Assessing the impact of social media analytics on the growth and profitability of SMEs in the Free State Province JM Moloi orcid.org/0000-0002-1170-2599 Mini-dissertation accepted in partial fulfilment of the requirements for the degree Master of Business Administration at the North-West University Supervisor: Dr NK Ndlovu Graduation: October 2025 ii | Page DECLARATION I, Jerry Moloi, hereby declare that the current mini dissertation, submitted to North-West University in partial fulfilment of the requirements for the Master of Business Administration degree, is my own original work. The work has not been submitted to any other institution for evaluation or publishing. 18 March 2025 iii | Page DEDICATION This work is dedicated to my family, the Makholokoe a ha Lehasa la Matsimela, the great- grandchildren of Kampo Mtwana Moloi. This achievement is a testament to breaking generational limitations. May this milestone inspire future generations to strive for excellence and continue building on this foundation. iv | Page ACKNOWLEDGEMENTS I would like to express my heartfelt gratitude to those who supported me throughout this research journey. To my beloved wife, Karabo Kgopa, thank you for your unwavering love, patience, and encouragement, which have been my foundation. Your belief in me kept me motivated through every challenge. To my friends, thank you for always encouraging me to continue and pursue my goals. Your support and companionship have been invaluable. I am profoundly grateful to my study leader, Prof. Kaizer Ndlovu, for his guidance, insightful feedback, and unwavering commitment to my progress. His mentorship was instrumental in shaping this research. My appreciation extends to the NWU Bursary Office for facilitating the financial support that made this study possible. This accomplishment would not have been possible without each of you, and I am deeply thankful. v | Page ABSTRACT This study aimed to explore the perceived impact of social media analytics on the growth and profitability of SMEs that run driving school companies in Free State Province, South Africa. Unlike large organisations, most SMEs in South Africa find it difficult to leverage social media analytics, primarily due to a lack of financial resources and expertise. To examine this issue, the study adopted a qualitative research approach, grounded in the principles and assumptions of the interpretivism research paradigm, which insists on subjectivism. Regarding research design, the study was premised on the phenomenology research design, which values digging deep into the participants’ experiences and opinions pertaining to an issue under investigation. The target population of the study were owners and managers of driving schools in Free State province. From this target population, a sample of 15 participants was selected. In-depth interviews were used to collect data from the participants. The data was analysed using thematic analysis to establish themes that permeated across all interview transcripts. The study found that social media analytics significantly have a significant impact on the growth and profitability of SMEs in the Free State Province. Firstly, these analytics facilitate market expansion through effective social media marketing. Secondly, they enhance profitability by increasing sales, revenue, and overall profits while reducing costs. However, SMEs face challenges in utilising these analytics, such as resource shortages, a lack of awareness about available tools, the complexities of their use, and inadequate technological infrastructure. Additionally, platforms such as Facebook, WhatsApp, and Instagram play crucial roles in improving customer engagement, fostering stronger relationships and loyalty. Lastly, tools like Google Analytics and Facebook Insights were found to effectively enhance customer engagement and contribute to the profitability and sustainability of SMEs. Therefore, it is recommended that SMEs in the Free State Province invest in training and resources to enhance their understanding and utilisation of social media analytics, while also leveraging social media platforms to improve customer engagement and drive growth. Keywords: Social media analytics, growth, profitability, driving schools, Free State Province vi | Page Table of Contents DECLARATION ............................................................................................................ I DEDICATION III ACKNOWLEDGEMENTS ........................................................................................... IV ABSTRACT V TABLE OF CONTENTS ............................................................................................. VI LIST OF FIGURES ...................................................................................................... X LIST OF TABLES ........................................................................................................ X CHAPTER 1: INTRODUCTION TO THE STUDY ........................................................ 1 1.1 Introduction ............................................................................................................ 1 1.2 Background of the study......................................................................................... 1 1.3 Problem statement ................................................................................................. 3 1.4 Research objectives ............................................................................................... 4 1.5 Research questions ............................................................................................... 4 1.6 Significance of the study ........................................................................................ 5 1.7 Research methodology .......................................................................................... 5 1.8 Delimitations of the study ....................................................................................... 6 1.9 Limitations of the study .......................................................................................... 7 1.10 Dissertation structure ........................................................................................... 7 vii | Page 1.11 Chapter summary ................................................................................................. 8 CHAPTER 2 LITERATURE REVIEW .......................................................................... 9 2.1 Introduction ............................................................................................................ 9 2.2 Theoretical framework ............................................................................................ 9 2.3 Empirical support of the theoretical framework ..................................................... 12 2.4 Analysis of social media analytics and small to medium enterprises .................... 15 2.5 Characterising social media analytics ................................................................... 16 2.6 Overview of SMEs in Free State Province ............................................................ 17 2.7 The role of social media in modern business practices ........................................ 18 2.8 Analytics of social media ...................................................................................... 19 2.9 Analysis of prominent social media analytics instruments .................................... 19 2.10 Essential metrics and insights obtained from social media analytics .................. 20 2.11 Challenges in implementing social media analytics in Small and Medium-sized Enterprises .......................................................................................... 21 2.12 Deficiency in technical proficiencies and instruction ........................................... 22 2.13 Prospects for small and medium enterprises in Free State Province .................. 23 2.14 Utilising social media analytics for competitive edge .......................................... 23 2.15 Emerging trends in social media analytics for small and medium enterprises ..... 29 2.16 Analytics of social media in rural and semi-urban regions .................................. 30 2.17 Chapter summary ............................................................................................... 31 CHAPTER 3 METHODOLOGY ................................................................................. 33 3.1 Introduction .......................................................................................................... 33 viii | Page 3.2 Research paradigm .............................................................................................. 33 3.3 Research approach .............................................................................................. 35 3.4 Research design .................................................................................................. 36 3.5 Target population ................................................................................................. 38 3.6 Data collection ..................................................................................................... 39 3.7 Data analysis ....................................................................................................... 39 3.8 Trustworthiness .................................................................................................... 40 3.9 Ethical considerations .......................................................................................... 41 3.10 Chapter summary ............................................................................................... 41 CHAPTER 4 PRESENTATION AND ANALYSIS OF FINDINGS ............................... 43 4.1 Introduction .......................................................................................................... 43 4.2 Demographics of participants ............................................................................... 43 4.3 Influence of social media analytics on the expansion of Small and Medium-Sized Enterprises .......................................................................................... 44 The study aimed to assess the influence of social media analytics on the expansion of SMEs in Free State Province. The participants in the study highlighted that social media analytics had helped in the expansions of the SMEs. One of the interview participants stated: .............................................. 44 4.4 Effect of social media analytics on profitability within SMEs in the Free State Province .............................................................................................. 46 4.5 Obstacles Small and Medium-sized Enterprises in the Free State Province encountered using social media analytics ........................................... 50 4.6 The functions of social media platforms in augmenting customer engagement for Small and Medium-sized Enterprises .................................................. 54 ix | Page 4.7 Efficacy of existing social media analytics instruments employed by Small and Medium-sized Enterprises in the Free State Province ......................... 55 4.8 Chapter summary ................................................................................................. 56 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS ..................................... 58 5.1 Introduction .......................................................................................................... 58 5.2 Re-cap of research objectives .............................................................................. 58 5.3 Summary of the study .......................................................................................... 58 5.4 Summary of key findings ...................................................................................... 59 5.5 Conclusions from findings .................................................................................... 61 5.6 Recommendations ............................................................................................... 61 5.7 Contributions of the study ..................................................................................... 62 5.8 Limitations and areas for further study ................................................................. 62 5.9 Chapter summary ................................................................................................. 63 REFERENCES .......................................................................................................... 64 ANNEXURE A: ETHICS CLEARANCE ..................................................................... 97 ANNEXURE B: INFORMED CONSENT .................................................................... 98 ANNEXURE C: INTERVIEW GUIDE ......................................................................... 99 ANNEXURE D: TURNITIN REPORT ....................................................................... 100 ANNEXURE E: LANGUAGE EDITOR CERTIFICATE ............................................ 101 x | Page LIST OF FIGURES Figure 2.1: Theoretical framework of the Technology Acceptance Model……………………………………………………………………................................14 Figure 2.2: Theoretical framework of the Resource-Based View Theory......................................................................................................................................14 Figure 2.3: Theoretical framework of the Diffusion of Innovation Theory......................................................................................................................................15 LIST OF TABLES Table 4.1: Background characteristics of participants...............................................................................................................................41 CHAPTER 1: INTRODUCTION TO THE STUDY 1.1 Introduction The evolution of social media technologies is fundamentally changing how businesses grow and expand (Nkosana, 2022; Pour et al., 2021). Among these social media advancements, as Cennamo et al. (2020) emphasise, social media analytics has emerged as an important tool for organisations that aim to leverage data-driven decision-making. Accordingly, Moyo (2019) explains that social media platforms generate various types of important data that, when gathered and analysed effectively, help to get insights into developing business growth and improving profitability strategies. Thus, Tarr (2021) argue that for SMEs, which usually encounter intense competition and operate within limited resources, embracing social media analytics presents a unique opportunity for growth and profitability. However, Patrick (2022) asserted that the degree to which SMEs in developing economies such as South Africa are adopting social media analytics remains unexplored. This study aimed to broaden our understanding by exploring the perceived impact of social media analytics on business growth and profitability, particularly focusing on SMEs that run driving schools in Free State Province, South Africa. 1.2 Background of the study Globally, SMEs are increasingly leveraging social media analytics as a strategic tool for business growth and expansion (Akpan et al., 2024). According to Mpungose et al. (2024), the introduction of social media platforms such as Facebook, Twitter, and WhatsApp presents an unprecedented opportunity for businesses to enhance their profitability and develop their brands. Cheung et al. (2020) indicate that companies that use social media analytics to evaluate the effectiveness of marketing efforts, establish emerging customer preferences, and benchmark their performance against competitors often achieve better results. Nogroho and Soewarno (2024) add that companies effectively integrating social media analytics into business operations experience improved decision-making processes, informed innovations, and higher profitability. Sashi et al. (2019) and Guo et al. (2020) agree that organisations employing social media analytics are typically more adept at anticipating customers' needs and responding to market changes with greater efficacy. 2 | Page Velempini and Kabanda (2024) assert that SMEs that fail to adopt social media analytics risk being left behind as technological advancement continues to transform industries worldwide. In South Africa, however, SMEs experience various challenges in adopting social media analytics, regardless of its ability to improve business growth and profitability (Swaartbooi, 2022). Mpungose et al. (2024) reveal that most of these SMEs run their businesses in resource-constrained environments, where financial constraints, limited access to technology, and inadequate digital literacy obstruct investment in modern analytics tools. Patrick (2022) establishes that the cost of data and internet connectivity is high in South Africa, which is a significant challenge for SMEs to effectively use analytics tools and social media platforms. Tarr (2021) also highlights that a lack of robust digital infrastructure, especially in rural areas, worsens this challenge and complicates the collection and analysis of social media data. Akpan et al. (2024) highlight a significant lack of awareness among SMEs owners regarding the importance of social media analytics and its integration into business strategies. This concern is echoed by Nkosana (2022), who observes that many SME owners in South Africa hesitate to adopt such tools due to a limited understanding of their potential to grow brands and enhance profitability. Moyo (2019) and Mpungose et al. (2024) add that these challenges are particularly pronounced in developing economies. Small and Medium-sized Enterprises are vital to local economies but often face disadvantages when competing with larger firms with greater access to digital expertise and resources. Despite these barriers, Velempini and Kabanda (2024) argue that SMEs should view social media analytics as a worthwhile investment for driving growth and long-term profitability. Nugroho (2024) cautions that avoiding or neglecting these tools can lead to missed opportunities, as businesses without data-driven insights risk losing customers to more agile, digitally savvy competitors. Similarly, Nkosana (2022) explains that the absence of social media analytics can weaken customer engagement, diminishing retention and brand loyalty. 3 | Page Tlapana et al. (2023) emphasise that without the ability to track and interpret customer behaviour and market trends, many SMEs struggle to make informed decisions, often resulting in misaligned marketing strategies. Moyo (2019) similarly points out that lacking analytic insights may lead to ineffective marketing expenditure, wasting already limited resources. McGuirk and Nunn (2024) further argue that real-time analytics are essential for responsiveness in a volatile marketplace, noting that SMEs are less adaptable to shifting customer expectations and economic conditions without such tools. As Nugroho (2024) concludes, failure to incorporate social media analytics can undermine competitiveness and long-term sustainability in today’s evolving digital business environment. 1.3 Problem statement Small and Medium-sized Enterprises in South Africa, particularly in the Free State Province, face various challenges that hinder their ability to adopt social media analytics, ultimately affecting their growth and profitability (Velempini & Kabanda, 2024). Key barriers include the high costs of data and connectivity (Tarr, 2022), limited access to digital infrastructure (Akpan et al., 2024), a lack of awareness about the benefits of social media analytics (Swaartbooi, 2022), and inadequate digital skills and expertise (Nkosana, 2022). Collectively, these issues pose significant obstacles to SMEs’ ability to harness data-driven insights that could inform more effective marketing strategies. The impact of these challenges is particularly severe in resource-constrained environments, where many SMEs struggle to justify the financial burden associated with adopting new technologies. Mpungose et al. (2024) note that this situation often results in missed opportunities for innovation and market engagement, leaving businesses ill-equipped to respond to evolving consumer preferences and emerging trends. Consequently, the inability to leverage social media analytics contributes to ineffective marketing efforts, reduced competitiveness, stagnated growth, and ultimately, low profitability. While previous research has explored the general adoption of social media analytics among SMEs, few studies have specifically examined the perceived impact of these 4 | Page tools on business growth and profitability, particularly within sector-specific and regional contexts. Against this backdrop, the present study investigates the perceived influence of social media analytics on the growth and profitability of SMEs operating driving schools in the Free State Province of South Africa. 1.4 Research objectives The study aimed to achieve the following objectives: 1.4.1 Primary research objective ● To explore the perceived impact of social media analytics on the growth and profitability of SMEs operating driving schools in Free State Province. 1.4.2 Secondary research objectives ● To explore the influence of social media analytics on the expansion of SMEs running driving schools in the Free State Province. ● To examine the relationship between social media analytics and profitability in SMEs that run driving schools in the Free State Province. ● To determine the challenges faced by SMEs operating driving schools in the Free State Province in utilising social media analytics. ● To examine the role of social media platforms in enhancing customer interaction for SMEs running driving schools in the Free State Province and its impact on profitability. ● To explore the efficacy of existing social media analytics instruments employed by SMEs operating driving schools in the Free State Province. 1.5 Research questions The study sought to answer the following questions: 1.5.1 Primary research question ● What is the perceived impact of social media analytics on the growth and profitability of SMEs operating driving schools in the Free State Province? 5 | Page 1.5.2 Secondary research questions ● What is the influence of social media analytics on the expansion of SMEs running driving schools in the Free State Province? ● What is the relationship between social media analytics and profitability in SMEs that run driving schools in the Free State Province? ● What are the challenges faced by SMEs operating driving schools in the Free State Province when utilising social media analytics? ● What is the role of social media platforms in enhancing customer interaction for SMEs running driving schools in the Free State Province and its impact on profitability? ● How effective are existing social media analytics instruments employed by SMEs operating driving schools in the Free State Province? 1.6 Significance of the study The study makes a two-fold contribution, namely theoretical and practical. Theoretically, the study expands the knowledge boundaries of the existing literature on social media analytics and its perceived impact on SMEs’ growth and profitability endeavours. In particular, examining the unique challenges that SMEs in the Free State Province face in adopting social media analytics. The study fills a critical gap in understanding how social media analytical tools influence growth and profitability, particularly in resource-constrained environments. Practically, the findings of the study establish valuable insights for SME owners and management who seek to enhance their competitive edge in an increasingly digital business landscape. Moreover, the study informs stakeholders and policymakers about the specific barriers that hinder social media analytics adoption, providing a guide for the development of support mechanisms and targeted intervention to promote digital transformation. As SMEs play an important role in job creation and economic development, understanding how social media analytics impacts operation is important for fostering sustainable economic growth. 1.7 Research methodology 6 | Page As fully explored in chapter three, this study adopted a qualitative research methodology. According to Morse (2020), the primary aim of qualitative research is to explore people’s experiences, opinions, and observations regarding particular issues. Thus, the findings of this study are grounded in the experiences and opinions of SME owners and managers who run driving schools in the Free State Province as far as adopting social media analytics for enhancing growth and profitability is concerned. The study was premised on the principles of the interpretivism research paradigm. Allan (2020) explains that interpretivism emphasises that reality is subjective; therefore, every individual experiences the social world differently. Therefore, the researcher assumed that the experiences and opinions of the SMEs owners and managers running driving schools in the Free State Province concerning the adoption of social media have some variation and are not the same, hence grounding the study in the interpretivism research paradigm. Semi-structured in-depth interviews were used to collect data from 15 participants who were owners and managers of SMEs that run driving schools in the Free State Province. In-depth interviews, according to Morse (2020), remain the most effective way of collecting qualitative data, mainly because they allow participants to empty their minds regarding a particular issue. As Vaivio (2012) advise, the researcher uses the probing technique to delve deeper into participants’ responses, enabling a comprehensive understanding of participants’ experiences and perspectives. The data was analysed using thematic analysis, which, according to Nassaj (2020), is a qualitative data analysis technique that focuses on identifying and establishing main themes or patterns that permeate across all interview or focus group transcripts. 1.8 Delimitations of the study The study was narrowed down to three delimitations, namely geographical, conceptual, and sectoral: i.) Geographical delimitations: The study focuses exclusively on the Free State Province of South Africa. This choice is important because it considers that the province has its own unique economic, social, and infrastructural challenges. This choice is important because it considers that the province has its own unique economic, social, and infrastructural challenges. 7 | Page Therefore, by limiting the scope of the study to this province, it provides a detailed examination of local conditions, such as internet connectivity and access to technology. ii.) Conceptual delimitations: The study is grounded on three distinct main constructs, namely social media analytics, growth and profitability. Therefore, other sub-constructs explored in this study, in one way or the other, link back to these main constructs. iii.) Sectoral delimitations: The study focuses exclusively on driving schools as the specific type of SME under examination. Thus, by focusing on this sector, the study uncovers sector-specific insights into how social media analytics can facilitate growth and profitability. 1.9 Limitations of the study The study was exposed to the following limitations: i.) Limited generalisability: The findings of qualitative research are usually based on small sample sizes, which minimises the ability to generalise the findings to larger populations (Patel & Patel, 2020). ii.) Data complexity: The descriptive and rich nature of qualitative data often makes analysis challenging and complex, complicating identification of clear patterns and conclusions (Nassaj, 2020). iii.) Replicability issues: Due to the context-specific and subjective nature of qualitative research, replacing studies is difficult because of variations in responses, the researcher’s interpretation and the setting (Allan, 2020). 1.10 Dissertation structure This study follows a five-chapter report structure as described below: Chapter one–Introduction: The chapter introduces the main elements of the study, which include the problem statement, research objectives, research questions, and significance of the study. 8 | Page Chapter two—Literature Review: The chapter reviews various scholarly opinions, observations, and submissions relating to the adoption of social media analytics by SMEs for business growth and profitability. Chapter three–Methodology: The chapter presents the research methodology adopted to answer the research questions and achieve the research objectives. Chapter four–Presentation and discussion of findings: The chapter presents and discusses the findings of the study guided by research objectives. Chapter five–Conclusions and recommendations: The chapter presents the conclusion and recommendations of the study, drawing on the key findings of the study. 1.11 Chapter summary The chapter explores the importance of adopting social media analytics for SMEs pursuing growth and profitability. In particular, the chapter highlights the evolving role of social media analytics in business operations, the challenges faced by SMEs in adopting these tools, and the potential consequences of failing to embrace social media analytics in this digital era. This chapter presents a detailed overview of the research methodology employed in this investigation. The following chapter analyses the relevant literature related to the subject of the investigation. 9 | Page CHAPTER 2 LITERATURE REVIEW 2.1 Introduction This chapter explores the concepts of social media analytics and digital transformation, highlighting their significance in driving growth and profitability, especially for SMEs. It discusses various theoretical perspectives and frameworks that support the use of social media analytics in the context of business development. The conversation emphasizes how digital transformation reshapes organizational processes, making social media analytics a key component of this change. Additionally, the chapter outlines the benefits and limitations of leveraging data-driven insights from social media platforms, focusing on how organizations can strategically use this information to enhance their competitive advantage and overall performance. 2.2 Theoretical framework Theoretical frameworks are fundamental to research as they offer a systematic and structured approach to understanding and examining phenomena of interest. A conceptual framework serves as a guiding template that outlines key concepts, variables, and the relationships among them within the context of a study (Kaniušonytė et al., 2021; Pearse, 2022a). One of the primary advantages of using a conceptual framework is that it allows researchers to clearly articulate the theoretical underpinnings of their work (Imura et al., 2014; Grant & Osanloo, 2014; Pearse, 2022b). By anchoring the research in an established conceptual framework, the researcher ensures alignment with relevant theories and prior scholarship (Mugizi, 2022; Lara- Cinisomo et al., 2016; Abbott, 2015). This method enhances the rigor and credibility of the study by establishing a coherent foundation for the research design, data collection, and subsequent analysis (Sakalasuriya et al., 2018; Alam et al., 2018). This study draws upon three key theoretical frameworks, each discussed in detail in the following sections. 2.2.1 Social media marketing theory 10 | Page Social media marketing has become crucial to contemporary marketing strategies (Cheung et al, 2021:362). It involves utilising social media sites such as Facebook, Instagram, “X” previously Twitter, and LinkedIn to market products, services, and brands (Ezhumalai & Vinoth, 2018). Numerous studies have examined the influence of social media marketing on brand development and equity. Stringfellow et al. (2019) investigated the mediating functions of brand equity and identity in the relationship between social media marketing, electronic word-of-mouth (eWOM), and willingness to pay. Lin et al. (2020) established a conceptual framework elucidating the relationship between social media marketing, the Technology Acceptance Model (TAM), and the aspiration to sustain organisational competitiveness. Lilo et al. (2023:351) found that customers heavily rely on peer feedback on social media, leading marketers to focus on strategies that improve customer engagement on these channels. Stringfellow et al. (2019) assert that the criteria by which SMEs choose social media platforms emphasize functional dimensions like identification, discourse, sharing, presence, relationships, reputation, and group dynamics. Chung and Cho (2017:482) established a framework for a performance assessment system to quantify social media contributions, address measurement challenges, and delineate a future research agenda. Cheung et al. (2020:363) identified five components of social media marketing capabilities: identity, discourse, sharing, presence, and relationships. These characteristics represent the diverse marketing strategies that can be implemented on social media platforms to improve business performance. Rowley and Keegan (2019:726) assert that studies are required to comprehend the effects of social media on purchasing selections, encompassing impulsive buying and the consumer’s progression through the decision-making phases. 2.2.2 Resource-based view theory The resource-based view (RBV) theory is a significant theory in strategic management that discusses how organisations can attain and maintain competitive advantage (Petružis, 2018; Ando & Ding, 2014). The RBV posits that the assets and skills of a company are the principal factors influencing its performance and competitive standing (Sirmon, Hitt, Ireland, & Gilbert, 2010; Kontesa & Lako, 2021). The fundamental 11 | Page principle of the RBV is that organisations exhibit heterogeneity in their resources and competencies (Kumar, 2021; Ziyae & Sadeghi, 2020). Resources and competencies can be classified as tangible (physical assets, financial resources) or intangible (knowledge, skills, reputation) (Newbert, 2008; Cai et al., 2010). The RBV states a resource must be valuable, rare, inimitable, and non-substitutable (VRIN) to confer a sustained competitive advantage (Hussain & Waheed, 2019; Cragg, 2008). Valuable resources allow an organisation to implement strategies that enhance efficiency and effectiveness, increasing its competitiveness (Sirmon & Hitt, 2003; Andersén, 2011). Scarce resources are infrequently possessed by current and potential competitors (Arbelo, Arbelo-Pérez & Pérez-Gómez, 2020; Lin, 2003), yet distinctive resources are difficult for rivals to replicate or acquire (Runyan et al., 2007; Steensma et al., 2005). Non-substitutable resources lack immediate strategic options (Das & Teng, 2000; Barney, 2001). The RBV emphasises that businesses must own VRIN resources and adeptly manage and utilise these resources to generate value and attain competitive advantage (Riahi‐ Belkaoui, 2003; Connor, 2002). Companies must engage in resource management and orchestration if they want to properly exploit their resources (Wilk & Fensterseifer, 2003). Extensively used in many settings, including small and medium-sized firms (Barthélémy, 2022), international corporations (Ando & Ding, 2014), and family enterprises (Hayton, 2005), the RBV has also been used to clarify the relevance of resources like human capital, intellectual capital (Riahi-Belkaoui, 2003), and Research and Development (Wright, 2001) in improving corporate performance and competitive advantage. Though RBV has received notable empirical support, many studies have underlined the need of greater honing and integration with other theoretical frameworks (Newbert, 2006; Galpin, 2019). In terms of study and implementation in strategic management, the RBV is still a leading and powerful paradigm. 2.2.3 Dynamic capabilities theory Dynamic Capabilities Theory is a paradigm that explains how organisations can attain and maintain competitive advantage in swiftly evolving settings (Mukhtar, 2023; Teece, 2018). The theory asserts that organisations must consistently integrate, 12 | Page develop, and reorganise internal and external resources and skills to respond to alterations in their business environment (Yung-Chul, 2021; Barreto, 2009). Dynamic Capabilities Theory posits that businesses must cultivate particular “dynamic capabilities” talents to adapt and respond to external changes (Yan et al., 2022; Zou et al., 2017). These dynamic capabilities empower organisations to identify opportunities and dangers, capitalise on opportunities, and adjust their resource base accordingly (Newey & Zahra, 2009; Fallon-Byrne & Harney, 2017). The theory suggests dynamic skills are not fixed but develop via organisational learning and experience (Danneels, 2010; Yu et al., 2022). Organisations can cultivate dynamic capacities through intentional activities, including knowledge management, organisational routines, and managerial decision-making (Olazabal & Avila, 2022; Rotjanakorn et al., 2020). Dynamic skills differ from “ordinary” or “operational” capabilities, which concentrate on efficiently executing daily tasks. Conversely, dynamic capabilities are superior capabilities that enable organisations to modify and reorganise their operational competencies in reaction to environmental changes (Aggarwal & Kapoor, 2018; Ambrosini et al., 2009). The theory argues that the significance of dynamic capacities depends on environmental dynamism. Standard capabilities may suffice in stable situations, but in quickly evolving contexts, dynamic capabilities are essential for maintaining competitive advantage (Xing et al., 2020; Soluk et al., 2021). Empirical studies indicate that dynamic capabilities enhance business performance, innovation, and sustainability, especially in volatile markets (Zhang et al., 2018; Bai & Guo, 2014). However, the theory recognises that developing and implementing dynamic skills may be complex and contingent upon prior paths (Ju et al., 2016; Mudalige et al., 2018). The Dynamic Capabilities Theory offers a paradigm for comprehending how organisations can adapt and prosper in fluctuating business environments by renewing their resource base and capabilities (Feng et al., 2022; Li, 2022). 2.3 Empirical support of the theoretical framework The amalgamation of these diverse theoretical frameworks offers a thorough comprehension of how SMEs can use social media analytics to improve their 13 | Page performance and competitiveness. The theory of social media marketing emphasises using social media platforms to interact with customers, enhance brand recognition, and achieve corporate objectives (Alam et al., 2023; Scuotto et al., 2017; Brooks et al., 2022; Ghazwani & Alzahrani, 2023; Prayudanti & Rohmah, 2018). The RBV theory underscores the significance of SMEs' internal resources and capabilities, such as social media data and analytics, as sources of competitive advantage (Shauri et al., 2023; Kikawa et al., 2022; Tajvidi & Karami, 2021). The Dynamic Capabilities Theory posits that SMEs must cultivate the capacity to identify opportunities, capitalise on them, and reorganise their resources to effectively utilise social media analytics in a swiftly evolving digital environment (Ahmad et al., 2019; Qalati et al., 2022; Yang, 2019). The researcher analysed three additional conceptual frameworks to comprehend the utilisation and influence of social media analytics on the commercial performance of SMEs in the Free State Province: the Technology Acceptance Model (TAM), Dynamic Capability Theory, and Diffusion of Innovation Theory. The TAM explains the determinants affecting SMEs' acceptance and utilisation of social media analytics tools, including perceived utility and ease of use (Vanninen et al., 2022; Akgül and Tunca, 2018; Bruce et al., 2022). The Dynamic Capabilities Theory illustrates how SMEs may adapt and respond to changes in their business environment, including adopting and utilising social media analytics (Ahmad et al., 2019; Qalati et al., 2022; Yang, 2019). The Diffusion of Innovation Theory clarifies the acceptance and spread of social media analytics tools within SMEs, highlighting factors such as relative advantage, compatibility, and observability (Ekanem & Erukusin, 2021; Solomon et al., 2023; Durkin et al., 2013). These theories assert that SMEs must develop dynamic competencies, such as the ability to recognise opportunities, leverage them, and reorganise their resources to effectively employ social media analytics in a rapidly changing digital landscape (Ahmad et al., 2019; Qalati et al., 2022; Yang, 2019). These theoretical viewpoints collectively offer a unified framework for understanding the intricate function of social media analytics in strengthening SMEs’ marketing, technological, resource-based, and inventive capacities, improving business performance and competitiveness. 14 | Page Figure 2.1 explains how TAM users form attitudes and intentions to use technology, particularly in SMEs' adoption of social media analytics. Figure 2.1: Theoretical framework of the Technology Acceptance Model Source: Davids (1989) Figure 2.2 illustrates the RBV Theory, which emphasises the importance of an organisation's internal resources and capabilities in achieving competitive advantage (Shauri et al., 2023; Kikawa et al., 2022; Tajvidi & Karami, 2021). In the context of social media analytics for SMEs, this theory suggests that the effective utilisation of social media data and analytics capabilities can be a source of competitive advantage (Shauri et al., 2023; Kikawa et al., 2022; Tajvidi & Karami, 2021). Figure 2.2: Theoretical framework of the Resource-Based View Theory Source: Barney (1991) Diffusion of Innovation Theory provides insights into how social media analytics technologies are adopted and spread among SMEs (Ekanem & Erukusin, 2021; Solomon et al., 2023; Durkin et al., 2013). Figure 2.3. explains how innovations spread 15 | Page among organisations (Rogers, 2003:11). For SMEs, understanding the diffusion of social media analytics technologies can help them develop effective strategies to promote the adoption and use of these technologies within their organisations and the broader SME community (Ekanem & Erukusin, 2021; Solomon et al., 2023; Durkin et al., 2013). Figure 2.3: Theoretical framework of the Diffusion of Innovation Theory Source: Rogers (2003) This study attempts to demonstrate the applicability of various theories to understanding and enhancing business performance through technology adoption and resource management in social media analytics in SMEs. 2.4 Analysis of social media analytics and small to medium enterprises Social media platforms are integral to SMEs’ marketing and operational plans; their cost-effectiveness and accessibility make them vital tools to compete with larger enterprises (Amoah & Jibril, 2021; Alam, 2023). Research indicates that social media engagement by SMEs positively influences customer awareness and perceptions of the company’s products and services (Amoah & Jibril, 2021; Pellegrino & Abé, 2023). Social media empowers SMEs to increase awareness of their products and services, influence potential clients, and improve organisational performance (Alam, 2023; 16 | Page Amoah & Jibril, 2021). It fosters business growth and sustainability for SMEs by promoting adoption and capitalising on opportunities (Amoah et al., 2021; Ekanem & Erukusin, 2021; Shauri et al., 2023). During the COVID-19 pandemic, SMEs have had to dynamically adapt their social media strategies to the new normal, leveraging their sensing, seizing, and reconfiguration capabilities (Hu, Olivieri & Rialti, 2023:1675). Social media has been particularly beneficial for SMEs in developing countries, where it can help them overcome geographical boundaries and interact directly with customers (Qalati et al., 2022; Patma et al., 2021). Small and Medium-sized Enterprises must manage their social media presence effectively by learning to keep followers informed, respond to queries, and elaborate on feedback to grasp innovation opportunities (Carbonara & Tagliaventi, 2023). There is a need for more awareness and the development of social media analytics tools specifically tailored for SMEs to help them better utilise and manage their social media activities (Madila et al., 2022:87). 2.5 Characterising social media analytics Social media analytics means collecting, analysing, and interpreting data from social media platforms to derive significant insights (Madila et al., 2021:87). This interdisciplinary field integrates informatics, statistics, and computational linguistics to derive meaningful insights from the extensive user-generated content on social media (Drescher, 2023). A notable benefit of social media analytics is its capacity to swiftly explore community perceptions about a product, especially during pivotal events like the COVID-19 pandemic. By analysing social media data, companies can swiftly comprehend popular sentiments and concerns, facilitating more informed and community-oriented decisions (Yigitcanlar et al., 2020:3). This feature allows companies to make informed decisions based on real-time data and public sentiment. Social media analytics encompasses diverse methodologies and instruments, including sentiment analysis, topic modelling, and network analysis, to gather, monitor, analyse, summarise, and visualise social media data (Madila et al., 2021; Batrinca & Treleaven, 2014:89). These instruments and methodologies can be employed to ascertain both quantitative and qualitative evidence of the prospective or actual social 17 | Page impact of research disseminated on social media (Joan et al., 2022). Social media analytics is essential in several fields, namely business, politics, and public health. In a corporate environment, it aids organisations in collecting customer insights, exploring the efficacy of marketing initiatives, and obtaining helpful consumer feedback (Madila et al., 2021; Drescher, 2023). In the political sphere, social media analytics can explore sentiment, discern patterns, and enhance comprehension of voter preferences and behaviours (Darapaneni, 2023). The use of social media analytics has been examined in diverse situations, such as the analysis of online hotel reviews (He et al., 2017:922), the comprehension of food consumption trends (Drescher, 2023), and the investigation of social media’s influence on product innovation (Cheng & Sheu, 2023:229). Furthermore, social media analytics has been employed to examine the utilisation of social media inside municipal government (Moss et al., 2015:288) and the influence of social media analytics on SMEs (Nugroho & Angela, 2024; Madila et al., 2022). Social media analytics allows organisations and researchers to understand public perceptions, behaviours, and preferences, improving decision-making and cultivating strategic advantages. 2.6 Overview of SMEs in Free State Province Small and medium enterprises play a crucial role in the economic growth and development of South Africa (Langton & Mafini, 2022:10). They contribute significantly to job creation, production, and export volumes (Viljoen & Struweg, 2016). However, SMEs in South Africa, including those in the Free State Province, have faced various challenges that have hindered their performance and growth (Langton & Mafini, 2022; Viljoen & Struweg, 2016). A primary concern for SMEs in the Free State Province is the effect of electricity shortages and load-shedding. Research indicates that a 1% reduction in energy usage can result in a 4.23% decline in GDP; since SMEs account for around 57% of the South African economy, the electricity crisis has substantially adversely affected their operations and performance (Viljoen & Struweg, 2016). Alongside the electrical crisis, SMEs in the Free State Province and other provinces of South Africa have had difficulties concerning access to finance, resource limitations, and the economic climate (Doacă, 2022:26). The absence of financial access, 18 | Page especially during economic downturns, can significantly affect the viability and expansion of SMEs (Doacă, 2022:26). The leadership and management styles of SME entrepreneurs and administrators are pivotal factors influencing the success and expansion of SMEs in South Africa. Research indicates that adaptable and entrepreneurial leadership styles can enhance tactical orientation and innovation in SMEs, which are crucial for their competitiveness and success (Dzomonda et al., 2017; Buchanan et al., 2022). 2.7 The role of social media in modern business practices The significance of social media in contemporary business practices is extensively recorded in the literature. Social media has evolved into a fundamental component of corporate operations, functioning as a potent instrument for communication, marketing, and customer engagement (Setiawan et al., 2022; Putri & Kurniasih, 2020; Rohmawati & Winata, 2021; Kwayu, 2021; Gupta, 2023; Dwivedi et al., 2019; Sutrisno, 2023; Gökerik, 2024). Social media offers businesses a cost-effective and efficient means to engage a broad audience and advertise their products or services (Putri & Kurniasih, 2020; Kwayu, 2021; Lupo & Stroman, 2020; Gökerik, 2024; Novandari, 2023). Social media platforms such as Facebook, X (previously Twitter), TikTok, and Instagram have emerged as prominent marketing channels, enabling businesses to enhance brand awareness, interact with customers, and execute targeted advertising campaigns (Rohmawati & Winata, 2021; Gupta, 2023; Gökerik, 2024; Javier, 2024; Novianti & Erdiana, 2020; Jeswani, 2023). Social media allows organisations to obtain valuable customer insights and feedback, which can enhance their products, services, and overall business strategies (Dwivedi et al., 2019; Simangunsong & Handoko, 2020; Prodanova & Looy, 2019; Pitafi, 2024). Through the analysis of social media discourse and engagement, companies can gain insights into their consumers’ requirements, preferences, and challenges, thus enabling more informed decision-making (Dwivedi et al., 2019; Simangunsong & Handoko, 2020; Prodanova & Looy, 2019). Social media has become essential for customer relationship management (CRM) (Rivanto & Novianti, 2019; Pitafi, 2024). Companies can use social media to engage with customers, resolve issues, and cultivate enduring relationships (Rivanto & Novianti, 2019; Pitafi, 2024). This may 19 | Page enhance customer loyalty and satisfaction and improve corporate success (Wibowo et al., 2020; Pitafi, 2024). Social media significantly influences the digital transformation of organisations, aiding their adaptation to the evolving technological landscape (Dwivedi et al., 2019; Simangunsong & Handoko, 2020; Prodanova & Looy, 2019). Integrating social media into business operations enables organisations to improve operational efficiency, stimulate innovation, and secure a competitive edge in the market (Dwivedi et al., 2019; Simangunsong & Handoko, 2020; Prodanova & Looy, 2019). The importance of social media in modern business practices is highlighted by its impact on entrepreneurship and business sustainability. Social media can inspire entrepreneurs to enhance innovation, creativity, and audacity in pursuing new company ventures (Jeswani, 2023; Novandari, 2023). Incorporating social media into corporate plans can improve performance and ensure long-term sustainability (Jeswani, 2023; Novandari, 2023; Wibowo et al., 2020). 2.8 Analytics of social media Social media analytics is a dynamic domain that employs diverse tools and approaches to comprehend and derive insights from the extensive data produced on social media platforms (Koohang, 2017; Shahbaznezhad et al., 2021; Dolan et al., 2019). These analytical methods and methodologies are utilised to judge, analyse, and interpret user engagement, content efficacy, and the overall influence of social media activities (Dolan et al., 2019; Odiboh et al., 2020). A crucial tool in social media analytics is using devices addressing privacy concerns. The tools, including those created by Koohang et al. (2017), explore users’ apprehensions regarding acquisition, secondary utilisation, inaccuracies, unauthorised access, control, and awareness of their personal information on social media platforms. Understanding these privacy concerns is essential for organisations to establish trust and effectively connect with their audiences (Koohang, 2017; Koohang et al., 2018). 2.9 Analysis of prominent social media analytics instruments Social media analytics is a growing and crucial field of study dedicated to analysing social media data to derive pertinent insights (Pääkkönen et al., 2020; Darapaneni, 20 | Page 2023). It involves the systematic collection, monitoring, analysis, and visualisation of social media data to extract meaningful insights (Pääkkönen et al., 2020; Mukti & Putri, 2021; Adnan et al., 2021). A key component of social media analytics is its ability to evaluate quantitative and qualitative aspects of social media activities (Lee et al., 2020:671). The process of social media analytics typically involves several steps, including data collection, preparation, analysis, and visualisation (Pääkkönen et al., 2020; Andryani et al., 2019; Stieglitz et al., 2018). Data collection entails the acquisition of pertinent social media data, including posts, comments, and metadata, from many social media platforms (Pääkkönen et al., 2020:791). Data preparation entails the cleaning, structuring, and organising data to render it appropriate for analysis (Stieglitz et al., 2018:157). Data analysis entails utilising many methodologies, including text mining, sentiment analysis, and network analysis, to get insights from the data (Pääkkönen et al., 2020; Dang et al., 2020; Subroto & Apriyana, 2019). Data visualisation entails clearly and intelligibly presenting analytical results, utilising tools such as charts, graphs, and dashboards (Pääkkönen et al., 2020; Andryani et al., 2019). Though social media analytics offers numerous advantages, various problems and ethical considerations must also be addressed (Petrescu & Krishen, 2020; Watson et al., 2020). These concerns encompass data privacy, algorithmic bias, and the possible exploitation of social media data (Petrescu & Krishen, 2020). Researchers and organisations must acknowledge these problems and create suitable regulations and frameworks to guarantee the ethical and responsible use of social media analytics (Watson et al., 2020:459). 2.10 Essential metrics and insights obtained from social media analytics Social media analytics has become essential for businesses to obtain information and make informed decisions. Organisations can use the extensive data produced on social media platforms to derive significant insights that inform their strategic and operational objectives (Ausat, 2023; Fitzpatrick & Weissman, 2021). A crucial statistic derived from social media analytics is audience engagement. This measure offers insights into user engagement with a brand’s content, including the number of likes, shares, comments, and click-throughs (Madila, 2024:220). By analysing audience 21 | Page engagement, organisations can determine which content resonates most effectively with their target demographics and subsequently modify their social media strategies (Dewi and Nugroho, 2024). Social media analytics can be essential for organisations, allowing them to make informed decisions, enhance consumer experiences, and secure a competitive advantage in the market (Davcheva & Benlian, 2018:1319). Organisations can use this data to improve their marketing strategies, refine product development, and ultimately stimulate corporate growth (Abu-Salih et al., 2021). 2.11 Challenges in implementing social media analytics in Small and Medium- sized Enterprises Social media has become recognised as an essential component of SMEs' marketing and communication strategies (Alam et al., 2023; Odoom et al., 2017). Utilising social media platforms, SMEs can competently rival larger firms, augment their brand visibility, and increase overall organisational performance (Alam et al., 2023; Odoom et al., 2017). However, incorporating social media analytics in small and medium-sized enterprises poses numerous challenges. A significant challenge is the lack of knowledge and skills among SME managers and staff about social media analytics (Madila et al., 2022:87). Numerous SMEs are unaware of the many social media analytics tools and frameworks that can enhance the use of social media analytics (Madila et al., 2022:87). As a result, they often rely on basic metrics such as likes, comments, and shares, without a comprehensive understanding of how to analyse and utilise the data (Madila et al., 2022:88). A further barrier is the constrained financial and human resources SMEs can allocate to social media analytics (McCann & Barlow, 2015:274). Small and medium-sized enterprises frequently lack the time and staff to efficiently strategise, execute, and explore their social media initiatives’ return on investment (ROI) (McCann & Barlow, 2015:274). This may result in the absence of a systematic methodology for social media analytics, which is essential for attaining desired results (McCann & Barlow, 2015:275). Notwithstanding these limitations, SMEs possess considerable opportunities to use social media analytics. Social media enables SMEs to gain insights into customer 22 | Page behaviour, discover new market opportunities, and improve their marketing and communication strategies (Alam et al., 2023). Through the proficient application of social media analytics, SMEs can acquire significant insights into customer behaviour, preferences, and engagement, thereby informing their product development, pricing strategies, and promotional activities (Odoom et al., 2017). One of the primary obstacles is the lack of technical skills and knowledge among SME owners and managers (Qalati et al., 2022; Effendi et al., 2020; Madila et al., 2022; Maharjan, 2024). Numerous SMEs doubt the advantages of social media analytics and are reluctant to embrace these technologies due to perceived complexity and ambiguity around their application. Resource limitations, including restricted financial and human capital, hinder SMEs from investing in and executing social media analytics tools and strategies (Qalati et al., 2022; Maharjan, 2024; Istanto et al., 2022). Competitive pressures and market dynamics can affect SMEs’ adoption of social media analytics, as businesses may feel obligated to implement these technologies to maintain competitiveness (Alsharji et al., 2018; Burgess et al., 2017). Moreover, in many developing nations, insufficient governmental assistance and inadequate technological infrastructure can present considerable obstacles to adopting social media analytics in small and medium-sized enterprises (Maharjan, 2024; Istanto et al., 2022). 2.12 Deficiency in technical proficiencies and instruction Numerous SMEs are merely conducting fundamental social media metrics, such as tallying likes, comments, and shares, lacking a comprehensive grasp of how to use social media analytics for strategic decision-making (Madila et al., 2022:88). This is due to SMEs' deficiency in requisite skills, knowledge, and implementation frameworks for efficiently executing SMA (Madila et al., 2022:88). The recent rise of social media as a key marketing tool has led to a lack of necessary knowledge among both researchers and professionals about how to turn social media data into useful information. This deficiency arises from the necessity for certain organisational competencies to adopt modern social media marketing tactics that SMEs frequently lack, including expertise in data and customer analytics (Li et al., 2021). 23 | Page Studies indicate that SMEs lack a definitive plan for utilising social media and frequently underestimate its impact on their financial success (Belás, Amoah, Dvorský & Šuleř, 2021:119). This is intensified by the reality that SMEs typically possess constrained resources and experience relative to larger enterprises, making it difficult to invest in and utilise sophisticated social media analytics technologies (Willetts, Atkins & Stanier, 2020). This underscores the necessity for specialised training and infrastructure enhancements to assist SMEs in addressing their shortcomings in technical skills and understanding of social media analytics (Maharjan, 2024:27). 2.13 Prospects for small and medium enterprises in Free State Province Research indicates that opportunities for SME development in the Free State Province are influenced by internal and extrinsic factors related to sustainable entrepreneurship in adjacent provinces, mainly Gauteng (Nhemachena & Murimbika, 2018:115). This indicates potential analogous reasons for sustainable enterprise in the Free State Province. Moreover, governmental regulations and initiatives, like the Small Business Act, have fostered entrepreneurial endeavours and enhanced resource accessibility for SMEs in South Africa (Akinyemi & Adejumo, 2018:3). Research has underscored the significance of social entrepreneurship and the impact of social entrepreneurial role models on entrepreneurial aspirations and behaviours in South Africa (Maziriri, 2024:278). Promoting and facilitating social entrepreneurship may be a viable strategy for developing SMEs in the Free State Province, as it corresponds with the region's need to tackle social and environmental issues. Although concrete information about these sectors in the Free State Province is scarce, the research indicates that offering extensive training and education may enhance driving schools (Malkin et al., 2021:1466). However, there is insufficient information to validate claims regarding the specific environmental conditions required by upholstery enterprises, such as drought, as the sources do not directly relate to this sector. 2.14 Utilising social media analytics for competitive edge The effective execution of social media analytics necessitates a synthesis of technological competencies, organisational innovation, and entrepreneurial mindsets 24 | Page (Nugroho, 2024; Onngam & Charoensukmongkol, 2024; Abdurohim et al., 2022; Venciute et al., 2023). Organisations must integrate social media data with additional business intelligence sources to extract significant insights (He et al., 2019; Santos, 2023). The utilisation of social media analytics has demonstrated a beneficial effect on organisational performance and competitiveness, particularly for SMEs (Macatumbas-Corpuz & Bool, 2021; Rambe et al., 2019; Eze et al., 2021). Using social media data, SMEs can surmount resource limitations and gain a competitive advantage in the marketplace (Macatumbas-Corpuz & Bool, 2021; Rambe et al., 2019). Social media analytics enables organisations to discern developing trends and observe competitors' activity (Horng et al., 2022; Mehmood et al., 2022; Madila et al., 2022). This allows them to swiftly adjust their plans and maintain a competitive advantage (Sasmita et al., 2023; Onngam & Charoensukmongkol, 2024). Social media analytics can assist firms in improving brand awareness, customer interaction, and online reputation (Jian et al., 2021; Thaker et al., 2020; Zulfikar et al., 2022). It can also formulate more successful marketing strategies, enhance customer service, and facilitate more educated company decisions (Nugroho & Angela, 2024; Hruška & Marešová, 2020; Zulfiqar et al., 2022). 2.14.1 Facebook Facebook (now Meta) is a leading worldwide social media platform. It offers businesses tools to analyse user behaviour, preferences, and interactions, facilitating the effective customisation of their marketing strategies. Businesses can leverage data from Facebook analytics to determine which demographic categories exhibit the highest engagement with their content, enabling them to concentrate their marketing efforts on these groups for optimal impact (Rahardja, 2022:176). Facebook's analytical features allow businesses to categorise their audiences according to demographic criteria, like age, location, and interest. This segmentation is essential for targeted marketing tactics since it enables organisations to customise their messages for certain groups, enhancing the relevance and efficacy of their efforts (Piranda et al., 2022:2). 25 | Page By analysing these measures, firms may ascertain which content types most effectively engage their audience and modify their strategy accordingly. If a specific post style, such as videos or infographics, receives much more engagement than others, firms might prioritise these formats in subsequent ads (Singh et al., 2023). This iterative content creation method improves user engagement and promotes a more dynamic interaction between brands and consumers. 2.14.2 WhatsApp WhatsApp enables significant user interaction, which can be quantitatively assessed using message frequency, response times, and group participation rates. Grebelsky- Lichtman et al. (2020:73) extensively examined WhatsApp profiles and user interactions, uncovering insights into the motives for user participation and the app's communication capabilities. Their findings suggest that individuals frequently reconcile their need for privacy with their tendency to share information, which can be quantitatively assessed by user behaviour analytics. WhatsApp facilitates individual and group communication, making it an essential instrument for social engagement, information distribution, and marketing tactics. Analytics obtained from WhatsApp usage can yield insights into user behaviour. communication patterns, and the overall influence of social media on numerous life elements. Naeem and Ozuem (2021:1029) examined the role of instant messaging applications, such as WhatsApp, in facilitating information sharing among healthcare workers and augmenting productivity and engagement in public sector hospitals. This use case illustrates the application of social media analytics to evaluate the efficacy of communication tools in professional settings, yielding significant insights into user interactions and their consequent effect on organisational performance. Ohme et al. saw a notable rise in mobile messaging during pivotal periods of the pandemic, such as governmental pronouncements, demonstrating how real-time analytics can monitor user behaviour in reaction to external events (Ohme et al., 2020:2). This situational analysis of messaging patterns can guide public health policies and communication initiatives, illustrating the capacity of social media analytics to impact real-world results. The research underscores the significant role of WhatsApp and similar platforms as potent marketing instruments for shaping consumer inclination and 26 | Page behaviours instruments shaping consumer inclination and behaviour across diverse sectors (Diantoro, 2024:84). By analysing user interactions and responses to marketing communications, organisations may customise their strategies to address consumers' wants more effectively. 2.14.3 “X” previously Twitter A fundamental part of Twitter (X) analytics is measuring user engagement. Engagement indicators, including retweets, likes, and replies, reflect the resonance of content with audiences. Leary et al. (2018:1) assert that while engagement rates on “X” are low in absolute terms, they may be considered high relative to the platform’s norms, indicating that even a minor percentage of engagement can reflect substantial interest or influence. This corresponds with the findings of Sharp et al. (2020:523), who underscore the significance of retweets as an indicator of diffusion, highlighting their role in revealing the reach and impact of particular tweets. Moreover, the relationship between social media activity and organisational reputation has been documented. Triemstra et al. (2018) demonstrate a relationship between hospitals' social media participation and their reputation scores, suggesting that increased interaction can improve perceived trustworthiness. By analysing the attitudes conveyed via tweets, scholars can explore public opinion on diverse subjects, ranging from political matters to consumers’ goods. Yang (2022:1) illustrates how social media sentiment may profoundly affect stock values, highlighting Twitter's importance in financial markets. This sentiment analysis encompasses financial circumstances, public health, and social issues. The research by Xue et al. (2024) emphasises that Twitter serves as a medium for raising awareness and facilitating discourse on sexual violence, thereby enhancing social capital among many communities. The capacity to analyse attitudes enables organisations to customise their communication strategies efficiently, address public issues, and improve involvement. 2.14.4 TikTok TikTok's distinctive algorithm and user-generated content (UGC) framework offer a fertile ground for examining social media interactions and trends. TikTok's technology 27 | Page enhances user engagement by tailoring content distribution according to users' behaviours. The For You Page (FYP) of the platform employs intricate algorithms that explore user activities, including likes, shares, and comments, to create a tailored feed that optimises engagement (Bhandari & Bimo, 2020). This algorithmic method improves user experience and offers significant insights for marketers and content developers targeting specific groups. Research indicates that TikTok's user demographic primarily comprises younger persons, with around 62% of users aged 10 to 29 years (Fraticelli et al., 2021:2). This demographic knowledge is essential for brands targeting younger people via social media marketing. The interaction metrics on TikTok, including views, likes, comments, and shares, are essential markers of content performance. Studies have shown that certain content characteristics, including humour and relatability, affect user engagement (Li et al., 2021:262). During the COVID-19 pandemic, public health organisations used TikTok to convey information, with videos in engaging styles achieving excellent interaction rates (Li et al., 2021:262). This underscores the significance of content planning in optimising interaction on the platform. 2.14.5 Instagram Instagram's analytics functionalities, primarily via its Insights feature, provide users with essential data regarding audience engagement, content efficacy, and overall activity. This tool enables organisations and individual users to monitor metrics such as impressions, reach, and interactions, which are crucial for exploring the efficacy of their content initiatives (Alfajri et al., 2019:42). Visualising this data will improve the decision-making process, allowing users to customise their content more effectively to align with the demands and interests of their audience (Alfajri et al., 2019:42). Moreover, the incorporation of social media analytics into marketing plans has markedly enhanced engagement and brand recognition, as demonstrated by numerous studies that underscore the positive relationship between the effective use of analytics and marketing results (Kuntjoro, 2023; Soelaiman, 2023). The influence of Instagram on users' behaviours and perceptions is significant. Studies demonstrate that the extent of Instagram usage can markedly affect users’ self-esteem 28 | Page and social connections (Sekarlangit et al., 2022; Romero-Rodríguez et al., 2020). Users frequently partake in social comparison, exploring their lives against the curated photos and lifestyles showcased by others on the platform. This phenomenon may result in beneficial and detrimental psychological effects, contingent upon the information consumed and the user's self-image (Trajković, 2022; Liesay, 2023). Thus, comprehending these dynamics via analytics can aid in creating material that promotes positive engagement and alleviates adverse impacts. 2.14.6 YouTube YouTube is a leading place for sharing content and interacting with people, giving businesses and creators the information, they need to plan their strategies. YouTube measures performance mainly by looking at watch time and audience retention, which give an idea of how long people stay with a video. Goncalves (2017) proved that the length of time a video is watched relates to more likes, shares, and emotional reactions in comments, meaning that retention strongly reflects how involved the audience is. YouTube’s algorithm gives a lot of importance to watch time when deciding which videos to suggest to people who are new to the platform. Besides tracking views, YouTube offers creators useful insights about their viewers’ age, how they find the channel, and what devices they are using. Organisations can use likes, comments, and shares to find out how their content affects people’s emotions and actions (Claesson and Mars, 2024). If many people stop watching soon after the video starts, it might be a sign that the beginning or the speed of the video should be improved. It has been found in social media marketing that analysing these patterns frequently helps improve the way content is presented, the brand’s messaging, and how long viewers stay engaged (Georgakopoulo et al., 2020). YouTube makes it possible for users to monitor the click-through rate on cards and end screens, so they can judge how well a video directs viewers to take actions like subscribing or visiting a site. As a result, marketers and communicators can make their strategies better by relying on data from users to increase visibility and engagement. For this reason, YouTube acts as a place for content and also collects a lot of 29 | Page information, which is important for groups trying to reach and engage people from various age groups. 2.15 Emerging trends in social media analytics for small and medium enterprises Incorporating social media analytics into the operational structures of SMEs has become a crucial trend, influencing their strategic decision-making and overall business performance. Nugroho (2024:169) asserts that proficiently applying social media analytics can augment SMEs’ strategic orientation and adaptability, enhancing their decision-making processes and overall market performance. Gupta (2023) emphasises the necessity of integrating developed design trends into social media plans to improve business efficiency and growth, especially among Indian SMEs. The fluidity of social media requires SMEs to consistently modify their strategies to correspond with changing consumer behaviours and market dynamics (Kwayu, 2021). Li et al. (2020) contend that, although social media has evolved into a strategic marketing instrument, numerous SMEs lack the expertise to transform social media data into actionable insights. This gap highlights the necessity for SMEs to cultivate specific organisational competencies that enable the efficient use of social media analytics. By cultivating a culture that promotes innovation and data-informed decision-making, SMEs can more effectively leverage social media to improve their marketing strategies and consumer engagement initiatives (Li et al., 2020). Khanal, Akhtaruzzaman and Kularatne (2021) propose that social media can augment knowledge and queries among stakeholders, improving the total engagement of SMEs with their communities. Nonetheless, they warn that social media may not consistently provide a comprehensive grasp of Corporate Social Responsibility issues, thus hindering crucial debates. Trifiro et al. (2022:2) underscore that SMEs can leverage media events to enhance their exposure and engagement within their communities, indicating that involvement in prominent social and media events can produce considerable advantages for SMEs. This corresponds with Tiwasing’s (2021:1897) results, which suggest that social media networks are essential for enhancing business performance and growth in SMEs. 30 | Page Tajvidi and Karami (2021) contend that social media augments SMEs’ capacities to improve their performance by generating value through efficient consumer involvement. Marolt et al. (2022:3) corroborate this claim, emphasising the intermediary function of relational social commerce capabilities and competitive advantage in utilising social media to enhance commercial results. Engaging clients via social media enhances brand loyalty and promotes recommendations and repeat business, which is essential for the viability of SMEs. 2.16 Analytics of social media in rural and semi-urban regions The analysis of social media in rural and semi-urban areas is a complex topic that includes communication patterns, health information distribution, community involvement, and economic growth. Studies demonstrate that social media platforms can facilitate information diffusion, enabling rural communities to obtain essential health information and participate in debates that geographical limitations would otherwise restrict (Wang et al., 2021; Wigh et al., 2018). During the COVID-19 pandemic, social media emerged as a principal information source for rural communities, enabling conversations regarding health guidelines and community solutions (Cuomo et al., 2020; Ahmed et al., 2023). This transition highlights the significance of social media as a medium for public health communication, especially in regions with restricted healthcare access (Boyd et al., 2023). The psychological effects of social media use in rural locations have been examined, indicating that persons in these locales frequently employ social media to mitigate feelings of loneliness and to obtain social support (Mehmet et al., 2020; Coman et al., 2023). Numerous research indicates that the augmented duration of social media usage during lockdowns underscores a dependence on these platforms for sustaining social connections and obtaining information (Mehmet et al., 2020; Tiwari, Lane & Alam, 2019). This phenomenon indicates that social media can improve mental well- being in rural populations by offering a sense of community and belonging that may be absent in their physical surroundings (Ahmed, Vidal-Alaball & Vilaseca, 2021; Vos, 2023). 31 | Page Social media analytics can substantially impact the economic outcomes of SMEs in rural regions. Social media for marketing and consumer involvement is essential for the sustainability and expansion of these enterprises (Escobar-Viera et al., 2022; Tiwasing, 2021). Research indicates that women-led enterprises in rural areas frequently encounter digital barriers, hindering their engagement in social media business networks (Escobar-Viera et al., 2022). Individuals who use social media can augment their visibility and outreach, facilitating local economic development (Jones et al., 2021; Ali, 2023). Analytics obtained from social media interactions can guide business strategy, enabling rural firms to comprehend consumer behaviour and preferences more effectively (Subejo et al., 2019:333). Community participation constitutes a vital component of social media analytics in rural environments. Social media platforms enable community members to mobilise around local concerns, allowing their participation in decision-making processes that impact their lives (Zulfiqar et al., 2022; Zhang, 2024). Social media has facilitated engagement in rural tourist development, enabling residents to disseminate their experiences and advocate for local attractions (Zhang, 2024; Sang & Ha, 2020). This participative method empowers individuals and cultivates a sense of ownership and pride among their communities (Joo et al., 2020:2). Notwithstanding the myriad advantages, obstacles persist in the efficient use of social media in rural regions. The digital divide, marked by inequalities in technology access and digital literacy, persists in obstructing the complete potential of social media (Boyd et al., 2023; Liu et al., 2023). Many rural inhabitants have inadequate internet connectivity, hindering their ability to interact successfully with social media platforms (Boyd et al., 2023; Mehmet et al., 2020). The prevalence of disinformation on social media presents considerable dangers, especially in health communication, where erroneous information can result in detrimental behaviours (Liegel et al., 2019; Escobar-Viera et al., 2020). To tackle these issues, focused measures are necessary to advance digital infrastructure and elevate media literacy within rural communities (Liu et al., 2023:2). 2.17 Chapter summary 32 | Page This chapter on social media analysis for SMEs thoroughly examined social media's crucial impact on improving these businesses’ performance and sustainability. The literature suggests that social media is an essential instrument for SMEs, facilitating consumer engagement, product promotion, and enhancement of market standing. This chapter consolidated findings from various research to clarify the diverse advantages of social media for SMEs, especially in marketing, customer interaction, and overall business performance. Social media platforms were widely acknowledged as economical marketing instruments for SMEs, enabling them to compete with larger entities despite constrained resources. Still, some obstacles were also recognized. SMEs often find it hard to use the data they get from social media to make useful decisions. There are not many opportunities for training and using digital tools in rural and semi-urban communities. A lack of proper infrastructure and the absence of fast internet still prevent some people from using social media well. Besides, when people have low digital literacy and receive misinformation, it becomes a significant threat in public health settings. This means that even though social media has a huge potential, its advantages are not open to everyone, so extra help and regulations are needed. The following chapter, Chapter 3: Methodology, outlines the study’s methodology, how data was collected, and the ways it was analyzed. It outlines the study’s approach and lets readers know how reliability, validity, and ethics were managed. 33 | Page CHAPTER 3 METHODOLOGY 3.1 Introduction This chapter presents the methodology that was adopted to achieve the set research objectives and answer the raised research questions. This entails that the presented methodology was considered the most appropriate to adopt for gaining insights into the research approach, the research design, the data collection method, the data collection analysis method, and ethical considerations. 3.2 Research paradigm A research paradigm, according to Singh (2019), is a set of practices and beliefs that guide researchers in undertaking research, including the nature of knowledge, the nature of reality, and the methods used to explore phenomena. Kaushik & Walsh (2024) defines a research paradigm as a framework containing ways of thinking, basic assumptions, and methodologies commonly accepted within a scientific community, which shapes the way research problems are approached and understood. A common feature across these definitions is the emphasis that a research paradigm is a guiding framework that shapes researchers’ methodologies, beliefs, and approaches to investigate and understand a phenomenon. Coe et al. (2021), Nassaj (2020), and Morse (2020) identify positivism, interpretivism, and pragmatism as three common research paradigms. Positivism is grounded in the belief that knowledge is drawn from observable phenomena and empirical evidence (Muzari et al., 2022). According to Nassaj (2020), positivism insists on the use of scientific methods to collect data, usually through measurable approaches such as surveys and experiments. Kaushik & Walsh (2024) highlights that positivism emphasises that reality is objective; therefore, it can be understood through measurement and analysis. Morse (2020) explains that positivism is mainly characterised by a focus on generalisation, causality, and theoretical development of outcome predictions. Allan (2020) points out that scholars operating within the positivism paradigm usually seek to establish principles and laws that have a universal application, relying on quantitative methods to validate findings. Patel and 34 | Page Patel (2019) emphasise that positivism is instrumental in fields such as the social sciences and natural sciences, where objective data collection and analysis are important for establishing reliable conclusions. On the other hand, as Morse (2020) highlights, interpretivism is centred on the subjective nature of human experience and the value of examining the meaning individuals attach to their interactions and actions. Nassaj (2020) explains that interpretivism is rooted in the belief that reality varies from person to person because it is socially constructed. Thus, Ugwu et al. (2021) clarify that interpretivism favours qualitative methods, such as interviews, focus groups, and case studies, to gain in- depth insights into participants’ opinions, contexts, and experiences. Singh (2019) argues that, by focusing on the complexities of social phenomena, interpretivism seeks to get into the underlying motivations, cultural factors, and beliefs that shape human behaviour. Accordingly, as Patel and Patel (2019) indicate, interpretivism is valuable where the understanding of a research problem is grounded in understanding human experience, whether in society or the workplace. Pragmatism offers a more practical and flexible approach, integrating elements from both interpretivism and positivism (Harris et al., 2019). Morse (2020) explains that pragmatism emphasises that the truth of an idea or theory is determined by its applicability to real-world problems and practical consequences. This, according to Patel and Patel (2019), makes the researcher within this paradigm open to employing a mix of qualitative and quantitative methods, adopting the most reliable tools based on the research questions. Therefore, Nassaj (2020) views pragmatism as an adaptive approach that enables a more comprehensive understanding of complex issues, as it values both subjective interpretations and objective measurements. This study is grounded in the principles of interpretivism, which, according to Patel and Patel (2019), emphasise that reality is subjective and varies from person to person. This paradigm allowed the researcher to delve deeply into the subjective interpretations and experiences of social media analytics in different SMEs driving schools in Free State. Thus, by adopting interpretivism, the study captured the contextual and rich insights that quantitative measures usually miss. 35 | Page 3.3 Research approach Research approach, according to Harris et al. (2019), entails the general plan for undertaking a study, encompassing the overall strategy, data collection and analysis methods. Allan (2020) clarifies, noting that research approach is simply a framework that outlines the way or process of conducting research, which includes quantitative, mixed-methods and qualitative. Three research approaches that permeate across research methods are quantitative, mixed-methods and qualitative (Allan, 2020 & Muzari et al., 2022). Quantitative methods emphasise the systematic collection and analysis of numerical data to identify relationships, patterns, and causal connections across variables (Coe et al., 2021). Rooted in positivism, as Kaushik & Walsh (2024) indicates, this approach embraces the assumption that reality can be objectively quantified and measured. Singh (2019) explains that when employing quantitative methods, researchers use controlled experiments, structured surveys, and statistical analyses to collect data from large samples, enabling the generalis ability of findings to broader populations. Allan (2020) highlights that quantitative research involves applying statistical tools to establish correlations and hypotheses, providing a structured and rigorous framework for understanding phenomena. In contrast, qualitative research focuses on understanding the richness of social contexts and human experiences (Patel & Patel, 2019). According to Kaushik & Walsh (2024), qualitative research is anchored on the belief that people, whether in society or the workplace, construct meaning through their perception and interaction. Therefore, Harris et al. (2019) establish that in qualitative research, researchers use methods such as focus groups and in-depth interviews to collect detailed accounts from individuals, allowing them to explore experiences and opinions regarding a particular matter. Patel and Patel (2019) explain that the depth of qualitative research uncovers insights that are often missed in quantitative research, such as reasons behind certain actions or the perceived impact of social dynamics. Nassaj (2020) points out that typically in qualitative research, the analysis involves identifying themes and patterns through coding, which helps to illuminate the complex realities of participants’ experiences. Singh (2019) emphasises that qualitative research provides 36 | Page an important lens for understanding the complexities of social phenomena, making it crucial for exploring under-researched and new areas. Mixed-methods research combines the strengths of both quantitative and qualitative approaches, building a more holistic understanding of a complex issues (Morse, 2020). This approach, according to Nassaj (2020), embraces the notion that different aspects of a research question may call for different types of data, allowing researchers to use quantitative insights to inform qualitative analysis and vice versa. According to Muzari et al. (2022), the integration cements the richness of the findings, as it captures both personal narratives and statistical trends. Singh (2019) argues that triangulating data from various sources using mixed-methods research not only increases the validity of the findings but also provides a comprehensive view that can inform more informed conclusions and practical applications. The study adopted a qualitative research approach. The decision to adopt qualitative research stemmed from the realisation that social media analytics was a subjective phenomenon. There are variations in the types of social media analytics and strategies each company uses. This decision is supported by Kaushik and Walsh (2024), who argues that qualitative research is the best fit for exploring subjective issues. 3.4 Research design The term research design has received varying definitions in literature (Allan, 2020; Coe et al., 2021; Patel & Patel, 2019). However, Nassaj (2020), Muzari et al. (2022) and Morse (2020) identify qualitative research design as phenomenology, grounded theory, ethnography, case study and narrative inquiry. Ethnography, according to Allan (2020), is a qualitative research design grounded in anthropology that involves the study of communities and culture. Coe et al. (2021) point out that, using ethnography, researchers engage in participant observation by spending long periods of time within a particular community to gain a close understanding of the social practices, beliefs, and behaviours of its members. Nassaj (2020) points out that in ethnography, data collection methods include interviews, field notes, and artefacts, providing a holistic view of the cultural dynamics at play. Ugwu 37 | Page et al. (2021) emphasise that ethnography is more useful for exploring complex social interactions and understanding how cultural contexts shape individuals’ experiences and behaviours. The case study’s research design is centred on an in-depth exploration of a particular individual, situation, and group within their real-life context (Patel & Patel, 2019). Researchers gather data from multiple sources, such as observations, interviews, and documents, to gain a comprehensive understanding of the case (Harris et al., 2019). According to Coe et al. (2021), case s