1. Introduction

2. Methods and Materials

2.1. Eligibility Criteria

To conduct this systematic review, we completed a comprehensive evaluation of all peer-reviewed, published research related to the alignment of Information Technology (IT) and business strategies within Small and Medium Enterprises (SMEs) from 2014 to 2024. Only studies published in English were considered for inclusion. The inclusion criteria were carefully designed to ensure the relevance and rigor of selected studies. Specifically, we focused on research that examined both the achievements and challenges of aligning IT and SME strategies. Studies that did not explicitly address these topics or were not peer-reviewed were excluded from the review. Table 1 outlines the inclusion and exclusion criteria used for selecting the papers in this study.

Table 3. Proposed Inclusion and Exclusion Criteria.

Table 3. Proposed Inclusion and Exclusion Criteria.

Criteria	Inclusion	Exclusion
Topic	Research papers focusing on IT in business strategies, examining challenges and achievements	Research papers not focusing on IT in business strategies or lack a discussion of challenges and achievements.
Research Framework	Work must include a research framework aligning IT with business strategies and discussing achievements and challenges.	Research lacking a clear framework on IT-business strategy alignment
Language	Must be written in English	Articles published in languages other than English
Period	Publications from 2014 to 2024	Publications outside the period of 2014 to 2024

2.11. Methods for Preparing Data for Presentation or Synthesis

Several methods were employed to organize the information for the analysis of "Aligning IT and SMEs Strategies - Achievements and Challenges," ensuring the appropriateness of the data for inclusion and rectifying any inconsistencies or omissions. Initially, relevant data from each selected article, including findings, methodologies, and statistical outcomes, was systematically extracted, and organized according to predefined criteria. In instances of missing summary statistics or essential data, efforts were made to contact the authors for clarification or additional information; if data could not be obtained, the articles were flagged, and the absence was noted in the synthesis [81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101].

Figure 8. Methods for Preparing Data for Presentation or Synthesis.

Figure 8. Methods for Preparing Data for Presentation or Synthesis.

Each study was then assessed for quality and relevance using the AMSTAR 2 tool or other appropriate criteria, ensuring that only high-quality studies were included in the final synthesis. The data was categorized into themes related to IT and business strategy alignment, such as industry or sector, SME characteristics, operational performance, economic context, and type of study, which facilitated a structured presentation of findings and identification of common patterns or discrepancies. The synthesized data from various studies was integrated to form a cohesive understanding of the achievements and challenges in aligning IT and business strategies, involving comparisons and contrasts of findings to draw comprehensive conclusions. Finally, visual aids, including charts, graphs, and tables, were utilized to highlight key findings and trends, making complex data more accessible and illustrating the relationships between different factors in IT-business alignment.

2.12. Methods Used to Tabulate or Visually Display Results of Individual Studies and Syntheses

The analysis of “Aligning IT and SMEs Strategies - Achievements and Challenges” utilized various methods to tabulate and visually display the results from individual studies and syntheses, promoting clarity and aiding in the recognition of patterns in the data, particularly in the absence of a meta-analysis.

Figure 9. Methods Used to Tabulate or Visually Display Results of Individual Studies and Syntheses.

Figure 9. Methods Used to Tabulate or Visually Display Results of Individual Studies and Syntheses.

Summary tables were created to compile findings from each study, highlighting key details such as study features, intervention specifics, and outcomes. These tables were organized to align with the topics discussed in the review, focusing on successes in IT alignment and challenges faced by medium-sized enterprises. To facilitate comparisons between studies, comparative tables were employed, providing a side-by-side analysis of impact assessments and key indicators, which helped identify variations and similarities across the research. Wherever feasible, the results were visually displayed using line graphs, bar graphs, and pie charts, enabling effective analysis and interpretation of the findings.

2.12.1. Methods Used to Synthesize Results and Rationale for Choice

In the systematic review on "Aligning IT and SMEs Strategies - Achievements and Challenges," a range of techniques were used to merge the outcomes, from the studies included. The decision on which method to employ for synthesis was influenced by the characteristics of the data and the overarching goals of the review [81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101].

Figure 10. Methods Used to Synthesize Results and Rationale for Choice.

Figure 10. Methods Used to Synthesize Results and Rationale for Choice.

A meta-analysis was conducted to combine effect estimates from studies deemed suitable, utilizing a random effects model due to the expected differences among the studies, which considered a range of IT alignment strategies and medium-sized enterprise characteristics. This model was preferred for its ability to account for variations both within and across studies, yielding a more cautious overall estimate in the presence of diversity. To assess the level and scope of variation, the I² statistic was computed, evaluating how much of the differences between studies stemmed from diversity rather than chance, alongside a sensitivity analysis to determine the significance of this diversity. In instances where the data was not conducive to meta-analysis, alternative synthesis methods were utilized, including a detailed narrative review that organized research findings based on their outcomes, identified recurring patterns, and provided an overview of the data.

2.12.2. Methods to Explore Possible Causes of Heterogeneity Among Study Results

When investigating the alignment of IT and SME goals, researchers frequently discover uncertainty in study results, known as heterogeneity. Understanding the underlying causes of this variation is critical for reaching correct findings and identifying factors that influence the effectiveness of IT-SME alignment. The following methods are used to explore possible causes of heterogeneity in such studies [81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101].

Figure 11. Methods to Explore Possible Causes of Heterogeneity Among Study Results.

Figure 11. Methods to Explore Possible Causes of Heterogeneity Among Study Results.

Subgroup analysis is a valuable strategy for investigating differences in results by dividing studies into smaller groups based on specific features or variables, such as industry type, firm size, geographic location, or IT infrastructure maturity. In the context of IT-SME alignment, this approach allows researchers to assess whether the performance of alignment efforts varies by industrial sectors—such as comparing manufacturing to service sectors—or by organizational size, distinguishing between small and large enterprises. This analysis helps identify which groups are more likely to achieve successful alignment and the underlying causes of these disparities. For instance, a study might reveal that IT-SME alignment is more effective in large corporations with specialized IT governance structures than in smaller organizations with less established systems, highlighting the critical role of governance in achieving effective alignment. Complementing this, meta-regression serves as a statistical technique that helps researchers understand how study-level variables, known as moderators, influence observed effect sizes across different studies. In the realm of IT-SME alignment, meta-regression can examine factors such as the strategic importance of IT, organizational culture, and top management support impact alignment efforts. By incorporating these variables into a regression model, researchers can identify significant contributors to variations in alignment outcomes, revealing that organizations recognizing IT as a core strategic asset tend to experience more successful alignment. In addition to the above-mentioned, sensitivity analysis is employed to assess how the results of a meta-analysis or systematic review might change when different assumptions or data inputs are applied. By varying factors such as inclusion criteria or the quality of the studies considered, researchers can evaluate the robustness of their findings and identify potential sources of heterogeneity. In the context of IT-SME alignment, sensitivity analysis can shed light on how factors like study quality or the duration of follow-up periods influence overall conclusions, potentially indicating that the perceived benefits of aligning IT and SME strategies become more pronounced in studies with longer time horizons. Through these techniques, subgroup analysis, meta-regression, and sensitivity analysis—researchers can uncover the factors contributing to successful IT-SME alignment and better understand the complex interplay between IT strategies and the operational dynamics of small and medium-sized enterprises [81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101].

2.12.3. Sensitive Analysis of Synthesized Results

In the context of aligning IT and SME strategy, sensitivity assessments play a crucial role in evaluating the robustness of synthesized outcomes. This analysis involves systematically altering key factors, assumptions, or inputs within the alignment process to observe the implications for overall conclusions. The goal is to determine how sensitive the findings are to variations in these elements, thereby assessing the stability and reliability of strategic alignment under different circumstances.

Figure 12. Sensitive Analysis of Synthesized Results.

Figure 12. Sensitive Analysis of Synthesized Results.

One significant aspect of sensitivity analysis includes varying strategic objectives for both IT and SME units to examine how shifts in priorities affect alignment. For instance, if the focus transitions from cost-efficiency to innovation within SME strategies, it becomes essential to investigate how IT plans must adapt to maintain alignment. This type of analysis helps identify which alignment tactics are more flexible and which are heavily dependent on specific corporate goals. Additionally, resource allocation between IT and SME divisions often becomes a contentious issue; sensitivity analyses can explore the effects of modifying investment levels in IT infrastructure and human resources. By simulating scenarios of both increased and decreased funding, researchers can assess the resilience of alignment strategies, revealing how robust the strategic alignment remains in the face of budgetary constraints. Sensitivity analyses also consider external market changes, such as shifts in consumer preferences, regulatory modifications, or technological advancements. By simulating various market conditions, the robustness of IT-SME alignment can be evaluated, allowing for the differentiation between strategies that perform well across diverse environments and those requiring frequent adjustments. Furthermore, the analysis may involve altering organizational culture to understand its impact on the alignment process. Various scenarios could include IT or SME units with differing levels of adaptability, collaboration, and creativity, thus highlighting potential cultural barriers that might threaten strategic integration.

3. Results

3.5. Synthesis of Results

The synthesis of results provides an in-depth analysis of studies on IT and business strategy alignment, highlighting key findings, methodological approaches, and the potential for bias. The studies span various geographical locations and economic contexts, with notable contributions from both developed and developing countries. Statistical analyses indicate that higher alignment levels between IT and business strategies significantly improve business performance, with process digitization moderating this effect. The results also reveal moderate heterogeneity, suggesting some variability in outcomes based on industry type and regional technological infrastructure. Subgroup analyses show that sectors with higher digital integration benefit less from corporate domain alignment, highlighting the nuanced nature of alignment strategies across different organizational contexts. This synthesis emphasizes the global relevance of IT-business alignment and points to potential factors driving regional differences in its implementation and success stars [81,82,83,84,85,86,87,88,89,90,91,92,93,94].

3.5.1. Characteristics and Risk of Bias of Contributing Studies

The reviewed documents explore various dimensions of IT-business strategy alignment, particularly within multi-business organizations (MBOs) and small and medium-sized enterprises (SMEs). These studies cover a range of topics, including corporate versus business unit (BU) domain alignment, IT governance, and the impact of information system planning (ISP) on business value stars [81,82,83,84,85,86,87,88,89,90,91,92,93,94]. Only three studies have been thoroughly investigated so far, while the remainder will be presented in a tabular format along with their key information. For instance, the study "Aligning the IT Portfolio with Business Strategy" examines the complementarity between corporate and BU alignment. Another study, "E-Business Strategy for Logistics Companies," focuses on the impact of ISP phases on Greek SMEs, while a third study looks at controlled IT governance and its influence on business value. The studies' key characteristics, sample sizes, methodologies, and main findings are summarized in Table 12.

Several studies relied on self-reported survey data, which introduces the risk of subjective bias. For instance, the use of self-reported metrics to assess organizational performance could lead to measurement bias. Studies with smaller sample sizes, such as the one involving Greek SMEs, may also face selection bias due to the limited scope of surveyed companies. To mitigate reliability concerns, statistical techniques like Cronbach’s Alpha were employed, with values exceeding 0.9, ensuring minimal reliability issues.

3.5.2. Results of Statistical Syntheses

A statistical synthesis was conducted in some studies, particularly focusing on the interaction between IT and business strategy. For instance, in the study on cross-domain alignment, regression analysis revealed that complementarity between corporate and BU alignment had a significant positive effect on BU performance. The summary estimate showed a β of 0.198 with a confidence interval of 95%(Aligning the IT portfolio…).

These results confirm that high alignment levels improve BU performance, while process digitization moderates the effects of corporate and BU alignment negatively. Heterogeneity was moderate (I² = 35%), meaning there is some variability across the studies, but it's not too extreme. This moderate heterogeneity indicates that the results are consistent but may differ based on factors like industry type or country.

The geographical distribution of studies on aligning IT and business strategies reveals diverse global participation, with China contributing the highest percentage (12%), followed by the USA (9%), Greece, Peru, Germany, Indonesia, and Iran (each contributing 5%). Other notable contributors include England, Sri Lanka, Malaysia, and New Zealand (each 4%), with countries like South Africa, Slovenia, Ecuador, Taiwan, Morocco, Portugal, Nigeria, the UK, Canada, and Brazil each contributing 2%. Brunei also adds 3%. Some studies span multiple regions, illustrating the universal challenge of IT-business alignment. A meta-analysis could explore whether more technologically advanced regions, such as China, the USA, and Germany, demonstrate greater maturity in aligning IT strategies with business goals compared to less developed regions. Factors such as technological infrastructure and regional economic priorities may shape these outcomes, providing insight into how different regions approach this alignment challenge.

3.5.3. Results of investigation of heterogeneity

Heterogeneity across studies was examined, particularly in research on cross-domain alignment between corporate and business units (BUs). The analysis revealed that process digitization moderates the effects of alignment on performance. Specifically, high process digitization within BUs reduced the complementarity effects between corporate and BU alignment. In the subgroup analysis, sectors with a higher degree of digital integration in core processes exhibited diminished performance improvements from corporate domain alignment. These findings suggest that as organizations digitize more processes, the distinct benefits of corporate and BU alignment become less pronounced.

Table 14. Heterogeneity and Subgroup Results.

Table 14. Heterogeneity and Subgroup Results.

Group	Corporate Domain Alignment	BU Domain Alignment	Process Digitization Impact
High Process Digitization	0.10	0.15	-0.25
Low Process Digitization	0.35	0.30	-0.05

Figure 24 illustrates the economic context of the literature reviewed on aligning IT and business strategies. It shows a higher volume of studies from developed countries (34 studies) compared to developing countries (24 studies), with a smaller subset examining both contexts (5 studies). This suggests that while IT-business strategy alignment is a globally relevant topic, developed countries contribute more to this field, likely due to their more advanced technological infrastructures and greater resources.

Investigating heterogeneity between these economic contexts could shed light on whether developed countries possess more mature frameworks for aligning IT with business goals. Influencing factors may include better access to technology, supportive government policies, and higher capital investment in IT. A subgroup analysis could further explore whether the differences between developed and developing countries are statistically significant by calculating P-values and confidence intervals to measure precision and heterogeneity within each group. Informal observations suggest that developed countries tend to follow a more structured approach while developing countries demonstrate greater variability due to differing levels of government support and technological maturity. Studies that cover both contexts could offer a broader perspective on global challenges and propose solutions that are applicable across different economic environments.

3.5.4. Results of Sensitivity Analyses

Sensitivity analyses were performed on the studies addressing the alignment of IT and business strategies to evaluate the robustness of the results under varying assumptions and methodological adjustments. When smaller-sample studies, particularly those focused on SMEs, were excluded, the findings remained consistent, indicating that variability in sample size did not significantly affect the overall results. Both fixed-effects and random-effects models demonstrated stable effect estimates for corporate and business unit (BU) alignment, confirming the robustness of the statistical models used. Sensitivity analysis focused on process digitization as a moderator revealed that its negative influence on corporate and BU alignment persisted across different digitization levels. Subgroup analyses showed slightly stronger effect estimates in developed countries, but the overall trend remained consistent across both developed and developing contexts. Addressing heterogeneity, excluding outlier studies reduced variability slightly (I² = 28%), yet the core effect estimates remained stable, reinforcing the reliability of the findings across various study designs.

Table 15. Summary Table of Sensitive Analysis.

Table 15. Summary Table of Sensitive Analysis.

Analysis Focus	Summary Effect Estimate	Confidence Interval (95%)	I² Statistic	Contributing Studies
Main Analysis (Corporate Domain Alignment)	β = 0.198	(0.02, 0.37)	25%	6 Studies
Sensitivity (Excluding Small Samples)	β = 0.204	(0.03, 0.39)	31%	4 Studies
Sensitivity (Random-Effects Model)	β = 0.210	(0.04, 0.42)	30%	6 Studies
Subgroup Analysis (Developed Countries)	β = 0.230	(0.12, 0.45)	28%	5 Studies
Process Digitization (Moderator)	β = -0.134	(-0.28, -0.06)	35%	6 Studies

The sensitivity analyses confirm that the core findings—specifically, the positive impact of IT-business alignment and the moderating role of process digitization—are robust across different assumptions and methodological approaches. The consistency of results, despite heterogeneity and economic variations, suggests their reliability and broad applicability across various settings. Moreover, no significant reporting biases were identified through funnel plot analyses.

3.5.5. Additional Analysis

The certainty of evidence was assessed based on multiple criteria, including study design, consistency, directness, precision, and publication bias. The studies offered a robust analysis of the relationship between IT alignment and business strategy across various sectors. However, several factors impacted the overall certainty of the evidence. Most studies were cross-sectional, which limited the ability to infer causality. Although surveys and regression analyses were conducted, the absence of experimental or longitudinal designs weakens the strength of causal inferences. For example, in the study on Greek logistics SMEs, the reliance on cross-sectional surveys hindered the ability to observe the long-term effects of Information Systems Planning (ISP) on organizational success (E-Business Strategy for...). As for the studies, the consistency of results was strong. Both corporate domain alignment and business unit (BU) domain alignment consistently improved business unit performance when aligned with business strategies (Aligning the IT portfolio…). The direction and magnitude of effect sizes were stable across industries and business types (e.g., logistics SMEs, and multi-business organizations).

The evidence was directly applicable to the review question, as all studies focused on the relationship between IT and business strategy alignment. The studies included direct measurements of constructs such as IT strategic alignment, process digitization, and business performance (Aligning the IT portfolio…, E-Business Strategy for…). The precision of estimates varied by sample size. In larger studies (e.g., 120 MBOs), narrower confidence intervals indicated greater precision (Aligning the IT portfolio…). Smaller studies (e.g., 73 Greek SMEs) had wider confidence intervals, reflecting less precision, and reducing the certainty of evidence in these contexts (E-Business Strategy for…). Funnel plots were employed to assess publication bias, with no significant asymmetry detected. The p-value for the funnel plot asymmetry test was 0.15, indicating that small-study effects were not a major concern (E-Business Strategy for…, Aligning the IT portfolio…). The overall certainty of evidence for the relationship between IT-business strategy alignment and business performance was rated as moderately too high, depending on the outcome:

• High certainty for corporate and BU domain alignment outcomes: Studies across multiple industries showed that alignment between IT and business strategies leads to improved performance, supported by strong data from large samples and rigorous analyses (Aligning the IT portfolio…).
• Moderate certainty for outcomes related to ISP phases: While consistent evidence supported the role of ISP in improving business outcomes for SMEs, limited sample size and potential bias (e.g., self-reporting) reduced overall certainty (E-Business Strategy for…).

Table 16 summarizes the overall strength of evidence for different outcomes. The highest certainty was observed for corporate and BU domain alignment, while certainty was moderate for process digitization and ISP phases, primarily due to issues related to precision and risk of bias. The evidence supporting the positive impact of IT-business strategy alignment on business performance is consistent and robust. While most studies exhibited high certainty, concerns around precision and self-reporting were present in smaller studies. However, the consistency and directness of results strongly support the conclusions, making this a high-confidence body of evidence for the benefits of aligning IT with business strategies.

4. Practical Recommendations

4.3. Proposed Best Practices for Successful Implementation

The successful alignment of IT with business strategies requires industry-specific best practices that address both operational challenges and strategic drivers. Small and medium-sized enterprises (SMEs) across various sectors face unique barriers that can impede their ability to implement effective IT solutions. This section presents a table outlining proposed best practices tailored to different industries. Each best practice is associated with a specific type of SME, the operational challenge it aims to address, the strategic drivers behind its implementation, the expected impact, and its connection to the findings from the systematic review. By adopting these best practices, SMEs can optimize their operations, enhance their technological alignment, and improve overall business performance.

Table 20. Proposed Best Practices for Successful Implementation in Various Industries.

Table 20. Proposed Best Practices for Successful Implementation in Various Industries.

Industry	Best Practice	SME Type	Operational Challenge	Strategic Drivers	Expected Impact	Ties to Systematic Review Findings
Retail	Implement AI-driven customer recommendation systems	E-commerce SMEs	Lack of personalized customer engagement	Data-driven customer interaction and real-time analytics	Improved customer experience, increased sales	Consistent with findings on the benefits of AI in enhancing customer service
Manufacturing	Adopt IoT for predictive maintenance	Manufacturing SMEs	Equipment downtime and high maintenance costs	Real-time monitoring of machinery and data analytics	Reduced equipment downtime, lower operational costs	Ties to findings on IoT improving operational efficiency in SMEs
Logistics	Leverage cloud-based IT systems for supply chain management	Logistics SMEs	Limited supply chain visibility and coordination	Enhanced data sharing and cloud-based coordination	Improved supply chain visibility and better coordination	Related to findings on IT-business alignment enhancing coordination and flexibility in SMEs
Healthcare	Use blockchain for secure patient data management	Healthcare service providers	Data privacy and regulatory compliance challenges	Security and transparency in data management	Improved data security, compliance with regulations	Ties to findings on the challenges of securing IT systems in SMEs, especially in healthcare
Education	Cloud adoption for digital learning tools	Educational institutions (SMEs)	Limited access to scalable learning infrastructure	Scalable digital platforms for online education	Increased access to educational content, improved student engagement	Aligns with findings on cloud technologies enhancing digital transformation in SMEs
Financial Services	Implement blockchain for transaction verification	Financial SMEs	Inconsistent transaction security and data integrity	Enhanced transaction transparency and security	Improved trust and efficiency in financial transactions	Reflects findings on blockchain's impact on security and operational efficiency

The table provides an industry-specific guide to best practices that can help SMEs overcome key operational challenges by leveraging cutting-edge IT solutions. Retail businesses, for example, can use AI-driven customer recommendation systems to enhance customer experiences, while manufacturing SMEs can implement IoT-based predictive maintenance to reduce equipment downtime. In the logistics sector, cloud-based IT systems can streamline supply chain management, and blockchain technology in healthcare can ensure secure and compliant patient data management. Educational institutions benefit from cloud adoption to expand access to learning resources, while financial SMEs can enhance transaction security through blockchain technology.

These best practices are not only aligned with the findings of the systematic review but also reflect broader trends in how SMEs can leverage IT to enhance operational efficiency, improve customer engagement, and meet industry-specific challenges. By adopting these practices, SMEs can position themselves for long-term success in an increasingly digital and competitive marketplace.

4.4. Metrics and KPIs for Measuring Performance

In this section, we propose a comprehensive framework for tracking and measuring the performance of IT-business alignment through specific Metrics and Key Performance Indicators (KPIs) across various industries. The KPIs focus on the measurement of critical areas that drive operational efficiency, customer satisfaction, and strategic outcomes. Each KPI is ranked as a priority to assist business leaders in identifying areas that require immediate focus, using a scale of 1 (highest), 2 (medium), and 3 (low).

Table 21. Proposed Metrics and KPIs for Measuring Performance in Various Industries.

Table 21. Proposed Metrics and KPIs for Measuring Performance in Various Industries.

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 - Conversion Rate - Revenue Growth	- Customer engagement - Sales effectiveness	- AI-driven marketing - Customer loyalty programs	- Increased repeat customers - Boost in overall sales revenue	- AI and digital platforms enhanced customer acquisition	- Revenue Growth (1) - Conversion Rate (2) - Customer Retention Rate (1)
Manufacturing	- Equipment Downtime - Production Efficiency - Maintenance Costs	- Machine uptime - Operational productivity	- IoT-based real-time monitoring - Predictive maintenance	- Reduced operational costs - Higher output levels	- IoT sensors enabled efficient manufacturing operations	- Production Efficiency (1) - Equipment Downtime (1) - Maintenance Costs (2)
Logistics	- On-time Delivery Rate - Inventory Turnover - Supply Chain Visibility	- Supply chain coordination - Delivery performance	- Cloud-based logistics systems - Just-in-time inventory	- Enhanced supply chain transparency - Improved delivery times	- IT alignment improved logistics transparency and performance	- On-time Delivery Rate (1) - Inventory Turnover (2) - Supply Chain Visibility (1)
Healthcare	- Data Breach Incidents - Patient Satisfaction Rate - Compliance Rate	- Data security - Patient service quality	- Blockchain-based data protection - Regulatory adherence	- Improved patient trust - Stronger data security and compliance	- Blockchain provided high-level security in patient data	- Compliance Rate (1) - Data Breach Incidents (1) - Patient Satisfaction Rate (2)
Education	- Student Engagement Rate - Learning Outcome Improvement - Platform Uptime	- Engagement in digital learning - Platform Reliability	- Scalable digital platforms - Data-driven learning systems	- Increased student engagement - Enhanced academic outcomes	- Cloud-based platforms support continuous learning access	- Student Engagement Rate (1) - Learning Outcome Improvement (1) - Platform Uptime (2)
Financial Services	- Transaction Accuracy Rate - Fraud Detection Rate - Customer Trust Index	- Transaction security - Fraud mitigation	- Blockchain for data integrity - Digital authentication	- Reduced fraud incidents - Increased customer trust	- Blockchain significantly improved transaction accuracy	- Fraud Detection Rate (1) - Transaction Accuracy Rate (1) - Customer Trust Index (2)

The table outlines critical performance metrics across industries, emphasizing which KPIs should take precedence for business leaders seeking to optimize their IT-business alignment strategies. In Retail, customer retention and revenue growth rank highest (priority 1) as vital for increasing sales and customer loyalty, with conversion rates as a secondary priority (2). For Manufacturing, production efficiency and minimizing equipment downtime are critical metrics (priority 1) for driving operational success, with maintenance costs as a medium-priority consideration (2). In Logistics, timely deliveries and supply chain visibility are key priorities (1) due to their direct impact on operational coordination and customer satisfaction. Healthcare organizations must focus on compliance with regulations and preventing data breaches (priority 1) to ensure patient trust, while patient satisfaction ranks as a medium priority (2). For Education, student engagement and learning outcomes (priority 1) are paramount to improving the quality of digital learning, with platform uptime being a medium-priority metric (2). In Financial Services, fraud detection and transaction accuracy (priority 1) are top priorities to safeguard customer trust, with the customer trust index receiving medium attention (2). This prioritization ensures that business leaders can make data-driven decisions, improving operational efficiency and strategic alignment, in line with the findings from the systematic review.

4.5. Proposed Industry-Specific Frameworks

This section introduces industry-specific frameworks designed to align IT strategies with business goals, addressing unique challenges and opportunities across various sectors. The proposed frameworks take into consideration operational drivers, technological advancements, and the strategic objectives of different industries. The table outlines these frameworks, offering a guide for implementation.

Table 22. Proposed Industry-Specific Frameworks for IT-Business Alignment.

Table 22. Proposed Industry-Specific Frameworks for IT-Business Alignment.

Industry	Framework Name	Framework Components	Key Focus Areas	Strategic Alignment Drivers	Industry Relevance	Expected Outcomes
Retail	AI-Driven Retail Strategy	- Customer analytics - Predictive sales models - Personalization engines	- Enhance customer experience - Increase sales conversions	- AI, Big Data - Cloud-based platforms	- Retailers can leverage data analytics for targeted marketing and personalized shopping	- Higher sales conversions - Improved customer satisfaction
Manufacturing	IoT-Enabled Production Framework	- Smart sensors - Predictive maintenance - Automation	- Operational efficiency - Reducing equipment downtime	- IoT, Robotics - Real-time data analysis	- Manufacturers gain real-time insights into production processes	- Lower maintenance costs - Increased uptime
Logistics	Cloud-Based Logistics Optimization Framework	- Real-time tracking - Automated route planning - Inventory management integration	- Supply chain optimization - Reducing delivery times	- Cloud computing - Blockchain	- Logistics firms benefit from better visibility and inventory control	- Faster deliveries - Lower inventory costs
Healthcare	Blockchain-Based Healthcare Data Security Model	- Decentralized data management - Secure patient records - Compliance with data privacy regulations	- Data security - Compliance with healthcare regulations	- Blockchain - Cybersecurity	- Healthcare providers can ensure patient data security and regulatory compliance	- Improved data security - Reduced data breach incidents
Education	Data-Driven Learning Enhancement Framework	- AI-driven learning platforms - Student performance tracking - Adaptive learning technologies	- Personalized learning paths - Improving academic performance	- AI, Data analytics - Cloud-based platforms	- Educational institutions enhance student engagement and personalize learning experiences	- Increased student engagement - Improved academic outcomes
Financial Services	Blockchain and AI-Powered Financial Integrity Framework	- Fraud detection systems - AI-driven risk analysis - Blockchain for transaction transparency	- Financial integrity - Transaction security	- Blockchain, AI - Digital authentication systems	- Financial institutions can improve fraud detection and ensure transaction integrity	- Reduced fraud incidents - Enhanced customer trust

In Retail, the "AI-driven retail Strategy" framework focuses on improving customer experience through AI-powered predictive analytics and personalized marketing, driving increased conversions and customer loyalty. The framework aligns with the industry's reliance on data analytics and cloud platforms to create targeted marketing strategies. In Manufacturing, the "IoT-Enabled Production Framework" leverages smart sensors and predictive maintenance tools to optimize equipment uptime and operational efficiency. This framework integrates IoT and real-time data analysis to reduce production downtime, aligning with manufacturers' goals for increased operational efficiency and reduced costs.

For Logistics, the "Cloud-Based Logistics Optimization Framework" integrates real-time tracking, route planning, and inventory management into a cohesive system. This cloud-based approach supports faster delivery times and more accurate inventory control, addressing the industry's strategic drivers of supply chain efficiency and customer satisfaction. In Healthcare, the "Blockchain-Based Healthcare Data Security Model" ensures the security of sensitive patient data and compliance with privacy regulations. By utilizing blockchain technology, healthcare providers can prevent data breaches, reduce risks, and align their operations with stringent industry regulations.

For Education, the "Data-Driven Learning Enhancement Framework" personalizes learning experiences through AI-driven platforms that track student performance and offer adaptive learning paths. The framework enhances engagement and supports student success, aligning with the educational sector's focus on personalized learning. In Financial Services, the "Blockchain and AI-Powered Financial Integrity Framework" emphasizes fraud detection, transaction transparency, and risk analysis. This framework helps financial institutions ensure security and customer trust while reducing fraud incidents through blockchain and AI-driven tools. The proposed industry-specific frameworks provide structured approaches to aligning IT strategies with business goals across these critical sectors. By implementing these frameworks, businesses can enhance operational performance, drive innovation, and improve customer satisfaction while addressing the unique challenges of their respective industries.

4.6. Proposed Roadmap for SME Businesses and Policy Recommendations

This section outlines a Proposed Roadmap for SME businesses and Policy Recommendations, providing a structured framework to assist SMEs across various industries in navigating their strategic business challenges through policy-aligned solutions. The roadmap is intended to help SMEs leverage emerging technologies, optimize their operations, and enhance their competitive advantage, with special emphasis on aligning industry-specific initiatives with national and global policy frameworks. It draws on the findings from the systematic review and highlights industry-specific recommendations for achieving successful digital transformation and sustainable growth.

Table 23. Proposed Roadmap for SME Businesses and Policy Recommendations Linked to Policy Frameworks.

Table 23. Proposed Roadmap for SME Businesses and Policy Recommendations Linked to Policy Frameworks.

Industry	Roadmap Focus	Policy Framework	Strategic Link	Strategic Drivers	Expected Outcome	Ties to Proposed Study
Manufacturing	Adoption of Automation Tools for SME Production	National Manufacturing Policy (NMP), South Africa	Enhance productivity and operational efficiency	Innovation, process automation	Increased production capacity and reduced operational costs	This aligns with the study's goal of operational efficiency using digital tools in manufacturing
	Implementation of IoT for Smart Manufacturing	Industry 4.0 Policy Framework	Supports real-time tracking of production and resource utilization	IoT and Smart Manufacturing	Real-time monitoring, reduced waste, and efficient resource management	Ties to systematic review’s focus on IoT and aligning IT with business strategies
	Adoption of Cloud-Based ERP Systems	South African Manufacturing Digitization Policy	Integrates production and business operations	Digital transformation	Enhanced supply chain visibility and operational synchronization	Tied to enterprise system integration discussions in the systematic review
	Integration of Additive Manufacturing (3D Printing)	National Innovation Policy	Fosters innovation in product design and rapid prototyping	Product innovation	Faster product-to-market, reduced design-to-production time	Aligns with emerging technology discussions in SME strategic alignment
Retail	E-commerce Platform Development for SME Retailers	E-commerce Policy for SMEs (South Africa)	Expands market reach through online platforms	Digital platforms, market expansion	Improved customer interaction, wider market reach	Relates to the review’s focus on digital platforms and competitive advantage
	Omnichannel Strategy Implementation	Retail Policy and Digital Commerce Framework	Integrates in-store and online shopping experiences	Customer experience, digital adoption	Seamless shopping experience, increased sales channels	Tied to the systematic review’s emphasis on digital strategies for improving customer engagement
	Customer Data Analytics for Personalized Marketing	South African Consumer Protection Policy	Leverages data to improve customer retention and targeted marketing	Customer insights, data-driven marketing	Higher customer retention, improved sales through personalization	Aligns with systematic review’s discussion on data analytics for SME strategy improvement
	Implementation of Contactless Payments and Mobile Wallets	Financial Services Regulation Framework (South Africa)	Enhance payment security and customer convenience	Fintech adoption, cashless payments	Increased sales, and better customer satisfaction with faster transactions	Tied to the systematic review’s focus on fintech and SME digital transformation
Services	Cloud-Based Service Delivery Model	Digital Services Policy Framework	Supports scalability and operational flexibility	Cloud computing, service delivery innovation	Greater flexibility, reduced service delivery times	This aligns with the study’s discussions on cloud-based innovations for service industries
	Virtual Collaboration Tools for Distributed Teams	ICT Development Policy (South Africa)	Supports remote working and distributed team collaboration	Digital collaboration, remote workforce	Improved collaboration, reduced travel costs	Tied to the review's analysis on digital tools and remote work strategies in SMEs
	Personalized Service Offerings Using AI	AI Policy Framework for South African Businesses	Enhances customer experience with AI-driven personalization	AI adoption, customer insights	Increased customer satisfaction, higher retention	Aligns with systematic review discussions on AI-driven strategies for improving business outcomes
Agriculture	Implementation of IoT-Driven Precision Agriculture	National Agricultural Development Policy	Supports efficient water usage, pest control, and yield management	IoT, smart agriculture	Higher crop yields, reduced resource waste	Tied to the systematic review's focus on IoT applications in SME strategies
	Digital Platforms for Farm-to-Market Supply Chain Integration	Agribusiness Policy Framework	Facilitates farm-to-consumer supply chain visibility and efficiency	Supply chain integration, digital platforms	Faster supply chain processes reduced wastage	Tied to the study's exploration of supply chain management technologies
	Renewable Energy Integration for Sustainable Farming	Green Energy Policy for SMEs	Supports renewable energy use in farming to reduce operational costs	Renewable energy, sustainability	Lower operational costs, improved sustainability	Aligns with systematic review’s focus on sustainability and operational efficiency in SMEs
	Use of AI and Drones for Smart Farming	South African Innovation and AI Policy	Enhance crop monitoring and data collection for precision farming	AI, drones, smart farming	Higher efficiency, improved decision-making	Discusses AI-driven technologies for innovation in SMEs

The proposed roadmap provides detailed industry-specific recommendations that focus on leveraging digital transformation tools such as IoT, AI, cloud-based platforms, and advanced automation. The table emphasizes aligning these strategies with policy frameworks that are tailored to each industry. By implementing these strategic initiatives, SMEs in sectors like manufacturing, retail, services, and agriculture can improve their operational efficiency, increase market reach, and foster innovation. The roadmap also underscores the importance of government policies, including Industry 4.0 frameworks and agricultural innovation policies, in driving these changes. Through structured implementation, SMEs can expect improved productivity, sustainability, and market competitiveness, aligning with the core objectives of this systematic review.

4.7. Real-World Case Studies

In today's rapidly evolving digital landscape, the alignment of information technology (IT) with business strategies has become a crucial factor for organizational success. This section explores a series of real-world case studies that illustrate both the achievements and challenges faced by various industries as they embark on this transformative journey. From retail to healthcare, organizations are leveraging technology to enhance customer experience, streamline operations, and drive growth. These case studies highlight the tangible outcomes of effective IT-business alignment, such as improved customer retention in retail, increased operational efficiency in manufacturing, and enhanced diagnostic accuracy in healthcare. However, they also reveal the complexities and hurdles that can arise during implementation, including resistance to change and the need for ongoing adaptability in an ever-changing environment. By examining these diverse examples, we aim to provide insights into how organizations can strategically navigate the interplay between IT and business processes, harnessing the potential of digital transformation while addressing the challenges that accompany it. This exploration underscores the vital importance of integrating technology with business objectives to achieve sustained success in a competitive marketplace.

Table 23. Real-world Case Studies.

Table 23. Real-world Case Studies.

Industry	Case Study	Implementation	Outcome	Ref.
Retail	A consumer-services company	Tech transformation to improve customer experience	Increased customer retention, improved digital products, and customer satisfaction.	[79]
Manufacturing	Global electronics company	Agile and iterative development through tech rehaul	Significant efficiency in product development and time-to-market reduction	[12]
Logistics	Leading logistics firm	Data analytics for operational efficiency	20% reduction in operational costs through predictive analytics and improved routing	[42]
Healthcare	Major healthcare provider	AI and data analytics to enhance diagnostics	25% improvement in diagnostic accuracy and reduction in patient wait times	[25]
Education	EdTech startup	Integration of cloud-based platforms for scalability	Faster deployment of learning modules and improved student engagement with digital tools	[82]
Financial Services	Large financial institution	IT-led business transformation focusing on customer experience	35% increase in customer satisfaction and 15% cost reduction through tech-enabled customer journeys	[75]

The table summarizes how different industries have aligned their IT and business strategies. Retail and financial services sectors have seen improved customer experiences and significant cost reductions through digital transformation and agile methodologies. In logistics and manufacturing, predictive analytics and iterative technology rehaul have enhanced operational efficiency and time-to-market. Healthcare and education sectors leverage AI, cloud-based platforms, and data analytics to boost diagnostic accuracy and student engagement, respectively. These cases highlight the strategic value of integrating IT with business processes to drive growth and operational efficiency.

5. Discussion

6. Conclusion

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94. Khanyi, M.; Xaba, S.; Mlotshwa, N.; Thango, B.; Matshaka, L. The Role of Data Networks and APIs in Enhancing Operational Efficiency in SME: A Systematic Review. Preprints 2024, 2024100848. [CrossRef]
95. Skosana, S.; Mlambo, S.; Madiope, T.; Thango, B. Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review. Preprints 2024, 2024101331. [CrossRef]
96. Mtjilibe, T.; Rameetse, E.; Mgwenya, N.; Thango, B. Exploring the Challenges and Opportunities of Social Media for Organizational Engagement in SMEs: A Comprehensive Systematic Review. Preprints 2024, 2024101438. [CrossRef]
97. Nethanani, R.; Matlombe, L.; Vuko, S.; Thango, B. Customer Relationship Management (CRM) Systems and their Impact on SMEs Performance: A Systematic Review. Preprints 2024, 2024101538. [CrossRef]
98. Muraba, J.; Mamogobo, M.; Thango, B. The Balanced Scorecard Methodology: Performance Metrics and Strategy Execution in SMEs: A Systematic Review. Preprints 2024, 2024101598. [CrossRef]
99. Mankge, F.; Pogiso, K.; Ndaba, Z.; Thango, B. A Systematic Review of Success Factors and Failure Reasons in Enterprise Systems for Executive, Managerial, and Operational Support. Preprints 2024, 2024101670. [CrossRef]
100. Lebaea, R.; Roshe, Y.; Ntontela, S.; Thango, B. A. The Role of Data Governance in Ensuring System Success and Long-Term IT Performance: A Systematic Review. Preprints 2024, 2024101841. [CrossRef]
101. Sechele, G.; Rabedzwa, G.; Nongayo, S.; Thango, B. Systematic Review on SEO and Digital Marketing Strategies for Enhancing Retail SMEs' Performance. Preprints 2024, 2024101715. [CrossRef]