The Relationship between Performance Expectancy and Social Media Usage ()
1. Introduction
In today’s digital age, social media platforms have become an integral part of businesses’ daily lives, and their impact on various aspects of business lives, including performance expectancy, has been a topic of growing interest. This research paper aims to investigate the relationship between performance expectancy and social media usage, exploring how users’ perceptions of technology’s ability to enhance their performance can influence their engagement with social media.
Previous scholars have suggested performance expectancy as one of the factors which might influence social media usage (Serben, 2014; Shy Hong, Kah Sin, Wai Lun, & Guan Zhou, 2015; Ceyrat & Oliveira, 2017; Thomas, Singh, & Aulia 2017; Bakar & Zaini, 2022). The concept of performance expectancy, as defined within the Unified Theory of Acceptance and Use of Technology framework, refers to the degree to which an individual believes that utilizing a particular system will help them achieve gains in their job performance (Venkatesh et al., 2003).
Given the ubiquity of social media and its potential impact on business productivity, it is important to understand how performance expectancy shapes users’ social media consumption and engagement. Without social media, micro, small, and medium enterprises (MSMEs) could not align their strategic objectives with the business processes, and therefore fail to achieve target performance (Hazen, Kung, Cegielski, & Jones Farmer, 2014).
Problem Statement
The study aims to explore the linkage between performance expectancy and social media usage, particularly within the context of businesses. Specifically, this research paper seeks to address the following question: Is there any influence of performance expectancy toward social media usage as a marketing tool?
A recent study finds that a significant 87% of micro, small, and medium enterprises utilize social media for marketing, with Facebook and Instagram being the most commonly used platforms (Pleno, 2023). However, a World Bank report from 2018 indicates that the majority of these enterprises have not fully embraced social media marketing due to factors such as limited knowledge or experience, time constraints in managing social media, financial constraints, lack of information about relevant and up-to-date social media applications, and fear of negative feedback (Almaiah, Al-Khasawneh, & Althunibat, 2020; Bank, 2022). There are diverse perspectives on which businesses are eligible to utilize social media as a marketing tool. Although social media platforms offer equal opportunities for micro, small, and medium enterprises, regardless of size, a perception persists that only large companies leverage social media for marketing purposes (Nadaraja & Yazdanifard, 2013). This perception exists among micro, small, and medium enterprises in Malaysia, who believe that only prominent companies like KFC and McDonald’s utilize social media as a marketing strategy (Pleno, 2023). Examining this issue can provide valuable insights into how businesses can leverage social media to improve their overall performance and productivity.
2. Literature Review
There is growing evidence in the literature that suggests a strong correlation between performance expectancy and social media usage. Existing research suggests that communication with customers through social media, as well as the use of video advertising on social media, is positively associated with the performance of small and simple enterprises (Batumalai & Sahid, 2022). Moreover, the literature indicates that when people strongly believe that technology can help them do their jobs, their intentions to use it grow (Gadiman, Jaafar, & Lunyai, 2023).
For instance, a recent study reported that more than 50 million firms utilize Facebook business pages, including 2 million for direct advertising purposes (Pellegrino & Abé, 2023). Furthermore, the study highlights the benefits of social media usage, such as increased brand awareness, enhanced customer engagement, and improved marketing effectiveness (Chivandi, Samuel, & Muchie, 2019).
Another study aimed to test the relationship between communication with customers in social media and the performance of small and simple companies. The findings suggest that communication using social media can be an effective strategy to improve the performance of small enterprises, as it reduces costs, increases efficiency, and leads to positive results in terms of marketing effectiveness.
Additionally, the literature suggests that the successful use of social media for business purposes is contingent on the alignment of social media strategies with other critical business functions, such as product strategies, pricing strategies, and distribution strategies.
Overall, the existing literature highlights the significant role that performance expectancy plays in shaping social media usage, particularly in the context of micro, small, and medium enterprises (MSMEs). Based on the discussed literature, the debate highlights that performance expectancy significantly influences social media usage among micro, small, and medium enterprises (MSMEs), a finding that aligns with the Theory of Reasoned Action (TRA), which posits that behavioral intentions are shaped by attitudes toward the behavior and subjective norms. When micro, small, and medium enterprise (MSME) owners perceive social media as useful in improving efficiency, reducing costs, and enhancing customer engagement, their attitudes toward adopting it become favorable, while the widespread adoption of platforms like Facebook, Instagram, and TikTok for business purposes reinforces positive subjective norms. This interaction strengthens the intention to integrate social media into business strategies, thereby supporting performance outcomes. The conclusion underscores that theoretical contributions lie in reinforcing the Theory of Reasoned Action’s explanatory power in social media usage as a marketing tool, particularly by demonstrating how performance expectancy shapes both attitudes and intentions toward social media use. Practically, the findings recommend that micro, small, and medium enterprises should not only use social media for marketing effectiveness but also ensure its alignment with core business functions such as product, pricing, and distribution strategies to maximize impact. By matching theoretical insights with actionable management, the argument illustrates that performance expectancy is not merely a predictor of usage but also a driver of competitive advantage when strategically rooted inside micro, small, and medium enterprises’ business models.
The Theory of Reasoned Action (TRA) explains MSMEs’ behaviour through two key determinants: attitudes toward the behaviour and subjective norms, which together shape behavioural intentions. However, in the context of social media usage as a marketing tool for MSMEs, extending TRA by adding performance expectancy provides robust descriptive power. Performance expectancy—posited as the belief that using a technology will improve individual job performance—influences attitudes by shaping positive perceptions of social media’s usefulness, such as introducing new products or services, encouraging repetitive purchase behaviour, enhancing customer experiences, and reducing marketing costs. At the same time, as more MSMEs adopt social media strategies, subjective norms become reinforced, creating social pressure and validity for its use. By incorporating performance expectancy into TRA, the model better captures the motivations behind MSMEs’ adoption of social media, illustrating that intention is not only a function of attitudes and norms but also of the perceived ability of the tool to deliver tangible business outcomes. This theoretical extension is particularly valuable as it bridges the gap between classical behavioural theory and contemporary digital business practices, offering a more robust framework for understanding and predicting social media adoption in marketing.
3. Methodology
The performance expectancy measurement used in this study is based on the work of Venkatesh et al. (2003). They defined performance expectancy as the degree to which an individual believes that using an information system can enhance their job performance. This suggests that micro, small, and medium enterprises (MSMEs) would be more likely to utilize social media as a marketing tool if they perceive it can benefit their business outcomes. In other words, if these enterprises recognize that social media for digital marketing can improve their business performance, they may be more inclined to incorporate it into their marketing activities.
3.1. Proportional Random Sampling
In this study, the population is micro, small, and medium enterprises (MSMEs) from all states in Malaysia. By grouping those micro, small, and medium enterprises (MSMEs) by state, the sample becomes a stratified sample. To determine the stratified sample, the elements in the population are divided into non-overlapping groups. Probability sampling was used to determine the samples in this study.
Zikmund et al. (2010) stated that probability sampling is a technique in which every element or member of the population has a probability of being chosen. Sekaran and Bougie (2020) also stated that every member in the population has the same probability of being chosen. Proportional stratified sampling is a stratified sampling in which the number of sample units is removed from each stratum in proportion to the size of the stratum population (Zikmund et al., 2010).
The total number of micro, small, and medium enterprises (MSMEs) involved in the food and beverage sector in Malaysia is 9955 (Table 1). It means that the number of individuals studied is 9955 people. Based on the Israeli table (2012), for a population of almost 10,000 people, the minimum number of samples is 385 people. Meanwhile, the sampling framework is the number of micro, small, and medium enterprises (MSMEs) that use social media as a marketing tool. The number of MSMEs is then divided into several strata according to the state in which the micro, small, and medium enterprises (MSMEs) are doing business. The researcher then calculated the percentage of micro, small, and medium enterprises (MSMEs) for each state. Once the percentage is identified, proportional random sampling will be done in selecting the respondents for the study.
Table 1. Total micro, small, and medium enterprises (MSMEs) in Malaysia by state.
Number |
State |
Total |
Percentage |
1. |
Perak |
997 |
9.81 |
2. |
Kedah |
971 |
9.75 |
3. |
Selangor |
941 |
9.45 |
4. |
Pahang |
899 |
9.03 |
5. |
Sabah |
792 |
7.96 |
6. |
Negeri Sembilan |
769 |
7.72 |
7. |
Johor |
754 |
7.57 |
8. |
Pulau Pinang |
720 |
7.23 |
9. |
Wilayah Persekutuan |
695 |
6.98 |
10. |
Sarawak |
634 |
6.37 |
11. |
Terengganu |
603 |
6.06 |
12. |
Kelantan |
558 |
5.06 |
13. |
Melaka |
385 |
3.86 |
14. |
Perlis |
237 |
2.38 |
Total |
|
9955 |
100 |
Table 2 shows sample calculations for micro, small, and medium enterprises (MSMEs) who use social media as a marketing tool. Each stratum has been assigned a percentage and sample size at a given percentage based on the given percentage. The total sample was 400 micro, small, and medium enterprises (MSMEs) from various states in Malaysia.
Table 2. Sample calculation of study selection.
Number |
State |
Total |
Percentage |
1. |
Perak |
40 |
10 |
2. |
Kedah |
39 |
9.75 |
3. |
Selangor |
38 |
9.50 |
4. |
Pahang |
36 |
9.00 |
5. |
Sabah |
32 |
8.00 |
6. |
Negeri Sembilan |
31 |
7.75 |
7. |
Johor |
30 |
7.50 |
8. |
Pulau Pinang |
29 |
7.25 |
9. |
Wilayah Persekutuan |
28 |
7.00 |
10. |
Sarawak |
26 |
6.50 |
11. |
Terengganu |
24 |
6.00 |
12. |
Kelantan |
22 |
5.05 |
13. |
Melaka |
15 |
3.75 |
14. |
Perlis |
10 |
2.50 |
Total |
|
400 |
100 |
3.2. KMO and Bartlett’s Test Values
Hair et al. (2007) suggests that if the partial correlation value is 0.7 and above, it is considered high. The Bartlett Test and the Kaiser-Meyer Olkin Test measuring the adequacy of sampling were used to identify the factoring of the matrix. The Bartlett test is a statistical test to detect correlations between variables. It provides a statistical probability in which the matrix correlation has a significant correlation between at least some of the variables (Hair et al., 2007). The study used a significance level of p < 0.05. The Kaiser-Meyer Olkin (KMO) test measures the adequacy of sampling that meets the degree of interrelation between variables and the appropriateness of factor analysis. BD Hill (2011) stated that 0.7 and above is appropriate. While according to Hair et al. (2007), KMO 0.8 is considered good, 0.7 and above is moderate, 0.6 and above is not very good, 0.5 and above is unsatisfactory, and 0.5 and below is not accepted. This study used KMO 0.7 to measure the adequacy value of sampling.
4. Results
4.1. KMO and Bartlett’s Test Values
A factor analysis with varimax rotation was conducted to validate whether the respondents perceived all constructs as distinct. The results showed all factor solutions with eigenvalues greater than 1.0, and the total variance explained was 65.56% of the total variance. The KMO measure of sampling adequacy was 0.932, indicating sufficient intercorrelations, while Bartlett’s Test of Sphericity was significant (Chi-Square = 13601.592, p < 0.001). The criteria used by Igbaria et al. to identify and interpret factors were that each item should load 0.50 or greater on one factor and 0.35 or lower on another factor.
4.2. Factor Analysis for Performance Expectancy
According to result from factor analysis, items related to performance expectancy included statements like “Social media use helps me accomplish things more quickly”, “Social media use helps increase my productivity”, “Social media use increases my chances of gaining more benefit for my business”, “Social media use may increase my chances of achieving my target sales”, and “I find social media useful in my business activity”. The factor analysis showed these items had values of 0.808, 0.744, 0.694, 0.689, and 0.589, respectively. The Eigenvalue for this component was 4.289, with a percentage variance of 8.409. This component three represented the factor of performance expectancy.
The results indicate that the higher the performance expectancy, the more likely businesses are to leverage social media as a marketing tool. Specifically, if businesses perceive that using social media can improve their productivity, help them achieve their target sales, and increase the overall benefits for their operations, they are more inclined to adopt social media as part of their marketing strategy. The findings of this study offer valuable insights for micro, small, and medium enterprises seeking to enhance their marketing efforts through social media platforms.
The performance expectancy variable exhibited an alpha value of α = 0.894. This Cronbach’s alpha coefficient, ranging from 0.6 to 1.0, indicates that the measurement instrument is reliable and suitable for use in the study (Hayes & Preacher, 2014).
4.3. Correlation Analysis Results
The analysis results revealed a significant, positive, and moderate correlation between performance expectancy and social media usage. The results of model 1 indicate that the relationship between performance expectancy and social media usage is significant and has a positive influence, with a β-value of 0.408. The results suggest that a one-unit increase in performance expectancy leads to a 0.408 increase in social media usage. The R-squared value indicates that performance expectancy accounts for 16.7% of the variation in social media usage. The results of the analysis found that there was a significant, positive, and moderate relationship between performance expectancy and social media usage (r = 0.63, p < 0.01).
H1 Performance expectancy has a positive effect on social media usage.
The hypothesis is confirmed.
5. Discussion
The findings of this study align with the broader literature on technology adoption, which suggests that performance expectancy is a critical factor influencing the use of information systems (Schultz et al., 2012).
The first objective of this study was to examine the relationship between performance expectancy and social media usage as a marketing tool. Performance expectancy is represented by a single, key dimension after factor analysis was conducted. Previous researchers, such as Venkatesh, Michael, Gordon, and Fred (2003), have also identified performance expectancy as a one-dimensional variable.
The findings of this study indicate that performance expectancy is a significant predictor of social media usage among micro, small, and medium enterprises. The performance expectancy variable refers to the degree to which an individual believes that using a particular system can enhance their job performance. In this study, performance expectancy relates to the expectation that using social media as a marketing tool can help micro, small, and medium enterprises reach their target sales, conduct marketing activities, accomplish objectives, increase productivity, and gain more benefits.
Micro, small, and medium enterprises find social media useful for their business activities. Additionally, they utilize social media to enhance their chances of meeting their sales targets. Another reason why micro, small, and medium enterprises use social media is that it helps them accomplish business tasks more efficiently. Additionally, micro, small, and medium enterprises believe that using social media would help increase their productivity. Furthermore, they perceive that social media usage would enhance their chances of gaining more business benefits.
The study’s findings indicate that performance expectancy has a positive and significant relationship with social media usage as a marketing strategy. This aligns with the research by Calderón, López, and Peña, which identified performance expectancy as the strongest predictor when adopting new technology. The relationship between performance expectancy and social media usage demonstrates that the performance expectancy factor is crucial in influencing micro, small, and medium enterprises to utilize social media for marketing purposes.
For micro, small, and medium enterprises in the food and beverage sector, performance expectancy is a crucial factor in deciding to adopt new technology. These enterprises not only sell products but also offer various services, such as gastronomy, ambiance, comfort, and safety, catering to customer expectations (Perumal, Devi Krisnan, & Abdul Halim, 2017). Given the highly competitive nature of the food and beverage industry due to similar offerings, these enterprises carefully evaluate whether new technologies can bring significant benefits to their performance and productivity.
The use of social media can effectively communicate this feature, making micro, small, and medium enterprises in the food and beverage sector more profitable. As a result, these enterprises anticipated that their sales targets could be easily achieved through social media usage. This aligns with the findings of Hazen, Kung, Cegielski, and Jones-Farmer (2014), who discovered that without social media, micro, small, and medium enterprises were unable to align their strategic objectives with their business processes, ultimately failing to meet their performance goals. Another reason why micro, small, and medium enterprises utilized social media was that it helped them accomplish tasks more quickly. This is consistent with the findings of Kumar and Ayedee (2021), who revealed that the use of social media as a marketing tool during the pandemic has assisted micro, small, and medium enterprises in Indonesia in increasing their sales demand.
The study found that micro, small, and medium enterprises in the food and beverage sector perceived that using social media would boost their productivity. Prior research supports this, showing that social media usage can enhance the productivity of these enterprises through better information exchange, knowledge sharing, and decision-making. Additionally, the study revealed that micro, small, and medium enterprises in the food and beverage sector believed that using social media would provide more business benefits, such as increased customer engagement, improved brand awareness, and higher sales revenue.
6. Conclusion
The results of this study have several important implications. First, the findings suggest that improving the performance expectancy of micro, small, and medium enterprises is crucial for increasing their adoption and usage of social media as a marketing tool. Enterprises should focus on educating and training their employees on how social media can enhance their business performance, productivity, and overall competitiveness.
Second, policymakers and industry associations should provide support and resources to help micro, small, and medium enterprises understand the potential benefits of social media usage. This could include workshops, training programs, and case studies demonstrating successful social media marketing strategies.
Third, social media platform providers should work closely with micro, small, and medium enterprises to design and develop features that better cater to their specific needs and performance expectations. By addressing the performance expectancy factor, micro, small, and medium enterprises in the food and beverage sector can be encouraged to more readily adopt and utilize social media as an effective marketing strategy.
Limitation of the Study
This article only discussed performance expectancy and its relationship to social media usage as a marketing tool. There are many other factors that may influence MSMEs in adopting social media as a marketing tool. Moreover, the study only concentrated on MSMEs; other entrepreneurs in small and medium enterprises also need to be studied in the future.
Another limitation of this study lies in its reliance on self-reported data collected through questionnaires, which raises the possibility of common method bias (CMB) and social desirability bias, potentially inflating the observed correlations between constructs. To address this, an assurance of anonymity and confidentiality may reduce social desirability effects. In future research, methodological improvements should therefore be considered, including the use of multi-method data collection such as adopting longitudinal designs to track behavioral changes over time, and incorporating multiple data sources from employees, managers, and customers to reduce single-source bias. These steps would help strengthen the validity of the findings and ensure that reported relationships between performance expectancy, social media usage, and business outcomes are less influenced by method-related issues.
Appendixes
Section B: The use of social media as a marketing tool
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Strongly Disagree |
Disagree |
Somewhat disagree |
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Agree |
Strongly Agree |
1. |
I regularly use social media as a marketing tool within the last two weeks. |
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2. |
I often spend on average more than 30 minutes to update and upload product information in the social media. |
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I regularly use social media for my marketing activities. |
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I prefer communicating through social media platforms to reach my customers. |
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5. |
I promote the use of social media to my business acquaintances. |
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Performance expectations due to the use of social media
Please state your performance expectancy from the use of social media as a marketing tool based on the following scale.
6. |
I find social media useful in my business activity. |
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7. |
Social media use may increase my chances of achieving my target sales. |
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8. |
Social media use helps me accomplish things more quickly. |
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9. |
Social media use helps increase my productivity. |
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10. |
Social media use increases my chances of gaining more benefit for my business. |
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11. |
Social media use increases the competitiveness of my business. |
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