication thereby enabling business people to communicate without limits at any time anywhere (Chen, 2008). Several studies have researched on the use of social media applications among SMEs and found that social media is used mainly for marketing, communication, sales, advertising, innovation, problem resolution, and customer service.

Meske & Stieglitz (2013) claims that SMEs use social media applications such as twitter to communicate with their customers and also to for internal communication within the staff.

Factors Affecting Adoption of Social Media Marketing

A research done on SMEs managers in USA, England and Australia found out that innovativeness, age and geographic location of a company have a significant effect on social media adoption by the Small and Medium Enterprises (Wamba & Carter, 2013). Zeiller & Schauer (2011) found out that Small and Medium Enterprises (SMEs) will use social media only if the applications provide a significant, valuable and timely information related to their businesses. A study by Wang et al. (2009), found that factors such as performance expectancy, facilitating conditions, social influence, hedonic value and perceived price advantage affect social media adoption. These factors that influence adoption of social media platforms for advertising have been explained below:

Performance expectancy

Venkatesh et al. (2003), defines performance expectancy as the degree to which an individual believes that using the system will help him/her to attain gains in job performance. In their study, performance expectancy is about how SMEs believe that social media will help in advertising their products and services. Social media is believed to spread information faster than any other medium for advertising. According to Gupta et al. (2008), performance expectancy impacts the use of internet positively. In a study to find out the factors that affect the spread of mcommerce in Kuwait by Alkhunaizan & Love (2012), they found out that performance expectancy positively related to behavioral usage intention of m-commerce. Also, another study by Sin Tan et al. (2013) on the factors influencing intention in internet marketing usage between Malaysia and South Korea found out that performance expectancy had a significant positive effect on intention in Internet marketing usage for both countries. From all these literatures, it is evident that performance expectancy has a positive effect on behavioural intention and usage by use of UTAUT model.

Facilitating conditions

Venkatesh (2003), defined facilitating conditions as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system. A study by Escobar-Rodríguez (2014) found out that facilitating conditions in the use of social media platforms positively impacted perceived relevance of social media platform. These findings suggest that the perception about the support available for social media users across the world influences their intention to use it.

Social influence

Venkatesh (2003), defines social influence as the degree to which an individual perceives that others believe he/she should use the new system. Social influence significantly affects the behaviour intention to use m-commerce in Kuwait. This is according to Alkhunaizan and Love (2012) Also, social influence will make a non-user to try a new venture given that his\her peers are using it.

Hedonic value

This is the pleasure value of the activity. Hedonic value is about whether the SME owners feel pleasurable as they advertise their businesses and products and services using social media platforms. The pleasure when adopting a social media platform would increase the motivation among other SME owners to advertise their businesses on social media platforms. Pleasure value of an activity increases the motivation to keep doing the activity. Kumar (2013) found out that perceived pleasure has a strong positive relationship with behavioural intention. In a similarly manner, Guo (2014) concluded that perceived pleasure is important in convincing new people to use a given technology or system for a given purpose. He came to this conclusion since most people tend to achieve pleasure. This implies that hedonic value has a positively affects behavioural intention to use social media for online advertisement.

Methodology

This study would test the following hypotheses stated below.

Hypotheses

H1: Performance expectancy positively affects behavioural intention to use social media for online advertisement

H2: Facilitating conditions positively affect behavioural intention to use social media for online advertisement

H3: Social influence positively affects behavioural intention to use social media for online advertisement

H4: Hedonic value positively affects behavioural intention to use social media for online advertisement

H5: Perceived price advantage positively affect behavioural intention to use social media for online advertisement.

Data Collection

The research was aimed at collecting accurate and relevant data that would help to generate quality outcomes. The study used primary data. Questionnaires were used in collection of the primary data from Kuwaiti consumers and Small and Medium Enterprise owners regarding social media advertisement. The collected data was analysed quantitatively to determine the consumers Small and Medium Enterprise owners view on social media advertising. Questionnaires were chosen as the data collection instrument because all participants were not in the same place and therefore, it would be easier, faster and cheaper to send questionnaires rather than travel to each participant to collect data in person. To ensure the questionnaire’s effectiveness, a pilot test involving three participants was conducted to check if the questionnaires would work and also to get suggestions that would improve the quality of the questionnaires before they were sent to the intended respondents. This being a questionnaire intended to be used by a large sample size, it used closed questions such that the respondents had to select an answer from the given multiple choices. The answers were in the 5-likert scale of strongly disagree, disagree, neutral, agree and Strongly agree which were coded as follows; Strongly disagree 1, Disagree - 2, Neutral - 3, Agree 4, Strongly agree – 5.

Sampling Techniques

The research adopted non-probability sampling. Due to the large sample size, I used snowball sampling to determine my sample size. A sample of 206 Kuwaitis was used.

Questionnaire Distribution

The questionnaires were created by Google sheets and distributed to the selected respondents through emails and Facebook. Facebook was used because it is the most used social media platform in Kuwait. This allowed the respondents to provide answers online. Participants were allowed to submit the questionnaires at any time they were comfortable since the participation was voluntary.

Components of the Questionnaire

The questionnaire had of 21 questions. The questions included basic information of the respondents, occupation, how they perceive social media advertisement, the pleasure value of the advertised commodities and social influence among others. All the responses were provided in a 5 Likert scale that was coded 1, 2, 3, 4 and 5 for strongly disagree, disagree, neutral, agree and strongly agree respectively.

Reliability of Outcomes

The raw data from the respondents’ questionnaires was to Statistical Package for Social Sciences, SPSS, through Epidata software so as not to lose any information. Outcome reliability was measured through the Statistical Package for Social Sciences (SPSS) computer software. Data was tabulated and the tables which were then interpreted in detail.

Data Analysis

Data analysis was done using the SPSS computer software. The data was first analysed to produce the descriptive statistics of the responses. A comparison of means test, one - sample ttest, was used to explore the diverse attitudes of Kuwaiti consumers regarding social media advertisement. The relationships between the different consumer perceptions towards social media advertisement were investigated via correlation analysis.

Findings, Discussion And Conclusion

Descriptive Statistics

The descriptive statistics are presented in the Table 1 below;

Table 1: Descriptive Statistics
    Minimu Maximu   Std.
N m m Mean Deviation
behavioral intention 206 1 5 3 1.463
performance expectancy 206 1 5 2.99 1.488
facilitating conditions 206 1 5 3.04 1.463
social influence 206 1 5 3 1.43
hedonic value 206 1 5 2.9 1.465
perceived price advantage 206 1 5 3.09 1.416
Valid N (listwise) 206        

From the Table 1 above, we know that the sample size, n, for this study was two hundred and six Kuwaitis. The data collected was coded using the five Likert Scale of strongly disagree, disagree, neutral, agree, strongly agree represented by 1, 2, 3, 4 and 5 respectively.

From the t-test results in the table above, behavioural intention responses range between strongly disagree and strongly agree and have a mean of 3.00, which implies neutral and a standard deviation of 1.463. Performance expectancy responses range between strongly disagrees and strongly agrees and has a mean of 2.99, which translates to neutral, and a standard deviation of 1.488. Facilitating conditions responses range between strongly disagree and strongly agree and has a mean of 3.04, implying the average response was neutral, and a standard deviation of 1.463. Social influence responses range between strongly disagree and strongly agree and has a mean of 3.00, meaning that the average response was neutral, and a standard deviation of 1.430. Hedonic value responses range between strongly disagree and strongly agree and has a mean of 2.90, which is rounded off to 3, implying neutral, and a standard deviation of 1.465. Perceived price advantage responses range between strongly disagree and strongly agree and have a mean of 3.09, implying the average response was neutral, and a standard deviation of 1.416. The means of all the variables are all close to 3, which imply neutral, from the employed Likert scale. This implies that all the products and services advertised through social media are satisfactory according to the populations’ responses.

One Sample T–test

The results of a one sample t-test are as shown in the Table 2 below:

Table 2: One-Sample Test
  Test Value = 0
  t df Sig. (2-
tailed)
Mean Difference 95% Confidence
Interval of the Difference
 
Lower Upper
performance expectancy 28.841 205 0 2.99 2.79 3.19
facilitating conditions 29.866 205 0 3.044 2.84 3.24
social influence 30.069 205 0 2.995 2.8 3.19
hedonic value 28.437 205 0 2.903 2.7 3.1
perceived price advantage 31.335 205 0 3.092 2.9 3.29

Performance expectancy has a t-value of 28.841 with 205 degrees of freedom and a pvalue of 0 at 0.025, two tailed, level of significance. The mean difference is 2.99 which is within the 95% confidence interval. Facilitating conditions has a t-value of 29.866 with 205 degrees of freedom and a p-value of 0 at 0.025, two tailed, level of significance. The mean difference, 3.044, is within the 95% confidence intervals. Social influence has a t-value of 30.069 with 205 degrees of freedom and a p-value of 0 at 0.025, two tailed, level of significance. The mean difference is 2.995, which is within the 95% confidence interval. Hedonic value has a t-value of 28.437 with 205 degrees of freedom and a p-value of 0 at 0.025, two tailed, level of significance. It has a mean difference of 2.903, which is within the 95% confidence interval. Perceived price advantage has a t-value of 31.335 with 205 degrees of freedom and a p-value of 0 at 0.025, two tailed, level of significance. It has a mean difference of 3.092, which is within the 95% confidence interval. This implies that means of performance expectancy, facilitating conditions, social influence, hedonic value and perceived price advantage t statistics are not statistically different, i.e. they have equal means.

Correlation Analysis

The relationships between the various consumer perceptions towards social media advertisement were investigated via correlation analysis and the results are presented in the Table 3 below:

Table 3: Correlations
  Behavioral Intention Performance Expectancy Facilitating Conditions Social Influence Hedonic Value Perceived Price Advantage
Behavioral Intention Pearson Correlation 1 0.063 -0.055 0.084 0.068 -0.016
Sig. (2-tailed)   0.371 0.436 0.23 0.331 0.817
N 206 206 206 206 206 206
Performance Expectancy Pearson Correlation 0.063 1 .139* -0.005 0.073 -0.037
Sig. (2-tailed) 0.371   0.046 0.948 0.294 0.601
N 206 206 206 206 206 206
Facilitating Conditions Pearson Correlation -0.055 .139* 1 0.012 -0.014 -0.047
Sig. (2-tailed) 0.436 0.046   0.867 0.842 0.505
N 206 206 206 206 206 206
Social Influence PEARSON Correlation 0.084 -0.005 0.012 1 0.018 0.063
Sig. (2-tailed) 0.23 0.948 0.867   0.793 0.369
N 206 206 206 206 206 206
Hedonic value Pearson Correlation 0.068 0.073 -0.014 0.018 1 -0.014
Sig. (2-tailed) 0.331 0.294 0.842 0.793   0.836
N 206 206 206 206 206 206
Perceived Price Advantage Pearson Correlation -0.016 -0.037 -0.047 0.063 -0.014 1
Sig. (2-tailed) 0.817 0.601 0.505 0.369 0.836  
N 206 206 206 206 206 206

Behavioural intention has a weak positive correlation with performance expectancy, social influence and hedonic value and a weak negative correlation with facilitating conditions and perceived price advantage. Performance expectancy has a weak positive correlation with behavioural intention, facilitating conditions and hedonic value and a weak negative correlation with social influence and perceived price advantage. Facilitating conditions have a weak positive correlation with performance expectancy and social influence and weak negative correlation with behavioural intention, hedonic value and perceived price advantage. Social influence shows a very weak positive correlation with behavioural intention, facilitating conditions, hedonic value and perceived price advantage and very weak negative correlation with performance expectancy. Hedonic value has a very weak positive correlation with behavioural intention, performance expectancy and social influence and very weak negative correlation with facilitating conditions and perceived price advantage. Perceived price advantage shows a very weak positive correlation with social influence and very weak negative correlation with behavioural intention, performance expectancy, social influence and hedonic value.

Discussion

Performance expectancy shows a positive correlation with behavioural intention which implies that performance expectancy positively affect behavioural intention to use social media for online advertisement. Facilitating conditions negatively affects behavioural intention to use social media for online advertisement due to the weak negative correlation results. Social influence positively affects behavioural intention to use social media for online advertisement due to the positive correlation results. Hedonic value is positively correlated with behavioural intention implying that it positively affects behavioural intention to use social media for online advertisement. Perceived price advantage is negatively correlated with behavioural intention implying that it negatively affects behavioural intention to use social media for online advertisement. This implies that performance expectancy, facilitating conditions; peer influence and pleasure value increases the need for social media advertisement. Therefore, for a small and medium enterprise to use social media as a medium for marketing and advertisement, it will need to have a high performance expectancy and facilitating conditions and also have pleasurable products because these are the factors the positively affect social media marketing. Also, the company should put measures in place to ensure that its customers’ perceived price is lower. High price perception negatively affects social media marketing.

Limitations

This study examines the factors that affect small and medium enterprises in Kuwait to use social media for online advertisement of their products. The factors included performance expectancy, facilitating conditions, social influence, hedonic value and perceived price advantage. All these factors cannot be measured numerically and therefore I had to use a Likert scale to quantify each variable. Quantifying non-numeric variables may result to collecting data that is not 100% accurate.

Future Recommendations

This research was carried out to determine the factors affecting use of social media in advertising small and medium enterprises. In this case, there is still room for further investigation in this field of social media advertising. My suggestions for further research studies are outlined below:

1. Effects of small and medium enterprises advertisements on social media.

2. The impact social media advertising on the growth of small and medium enterprises.

References