1. Introduction

2. Materials and Methods

2.3. Search Strategy

The process of conducting a systematic review, beginning with the formulation of research questions, is depicted in Figure 3. These inquiries support the selection of a research approach, including organizing an SLR (Systematic Literature Review), defining inclusion and exclusion criteria, choosing research sources, and selecting search terms. The process then moves on to selecting research articles via a search and assessment process, which is essential to guaranteeing references of the highest calibre. The next step involves taking data out of the selected research articles, with an emphasis on assessing data integrity. In order to answer the research questions and wrap up the research process, data must be compiled during the execution phase of the research data.

To find studies that provided characteristics on functional systems, assistance for managers, and support for employees and SMEs, search keywords were developed specifically for this systematic review. A duplicative process of exploratory searches was used to choose index key-terms. Terms that failed to produce studies meeting the inclusion requirements were eliminated. The comparable terms of the recommended phrases were determined in order to make sure that they included the equivalent terms of the core keywords. Matchable terms were found using synonyms; for example, "Functional Systems" might also mean "Support Systems." We got, "Small and Medium Enterprises" for "SMEs" and "Workers" for "Managers". The code string was ("Enterprise Resource Planning" OR ERP) AND ("Customer Relationship Management" OR CRM) AND ("Human Resource Management" OR HRM) AND ("Small and Medium Enterprises" OR SME OR SMEs) AND (Manager* OR Employee*) AND (Support OR Satisfaction OR Efficiency OR Performance) which had the use of AND & OR, based on the research title: Functional Systems and Their Support for Managers and Workers in SMEs. The logic "OR" operator encompasses any of the selected terms, while the logic "AND" operator includes all of the selected keywords. The code string helped us finding 17800 research articles in Google scholar from 2014-2024 in 0.40 seconds.

Table 3. Search Terms Used in SLR.

Table 3. Search Terms Used in SLR.

Search Terms	Data Bases	Fields
Enterprise Resource Planning OR ERP AND Customer Relationship Management OR CRM AND Human Resource Management OR HRM AND Small and Medium Enterprises OR SMEs AND Manager OR Employee AND Support OR Satisfaction OR Efficiency OR Performance	SCOPUS Google Scholar Web of Science	Title, Abstract Keywords

2.6. Data Items

The purpose of this Systematic Literature Review is to compile data that explores how big data affects small and medium-sized businesses (SMEs). To ensure a thorough understanding of the subject, the results are categorized based on the research questions.

2.6.1. Data Collection Method

The outcomes for which the data were searched are listed and defined in this part. These outcomes include performance gains, cloud computing adoption, and the operational performance of SMEs. All relevant results, covering a range of time points, techniques, and analyses, that were compatible with these metrics were sought for each outcome area. Based on predefined criteria, a systematic review was utilized to highlight the most appropriate and dependable data when many outcomes were convenient inside the same domain. This made sure that each outcome's analysis provided a methodologically sound and comprehensive picture.

The following are the main factors that are used to evaluate how ERP, CRM, and HRM systems affect the functionality of SMEs' systems. The most crucial components, methods, and findings that establish the effectiveness of those functional systems' use are shown in the figure.

2.6.2. Variable Data Collection

The characteristics of the participants as well as the specifics of the intervention are defined and listed in this section together with all other variables for which information were searched. Additionally, factors like the study design were well documented. Certain information was acquired to fill in the blanks, when necessary, either based on reasonable information gathered from the information that was accessible or by following standard procedures from the reference. To guarantee clarity and minimize the effect of missing or inconsistent data on the study, the collected data was expressed explicitly.

Table 4. Data Items with their Descriptions.

Table 4. Data Items with their Descriptions.

Fields	Description
Title	The name of the research work or publication.
Year	The year the research was published.
Online Database	The database where the research is indexed (e.g., Google Scholar, SCOPUS).
Journal Name	The name of the journal or publication where the research papers.
Research Type	The format of the research paper (e.g., journal article conference paper)
Discipline or subject area	The academic field or area of study (e.g., functional systems, management support).
Industry Context	The industry or sector where the research is applied (e.g., SMEs, startups).
Geographic Location	The geographic area where the research was conducted or is focused.
Economic Context	The economic environment addressed by the research (e.g., developed vs developing countries)
Type of Functional Systems	The types of systems discussed (e.g., financial management, supply chain management).
Technology Providers	The companies or platforms providing the technology discussed (e.g., SAP, Oracle).
Technology Implementation Model	The model used for implementing (e.g., on-premises, cloud-based).
Research Design	The approach used in the research (e.g., experimental, case study).
Type of Study	The nature of study (e.g., quantitative, qualitative, mixed methods).
Sample Size	The number of participants or units in the study.
Sample Characteristics	The nature of the sample (e.g., SMEs, managers).
Data Collection Methods	Techniques used to gather data (e.g., surveys, interviews).
Data Analysis Techniques	Methods used to analyze the collected data (e.g., statistical analysis, thematic analysis).
Managerial Support Metrics	Measures of support provided to management (e.g., decision-making efficiency)
Worker Support Metrics	Measures of support provided to management (e.g., decision-making efficiency)
Business Performance Metrics	Indicators of business performance (e.g., task efficiency).
	Indicators of business performance (e.g., revenue growth, cost savings).
Organizational Outcomes	Results related to organizational effectiveness (e.g., employee satisfaction).
Long-term Impacts	The enduring effects of the research outcomes (e.g., competitive advantage, business sustainability).

2.9. Synthesis Methods

We used a methodical approach to find high-quality studies to compile the research required for our systematic literature review about the effects of customer resource management, enterprise resource management, and human resource management on the performance of small and medium-sized enterprises (SMEs). As shown in Figure 5, the overall tasks and the synthesis processes are important. The crucial elements in maintaining the integrity and relevance of the study's conclusions are the additional procedures used throughout the review. To make sure that we included important, high-quality studies, we followed a methodical approach when gathering the research for our systematic literature review on the effects of customer resource management, enterprise resource management, and human resource management on the performance of small and medium-sized enterprises (SMEs) [110,111,112,113,114,115,116,117,118,119].

Included are the qualifying requirements, data preparation for synthesis, data accumulation and presentation, and data synthesis. In addition, we examined the causes of research result heterogeneity and performed a sensitivity analysis to evaluate the reliability of our synthesized data. Figure 6.

2.9.1. Selection of Studies and Eligibility for Synthesis

To determine how well the research matched our main themes and conclusions about ERP, CRM, HRM, and SMEs, we examined their features. Papers having multiple citations and those directly connected to our issue were given preference in our selection criteria. To understand the goals and methods of each study, we carefully read the abstracts. We searched for research that used techniques to investigate how functional systems affect SMEs. Literature evaluations were left out to maintain the reliability of our sources. Includes only sources' peer-reviewed articles. We avoided publications that testified to the reliability and authority of the studies in our review by employing online filters. The research that was chosen for examination included theses, dissertations, conference papers, journals, articles, and articles published between 2014 and 2024.

2.9.2. Selecting Which Syntheses Are Eligible

Based on their objectives and the results they assessed, studies were categorized into several synthesis groups. Research on how functional systems affect managerial decision-making, for example, was combined with studies on worker task efficiency, which were synthesized separately. This strategy minimized the possibility of bias and improved the validity of the results by ensuring that only comparable studies were included in each synthesis.

2.9.3. Preparing Data for Synthesis and Display

An attempt was made to get the missing data for studies that lacked summary statistics (such as missing means or standard deviations) by getting in touch with the authors. The study was not included in any quantitative meta-analysis; instead, it was incorporated into the narrative synthesis in the event that data could not be collected. Data conversions were carried out using accepted statistical procedures to permit inclusion in quantitative synthesis in studies that reported medians and interquartile ranges rather than means and standard deviations. When needed, effect sizes like odds ratios, risk ratios, and hazard ratios were transformed into a standard metric (such standardized mean difference) to make comparing results between studies easier. This was especially crucial for research utilizing various scales or methodologies to measure comparable outcomes [110,111,112,113,114,115,116,117,118,119].

2.9.4. Synthesis Procedures

The study findings were synthesized in an organized way using a set of criteria arranged in an Excel spreadsheet. With an emphasis on characteristics like the title, publication year, and source databases (such Google Scholar, SCOPUS, and Web of Science), this organized strategy assisted in comparing and assessing various elements of the studies included. Table 7 shows the number of studies for each of these databases. Journal name, research kind (article journal, conference paper, book chapter, dissertation/theses), and citation count were used to categorize the studies [110,111,112,113,114,115,116,117,118,119].

Table 8. Results Obtained from Literature Search.

Table 8. Results Obtained from Literature Search.

No.	Online Repository	Number of Results
1	SCOPUS	37
2	Google Scholar	51
3	Web of Science	20
Total		108

2.9.5. Exploration of Heterogeneity Causes

Many factors contribute to the observed variety in results when examining the heterogeneity of findings among studies from 2014 to 2024 on ERP, CRM, and HRM systems for SMEs with an emphasis on managerial and staff assistance. The varying sizes and industries of SMEs are a primary source of heterogeneity, as they have an important effect on system outcomes, uptake, and implementation success. For instance, ERP systems may provide different levels of assistance to manufacturing SMEs than to service-based SMEs, where CRM systems are more important. The availability of trained personnel and regional IT infrastructure are two examples of geographic factors that influence the efficacy and user satisfaction levels.

Some systematic reviews have used subgroup analyses to separate findings depending on company size, industry type, and region to investigate these reasons of heterogeneity. Furthermore, there are differences in the software integration capacities and customization choices amongst various systems (CRM, HRM, ERP), which leads to inconsistent results concerning usability and performance impact. Further adding to the variation are studies that go further into the employee perspective and frequently describe varying experiences with system usability, training, and engagement.

2.9.6. Analysis of Sensitivity

For this systematic review, both quantitative and qualitative synthesis methods were employed to ensure a comprehensive analysis of ERP, CRM, and HRM systems in SMEs. Where sufficient data were available, a meta-analysis was conducted using a random-effects model to account for heterogeneity in study designs, SME sizes, and system implementations. This allowed for a more robust quantitative synthesis of key outcomes, such as improvements in managerial decision-making and employee productivity. In cases where meta-analysis was not feasible, a thematic analysis was applied to qualitative data, identifying common themes like "decision-making improvements," "barriers to adoption," and "employee support." This combined approach strengthens the overall impact of the findings by providing both statistical insight and deeper thematic understanding [110] – [119].

Figure 7. Synthesis Procedures.

Figure 7. Synthesis Procedures.

The synthesis procedures included preparing data for analysis, classifying research by objectives, and choosing studies based on qualities. Based on research compatibility, narrative synthesis or meta-analysis was used, with sensitivity and subgroup analyses guaranteeing solid, objective conclusions.

3. Results

3.1. Study Selection

The method used to choose research papers is depicted in Figure 7 below. Three databases yielded a total of 108 research papers, the distribution of which was shown in percentages. Most of the papers 47 % came from Google Scholar, Web of Science (34%), and SCOPUS (19%). To make sure that only relevant studies were included in the review's final analysis, these papers were chosen based on inclusion and exclusion criteria.

Figure 9. Distribution of Online Data Sources.

Figure 9. Distribution of Online Data Sources.

3.2. Study Characteristics

This study comprised 108 studies ranging from 2014 to 2024. The majority of these were journal papers (77 in total), with a significant increase in publications in 2023 and 2024. Many of these publications were obtained via SCOPUS and Google Scholar, with fewer coming from Web of Science. In addition to journal articles, other publishing types included conference papers (8), research articles (15), and a smaller number of editorial materials, systematic reviews, study protocols, and theses. The studies were gathered from three key databases: Google Scholar, SCOPUS, and Web of Science, with Google Scholar accounting for the highest share across all types. SCOPUS had the most presence in conference papers and research articles, especially in recent years. The significant increase in publications between 2023 and 2024 indicates the field's rising research focus, with more recent papers dominating the dataset. This rising trend demonstrates growing interest and evolving research contributions from a variety of experts and organizations.

Table 11. Distribution of Conference Papers and Journal Articles by Publication Year.

Table 11. Distribution of Conference Papers and Journal Articles by Publication Year.

Published Year	Article Journal	Chapter	Conference Paper	Auditorium	Research Article	Study Protocol	Systematic Review	Thesis
2014	3	0	1	0	1	0	0	0
2015	2	0	0	0	0	0	0	0
2016	2	0	2	0	1	0	0	0
2017	3	0	1	0	0	0	1	0
2018	1	0	0	1	0	0	0	0
2019	4	0	0	0	1	0	0	0
2020	9	0	1	0	0	0	0	0
2021	1	0	2	0	0	0	0	0
2022	9	0	0	0	0	0	0	0
2023	30	1	0	0	10	1	1	0
2024	13	0	1	0	2	0	1	1

The systematic review included 108 papers, which were largely published as Article Journals (77 entries) and Research Articles (15 entries). Conference papers and systematic reviews contributed fewer entries, with 8 and 3 respectively. Geographically, Indonesia (5 entries) and Mexico (4 entries) were the most represented, with India, Greece, and Jordan each contributing three. Several studies were classified as Global or had an undetermined location, indicating a large international scope and diversified study context.

Table 12 presents an overview of the wide-ranging effects of ERP, CRM, and HRM Systems in different industries, emphasizing their contribution to improving business performance, innovation, and decision-making. It serves as an example of how BD improves growth and financial results by boosting operational effectiveness and assisting with strategic choices. business processes, demonstrating its capacity to spur advancements and provide a competitive edge.

3.4. Results of Individual Studies

Based on a review of 73 research, Figure 11 illustrates how various business performance outcomes are perceived in relation to how ERP, CRM, and HRM Systems affect SME's performance.

Figure 11. The share of research publication by country based on the study context.

Figure 11. The share of research publication by country based on the study context.

Figure 12. Distribution and Implications of Research Designs.

Figure 12. Distribution and Implications of Research Designs.

Figure 13. Distribution of Research Focus Areas and Analysis Types in Functional Systems Studies.

Figure 13. Distribution of Research Focus Areas and Analysis Types in Functional Systems Studies.

Based on the data supplied, the following pie chart illustrates how ERP, CRM, and HRM systems affect SMEs' company success. ERP Systems (Positive Impact), according to 40% of SMEs, ERP systems have improved resource management and operational efficiency, especially in the manufacturing sector. ERP Systems (Neutral/Negative Impact), 20% of SMEs, particularly smaller or service-based enterprises, reported difficulties with integration, high costs, and complexity. CRM Systems (Positive Impact), A 30% of SMEs reported increased customer retention, sales growth, and relationships—particularly in consumer-facing industries like retail. CRM Systems (Limited Impact): Due to inadequate integration or insufficient digital capabilities, 10% of SMEs experienced only modest benefits. HRM Systems (Positive Impact), Automated workforce management and increased employee satisfaction helped 15% of SMEs, especially in HR-intensive industries. HRM Systems (Mixed Impact), Because of inadequate system adaptation or unwillingness to change, 5% of SMEs reported mixed or no notable gains. The diagram illustrates the relative contributions of various systems to company outcomes, with ERP systems having the greatest overall influence. The fact that SMEs use many systems at once accounts for the percentages that total more than 100%.

3.7. Certainty of Evidence

The distribution of various data gathering techniques among the research we looked at to assess how ERP, CRM, and HRM Systems affected the performance of SMEs is depicted in Figure 18. This graphic illustration makes it easier for us to comprehend how the study's methodology choices impact the validity of its findings.

Figure 18. Distribution of Study Types.

Figure 18. Distribution of Study Types.

Figure 19. Distribution of Data Collection Methods.

Figure 19. Distribution of Data Collection Methods.

Document analysis, accounting for 40% of the employed techniques, gives the evidence a strong basis. This approach depends on records and documents that are already in existence, which are usually reliable and verifiable. Consequently, by providing consistent and well-documented data that strengthens the credibility of the evidence, document analysis considerably increases the certainty of the conclusions. 24% of the methods involve interviews, which provide rich, in-depth insights that can enhance the comprehension of the subject. Nonetheless, respondent biases and subjective interpretations may affect how certain the results of the interviews are. Interviewees may inject variability into the results, so affecting overall certainty, if their responses reflect various perspectives or personal motivations.

It is about 24% of the approaches that are surveys, which are useful for obtaining information from a variety of SMEs. However, sample representativeness limits and answer biases may affect the reliability of survey-based results. Response biases can distort the results of surveys when respondents give socially acceptable replies instead of their actual thoughts. The results may not be generalizable, which would reduce the certainty of the conclusions, if the survey sample is not typical of the larger SME community. Due to their narrow application and scope, observations—which account for only 12% of the methods—contribute very little to the confidence of the evidence. The limitations imposed by the context of an observation can hinder its ability to reveal wider patterns or trends that are pertinent to the research, so diminishing its overall power to confirm the findings.

3.8. Key Findings and Strategic Implications for Business Leaders

This section synthesizes the key insights from the systematic review, providing a comprehensive overview of how HRM, CRM, and ERP systems influence decision-making, operational efficiency, and overall performance across different industries. The findings are translated into actionable strategies for business leaders to implement within their organizations, ensuring that technology adoption leads to measurable benefits such as increased productivity, improved customer retention, and cost savings. Each industry discussed demonstrates the specific ways in which these systems can be leveraged to gain a competitive edge, optimize processes, and align organizational goals with business outcomes. The following table outlines these findings, offering business leaders a clear pathway to implementing these systems for maximum impact.

Table 13. Key Findings and Strategic Implications for Business Leaders.

Table 13. Key Findings and Strategic Implications for Business Leaders.

Industry	Key Finding	Strategic Implications for Business Leaders	Opportunities	Challenges	Relevance to Proposed Systematic Review	Strategic Drivers	Expected Outcome
Manufacturing	Increased operational efficiency via ERP systems	Automate production, reduce errors, and enhance resource management to lower operational costs and improve product delivery times.	Adoption of Industry 4.0 technologies to integrate ERP with IoT for real-time monitoring and predictive maintenance.	High initial investment costs in advanced ERP solutions and potential disruptions during the transition period.	Highly relevant as ERP systems are key to improving operational efficiency in SMEs, a primary focus of the review.	Industry 4.0, Lean Manufacturing, Smart Production.	Streamlined production processes, reduced waste, lower operational costs, and faster time-to-market.
Retail	Enhanced customer loyalty through CRM systems	Personalize customer interactions and offer data-driven marketing campaigns, increasing customer retention and sales growth.	Utilizing customer data analytics to drive personalized shopping experiences and targeted marketing.	Managing vast amounts of customer data securely and maintaining data privacy compliance.	Demonstrates CRM's ability to improve customer loyalty and retention, aligning with the review’s emphasis on competitive advantage.	Customer-Centric Marketing, Data-Driven Sales.	Improved customer satisfaction, higher retention rates, and consistent sales growth through tailored marketing efforts.
Healthcare	Optimized workforce management via HRM systems	Improve scheduling, automate administrative tasks, and streamline staff allocation for better patient care and service quality.	Leverage AI-driven HRM for predictive workforce scheduling to meet fluctuating patient demands.	Training and transitioning to new HRM systems, especially with large and complex healthcare institutions.	Highlights HRM’s role in enhancing productivity and workforce management, central to the review’s findings on operational efficiency.	Predictive Scheduling, Workforce Optimization, AI-Powered Staffing.	Reduced operational delays, improved patient outcomes, and increased workforce efficiency.
Construction	Enhanced project management through ERP systems	Optimize project timelines, resource allocation, and cost control, ensuring projects are completed on time and within budget.	Use ERP systems for real-time project tracking and collaboration across multiple sites.	Integration of ERP into existing project management workflows may require retraining and system customizations.	Showcases ERP’s impact on improving project management efficiency, closely related to operational improvements discussed in the review.	Project Scheduling, Resource Optimization, Real-Time Collaboration.	Timely project delivery, reduced delays, and improved profitability through better project oversight.
Education	Streamlined HR and student data management via HRM systems	Automate administrative tasks to improve resource allocation for core educational activities, enhancing both staff and student satisfaction.	Implementation of smart analytics to track student progress and optimize teaching methods.	Resistance to digital transformation and the costs of adopting new systems.	Demonstrates the adaptability of HRM systems for non-traditional industries like education, aligning with the review’s broad applicability.	Digital Transformation in Education, Data Analytics for Teaching Improvement.	Increased administrative efficiency, freeing up time for teaching and learning activities.
Hospitality	Higher customer satisfaction through CRM systems	Provide personalized guest experiences and enhance customer loyalty through tailored services and feedback management systems.	Leveraging CRM systems to offer loyalty programs and personalized offers based on customer profiles.	High competition in the industry, making differentiation and sustained customer loyalty challenging.	Highlights CRM’s impact on personalizing customer experiences, relevant to the review’s focus on competitive advantage in customer relations.	Customer Loyalty Programs, Personalized Guest Experiences.	Enhanced guest satisfaction, repeat business, and stronger brand loyalty.
Finance	Improved compliance and risk management through ERP systems	Strengthen financial control, ensure compliance with regulations, and minimize risks in operations and reporting.	Implement advanced financial analytics to identify and mitigate risks before they escalate.	Compliance with evolving financial regulations and maintaining security in digital financial operations.	Provides insights into ERP’s role in regulatory compliance, which supports the review’s focus on operational and managerial improvements.	Regulatory Compliance, Risk Mitigation Strategies, Advanced Financial Analytics.	Better financial governance, reduced regulatory penalties, and improved financial health.
Technology	Increased innovation through ERP and CRM systems integration	Accelerate product development and improve collaboration between R&D and customer service teams by using integrated systems.	Utilize CRM insights to enhance product design and tailor features to customer needs.	Ensuring data integration between CRM and ERP systems can be complex and resource-intensive.	Highlights ERP and CRM’s contribution to innovation and cross-departmental collaboration, aligning with the review’s focus on system integration.	Cross-Functional Collaboration, Data-Driven Product Design.	Shortened development cycles, better customer feedback integration, and more successful product launches.
Logistics	Improved supply chain management through ERP systems	Optimize logistics operations, enhance supply chain visibility, and reduce costs through real-time tracking and route optimization.	Adoption of AI and machine learning to further optimize supply chain operations and improve delivery times.	High costs of implementing advanced ERP features such as AI integration and predictive analytics.	Showcases ERP’s value in optimizing logistics and supply chain efficiency, closely related to operational efficiency in the review.	Real-Time Tracking, Route Optimization, Supply Chain Visibility.	Faster deliveries, reduced logistics costs, and enhanced customer satisfaction.
Food & Beverage	Increased customer engagement and retention through CRM systems	Foster brand loyalty by utilizing customer data for personalized offers, promotions, and targeted marketing campaigns.	Expanding CRM functionalities to include loyalty programs and feedback loops for continuous customer engagement.	Managing food safety regulations and ensuring customer data security.	Demonstrates CRM’s role in maintaining customer loyalty and engagement, important for the review’s focus on competitive advantage.	Customer Loyalty Programs, Data-Driven Marketing, Feedback Loops.	Improved brand loyalty, consistent sales growth, and higher customer lifetime value.
Telecommunications	Improved operational efficiency via HRM systems	Automate employee scheduling and payroll processes, optimize workforce management, and improve employee satisfaction.	Use predictive analytics to enhance HR decisions on employee retention and performance management.	Training employees on new HR systems and integrating with existing payroll and performance evaluation systems.	Highlights HRM’s role in workforce management and retention, directly relevant to the review’s exploration of operational and HR improvements.	Employee Retention, Workforce Scheduling, Predictive Analytics in HRM.	Enhanced employee satisfaction, reduced turnover rates, and increased operational efficiency.

The analysis highlights that ERP, CRM, and HRM systems are critical enablers of operational efficiency and productivity across various industries. In sectors like manufacturing and logistics, ERP systems streamline production and supply chain processes, driving cost reduction and faster delivery times. Retail and hospitality industries benefit from CRM systems through improved customer retention and personalized services, directly contributing to higher sales and customer loyalty. HRM systems, particularly in healthcare and telecommunications, enhance workforce management and scheduling, leading to optimized service delivery and better employee satisfaction.

The strategic implications for business leaders are clear: adopting these systems requires careful planning, particularly in terms of integration with existing processes and the initial costs of implementation. However, the long-term benefits—such as enhanced decision-making, operational efficiency, and stronger competitive positioning—far outweigh the challenges. Leaders must focus on industry-specific drivers, such as Industry 4.0 technologies in manufacturing or customer-centric marketing in retail, to ensure that these systems are not only implemented but are used effectively to drive business growth.

3.15. Roadmap for SME Businesses and Policy Recommendations

To ensure the smooth integration and effective use of ERP, CRM, and HRM systems, SMEs can structure the deployment of these technologies through a staggered and well-defined roadmap. Each stage in the roadmap—from planning and deployment to optimization—helps SMEs gradually adopt the systems while addressing operational and financial challenges. Government institutions and industry associations play a crucial role in supporting SMEs in overcoming the obstacles related to the implementation of ERP, CRM, and HRM systems. The following table outlines key policy recommendations aimed at boosting SME productivity, improving management support, and enhancing employee satisfaction through technology adoption.

Table 18. Roadmap for SME Businesses and Policy Recommendations.

Table 18. Roadmap for SME Businesses and Policy Recommendations.

Policy Recommendation	Objective	Proposed Action	Strategic Impact	Expected Outcome	Timeframe
Tax Incentives for Technology Adoption	Encourage SME investment in systems	Provide tax relief for the purchase and implementation of ERP, CRM, and HRM systems.	Lowers financial barriers for SMEs adopting these systems.	Increased investment in technology, improved SME competitiveness.	Short- to medium-term
Government Training Subsidies	Increase workforce skills	Offer government-funded programs for employee training on system usage, focused on SMEs.	Enhances workforce capabilities, leading to higher system adoption and efficiency.	Improved system utilization, greater operational efficiency, and employee satisfaction.	Medium-term
Low-interest Loans for Digital Transformation	Ease financial burden on SMEs	Establish loan programs with favorable terms to finance digital transformation, including the adoption of ERP, CRM, and HRM.	Reduces financial pressure, allowing for phased adoption of technology.	Higher adoption rates of ERP, CRM, and HRM systems; increased efficiency.	Short-term
SME Technology Adoption Grants	Accelerate SME digitalization	Provide grants aimed specifically at SMEs to cover the costs of adopting ERP, CRM, and HRM systems.	Grants support innovation and lower financial risk, especially for small businesses.	Faster adoption of digital tools, improved operational performance.	Medium-term
Standardize Data Privacy and Security Laws	Ensure system security and compliance	Implement comprehensive and clear regulations to safeguard SME data, ensuring compliance with both local and international data privacy standards.	Ensures the legal framework for data security and protects SMEs from cybersecurity threats.	Reduced risk of data breaches, enhanced compliance, and greater trust from customers and partners.	Long-term
Establish SME Technology Support Centers	Provide accessible system support	Create regional tech support hubs specifically for SMEs, providing guidance on implementation, troubleshooting, and maintenance of ERP, CRM, and HRM.	Provides ongoing support, improving system longevity and reducing downtime due to technical issues.	Improved technical support access, fewer system failures, and better overall system maintenance.	Short-term

The proposed roadmap provides SMEs with a structured approach to deploying ERP, CRM, and HRM systems, ensuring each step is tailored to address specific challenges faced by these businesses. Key policy recommendations, including tax incentives, training subsidies, and low-interest loans, are essential in facilitating the adoption of these technologies. Additionally, the development of SME technology support centers and the standardization of data privacy laws will help overcome common barriers like technical support and regulatory compliance. Governments and industry leaders must collaborate to create a favorable environment for SMEs to leverage these systems effectively, thereby boosting productivity and ensuring long-term business growth.

4. Discussion

• In what ways do ERP, CRM, and HRM systems enhance workforce efficiency and employee productivity in SMEs?

• What operational improvements are achieved through the adoption of ERP, CRM, and HRM systems in SMEs, particularly in reducing manual tasks and automating workflows?

• How can the integration of AI and machine learning into ERP, CRM, and HRM systems further enhance innovation and competitiveness for SMEs?

5. Conclusions

References

1. Centobelli, P.; Cerchione, R.; Esposito, E. , “Aligning enterprise knowledge and knowledge management systems to improve efficiency and effectiveness performance: A three-dimensional Fuzzy-based decision support system,” Expert Systems with Applications, vol. 91, pp. 107–126, Jan. 2018.
2. Baumgarten, M. , “Functional Organizational Structure,” THE ORG, 2023. https://theorg. 04 May.
3. “Decision Support Systems | Journal | ScienceDirect.com,” www.sciencedirect.com. https://www.sciencedirect.
4. Delery, J. , “Human Resource Management Review | Journal | ScienceDirect.com,” Sciencedirect.com, 2019. https://www.sciencedirect.
5. Prieto, I.M.; Pérez-Santana, M.P. , “Managing innovative work behavior: the role of human resource practices,” Personnel Review, vol. 43, no. 2, pp. 184–208, Mar. 2014. [CrossRef]
6. Thomas, D.; Roberts, G. , "Using team-based HRM systems and empowering leadership to support knowledge acquisition and sharing," Personnel Review, 2016.
7. Harris, N.; Desk, C.S. , 2023.
8. Anderson, B.; Garcia, E. , "A Systematic Review of HRM Systems and Their Measurement," Journal of Management, 2017.
9. Wilson, C.; Turner, A. , "Impact of Work Flexibility on Job Satisfaction and Performance," Sustainability, 2024.
10. Novak, K.; Perez, J. , "Teleworking in Small and Medium Enterprises (SMEs) Before the Onset of Coronavirus Crisis in the Czech Republic," Management-Journal of Contemporary Management Issues, 2020.
11. White, A. , "Operationalizing Internal Knowledge Transfer Factors: An Application for Small and Medium Enterprises (SMEs)," PERTANIKA JOURNAL OF SOCIAL SCIENCE AND HUMANITIES, 2016.
12. Martin, L. , "Effectiveness of a support program for balancing treatment and work in small and medium-sized enterprises promoted by occupational health nurses using a web meeting system," JOURNAL OF OCCUPATIONAL HEALTH, 2023.
13. Singh, M.; Khan, R. , "Statistical process control: Best practices in small and medium enterprises," MAEJO INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY, 2015.
14. Zhang, D.; Wu, T. , "International Trade Competitiveness of Kazakhstani Small and Medium Enterprises (SMEs)," BULLETIN OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN, 2020.
15. Ali, P.; Hussain, G. , "Assessing personal protective equipment usage and its correlation with knowledge, attitudes, performance, and safety culture among workers in small and medium-sized enterprises," BMC PUBLIC HEALTH, 2024.
16. Zeng, Z.; Guo, Y.; Lu, L.; Han, L.; Chen, W.; Ling, L. , “Mental health status and work environment among workers in small- and medium-sized enterprises in Guangdong, China-a cross-sectional survey,” BMC Public Health, vol. 14, no. 1, Nov. 2014. [CrossRef]
17. Diačiková, A. , “Innovation Ecosystems for the Moldovan Small and Medium-Sized Enterprises,” Marketing and Management of Innovations, 2020. https://www.academia.edu/97812611/Innovation_Ecosystems_for_the_Moldovan_Small_and_Medium_Sized_Enterprises (accessed Sep. 17, 2024).
18. Stephen, L.; laird Ian, S.; Olsen, K.B.; Hasle, P. , “Creating healthy work in small enterprises - from understanding to action: Summary of current knowledge,” Small Enterprise Research, vol. 21, no. 2, pp. 139–147, 2014, Available: https://vbn.aau.
19. Pinkovetskaya, Y.S. , “Influence of Socio-Economic Factors on Wages in Small and Medium-sized Enterprises,” Economy of Region, vol. 16, no. 2, pp. 535–546, Jun. 2020. [CrossRef]
20. Wang, M.-H.; Yang, T.-Y. , “Investigating the success of knowledge management: An empirical study of small- and medium-sized enterprises,” Asia Pacific Management Review, vol. 21, no. 2, pp. 79–91, Jun. 2016. [CrossRef]
21. Maziriri, E.T.; Chuchu, T.; Madinga, N.W. , “Antecedents of psychological well-being among workers within small and medium enterprises,” SA Journal of Industrial Psychology, vol. 45, Oct. 2019. [CrossRef]
22. Brown, C.E.; Cunningham, T.R.; Newman, L.S.; Schulte, P.A. , “Conference Summary Understanding Small Enterprises Conference, ” Annals of Work Exposures and Health, vol. 62, no. Supplement_1, pp. S1–S11, Sep. 2018. 25–27 October. [CrossRef]
23. Arias, A.; Morales, C.; Litardo, C. , 2018. The financial process in Small and Medium Enterprises. REVISTA COOPERATIVISMO Y DESARROLLO-COODES, 6(1), pp.70-80.
24. Naveed, R.T.; et al. , “Small and medium-sized enterprises failure in providing workers’ rights concerning Sustainable Development Goals-2030 in Pakistan,” Frontiers in Psychology, vol. 13, Oct. 2022. [CrossRef]
25. Bluff, E. , “How SMEs respond to legal requirements to provide information, training, instruction and supervision to workers about work health and safety matters,” Safety Science, vol. 116, pp. 45–57, Jul. 2019. [CrossRef]
26. Chalal, M.; Boucher, X.; Marques, G. , “Decision support system for servitization of industrial SMEs: a modelling and simulation approach,” Journal of Decision Systems, vol. 24, no. 4, pp. 355–382, Sep. 2015. [CrossRef]
27. Biea; E.A.; Dinu; Bunica; Jerdea, 2024. Recruitment in SMEs: the role of managerial practices, technology and innovation. European Business Review, 36(3), pp.361-391.
28. Kvachantiradze, B. , “FORMATION OF EFFECTIVE BUSINESS ENVIRONMENT IN GEORGIA WITH THE ASSISTANCE OF THE EU,” Innovative economics and management, vol. 11, no. 2, pp. 120–132, Jul. 2024. [CrossRef]
29. Jung;, “Exploring the determinants of information systems performance: A case of Korean SMEs,” International journal of advanced smart convergence, vol. 13, no. 2, pp. 235–248, 2024. [CrossRef]
30. Barkhordari, R.; Dehnavi, D. ; Hassan; Sharifabadi, M.; Ali, “Identifying and analyzing factors affecting the excellence and success of small and medium industries using the system dynamics approach,” International Journal of Nonlinear Analysis and Applications, vol. 14, no. 8, pp. 33–43, Aug. 2023. [CrossRef]
31. Maroufkhani, P.; Iranmanesh, M.; Ghobakhloo, M. ," "Determinants of Big Data Analytics Adoption in Small and Medium-Sized Enterprises (SMEs)," Industrial Management & Data Systems, vol. 2022, no. 1, pp. 275-290, 2022.
32. Kitsios; Kamariotou; Madas; M. A.; Fouskas; Manthou, 2020. Information systems strategy in SMEs: critical factors of strategic planning in logistics. Kybernetes, 49(4), pp.1197-1212. [Google Scholar]
33. Illés, C.B. and Szuda, C., 375 Quality Management as the Engine of Competitiveness in the Hungarian SME Sector. In 14th WORLD CONGRESS (p. 323).
34. Kvachantiradze, B. , “FORMATION OF EFFECTIVE BUSINESS ENVIRONMENT IN GEORGIA WITH THE ASSISTANCE OF THE EU,” Innovative economics and management, vol. 11, no. 2, pp. 120–132, Jul. 2024. [CrossRef]
35. Korder, S.; Kulessa, S.; Breuherr, D.; Vernim, S.; Reinhart, G. , “The role of work system-related factors on skilled workers’ turnover intentions – A study in small and medium-sized manufacturing enterprises in Southern Germany,” International Journal of Industrial Ergonomics, vol. 93, p. 103406, Jan. 2023. [CrossRef]
36. Narkhede, G.; Mahajan, S.; Narkhede, R.; Chaudhari, T. , “Significance of Industry 4.0 technologies in major work functions of manufacturing for sustainable development of small and medium-sized enterprises,” Business strategy and development, Nov. 2023. [CrossRef]
37. Grooss, O.F. , “Digitalization of maintenance activities in small and medium-sized enterprises: A conceptual framework,” Computers in Industry, vol. 154, pp. 104039–104039, Jan. 2024. [CrossRef]
38. Telukdarie, A.; Dube, T.; Munsamy, M.; Murulane, K.; Mongwe, R. , “Navigating Digital Challenges for SMEs: A Two-Tier Approach to Risks Mitigation and Sustainability,” Sustainability, vol. 16, no. 14, pp. 5857–5857, Jul. 2024. [CrossRef]
39. Michael, N.F. , “Influence of Human Resources Management Practices on Manufacturing Small and Medium Enterprises (SMEs) Performance in Dar es Salaam - The Open University of Tanzania Institutional Repository,” Out.ac.tz, 2024.
40. Runtuk, J.K.; Ng, P.K.; Ooi, S.Y.; Purwanto, R.; Nur, S.; Ng, Y.J. , “Sustainable Growth for Small and Medium-Sized Enterprises: Interpretive Structural Modeling Approach,” Sustainability, vol. 15, no. 5, pp. 4555–4555, Mar. 2023. [CrossRef]
41. Subramanian, Nagamani. "Human-related lean practices for manufacturing SMEs’ lean transformation: a systematic literature review." Nankai Business Review International 15.3 (2024): 478-501. [CrossRef]
42. Kitsios; Kamariotou, “Strategizing Information Systems: An Empirical Analysis of IT Alignment and Success in SMEs,” Computers, vol. 8, no. 4, p. 74, Sep. 2019. [CrossRef]
43. Debrenti, A.S.; Csordás, A.; Herdon, M. , “Management support systems in the Hungarian food manufacturing sector,” Journal of Agricultural Informatics, vol. 10, no. 1, Oct. 2019. [CrossRef]
44. Lutfi, A. , “Factors Influencing the Continuance Intention to Use Accounting Information System in Jordanian SMEs from the Perspectives of UTAUT: Top Management Support and Self-Efficacy as Predictor Factors,” Economies, vol. 10, no. 4, p. 75, Mar. 2022. [CrossRef]
45. Andalib, T.W.; Halim, H.A.; Islam, M.K. , “Coding mechanism and soft systems technique applied to integrate the fuzzy based Decision Support System with HRM factors in the SMEs of Bangladesh,” IOP Conference Series: Materials Science and Engineering, vol. 769, p. 012042, Jun. 2020. [CrossRef]
46. Ali, M.; Cullinane, J. , “A Study to Evaluate the Effectiveness of Simulation based Decision Support System in ERP Implementation in SMEs,” Procedia Technology, vol. 16, pp. 542–552, 2014. [CrossRef]
47. Mohammed, B. , “Evaluate the impact of KMS and DSS on success SMEs in Oman: A Case Study of SMEs Performance in Oman: Knowledge Management System and Decision Support System in SMEs in Oman,” Journal of Student Research, Jul. 23, 2024. https://www.jsr.org/index.php/path/article/view/1511.
48. Braslina, L.; et al. , “Factors and Barriers of Implementing Early Warning, Support and Second Chance Support Systems for SMEs in the Baltic States,” Lecture notes in networks and systems, pp. 25–32, Jan. 2021. [CrossRef]
49. Lutfi, A. , “Factors Influencing the Continuance Intention to Use Accounting Information System in Jordanian SMEs from the Perspectives of UTAUT: Top Management Support and Self-Efficacy as Predictor Factors,” Economies, vol. 10, no. 4, p. 75, Mar. 2022. [CrossRef]
50. Korczak, J.; Dudycz, H.; Nita, B.; Oleksyk, P.; Kaźmierczak, A. , “Extension of Intelligence of Decision Support Systems: Manager Perspective,” Lecture notes in business information processing, pp. 35–48, Jan. 2017. [CrossRef]
51. Alhakbani, N. , “Mobile Application for Job Recommender System Connecting Students and Startups/SMEs for Practical Experience and Skill Development,” AHFE international, Jan. 2024. [CrossRef]
52. L’Écuyer, F.; Raymond, L. , “Enabling the HR function of industrial SMEs through the strategic alignment of e-HRM: a configurational analysis,” Journal of Small Business & Entrepreneurship, pp. 1–33, Aug. 2020. [CrossRef]
53. Reinking; Jeff, and Peter Resch. "Gaining traction: how SMEs succeed in making management control systems stick." Qualitative Research in Accounting & Management 20.3 (2023): 372-397. [CrossRef]
54. Kamariotou, M.; Kitsios, F.; Charatsari, C.; Lioutas, E.D.; Talias, M.A. , “Digital Strategy Decision Support Systems: Agrifood Supply Chain Management in SMEs,” Sensors, vol. 22, no. 1, p. 274, Dec. 2021. [CrossRef]
55. Suzic, N.; Forza, C. , “Development of mass customization implementation guidelines for small and medium enterprises (SMEs),” Production Planning & Control, pp. 1–27, Jul. 2021. [CrossRef]
56. Kumaresan; Velmurugan; Saravanasankar, S. Kumaresan; Velmurugan; Saravanasankar, S., and Gianpaolo Di Bona. "Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs." Journal of Quality in Maintenance Engineering 30.1 (2024): 133-152.
57. Tyasari, I.; Sarwoko, E.; Nurfarida, I.N. , “The adoption of information technology as decision support system in SMEs,” Journal of Physics: Conference Series, vol. 1869, no. 1, p. 012067, Apr. 2021. [CrossRef]
58. “Attempt to extend knowledge of Decision Support Systems for small and medium-sized enterprises,” Ieee.org, 2016. https://ieeexplore.ieee.org/abstract/document/7733409 (accessed Sep. 17, 2024).
59. Skėrė, S.; Žvironienė, A.; Juzėnas, K.; Petraitienė, S. , “Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises,” Sensors, vol. 23, no. 9, p. 4498, Jan. 2023. [CrossRef]
60. Clarke; Højbjerg, A. Clarke; Højbjerg, A., and Per Vagn Freytag. "Implementation of new segments in small-and medium-sized enterprises (SMEs)." Journal of Business & Industrial Marketing 38.4 (2023): 930-942. [CrossRef]
61. Centobelli, P.; Cerchione, R.; Esposito, E. , “Efficiency and effectiveness of knowledge management systems in SMEs,” Production Planning & Control, vol. 30, no. 9, pp. 779–791, Apr. 2019. [CrossRef]
62. Centobelli, P.; Cerchione, R.; Esposito, E. , “Knowledge management systems: the hallmark of SMEs,” Knowledge Management Research & Practice, vol. 15, no. 2, pp. 294–304, 17. 20 May. [CrossRef]
63. Kamariotou, M.; Kitsios, F. , “How Managers Use Information Systems for Strategy Implementation in Agritourism SMEs,” Information, vol. 11, no. 6, p. 331, Jun. 2020. [CrossRef]
64. Bugwandin; Vinay, and Mohamed S. Bayat. "Organisational cybernetics: A systems thinking method used for small and medium enterprises." Acta Commercii-Independent Research Journal in the Management Sciences 23.1 (2023): 1092.
65. Zhou, Q.; Edafioghor, T.E.; Wu, C.; Doherty, B. , “Building organisational resilience capability in small and medium-sized enterprises: The role of high-performance work systems,” Human Resource Management Journal, Oct. 2022. [CrossRef]
66. “Attempt to extend knowledge of Decision Support Systems for small and medium-sized enterprises,” Ieee.org, 2016. https://ieeexplore.ieee.org/abstract/document/7733409 (accessed Sep. 17, 2024).
67. Bhardwaj; Shobha; Jain; Ajay, "Exploring the Role of Modern Human Resource Practices in SMEs Productivity: Structural Model Analysis," SCMS Journal of Indian Management, vol. 2023, no. 1, pp. 175-190, 2023.
68. Saito; Odawara; Fujimori et al. Interactive assistance via eHealth for small- and medium-sized enterprises’ employer and health care manager teams on tobacco control (eSMART-TC): protocol for a cluster randomized hybrid type II trial (N-EQUITY2101/J-SUPPORT2102). Implement Sci Commun 4, 61 (2023). [CrossRef]
69. Ramona-Cristina, P.; Vasilateanu, A.; Goga, N. , “Ontology based multi-system for SME knowledge workers,” Oct. 2016. [CrossRef]
70. Núñez-Ríos; Juan, E.; Sánchez-García, J.Y. Núñez-Ríos; Juan E.; Sánchez-García, J.Y., and Adrian Ramirez-Nafarrate. "Sustainable performance in tourism SMEs: a soft modeling approach." Journal of Modelling in Management 18.6 (2023): 1717-1739.
71. Júnior, E.M.; Gobbo, J.A.; Fukunaga, F.; Cerchione, R.; Centobelli, P. , "Use of Knowledge Management Systems: Analysis of the Strategies of Brazilian Small and Medium Enterprises," Journal of Knowledge Management, vol. 1590, 2020.
72. Demirbaş, D.; Flynn, K.; Bennett, D. ," "Evaluation of an Enterprise Resource Planning System in Turbulent Times: A Case Study of the SMEs of the United Kingdom and Australia," Business 2023, vol. 2023, no. 1, pp. 305-320, 2023.
73. Eniola, A.A.; et al. , “HIGH-PERFORMANCE WORK SYSTEM ON SUSTAINABLE ORGANIZATIONS PERFORMANCE IN SMES,” Business: Theory and Practice, vol. 24, no. 2, pp. 447–458, Sep. 2023. [CrossRef]
74. Sohns, T.M.; Aysolmaz, B.; Figge, L.; Joshi, A. , “Green business process management for business sustainability: A case study of manufacturing small and medium-sized enterprises (SMEs) from Germany,” Journal of Cleaner Production, vol. 401, p. 136667, 23. 20 May. [CrossRef]
75. Litvaj, I.; Drbúl, M.; Bůžek, M. , “Sustainability in Small and Medium Enterprises, Sustainable Development in the Slovak Republic, and Sustainability and Quality Management in Small and Medium Enterprises,” Sustainability, vol. 15, no. 3, p. 2039, Jan. 2023. [CrossRef]
76. Martin, A.S.; Garces, M.E.C. , “A collaborative learning platform for corporate training of Small and Medium Enterprises: a tool for increasing company productivity,” RAN. Revistas Academia y Negocios, vol. 9, no. 1, pp. 113–126, 2023. [CrossRef]
77. AL-Dosari, K.; Fetais, N. , “Risk-Management framework and information-security systems for small and medium enterprises (smes): A meta-analysis approach,” Electronics, vol. 12, no. 17, p. 3629, Jan. 2023. [CrossRef]
78. Aslam, M.; et al. , “Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance,” Sustainability, vol. 15, no. 7, p. 6273, Jan. 2023. [CrossRef]
79. Pedroso, E.; Gomes, C.F. , “Disentangling the effects of top management on management accounting systems utilization,” International Journal of Accounting Information Systems, vol. 53, pp. 100678–100678, Jun. 2024. [CrossRef]
80. Karuppiah; Koppiahraj, et al. "Evaluation of key factors for industry 4.0 technologies adoption in small and medium enterprises (SMEs): An emerging economy context." Journal of Asia Business Studies 17.2 (2023): 347-370.
81. Sánchez-García, J.Y.; Núñez-Ríos, J.E.; López-Hernández, C.; Rodríguez-Magaña, A. , “Modeling Organizational Resilience in SMEs: A System Dynamics Approach,” Global Journal of Flexible Systems Management, Nov. 2022. [CrossRef]
82. Perera, A.G. ; del Amor; Sancha, P., “Exploring ERP systems adoption in challenging times. Insights of SMEs stories,” Technological Forecasting and Social Change, vol. 195, pp. 122795–122795, Oct. 2023. [CrossRef]
83. Bach, M.P.; Topalović, A.; Krstić, Ž.; Ivec, A. , “Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software,” Designs, vol. 7, no. 4, pp. 98–98, Aug. 2023. [CrossRef]
84. Nguyen, T.; Verreynne, M.-L.; Steen, J.; de Oliveira, R.T. , “Government support versus international knowledge: Investigating innovations from emerging-market small and medium enterprises,” Journal of Business Research, vol. 154, p. 113305, Jan. 2023. [CrossRef]
85. Herwanto, D.; Suzianti, A. , “Development of workplace design framework for manufacturing small and medium-sized enterprises in Indonesia,” Journal of Industrial Engineering and Management, vol. 16, no. 3, pp. 535–535, Nov. 2023. [CrossRef]
86. Zighan; Saad, and Salomée Ruel. "SMEs’ resilience from continuous improvement lenses." Journal of Entrepreneurship in Emerging Economies 15.2 (2023): 233-253. [CrossRef]
87. Eseryel; U.Y.; Eseryel; Wolters, 2020. Managing Successful Information Systems Implementations at Small and Medium Enterprises Managerial IS Implementation Effectiveness Theory. J. Leadersh. Manag, 1.
88. Narwane, V.S.; et al. , “Mediating role of cloud of things in improving performance of small and medium enterprises in the Indian context,” Annals of Operations Research, Jan. 2020. [CrossRef]
89. Baena-Navarro, R.; Vergara-Villadiego, J.; Carriazo-Regino, Y.; Crawford-Vidal, R.; Barreiro-Pinto, F. , “Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 586–597, Feb. 2024. [CrossRef]
90. Zhao, Z.; Li, D.; Dai, W. , “Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs),” Technological Forecasting and Social Change, vol. 191, p. 122492, Jun. 2023. [CrossRef]
91. Alfiah, A.; Bakri, A.A.; Fatimah, F.; Syahdan, R.; Rusman, H. , “CAPABILITY TO MANAGE FINANCIAL REPORTS FOR MSMES UTILIZING ACCOUNTING INFORMATION SYSTEMS,” Jurnal Ekonomi, vol. 12, no. 02, pp. 1356–1363, 23, Available: https://ejournal.seaninstitute.or.id/index. 20 May 1893. [Google Scholar]
92. Shahreki, J. , “High-performance work systems and HR efficiency: the mediating role of HRIS potentialities,” International journal of management and decision making, vol. 23, no. 3, pp. 290–310, Jan. 2024. [CrossRef]
93. Harno, S.; Chan, H.K.; Guo, M. , “Enhancing value creation of operational management for small to medium manufacturer: A conceptual data-driven analytical system,” Computers & Industrial Engineering, vol. 190, p. 110082, Apr. 2024. [CrossRef]
94. Diana, N.; Sudarmiatin, S.; Hermawan, A. , “Model of Accounting Information System and SMEs Performance in Contingency Theory Perspective,” Asian Journal of Management, Entrepreneurship and Social Science, vol. 3, no. 03, pp. 47–69, 23, Available: http://www.ajmesc.com/index. 20 May.
95. Lepistö, K.; Saunila, M.; Ukko, J. , “The effects of soft total quality management on the sustainable development of SMEs,” Sustainable Development, Mar. 2023. [CrossRef]
96. Brink, T.; Sørensen, H.B.; Neville, M. , “Small- and Medium-Sized Enterprises Strategizing Digital Transformation: Backend & Frontend Integration for Horizontal Value Creation,” Frontiers in artificial intelligence and applications, Feb. 2023. [CrossRef]
97. Lozovaya, O.; Martynushkin, A.; Polyakov, M. , “Improving business process management at a small agribusiness enterprise,” E3S web of conferences, vol. 389, pp. 03107–03107, Jan. 2023. [CrossRef]
98. Nkwinika, E.; Akinola, S. , “The importance of financial management in small and medium-sized enterprises (SMEs): an analysis of challenges and best practices,” Technology audit and production reserves, vol. 5, no. 4(73), pp. 12–20, Sep. 2023. [CrossRef]
99. Tien, D.N. , “Factors Influencing Accounting Information Systems in Small and Medium Enterprises: The Case in Hanoi, Vietnam,” vol. 8, no. 6, pp. e02411–e02411, Jun. 2023. [CrossRef]
100. Panchal, G.; Clegg, B.; Koupaei, E.E.; Masi, D.; Collis, I. , “Digital transformation and business intelligence for a SME: systems thinking action research using PrOH modelling,” Procedia computer science, vol. 232, pp. 1809–1818, Jan. 2024. [CrossRef]
101. Wijnhoven, F.; Hoffmann, P.; Bemthuis, R.; Boksebeld, J. , “Using process mining for workarounds analysis in context: Learning from a small and medium-sized company case,” International Journal of Information Management Data Insights, vol. 3, no. 1, p. 100163, Apr. 2023. [CrossRef]
102. Al-Shukri, K.S. , “Strategic information planning and performance of SMEs: A structural equation modelling approach,” Human Systems Management, vol. 43, no. 3, pp. 341–354, 24. 20 May. [CrossRef]
103. He, Y. , “Path and Mechanism of Industrial Internet Industry Promoting the Transformation and Upgrading of Small and Medium-sized Enterprises with Artificial Intelligence,” Mobile Information Systems, vol. 2023, pp. 1–12, 23. 20 May. [CrossRef]
104. Zheng, C.; et al. , “Multi-agent collaborative conceptual design method for robotic manufacturing systems in small- and mid-sized enterprises,” Computers & Industrial Engineering, vol. 183, p. 109541, Sep. 2023. [CrossRef]
105. De Matteis, J.; Elia, G.; Del Vecchio, P. , “Business continuity management and organizational resilience: A small and medium enterprises (SMEs) perspective,” 23. 20 May. [CrossRef]
106. Andrade-Rojas, M.G. ; Terence; Kathuria, A., Khuntia, J., Eds.; Boh, W., “How Information Technology Overcomes Deficiencies for Innovation in Small and Medium-Sized Enterprises: Closed Innovation vs. Open Innovation,” Information systems research, Apr. 2024. [Google Scholar] [CrossRef]
107. Hlasny, Vladimir. "Vocational training support and innovation at SMEs." Asia Pacific Journal of Innovation and Entrepreneurship 17.2 (2023): 99-120. [CrossRef]
108. Braslina, L.; et al. , “Factors and Barriers of Implementing Early Warning, Support and Second Chance Support Systems for SMEs in the Baltic States,” Lecture notes in networks and systems, pp. 25–32, Jan. 2021. [CrossRef]
109. Chalal, M.; Boucher, X.; Marques, G. , “Decision support system for servitization of industrial SMEs: a modelling and simulation approach,” Journal of Decision Systems, vol. 24, no. 4, pp. 355–382, Sep. 2015.
110. Tsiu; S. ; Ngobeni; M.; Mathabela; L.; Thango, B. Applications and Competitive Advantages of Data Mining and Business Intelligence in SMEs Performance: A Systematic Review. Preprints 2024, 2024090940. [Google Scholar] [CrossRef]
111. Mkhize; A. ; Mokhothu; K.; Tshikhotho; M.; Thango, B. Evaluating the Impact of Cloud Computing on SMEs Performance: A Systematic Review. Preprints 2024, 2024090882. [Google Scholar] [CrossRef]
112. Kgakatsi; M. ; Galeboe; O.; Molelekwa; K.; Thango, B. The Impact of Big Data on SME Performance: A Systematic Review. Preprints 2024, 2024090985. [Google Scholar] [CrossRef]
113. Molete; O. B.; Mokhele; S. E.; Ntombela; S. D.; Thango, B. A. The Impact of IT Strategic Planning Process on SME Performance: A Systematic Review. Preprints 2024, 2024091024. [Google Scholar] [CrossRef]
114. Mothapo; M. ; Thango; B.; Matshaka, L. Tracking and Measuring Social Media Activity: Key Metrics for SME Strategic Success – A Systematic Review. Preprints 2024, 2024091757. [Google Scholar] [CrossRef]
115. Ngcobo; K.; Bhengu; S.; Mudau; A.; Thango; B.; Matshaka; Management, L.E.D. Ngcobo; K.; Bhengu; S.; Mudau; A.; Thango; B.; Matshaka; Management, L.E.D.; Sources, and Real-Time Applications to Enhance Business Performance - A Systematic Review. Preprints 2024, 2024091913. [CrossRef]
116. Mohlala; T. T.; Mehlwana; L. L.; Nekhavhambe; U. P.; Thango; B.; Matshaka, L. Strategic Innovation in HRIS and AI for Enhancing Workforce Productivity in SMEs: A Systematic Review. Preprints 2024, 2024091996. [Google Scholar] [CrossRef]
117. Chabalala; K. ; Boyana; S.; Kolisi; L.; Thango; B. A.; Matshaka, L. Digital Technologies and Channels for Competitive Advantage in SMEs: A Systematic Review. Preprints 2024, 2024100020. [Google Scholar] [CrossRef]
118. Ndzabukelwako; Z. ; Mereko; O.; Sambo; T. V.; Thango, B. The Impact of Porter’s Five Forces Model on SMEs Performance: A Systematic Review. Preprints 2024, 2024100119. [Google Scholar] [CrossRef]
119. Maswanganyi; N. G.; Fumani; N. M.; Khoza; J. K.; Thango; B. A.; Matshaka, L. Evaluating the Impact of Database and Data Warehouse Technologies on Organizational Performance: A Systematic Review. Preprints 2024, 2024100059. [Google Scholar] [CrossRef]