The Impact of Big Data on SME Performance: A Systematic Review
Abstract
:1. Introduction
1.1. Research Questions
- How does Big Data capability impact SMEs’ performance?
- What are the critical factors influencing the successful implementation of Big Data in SMEs?
- How can awareness and understanding of Big Data be leveraged to enhance productivity in SMEs?
- What are the challenges SMEs face in integrating Big Data into their existing systems and operations?
- How can SMEs effectively adapt their decision-making processes to harness the full potential of Big Data analytics?
1.2. Research Rationale
1.3. Research Objectives
1.4. Research Contribution
- This research establishes BD adoption as a crucial enabler of innovation for SMEs operating in resource-constrained environments. In these contexts, BD adoption extends beyond operational efficiency, acting as a catalyst for innovation. By leveraging data-driven insights, SMEs can overcome limitations in financial, human, and technological resources. This study highlights how BD can be strategically utilized as an innovation resource, allowing SMEs to develop dynamic capabilities and foster continuous innovation despite their size and limitations.
- A key contribution of this study is the proposed conceptual frameworks for various industries that demonstrate how BD capabilities, alongside KM practices, positively influence SME performance. The framework underscores the importance of integrating technological infrastructure with managerial support to maximize the impact of BD adoption. Additionally, KM practices act as mediators, amplifying the effects of BD on innovation and competitiveness. This integration emphasizes the need for a techno-human collaboration that combines data-driven insights with KM systems to drive sustainable competitive advantage.
- This research introduces a taxonomy categorizing SMEs based on their level of BD adoption—low, medium, and high adopters. Low adopters rely on basic analytics for operational decisions, while medium adopters integrate BD with KM practices to gradually enhance performance. High adopters leverage BD for strategic innovation, transforming business models and enabling the development of new products or services. This taxonomy provides SMEs with a clear roadmap to assess their BD maturity level and offers practical implications for implementing advanced data-driven strategies.
- This study provides actionable insights for SME practitioners by demonstrating how BD can enhance both operational and long-term innovation performance. The findings serve as a practical guide for SME managers to strategically adopt BD, optimizing not only their efficiency but also fostering a culture of innovation that strengthens their market competitiveness. The study offers a structured approach for integrating BD with KM practices, enabling SMEs to unlock the full potential of data-driven decision-making.
1.5. Research Novelty
2. Materials and Methods
2.1. Eligibility Criteria
2.2. Information Sources
2.3. Search Strategy
2.4. Selection Process
2.5. Data Collection Process
2.6. Data Items
2.6.1. Results and Data Collection
2.6.2. Contributor Characteristics
2.7. Study Risk of Bias Assessment
2.8. Effect Measures
2.9. Synthesis Methods
2.9.1. Study Eligibility Criteria
2.9.2. Data Preparation for Synthesis
2.9.3. Data Visualization and Tabulation Methods
2.9.4. Synthesis Methodology
2.9.5. Exploration of Heterogeneity Causes
2.9.6. Sensitivity Analysis
2.10. Reporting Bias Assessment
2.11. Certainty Assessment
3. Results
3.1. Study Selection
3.2. Study Characteristics
3.3. Risk of Bias
3.4. Results of Individual Studies
3.5. Results of Syntheses
3.5.1. Study Characteristics and Bias Assessment
3.5.2. Statistical Synthesis Results
3.5.3. Factors Contributing to Result Variability
3.5.4. Sensitivity Analyses
3.6. Reporting Biases
3.7. Certainty of Evidence
4. Practical Recommendations
4.1. Key Findings and Strategic Implications for Business Leaders
4.2. Proposed Decision-Making Framework for Implementation
4.3. Proposed Best Practices for Successful Implementation
4.4. Proposed Metrics and KPIs for Measuring Performance
4.5. Proposed Industry-Specific Frameworks
4.6. Real-World Case Studies Related to Proposed Systematic Review
4.7. Proposed Roadmap for SMEs and Policy Recommendations
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Ref. | Citations | Year | Contribution | Pros | Cons | Critical Gaps and Comparative Insights | Theoretical Framework |
---|---|---|---|---|---|---|---|
[21] | 1583 | 2016 | Developed a Big Data Capabilities (BDC) model integrating management, technology, and talent dimensions, validated through Delphi studies and surveys. | Highlights the importance of aligning analytics capabilities with business strategy; provides a hierarchical model of BDC. | Lacks detailed empirical evidence on the direct impact of BDC on firm performance; potentially limited generalizability of findings. | Gap—BDC’s performance impact leaves questions about its true efficacy. Our review’s contribution—We build on this by providing more detailed performance metrics for SMEs, focusing on how different BDCs affect operational and financial performance. | Not specified |
[22] | 233 | 2017 | Proposed a Big Data adoption model for Indian firms using PSV and TOE frameworks. | Insights into Big Data adoption in emerging economies; practical for managers. | Limited generalizability; small sample. | Gap—Study’s focus on Indian firms limits its applicability globally. Our review’s contribution—We extend the geographical scope, comparing Big Data adoption models in SMEs from diverse regions to provide more globally applicable insights. | TOE framework |
[23] | 54 | 2018 | Review of Big Data as a source of competitive advantage. | Identifies key benefits and sources of competitive advantage from Big Data and practical implications for various industries. | Requires managerial awareness for effective implementation; focuses on conceptual benefits without in-depth empirical analysis. | Gap—Conceptual focus lacks real-world testing. Our review’s contribution: We add validation of how Big Data creates competitive advantages in various SME contexts through comparison with our findings. | Not specified |
[24] | 50 | 2019 | Examined the early-stage adoption of Big Data in international marketing, especially in SMEs and developing countries. | Provides insights into the current state of Big Data adoption and highlights future research directions. | Limited research on Big Data adoption in international marketing, especially among SMEs in developing countries. | Gap—The lack of depth on specific SME challenges in international contexts. Our review’s contribution: We focus specifically on SME performance and Big Data in global supply chains, addressing gaps in international marketing contexts. | Not specified |
[25] | 3 | 2019 | BI in decision support systems. | Enhances decision-making quality, supports strategic decisions, and improves efficiency. | Requires complex setup, can be costly, and data integration challenges. | Gap—Lacks detailed insights into cost-benefit analyses for SMEs. Our review’s contribution—We incorporate cost-performance metrics in SMEs using innovative financial models, filling this gap. | Not specified |
[26] | 24 | 2020 | Review of decision-making (DM) and knowledge management (KM) in small transport SMEs, proposing new assessment tool. | Highlights DM–KM benefits for SMEs, especially in transportation. | Limited empirical evidence on SMEs in transportation; research relies on literature. | Gap—Limited practical insights and empirical backing for the assessment tool. Our review’s contribution—We propose new, empirically tested models, addressing gaps in DM–KM integration in SMEs beyond transportation. | Not specified |
[27] | 54 | 2021 | Comprehensive identification of the impact of open innovation on company performance through a systematic literature review. | Provides a clear picture of the importance of organizational readiness for open innovation. | Focuses primarily on the management domain, potentially limiting applicability to other fields. | Gap—Open innovation’s practical application in sectors beyond management is underexplored. Our review’s contribution—We assess the intersection of Big Data and open innovation in SMEs, including practical examples across sectors. | Not specified |
[28] | 108 | 2021 | Review and bibliometric analysis of Big Data adoption. | A broad analysis of Big Data across sectors; highlights research gaps and trends. | Limited to English-language studies; may miss relevant research due to keyword selection. | Gap—Limited to English-language studies, which may overlook non-Western perspectives. Our review’s contribution—We integrate research from multilingual databases, offering a more comprehensive global perspective. | Not specified |
[29] | 159 | 2021 | IoT and Big Data in supply chain decision-making: a review. | Promotes autonomous decision-making and distributed data processing. | Challenges in fully leveraging IoT-generated data for SCM decisions due to limited autonomy. | Gap—Lack of detailed case studies on IoT–Big Data integration. Our review’s contribution—We include case studies of SMEs successfully adopting IoT–Big Data solutions for supply chains, providing actionable insights. | Not specified |
[30] | 15 | 2021 | A systematic review of Big Data adoption challenges in Malaysian SMEs. | Highlights Lessig’s four modalities’ relevance and SMEs’ challenges insights. | Limited to Malaysian SMEs; focus on literature review rather than empirical data. | Gap—The study’s narrow focus on Malaysian SMEs restricts broader generalizability. Our review’s contribution—We expand the scope to address adoption challenges across multiple countries, enhancing its global relevance. | TOE framework |
[31] | 25 | 2022 | Analyzed the impact of inventory management on SMEs’ operational performance using bibliometric and systematic review methods. | Revealed trends and gaps in inventory management research. Identified emerging themes and technologies. | Limited to articles only in English and from Scopus; some papers only addressed IM or OP separately. | Gap—Limited depth in combining IM and operational performance studies. Our review’s contribution: We propose best practices that link directly to operational and financial performance in SMEs, adding depth. | Not specified |
[32] | 11 | 2022 | Development of a Big Data adoption model in B2B, four-category classification, systematic literature review. | Comprehensive, structured approach; clarifies adoption motives; broad view identifies research gaps. | Lacks practical details, may miss contexts; too theoretical, lacks empirical validation. | Gap—Too abstract, lacking practical applications. Our review’s contribution—We provide real-world applications of Big Data models in B2B SMEs. | TOE framework |
[33] | 246 | 2022 | Overview of Big Data in intelligent manufacturing; proposes a decision-making framework. | Provides theoretical basis and practical insights; highlights real-time dynamic perception. | Limited to one year; may not cover emerging technologies beyond 2021. | This review’s contribution is narrow in terms of timeframe and lacks depth on sustainability aspects. The proposed review bridges this gap by extending the analysis to long-term sustainable impacts on SMEs. | Decision-making framework |
[34] | 3 | 2023 | Examined factors influencing the adoption of Big Data in SMEs, identifying 13 key factors. | Provides a thorough analysis with practical insights; enhances academic understanding; useful for SMEs. | Focuses mainly on SMEs and may overlook some emerging trends or factors. | While this study provides a focused analysis on SMEs, it lacks quantitative performance metrics. The proposed review contributes by introducing various frameworks to assess SME performance using Big Data. | TOE framework |
[35] | 19 | 2023 | Analyzed COVID-19 impact on SMEs’ supply chains. | Provides current insights. | Limited to a specific population. | The COVID-19 context limits generalizability. The proposed review broadens the scope beyond pandemic-related impacts to offer broader insights into Big Data and sustainability in various sectors. | Not specified |
[36] | 111 | 2023 | Reviewed the use of data science in SMEs’ digital marketing strategies. Identified seven state-of-the-art uses and proposed four future research directions. | Provides a comprehensive overview of current data science applications in SMEs; identifies gaps and future research areas. | Limited to existing literature; may not fully capture emerging trends in data science. | While this study focuses on digital marketing, the proposed review expands to multiple business functions within SMEs, such as operations and supply chain, to provide a holistic perspective. | Not specified |
[37] | 9 | 2023 | A systematic review of Cloud ERP, linking enablers and barriers to innovation outcomes. | A thorough analysis of benefits and challenges; a useful framework; identifies future research areas. | Limited to literature up to February 2022; primarily based on Indian studies; lacks some empirical data. | The narrow focus on Cloud ERP and its regional limitations are addressed in the proposed review by encompassing a wider range of technological setups and geographical contexts for SMEs. | TOE framework |
[38] | 161 | 2023 | Identified initial steps for MSMEs in digital transformation. | Empowers MSMEs, fosters innovation, and enhances reputation. | Requires cultural change and stakeholder management. | This study emphasizes cultural aspects of digital transformation, but lacks a deep dive into performance metrics. The proposed review fills this gap by providing detailed performance indicators and innovative models for evaluation. | Not specified |
[39] | 0 | 2024 | Examines how Industry 4.0 skills impact sustainable manufacturing in SMEs, highlighting rational culture’s moderating effect and stressing the need for these competencies to boost sustainability. | The study offers insight into how Industry 4.0 competencies can boost sustainable manufacturing for SMEs, identifies literature gaps, and underscores the moderating role of rational culture. | The study’s focus on Malaysian SMEs may limit its broader applicability, and reliance on existing literature might overlook recent Industry 4.0 and sustainable manufacturing trends. | The proposed review expands the scope to cover broader contexts, including non-manufacturing SMEs, and introduces performance metrics to track the impact of Big Data in fostering sustainability. | Not specified |
[40] | 6 | 2024 | Reviews the impact of inventory management practices on SMEs’ operational performance through bibliometric and systematic analysis. | Highlights key inventory management strategies, identifies research gaps, and provides a roadmap for future studies. | Focuses broadly on inventory management without in-depth analysis of specific practices or technologies. | While the study provides valuable insights into inventory management, it lacks integration with other business processes. The proposed review addresses this by examining how Big Data applications can optimize various SME operations. | Not specified |
[41] | 2 | 2024 | Examines cloud computing’s role in the circular economy for SMEs using TOE and institutional isomorphism frameworks. | A comprehensive framework identifies research gaps and rigorous methodology. | Limited empirical data on cloud computing’s impact, and complex framework. | This review is narrow in focus (cloud computing), whereas the proposed review integrates multiple technologies like Big Data, Cloud, and IoT to offer a more comprehensive view on SME transformation. | TOE framework |
[42] | 0 | 2024 | Explores the negative implications of Industry 4.0 on sustainability and presents a framework for addressing these issues. | Highlights Industry 4.0’s negative impacts like job losses, wage gaps, and environmental issues, and suggests ways to address them. | The emphasis on negative impacts may overshadow Industry 4.0’s benefits and relies mainly on Indian literature with limited empirical data. | The proposed review takes a balanced approach, discussing both positive and negative implications of Industry 4.0 on SMEs, while offering innovative financial models to better assess outcomes. | Not specified |
[43] | 1 | 2024 | Systematic review of integrating analytics in enterprise information systems (EISs). | A comprehensive review of global literature; highlights adoption challenges and strategic impacts; utilizes PRISMA 2020 and TOE framework. | May overlook non-English-language studies; Limited by selected databases and search terms. | The focus on EIS limits generalizability across all SME setups. The proposed review contributes by examining diverse Big Data applications across different SME sectors to provide a holistic understanding. | TOE framework |
[44] | 50 | 2024 | Systematic review of business analytics for competitive advantage in emerging markets. | Comprehensive analysis of recent literature; identifies key impacts and challenges. | Excludes non-English-language and non-peer-reviewed sources; limited to recent publications. | This review focuses on business analytics in general, whereas the proposed review narrows in on specific Big Data applications in SMEs to provide more targeted, actionable insights. | Not specified |
Criteria | Inclusion | Exclusion |
---|---|---|
Topic | Articles must focus on the impact of Big Data on SME performance. | Articles unrelated to the impact of Big Data on SME performance. |
Research Framework | The articles must comprise a research framework for the impact of Big Data on SME performance. | Articles with inadequate research framework focusing on the impact of Big Data on SME performance. |
Language | Papers written in English. | Papers not written in English. |
Publication Period | Publications between 2014 and 2024. | Publications outside 2014 and 2024. |
Search Terms 1 | Databases | Fields | ||||||
---|---|---|---|---|---|---|---|---|
Big Data OR Data Analytics OR Data Mining | AND | SMEs OR Small and Medium Enterprises OR Small and Medium-sized Businesses | AND | Performance OR Business Performance OR Organizational Performance | AND | Impact OR Effect OR Influence OR Role | Google Scholar Web of Science Scopus | Title, Abstract Keywords |
Fields | Description | Selections |
---|---|---|
Title | The name of the research article or paper. | None |
Year | The publication year of the study. | None |
Online database | The database where the article was sourced. | Google Scholar, Scopus, Web of Science |
Journal name | Represents data as slices of a whole, ideal for showing proportional or percentage distribution of categories. | None |
Research type | Shows parts of a whole, allowing multiple variables to be represented in the same category for easier comparison. | Article journal, conference paper, book chapter, dissertation, thesis |
Discipline or subject area | Uses color coding to represent data intensity or frequency, useful for spotting patterns in large datasets. | Big Data, SME performance, business analytics |
Industry context | The industry or sector the research is focused on. | SMEs, startups, small businesses |
Geographic location | The region or country where the study was conducted or focused. | None |
Economic context | The economic environment of the study. | Developed, developing |
Types of Big Data technologies | The specific Big Data technologies used in the research. | Hadoop, Spark, NoSQL databases |
Big Data analytics techniques | The analytical methods employed. | Machine learning, data mining, predictive analytics |
Technology providers | Companies or organizations providing the technology. | Cloudera, Hortonworks, IBM, AWS |
Technology implementation model | The mode of technology deployment. | On-premises, cloud-based, hybrid |
Research design | The design of the study. | Experimental, quasi-experimental, case study, survey |
Type of study | The methodology used. | Qualitative, quantitative, and mixed methods |
Sample size | The number of participants or entities involved in the study. | None |
Sample characteristics | Demographic or specific features of the sample. | SMEs, Big Data, IT professionals |
Data collection methods | Techniques used to gather data. | Interviews, surveys, observations, document analysis |
Big Data techniques | Methods used to analyze the data. | Statistical analysis, thematic analysis |
IT performance metrics | Measures related to technological performance. | Data processing speed, scalability, data accuracy |
Business performance | Measures of business outcomes. | Operational efficiency, revenue growth, cost savings |
Organizational outcomes | Results related to the organization. | Employee satisfaction, customer satisfaction |
Long-term impacts | The extended effects of the study findings. | Business sustainability, competitive advantage |
Ref. | Selection (0–4 Stars) | Comparability (0–2 Stars) | Outcome/Exposure (0–3 Stars) | Total Stars | Quality Rating |
---|---|---|---|---|---|
[60,101,111] | ★★ | ★ | ★★ | 5 | Low |
[62,66,68,82,93,98,100,107,109,126,129,135] | ★★ | ★★ | ★★ | 6 | Low-Moderate |
[50,53,55,58,59,67,70,75,77,80,84,86,87,95,106,110,116,118,119,121,123,124,129,135] | ★★★ | ★★ | ★★ | 7 | Moderate |
[45,47,48,52,54,56,57,61,63,64,69,71,74,80,85,87,88,93,96,97,104,106,109,113] | ★★★ | ★★ | ★★★ | 8 | Moderate-High |
[46,49,51,65,72,73,76,78,81,83,92,94,99,102,104,108,115,117,124,130] | ★★★★ | ★★ | ★★★ | 9 | High |
Chart Type | Purpose | Data Representation Format |
---|---|---|
Bar chart | Displays categorical data with rectangular bars, ideal for comparing different categories or variables in a dataset. | Numbers |
Column chart | Similar to a bar chart, but with vertical bars, it is useful for comparing the frequency or number of categories. | Numbers |
Line chart | Shows trends over time by connecting data points with a continuous line. | Numbers |
Pie chart | Represents data as slices of a whole, ideal for showing proportional or percentage distribution of categories. | Percentages (%) |
Stacked bar chart | Shows parts of a whole, allowing multiple variables to be represented in the same category for easier comparison. | Numbers and Ppercentages (%) |
Scatterplot | Plots individual data points on an X and Y axes to explore relationships or correlations between two variables. | Numbers |
No. | Online Repository | Number of Results |
---|---|---|
1 | Google Scholar | 64 |
2 | Web of Science | 233 |
3 | Scopus | 13 |
Total | 315 |
Types | Description |
---|---|
Hadoop | A framework developers can use for managing very large datasets in a distributed environment using simple programming models that span multiple clusters. It enables the expansion of additional machines in addition to the storage servers to a hundred thousand with a local processing unit and a local disk. |
Spark | An analytics system that can process an entire Big Data stack in one tool that includes stream processing, SQL, machine learning, and graph computation processing engine. A particular processing framework that brings data into memory and processes it there instead of inputting data from a disk every single time; therefore, it is appropriate for real-time analysis of data. |
NoSQL Databases | This approach of database management systems is suitable for systems that require support for a variety of data formats such as relational, document, column-oriented, and graph databases. NoSQL databases are built with specific principles in mind, and they are most efficiently used in a Big Data environment with a great deal of data that are advancing in complexity. |
Questions(Q) | Research Quality Questions |
---|---|
Q1 | Are the research objectives explicitly outlined and well defined? |
Q2 | Is the research methodology comprehensively detailed? |
Q3 | Is the impact of Big Data on SME performance thoroughly and clearly analyzed? |
Q4 | Are the methods of data collection comprehensively detailed and appropriate? |
Q5 | Do the research findings add to the existing literature on the topic? |
Ref. | Q1 | Q2 | Q3 | Q4 | Q5 | Total | % |
---|---|---|---|---|---|---|---|
[45,46,49,51,52,53,56,57,58,61,64,65,71,72,73,74,75,77,80,82,84,85,87,90,107,123,125,127,128,133] | 1 | 1 | 1 | 1 | 1 | 5 | 100% |
[47,48,55,59,60,77,79,104,111,112,125,127,129,132] | 1 | 1 | 0.5 | 1 | 1 | 4.5 | 90% |
[54,63,80,92,95,96,97,98,99,100,101,102,103,105,109,124,134,137] | 1 | 0.5 | 0.5 | 1 | 1 | 4 | 80% |
[69,70,86,87,89,90,113,114,137,141] | 1 | 0.5 | 0.5 | 0.5 | 1 | 3.5 | 70% |
[50,62,66,68,85,94,106,113,114,115,116,117,118,119,120,121,122,123] | 1 | 0.5 | 0.5 | 0 | 1 | 3 | 60% |
[67,82] | 1 | 0.5 | 0 | 0 | 1 | 2.5 | 50% |
Published Year | Conference Paper | Journal Article |
---|---|---|
2016 | 3 | 2 |
2017 | 2 | 6 |
2018 | 1 | 2 |
2019 | 3 | 7 |
2020 | 3 | 13 |
2021 | 1 | 11 |
2022 | 2 | 10 |
2023 | 0 | 15 |
2024 | 0 | 12 |
Category | Ref. | Contribution |
---|---|---|
Big Data (BD) and Firm Performance | [45,47,52,53,59,62,72,74,86,87,100,105,126,134] | BD enhance financial, growth, innovation, and environmental performance in SMEs. Key drivers include organizational readiness and top management support. Proposes a conceptual framework linking BD adoption to enhanced innovation in resource-constrained environments. |
Industry 4.0 and Digital Capabilities | [46,50,55,60,63,75,78,91,95,129,133] | The adoption of Industry 4.0 technologies improves operational, financial, and innovation performance, particularly in manufacturing. Suggests a taxonomy of digital capabilities that SMEs can leverage for effective BD implementation and innovation outcomes. |
BD for Decision-Making and Knowledge Management | [48,51,66,67,69,81,96,101,108,109,125] | BD enhance decision-making and knowledge management, fostering productivity. Proposes a conceptual framework integrating KM models that leverage BD for strategic advantage, addressing barriers like lack of expertise and complexity in resource-limited contexts. |
BD and Competitive Advantage | [58,61,72,76,81,82,88,94,106,122] | BD improve competitive advantage through enhanced market performance and supply chain coordination. Discusses how entrepreneurial orientation and co-innovation can form a theoretical basis for understanding BD’s role in resilience and competitive positioning in SMEs. |
Adoption Challenges and Barriers to BD | [93,101,113,114,126,135,136,137] | Identifies common barriers to BD adoption such as financial constraints and lack of expertise. Suggests a comprehensive model based on the TOE framework that considers organizational readiness as a critical moderator in resource-constrained environments. |
BD in Supply Chain Management | [71,75,78,87,92,98,112] | BD enhance supply chain efficiency through improved visibility and real-time adjustments. Proposes a framework connecting BD capabilities to green product development and sustainable supply chain outcomes, emphasizing the unique challenges faced by SMEs. |
Cloud-Based BD and Scalability | [68,70,84,89,112,115] | Cloud computing provides scalable solutions for SMEs to access BD technologies. Introduces a research agenda on the role of cloud-based BD in overcoming scalability and security challenges in innovation for SMEs. |
BD and HR Practices | [80,82] | BD improve HR service quality and innovation competency. Proposes a conceptual framework illustrating how BD can enhance HR practices, focusing on the importance of openness in change and technical skill development in SMEs. |
Big Data-Driven Innovation | [59,73,79,97,118,124] | BD fosters green innovation, improving economic and environmental outcomes. Suggests developing a taxonomy of data-driven business models that enhance innovation and value creation, particularly in Industry 4.0 contexts. |
BD in Financial Services | [77,103,107,129] | BD supports SMEs in credit assessment and financing, reducing information asymmetry. Proposes a framework integrating financial and non-financial data for credit evaluations, particularly for SMEs with weaker financial conditions. |
BD and Project Performance | [85,135] | BD positively influences project performance by mediating relationships between knowledge management, green purchasing, and operational capabilities. Suggests a model combining DEA with machine learning techniques to improve performance prediction accuracy for SMEs. |
BD and Network Security | [93,99] | Security frameworks that integrate BDA enhance network reliability and data validity, addressing privacy concerns. Proposes a research agenda for developing advanced security techniques, like fog computing, tailored for SMEs to protect their BD investments. |
BD in Agriculture and SMEs | [102,104] | BD affect management control systems in agricultural SMEs. Proposes a framework illustrating the interplay between leadership, managerial culture, and BD’s role in stabilizing or changing management practices in resource-limited agricultural contexts. |
BD and Innovation Efficiency | [131,133] | Absorptive capacity is pivotal for sustainable economic performance, influencing product innovation efficiency. Proposes a conceptual framework highlighting the mediating effects of BD capabilities in linking market development strategies to innovation efficiency. |
BD in Traffic Systems | [136] | Crowdsourced traffic data enhances accuracy in traffic event detection. Suggests a theoretical framework linking BD integration with machine learning to improve urban traffic management, providing insights into cost reduction compared to conventional methods. |
Industry | Key Finding | Strategic Implications for Business Leaders | Opportunities | Challenges | Relevance to Proposed Systematic Review | Strategic Drivers | Expected Outcome |
---|---|---|---|---|---|---|---|
Retail | Big Data enable demand forecasting, optimizing stock levels | Leaders should prioritize data-driven decision-making for inventory management | Use Big Data for precision forecasting | Data literacy and technology integration challenges | Aligns with findings on operational efficiency | Integration of BD technologies and skills development | Improved inventory management and reduced stockouts |
Manufacturing | Big Data improve process optimization and reduce downtime | Invest in real-time data analytics for machinery performance monitoring | Real-time insights into equipment health | High costs of analytics infrastructure | Related to BD’s role in process innovation | Emphasis on operational excellence and cost-effectiveness | Reduced downtime, increased productivity |
Healthcare | Big Data enhance patient outcome tracking and predictive healthcare | Utilize predictive analytics for personalized treatments | Improved patient care and outcomes | Privacy concerns, data security issues | Relevant to BD in decision-making improvements | Focus on data-driven healthcare solutions | Enhanced patient satisfaction and care quality |
Finance | Big Data allow for advanced risk modeling and fraud detection | Develop comprehensive risk management strategies using analytics | Better fraud detection and risk mitigation | Lack of skilled data analysts | Tied to BD’s impact on risk management strategies | Drive innovation in financial analysis tools | Reduced financial risk and fraud cases |
Logistics | Data analytics improves route optimization and reduces fuel consumption | Adopt analytics for logistics and transportation management | Better route planning, cost reduction | Integration with existing systems | Linked to resource optimization through BD | Focus on eco-friendly, cost-efficient operations | Improved efficiency, lower operational costs |
Industry | Step | Framework Focus | Key Features | Strategic Drivers | Expected Outcome | Ties to Proposed Study |
---|---|---|---|---|---|---|
Retail | 1. Assess Data Needs | Identifying critical data sources for forecasting | Customer behavior, sales data, market trends | Data-driven decision-making, customer-centric strategies | Optimized stock levels, reduced overstock/stockouts | Aligned with operational efficiency and demand forecasting |
2. Implement Real-Time Analytics | Real-time analysis of customer preferences | Real-time sales tracking, dynamic pricing | Enhanced customer engagement, personalized marketing | Increased customer satisfaction, improved sales | Links to BD’s impact on customer satisfaction | |
3. Optimize Inventory Management | Data-driven inventory control | Inventory turnover analysis, stock level monitoring | Efficient resource allocation, minimized waste | Reduced inventory costs, faster restocking | Ties to BD’s role in operational efficiency | |
4. Monitor Market Trends | Predicting future market changes | Predictive analytics, market sentiment tracking | Business agility, competitive positioning | Improved market responsiveness | Relevant to BD’s role in competitive advantage | |
Manufacturing | 1. Conduct Data Audits | Assess existing production and process data | Production KPIs, machinery data | Operational excellence, process optimization | Reduced downtime, improved production rates | BD in process innovation |
2. Integrate Predictive Maintenance | Predict machine failures using data | Machine learning, sensor data | Cost-effectiveness, reduced equipment failure risk | Improved asset lifespan, reduced maintenance costs | Relevant to operational efficiency | |
3. Implement Quality Control Tools | Data-driven monitoring of product quality | Real-time quality checks, anomaly detection | Enhanced product consistency, reduced waste | Higher product quality, minimized defects | Links to BD in quality control | |
4. Automate Workflow Processes | Using data to automate production workflows | Robotics, real-time process monitoring | Productivity, cost-effectiveness | Optimized production, reduced manual labor | Relevant to BD in productivity | |
Healthcare | 1. Ensure Data Privacy Compliance | Protecting patient data during BD use | Data encryption, compliance checks | Data security, patient trust | Increased patient data security, compliance | Relevant to ethical data management |
2. Implement Predictive Analytics | Using data for predicting patient outcomes | Predictive models, health data analysis | Improved patient outcomes, proactive care | Enhanced healthcare delivery, lower readmission rates | Relevant to BD in healthcare performance | |
3. Streamline Clinical Processes | Optimizing operational processes in healthcare | Process mapping, resource allocation tools | Cost-effectiveness, process optimization | Reduced waiting times, improved service quality | Tied to operational improvements in healthcare | |
4. Utilize AI for Diagnosis Support | Supporting diagnosis with AI-based data tools | Machine learning, AI-assisted diagnostics | Accuracy in diagnosis, faster treatment decisions | Improved diagnostic accuracy, reduced medical errors | Relevant to innovation in healthcare delivery | |
Finance | 1. Build Risk Management Models | Use BD to enhance risk management strategies | Fraud detection, credit risk analysis | Improved financial analysis, fraud mitigation | Reduced financial risk, better credit assessments | Tied to risk management in financial performance |
2. Optimize Investment Decisions | Data-driven investment forecasting | Predictive models, market analysis | Investment efficiency, risk-adjusted returns | Higher returns, optimized portfolio management | Relevant to BD in financial decision-making | |
3. Automate Compliance Monitoring | Ensure regulatory compliance through data | Real-time monitoring, regulatory data checks | Compliance risk reduction, regulatory adherence | Reduced compliance risks, streamlined audits | Relevant to BD in regulatory management | |
4. Improve Customer Personalization | Enhancing customer services using data | Customer behavior analysis, financial trends | Customer satisfaction, loyalty | Increased customer retention, personalized services | Tied to customer experience enhancement | |
Logistics | 1. Assess Supply Chain Data | Data-driven insights on supply chain operations | Route data, delivery time analysis | Cost-effectiveness, sustainable operations | Reduced fuel costs, optimized delivery routes | Aligned with BD in supply chain optimization |
2. Optimize Fleet Management | Monitoring and improving fleet performance | GPS data, fuel consumption tracking | Operational efficiency, reduced carbon footprint | Increased fleet efficiency, reduced operational costs | Relevant to eco-friendly logistics strategies | |
3. Implement Predictive Route Planning | Dynamic route optimization using BD | Real-time traffic data, predictive route models | Faster deliveries, cost reduction | Improved delivery times, optimized supply chain | Links to data-driven logistics | |
4. Manage Inventory Efficiently | Enhance supply chain inventory control | Real-time inventory tracking, stock monitoring | Reduced stockouts, cost-effectiveness | Optimized supply chain, minimized inventory costs | Tied to BD in supply chain management |
Industry | Best Practice | SME Type | Operational Challenges | Strategic Drivers | Expected Impact | Ties to Systematic Review Findings |
---|---|---|---|---|---|---|
Retail | Adopt real-time analytics for customer data | E-Commerce, Brick-and-Mortar | Managing large volumes of customer data | Customer-centric strategies, real-time decision-making | Improved customer engagement, increased sales | Aligned with operational efficiency and customer satisfaction |
Integrate demand forecasting tools | Apparel, Grocery Retailers | Stockouts and inventory mismanagement | Inventory control, predictive demand analysis | Reduced inventory costs, improved stock levels | Relevant to BD’s role in operational efficiency | |
Utilize dynamic pricing models | Online Retailers | Staying competitive in fluctuating markets | Market responsiveness, competitive positioning | Increased market competitiveness, optimized pricing strategies | Ties to competitive advantage in the systematic review | |
Manufacturing | Implement predictive maintenance systems | Light Manufacturing, Electronics | Equipment breakdown and downtime | Cost-effectiveness, resource management | Reduced downtime, extended equipment lifespan | Relevant to BD’s role in operational efficiency |
Utilize automated quality control | Pharmaceuticals, Food Processing | Quality consistency and compliance | Regulatory adherence, product consistency | Improved product quality, minimized waste | Aligned with BD’s role in process optimization | |
Leverage workflow automation | Automotive, Heavy Machinery | Complex workflows, production bottlenecks | Operational excellence, productivity | Enhanced production rates, optimized resource allocation | Ties to productivity enhancements in SMEs | |
Healthcare | Prioritize data security and privacy compliance | Clinics, Healthcare Facilities | Data breaches and patient trust | Data security, regulatory compliance | Increased patient trust, adherence to data privacy regulations | Relevant to BD’s role in ethical data management |
Use AI for diagnostic support | Hospitals, Diagnostic Centers | Diagnostic errors and delays | Diagnostic accuracy, faster treatment decisions | Improved diagnostic accuracy, reduced errors | Aligned with innovation in healthcare delivery | |
Optimize patient flow through predictive analytics | Primary Care, Urgent Care | Patient congestion, long waiting times | Process optimization, patient satisfaction | Reduced waiting times, improved service delivery | Tied to operational improvements in healthcare | |
Finance | Enhance risk management with real-time analytics | Fintech, Banks | Fraud detection, risk assessment challenges | Fraud mitigation, risk-adjusted decision-making | Reduced financial risk, improved credit assessments | Aligned with risk management in financial performance |
Automate compliance reporting | Financial Services, Insurance | Complex regulatory compliance requirements | Compliance risk reduction, streamlined audits | Reduced compliance risk, improved audit readiness | Relevant to regulatory management through BD | |
Leverage predictive analytics for investment strategies | Investment Firms, Asset Management | Market volatility and forecasting inaccuracy | Market foresight, investment efficiency | Increased returns, optimized portfolio management | Aligned with financial decision-making strategies | |
Logistics | Optimize supply chain management with data insights | Freight, Delivery Services | Inefficient inventory control, high fuel costs | Supply chain visibility, cost-effectiveness | Reduced operational costs, optimized deliveries | Relevant to supply chain management and efficiency |
Implement predictive route optimization | Fleet Management, Last-Mile Delivery | Route inefficiencies, delivery delays | Route optimization, customer satisfaction | Faster delivery times, reduced fuel consumption | Tied to data-driven logistics strategies | |
Leverage real-time tracking for inventory | Warehousing, Distribution | Inventory mismanagement, stockouts | Inventory optimization, cost reduction | Optimized inventory levels, reduced stockouts | Aligned with operational efficiency in logistics |
Industry | Key Metrics/KPIs | Measurement Focus | Strategic Drivers | Expected Outcome | Ties to Systematic Review Findings | Priority (1 = Highest, 2 = Medium, 3 = Low) |
---|---|---|---|---|---|---|
Retail | Customer Retention Rate | Customer Engagement | Customer Loyalty, Brand Experience | Higher retention, increased repeat business | Big Data improving customer targeting and loyalty | 1 |
Average Order Value | Sales Performance | Profitability, Customer Spending | Higher sales, increased revenue | Data analytics boosting profitability | 1 | |
Inventory Turnover | Inventory Management | Stock Optimization, Demand Forecasting | Reduced overstock/stockouts, cost control | BD improving demand forecasting and stock management | 2 | |
Conversion Rate | Marketing Efficiency | Sales Funnel Optimization, Digital Engagement | Increased conversion, better ROI on marketing | Insights from Big Data on consumer behavior and conversion | 2 | |
Manufacturing | Production Throughput | Operational Efficiency | Cost Reduction, Process Optimization | Enhanced productivity, lower operational costs | Big Data driving operational efficiency and innovation | 1 |
Defect Rate | Quality Control | Product Quality, Waste Reduction | Improved product quality, less waste | Impact of BD on maintaining high-quality standards | 2 | |
Equipment Downtime | Maintenance Efficiency | Predictive Maintenance, Cost Control | Increased uptime, reduced maintenance costs | Big Data predictive analytics for maintenance efficiency | 2 | |
Order Fulfillment Cycle Time | Supply Chain Management | Lead-Time Reduction, Demand Fulfillment | Faster fulfillment, Improved customer satisfaction | Enhanced supply chain operations through data insights | 1 | |
Healthcare | Patient Satisfaction Score | Service Quality | Patient Care, Service Delivery | Improved care outcomes, patient trust | Data analytics improving patient satisfaction | 2 |
Average Treatment Cost | Cost-Effectiveness | Healthcare Cost Management | Lower costs, better allocation of resources | BD supporting resource optimization in healthcare | 2 | |
Treatment Success Rate | Clinical Outcomes | Quality of Care, Patient Outcomes | Improved health outcomes, fewer readmissions | Big Data predictive analysis for patient care | 1 | |
Finance | Loan Default Rate | Credit Risk Management | Risk Reduction, Customer Creditworthiness | Lower default rates, increased risk mitigation | BD enhancing credit scoring and risk analysis | 1 |
Net Interest Margin | Profitability | Revenue Generation, Cost of Funding | Higher profits, improved loan and deposit management | Big Data improving financial decision-making | 1 | |
Fraud Detection Rate | Security and Compliance | Transaction Monitoring, Fraud Prevention | Reduced fraud losses, increased compliance | Data-driven fraud prevention and detection strategies | 2 | |
Customer Acquisition Cost | Customer Acquisition | Marketing Efficiency, Profitability | Lower acquisition costs, better customer targeting | Insights from BD for reducing acquisition costs | 2 | |
Logistics | On-Time Delivery Rate | Supply Chain Management | Delivery Optimization, Customer Satisfaction | Improved delivery times, increased customer trust | Big Data optimizing logistics and supply chain processes | 1 |
Transportation Cost per Mile | Operational Efficiency | Cost Control, Route Optimization | Lower costs, increased operational efficiency | Big Data improving transport logistics and cost-effectiveness | 2 | |
Fleet Downtime | Asset Management | Maintenance Planning, Cost Control | Reduced downtime, lower maintenance costs | BD enabling predictive maintenance for fleet management | 2 | |
Shipment Tracking Accuracy | Customer Experience | Transparency, Service Quality | Better shipment tracking, Improved customer satisfaction | Real-time data insights enhancing shipment tracking | 1 |
Industry | Framework Component | Key Focus Area | Implementation Steps | Challenges Addressed | Strategic Drivers | Expected Outcome | Ties to Systematic Review Findings |
---|---|---|---|---|---|---|---|
Retail | Data-Driven Marketing | Customer Engagement | Step 1: Define target segments based on data analytics Step 2: Optimize marketing channels with insights Step 3: Leverage predictive analytics for personalized offers | Addressing customer retention, personalization gaps | Customer Loyalty, Sales Growth | Improved customer targeting, higher sales | Data supporting decision-making and customer loyalty |
Inventory Optimization | Supply Chain Management | Step 1: Implement demand forecasting tools Step 2: Utilize BD for inventory tracking Step 3: Automate restocking with predictive models | Stockouts, overstocking, supply chain disruptions | Operational efficiency, Cost Reduction | Reduced stockouts, improved supply chain management | BD enhancing stock management and forecasting | |
Manufacturing | Predictive Maintenance | Equipment Efficiency | Step 1: Use sensors for real-time equipment monitoring Step 2: Analyze data to predict maintenance needs Step 3: Automate alerts for proactive maintenance actions | Equipment downtime, high maintenance costs | Operational Efficiency, Cost Control | Increased equipment uptime, reduced costs | Big Data improving predictive maintenance |
Process Optimization | Production Output | Step 1: Collect data from production processes Step 2: Analyze workflow bottlenecks Step 3: Implement data-driven process adjustments | Inefficiencies in production, high defect rates | Process Optimization, Quality Control | Enhanced productivity, lower defect rates | BD streamlining production efficiency | |
Healthcare | Patient Care Analytics | Clinical Outcomes | Step 1: Aggregate patient data from multiple sources Step 2: Use predictive models to identify at-risk patients Step 3: Integrate data into care plans for personalized treatment | High treatment costs, patient readmissions | Patient Satisfaction, Quality of Care | Improved patient outcomes, lower readmission rates | Big Data driving patient care and clinical success |
Resource Allocation | Healthcare Efficiency | Step 1: Track usage of medical resources in real time Step 2: Use BD to forecast resource needs Step 3: Automate resource scheduling based on demand | Inefficient resource usage, overcrowded facilities | Cost Control, Efficiency | Optimized resource usage, cost savings | Data supporting optimal resource allocation | |
Finance | Credit Risk Assessment | Risk Management | Step 1: Collect customer financial data Step 2: Analyze credit risk using BD models Step 3: Implement automated risk scoring systems | Loan default, poor risk analysis | Risk Mitigation, Profitability | Lower default rates, better risk management | Big Data improving credit scoring and risk assessment |
Fraud Detection | Security and Compliance | Step 1: Implement real-time transaction monitoring systems Step 2: Use machine learning for anomaly detection Step 3: Automate fraud alerts and responses | Fraudulent transactions, security risks | Compliance, Risk Control | Reduced fraud incidents, improved security | Data-driven fraud prevention | |
Logistics | Route Optimization | Supply Chain Efficiency | Step 1: Use GPS data for real-time route tracking Step 2: Implement predictive analytics for delivery time estimation Step 3: Automate route adjustments based on traffic patterns | Delays in delivery, high transportation costs | Customer Satisfaction, Cost Control | Faster deliveries, lower transportation costs | BD optimizing logistics and delivery routes |
Fleet Management | Operational Efficiency | Step 1: Collect data from fleet sensors Step 2: Analyze vehicle performance data Step 3: Use BD insights for fleet maintenance scheduling | Fleet downtime, inefficient asset management | Operational Efficiency, Asset Utilization | Reduced downtime, improved fleet performance | Big Data improving asset and fleet management |
Industry | Case Study | Implementation | Outcome | Reference |
---|---|---|---|---|
Retail | Amazon | Amazon utilizes Big Data for personalized recommendations, price optimization, and shipping logistics. It leverages customer data from browsing habits and voice interactions with Alexa. | Increased sales by 35% from recommendations, improved customer retention, and enhanced operational efficiency. | https://www.businesstechweekly.com/ (Accessed on 20 September 2024) |
Retail | Walmart | Walmart harnesses Big Data through Hadoop and NoSQL to analyze customer preferences in real time, optimizing product recommendations and supply chain operations. | Boosted conversion rates, enhanced customer satisfaction, and streamlined supply chain processes. | https://datafortune.com/ (Accessed on 20 September 2024) |
Finance | Visa | Visa employs Big Data to detect fraudulent transactions in real time by analyzing billions of transaction records across the globe. | Minimized fraudulent transactions, leading to greater trust in the Visa network and enhanced customer protection. | https://usa.visa.com/ (Accessed on 20 September 2024) |
Logistics | UPS | UPS uses Big Data to optimize delivery routes and improve fleet management, utilizing its ORION system to analyze vehicle sensor data and GPS information. | Saved millions of gallons of fuel annually, reduced carbon emissions, and improved delivery times. | https://hbr.org/ (Accessed on 20 September 2024) |
Entertainment | Netflix | Netflix employs Big Data for content recommendations, tracking user preferences, viewing patterns, and ratings to curate personalized viewing experiences. | Increased subscriber retention, personalized user experiences, and higher content engagement rates. | https://towardsdatascience.com (Accessed on 20 September 2024) |
Industry | Roadmap Focus | Policy Framework | Strategic Link | Strategic Drivers | Expected Outcome | Ties to Proposed Study |
---|---|---|---|---|---|---|
Retail | Step 1: Adopt digital platforms such as e-commerce and CRM | EU Digital Strategy for SMEs | Improve digital competitiveness | Digital transformation and customer engagement | Increased market reach and improved customer satisfaction | Supports findings on digital tools improving SME performance |
Step 2: Implement data analytics for customer behavior insights | Enhance customer targeting through data | Data-driven innovation | Customized marketing campaigns, higher customer retention | Links to improved decision-making and customer insights | ||
Step 3: Transition to digital payments | EU Fintech Policy | Align with global e-payment trends | Digital payment systems | Improved transaction efficiency and trust | Reinforces tech adoption for operational efficiency | |
Step 4: Train workforce in data utilization | EU Skills Agenda | Boost workforce competency in data analytics | Upskilling and employee training | Enhanced data literacy and workforce productivity | Aligns with digital skills improvement in SMEs | |
Manufacturing | Step 1: Implement IoT for process automation | Industry 4.0 | Integrate advanced manufacturing technologies | IoT, automation, and smart manufacturing | Increased operational efficiency, reduced downtime | Aligns with findings on operational efficiency through tech adoption |
Step 2: Utilize predictive maintenance tools | Reduce equipment failure rates | Data analytics and predictive algorithms | Improved machine longevity and reduced maintenance costs | Reinforces predictive maintenance as a driver for performance | ||
Step 3: Incorporate AI in production decision-making | China’s Made in China 2025 | Enhance real-time decision-making capabilities | AI in manufacturing | Reduced decision time, optimized production processes | Links to AI-driven decision-making for SMEs | |
Step 4: Foster a culture of continuous improvement (Kaizen) | Lean Manufacturing Policy Framework | Emphasize ongoing process improvements | Continuous improvement | Increased innovation, reduced waste, improved product quality | Aligns with process optimization for SME growth | |
Healthcare | Step 1: Deploy AI-driven diagnostic tools | European Health Data Space | Improve diagnostics accuracy and reduce costs | AI in healthcare | Improved patient outcomes and reduced diagnostic errors | Supports health tech adoption to improve efficiency |
Step 2: Expand telemedicine capabilities | US SME Policy Act for Healthcare Innovation | Increase healthcare access to underserved populations | Remote healthcare solutions | Greater access to healthcare, improved patient satisfaction | Links telemedicine to improved service delivery | |
Step 3: Integrate wearable health monitoring devices | Enable real-time patient monitoring | IoT in healthcare | Continuous health monitoring, timely interventions | Reinforces IoT’s role in improving health outcomes | ||
Step 4: Train healthcare staff in digital tools | EU Health Workforce Skills Policy | Equip healthcare providers with the necessary digital skills | Upskilling and employee training | Enhanced workforce competency, improved patient interactions | Aligns with upskilling to support tech adoption in healthcare | |
Finance | Step 1: Strengthen cybersecurity measures | Basel III | Ensure data security in digital transactions | Cybersecurity and data protection | Increased trust in financial services, reduced fraud risks | Supports secure fintech integration for SME growth |
Step 2: Adopt blockchain for transaction transparency | African Continental Free Trade Area (AfCFTA) SME Policy | Facilitate secure, transparent transactions | Blockchain in finance | Improved transaction security, increased client trust | Links blockchain to enhanced financial performance | |
Step 3: Enhance accessibility to digital finance tools | Promote financial inclusion and access to credit | Digital finance adoption | Increased financial service access for SMEs, improved financial inclusion | Reinforces financial access as a growth driver for SMEs | ||
Step 4: Integrate AI for risk management | Automate risk assessment and fraud detection | AI in finance | Reduced financial risks, improved decision-making | Aligns with AI-driven innovation in finance | ||
Logistics | Step 1: Implement blockchain for supply chain transparency | EU SME Green Deal | Enhance transparency and sustainability in logistics | Blockchain in logistics | Improved visibility, enhanced supply chain transparency | Supports findings on blockchain’s role in enhancing supply chain efficiency |
Step 2: Optimize routes using Big Data analytics | US Infrastructure Investment and Jobs Act | Reduce operational costs through efficient route management | Data analytics for logistics | Reduced transportation costs, improved delivery times | Links Big Data to operational efficiency in logistics | |
Step 3: Adopt real-time tracking and fleet management systems | Improve asset utilization and tracking capabilities | IoT in logistics | Improved fleet efficiency, reduced downtime | Supports IoT adoption to improve logistics management | ||
Step 4: Train employees in digital tools for logistics management | Equip logistics workers with digital tools | Upskilling and employee training | Enhanced workforce productivity, improved logistics operations | Reinforces upskilling for digital transformation in logistics |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kgakatsi, M.; Galeboe, O.P.; Molelekwa, K.K.; Thango, B.A. The Impact of Big Data on SME Performance: A Systematic Review. Businesses 2024, 4, 632-695. https://doi.org/10.3390/businesses4040038
Kgakatsi M, Galeboe OP, Molelekwa KK, Thango BA. The Impact of Big Data on SME Performance: A Systematic Review. Businesses. 2024; 4(4):632-695. https://doi.org/10.3390/businesses4040038
Chicago/Turabian StyleKgakatsi, Mpho, Onthatile P. Galeboe, Kopo K. Molelekwa, and Bonginkosi A. Thango. 2024. "The Impact of Big Data on SME Performance: A Systematic Review" Businesses 4, no. 4: 632-695. https://doi.org/10.3390/businesses4040038
APA StyleKgakatsi, M., Galeboe, O. P., Molelekwa, K. K., & Thango, B. A. (2024). The Impact of Big Data on SME Performance: A Systematic Review. Businesses, 4(4), 632-695. https://doi.org/10.3390/businesses4040038