Social Media as a Promotional Tool Towards SME’s Development: Evidence from the Financial Industry in a Developing Economy

Abstract

The notable contribution of small and medium enterprises (SMEs) in the development of socio-economics has been very encouraging in terms of its GDP contribution and employment creation. Both developed and developing countries are living testimonies of the contributions by SMEs. The primary aim of this paper is to assess the use of social media as a promotional tool for SME development. To achieve this objective, a total of 800 structured questionnaires were developed through an intercept and online survey to obtain data for the exercise. A non-probability sampling technique was employed for the collection of the data of which 648 respondents were dully accurate for the data processing/analysis. Using PLS-SEM with the help of ADANCO software v 2.2.1, findings revealed that the use of social media as an advertising tool has a significant effect on a firm’s financial performance, business motivation, customers’ perspective/attraction, and an increase in market share, thus, a key in reflecting a positive change in their marketing and promotional activities in the consumer market. The findings of the study are both beneficial to practitioners/owners of SMEs and the social media marketing literature in general. This study further strengthens the need for businesses to invest in digital marketing tools and employ them effectively to stay competitive in the long term for the firm’s growth and survival.

Keywords:

• Social media
• advertising
• promotion tool
• SME’s
• financial industry
• ghana

PUBLIC INTEREST STATEMENT

The use of social media as a “promotional tool” has attracted attention to scholars and practitioners particularly within the space of digital marketing. Although social media serve not only as a digital entertainment platform but also as a medium of marketing communication. Again, the advent of technology and the competitive nature of businesses in today’s marketing era cannot be underestimated. Therefore, this article sought to investigate the effect of social media as a promotional tool toward SME’s development in the financial industry of a developing country (Ghana). The findings of this study would be a wake-up call to practitioners of existing and start-up businesses on the need to integrate social media channels as effective and efficient marketing communication tools for a medium-to-long-term business operation. The study not only adds to the literature but a reminder to scholars regarding the need for developing countries to intensify the adoption of digital marketing.

1. Introduction

The role of SMEs in promoting the socio-economic development of countries, especially, developing countries cannot be overstated. SMEs are seen to contribute tremendously toward the reduction of the country’s unemployment rate through job creation and GDP (Kumar, 2017; Kwaku Amoah, 2018). Businesses and societies are being shaped with advancements in technology which has affected the way interactions are facilitated. The convenience, affordability, and portability in the use of smartphones have brought about what has been known as “E-commerce” (Laudon & Traver, 2016). Such dynamism sparked by technology has evolved and challenged the status quo of the traditional way of conducting business: marketing, advertisement, innovation, among others (Wright et al., 2019). Significantly, social media has become an enabling factor that has catalyzed this evolution, with varied impacts for businesses (Asiodu et al., 2015; Boyd, 2007). The basis of the growth of businesses, especially SMEs, largely depends on marketing and the promotion of its products and services as they are noted to mostly have challenges with obtaining external finances for their businesses. Therefore, marketing activities have the potential to speed up the exchange process of products or services to money or other benefits. It has also been noted that marketing strengthens and prolongs the life of the business through the modification of products to meet customers’ needs. Promotion involves the dissemination of information on the product, product line, brand or company, and is one of the four most important aspects of mixed marketing besides product management, pricing, and distribution. The advent and utilization of Information and Communication Technology (ICT) provide opportunities for the promotion of products and services of SMEs. Thus, the use of social media has become an important tool for SMEs in this regard (Che Mohd Zulkifli Che Omar, 2014; Jibril et al., 2019; Kaplan & Haenlein, 2010).

According to Hassan et al. (2015); Wardati and Er (2019) with the presence of social media, SMEs are enabled to carry out advertising activities in an efficient manner. Thus, through social media, the prospects of SMEs to promote their goods or services and market their brands worldwide have increased due to enhanced access to external resources, and the development of product innovations which affects their sales performance positively (Bocconcelli et al., 2017; Wardati & Er, 2019). Social media currently mediate the communication between SMEs and their customers, providing opportunities for consideration as the preferred advertising tool for their promotion activities. Thus, the SME-customer relationship is enhanced as they are freely allowed to communicate directly with the companies, and also receive feedback within the shortest possible time at a lower cost and higher efficiency as compared with the traditional communication networks like radio and TV(Crammond et al., 2018; Tajudeen et al., 2018; Wright et al., 2019). Also, customers/consumers can monitor their favorite brands, post questions, and make comments related to services or products on social media platforms like Facebook, Twitter, YouTube, LinkedIn, Yelp, and Instagram. Businesses, on the other hand, can connect directly with consumers and then ascertain discussions about their brands through the various platforms on social media (Talal et al., 2018). In a Ghanaian contextual study, Dzisi and Ofosu (2014) assessed how the strategies in promotion have influenced the performance of SMEs. It was observed that in the marketing of products and services by SMEs, the use of social media is preferable to traditional media (Adzovie et al., 2017). Social media is therefore considered as one of the most successful advertising tools for adoption by businesses, especially SMEs in across the globe (Bernhard and Abukar (2012); Talal et al., (2018) assessed how SMEs are using social media in their communication processes and proved that the various social media platforms play a vital role, by enabling rapid responses to customers, appropriate interactions, and feedback which increases collaborative communication on the platforms. Therefore, the success of an SME to some extent is influenced by the elements of effective marketing. Additionally, it is noted that social media has provided the capacity for SMEs to compete with large corporations, by making their marketing activities effective (Hassan et al., 2015). Although in the Ghanaian context, SMEs are responsible for employing 70% of the country’s total population, yet their growth is incommensurate to the large space they occupy within the economy. SMEs in Ghana find it difficult to leverage their key position as major contributors to the GDP achievement to advertise their products and services through modern advertising channels such as social media (Lekhanya, 2013). This is attributed to the lack of a proper strategy in using social media as a marketing tool impeding the ability of SMEs to advertise their products and services (Bernhard & Abukar, 2012).

From the foregoing, this current study aims at assessing the use of social media as an advertising tool for SMEs in Ghana, specifically, rural banks. In Ghana, a very large sector of the economy rests on the primary sector which is mostly indigenously manned with low capital inputs. Therefore, rural banks were set up to assist the rural folks financially in their various occupations to increase productivity in the short run, and economic development in the long run. They perform such a significant function in facilitating the financial activities of SMEs through products and services. However, they are essentially also considered as SMEs due to the number of employees, size of assets, and operating capital as defined by the National Board for Small Statistical Industry (NBSSI). Thus, considering how they innovate, market their products and services, and engage customers is critical to their growth and sustainability.

Thus, this research specifically targets this niched industry in Ghana, as extant literature (Ackah, Kondegri & Agboyi, 2015) has mainly focussed on the support provided by SMEs; and not on rural banks, in their rights, being considered as SMEs. The researchers find it most appropriate to carry out this study since scanty research has been conducted on the adoption of social media usage by SMEs (rural banks in the financial industry) in a developing country context. The outcome of the study would add knowledge to the existing literature and fill the literary gap in a developing country context. Thus, this study would examine the usefulness of integrating the new technology (social media) as an advertising/communication tool toward SME’s development in Ghana. The result will also benefit industry players to make strategic decisions on how to implement the findings to achieve their objectives. The paper, therefore, contains a literature review, methodology, results/findings, discussions, and conclusion in other sections.

2. Literature review

2.1 Social media usage

This variable has been investigated extensively and extant literature has proven that social media provide user-friendly tools that SMEs can adopt for the promotion of their products and services, and provides a channel of communication with their customers, while supporting internal communication and collaborations (Meske & Stieglitz, 2013; Öztamur & Karakadılar, 2014). Also, Attai et al. (2015) identified that positive relationships exist in using social media which provides information and feedback to customers. Bianchi and Andrews (2015) indicated that social media have not only altered how SMEs and their brands interact with their customers but also changed the way businesses are conducted. Zhang et al. (2010) identified that users vary in posting activities, reading behaviors, and perceived benefits. The study also identified barriers for social media adoption such as noise-value ratio paradoxes. Furthermore, Ainin, Parveen, Moghavvemi et al. (2015); Attai et al. (2015) revealed that SMEs have been using social media for various organizational objectives, such as marketing, communication, sales, advertising, innovation, problem resolution, customer service, human resources, information technology, and driving cultural changes. According to Algharabat et al. (2020); Bonsón and Ratkai (2013), SMEs can fully use social media for selling, advertising, and marketing at a lower cost, and also promote their products and services through messaging, tagging, commenting, and notifying. Finally, Muslim et al. (2020) established that customers can now be reached for businesses through social media and this has improved brand awareness.

2.2. Customer perspective

The adoption of social media has helped in establishing an effective feedback loop between an enterprise and its customers. Thus, instant feedback of responses is provided and received from customers of small and medium enterprises while customers generate information to be shared among other friends, organizations, and broader online communities (Bailey et al., 2018; Bianchi & Andrews, 2015). According to Algharabat et al. (2020), social media establishes a meaningful relationship with customers and Small Medium Enterprises which improves their self-expressive brands, consumer participation, and builds co-creative experiences. A study by Goleman et al. (2019) also confirmed that social media supports SMEs to attract new customers better than the use of traditional channels like radio and Television. The said publication suggested that adopting social media as an approach by SMEs adds more value than the traditional approaches for obtaining insights about products and services. Moreover, existing products usually offer insight and information to SMEs to dwell upon for the betterment of the design or decision-making process. However, social media has some limitations which sometimes prevent both SMEs and customers from its usage due to limited access to the internet and disparities in the digital world. The concept of social media has brought about customer participation in the frequency of online visits to Small Medium Enterprise sites which have called for customer interactions to be considered (Al-Qaysi et al., 2020; Kamboj et al., 2018). Moreover, since social media creates new ways of communication with users; businesses can include users in the product development process to improve and refine their products or services for the next generation.

2.3. Financial performance

Social media innovations have enhanced an important role in businesses and Small Medium Enterprise’s performance in the past two decades. Some research on social media and its financial performance perspective has been investigated in extant literature (Kim et al., 2016; Tajvidi & Karami, 2017) though many gaps remain. Previous studies have investigated organizational usage, however only a few examined the impact of social media on organizational financial performance. For example, Rodriguez et al. (2015) affirmed that social media significantly impacts the sales performance of an organization through its customer-oriented processes, while Wong (2012) suggests that the use of social media positively impacts the performance of an organization either financially or non-financially. A similar paper published by Kwok and Yu (2013), concluded that social media increased sales volume and helped achieve the sales target of Small Medium Enterprises. Rauniar et al. (2014) revealed that social media have helped organizations to achieve their sales and financial targets through the various social media handlers such as blogs (open diary), social networking (Facebook), and communities (YouTube). Social media applications, also, positively influence the financial performance of SMEs by improving the sourcing assistance through the quality relationship established between the organization and the customers (Rodrigues Pinho & Soares, 2011). Social media is claimed to have bridged the connection between Small and Medium Enterprises and prospective consumers hence leading to improvement in the financial performance of SMEs. The holistic adoption of social media through its handlers like Facebook, Twitter, and others have also reduced the financial ailments of SMEs and create opportunities to compete internationally. Ainin, Parveen, Moghavvemi et al. (2015) in their studies revealed that social media reduces the cost of the marketing campaign and helps organizations’ awareness creation, while creating a meaningful customer-driven product innovation and boosting sales income.

2.4. Business motivation

Zhang and Pentina (2012) revealed that social media in the form of Facebook, and YouTube integration has brought about marketing intelligence. Cohen and Lancaster (2014) also affirmed that the introduction of social media has enabled SMEs to create company awareness and also promote the company from different perspectives with minimal cost. Moreover, a lot more of the SME’s information is shared through social media platforms. The essence of cost in the adoption and utilization of social media has been studied and it has been established that there is a direct and significant relationship between cost and the adoption of technology, especially, social media (Alam & Mohammad Noor, 2009; Jibril et al., 2019). The introduction of social media has created opportunities for organizations to communicate continuously with customers hence promoting their customers’ interest in the products and services of the organizations (Majchrzak et al., 2013), while the organizations can transfer the firm’s information to the customer. According to Kamboj et al. (2018), business motivation is achieved because customer brand loyalty and trust are gained through the usage of social media. Customers who become addicted to particular goods and services through social media maintain brand co-creation and therefore impact positively on the firm. Giannakis-Bompolis and Boutsouki (2014) point out that because customers become the focal point of the firm through social media, firms can achieve their targets since customer relationship managers are assigned to each customer to respond to their challenges. Bazi et al. (2020) similarly confirm that firms through social media are always in close interactions and good contacts with customers. By so, new customers are attained based on recommendations from customers to others. This, therefore, helps SMEs resulting in a reduction in the cost of a marketing campaign.

2.5. Increased market share

Scholars have raised concerns about how social media affects the market share of SMEs, whether positively or negatively. The answer to this lies in the ability of SMEs to critically access the perception of customers about their products and services based on shared information and experience (Tajvidi & Karami, 2017). Thus, the introduction of social media has brought about an increase in the market share of SMEs. Xenos et al. (2014) strongly assert that Small and Medium Enterprises through social media help in building engagements with customers. Jara et al. (2014) also concluded that social media improves customer relationships with Small and Medium Enterprises since information about goods and services can be retrieved and shared with others within few minutes, and feedback received in real-time. Such an interaction thus improves customer participation in the business of the firm (Barney et al., 2001; Moreno-Munoz et al., 2016). Managers of small and medium enterprises can therefore build customer relations and more business connections with their customers through the marketing competencies of social media (Palacios-Marqués et al., 2015). However, Gao and Lee (2017) maintain that the local government should also build their relationship with the public as customers through the adoption of social media and not leave it for only small and medium enterprises. This is for disseminating information, reaching the community, and informing public services which ultimately reduce cost. Besides, Jacobson et al. (2020); Dwivedi et al. (2017) revealed that consumers are comfortable using social media to access small and medium enterprises’ products and services. The experiences gained by consumers are therefore shared with others by recommending the benefits derived from the firms, hence promoting customer-driven sales. Social media have helped in reducing the cost incurred by SMEs in their marketing campaigns (Palmer et al., 2018).

3 Summary of research hypotheses and conceptual model

To conclude the literature review, we deduce the following propositions for the research aim(s):

Hypothesis (H1): Customers’ knowledge/perspective for using social media would be significantly predicted by social media usage (SMU) by SME’s.

Hypothesis (H2): Social media usage by SME’s would be postively relate to the financial performance of the firm.

Hypothesis (3): Social media usage by SME’s would significantly predict the positive image/brand (brand motivation) of the firm

Hypothesis (H4): Social media usage will trigger a positive relationship between SMEs and their market share

4. Methodology

4.1. Sample data and demographics

The researchers adopted a quantitative approach to achieve the objective of the study. To test the proposed research model and the hypotheses, a questionnaire was administered to employees from the Small and Medium Enterprises in the financial sector through the online-based approach. The questionnaire was targeted at the employees of the SMEs in the rural banking sector in the Greater Accra, Central and Western Regions of Ghana. A type of non-probability sampling technique specifically, convenience sampling, was adopted to select the participants/respondents for the answering of the research questionnaire. The researchers adopted convenience sampling because it was necessary to aid within the data collection process and also to reduce time spent (Heckathorn, 2011). Again, the sampling technique was adopted based on respondents’ availability, participation, and willingness to produce information needed for the data analyses/processing (Amoah & Jibril 2020; Etikan et al 2016). A total of 800 questionnaires were distributed to the respondents of selected financial institutions of which 749 (93%) were returned. The participants were drawn from four (4) domestic banks of Ghana—particularly, one investment bank (National Investment Bank) and three rural banks (Gomoa Community Rural Bank, Fiaseman Rural Bank, and Ahantaman Rural Bank. In all, 648 (92%) out of the 749 received were dully completed for the analysis. The collection of the data was done between September to November 2020. The structured questionnaire took each respondent an average of five minutes to fill. Because of secrecy, the respondent’s various names were not included in the questionnaire so as to ensure the ethical standard of the study. In conclusion, 648 (86%) responses were dully valid for the researchers to use for data processing and analysis. The researchers adopted PLS-SEM with the help of ADANCO 2.1 software version to analyze the data. Table 1 below shows the summary of repondents’ profile.

Table 1. Demographic characteristics

Details		Frequency	Percentage(%)
Gender	Male	445	68.60
	Female	203	31.40
Age	20–30	313	48.30
	31–40	275	42.40
	41–50	46	7.00
	51 and above	14	2.30
Educational Level	Certificate/Diploma/HND	188	29.00
	Undergrat/Bachelor Degree	305	47.10
	PGD/Master’s/PhD	135	20.90
	Senior High School	19	2.98
Company Size	Micro(1–10 employees)	64	9.90
	Small(11–50 employees)	147	22.60
	Medium(51–100 employees)	150	23.20
	Large(100 above)	287	44.30
Work Experience	1–5 years	460	71.00
	6–10 years	125	19.20
	10–15 years	31	4.80
	15 and above	32	5.00
Company websites	Yes	423	65.30
	No	225	34.70
Information on Company websites	Yes	485	74.90
	No	163	25.10
Social Media (Sales)	Yes	534	82.40
	No	23	3.60
	Maybe	91	14.00
Social Media Tools	Wall Postings	268	41.40
	Video	111	17.10
	Photo Gallery	232	35.80
	Pop-Ups	37	5.70

Sources: Authors’ field survey, Sept-Nov. 2020

4.2. Data analysis technique

The researchers adopted Partial Least Square and Structural Modeling (PLS-SEM) specifically ADANCO 2.0 for the testing of the research model. The researchers adopted PLS-SEM over Co-Variance-Based Structural Equation Modeling (CB-SEM). While CB-SEM requires data to be normally distributed, PLS-SEM holds no assumption about distributions of data. Thus, the overall results of a statistical test are not contradicted by non-normal data (Goodhue et al., 2012), hence the use of PLS-SEM. The adoption of the Partial Least Square (PLS) approach was used by the researchers for the data analysis because it explained the variance of the detailed variables (J. F. Hair et al., 2019; J. Hair et al., 2017). According to Jibril et al., (2019), it is appropriate for the researchers to use PLS since it is an exploratory study. Smart PLS 3.2.9 was used in testing the research hypotheses.

4.3. Measurement of the constructs

Existing literature was used for the measurement of the research constructs. A slight change was done in both measurements and scale of the constructs. The study adopted the use of new scales for the current theme on a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree). This was used to determine the respondent’s level of idea concerning how they agree or disagree with the measurement of the constructs. A positive statement was received about the measurement. Table 2 depicts a summary of the construct’s indicators questionnaire and the various literature sourced and scale measurement.

Table 2. Research constructs and measurement

Constructs	Operationalization	Literature Adapted
Social Media Usage	1. Social media is user friendly 2. Customers can use Social media to access the firm’s products 3. Social media helps SMEs to engage in business with distant customers 4. Create a meaningful relationship with customers through social media	Öztamur and Karakadılar (2014), Tajudeen et al., 2018)
Customer Perspective	1. It is an easy way to learn about your audience 2. It helps the targeted audience more effectively 3. It helps find new customers and expand 4. It allows you to receive instant feedback from your customers	Algharabat et al. (2020), Bailey et al. (2018), Owusu-dampare et al. (2018)
Financial Perspective	1. Social media increases the profitability of SMEs 2. Social media also increases the sales volume of SMEs 3. Through social media, SMEs can maintain a solid financial position 4. Social media also helps to achieve the sales target of SMEs	Kim et al. (2016), Lechuga Sancho et al. (2018), Tajudeen et al. (2018)
Business Motivation	1. It helps in sales and income-driven 2. Social media helps in creating company awareness 3. Social media helps in a customer-driven 4. It reduces the cost of the marketing campaign	Cohen and Lancaster (2014), Ainin, Parveen, Moghavvemi et al. (2015), Kamboj et al. (2018)
Increased Market Share	1. Social media helps in the large audience captured 2. Social media helps builds more business connections 3. It improves customer relationship 4. It also helps to know about customers perceptions of the company	Cattedra (2019), Dwivedi et al. (2017), Xenos et al. (2014)

5. Empirical results

5.1. Model measurement

Per the recommendation from scholars like (J. Hair et al., 2017), the authors took inspiration from the PLS-SEM literature (Bagozzi & Yi, 1988; J. F. Hair et al. (2019); thus, the constructs’ reliabilities were assessed vigorously using Dijkstra-Henseler’s rho along with Cronbach’s alpha coefficients. From Table 3, all the values exceeded the threshold of 0.5 indicating strong coefficients of construct’s reliability as suggested by (Bagozzi & Yi, 1988; J. F. Hair et al. (2019). The software ADANCO 2.0 version (Henseler et al., 2015) was used to evaluate the psychometric properties of the constructs and their underlying items. Concerning the composite reliability of the constructs, the Jöreskog’s rho (pc) with a minimum threshold of 0.7 and Dijkstra-Henseler’s rho (ρA) with the threshold of 0.8 were evaluated and our analysis fulfills those requirements. Hence, the result presented by Dijkstra-Henseler’s rho (ρA) with a minimum reliability coefficient of 0.9281 and a maximum of 0.9559, while convergent validity was presented by average variance extracted (AVE) which also exceeded the minimum threshold of 0.5 (see table 3).

Table 3. Construct reliability and validity

Constructs	Dijkstra-Henseler’s rho (ρA)	Jöreskog’s rho (ρc)	Cronbach’s alpha(α)	The average variance extracted (AVE)
Social media usage	0.9320	0.9476	0.9308	0.7836
Customer perspective	0.9559	0.9602	0.9525	0.7513
Financial perspective	0.9318	0.9397	0.9198	0.7574
Brand motivation	0.9400	0.9529	0.9381	0.8019
Increase market share	0.9281	0.9481	0.9270	0.8205

Source: Authors’ processing from ADANCO 2.0 version

Moreover, the indicator loadings of the latent constructs were assessed and loaded meaningfully to their respective construct. Per the recommendation of Bagozzi and Yi (1988), a factor loading above a threshold of 0.6 is the best measure/indicator. The result of the indicator-variables show a loading of (0.7922) and (0.9259) for minimum and maximum load, respectively. Hence, the summary of all the research constructs as well as their items are shown in Table 4 with their corresponding loadings (coefficients). Again, we took into consideration the presence of multicollinearity in detecting evidence of common method variance (CMV) of the measurement scales using the variance inflation factor (VIF). The results of this post-hoc measure indicate that CMV is not a key concern since the computed VIFs are less than five (5) considering the maximum threshold of ten (10) (see Kwarteng et al., 2020; MacKenzie & Podsakoff, 2012). Therefore, the concerns about CMV are minimal here; hence, in this analysis, CMV is not an issue (see table 4).

Table 4. Factor loadings

Indicator	Social media usage (SMU)	Customer perspective (CP)	Financial perspective (FP)	Brand motivation (BM)	Increase-market-share (IMS)	VIF
SMU1	0.8739					2.8385
SMU2	0.8761					3.0387
SMU3	0.9156					3.9485
SMU4	0.9020					3.4643
SMU5	0.8573					2.6735
CP1		0.8151				2.7235
CP2		0.8568				3.4092
CP3		0.9116				4.7117
CP4		0.8953				4.0647
CP5		0.8924				3.9903
CP6		0.8112				2.4767
CP7		0.8865				3.7468
CP8		0.8595				3.1183
FP1			0.8903			3.2843
FP2			0.9000			3.6629
FP3			0.9108			3.7478
FP4			0.8528			2.8949
FP5			0.7922			2.1959
BM1				0.8775		3.1369
BM2				0.9250		4.3538
BM3				0.8989		3.8757
BM4				0.8669		3.0995
BM5				0.9079		3.8385
IMS1					0.8958	3.0722
IMS2					0.9259	3.8760
IMS3					0.8869	2.8445
IMS4					0.9140	3.4910

1- Social media usage, 2- Customer Perspective, 3- Financial Performance, 4-Business Motivation, 5-Increased Market Share

Source: Author’s processing from ADANCO 2.0 version

Nonetheless, in assessing the discriminant validity of the construct, Fornell-Larcker’s (1981) criterion was used to assess the presence of discriminant validity among the latent variables (Henseler et al., 2015). It is worth noting that the values in the diagonal (in bold) of the Fornell-Larcker’s table 5 indicate AVE’s of the measured constructs of which per the recommendation from experts (see J. F. Hair et al., 2019; Henseler et al., 2015) must be greater than 0.5. Notwithstanding, each construct’s AVE should be of a higher coefficient at both column and row position over other constructs so that discriminant validity could be established. The result shows that the constructs satisfy both basic and stringent assumptions and therefore establishes discriminant validity.

Table 5. Test of discriminant validity—fornell-larcker criterion

Construct	1	2	3	4	5
1 = Management Skills	0.7836
2 = Cost/financia	0.6849	0.7513
3 = Social media usage	0.4473	0.5394	0.7574
4 = Brand motivation	0.6487	0.6762	0.5456	0.8019
5 = increase-market-share	0.5690	0.6453	0.4824	0.7255	0.8205

the diagonal (in bold) are the average variance extracted (AVE) Sources: Author’s processing from ADANCO 2.0 version

5.2. Structural modeling- path analysis

Going forward, after assessment of the model fit, the structural model (path analysis) of these is required. Importantly, this step is imperative in the analysis since it identifies and establishes the causal-effect (or relationships) of the underlined research construct. Concerning effect, the study (empirical findings) shows that the construct, social media usage (SMU) by SMEs, has a positive and significant effect on the constructs: customer perspective (CP), financial perspective (FP), brand motivation (BM), and increase market share (IMS). The regression coefficients; Beta (β), and the significant values; T-values >1.96 (or P-values < 0.05) of these constructs are indicated in Table 6.

Table 6. Hypothetical path coefficient sources

Relationship	Beta (β)	Standard bootstrap results					Empirical remarks
		Mean value	SD error	t-value	Effect size (Cohen’s f^2)	P-value
H1: SMU -> CP	0.8276	0.8267	0.0238	34.7255	2.1737	0.0000	Supported
H2: SMU -> FP	0.6688	0.6684	0.0303	22.0611	0.8094	0.0000	Supported
H3: SMU -> BM	0.8054	0.8051	0.0220	36.5999	1.8463	0.0000	Supported
H4: SMU -> IMS	0.7543	0.7534	0.0265	28.4196	1.3199	0.0000	Supported
Control variable:
Company-size -> SMU	−0.0103	−0.0118	0.0511	−0.2010	0.0001	0.8408	Not supported
Company website -> SMU	−0.0009	−0.0020	0.0478	−0.0190	0.0000	0.9848	Not supported
Company’s business strategy -> SMU	−0.1382	−0.1371	0.0387	−3.5747	0.0194	0.0004	Supported
Dependent variable:	Coefficient of determination (R^2)				Adjusted R^2
Customer-Perspective (CP)	0.6849				0.6844
Financial-Perspective (FP)	0.4473				0.4465
Brand-Motivation (BM)	0.6487				0.6481
Increase-market-share (IMS)	0.5690				0.5683

Note SMU = social media usage. Source: Author’s processing from ADANCO 2.0 version

Additionally, the structural model shows a control variable effect on the predictor variable (thus, SMU). Per the estimate, out of the three (3) control variables (company’s size, company’s business strategy, and company’s website presence) examined, the results show ‘company’s business strategy’ was revealed as a significant control variable toward social media usage among SMEs in Ghana (a developing country) (see Table 6 and Figure 1).

Figure 1. Estimated research model

Source: authors’ processing from ADANCO 2.1 version

Regarding the predictive power (coefficient of determination) of the research model, the coefficient of determination (R²) of the regression model was assessed. The coefficient indicates the percentage of variation in the dependent variable that is been explained by the predictor (independent) variable. Thus, R2 of CP (68%), FP (45%), BM (65%), and IMS (57%) are all explained by SMU (predictor variable) (see Table 6 and Figure 1). More importantly, the authors would like to remind readers that: supplementary information regarding exploratory factor analysis (EFA) and descriptive statistics are given in tables (see Appendix).

6. Discussion

The research findings show that social media as an advertising tool is in a positive direction for Small and Medium Enterprises to adopt it for their advertisement or marketing activities. Thus, with respect to hypothesis (H1): Social media usage by SME’s would be positively predict customers’s knowledge/percepective on product/service provided by the firm as proven from the above computaton. Hence, the relationship between customer perspective and social media usage as an advertising tool is consistent with the research works of (Algharabat et al., 2020; Bianchi & Andrews, 2015; Casaló et al., 2020; Owusu-dampare et al., 2018; Rusok et al., 2017; Tajudeen et al., 2018) where it was strongly established that social media usage tremendously influences a mutual relationship between Small and Medium Enterprises and its customers, makes target audience more effective and allows Small Medium Enterprises to find new customers and expand its markets both locally and globally. Also, with the introduction of social media by smartphones, Small and Medium Enterprises are now satisfied to interact with their customers over the internet regularly. It is in this opinion that the researchers revealed that both SMEs and their customers receive instant feedback within the shortest possible time. Also, social media usage as an advertising tool stands better than the traditional way of advertisement in this current era. Besides, the current study through its findings revealed that social media establishes a meaningful relationship between SMEs and their customers, improves upon customers’ self-expressive brands, consumer participation, builds co-creative experiences (Parveen et al., 2016).

Also, per the valid responses received for the current study, the findings strongly supported the hypothesis (H2) that is Social media usage by SME’s would significantly improve the financial performance of the firm. This assertion has been confirmed by (Lechuga Sancho et al., 2018; Tajudeen et al., 2018) where it was affirmed that social media significantly impacts the sales performance of Small and Medium Enterprises through its customer-oriented processes. Besides, the previous works opined that social media as an advertising tool has helped Small Medium Enterprises to achieve their sales and financial targets through its handlers such as blogs (open diary), social networking (Facebook), communities (YouTube). The researchers, hence, argue that social media applications positively influence the financial performance of SMEs by improving their sourcing assistance through the quality relationship established between the organization and its customers. The current study in analyzing social media as an advertising tool for Small and Medium Enterprises classified it as crucial because SMEs always want to expand their marketing activities from the traditional way to an advanced means which must reflect in their financial base. Based on the findings, small and medium enterprises in underdeveloped countries like Ghana can fully adopt social media for advertising purposes in the postmodern era of business dealings which will ultimately affect sales volume positively, a direct indicator of financial performance. Therefore, there is a direct and significant relationship between cost and social media as a technology adoption for SMEs.

Again, the findings strongly supported the hypothesis (H3): Social media usage by SME’s would significantly predict the positive image/brand (brand motivation) of the firm. This means that there is a positive relationship between social media usage for Small and Medium Enterprises and business motivation (image brand) of the firm. The researchers, thus, agree with the findings from (Kamboj et al., 2018; Majchrzak et al., 2013; Ņikitina et al., 2020) that social media has created opportunities for SMEs to communicate continuously with customers hence promoting their customers’ interest in the products and services of the SMEs. Also, the integration of social media in the form of Facebook, YouTube, etc has brought about marketing intelligence. Customers who become addicted to particular goods and services through social media maintain brand co-creation and therefore impact positively on the firm. The findings of the above researches further emphasized that customer brand loyalty and trust are gained through social media usage. Thus, the researchers are of the view that when SMEs adopt social media toward their advertisements, it will enable them to create company awareness and also promote the company from different perspectives with minimal cost, reduces marketing campaign cost, and helps in awareness creation, creating a meaningful customer-driven product innovation.

Lastly, social media usage will trigger a positive relationship between SMEs and their market share (H4) is significantly supported by the findings of the current research. Hence, there is a direct relationship between social media and increased market share. Thus, previous studies have also confirmed this in a positive direction as found in (Cattedra, 2019; Nepelski & Van Roy, 2020) that SMEs can now access or know the perception of customers about their products and services based on shared information and experience through social media and build customer engagements. The supported findings further established that social media have made it possible for Small Medium Enterprises to build customer relationships, facilitate business connections with their customers, and above all disseminate information at a reduced cost. Given this, the researchers are of the view that social media continues to contribute largely to the expansion of Small Medium Enterprise’s markets and increased its customer base. Furthermore, the experience gained by customers as a result of products or service usage through social media is shared with others by recommending the benefits derived from the firm. Also, information about SMEs products and services can be retrieved and shared with others within few minutes, received feedback in real-time.

7. Research implication and conclusion

7.1. Conclusion

The current study mainly focused on the adoption of social media as an advertising tool for Small and Medium Enterprises in the Ghanaian financial industry. Hence, the present study seeks to examine the usefulness of integrating the new technology (social media) as an advertising or communication tool toward Small and Medium Enterprises’ development in Ghana. Additionally, the researchers explained the possible benefits that Small Medium Enterprises in Sub-Saharan African like Ghana will gain from social media adoption as an advertising tool. The researchers also revealed that Small and Medium Enterprises after fully adopting social media will be able to compete with large companies in the field of advertising or marketing of their products and services and thus, boost their competitive advantage. The study employed a quantitative research approach by developing a structured questionnaire to gather data or information from staff of Small and Medium Enterprises in the financial sector, that is, from the Greater, Central, and Western Regions of Ghana. A non-probability sampling technique particularly convenience sampling was used to select the respondents or participants for the data collection process. Besides, 648 valid responses received were analyzed vigorously by using Partial Least Square and Structural Equation Modeling (PLS-SEM) with the aid of ADANCO 2.1 statistical software.

7.2. Theoretical relevance

Although the study is limited in scope, its findings are very useful. Upon the relevance of the information or data gathered in the study area where less research or publications have been done about the use of social media for advertising purposes, the study, therefore, fills the vacuum in literature by this addition and contribute to the missing gap that has existed for a while in underdeveloped country settings like Ghana. The present study per its findings would also offer new insights and valuable contributions to social media marketing or activities. Other scholars and researchers are therefore encouraged to conduct further research to re-examined the reliability and validity of the research constructs, research model in different developing country contexts. Again, the empirical findings also revealed that Small Medium Enterprises would enjoy an increase in financial base, attracting more customers, expand their business relationships, and increased market shares through the use of social media.

7.3. Practical relevance

Small Medium Enterprise managers/owners would take inspiration from the findings of the current study with relevance to social media usage as an advertising tool toward their development and benefits that can be derived from its adoption. The study also provides a clear roadmap for Small Medium Enterprises to formulate a framework combined with the research constructs to achieve the key purpose of how they can effectively and efficiently advertise their products and services through this medium. Above all, practitioners will also enjoy the usefulness of the research constructs (social media usage, customer perspectives, financial performance, business motivation, increased market shares) in their marketing and communication activities. The results will also benefit industry players and practitioners to make strategic decisions on how to implement the findings to achieve the desire goals and appreciate the presence of social media.

7.4. Limitations and future directions

However, the current study is limited to Small and Medium Enterprises in Ghana; therefore, the results cannot be generalized to other developing countries. The current study was limited to only quantitative research techniques; the interviews of SMEs managers and owners through qualitative research may improve the results/findings.

Acknowledgements

This work is supported by Tomas Bata University in Zlin through; IGA/FaME/2021/005. Significant factors in the sustainability of economic growth with a focus on the SME segment. The article was further supported by the following projects: IGA/FaME/2020/002 and IGA/FaME/2021/012.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

John Amoah

John Amoah is a PhD scholar at the Tomas Bata University, Zlin Czech Republic at the faculty of Management and Economic Department. He holds a master’s degree in Marketing from the Pentecost University of Ghana. His main research area is on SMEs development, Social media Analysis, Service Marketing in Developing Countries. The results of his research have been published in peer-reviewed scientific journals and presented at numerous international conferences around the globe.

Abdul Bashiru Jibril is currently a Doctoral Researcher at the Department of Management and Marketing at the Tomas Bata University in Zlin. His research focuses on digital marketing applications, Social media analysis, Brand management, and SME development. He is particularly interested in deploying data mining techniques in extracting intelligence to improve business decision making especially for marketers. The results of his research have been published in peer-reviewed scientific journals and presented at numerous international conferences.

APPENDIX

Table A. Descriptive statistics of the measurement items/scales

Indicator	Minimum	Maximum	Mean	Variance	Skewness	Kurtosis
SMEB	1.000000000000	3.000000000000	1.318392581144	0.499088433877	1.863577149922	1.617228796598
SMU1	1.000000000000	5.000000000000	3.834621329212	1.197065761959	−1.020093894707	0.647889389364
SMU2	1.000000000000	5.000000000000	3.757341576507	1.097372488408	−0.928912426463	0.591398980447
SMU3	1.000000000000	5.000000000000	3.931993817620	1.286389671788	−1.111373430446	0.667257271114
SMU4	1.000000000000	5.000000000000	3.741885625966	1.105100463679	−0.896974392896	0.476024925849
SMU5	1.000000000000	5.000000000000	3.735703245750	1.284528258550	−0.888585556912	0.148075568214
CP1	1.000000000000	5.000000000000	3.476043276662	1.122877199363	−0.571245717532	−0.171297537956
CP2	1.000000000000	5.000000000000	3.588871715611	1.124829529957	−0.745580174931	0.041860012885
CP3	1.000000000000	5.000000000000	3.874806800618	1.156129026084	−1.120738043013	0.924494541527
CP4	1.000000000000	5.000000000000	3.768160741886	1.172173546877	−0.916780303653	0.432810882112
CP5	1.000000000000	5.000000000000	3.797527047913	1.112192017456	−0.970673046325	0.620448796516
CP6	1.000000000000	5.000000000000	3.520865533230	1.048712562386	−0.576954673984	0.046066401992
CP7	1.000000000000	5.000000000000	3.680061823802	1.221010522488	−0.827184170314	0.163542847438
CP8	1.000000000000	5.000000000000	3.683153013910	1.210598092650	−0.768302248024	0.058686696383
FP1	1.000000000000	5.000000000000	3.403400309119	1.064570463344	−0.596209172316	0.020298460581
FP2	1.000000000000	5.000000000000	3.547140649150	1.105746455419	−0.721762045790	0.152375558334
FP3	1.000000000000	5.000000000000	3.380216383308	1.102889736388	−0.477588122457	−0.167147528005
FP4	1.000000000000	5.000000000000	3.214837712519	1.128695910155	−0.203762975897	−0.488831012228
FP5	1.000000000000	5.000000000000	3.128284389490	1.161536216211	−0.249496480176	−0.501026607161
BM1	1.000000000000	5.000000000000	3.540958268934	1.041276479680	−0.777560468179	0.388022362555
BM2	1.000000000000	5.000000000000	3.823802163833	1.111321124887	−1.074882301753	0.973686458545
BM3	1.000000000000	5.000000000000	3.630602782071	0.994913413181	−0.842028943078	0.618978034320
BM4	1.000000000000	5.000000000000	3.676970633694	1.280939415545	−0.787542582426	0.032981008629
BM5	1.000000000000	5.000000000000	3.693972179289	1.160071968265	−0.874200575900	0.387894423033
IMS1	1.000000000000	5.000000000000	3.666151468315	1.095802967734	−0.859504252488	0.455836669002
IMS2	1.000000000000	5.000000000000	3.721792890263	1.083471703169	−0.960898384990	0.729275556579
IMS3	1.000000000000	5.000000000000	3.588871715611	1.075293926242	−0.643764437314	0.105881544351
IMS4	1.000000000000	5.000000000000	3.596599690881	1.064570463344	−0.676100822376	0.268122101130
Kaiser-Meyer-Olkin Measure of Sampling Adequacy → 0.973 Bartlett’s Test of SphericityApprox. Chi-Square → 18,592.343 df→ 351 Sig.→ 0.000

Source: Authors’ processing from ADANCO software

Table B. Exploratory factor analysis

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	17.283	64.010	64.010	17.283	64.010	64.010
2	1.530	5.665	69.676	1.530	5.665	69.676
3	1.050	3.889	73.564	1.050	3.889	73.564
4	.879	3.254	76.818
5	.564	2.089	78.907
6	.559	2.069	80.976
7	.477	1.766	82.742
8	.465	1.722	84.464
9	.385	1.424	85.889
10	.352	1.302	87.191
11	.328	1.214	88.404
12	.293	1.085	89.490
13	.257	.950	90.440
14	.249	.922	91.361
15	.245	.906	92.267
16	.230	.850	93.118
17	.220	.816	93.934
18	.209	.775	94.709
19	.200	.741	95.450
20	.187	.691	96.141
21	.177	.654	96.795
22	.171	.635	97.430
23	.162	.598	98.028
24	.153	.568	98.596
25	.139	.514	99.110
26	.132	.489	99.599
27	.108	.401	100.000

Extraction Method: Principal Component Analysis.—3 Components extracted. Sources: Authors’ processing from SPSS software.

Table C. Component Matrix of measurement items

	Component
	1	2	3
SMU1	.777	−.196	.077
SMU2	.780	−.093	.031
SMU3	.827	−.211	.015
SMU4	.823	−.144	.095
SMU5	.772	−.271	.123
CP1	.755	−.015	.288
CP2	.815	.009	.238
CP3	.887	−.114	.140
CP4	.861	−.125	.150
CP5	.865	−.061	.157
CP6	.733	−.142	.289
CP7	.832	−.100	.239
CP8	.790	−.191	.287
FP1	.760	.404	.009
FP2	.815	.342	−.022
FP3	.775	.455	.038
FP4	.647	.616	.127
FP5	.615	.543	.054
BM1	.833	.122	−.193
BM2	.868	−.130	−.224
BM3	.828	.048	−.224
BM4	.782	−.121	−.297
BM5	.848	−.089	−.241
IMS1	.800	−.118	−.249
IMS2	.837	−.095	−.276
IMS3	.791	−.054	−.230
IMS4	.825	.021	−.305

Extraction Method: Principal Component Analysis. a. 3 components extracted. Source: Authors’ processing from SPSS.

Table D. Godness of model fit—Using Standardized Root Mean Square Residual (SRMR)

	Value	HI95	HI99
SRMR	0.0786	0.0398	0.0427
dULS	4.5240	0.7358	0.8476
dG	0.7429	0.2603	0.2734

Because the SRMR is an absolute measure of fit, a value of zero indicates a perfect fit. The SRMR has no penalty for model complexity. A value less than . 08 is generally considered a good fit” (Hu & Bentler, 1999). Source: Author’s processing from ADANCO software