Next Article in Journal
Techno-Economic-Environmental Energy Management of a Micro-Grid: A Mixed-Integer Linear Programming Approach
Next Article in Special Issue
BSC’s Perspectives Ranking towards Organizational Performance: An Empirical Study Performed with Portuguese Exporters
Previous Article in Journal
Indoor Safety Monitoring for Falls or Restricted Areas Using Wi-Fi Channel State Information and Deep Learning Methods in Mega Building Construction Projects
Previous Article in Special Issue
American and Australian Tariff Policies: Do They Rock or Tango or Roll?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 22
10.3390/su142215029
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Social Media Use and Business Performance in SMEs: The Mediating Roles of Relational Social Commerce Capability and Competitive Advantage

by
Marjeta Marolt
1,*,
Hans-Dieter Zimmermann
2 and
Andreja Pucihar
1
1
Faculty of Organizational Sciences, University of Maribor, 4000 Kranj, Slovenia
2
School of Management, Eastern Switzerland University of Applied Sciences, 9000 St. Gallen, Switzerland
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15029; https://doi.org/10.3390/su142215029
Submission received: 12 October 2022 / Revised: 11 November 2022 / Accepted: 11 November 2022 / Published: 14 November 2022
(This article belongs to the Special Issue Circular Economy and Economy Sustainability)
Browse Figures
Versions Notes

Abstract

:
Social media (SM) enables micro, small, and medium sized enterprises (SMEs) to improve brand awareness and to engage their audience, which can lead to referrals, repeat business, and increased sales. However, the existing literature offers limited insights into how the ability to leverage SM for commercial activities that are beyond transactions (relational social commerce capability) can affect performance outcomes for SMEs. Drawing on the existing literature and insights from in-depth interviews with six SME managers/owners, we developed a conceptual research model and examined it empirically by using a dataset collected from Slovenian SMEs. This study identifies relational social commerce capability and competitive advantage as important mediators when exploring the impact of SM use on business performance. More specifically, the findings reveal the mediating role of relational social commerce capability between SM use and competitive advantage, while SM use was not found to have a direct impact on competitive advantage. Furthermore, the findings illustrate business performance as a result of the competitive advantage derived from relational s-commerce capability.

1. Introduction

Social media (SM) is one of the essential communication channels for businesses [1,2]. Due to the popularity of SM, customers can socially interact with, and share information relating to, brands [3,4]. This may lead to more rational purchase decisions [5]. Furthermore, SM seems to be an innovative approach for enterprises to increase sales by developing relationships with new customers and maintaining existing ones [6]. To reach and communicate with prospects and customers, many enterprises are building their SM presence. This means that enterprises constantly strive to publish brand-relevant content and to respond to customers’ comments, questions, and queries on time. As such, SM has changed the way enterprises interact with customers [7,8,9] and has created a new stream of electronic commerce (e-commerce), commonly known as social commerce (s-commerce).
As indicated by Huang and Benyoucef [10], s-commerce has been studied from various perspectives, including marketing, computer science, psychology, and sociology. Although s-commerce is a subset of e-commerce, there are differences between them [11]. For example, e-commerce focuses on improving search options, optimizing shopping cart functionality, and recommendation systems, while s-commerce is focused on social interactions [12,13]. In s-commerce, both customers and enterprises are actively involved. Customers engage through their purchasing journey, while enterprises try to facilitate those activities to improve customer experience [11], as well as by addressing customer rights, meeting customer expectations, and building a strong brand name and reputation.
Whilst s-commerce has received substantial interest among practitioners and researchers since 2010, several aspects need to be further investigated. In particular, most of the studies thus far have investigated the purchasing intentions of customers and the effect of different factors on the decision-making process of purchasing [14,15,16], thereby neglecting the enterprise perspective of s-commerce use. Furthermore, most of the studies that took into consideration the enterprise perspective have focused on the transactional s-commerce process. As such, the existing research offers limited insights into the use of SM in customer acquisition and retention processes which, according to Zhang and Benyoucef [17], are crucial for enterprises to make better decisions on how to serve customers. Moreover, whilst unique characteristics of s-commerce enable enterprises to strengthen their relationships with customers [6,18], the effect of s-commerce use on performance outcomes remains under-researched [11,19]. Additionally, the relevant empirical studies on performance outcomes primarily focus on financial performance [19], thus neglecting other aspects of marketing performance outcomes, such as customer mindset and customer behavior [20].
Advancements in digital technologies, such as SM, provide unprecedented opportunities for micro, small, and medium-sized enterprises (SMEs), which play an important role in the economic development of any country [21]. SM popularity among SMEs may be at least partially explained by its lower costs, minimum technical requirements [22], and positive and wide-ranging impacts [23,24]. For instance, SM creates value for SMEs in regard to marketing, customer service, sales, and internal operations [25]. Nevertheless, SMEs are usually unable to leverage SM to its full potential and reap its maximum benefits [26,27,28]. Furthermore, there have been very few studies exploring how SMEs use SM and the impact of this endeavor on their performance [29]. To succeed in the business environment, SMEs must be able to assist customers in pre-purchase decisions, as well as post-purchase behaviors [19]. Thus, it is essential to advance the understanding of SME’s ability to leverage SM for relational commercial activities and its impact on performance outcomes. Therefore, this study has the following objectives: (1) to investigate the role of SM in shaping relational s-commerce capabilities in SMEs; (2) to determine whether relational s-commerce capability mediates the relationship between SM use and competitive advantage in SMEs; and (3) to investigate the consequence of competitive advantages derived from relational s-commerce capability in SMEs. To achieve these stated objectives, we propose a conceptual research model and test it empirically with SMEs in Slovenia.
Overall, the contribution of this study is threefold. First, we conceptualize relational s-commerce capability and competitive advantage. Second, we study the effect of relational s-commerce capability on competitive advantage and business performance. Third, we explain the mediating roles of relational s-commerce capability and competitive advantage. The rest of this paper is organized as follows. The following section presents the theoretical background and the developed research hypotheses. The subsequent two sections present data collection and data analysis, demonstrate the validation of the conceptual research model and report the findings of this study. The last section discusses the study implications, as well as limitations and suggestions for further research.

2. Theoretical Background and Hypotheses Development

Broadly speaking, the literature on s-commerce focuses either on customer perspective [13,30,31] or on enterprise perspective [19,32]. The emphasis on the latter perspective is less prominent and encompasses themes such as business strategies, business models, and business performance [33]. Thus, many questions on how enterprises use SM features and functions for commercial purposes remain unanswered [11,19]. In essence, s-commerce refers to the interplay of SM and e-commerce [19,33]. SM harnesses the power of the internet to foster networked communities in order to socially connect and collaborate, while e-commerce supports customer decision making and encourages repeat purchases. The combination of these unique characteristics enables enterprises to strengthen their relationships with their customers [34,35] and boost performance [19,36,37]. There are two primary trends of s-commerce [7,38]. The first trend embeds SM on e-commerce sites such as Amazon, Walmart, Etsy, and eBay. The second trend adds commerce features to SM (e.g., Facebook Shop, Instagram Shopping). As the first trend of s-commerce limits possibilities for customer interactions (e.g., customers cannot tag others or are not able to send private messages), several recent studies have focused on the second trend of s-commerce [13,39,40].
Although enterprises have made considerable investments in s-commerce technology over recent years, they are still struggling to leverage their resources to properly support s-commerce initiatives. Thus, several studies have investigated s-commerce capability either from a transactional [32,41] or relational [42,43,44] perspective. Transactional s-commerce capability refers to an enterprise ability for using and leveraging SM to sell its products, while relational s-commerce capability refers to enterprise ability in purposely using and leveraging SM to build a relationship with customers beyond their transactions [11]. It can be argued that the latter perspective focuses on how enterprises are trying to create deeper, more meaningful relationships with customers [44,45]. Nevertheless, according to Liang and Turban [37], only customer engagement with commercial intentions should be taken into consideration when referring to relational s-commerce capability.
In contrast to large enterprises, SMEs usually have fewer resources, including knowledgeable and skilled employees, information and communication technology, and finances. Thus, SM seems to be the most suitable tool for SMEs as it is a cost-effective solution that enables them to reach potential customers, build customer trust, manage relationships, and gain knowledge about their customers [46,47,48]. As SM has transformed the way enterprises build relationships and execute transactions with their customers, several efforts have been made to analyze existing knowledge in the context of SMEs [25]. Some studies have focused on how SMEs use SM for business collaboration [49], customer relationship management (CRM) [50], customer engagement [51], and commercial activities [25]. Other studies have provided insights on how the use of SM for business purposes influences SME’s performance [52,53]. Nevertheless, the current research provides very limited insights on relational s-commerce capability and its impact on SMEs performance. Therefore, the resource-based view and dynamic capabilities perspective served as the theoretical basis for the development of a conceptual research model presented in Figure 1. As shown in Figure 1, the relational s-commerce capability is considered as a mediator between SM use for CRM and competitive advantage, which in turn contributes to business performance. SM use for CRM is considered as an information technology resource.

2.1. Effects of SM Use for CRM

Whilst SM was initially only targeted at individuals, in recent decades, it has become a common practice in many enterprises. SM can be defined as “online tools and platforms that allow internet users to collaborate on content, share insights and experiences, and connect for business or pleasure” [54]. In addition to the variety of definitions of SM, there are also different approaches to the classification of SM. The most prominent approach was introduced by Kaplan and Haenlein [55], who proposed a classification scheme to distinguish six different types of SM. Regardless of the SM classification, the term SM is usually used when referring to social networking sites, blogs, forums, microblogs, photo and video sharing, product/service reviews, evaluation communities, and social gambling [56].
As SM connects millions of users [57], it provides enterprises with opportunities to connect with a large number of customers to achieve different business objectives, such as marketing, customer service problem solving, and learning about customers [58,59]. Thus, SM provides an environment in which enterprises can build relationships with customers and differentiate themselves from their competitors [60,61]. For instance, studies have explored the impact of SM use on customer relationship management [51,62,63], customer knowledge [64], and innovation [65,66]. The majority of these studies neglect the involvement of commercial intentions in SM activities [59] or focus solely on sales process capability (sales) [67,68]. As prior research has provided evidence that SM use has a positive impact on building relationships with customers and has also a potential to improve sales, we hypothesize that:
H1: 
SM use for CRM has a positive impact on relational s-commerce capability.
There is a plethora of empirical research on the relationship between the adoption of information and communication technology and performance. However, little is known about the relationship between SM use and performance outcomes [69], particularly in the context of SMEs [19]. It was found that SM can improve firm success [70], enhance financial performance [71], and lead to higher sales by increasing brand awareness [72]. In the context of SMEs, the findings are inconsistent. While Tajvidi and Karami [52] provide evidence that SM use has a positive effect on the growth and profitability of SMEs, Foltean, Trif, and Tuleu [68] did not find a significant relationship between SM and business performance. As several recent studies [48,73] indicated that SM use can improve the performance of SMEs, we hypothesize that:
H2: 
SM use for CRM has a positive impact on business performance.

2.2. Mediating Role and Effects of Relational S-Commerce Capability

Previously, researchers have investigated the role of customer-facing aspects of organizational capability on different performance outcomes. Depending on the focus of each specific study, different conceptualizations of performance outcomes were taken into consideration. For instance, when conceptualizing performance outcome constructs, Braojos, Benitez, and Llorens [19] only used elements that are, theoretically, intended to be influenced directly by customer engagement. More specifically, they indicate that customer engagement has an impact on business performance that consists of two dimensions: innovation performance and customer service performance. When exploring the effect of marketing capability, several studies [53,62] have followed the organizational capabilities perspective, which argues that organizational capabilities empower enterprises to gain competitive advantage and increase business performance [74]. These studies focus either on one of these two constructs (competitive advantage or business performance) or treat them as interchangeable constructs. While competitive advantage and business performance should be differentiated [75], both aspects need to be taken into consideration. In this study, we consider three different relationship patterns: (1) the mediating role of relational s-commerce capability between SM use for CRM and competitive advantage; (2) the effect of relational s-commerce capability on competitive advantage; and (3) the effect of relational s-commerce capability on business performance.
Although the majority of enterprises have invested in customer-facing information technologies (e.g., customer relationship management, e-commerce, order management, sales force automation systems), their efforts do not usually meet the expected performance outcomes [76]. In other words, customer-facing information technologies alone do not produce competitive advantages [62,69], however, the ability to leverage these resources (organizational capabilities) could be a source of competitive advantage [77]. Organizational capabilities relating to customer-facing processes, marketing capability, customer relationship management capability, and e-commerce capability have been studied several times in the past [53,61,62,78,79]. More recently, s-commerce capability, in which sales and marketing are intertwined to build and maintain a long-term relationship and improve online customer engagement, [19] were taken into consideration. The role of sales is no longer exclusively focused on selling products or services, but also includes the management of relationships with customers, which includes persuasive promotional activities, coordinated delivery, and customer support [80]; these can create competitive advantages.
SM improves performance outcomes by developing strong relationships with customers, influencing their decision to buy, and gaining access to knowledge that is shared, not only between enterprises and customers, but also between customers themselves [62]. As such, SM provides opportunities for SMEs who want to build strong relationships with customers and improve their capabilities, which, in turn, affects their competitive advantage and business performance [68,81]. Despite the scholarly attention on s-commerce capability [19], the evaluation of how s-commerce capability mediates the role between SM use for CRM and competitive advantage remains understudied. As marketing capability is identified as crucial for obtaining competitive advantage and gaining business performance, we hypothesize that:
H3: 
Relational s-commerce capability mediates the relationship between SM use for CRM and competitive advantage.
Competitive advantage is formed by relational capabilities that create more value for the customers than their competitors and, as such, provide opportunities for improved business performance [77]. This can be achieved by creating, developing, and maintaining mutually beneficial relationships with customers [53]. Thus, we hypothesize that:
H4: 
Relational s-commerce capability has a positive impact on competitive advantage.
Different customer-facing aspects of organizational capabilities were taken into consideration when exploring the drivers of business performance. For instance, studies [53,82] have shown that marketing capability improves business performance. Furthermore, customer relationship management capability has also been identified as a driver of business performance [61,62,69]. Thus, we hypothesize that:
H5: 
Relational s-commerce capability has a positive impact on business performance.

2.3. Mediating Role and Effect of Competitive Advantage

Several studies have identified competitive advantage as a mediator in the relationship between organizational capabilities and business performance [83,84,85,86]. Furthermore, Khan, Yang, and Waheed [85] also showed that effective competitive advantage significantly positively contributes to business performance. Taking into consideration the fact that investment in branding capabilities boosts competitive advantages, which in turn enhance SMEs performance [86], we hypothesize that:
H6: 
Competitive advantage mediates the relationship between relational s-commerce capability and business performance.
H7: 
Competitive advantage has a positive impact on business performance.

3. Materials and Methods

3.1. Data Collection

The conceptual research model was developed based on the literature review and insights from six in-depth interviews with SME owners/managers. To validate the conceptual research model, we conducted a survey. The target population included SME managers/owners in Slovenia. SMEs are defined by the European Commission definition, in which SMEs are classified based on two criteria: employee numbers and annual turnover or balance sheet total. Taking into consideration the cost and impracticality of surveying the whole population relevant to the study, as well as the risk of a low-response rate [87], the decision was taken to include 2000 SMEs in the samples. SMEs were randomly selected from the Slovenian Business Register database, managed by the Agency of the Republic of Slovenia for Public Legal Records and Related Services.
Before the survey instrument was released to the final target respondents [88], it was first reviewed by two experts with backgrounds in digital business and statistics, and pretested with nine SME managers/owners. Based on the feedback and recommendations, the instrument was refined [88] and then an e-mail invitation was sent to the final target respondents. The invitation contained an introductory section, with information about the research, and the URL link to the online survey instrument. One reminder email was sent to motivate participation. Overall, 119 valid responses were received in a one month time frame.
The highest proportion of SMEs fell into the medium-sized category (37.8%), followed by small (35.3%) and micro (26.9%) enterprises. They mainly classified themselves in wholesale and retail trade (32.8%), manufacturing (26.8%), and other service activities (10.1%) sectors. All of the respondents held at least a middle management position in the enterprise, and 69.7% of them had more than five years of experience at the current or comparable position.

3.2. Instrument Development

The items for the proposed constructs were either: (1) adopted from previously used scales and then adjusted for the purpose of this research; or (2) developed based on existing conceptual studies and insights from in-depth interviews. With the exception of SM use, all of the items were measured with a five-point Likert-type scale (1—strongly disagree, 5—strongly agree). To measure SM use for CRM, we used a single score that was aggregated from the marked list of SM tools (Facebook, YouTube, Twitter, Instagram, LinkedIn, and Blogs), which was already conducted in past research [62,89]. This single score shows how many SM tools were used by each SME for the purposes of CRM. The new items in the relational s-commerce capability construct were drawn from the relevant literature [90,91] and insights from in-depth interviews. Altogether, seven items were designed to assess the ability in leveraging SM for commercial activities that are beyond transactions. After the reduction analysis was conducted, one item was eliminated due to the low item-total correlations. As competitive advantage is context-specific [75], the items to assess competitive advantage were also drawn from studies conducted by Hinton and Tao [92] and Li and Zhou [93] and based on the insights from the in-depth interviews. None of the four identified items were eliminated due to the low item-total correlations. To measure business performance, the marketing performance items were adapted from Katsikeas, Morgan, Leonidou, Hult [20], and Demirci Orel and Kara [94]. Table 1 presents the constructs, corresponding measurement items, and validity assessment.

4. Empirical Analysis and Results

The study employed Structural Equation Modeling (SEM) techniques, using R software (lavaan: An R package for structural equation modelling [95]) for data analysis. We used lavaan because it is easy to use, includes the required features, the results are very close to those reported by the current commercial programs, and it is completely open-source software [95]. We followed the two-stage approach proposed by Anderson and Gerbing [96]. A confirmatory factor analysis was first conducted to assess the measurement model in terms of reliability, convergent validity, and discriminant validity of the constructs. Then, hypotheses were tested using a structural equation modelling approach. Before the empirical analysis was performed, the minimum required sample was calculated. Based on the A-priori Sample Size Calculator for Structural Equation Models [97], the minimum required sample size to estimate the model is 116.

4.1. Assessment of Measurement Model

We assessed model fit by examining five types of indices, including Normed chi-square (chi-square/df), Root mean Square Error of Approximation (RMSEA), Tucker Levis index (TLI), Comparative Fit Index (CFI), and Goodness of Fit (GFI) [98]. As the results were not in the acceptable range, a revision of the model was conducted. Two items were deleted from the model, one by one, as a result of weak performance. Before deleting any items, we considered both statistical indicators and theoretical issues. This resulted in an acceptable model fit. The reliability and convergent validity of the measurement model were conducted using composite reliability (CR), Average variance extracted (AVE), and Cronbach’s alpha [99,100]. For all the constructs, the CR, AVE, and Cronbach’s alpha are above the cutoff values [100] (see Table 1). Moreover, discriminant validity was examined using the Fornell-Larcker criterion. As shown in Table 2, the correlations between each pair of constructs were lower than the square root of AVE for the relevant constructs.
Finally, the common method variance (CMV) was obtained using Harman’s single-factor tests. The largest variance explained by an individual factor was 32.164%, indicating that CMV is not a significant problem in this research [101].

4.2. Assessment of Structural Model

Following the evaluation of the measurement model, the structural model was assessed. First, we used the same set of fit indices to examine the fit of the structural model. In compression with the fit indices of the revised measurement model, only trivial changes were observed. Table 3 depicts results related to the direct paths, whereas Table 4 presents the results for the indirect paths. The path coefficients between the latent variables that were hypothesized in this research were significant, with the exception of H2 (influence of SM use on business performance) and H5 (influence of relational s-commerce capability on business performance). The use of SM for CRM has a significant effect on relational s-commerce capability (β = 0.166, t = 3.826, p ≤ 0.001), and relational s-commerce capability has a significant effect on competitive advantages (β = 0.477, t = 3.054, p < 0.01). Furthermore, relational s-commerce capability significantly mediates the relationship between SM use for CRM and competitive advantage (β = 0.116, t = 2.219, p < 0.05). Moreover, competitive advantage has a significant effect on business performance (β = 0.079, t = 2.531, p ≤ 0.05) and significantly mediates the relationship between the relational s-commerce capability and business performance (β = 0.426, t = 2.943, p ≤ 0.01). The results of the study also show that the model explains 15.3% of the variance in relational s-commerce capability, 12.7% of the variance in competitive advantage, and 55.4% of the variance in business performance. These results of the hypotheses testing are also presented in Figure 2.

5. Discussion

This research enriches the social commerce literature by focusing on the enterprise perspective and examining how SMEs use SM for CRM in order to gain competitive advantage through relational s-commerce capability and how this further affects their business performance. The findings suggest that, in the digital age, enterprises are facing ever-shifting customer expectations and, thus, enterprises need to alter their relational s-commerce capability by leveraging SM for CRM in order to gain competitive advantages, which in turn can drive business performance. As such, we believe that relational s-commerce capability encompasses a set of enterprise actions that enable the enterprise to outperform its competitors. Namely, these actions can help customers to be better informed and to evaluate products or services before purchasing [102]. Furthermore, by meeting customer expectations and building a strong brand name and reputation, enterprises can maintain long-lasting customer relationships [53]. These findings are important in the context of the increasing use of SM as a mean to support some of the key customer relationship activities among SMEs.
In particular, this study conceptualizes the relational s-commerce capability construct and competitive advantage construct and considers them as mediators to better understand how SM use can affect firm performance. The conceptualization of the relational s-commerce capability construct consists of relational activities that are beyond transactions and building meaningful relationships with customers [103]. It is expected that enterprises will evolve this capability over time, as they operate in a turbulent environment in which customers are more informed than ever before and, with their engagement, produce a large amount of data on daily basis [104]. Furthermore, new SM will emerge and additional functionalities will be added to existing ones over time, which will further affect the evolvement of relational s-commerce capability. As competitive advantage is context-specific, the suggested construct consists of competitive differentiation advantages that are, theoretically, directly influenced by relational s-commerce capability. Specifically, in this study, the competitive advantage is understood as increased brand recognition within the market and better understanding of customers, in comparison to competitors. Both conceptualized constructs were identified by this study as important mediators when exploring the impact that using SM for CRM has on business performance, in the context of SMEs.
Furthermore, this study did not find a direct impact of SM for CRM on business performance, but indicates that SM facilitates SMEs with relational s-commerce capability, which enables them to differentiate themselves from competitors and improve business performance. This finding is consistent with the existing literature (e.g., [53,89,105,106]), which implies that information technology without proper strategies rarely delivers the expected competitive advantages and performance. In other words, SMEs need to think strategically when leveraging their SM for relational s-commercial activities and build a deeper connection with customers. This may further result in the SME’s competitive advantage as they can promote themselves, identify customer requirements, and opportunities more effectively. Moreover, according to our findings, SMEs that succeed in differentiating themselves from competitors by facilitating relational s-commerce capability can expect to improve their performance. Therefore, our study confirms the importance of achieving a competitive advantage in enhancing business performance, which has already been acknowledged by several studies (e.g., [85,86]). Additionally, the mediating effect of both constructs, relational s-commerce capability and competitive advantages, between SM for CRM and business performance was analyzed at the same time, although this was not the aim of the study. The findings also indicate a positive mediating effect of these two constructs, which additionally supports our findings. An important implication of this association is that SME’s business performance can be improved by embedding the use of SM for CRM into their core relational commerce capabilities, which facilitates competitive advantages.
Moreover, by investigating competitive advantage and business performance, separately and as two different constructs, we found that gaining competitive advantage plays a critical role in achieving higher business performance [75]. Specifically, a higher level of competitive advantages enables firms to create superior value for customers, which in turn increases sales volume [84], market share, customer satisfaction, and loyalty.
For practitioners responsible for developing and adjusting such capability, our findings reinforced the need to align their relational commercial activities with emerging digital technologies. Thus, our study suggests that SMEs should commit to an ongoing process of bundling tangible and intangible resources into their capability. This will enable them to leverage digital technologies for relational activities more successfully and provide a better customer experience. Such endeavors can help strengthen the SME’s brand recognition and ultimately increase their revenue. Our study also suggests that SMEs need to move beyond experimental and informal patterns of activity and try to approach them more systematically, with a proper strategy. This will enable them to gain customer insights based on their endeavors and thus more successfully conduct follow-up relational, as well as commercial, activities. Furthermore, they will also gain better insights into what their competitors are doing and how the market is changing. As such, SMEs will be able to differentiate themselves from the competition by ongoing communication of their value proposition in a meaningful and effective way. This will help them remain competitive, which, in turn, may also impact their business performance.

6. Conclusions

SM enables enterprises to gain exposure, marketing insights, and increase traffic [107] and, as such, present an asset that SMEs use to increase competitive advantage and business performance. However, SM use for CRM does not necessarily lead directly to superior business performance [29]. Thus, the main aim of this research was to better understand the way SM use for CRM affects SME’s competitive advantages and business performance. Drawing upon resource-based views and a dynamic capabilities perspective, we argue that SM use for CRM enhances relational s-commerce capability in a way that increases the competitive advantage and business performance of SMEs.
Although the current study provides valuable insights into the s-commerce field, it is not without limitations. First, as cross-sectional data only provide one image in time, the identification of causal relationships is limited. Second, the findings lack generalizability as the data were collected exclusively from Slovenian SMEs. Third, this study applied a rather simple measure of SM use for CRM. For a more comprehensive evaluation of SM use for CRM, further studies should apply more sophisticated measures. In addition, future studies could extend the conceptual research model by examining moderating effects, for example, corporate social responsibility, environmental turbulence, entrepreneurial orientation, or, at least, longitudinally assess how relational s-commerce capability evolves. Moreover, enterprises typically undertake a whole range of marketing activities and use different information and communication technologies, including SM, as part of omnichannel marketing. Therefore, future studies should also consider a broader perspective by including other relational capabilities that arise, not only from SM, but also from other e-commerce and m-commerce platforms, as well as emerging technologies. Finally, other marketing performance indicators could be used to provide additional knowledge on market performance outcomes.

Author Contributions

Conceptualization, M.M. and A.P.; methodology, M.M.; formal analysis, M.M.; writing—original draft preparation, M.M.; writing—review and editing, A.P., H.-D.Z. and M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Slovenian Research Agency: Program No. P5-0018—Decision Support Systems in Digital Business.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Hanafizadeh, P.; Shafia, S.; Bohlin, E. Exploring the consequence of social media usage on firm performance. Digit. Bus. 2021, 1, 100013. [Google Scholar] [CrossRef]
  2. Mason, A.N.; Narcum, J.; Mason, K. Social media marketing gains importance after COVID-19. Cogent Bus. Manag. 2021, 8, 1870797. [Google Scholar] [CrossRef]
  3. Hajli, M.N. The role of social support on relationship quality and social commerce. Technol. Forecast. Soc. Chang. 2014, 87, 17–27. [Google Scholar] [CrossRef]
  4. Liao, S.H.; Widowati, R.; Hsieh, Y.C. Investigating online social media users’ behaviors for social commerce recommendations. Technol. Soc. 2021, 66, 101655. [Google Scholar] [CrossRef]
  5. Gupta, V. The influencing role of social media in the consumer’s hotel decision-making process. Worldw. Hosp. Tour. Themes 2019, 11, 378–391. [Google Scholar] [CrossRef]
  6. Hajli, N. Social commerce constructs and consumer’s intention to buy. Int. J. Inf. Manag. 2015, 35, 183–191. [Google Scholar] [CrossRef]
  7. Lin, X.; Li, Y.; Wang, X. Social commerce research: Definition, research themes and the trends. Int. J. Inf. Manag. 2017, 37, 190–201. [Google Scholar] [CrossRef]
  8. Riley, J. Sustaining customer engagement through social media brand communities. J. Glob. Sch. Mark. Sci. 2020, 30, 344–357. [Google Scholar] [CrossRef]
  9. Teng, X.; Wu, Z.; Yang, F. Research on the Relationship between Digital Transformation and Performance of SMEs. Sustainability 2022, 14, 6012. [Google Scholar] [CrossRef]
  10. Huang, Z.; Benyoucef, M. From e-commerce to social commerce: A close look at design features. Electron. Commer. Res. Appl. 2013, 12, 246–259. [Google Scholar] [CrossRef]
  11. Busalim, A.H.; Hussin, A.R.C. Understanding social commerce: A systematic literature review and directions for further research. Int. J. Inf. Manag. 2016, 36, 1075–1088. [Google Scholar] [CrossRef]
  12. Wang, C.; Zhang, P. The evolution of social commerce: The people, management, technology, and information dimensions. CAIS 2012, 31, 105–127. [Google Scholar] [CrossRef]
  13. Andijani, A.; Kang, K. Social Commerce Acceptance after Post COVID-19 Pandemic in Saudi Women Customers: A Multi-Group Analysis of Customer Age. Sustainability 2022, 14, 10213. [Google Scholar] [CrossRef]
  14. Dabrynin, H.; Zhang, J. The Investigation of the Online Customer Experience and Perceived Risk on Purchase Intention in China. J. Mark. Dev. Compet. 2019, 13, 2019. [Google Scholar]
  15. Lăzăroiu, G.; Neguriţă, O.; Grecu, I.; Grecu, G.; Mitran, P.C. Consumers’ Decision-Making Process on Social Commerce Platforms: Online Trust, Perceived Risk, and Purchase Intentions. Front. Psychol. 2020, 11, 890. [Google Scholar] [CrossRef]
  16. Liao, S.H.; Widowati, R.; Cheng, C.J. Investigating Taiwan Instagram users’ behaviors for social media and social commerce development. Entertain. Comput. 2022, 40, 100461. [Google Scholar] [CrossRef]
  17. Zhang, K.Z.K.; Benyoucef, M. Consumer behavior in social commerce: A literature review. Decis. Support Syst. 2016, 86, 95–108. [Google Scholar] [CrossRef]
  18. Oncioiu, I.; Căpușneanu, S.; Topor, D.I.; Tamaș, A.S.; Solomon, A.G.; Dănescu, T. Fundamental Power of Social Media Interactions for Building a Brand and Customer Relations. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1702–1717. [Google Scholar] [CrossRef]
  19. Braojos, J.; Benitez, J.; Llorens, J. How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence. Inf. Manag. 2019, 56, 155–171. [Google Scholar] [CrossRef]
  20. Katsikeas, C.S.; Morgan, N.A.; Leonidou, L.C.; Hult, G.T.M. Assessing Performance Outcomes in Marketing. J. Mark. 2016, 80, 1–20. [Google Scholar] [CrossRef]
  21. Rana, N.P.; Barnard, D.J.; Baabdullah, A.M.A.; Rees, D.; Roderick, S. Exploring barriers of m-commerce adoption in SMEs in the UK: Developing a framework using ISM. Int. J. Inf. Manag. 2019, 44, 141–153. [Google Scholar] [CrossRef]
  22. Cesaroni, F.M.; Consoli, D. Are Small Businesses Really Able to Take Advantage of Social Media? Electron. J. Knowl. Manag. 2015, 13, 257–268. [Google Scholar]
  23. He, W.; Wang, F.-K.; Chen, Y.; Zha, S. An exploratory investigation of social media adoption by small businesses. Inf. Technol. Manag. 2017, 18, 149–160. [Google Scholar] [CrossRef]
  24. Chatterjee, S.; Kar, A.K. Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India. Int. J. Inf. Manag. 2020, 53, 102103. [Google Scholar] [CrossRef]
  25. Abed, S.S. Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. Int. J. Inf. Manag. 2020, 53, 102118. [Google Scholar] [CrossRef]
  26. Braojos-Gomez, J.; Benitez-Amado, J.; Javier Llorens-Montes, F. How do small firms learn to develop a social media competence? Int. J. Inf. Manage. 2015, 35, 443–458. [Google Scholar] [CrossRef]
  27. Ahmad, S.Z.; Ahmad, N.; Bakar, A.R.A. Reflections of entrepreneurs of small and medium-sized enterprises concerning the adoption of social media and its impact on performance outcomes: Evidence from the UAE. Telemat. Inform. 2018, 35, 6–17. [Google Scholar] [CrossRef]
  28. Oyewobi, L.; Adedayo, O.F.; Olorunyomi, S.O.; Jimoh, R.A. Influence of social media adoption on the performance of construction small and medium-sized enterprises (SMEs) in Abuja–Nigeria. Eng. Constr. Archit. Manag. 2022; ahead-of-print. [Google Scholar] [CrossRef]
  29. Ahmad, S.Z.; Abu Bakar, A.R.; Ahmad, N. Social media adoption and its impact on firm performance: The case of the UAE. Int. J. Entrep. Behav. Res. 2018, 25, 84–111. [Google Scholar] [CrossRef]
  30. Akman, I.; Mishra, A. Factors influencing consumer intention in social commerce adoption. Inf. Technol. People 2017, 30, 356–370. [Google Scholar] [CrossRef]
  31. Handarkho, Y.D. Impact of social experience on customer purchase decision in the social commerce context. J. Syst. Inf. Technol. 2020, 22, 47–71. [Google Scholar] [CrossRef]
  32. Lee, K.; Lee, B.; Oh, W. Thumbs up, sales up? The contingent effect of facebook likes on sales performance in social commerce. J. Manag. Inf. Syst. 2015, 32, 109–143. [Google Scholar] [CrossRef]
  33. Baethge, C.; Klier, J.; Klier, M. Social commerce—state-of-the-art and future research directions. Electron. Mark. 2016, 26, 269–290. [Google Scholar] [CrossRef] [Green Version]
  34. Park, H.; Kim, Y.-K. The role of social network websites in the consumer–brand relationship. J. Retail. Consum. Serv. 2014, 21, 460–467. [Google Scholar] [CrossRef]
  35. Hennig-Thurau, T.; Malthouse, E.C.; Friege, C.; Gensler, S.; Lobschat, L.; Rangaswamy, A.; Skiera, B. The Impact of New Media on Customer Relationships. J. Serv. Res. 2010, 13, 311–330. [Google Scholar] [CrossRef] [Green Version]
  36. Hajli, N.; Sims, J. Social commerce: The transfer of power from sellers to buyers. Technol. Forecast. Soc. Chang. 2015, 94, 350–358. [Google Scholar] [CrossRef] [Green Version]
  37. Michaelidou, N.; Siamagka, N.T.; Christodoulides, G. Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands. Ind. Mark. Manag. 2011, 40, 1153–1159. [Google Scholar] [CrossRef] [Green Version]
  38. Liang, T.-P.; Turban, E. Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. Int. J. Electron. Commer. 2011, 16, 5–14. [Google Scholar] [CrossRef] [Green Version]
  39. Hajli, N.; Sims, J.; Zadeh, A.H.; Richard, M.-O. A social commerce investigation of the role of trust in a social networking site on purchase intentions. J. Bus. Res. 2017, 71, 133–141. [Google Scholar] [CrossRef] [Green Version]
  40. Li, C.-Y. How social commerce constructs influence customers’ social shopping intention? An empirical study of a social commerce website. Technol. Forecast. Soc. Chang. 2019, 144, 282–294. [Google Scholar] [CrossRef]
  41. Kim, S.; Park, H. Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. Int. J. Inf. Manag. 2013, 33, 318–332. [Google Scholar] [CrossRef]
  42. Bai, Y.; Yao, Z.; Dou, Y.-F. Effect of social commerce factors on user purchase behavior: An empirical investigation from renren.com. Int. J. Inf. Manag. 2015, 35, 538–550. [Google Scholar] [CrossRef]
  43. Liu, L.; Cheung, C.M.K.; Lee, M.K.O. An empirical investigation of information sharing behavior on social commerce sites. Int. J. Inf. Manag. 2016, 36, 686–699. [Google Scholar] [CrossRef]
  44. Shin, N.; Park, S.; Kim, H. Consumer satisfaction–based social commerce service quality management: BRQ Bus. Res. Q. 2020, 24, 34–52. [Google Scholar] [CrossRef]
  45. Kumar, V.; Aksoy, L.; Donkers, B.; Venkatesan, R.; Wiesel, T.; Tillmanns, S. Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value. J. Serv. Res. 2010, 13, 297–310. [Google Scholar] [CrossRef] [Green Version]
  46. Ghezzi, A.; Gastaldi, L.; Lettieri, E.; Martini, A.; Corso, M. A role for startups in unleashing the disruptive power of social media. Int. J. Inf. Manag. 2016, 36, 1152–1159. [Google Scholar] [CrossRef]
  47. Han, S.; Min, J.; Lee, H. Building relationships within corporate SNS accounts through social presence formation. Int. J. Inf. Manag. 2016, 36, 945–962. [Google Scholar] [CrossRef]
  48. Qalati, S.A.; Yuan, L.W.; Khan, M.A.S.; Anwar, F. A mediated model on the adoption of social media and SMEs’ performance in developing countries. Technol. Soc. 2021, 64, 101513. [Google Scholar] [CrossRef]
  49. Barnes, D.; Clear, F.; Dyerson, R.; Harindranath, G.; Harris, L.; Rae, A. Web 2.0 and micro-businesses: An exploratory investigation. J. Small Bus. Enterp. Dev. 2012, 19, 687–711. [Google Scholar] [CrossRef]
  50. Yawised, K.; Marshall, P.; Stockdale, R. Social CRM: A Review of the Academic and Practitioner Literatures and Research Agendas. In Proceedings of the Malaysian Conference on Information Systems, Sabah, Malaysia, 18–20 November 2013; pp. 101–107. [Google Scholar]
  51. Cheng, C.C.; Shiu, E.C. How to enhance SMEs customer involvement using social media: The role of Social CRM. Int. Small Bus. J. Res. Entrep. 2019, 37, 22–42. [Google Scholar] [CrossRef]
  52. Wang, W.Y.C.; Pauleen, D.J.; Zhang, T. How social media applications affect B2B communication and improve business performance in SMEs. Ind. Mark. Manag. 2016, 54, 4–14. [Google Scholar] [CrossRef]
  53. Tajvidi, R.; Karami, A. The effect of social media on firm performance. Comput. Hum. Behav. 2017, 115, 105174. [Google Scholar] [CrossRef]
  54. Strauss, J.; Frost, R.; Ansary, A.I. E-Marketing; Pearson Prentice Hall: Hoboken, NJ, USA, 2009. [Google Scholar]
  55. Kaplan, A.M.; Haenlein, M. Users of the world, unite! The challenges and opportunities of Social Media. Bus. Horiz. 2010, 53, 59–68. [Google Scholar] [CrossRef]
  56. Aichner, T.; Jacob, F. Measuring the Degree of Corporate Social Media Use. Int. J. Mark. Res. 2015, 57, 257–276. [Google Scholar] [CrossRef]
  57. Statista Number of Social Media Users Worldwide from 2018 to 2022, with Forecasts from 2023 to 2027. Available online: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ (accessed on 29 August 2022).
  58. Nisar, T.M.; Whitehead, C. Brand interactions and social media: Enhancing user loyalty through social networking sites. Comput. Hum. Behav. 2016, 62, 743–753. [Google Scholar] [CrossRef]
  59. Chaker, N.N.; Nowlin, E.L.; Pivonka, M.T.; Itani, O.S.; Agnihotri, R. Inside sales social media use and its strategic implications for salesperson-customer digital engagement and performance. Ind. Mark. Manag. 2022, 100, 127–144. [Google Scholar] [CrossRef]
  60. Wang, Y.-S.; Li, H.-T.; Li, C.-R.; Zhang, D.-Z. Factors affecting hotels’ adoption of mobile reservation systems: A technology-organization-environment framework. Tour. Manag. 2016, 53, 163–172. [Google Scholar] [CrossRef]
  61. Wang, Z.; Kim, H.G. Can Social Media Marketing Improve Customer Relationship Capabilities and Firm Performance? Dynamic Capability Perspective. J. Interact. Mark. 2017, 39, 15–26. [Google Scholar] [CrossRef]
  62. Trainor, K.J.; Andzulis, J.M.; Rapp, A.; Agnihotri, R. Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. J. Bus. Res. 2014, 67, 1201–1208. [Google Scholar] [CrossRef]
  63. Charoensukmongkol, P.; Sasatanun, P. Social media use for CRM and business performance satisfaction: The moderating roles of social skills and social media sales intensity. Asia Pac. Manag. Rev. 2017, 22, 25–34. [Google Scholar] [CrossRef]
  64. He, W.; Zhang, W.; Tian, X.; Tao, R.; Akula, V. Identifying customer knowledge on social media through data analytics. J. Enterp. Inf. Manag. 2019, 32, 152–169. [Google Scholar] [CrossRef]
  65. Muninger, M.I.; Hammedi, W.; Mahr, D. The value of social media for innovation: A capability perspective. J. Bus. Res. 2019, 95, 116–127. [Google Scholar] [CrossRef]
  66. Soto-Acosta, P.; Popa, S.; Palacios-Marqués, D. Social web knowledge sharing and innovation performance in knowledge-intensive manufacturing SMEs. J. Technol. Transf. 2017, 42, 425–440. [Google Scholar] [CrossRef]
  67. Rodriguez, M.; Ajjan, H.; Peterson, R.M. Social Media in Large Sales Forces: An Empirical Study of the Impact of Sales Process Capabillity and Relationship Performance. J. Mark. Theory Pract. 2016, 24, 365–379. [Google Scholar] [CrossRef]
  68. Bocconcelli, R.; Cioppi, M.; Pagano, A. Social media as a resource in SMEs’ sales process. J. Bus. Ind. Mark. 2017, 32, 693–709. [Google Scholar] [CrossRef]
  69. Foltean, F.S.; Trif, S.M.; Tuleu, D.L. Customer relationship management capabilities and social media technology use: Consequences on firm performance. J. Bus. Res. 2019, 104, 563–575. [Google Scholar] [CrossRef]
  70. Garrido-Moreno, A.; Lockett, N. Social Media Use in European Hotels: Benefits and Main Challenges. Tour. Manag. Stud. 2016, 12, 172–179. [Google Scholar] [CrossRef] [Green Version]
  71. Schniederjans, D.; Cao, E.S.; Schniederjans, M. Enhancing financial performance with social media: An impression management perspective. Decis. Support Syst. 2013, 55, 911–918. [Google Scholar] [CrossRef]
  72. Paniagua, J.; Sapena, J. Business performance and social media: Love or hate? Bus. Horiz. 2014, 57, 719–728. [Google Scholar] [CrossRef]
  73. Fan, M.; Qalati, S.A.; Khan, M.A.S.; Shah, S.M.M.; Ramzan, M.; Khan, R.S. Effects of entrepreneurial orientation on social media adoption and SME performance: The moderating role of innovation capabilities. PLoS ONE 2021, 16, e0247320. [Google Scholar] [CrossRef]
  74. Davcik, N.S.; Sharma, P. Marketing resources, performance, and competitive advantage: A review and future research directions. J. Bus. Res. 2016, 69, 5547–5552. [Google Scholar] [CrossRef] [Green Version]
  75. Ma, H. Competitive advantage and firm performance. Compet. Rev. 2000, 10, 15–32. [Google Scholar] [CrossRef]
  76. Reinartz, W.; Krafft, M.; Hoyer, W.D. The Customer Relationship Management Process: Its Measurement and Impact on Performance. J. Mark. Res. 2004, 41, 293–305. [Google Scholar] [CrossRef]
  77. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic capabilities and strategic management. Strateg. Manag. J. 1997, 18, 509–533. [Google Scholar] [CrossRef]
  78. Morgan, N.A.; Slotegraaf, R.J.; Vorhies, D.W. Linking marketing capabilities with profit growth. Int. J. Res. Mark. 2009, 26, 284–293. [Google Scholar] [CrossRef]
  79. Zhuang, Y.; Lederer, A.L. A resource-based view of electronic commerce. Inf. Manag. 2006, 43, 251–261. [Google Scholar] [CrossRef]
  80. Storbacka, K.; Ryals, L.; Davies, I.A.; Nenonen, S. The changing role of sales: Viewing sales as a strategic, cross-functional process. Eur. J. Mark. 2009, 43, 890–906. [Google Scholar] [CrossRef] [Green Version]
  81. Gensler, S.; Völckner, F.; Liu-Thompkins, Y.; Wiertz, C. Managing Brands in the Social Media Environment. J. Interact. Mark. 2013, 27, 242–256. [Google Scholar] [CrossRef]
  82. Ahmed, M.U.; Kristal, M.M.; Pagell, M. Impact of operational and marketing capabilities on firm performance: Evidence from economic growth and downturns. Int. J. Prod. Econ. 2014, 154, 59–71. [Google Scholar] [CrossRef]
  83. Kim, K.-H.; Kim, M.; Qian, C. Effects of Corporate Social Responsibility on Corporate Financial Performance: A Competitive-Action Perspective. J. Manag. 2015, 44, 1097–1118. [Google Scholar] [CrossRef]
  84. Saeidi, S.P.; Sofian, S.; Saeidi, P.; Saeidi, S.P.; Saaeidi, S.A. How does corporate social responsibility contribute to firm financial performance? The mediating role of competitive advantage, reputation, and customer satisfaction. J. Bus. Res. 2015, 68, 341–350. [Google Scholar] [CrossRef]
  85. Khan, S.Z.; Yang, Q.; Waheed, A. Investment in intangible resources and capabilities spurs sustainable competitive advantage and firm performance. Corp. Soc. Responsib. Environ. Manag. 2019, 26, 285–295. [Google Scholar] [CrossRef]
  86. Ferreira, J.; Coelho, A. Dynamic capabilities, innovation and branding capabilities and their impact on competitive advantage and SME’s performance in Portugal: The moderating effects of entrepreneurial orientation. Int. J. Innov. Sci. 2020, 12, 255–286. [Google Scholar] [CrossRef]
  87. Lefever, S.; Dal, M.; Matthíasdóttir, Á. Online data collection in academic research: Advantages and limitations. Br. J. Educ. Technol. 2007, 38, 574–582. [Google Scholar] [CrossRef]
  88. Callegaro, M.; Manfreda, K.L.; Vehovar, V. Web Survey Methodology; SAGE Publications: Thousand Oaks, CA, USA, 2015. [Google Scholar]
  89. Jayachandran, S.; Sharma, S.; Kaufman, P.; Raman, P. The Role of Relational Information Processes and Technology Use in Customer Relationship Management. J. Mark. 2005, 69, 177–192. [Google Scholar] [CrossRef] [Green Version]
  90. Andzulis, J.M.; Panagopoulos, N.G.; Rapp, A. A review of social media and implications for the sales process. J. Pers. Sell. Sales Manag. 2012, 32, 305–316. [Google Scholar] [CrossRef]
  91. Sigala, M. eCRM 2.0 applications and trends: The use and perceptions of Greek tourism firms of social networks and intelligence. Comput. Hum. Behav. 2011, 27, 655–661. [Google Scholar] [CrossRef]
  92. Hinton, M.C.; Tao, Y. Exploring sources of competitive advantage. J. Technol. Manag. China 2006, 1, 92–106. [Google Scholar] [CrossRef]
  93. Li, J.J.; Zhou, K.Z. How foreign firms achieve competitive advantage in the Chinese emerging economy: Managerial ties and market orientation. J. Bus. Res. 2010, 63, 856–862. [Google Scholar] [CrossRef] [Green Version]
  94. Orel, F.D.; Kara, A. Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market. J. Retail. Consum. Serv. 2014, 21, 118–129. [Google Scholar] [CrossRef]
  95. Rosseel, Y. lavaan: An R Package for Structural Equation Modeling. J. Stat. Softw. 2012, 48, 1–36. [Google Scholar] [CrossRef] [Green Version]
  96. Anderson, J.C.; Gerbing, D.W. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  97. Soper, D.S. A-Priori Sample Size Calculator for Structural Equation Models [Software]. Available online: https://www.danielsoper.com/statcalc/calculator.aspx?id=89 (accessed on 1 July 2021).
  98. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a Silver Bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  99. Chin, W.W. Issues and Opinion on Structural Equation Modeling. MIS Q. 1998, 22, 8–17. [Google Scholar]
  100. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39. [Google Scholar] [CrossRef]
  101. Mou, J.; Cohen, J.; Dou, Y.; Zhang, B. Predicting buyers’ repurchase intentions in cross-border e-commerce: A valence framework perspective. In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portuga, 5–10 June 2017; pp. 2382–2394. [Google Scholar]
  102. Tajudeen, F.P.; Jaafar, N.I.; Ainin, S. Understanding the impact of social media usage among organizations. Inf. Manag. 2018, 55, 308–321. [Google Scholar] [CrossRef]
  103. Wibowo, A.; Chen, S.C.; Wiangin, U.; Ma, Y.; Ruangkanjanases, A. Customer Behavior as an Outcome of Social Media Marketing: The Role of Social Media Marketing Activity and Customer Experience. Sustainability 2021, 13, 189. [Google Scholar] [CrossRef]
  104. Mikalef, P.; Pateli, A.; van de Wetering, R. IT architecture flexibility and IT governance decentralisation as drivers of IT-enabled dynamic capabilities and competitive performance: The moderating effect of the external environment. Eur. J. Inf. Syst. 2020, 30, 512–540. [Google Scholar] [CrossRef]
  105. Mao, H.; Liu, S.; Zhang, J.; Deng, Z. Information technology resource, knowledge management capability, and competitive advantage: The moderating role of resource commitment. Int. J. Inf. Manag. 2016, 36, 1062–1074. [Google Scholar] [CrossRef]
  106. Bilgihan, A.; Okumus, F.; Nusair, K.K.; Kwun, D.J.W. Information technology applications and competitive advantage in hotel companies. J. Hosp. Tour. Technol. 2011, 2, 139–153. [Google Scholar] [CrossRef]
  107. Dwivedi, Y.K.; Ismagilova, E.; Hughes, D.L.; Carlson, J.; Filieri, R.; Jacobson, J.; Jain, V.; Karjaluoto, H.; Kefi, H.; Krishen, A.S.; et al. Setting the future of digital and social media marketing research: Perspectives and research propositions. Int. J. Inf. Manag. 2021, 59, 102168. [Google Scholar] [CrossRef]
Sustainability 14 15029 g001 550
Figure 1. Conceptual research model.
Figure 1. Conceptual research model.
Sustainability 14 15029 g001
Sustainability 14 15029 g002 550
Figure 2. Results of the hypotheses testing. Sign. *** p ≤ 0.001 ** p ≤ 0.01; * p ≤ 0.05.
Figure 2. Results of the hypotheses testing. Sign. *** p ≤ 0.001 ** p ≤ 0.01; * p ≤ 0.05.
Sustainability 14 15029 g002
Table
Table 1. Constructs, measurement items, and validity assessment.
Table 1. Constructs, measurement items, and validity assessment.
ConstructItemsLoading
SM use for CRMSocial media use for CRM1.000
Relational
s-commerce capability
CR = 0.813
AVE = 0.505
Cronbach’s α = 0.798		Post new offerings
Inform customers about special offers
Share stories that build brand
Negotiate terms of exchange in real-time
Activate brand ambassadors and opinion leaders in campaign activities
Demonstrate a specific value proposition, using visual ads and additional demonstrations
Educate the potential customers on product or service through the two-way interaction 		**
**
0.738
0.660
*

0.793

0.694
Competitive Advantage
CR = 0.839
AVE = 0.567
Cronbach’s α = 0.837		Compared to our competitors, we have an advantage in
differentiating ourselves from others through effective: advertising and promotion campaigns
monitoring customer requirements more effectively
identifying new selling opportunities constantly
building a strong brand name

0.740
0.783
0.734
0.750
Business performance
CR = 0.860
AVE = 0.627
Cronbach’s α = 0.849		Customers are pleased with the quality of service provided by our organization
Our customers often speak positively about our organization
Market share has increased
Sales volume has increased 		0.603

0.593

0.940
0.934
Overall model fit: Chi square/df = 1.717; RMSEA = 0.078; TLI = 0.923; CFI = 0.941; GFI = 0.989
Notes: CR = composite reliability; AVE = average variance extracted; Cronbach’s α = Cronbach’s alpha; chi-square/df = Normed chi-square; RMSEA = Root mean Square Error of Approximation; TLI = Tucker Lewis index; CFI = Comparative Fit Index; GFI = Goodness of Fit. * Item deleted from further analysis due to low item-to-total correlations. ** Items deleted from further analysis due to weak performance.
Table
Table 2. Intercorrelation of the latent variables and the square root of AVE for the revised measurement model.
Table 2. Intercorrelation of the latent variables and the square root of AVE for the revised measurement model.
MeanStd. DevSMURSCCOABUP
SM use for CRM (SMU)2.8401.2751.000
Relational s-commerce capability (RSC)3.9791.3100.3840.711
Competitive advantage (COA)3.6740.9970.2270.3460.753
Business performance (BUP)2.6111.0830.2130.3140.7430.792
Table
Table 3. Testing for direct effect.
Table 3. Testing for direct effect.
Structural Pathβ S. E.t-Value
H1RSC ← SMU0.166***0.0433.826
H2BUP ← SMU0.031 0.0560.547
H4COA ← RSC0.477**0.1563.054
H5BUP ← RSC0.081 0.1590.509
H7BUP ← COA0.894***0.1346.658
Sign. *** p ≤ 0.001 ** p ≤ 0.01.
Table
Table 4. Testing for the indirect effect.
Table 4. Testing for the indirect effect.
Structural Pathβ Lower BoundUpper Bound
H3COA ← RSC ← SMU 0.079 *0.0180.140
H6BUP ← COA ← RSC0.426**0.1420.710
Sign. ** p ≤ 0.01; * p ≤ 0.05.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Marolt, M.; Zimmermann, H.-D.; Pucihar, A. Social Media Use and Business Performance in SMEs: The Mediating Roles of Relational Social Commerce Capability and Competitive Advantage. Sustainability 2022, 14, 15029. https://doi.org/10.3390/su142215029

AMA Style

Marolt M, Zimmermann H-D, Pucihar A. Social Media Use and Business Performance in SMEs: The Mediating Roles of Relational Social Commerce Capability and Competitive Advantage. Sustainability. 2022; 14(22):15029. https://doi.org/10.3390/su142215029

Chicago/Turabian Style

Marolt, Marjeta, Hans-Dieter Zimmermann, and Andreja Pucihar. 2022. "Social Media Use and Business Performance in SMEs: The Mediating Roles of Relational Social Commerce Capability and Competitive Advantage" Sustainability 14, no. 22: 15029. https://doi.org/10.3390/su142215029

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop