Enhanced Organizational Performance: Integrating Dimensions for Sustainable Growth

Abstract

This study examined how different factors—environmental, human resources, managerial, technological, and innovation—influence the organizational performance of small- and medium-sized enterprises (SMEs). For the study methodology, a correlational research design was used to examine the relationships between variables in a sample of 476 SMEs in Colombia. Advanced statistical techniques were used, such as regression and correlation analyses with Monte Carlo simulation. We evaluated four dimensions, 16 factors, and 197 related variables to understand their impact on business performance. The results show that all the studied factors, i.e., environment, structure, management, and people, have a positive relationship with organizational performance. Additionally, the results indicate that SMEs tend to have strong performance in the dimensions of finance, infrastructure, and production but weaker performance in those of international, socioenvironmental, technological, institutional philosophy, process, and human resources management. These findings provide valuable insight into the areas that SMEs may need to focus on to improve their performance while underscoring the importance of considering a variety of factors when developing strategies to improve organizational performance.

1. Introduction

In the vast landscape of the global economy, small- and medium-sized enterprises (SMEs) stand as foundational pillars, especially within Colombia. Their significance in this nation stretches beyond mere economic contribution into job creation and sociocultural enrichment [1,2]. There is a growing imperative to understand the multifaceted influences, notably the environmental, human resources, managerial, technological, and innovation factors that underpin SME performance, considering that organizational performance is defined by [3] the organization’s ability to achieve its goals and objectives. Such an understanding is not merely academic; it holds profound socioeconomic implications for Colombia and its alignment with broader sustainable development initiatives.

Attuned to the paramount role of SMEs in the economic schema, the Colombian government has been fostering an environment conducive to their growth. Through their strategic initiatives, particularly in endorsing technological and sustainable practices, SMEs aim to be robust contributors to achieving the nation’s sustainability benchmarks, in harmony with global Sustainable Development Goals (SDGs) [4].

The decision to venture into this study emerges from recognizing the unique confluence of economic, social, and environmental imperatives surrounding SMEs in Colombia. With the country presenting both challenges and opportunities distinct to its socioeconomic fabric, a nuanced exploration becomes indispensable.

Examining the determinants of organizational performance is essential for Colombian SMEs, given their pivotal role in the nation’s economic landscape and their engagement in both local and global markets [5]. The incorporation of technological advancements and adherence to sustainable practices not only amplifies their competitive positioning but also resonates with Colombia’s overarching sustainability objectives [4]. Consequently, their fortified performance and adaptability not only catalyze inclusive growth but also equip these enterprises to navigate prevalent economic adversities, cementing their significance in shaping Colombia’s economic and sociocultural trajectory [6].

Central to our exploration is general systems theory (GST), which offers an integrative framework for understanding organizations. Rooted in the foundational principles articulated in [7] and enriched by subsequent research [8,9,10,11], in GST, organizations are visualized as open systems, namely, entities in continuous dialogue with their environment, assuming roles beyond economic functions and acting as pivotal social agents, particularly within the SDG context [12]. The relevance of adopting GST in the context of SMEs underscores the framework of our study, promising fresh insights into SME dynamics in the Colombian setting.

Concluding this introduction, our methodological approach leverages tools such as Monte Carlo simulation, providing a nuanced departure from models with inherent constraints such as the Marinovic model. This innovative methodology is aimed at quantifying the impact and potency of the incorporated strategies, thereby empowering organizations with the provision of pragmatic tools for sustainable growth [13,14]. Using a structured exploration, spanning from GST literature reviews to intricate methodologies and discussions, this research pledges a comprehensive analysis, augmenting the discourse on SMEs in Colombia and beyond.

This work is structured as follows. Section 1 introduces the problem of this study. Section 2 is a discussion of the literature on GST and the company as a relational whole, in which the elements of a company’s integrated management are reviewed to improve the performance of various management indicators. Presented in Section 3 is the research methodology of this study and the statistical treatment used for its implementation. In Section 4 are the study results, analyses, and discussion. Section 5 contains the main conclusions from this study and the perceived limitations.

2. Literature Review

2.1. General Systems Theory

General systems theory (GST), proposed in [15] more than half a century ago, has proven to be a valuable tool for analyzing and understanding organizations and promoting a holistic and systemic approach to business management [16]. This approach has gained wide acceptance in various fields of study, including organizational management, where its application has been documented in numerous studies and desk reviews [17]. GST has enabled an evolution of the traditional organizational concept from the mechanistic vision of classical theory, in which a company was viewed as a closed system dedicated solely to generating profits, to a modern approach, wherein the company is seen as a complex entity that interacts formally and informally with its immediate and external environment [18].

According to this logic, companies not only actively participate in the propagation or perpetuation of problems in the world but also constitute economic actors who can contribute to solving the problems that arise in their environment. In this light, systems thinking as defined in [19] is understood as a combination of human abilities and attitudes that ground the perception of the real world in relation to totalities. This approach not only enables a more comprehensive analysis and deeper understanding of the elements of the system but also stimulates actions based on their overview.

Through application, GST justifies its relevance in this regard. GST provides a framework for understanding how individual elements of a system interact with each other to produce emerging behavior and emphasizes the interdependence and interrelationship between these components.

From the perspective of GST, the concept of closed and open systems was reinterpreted in the context of organizations (groups, companies, etc.) regarding their interaction with the environment [20]. In this sense, organizations that are considered closed systems are viewed as isolated from their environment, with a focus on internal structures and behaviors. On the other hand, organizations considered to be open systems are connected to their environment, with attention paid to how this interaction is managed.

Recent studies on GST, based on seven central principles, include the interdependence of components, the importance of adaptive systems, and the need to understand the system [21]. This perspective has been validated in various contexts ranging from ecology to economics [19,22,23,24]. In the case of the humanitarian supply chain, the use of GST by [25] provides a deeper and more nuanced understanding of how power drives decision-making in complex and challenging organizational contexts, such as humanitarian. In the context of organizations, systems thinking becomes indispensable. Its application stimulates the flexible creation of knowledge and enables companies to adapt to changes and challenges in the environment and to develop innovations. This approach can be used to catalyze the transformation of businesses into intelligent organizations capable of learning and proactively adapting to the changing environment.

GST therefore offers a theoretical and methodological framework for the development of robust systems thinking in a business environment. This framework promotes the understanding of organizations as dynamic and interdependent systems that can enhance decision-making, strategic planning, and innovation while fostering the building of more resilient and adaptable intelligent organizations. The true essence of systems thinking lies in recognizing the importance of each element of the system, even those that at first glance seem irrelevant or of secondary importance [26]. Using this rational, logical, and strategic approach, the aim is to identify and address the root causes that produce specific impacts in a system.

This implies considering the underlying structure of a system, which is crucial to its behavior [27]. When strategic decisions do not take such structure into account, one can inadvertently hit the limits of the system, which, in the long run, can turn what appears as initial success into a problem for the organization. In [26], the above is illustrated using an example of when a product’s sales fall short of expectations. The immediate response could be to focus on improving the sales department (motivation) when the problem may be systemic in nature and originate anywhere within the organization (e.g., quality) and externally (suppliers).

This scenario shows both the limits of linear thinking and the importance of adopting a systems view to tackle complex problems. In this context, measurement models conceive the enterprise as a social system that coexists in two domains, that of people and objects and the social domain, allowing for a more coherent view of organizations, where all functions and activities revolve around the system, whose goals rotate, rather than their operation as separate entities [28,29]. In this way, GST provides a valuable framework for understanding an organization and the functioning of companies from a systems perspective.

From the above, it can be concluded that GST has contributed significantly to analyzing and understanding organizations from a holistic and systemic perspective in business management [30]. However, in the current environment characterized by sustainability and global constraints, failure to incorporate the SDGs into a systematic analysis of organizations can lead to significant gaps in their ability to understand and address the complexities of sustainable growth [31]. From the perspective of GST, organizations not only interact in isolated ways but also have complex interactions with the external environment [29]. This implies an inherent responsibility of organizations to broader topics, including those highlighted in the SDGs. The SDGs set by the United Nations represent critical areas that have been identified as requiring attention to ensure a sustainable future. Neglecting these goals in organizational and strategic analysis could lead to short-term solutions and unsustainable efforts.

There is a clear opportunity to integrate the SDGs within the framework of GST. These goals address complex problems that require a systems approach to solve. For example, when considering goals such as responsible consumption and responsible production, companies should examine not only their internal operations but also how they interact with the broader business ecosystem [32]. Such a holistic perspective is consistent with the underlying philosophy of GST.

Therefore, although GST provides an essential framework for looking at organizations from a systems perspective [23], there is a perceived need to further develop and expand this framework to include the SDGs. This would not only facilitate a better understanding of global challenges but also actively contribute to developing and implementing sustainable solutions [33].

2.2. Organizational Performance and SDGs

The SDGs, which replaced the Millennium Development Goals, were adopted by UN member states in 2015 and comprise 17 goals and 169 targets that address a range of social, economic, and environmental challenges, including poverty, hunger, health, education, gender equality, clean water and sanitation, decent work and economic growth, industry, innovation and infrastructure, reducing inequality, sustainable cities, responsible consumption and production, climate change, life below water, life on land, peace and justice, and partnerships for the goals [34].

On the other hand, organizational performance has held a central place in the study of organizational management for decades and is a key pillar in the seminal work of [35]. However, the emergence of the United Nations Sustainable Development Goals (SDGs) in 2015 has sparked growing academic interest in how these goals can influence and improve organizational performance [36].

This renewed focus is driven by the growing recognition that sustainability and business performance are linked in ways that are not yet fully understood [37]. Recent studies support the idea that commitment to the SDGs can have a significant impact on multiple dimensions of business performance, including operational efficiency, business reputation, and financial performance [38,39].

Organizations that manage to align their strategies and operations with the SDGs benefit not only from improved performance but also from greater resilience to market turbulence and better stakeholder relationships [40]. By adopting the SDGs as a comprehensive framework for strategic decision-making, organizations can achieve sustainable competitive advantages while contributing to the achievement of social and environmental goals on a global scale [41].

However, this path to SDG mainstreaming is not without its challenges. Organizations can struggle to balance short-term needs with long-term goals [42]. Despite these challenges, the increasing pressure for greater transparency and corporate accountability from investors, regulators, and society at large suggests that incorporating the SDGs into organizational strategy and operations will increasingly be an expectation, not an option [43]. In this sense, the role of organizations in achieving the SDGs is a topic of growing interest in the scientific literature concerning SDGs 8, 9, and 12.

Goal 8: Decent work and economic growth—This goal emphasizes promoting inclusive and sustainable economic growth, productive full employment, and decent work for all. Despite its importance, its implementation poses significant challenges, particularly in developing countries. Some studies have highlighted that organizations can play a crucial role in achieving this goal, notably through fair, equitable, and inclusive employment practices and the promotion of entrepreneurship and innovation [44]. However, others argue that focusing solely on economic growth could undermine other SDGs, such as those related to the environment [45].

Goal 9: Industry, innovation, and infrastructure—This goal promotes the building of resilient infrastructure, inclusive and sustainable industrialization, and innovation. In achieving this goal, many studies have emphasized the role of organizations, particularly in the technology and innovation sectors. Some studies also suggest that organizations that adopt open innovation practices are better able to contribute to this goal [46]. However, as with SDG 8, there are concerns about possible trade-offs with other SDGs, particularly those related to environment and sustainability [47].

Goal 12: Responsible consumption and production—This goal focuses on ensuring sustainable patterns of consumption and production. Some studies argue that organizations play a pivotal role in promoting more sustainable consumption and production patterns by adopting practices such as developing sustainable products, adopting circular economy principles, minimizing waste, and promoting responsible consumption [48]. However, others point out that the responsibility for this goal should not lie solely with organizations but requires the participation of all actors in society, including consumers and policymakers [49].

Overall, the literature on the SDGs shows there are different perspectives, but there is a consensus on the important role that organizations play in achieving them. However, there is also recognition of the significant challenges and trade-offs to be considered, and that a more integrated and systemic approach is required to implement them.

The relationship between various organizational factors such as management [50], technology [51], innovation [52], and the performance of companies is a much-studied topic in corporate governance and sustainability. The main factors include environment, human resources, management, technology, and innovation.

The environmental field has become increasingly important in recent years and is recognized as a critical factor in business performance. In one study [53], the resilience of automotive and aerospace supply chains was analyzed in the face of the COVID-19 outbreak, and the need to integrate environmental factors into business planning and management was emphasized.

On the other hand, Ref. [54] examined the impact of knowledge management practices on green innovation and sustainable enterprise development. They found that such practices can improve organizations’ ability to develop green innovations, thereby contributing to corporate sustainability.

In the area of human resources and management, Ref. [55] examined the relationship between environmental ethics, environmental performance, and competitive advantage and found that environmental training plays a crucial role.

Regarding technology and innovation factors [56], how technological innovations are managed together with globalization has become a driving factor for service quality and organizational competitiveness. Finally, in the study by [57], it is pointed out that social networks can play a crucial role in knowledge formation that indirectly affects organizational performance.

Taken together, these studies demonstrate the importance of environmental, human, managerial, technology, and innovation factors in business performance and underscore the need for further research on how these factors interact and can be effectively managed to promote business performance and sustainability.

2.3. Structural Archetypes and Innovative Capacity in the Current Context

Traditional organizational design theory focuses on the existence of universal forms and the assumption of a single optimal form of organization, as shown by the work of [58] on bureaucracy and [59] on multidepartmental structure. However, in the 1960s and 1970s, this premise was challenged by contingency theory, and it is currently postulated that variations in organizational forms are adaptive responses to context demands [60].

In the current post-pandemic context, these approaches are becoming increasingly relevant as environmental change is more dynamic and complex than ever. Organizations are forced to adapt quickly to situations of uncertainty and volatility, for which innovation is required both in their internal processes and in the products or services they offer [61,62].

Borrowing from the archetypes of mechanistic and organic organizations proposed by [63], it can be concluded that organizations may need a more organic approach in the post-pandemic environment, thus being able to adapt quickly to new conditions created by accelerated change and innovation, which are constant [64]. Even mechanistic and organic structures can coexist in different parts of the same organization in response to the different demands of functional sub-environments [65]. In this sense, the notion of ambidextrous organizations, in which mechanistic and organic features are combined to adapt to both evolutionary and revolutionary changes [66,67,68], can be particularly useful. In an environment where innovation and adaptability are essential for survival and success, companies must be able to manage these two aspects effectively [69].

In addition, the demands on companies have changed due to the growing concern for sustainable development. Therefore, there is a need to balance profitability and competitiveness with a commitment to ethical, social, and environmental issues. In this sense, changes in industrial systems, resorting to practices based on circular economy, product–service systems, and the Sustainable Development Goals, are strategies that can be used to meet these market needs [70].

2.4. Organizational Performance Measurement and Monitoring Model

The systemic approach is used to investigate these relationships; consequently, it follows from GST that an organization is a complex of parts that interact with each other to form an organized whole. In this sense, Ref. [71] argues that organizational integration is viewed as a potential connection between people and organizations to achieve specific goals and create competitive advantage. Within this framework, a diagnostic tool is proposed to assess the current state of a company, which is the initial phase of the model [72].

Strategic measures are then implemented that aim to eliminate identified weaknesses and fortify the company’s strengths. After the implementation of these strategies, a new diagnosis is made to identify and quantify the evolution of key performance indicators. Using this feedback process, it is possible to monitor and measure the progress of SMEs and adjust strategies where necessary [73].

A model with an integral approach must include spatial configuration variables, temporal dynamization, and relational visualization [74] to meet the conditions for the viability of a business organization: internal coherence and congruence with the environment [75]. Achieving this requires overcoming the limitations of other models in terms of their practical application, limited focus on resources, difficulty in measuring their impact, and minimal focus on innovation. This creates value by enabling the understanding of the structure and functioning of companies toward facilitating the development of effective strategies to improve business performance.

3. Methodology

Taking into account the necessary conditions for the application of a model for measuring and monitoring business performance (discussed in Section 2.4), this paper proposes a stochastic model structured in a matrix that considers the SDGs that are particularly relevant for SMEs, such as 8 (“Decent work and economic growth”), 9 (“Industry, innovation, and infrastructure”), and 12 (“Responsible consumption and production”), thus improving innovation, digitalization, and environmental sustainability in a transversal way, complementing the spatial, temporal, relational, stability, and adaptability variables and solving the difficulty in measuring the impact by using Monte Carlo simulation.

In this matrix, the spatial variables refer to the physical aspects of the organization (equipment, technology, buildings, machinery, etc.), while the temporal variables allude to the processes and activities carried out within the organization, including innovation, digitalization, and incorporation of circular economy strategies in the interest of consolidating sustainability [76]. For their part, relational variables deal with the interactions between different actors and elements of the organization, both internal and external [77], with the latter including the efforts and strategies for achieving sustainability [78] and consequently supporting the SDGs.

In the proposed model, it is assumed that the company is an integrated system, in which all dimensions are interconnected and interact dynamically. Such a systemic conception makes it possible to understand that any individual change can have an impact on the system [79]. This approach is applicable to a wide range of organizations, from businesses to government agencies to non-profit organizations. By adopting this model, organizations can gain a more complete view of their structure and functioning, which in turn allows them to design more effective strategies to improve their performance [80,81].

The model starts from a two-step planning exercise, considered as an instrument to “analyze and identify the transit processes inherent to a strategic path”, from which strategies derived from Porter and Drucker’s proposals are built and implemented, and evaluation systems are applied to evidence the results and achievements attained by the different projects. Considering that complex systems are characterized by new states of order, which, in the business case, is the permanent emergence of new behaviors [82], the formulation of projects with work teams that undertake the most convenient strategies at each moment and maintain a structure is required, in accordance with the concept of business organization of [83], which must include other components or subsystems of the whole; for this, four dimensions of the company are proposed in this work: people, management, structure, and environment. The dimensions, in turn, are explained by 16 attributes—see Figure 1—composed of 47 variables explained with 197 indicators. Additionally, Figure 1 enumerates diverse dimensions, attributes, and variables pertinent to the management scanner. The selection of these variables is underpinned by an extensive array of sources, encompassing the works of [84,85,86,87]. These sources collectively constitute varied viewpoints and methodologies within the management domain, enhancing the comprehensiveness and robustness of variable selection.

The model makes it possible to understand how, by tracing the interdependence of the variables, the permanent emergence of new states of order can be observed [88]. In general, the interaction between the parts causes the system to look like an organized whole [81], making it possible to establish and measure the strategic purposes of the company, which is evident in the results of the current model.

3.1. Methodological Process

The proposed model is a tool that shows the result for each of the indicators and the effect, in real time, on the attributes and dimensions defined in Figure 1, making it possible to intervene on the fly, i.e., to manipulate the variables and adjust the projects to achieve sustainable transformation of the company. Consequently, it is possible to draw up strategic projects to intervene in the organization at the points identified using the model. The evolution of the indicators is monitored in real time with a Power BI dashboard.

The model, along the lines of [89], uses a multicriteria methodology that combines qualitative and quantitative criteria, with the assignment of rating and ranking by experts, for the evaluation and rating of company performance. To apply the multicriteria methodology to the strategic diagnosis problem, the following steps shown in

Table 1. Steps followed in the application of the multicriteria methodology.

Define the problem: Identify the problem to be solved and the objective to be achieved, determining the key areas to be evaluated.

2. Identify the evaluation criteria: Identify the criteria and factors that influence decision-making and the achievement of the strategic objective.

3. Establish the hierarchy of criteria: Hierarchize the identified criteria to establish the relative importance of each of them in decision-making.

4. Establish the valuation alternatives: Define the valuation alternatives: enhanced, viable, warning, and high risk.

5. Assign weights and scores: Assign weights to each of the criteria and scores to each of the dimensions evaluated to determine the total value of the company.

Source: Own elaboration.

are included.

Continuing with the methodological process applied, the structure for implementing the model in the companies is carried out in stages. The first stage corresponds to management diagnosis (1), in which a description is prepared with the support of figures on the state of the company, based on the perceptions of the people who comprise the organization. Meanwhile, the second stage refers to the measurement of indicators (2) for demonstrating the quantitative and qualitative aspects of the variables and indicators. Once the information for each dimension has been collected, each of the variables is quantified using the set of indicators. Finally, the third stage corresponds to the evaluation and monitoring of the variables in real-time (3), consisting of a permanent review process in each area, using a Power BI dashboard to record the evolution of the indicators over time [90] and in the company in a systemic manner.

Regarding the indicators for evaluation, decision-making, and strategy formulation in the company used in this study,

Table 2. Dimensions, attributes, and variables for the management scanner.

Dimension	Attribute	Variable
Structure	Financial Technological Infrastructure	Funds internal generation Company liquidity Business cycle Business profitability vs. non-quality costs Fulfillment of strategic organizational objectives Clean technology Technological innovation Computer equipment Manufacturing technology or servo production Digitalization Strategic location vs. suppliers and customers The physical structure in harmony with nature Spaces (areas): Use of physical spaces Information safety
Management	Processes Functions Philosophy Environmental Policies Sustainability	Management processes Mission processes Support processes Strategic functions Tactical functions Operational functions Vision Mission Principles Values Policies Environmental policies Environmental behavior Environmental attitude Environmental knowledge Sustainability benefits
Human Resources	Culture Climate Human talent Communication	Intrapersonal ties Interpersonal links Interorganizational linkages Cohabitation Stimulus Competences Compensation Formation Media Participation Impact
Environment	Market Government United Nations Culture	Clients Suppliers Government policies Stimuli Support entities Stakeholders Internship SDG 8–SDG 9 and SDG 12

Source: Own elaboration.

lists the dimensions, attributes, and variables that make up the management scanner. However, it is worth mentioning that the 197 measured indicators are not included because this model is in the process of being registered.

3.2. Sample and Data Collection

A total of 534 questionnaires were completed, which, following filtering, corresponded to 476 SMEs from all types of sectors in the department of Antioquia. The response rate was 89%. Primary data were collected with an online questionnaire addressed to the managers of the selected organizations. The sampling was non-probabilistic [91]; only formal companies were surveyed, with a willingness to belong to the group of subjects analyzed (authorization to use information was provided).

3.3. Model Construction

A structured questionnaire, designed by a team of experts [89], was used as an instrument, and with expert judgment, the weights of the indicators were obtained based on experience with the organization [91]. The process of validation using expert judgment involved obtaining opinions and ideas from ten experts in the relevant field regarding the instrument. These experts have extensive experience in the relevant area and focused on assessing the relevance and clarity of the items. The comments obtained from these experts served to refine and improve the quality of the instrument [92]. In addition, it is important to highlight that, in the first instance, a pilot test was carried out with the participation of three experts as part of this validation process. The details of the validation process are described below:

Feedback Analysis: Feedback from the 3 experts was collected and analyzed in a systematic manner, paying attention to areas where common problems or discrepancies arose in the experts’ responses.

Modifications to the Questionnaire: Based on the comments and previous analysis of the experts, the necessary modifications were made to the original questionnaire to address the problems identified.

Repetition of the Process: It was not necessary to repeat this cycle of testing and feedback with the same or other experts because the questionnaire reached an acceptable level of clarity and validity.

Content Validity: Content validity is a measure of the extent to which the instrument items effectively assess the construct in question. Expert validation serves as a widely used method for appraising content validity, involving the scrutiny of ten experts to ensure that the items are pertinent and representative of the construct [93].

Statistical Validation Tests: In tandem with expert validation, a battery of statistical tests was administered to evaluate the instrument’s validity and reliability. These tests encompass an analysis of internal consistency with the application of Cronbach’s alpha coefficient. The aim of this analysis is to elucidate the underlying structure and reliability of the instrument [94]. The dataset comprised 476 valid cases, signifying a completeness rate of 100%. No cases were excluded during the procedure.

Reliability Statistics: The Cronbach’s alpha coefficient, a key indicator of internal consistency, yielded a robust value of 0.906, underpinned by four elements within the instrument.

In summary, expert validation and statistical testing are essential components in the development of measuring instruments. These steps ensure that the instruments are valid, reliable, and appropriate for use in research or evaluation.

To conduct this study, a quantitative approach was used with the objective of examining the relationship between SDGs 8, 9, and 12 and organizational performance. The proposed questionnaire covers the dimensions, attributes, and variables listed in Table 2, complemented with indicators related to SDGs 8, 9, and 12 and the documentary review [95] to confront and refine the result under the criteria defined in the expert panel [89]. Each question was formulated using a 9-point Likert scale, allowing valuation from 1 (very weak) to 9 (great strength).

Then, a form was structured in Google Docs, and using a Power BI dashboard, one per company, data were collected and reported in real-time for the refinement of the model. Subsequently, each component was quantified, using the SM with reference weights for classification in the four levels of the management system (advanced, viable, alert, risk; see Table 1), and the necessary actions for the productive and sustainable transformation process were established with the participation of the teams in the analysis and resolution of problems.

3.4. Expert Judgment and AHP Matrix

To determine the weights of the dimensions, factors, and indicators in the matrix, an expert judgment was performed. The analytic hierarchy process (AHP) methodology was used to calculate the weights of the dimensions and factors in the matrix [96,97].

The AHP is a mathematical–analytical technique that allows decision-making in complex situations, especially when a comparison of different criteria, which can be quantitative or qualitative, is required [98]. The following is a description of the process and validation of AHP:

1. Hierarchical structuring: The first step in AHP is to decompose the decision problem into a hierarchy of sub-problems represented in levels. At the top is the main goal of the decision. Below are the criteria that influence the decision, followed by sub-criteria (if any), and finally the decision alternatives.

2. Pairwise comparisons: Once the hierarchy is established, pairwise comparisons of the criteria (or sub-criteria) are performed relative to the next higher level. These comparisons are rated on a scale from 1 (equally important) to 9 (very important). These pairwise comparisons are represented as a square matrix.

3. Weight derivation: The weights or eigenvalues of each criterion, sub-criterion, and alternative can be derived from the pairwise comparison matrices [99]. This makes it possible to assign each element a numerical weight that reflects its relative importance to the overall decision.

4. Consistency test: A key advantage of AHP is the ability to carry out a consistency test. Because human decisions can be subject to inconsistencies, the AHP methodology provides a consistency ratio that indicates how consistent the decision-maker’s pairwise comparisons were. A ratio that is too high (usually greater than 0.10) indicates that comparisons may be inconsistent and should be reviewed by the decision-maker [100].

5. Summary and decision: Finally, a synthesis of the weights is carried out to obtain a ranking or prioritization of the alternatives according to their ability to fulfill the main objective. Validation of AHP: The validation of AHP is based on its mathematical accuracy and its ability to handle quantitative and qualitative data. AHP enables a structured presentation of a complex problem and facilitates the inclusion of expert judgment in decision-making. Consistency testing is a fundamental feature that strengthens the validity of the process and ensures that peer comparisons are consistent and logical.

In summary, expert opinions were used to determine the weights of the dimensions, factors, and indicators in the matrix, just as the AHP method is used to calculate the weights of the dimensions and factors. This combination of expert judgment and AHP methodology ensures an informed and consistent decision, especially if the matrix passes the consistency test.

3.5. Monte Carlo Simulation

After data collection and coding, the Monte Carlo simulation technique was used in @Risk 8.1 software to construct a model to gauge company performance. Monte Carlo simulation, derived from statistical sampling techniques, essentially facilitates estimating the impact of risk due to uncertainty by allowing analysts to see a range of possible outcomes and their probabilities for any given choice of action.

In the process, each uncertain variable, factor, and dimension is replaced with a set of multiple possible values, which were determined by random sampling from their probability distributions. This simulation was then repeated thousands of times, each time calculating an outcome, which ultimately provided a distribution of potential results.

Consequently, this methodology allows for a deeper understanding of the system’s dynamics, identifying which variables have the most profound influence on organizational performance. By doing this, one can recognize the risks associated with various factors and indicators, offering a more holistic view of the uncertain environment in which companies operate.

To ensure the robustness and validity of the Monte Carlo simulation performed in @Risk software, a methodical validation process was implemented. Specifically, the validation of the simulation results was achieved using five separate runs (or iterations) of the simulation, each processing the dataset in its entirety.

The dataset consists of 476 data points, which represent individual measurements or observations related to a company’s performance metrics, factors, and dimensions. These data points represent the empirical basis on which the Monte Carlo simulation generates its probability distributions and subsequent results.

By conducting the simulation across five separate runs, the model’s stability and consistency are tested. Each run can potentially produce slightly different outcomes due to the inherent variability in the Monte Carlo process, which randomly samples from the defined distributions. The goal is to ensure that across all five runs, the results are consistent and converge around a stable set of values, which indicates the simulation’s reliability.

The following is a breakdown of the validation process.

Initialization: The Monte Carlo simulation was set up in @Risk software using the 476 data points to define the probability distributions for the variables, factors, and dimensions.

Run 1: The simulation was executed, generating thousands of potential scenarios for each of the 476 data points.

Runs 2 to 5: The simulation was repeated four more times, each time recalculating outcomes based on the random sampling process.

Analysis: After five runs, the outcomes were collated and analyzed. Key metrics, such as means, medians, and variances, were compared across the five runs to check for consistency.

Validation: If the results across the five runs are consistent (i.e., they converge to similar values and show minimal variance), this indicates the simulation is reliable. Any anomalies or significant variances would necessitate further investigation, potential model adjustments, and potentially more runs.

Using this rigorous approach, with five runs and 476 data points, ensured that the Monte Carlo simulation results were not only statistically valid but also representative of the complexities and uncertainties inherent in the company’s performance metrics.

3.6. Data Analysis Process

Descriptive analyses were performed to summarize and present the collected data. Inferential analyses were used to determine the statistical significance of the relationships between the dimensions (which include the SDGs) and organizational performance. A sensitivity analysis was also conducted to identify which variables have the greatest influence on organizational performance. Subsequently, @Risk was used to adjust the probability distributions of the responses using triangular distributions in which the minimum, maximum, and most probable values obtained with the survey were adjusted, and the model was defined under the structure set out in Equation (1).

$$f\left(X\right)={\sum }_{i=1}^n{D}_i\ast M_i\ast W_{di}\ast {\sum }_{j=1}^m{F}_j\ast W_{Fj}{\sum }_{l=1}^k{v}_l\ast W_{vl}$$

(1)

where $\left\{\begin{array}{c}D=\left\{\begin{array}{c}1. Management\\ 2. People \\ 3. Environment\\ 4. Structure \end{array}\right.\\ F=\mathrm{M}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{s} 1 until 16\\ V_l=\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{v}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e} \mathrm{L} \mathrm{t}\mathrm{o} \mathrm{K} (\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} \mathrm{L} \mathrm{c}\mathrm{a}\mathrm{n} \mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{s} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{s} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} 1 \mathrm{t}\mathrm{o} \mathrm{F}\\ K=1 until 197\\ M=weight regression coeficients of @Risk\\ W_{di}=weight of the i dimension (1 until 4)\\ W_{vl}=weight of the index i \left(1 until 197\right)\\ W_{Fj}=weight of the variable j in factor F\\ \end{array}\right.$

Finally, to complement the data analysis, the results of this study were validated with a careful review of the assumptions underlying the model, the internal consistency of the results, and their consistency with the existing literature in the field, as shown in

Table 3. Levels of classification of information for decision-making.

Valuation	Operationalization
Enhanced	Aspects of the company that are perceived as outstanding and reflect the company’s great achievements in the evaluation process.
	Factors that are given a very high rating between 8.0 and 9.0, which are high-value aspects of the company.
Viable	The company’s characteristics are perceived as good; some achievements have been made and indicate normal functioning of the company.
	There is room for improvement since the optimal level for the company has not yet been reached.
	Aspects with a score between 6.0 and 7.9 that reflect good performance but can be improved upon.
Warning	Characteristics of companies for which there are certain difficulties in implementation; at this level are factors that have demonstrated some failures and indicate early warnings.
	Aspects rated to values between 4.0 and 5.9. This scale indicates failures in the company’s various processes and reflects errors that have occurred.
High Risk	Characteristics of the company that can cause significant problems and place the company in a vulnerable situation.
	Typically scored between 0.0 and 3.9 and reflect negligence, poor practices, mistakes with serious consequences, and omissions that leave a negative image and affect the stability of the organization.

Source: Own elaboration with expert judgement.

.

4. Results and Discussion

From Figure 2, it can be deduced that, overall, there is a 97% probability of an organization’s performance being in the alert range, since the organization performance indicator has an average output value of between 5.20 and 5.99, with only 2.4% of the companies being in the range of usable performance and none in the advanced range.

Table 4. Organizational performance.

Dimensions/Variables	Minimum Value	Most Likely	Maximum Value
1. Environment (SDG 12)	4.25	5.03	5.80
1.1 Context	5.75	6.57	7.39
1.2 International	1.92	2.47	3.02
1.3 Legal	5.33	6.26	7.19
1.4 Socioenvironmental	3.90	4.75	5.60
2. Structure (SDG 8)	5.71	6.45	7.20
2.1 Finance	6.16	6.76	8.01
2.2 Infrastructure and production	5.84	6.64	7.45
2.3 Technology	5.09	5.94	6.80
3. Management	4.37	5.59	6.81
3.1 Institutional philosophy	4.37	5.53	6.69
3.2 Functions	5.04	6.15	7.26
3.2 Processes	4.13	5.42	6.72
4. People (SDG 9)	4.51	5.43	6.34
4.1 Organizational climate	4.65	5.51	6.37
4.2 Organizational culture	4.78	5.69	6.60
4.3 Human resources management	4.26	5.22	6.19
Organizational performance	4.73	5.63	6.53

Source: Own elaboration.

shows that the independent variables of environment, structure, management, and people have different ranges of values, indicating their different degrees of impact on organizational performance. Thus, the environment variable ranges from 4.25 to 5.80, with the most likely value being 5.03. The structure variable ranges from 5.71 to 7.20, and the most likely value is 6.45. Management ranges from 4.37 to 6.81, with a most likely value of 5.59, and the people variable ranges from 4.51 to 6.34, with a most likely value of 5.43.

Several studies in the existing literature suggest that these variables are fundamental to organizational performance. In the case of the environment, different studies highlight that environmental factors, including economic, regulatory, and market changes, can have a significant impact on an organization’s performance [101,102]. Regarding structure, the structure, hierarchy, division of labor, and coordination between departments of an organization can influence its performance. Effective organizational structures have been shown to facilitate communication, decision-making, and operational efficiency [102]. Meanwhile, management effectiveness is a critical factor in an organization’s performance. This includes, among other things, the ability to set and communicate clear goals, motivate and guide employees, make strategic decisions, and improve innovation processes in management [103]. Finally, regarding the people variable, employees are a crucial resource for any organization. Their skills, dedication, and satisfaction can have a major impact on company performance [104,105].

Continuing with the presentation of the results, the results shown in Figure 3 suggest that there is a growing trend, and as the percentiles in management, employees, environment, and structure (X) increase, so does the organizational KPI (Y). This indicates that changes in management, people, environment, and structure (X) have a positive impact on the organization’s performance.

With the regression coefficients in Figure 4, it is possible to determine the individual value of an SME with knowledge of the assessments of the dimensions using Equation (2). In this sense, the results indicated that the management variable was the best-valued variable with 0.62, followed by the people variable with 0.49, the environment variable with 0.42, and, finally, the structure variable with 0.40.

$$Y=0.62X1+0.49X2+0.42X3+0.40X4$$

(2)

where:

Y: Organizational performance.

X1: Management; X2: people; X3: environment; and X4: structure.

Figure 4. Regression coefficients. Source: Own elaboration using @Risk.

Figure 4. Regression coefficients. Source: Own elaboration using @Risk.

With regard to the application of Spearman’s correlation coefficients, Figure 5 shows some variations in the variables, indicating that not all changes in dimensions (X1: management; X2: people; X3: environment; and X4: structure) have the same impact on the organization’s average performance, it is observed that there is a strong and positive correlation between management (0.64), people (0.49), structure (0.42), and environment (0.41) and the average performance of the organization. From this, we can conclude that the organizational performance indicator behaves in the same direction as the management indicator score increases.

The regression coefficients allow the calculation of the organizational performance indicator for a specific SME. To achieve this, the value of each of the variables shown in Figure 6 must be calculated. The results indicate that the factors including processes at 0.37, institutional philosophy at 0.34, and functions at 0.31 were those with the highest values.

Spearman’s coefficient is a nonparametric measure of correlation used to assess the relationship between two ordinal variables or quantitative variables that do not follow a normal distribution (see Figure 7). Nonlinear relationships or outliers in the data are not considered. In addition, the Spearman coefficient does not provide any information about the strength of the relationship but only indicates whether there is a monotonic relationship between the variables or not. For the present case, it is observed that all variables have a positive relationship with organizational performance.

Figure 8 shows the impact of each factor on the average performance of the organizational KPI. The process factor, which carries the organizational key performance indicator in the range of (5.51 to 5.64), shows the greatest effect. The financial factor moves in between the factors shown. Similarly, the factors of institutional philosophy, functions, and human resources management were also shown to have an impact on organizational performance.

Finally, based on the values provided previously in Table 4, each dimension/variable can be classified into one of four categories (High Risk, Warning, Viable, Enhanced) according to the most likely values, as shown in

Table 5. Classification of dimensions and factors.

High Risk	International (Within the Environmental Dimension/SDG 12)	2.47
Warning	Environmental dimension (SDG 12)	5.03
	Socioenvironmental (within the environmental dimension/SDG 12)	4.75
	Technology (within the structural dimension/SDG 8)	5.94
	Management	5.59
	Institutional philosophy (within the management dimension)	5.53
	Processes (within the management dimension)	5.42
	People (SDG 9)	5.43
	Organizational climate (people dimension/SDG 9)	5.51
	Organizational culture (people/SDG 9 dimension)	5.69
	Human resources management (people dimension/SDG 9)	5.22
	Organizational performance	5.63
Viable	Context (within the environmental dimension/SDG 12)	6.57
	Legal (within the environmental dimension/SDG 12)	6.26
	Structure (SDG 8)	6.45
	Finance (structural dimension/SDG 8)	6.76
	Infrastructure and production (structural dimension/SDG 8)	6.64
	Functions (within the management dimension)	6.15
Enhanced	None of the dimensions/variables reach the level of excellence
	Organizational performance	5.63

Source: Own elaboration.

. The results suggest that while there are areas where SMEs are performing well, there are also some that are warned, and one dimension that is classified as High Risk.

Environmental dimension (SDG 12): The overall environmental dimension is on Warning, with particularly low performance on the international and social–environmental dimensions, with the latter on the verge of being classified as High Risk. Companies appear to have a good grip on context and legal aspects, but struggle with their performance in international contexts and socioenvironmental aspects, as previously stated by authors such as [99,106].

Structural dimension (SDG 8): The structural dimension shows good overall performance with strong results in the areas of finance, infrastructure, and production. However, technology is placed on Warning, suggesting the sector could improve in this regard.

Management dimension (SDG 9): The overarching management dimension finds itself in the Warning zone, yet there is a silver lining. The “Functions” sub-dimension fares relatively well, indicating areas of strength within management, as shown [107]. However, a deeper dive indicates possible bottlenecks in “Institutional Philosophy” and “Processes”—both of which are blinking a Warning signal.

The results clearly show that while there are areas where SMEs are performing efficiently, certain dimensions clearly require urgent attention and intervention. The granular insights from each dimension shed light on specific strengths and vulnerabilities, offering a clear roadmap for future strategies and interventions. This discussion aims to illuminate these nuances for a comprehensive understanding and effective decision-making.

5. Conclusions

The development of systems thinking requires an initial attempt to expand our vision beyond the limits of linearity, a challenge that inevitably forces us to step out of our comfort zone [27]. Despite the initial difficulties, systems thinking, similar to any learning process, becomes a natural and intuitive response to problems with time and practice.

This comprehensive and holistic approach involves considering the multiple elements and relationships that make up any system, including interactions with its environment [26]. This global perspective warns us about the mistakes that could be made by focusing solely on the individual parts of a system, where unexpected consequences or limitations on growth remain hidden.

This study is consistent with previous research in this area. The authors of [107], for example, found that organizational performance is closely related to management practices and organizational structure. Similarly, reference [107] concluded that environmental factors have a significant impact on firm performance much in the same sense as [99], who draw attention to the need to balance profitability and competitiveness with commitment to ethical, social, and environmental issues. Our study advances the understanding of the relationship between these factors and organizational performance by including the impact of workforce practices, technology, and innovation [108], factors that have been less studied in this context.

According to the data, there is a strong correlation between the dimensions of management, people, environment, and structure and the organizational performance of SMEs. This observation is supported by the results of previous studies that have identified these factors as being critical to organizational performance [109].

The results suggest that while organizations demonstrate strengths in areas such as context, legal aspects, finance, and infrastructure and production, in other areas, particularly those related to SDGs 8 and 12, there are also several areas for significant improvement. In particular, organizations need to work on the dimensions classified as “alert”, especially in the environmental dimension (SDG 12), where the international dimension represents a high risk, and in the management dimension. Ensuring improvements in these areas will help enhance the overall performance of the organization, which is also currently on alert.

On the other hand, this study has some limitations. First, it is based on a limited number of companies and therefore does not capture the full impact of these variables on company performance. Second, this study does not examine the interaction between the variables, which could provide a more comprehensive picture of how these variables work together to affect organizational performance. Third, this study assumes that all companies are subject to the same environmental conditions, which may not be the case in practice.

Regarding the practical implications, the results of this study have important practical implications for SMEs. It is noted that companies can improve their organizational performance by focusing on improving management, developing people, adapting to their environment, effectively structuring the organization, and contributing to achieving the SDGs. The findings also demonstrate the importance of investing in technology and innovation practices to improve organizational performance.

Finally, the results underline the need to pay special attention to the international and socioenvironmental aspects, as these seem to be high-risk areas for companies. In conclusion, this study represents a valuable contribution by providing companies with strategic guidance for proactively identifying and addressing risks and challenges. While it is possible to drill down and perform a specific analysis for an individual company that could provide personalized recommendations tailored to their individual needs, such detailed analysis is beyond the scope of this work. The strength of our research lies in providing a comprehensive and general view that can serve as a reference point for a wide range of companies in the industry. In future studies, the established set of variables and dimensions can be used to diagnose the business performance of SMEs belonging to specific sectors and compare their performance against the results obtained in this research.