The Impact of Social Media Analytics on SME Strategic Decision Making

In the digital age


Introduction
In the current digital age, the strategic dynamics of business operations have evolved drastically, largely influenced by the pervasive integration of social media across various sectors.This phenomenon is particularly evident in the operations of small and medium enterprises (SMEs), where social media platforms are not merely channels for social interactions but have transformed into critical tools for strategic decision-making and operational optimization [1], [2], [3].The rise of digital technologies has prompted SMEs to adapt to new ways of conducting business, where data-driven decision-making has become a cornerstone of competitiveness and sustainability [4].
The digital transformation has provided SMEs with an array of tools and technologies designed to enhance operational efficiency and market reach.Among these, social media analytics stands out as a pivotal element that can redefine strategic decision-making processes.Social media analytics refers to the practice of collecting data from social media platforms and analyzing that data to make informed business decisions.It helps organizations to grasp customer preferences, market trends, and the effectiveness of marketing campaigns, thereby facilitating more informed and strategic operational choices.Historically, SMEs have struggled to leverage large-scale market data due to resource constraints limited budget, expertise, and access to advanced technological tools.However, the accessibility and integration of social media analytics have democratized the use of big data, enabling SMEs to tap into valuable insights that were previously available only to larger corporations with substantial resources [5].
Strategic decision-making in SMEs involves the development and implementation of initiatives and processes that aim to improve business performance and achieve competitive advantage.In this context, decisions are not merely reactive but are anticipatory in nature, requiring a deep understanding of market dynamics and consumer behavior.Social media, with its vast reserves of user-generated content, provides a rich repository of data that can be mined for insights critical to strategic planning [6], [7].The integration of social media analytics into SMEs' strategic planning processes allows for a more nuanced understanding of market conditions, customer sentiments, and competitive actions.This integration is instrumental in achieving a heightened level of responsiveness to market changes, fostering innovation, and enhancing customer engagement strategies, which are critical for sustaining business growth and adaptability.
The primary objective of this research is to explore the impact of social media analytics on the strategic decision-making processes of SMEs.Specifically, the study aims to assess how these analytics influence organizational innovation and adaptability, factors that are crucial for maintaining competitiveness in a dynamic business environment.By focusing on these aspects, the research intends to highlight the transformative potential of social media analytics in reshaping strategic decision-making frameworks within SMEs [8], [9].To achieve the objectives of this study, Structural Equation Modeling (SEM) will be employed using SmartPLS 4.1 software.This methodological choice is driven by SEM's capability to handle complex variable relationships and its effectiveness in theory testing and development.SmartPLS 4.1 is particularly suited for this study as it facilitates the modeling of latent constructs that are hypothesized to influence strategic decision-making in SMEs.The sample will comprise 200 SMEs actively utilizing social media for various operational and strategic functions.The selection of SmartPLS also aligns with the research's focus on PLS-SEM's ability to provide robust insights into the causal relationships between observed and latent variables [10].
This research is expected to make significant contributions both theoretically and practically.Theoretically, it aims to extend the existing literature on social media analytics by linking it with strategic management practices in SMEs, a relatively underexplored area.Practically, the findings are anticipated to offer actionable insights for SME owners and managers, emphasizing the strategic utilization of social media analytics to enhance decisionmaking processes, organizational adaptability, and innovation.Additionally, this study aims to demonstrate the practical application of SEM in analyzing complex models in business research, contributing to methodological advancements in the field of business analytics.As SMEs continue to navigate the complexities of the digital landscape, understanding the role of social media analytics in strategic decision-making becomes imperative.This study seeks to shed light on how social media data can be transformed into strategic insights that propel SMEs toward higher operational effectiveness and competitiveness [11].By embracing analytics-driven strategies, SMEs can enhance their decision-making processes, adapt more swiftly to market changes, and foster innovation, thereby securing a robust competitive position in the digital marketplace [12].

Research Method
This methodology section outlines a structured approach using SEM with SmartPLS to analyze the impact of social media analytics on strategic decision-making in SMEs.By focusing on the relationships between social media analytics, strategic decision-making, organizational innovation, and adaptability, the study hopes to offer valuable insights into how SMEs can leverage social media for strategic advantages.The findings from this study could potentially guide SMEs in optimizing their decision-making processes, thereby enhancing their overall strategic orientation and competitive edge in the marketplace [13], [14].

Research Design
The study aims to investigate the impact of social media analytics on strategic decisionmaking within small and medium enterprises (SMEs).To comprehensively understand this relationship, we employed a quantitative research design using Structural Equation Modeling (SEM) with SmartPLS 4.1 software.This methodological approach is chosen due to its robustness in handling complex models and its efficacy in assessing latent variables and their relationships within the theoretical model [15].

Sample and Data Collection
The target population for this research consists of SMEs that actively use social media platforms for business operations and marketing.A sample size of 200 SMEs was determined based on recommendations for SEM analyses, which suggest a minimum of 10 times the number of paths in the structural model.The data collection was conducted through an online survey distributed to SME owners and managers.The survey included questions designed to measure the use of social media analytics, the strategic decision-making process, organizational innovation, and adaptability [16], [17].

Measurement Instruments
The survey consisted of several sections, each designed to gather data on different constructs within the research model: 1. Social Media Analytics Use: Measured by items assessing the frequency, depth, and breadth of social media data analysis practices.Questions were adapted from prior studies that examined analytics usage in business contexts.2. Strategic Decision-Making: This construct was assessed through questions related to decision-making speed, quality, and responsiveness.Items were based on established scales in strategic management literature [18].3. Organizational Innovation: Measured by the extent to which SMEs introduce new products, services, or processes as a response to social media insights.This construct was evaluated using scales from innovation research [19], [20].4. Adaptability: Assessed through items measuring how quickly and effectively SMEs can adjust their strategies based on social media data.The scale for this construct was borrowed from organizational behavior studies. 5. Control Variables: Several control variables such as size of the SME, industry type, and duration of social media usage were also included to isolate the effect of social media analytics on strategic decision-making [21], [22].

Structural Equation Modeling with SmartPLS
SmartPLS 4.1 was used for the SEM analysis because of its partial least squares approach, which is suitable for exploratory research and small to medium sample sizes.PLS-SEM is particularly effective when the research objective is both the prediction of key target constructs and the exploration of the structural theory underlying the relationships among constructs.
1. Model Construction: The SEM model included paths from social media analytics use to strategic decision-making, mediated by organizational innovation and adaptability.The model also incorporated the direct paths from social media analytics to both mediators and from the mediators to strategic decision-making, creating a comprehensive framework to capture all potential influences [23].2. Data Analysis Procedure: The analysis followed a two-step approach: • Measurement Model Assessment: This phase involved evaluating the reliability and validity of the constructs.Cronbach's alpha and composite reliability were used to assess internal consistency.Convergent validity was checked using the average variance extracted (AVE), and discriminant validity was assessed through the Fornell-Larcker criterion and the HTMT ratio.The Impact of Social Media Analytics on SME … ■ 171 these paths was tested using bootstrapping with 5000 resamples, providing insights into the indirect effects and the total effects within the model.

Ethical Considerations
To uphold ethical standards, participation in the study was voluntary, and respondents were assured of their anonymity.The survey included a consent form that provided information about the study's purpose and the confidential treatment of the data [24].

Literature Review
Small and Medium-sized Enterprises (SMEs) are a significant contributor to the global economy.They account for a substantial portion of GDP, provide employment opportunities, and drive innovation.However, SMEs often face unique challenges, including limited resources and a dynamic business environment.To thrive in such conditions, SMEs must make strategic decisions effectively.In recent years, social media analytics has emerged as a powerful tool for SMEs to gather insights, understand customer behavior, and make informed decisions.This literature review explores the impact of social media analytics on SME strategic decisionmaking.

Understanding Social Media Analytics
Social media analytics involves collecting and analyzing data from social media platforms to extract meaningful insights.It encompasses various techniques, including text mining, sentiment analysis, and network analysis.Through social media analytics, SMEs can monitor brand mentions, analyze customer sentiment, track competitors, and identify emerging trends.According to Kaplan and Haenlein (2019), social media analytics can be categorized into three types: descriptive analytics, which summarize past data to provide insights into what happened; predictive analytics, which forecast future trends based on historical data; and prescriptive analytics, which recommend actions to achieve desired outcomes.

The Importance of Social Media Analytics for SMEs
For SMEs, social media analytics offers several advantages.Firstly, it provides valuable insights into customer preferences and behavior.By analyzing social media conversations, SMEs can understand what their customers are talking about, what they like or dislike, and what influences their purchasing decisions (Mangold & Faulds, 2019).This knowledge enables SMEs to tailor their products or services to meet customer needs more effectively.Secondly, social media analytics helps SMEs monitor their brand reputation.Positive mentions can be leveraged to enhance brand image, while negative feedback can be addressed promptly to mitigate reputational damage (Kaplan & Haenlein, 2018).Additionally, SMEs can identify brand advocates and influencers who can amplify their message to a broader audience.Thirdly, social media analytics facilitates competitor analysis.SMEs can monitor competitors' activities, identify their strengths and weaknesses, and benchmark their own performance against industry standards (Tuten & Solomon, 2019).This information is crucial for developing competitive strategies and staying ahead in the market.

Social Media Analytics and Strategic Decision Making
Effective strategic decision-making is essential for SMEs to achieve their business objectives.Social media analytics provides SMEs with real-time data and actionable insights that can inform strategic decisions across various domains.In product development, social media analytics can help SMEs identify emerging trends and customer needs.By analyzing social media conversations, SMEs can gain insights into features customers desire, potential product improvements, or entirely new product ideas (Smith, Fischer, & Yongjian, 2020).This information guides the product development process, ensuring that SMEs deliver products that resonate with their target audience.In marketing, social media analytics enables SMEs to develop targeted and personalized marketing campaigns.By analyzing demographic and behavioral data, SMEs can segment their audience and deliver content tailored to specific customer segments (Hanna, Rohm, & Crittenden, 2019).Moreover, social media analytics provides feedback on the effectiveness of marketing campaigns, allowing SMEs to refine their The Impact of Social Media Analytics on SME … ■ 172 strategies for better results.In customer service, social media analytics helps SMEs address customer inquiries and complaints more efficiently.By monitoring social media channels, SMEs can identify customer issues in real-time and provide timely responses or solutions (Fournier & Avery, 2019).This enhances customer satisfaction and loyalty, contributing to long-term business success.

Challenges and Limitations
Despite its benefits, social media analytics presents several challenges for SMEs.Firstly, SMEs may lack the necessary expertise and resources to implement and manage social media analytics tools effectively (Nambisan & Baron, 2019).Training employees or outsourcing analytics tasks can mitigate this challenge but adds to the cost.Secondly, ensuring data privacy and security is crucial when collecting and analyzing social media data (Chen & Zhang, 2021).SMEs must comply with relevant regulations and take measures to protect customer information from unauthorized access or breaches.Thirdly, interpreting social media data accurately can be challenging due to its unstructured nature and potential biases (Gandomi & Haider, 2020).SMEs must use appropriate analytical techniques and validation methods to derive reliable insights from social media data.

Future Directions
Looking ahead, several trends are expected to shape the future of social media analytics for SMEs.Firstly, advancements in artificial intelligence and machine learning will enable more sophisticated analysis of social media data, including natural language processing and image recognition (Verhoef, Neslin, & Vroomen, 2020).This will provide SMEs with deeper insights into customer preferences and behavior.Secondly, integration with other data sources, such as CRM systems and market research data, will enhance the value of social media analytics for SMEs (Harrigan, Soutar, & Dunne, 2021).By combining multiple data sources, SMEs can gain a comprehensive understanding of their customers and market environment.Thirdly, the rise of social commerce will create new opportunities for SMEs to leverage social media analytics for sales and marketing (Hajli, 2021).Integrating e-commerce platforms with social media analytics tools allows SMEs to track customer interactions, identify purchase intent, and facilitate seamless transactions.Social media analytics holds great promise for SMEs seeking to improve their strategic decision-making processes.By harnessing the power of social media data, SMEs can gain valuable insights into customer behavior, market trends, and competitor activities.However, SMEs must overcome challenges related to expertise, data privacy, and interpretation to fully realize the benefits of social media analytics.Looking ahead, advancements in technology and integration with other data sources are expected to further enhance the value of social media analytics for SMEs.

Hypotheses
In this study, we will identify the main variables to be tested in the structural model using SmartPLS and formulate hypotheses supporting the relationships between these variables.Here are the variables to be used: 1. Independent Variable • Social Media Analytics Use: Refers to the extent to which small and medium enterprises (SMEs) utilize social media analytics tools and techniques to gather insights from various social media platforms.It includes practices such as monitoring brand mentions, analyzing customer sentiment, tracking competitors, and identifying emerging trends.This variable measures how actively and effectively SMEs employ social media data analysis in their business operations and decision-making processes.

Mediator Variables
• Organizational Innovation: This mediator variable refers to the degree to which SMEs introduce new products, services, or processes as a response to insights gained from social media analytics.Organizational innovation involves the ability of SMEs to generate and implement novel ideas, products, or practices that enhance their competitiveness and market position.

H1: Social Media Analytics Use has a positive impact on Organizational Innovation.
This hypothesis is based on the assumption that the more active and in-depth the use of social media analytics by SMEs, the more likely they are to generate innovation in their products, services, or organizational processes in response to insights from social media.

H2: Organizational Innovation has a positive impact on Strategic Decision-Making.
This hypothesis states that the more innovative an organization, the better its ability to make effective strategic decisions.Organizational innovation allows SMEs to identify opportunities and tackle challenges in a more creative and adaptive manner.

H3: Adaptability has a positive impact on Strategic Decision-Making.
This hypothesis is based on the assumption that organizations that are more adaptable to environmental changes, including those revealed through social media analysis, will have a better ability to make timely and effective strategic decisions.

H4: Social Media Analytics Use has a direct positive effect on Strategic Decision-Making.
This hypothesis assumes that the use of social media analytics directly influences SMEs' ability to make strategic decisions, without mediation through organizational innovation or adaptability.

H5: Organizational Innovation partially mediates the relationship between Social Media Analytics Use and Strategic Decision-Making.
This hypothesis suggests that part of the impact of Social Media Analytics Use on Strategic Decision-Making is explained by the level of organizational innovation generated by its use.

H6: Adaptability partially mediates the relationship between Social Media Analytics Use and Strategic Decision-Making.
This hypothesis assumes that part of the effect of Social Media Analytics Use on Strategic Decision-Making is explained by the enhanced level of adaptability resulting from its use.
By formulating these variables and hypotheses, we will conduct Structural Equation Modeling (SEM) analysis using SmartPLS 4.1 to test the relationships between variables and to evaluate whether these relationships are significant and consistent with existing theory.The Impact of Social Media Analytics on SME … ■ 174

Findings Measurement Model Assessment
To assess the reliability and validity of the constructs, we conducted a measurement model assessment using SmartPLS 4.1.
• Reliability Reliability measures how consistently a construct measures a particular concept.We evaluated reliability using Cronbach's alpha and composite reliability.Cronbach's alpha values above 0.7 indicate high internal consistency.Composite reliability measures the extent to which multiple indicators measure the same construct.Our results show high reliability for all constructs, with Cronbach's alpha and composite reliability values exceeding 0.7 for all constructs.  1 presents the reliability analysis results for the constructs in the measurement model.Cronbach's alpha and composite reliability were used to assess internal consistency.All constructs demonstrate high reliability, with Cronbach's alpha and composite reliability values exceeding the recommended threshold of 0.7.The high reliability indicates that the items within each construct are internally consistent and measure the underlying concept reliably.

• Convergent Validity
Convergent validity assesses the extent to which the items within a construct are related to each other.We evaluated convergent validity using the average variance extracted (AVE).AVE values above 0.5 indicate satisfactory convergent validity.2 displays the results of convergent validity analysis.Convergent validity was evaluated using the average variance extracted (AVE).All constructs demonstrated satisfactory convergent validity, with AVE values exceeding the recommended threshold of 0.5.The authors' results show that all constructs have AVE values exceeding 0.5, which indicates that the variance captured by the indicators is greater than the error variance, thus indicating satisfactory convergent validity.

• Discriminant Validity
Discriminant validity examines whether a construct is truly distinct from other constructs.We assessed discriminant validity through the Fornell-Larcker criterion and the HTMT ratio.According to the Fornell-Larcker criterion, the square root of the AVE • The path coefficient between Organizational Innovation and Strategic Decision-Making is 0.42, indicating a positive relationship.This suggests that organizations that innovate are better equipped to make strategic decisions effectively.

• The direct path coefficient between Social Media Analytics Use and Strategic
Decision-Making is 0.32, implying a positive direct effect.This indicates that social media analytics use directly influences strategic decision-making in SMEs.• The path coefficient between Social Media Analytics Use and Adaptability is 0.50, suggesting a strong positive relationship.This indicates that SMEs that use social media analytics are more adaptable to changes in their environment.• The path coefficient between Adaptability and Strategic Decision-Making is 0.35, indicating a positive relationship.This implies that organizations that are more adaptable tend to make better strategic decisions.
Table 4 presents the path coefficients analysis results.Path coefficients represent the strength and direction of relationships between variables.The t-value and p-value indicate the significance of each coefficient.Coefficients with p < 0.05 are considered significant."Supported" indicates the support for the corresponding hypothesis.The authors' show that all paths between constructs are significant at p < 0.001, indicating strong relationships.

• Mediation Effects
Mediation effects were analyzed using bootstrapping with 5000 resamples.Organizational innovation and adaptability partially mediate the relationship between the use of social media analytics and strategic decision making.The following are the results of the mediation effect: 1. We analyzed mediation effects to understand the role of organizational innovation and adaptability in mediating the relationship between social media analytics use and strategic decision-making.2. The mediation effect of organizational innovation is significant (0.25), suggesting that part of the impact of social media analytics use on strategic decision-making is mediated by organizational innovation.3. Similarly, the mediation effect of adaptability is significant (0.28), indicating that part of the effect of social media analytics use on strategic decision-making is mediated by adaptability.

Discussion
The results support all hypotheses proposed in this study, indicating a significant impact of social media analytics on strategic decision-making in SMEs, both directly and indirectly through organizational innovation and adaptability.
1. Direct Effects: Social media analytics use has a direct positive effect on strategic decision-making, emphasizing its importance as a tool for informed decision-making.2. Indirect Effects: Organizational innovation and adaptability partially mediate the relationship between social media analytics use and strategic decision-making, suggesting that SMEs that innovate and adapt based on social media insights are better positioned to make effective strategic decisions.3. Practical Implications: These findings suggest that SMEs can enhance their strategic decision-making processes by leveraging social media analytics to drive innovation and increase adaptability.By investing in social media analytics tools and fostering a culture of data-driven decision-making, SMEs can gain a competitive edge in the marketplace.4. Limitations and Future Research: While this study provides valuable insights, it is not without limitations.Future research could explore additional factors that may influence the relationship between social media analytics and strategic decision-making, such as organizational culture or external environmental factors.
The results of this study highlight the significant impact of social media analytics on strategic decision-making in SMEs and underscore the importance of innovation and

on Sustainable Digital Innovation (ITSDI) p-ISSN: 2686-6285 Vol. 5 No. 2 April 2024 e-ISSN: 2715-0461
• Structural Model Assessment: After confirming the measurement model's adequacy, the structural model analysis was conducted.Path coefficients were examined to determine the strengths of the relationships, and the significance ofIAIC Transactions

on Sustainable Digital Innovation (ITSDI) p-ISSN: 2686-6285 Vol. 5 No. 2 April 2024 e-ISSN: 2715-0461
The Impact of Social Media Analytics on SME … ■ 173 between Social Media Analytics Use and Strategic Decision-Making by explaining how the insights derived from social media analytics lead to innovative strategies and actions within the organization.• Adaptability: Adaptability is another mediator variable that represents the agility and flexibility of SMEs in adjusting their strategies and operations based on insights from social media analytics.It measures how quickly and effectively SMEs can respond to changes in their environment, including shifts in customer preferences, market trends, or competitive dynamics.Adaptability mediates the relationship between Social Media Analytics and Strategic Decision-Making by showing how the ability to adapt to new information influences the decisionmaking process.3. Dependent Variable • Strategic Decision-Making: Refers to the process by which SMEs formulate and implement strategic choices to achieve their long-term goals and objectives.It involves identifying opportunities, analyzing alternatives, and selecting courses of action that align with the organization's mission and vision.Strategic decisions may relate to areas such as market positioning, product development, resource allocation, and competitive strategies.This variable measures the quality, speed, and effectiveness of SMEs' strategic decision-making processes, influenced by factors such as Social Media Analytics Use, Organizational Innovation, and Adaptability.
It mediates the relationshipIAIC Transactions