Exploring the Role of Digital Technologies in Promoting Sustainable Entrepreneurship

The emergence of digital technology has drastically altered the landscape of entrepreneurship, bringing in a new era of digital sustainable entrepreneurship. As the digital revolution proceeds, entrepreneurs are increasingly leveraging the potential of digital tools and platforms to promote innovation while addressing important sustainability issues. The epidemic of 2019-COVID accelerated the usage of digital technologies, with physical distancing measures forcing firms to embrace online platforms and remote labour. This digital shift has created new opportunities for entrepreneurs to pursue, notably in the field of sustainability. Digital technologies like social media, Data analytics, the IoT, and cloud computing have enabled entrepreneurs to create new solutions to environmental and social challenges. These digital tools have allowed entrepreneurs to swiftly develop their businesses, reach worldwide markets, and engage with a varied range of stakeholders, all while reducing their environmental impact. This assumption builds on the power of digitalization to transform and alter the nature of entrepreneurship. Researchers and academics can increasingly use digital platforms to more efficiently commercialize their inventions, develop partnerships, and connect with industry partners. As the field of digital sustainable entrepreneurship evolves, academics and policymakers must investigate the dynamics and implications of this phenomenon. The most significant finding revealed that no entrepreneur considered digitalization as an individual goal for their organization, demonstrating the importance of businesses having an appropriate digitalization strategy that considers the opportunities and risks presented by the external business environment. Entrepreneurship has been identified as an important driver of economic development, and additional research is needed to determine how digital technologies improve entrepreneurship performance. Sustainable entrepreneurship is the integration of two words, sustainability & entrepreneurship. in particular, it integrates traditional entrepreneurship with the goal of sustainability. The specific concept of sustainable entrepreneurship is currently unknown. It takes a broader approach to encompass entrepreneurship. Sustainable entrepreneurship can be viewed as a subset of social entrepreneurship that focuses on overconsumption and climate change. A sustainable entrepreneurship enterprise, for example, will assess the sustainability of the products used in production (that is, whether the materials are eco-friendly, free of plastic, and/or biodegradable), the impact of the products and their business on the ecosystem and ecological resources, and the business's social value and profitability. However, it is easy to discover social entrepreneurs that are not completely sustainable; for example, a firm that works with local workers but uses plastic in product packaging. According to environmentalists, sustainable business must strike a balance between economic well-being, social justice, and environmental resilience. This entrepreneurship is the process of launching a business in which entrepreneurial goals are linked to social and environmental goals that promote the creation of long-term value. It is defined as an organization's ongoing commitment to moral behaviour, promoting economic advancement, and enhancing the standard of living for people worldwide as well as future generations. In the context of sustainable entrepreneurship research, some scholars have shifted their attention to the triple bottom line—the value provided by economic, ecological, and social factors—from a sustainability standpoint. Elkington developed the Triple Bottom Line (TBL) concept in 1994 to measure sustainability. Currently, many scholars employ the TBL concept to describe "sustainability" to the global sustainability strategy. In practice, TBL is used not only for conceptual explanations or descriptions of sustainability, but it is also an important tool for quantifying sustainability. Businesses are critical to achieving social goals, improving the environment, and promoting economic progress. In a nutshell, sustainable entrepreneurship in the digital age is a tremendous driver of innovation, development, and positive impact. As entrepreneurs, policymakers, and consumers, we must work together to address the challenges and embrace the opportunities presented by the intersection of technology and sustainability. We can all benefit from practicing sustainable enterprise.

LITERATURE REVIEW

1. Xu, Hou, & Zhang (2022). The paper highlights the importance of leveraging digital capabilities for sustainable entrepreneurship and emphasizes the role of innovation orientation in creating social and environmental value. Digital capabilities contribute to achieving net positive environmental impacts and driving sustainable value creation.

2. Gregori, & Holzmann, (2020). This paper highlights the importance of integrating sustainability and digital technology through a business model view and provides a comprehensive framework for recognizing that digital technologies can be used to promote sustainable entrepreneurship and create a positive social and environmental impact. The paper provides a solid foundation for exploration in the field of digital sustainable entrepreneurship, ultimately contributing to the theory and practice of sustainable entrepreneurship in the digital age.

3. Fuerst, Dominguez & Montes (2023) The paper focuses on the role of digital technologies in value enhancement, their delivery, and capture within the business models of sustainable entrepreneurship. The paper underscores the need for entrepreneurs to strategically leverage digital technologies to drive innovation, enhance operational efficiency, and create positive social and environmental impact in their ventures.

4. Avelar, Tiago, Almeida & Tiago (2024). The authors developed a model named Generalized Structural Equation Model (GSEM) to study the interactions between three essential variables: innovation, digitalization, and sustainability. This model was created to investigate how these variables affect a firm's reported growth Specifically, the combination of these factors can result in improved performance and competitive advantage. These variables influence the dynamics of sustainable entrepreneurship and its integration with innovation and digitization, affecting SMEs' growth and success in a competitive global market.

5. Gu, Pan, Hu, and Liu (2022). The study highlights the relationship between entrepreneurship and sustainability in 22 countries from 2005 to 2018, shedding light on how economic policy uncertainty affects sustainable practices. The study thereafter makes a distinction between innovative and business entrepreneurship, in which innovative entrepreneurship is divided into green and non-green spirits. It discovers that in high Human Development Index (HDI) countries, a green innovation spirit can greatly reduce environmental pollution, but this effect is less noticeable in lower HDI countries.

6. Petersen, Fuerst, and Torkkeli (2023) the authors study the nexus of sustainable entrepreneurship and digitization, emphasizing the transformative issues confronting today's entrepreneurs and managers and underlining the need to respond to these changes and create value in novel ways. This concept seeks to simplify the process of generating regenerative growth possibilities while addressing the difficulties of sustainability.

7.  George, Merrill, and Schillebeeckx (2020). The study focuses on how digital technologies are transforming the battle against climate change while promoting sustainable development. The research aims to spark new ideas and opinions in entrepreneurship, enterprise models, and ecosystems, eventually leading to a positive impact on society as well. The study underlines the importance of including socioecological value as a basic component of business models that prioritize environmental and social results, to promote sustainable practices. Observing firms that are actively involved with digital sustainability can provide insights into creative business approaches and methods that effectively include sustainability into their main business activities.

8. Usman, Kess-Momoh, Ibeh, Elufioye, Ilojianya, and Oyeyem (2024.) The study provides a comprehensive overview of how globalization and technological advancements are changing entrepreneurship. It identifies new trends, analyzes the transformative influence of technology developments, and investigates the complex consequences of globalization on business practices. An organization's potential to modernize and adapt in an economic environment that is rapidly changing is studied in this study. Entrepreneurs must adapt to technology improvements, embrace globalization's potential, along with its drawbacks.

Previous research suggests that new technologies can accelerate and expand national development while also positively impacting entrepreneurship. Scholars argue that the use of digital technologies in entrepreneurship signals a new digital revolution as well and digital transformation plays a crucial role in achieving sustainability. It may also lead to the development of new methods to preserve natural resources.

Research Gap

While there is a general study on the role of digital technologies in business, there is a scarcity of detailed studies that focus on specific technologies (e.g., IoT, AI, blockchain) and their direct impact on sustainable entrepreneurship. Few studies look at the barriers and challenges that entrepreneurs experience when implementing digital technology for sustainability. Understanding these hurdles is critical for creating effective support systems.

 1.3 Objectives of the study

1. To assess the awareness and usage of emerging digital technologies among entrepreneurs.
2. To identify the barriers and challenges faced by entrepreneurs in adopting emerging digital technologies.
3. To evaluate the potential of emerging digital technologies in enhancing sustainable entrepreneurship.
4. To analyse the perception and attitude of entrepreneurs towards sustainable entrepreneurship.
5. To investigate the relationship between the adoption of digital technologies and sustainable entrepreneurship outcomes.

1.4 Theoretical Framework

i) Digitalisation and Sustainable Entrepreneurship

Digitalization is the acceptance or use of digital technology by various stakeholders in a variety of contexts, such as applications and services. It applies to the sociotechnical method used for applying digitizing techniques to larger social and institutional contexts, transforming digital technologies into infrastructure. Research on employing digital technology for capitalizing on electronic opportunities began with the broad availability of the Internet. The rise of digital start-ups such as Airbnb, Uber, and Twitter has recently boosted the entrepreneurship movement. Recent research explores the potential benefits of digital artifacts for businesses, such as quick scalability and co-creation. Scholars have begun to explore how digital technologies impact sustainable entrepreneurship, laying the groundwork for future research. Digital sustainability is described as organizations that use technology to achieve sustainable development goals. In a word, digital sustainable entrepreneurship study focuses on how digital technology might help the creation of entrepreneurial activities that promote sustainability.

ii) The Contribution of Digital Technology in Sustainable Entrepreneurship

Digital technology improves efficiency, a key factor in promoting sustainable business performance. Automation and data analytics improve procedures in supply chain, manufacturing, and operations. This optimization reduces resource use and waste, promoting sustainable resource use.

• Data-driven decision-making: Data-driven decision-making is now important for long-term business growth. By collecting and analysing huge amounts of data, firms can acquire insights into their environmental impact. This information helps firms make informed decisions that promote sustainability, including energy efficiency and responsible sourcing.
• Supply Chain Transparency: Digital innovation is vital for developing accountable and transparent supply chains. Blockchain provides an immutable and decentralized database for documenting transactions, ensuring traceability across the supply chain. Transparency in the supply chain helps evaluate sustainability claims, promote fair labour standards, and improve overall ethics.
• Renewable Energy Integration: Integrating the Internet of Things (IoT) allows for real-time monitoring and management of energy consumption, leading to more sustainable practices. Companies may optimize energy usage and incorporate renewable energy sources like solar and wind into their operations. This decreases environmental impact and aligns businesses with the worldwide trend towards renewable and clean energy solutions.
• Improved communication and stakeholder engagement: Digital platforms enable communication and involvement between organizations and their stakeholders, such as consumers, employees, and communities. Social media, online collaboration tools, and e-commerce platforms enable firms to promote sustainability objectives, get feedback, and engage stakeholders in environmental and social goals. Connectivity promotes a culture of shared responsibility and sustainable consumption.
• Cost Savings and Innovation: Adopting digital technology for sustainable business development can result in significant cost reductions over time. Increasing efficiency, optimizing resources, and automating processes can lead to lower operating costs. Adopting digital technologies can boost sustainable practices, giving organizations a competitive advantage.
• Global Competitiveness: In a world increasingly focused on Businesses that use digital technology to foster sustainable practices, improve their worldwide competitiveness. Addressing environmental and socioeconomic accountability norms not only attracts environmentally diligent buyers. Digital transformation allows firms to experiment with and adopt new technology, leading to innovation. This covers AI, machine learning, IoT, and blockchain technologies, among others. Integrating these technologies enables firms to reinvent their products, services, and operations continuously.

1.5 Hypotheses of the Study

Based on the theoretical framework following hypotheses are proposed by the researcher

• H1: There is a significant positive relationship between the awareness of emerging digital technologies and the level of sustainable entrepreneurship.
• H2: Barriers and challenges in adopting digital technologies negatively affect sustainable entrepreneurship.
• H3: The potential of emerging digital technologies significantly enhances sustainable entrepreneurship.
• H4: Entrepreneurs with a positive perception and attitude towards emerging digital technologies are more likely to engage in sustainable entrepreneurship.

Sustainable entrepreneurs prioritize environmental and social causes, showing empathy, ethical integrity, and a forward-thinking mindset. They blend creativity, resilience, risk-taking, and adaptability to lead with a global perspective, learn continuously, and maintain a long-term commitment to positively impacting the world.

RESEARCH METHODOLOGY

This study adopted a quantitative research design to examine the relationship between digital technologies and sustainable entrepreneurship. The research aimed to explore underlying constructs, test hypothesized relationships, and assess entrepreneurs' perceptions through statistical analysis.

i) Sampling Technique and Data Collection

A purposive sampling technique was used to target entrepreneurs who actively engage in or are aware of digital technologies in their business operations. A total of 250 entrepreneurs were approached across various sectors. Out of these, 205 respondents returned completed questionnaires. After rigorous data cleaning—including checks for completeness, response bias, and outliers—136 valid responses were retained for analysis.

ii) Instruments and Measures

The questionnaire comprised multiple items measuring awareness, adoption, and impact of digital technologies, perceived benefits, and barriers related to sustainable entrepreneurship. All items were assessed using a five-point Likert scale ranging from “strongly disagree” to “strongly agree.”

iii) Data Analysis Techniques

The data were analyzed using descriptive statistics to summarize the demographic and response patterns. To identify the underlying constructs and reduce dimensionality, Exploratory Factor Analysis (EFA) was conducted using Principal Component Analysis with Varimax rotation. Subsequently, regression analysis was employed to examine the influence of extracted factors on sustainable entrepreneurship outcomes. All analyses were performed using SPSS software.

RESULTS AND DISCUSSION

Table 1: Demographic Profile

Source: Author Compilation

The demographic profile of respondents in the study "Exploring the Role of Digital Technologies in Promoting Sustainable Entrepreneurship" provides valuable insight into the diversity and characteristics of the sample. Out of 133 participants, a majority were male (71.3%), indicating a gender imbalance in either participation or engagement in sustainable entrepreneurship initiatives. In terms of age distribution, most respondents (77.9%) were under 40 years of age, suggesting that younger individuals are more inclined toward digital entrepreneurship or are more familiar with digital tools. Regarding experience, 70.6% of the respondents had less than five years of professional involvement, reflecting a predominance of early-stage or emerging entrepreneurs in the sample. This aligns with the observed trend of digital technologies attracting newer entrants to the entrepreneurial ecosystem due to their accessibility and innovation potential. Educationally, a substantial proportion of respondents held undergraduate (48.5%) or postgraduate (38.2%) qualifications, indicating a highly educated sample. The relatively small number of participants with only primary or secondary education suggests that digital entrepreneurship is more prevalent among those with higher academic attainment. Overall, the demographic analysis indicates that digital technologies are primarily adopted by young, educated, and relatively less experienced individuals in the realm of sustainable entrepreneurship. This insight is critical for designing inclusive and targeted digital support programs.

i) Reliability and Validity

Table 2. Reliability Statistics

Source: Compilation through SPSS Software

The reliability analysis of the constructs using Cronbach’s Alpha reveals high internal consistency across all measures. AU (α = 0.956) and SE (α = 0.955) demonstrate excellent reliability, indicating that their items are highly cohesive. PAE (α = 0.917) and BC (α = 0.878) also show strong reliability, suggesting consistent measurement within these constructs. Even PEDT, with only three items, achieves a solid alpha value of 0.856, reflecting good reliability. Overall, the results confirm that all five constructs—AU, BC, PEDT, PAE, and SE—are reliably measured and suitable for further statistical analysis within the context of the study.

ii) Exploratory Factor Analysis

We used EFA to assess the scale's consistency by evaluating the factor loadings of observed variables within the study's concept analysis. Factor loadings higher than 0.5 in all the above variables are being observed, hence all these variables show good Goodness of fit with their corresponding latent variables as well.

Table 3. KMO and Bartlett's test

Source: Values compiled through SPSS Software

The value of the KMO test in the table above is.0.932. This value represents the adequacy of the dataset for further processing. KMO tests between 0.8 and 1 indicate a suitable sample. Bartlett's sphericity test returned sphericity values less than 0.05, indicating the suitability of factor analysis.

Table 4. Rotated Component Matrix

 Source: SPSS 21 (Extraction Method: Principal Component Analysis, Varimax Rotation)

The Rotated Component Matrix, derived through PCA with Varimax rotation, reveals five distinct factors explaining the underlying structure of digital technology adoption and sustainable entrepreneurship. The loading values (ranging from 0.60 to 0.86) represent the strength of association between each item and its corresponding factor; values above 0.60 are considered strong and indicate good construct validity. Component 1 includes AU1 to AU10 with high loadings (0.66–0.83), indicating a strong alignment with the construct Adoption and Use of Digital Technologies. These values reflect a consistent and reliable grouping, emphasizing the importance of digital tools (AI, IoT, cloud computing) in business operations. Component 2, covering SE1 to SE8, shows loadings from 0.63 to 0.80. This indicates a solid grouping under Sustainable Entrepreneurship Awareness, suggesting respondents have a clear understanding and positive perception of sustainability's role in business. Component 3 (BC1–BC4) has loadings between 0.64 and 0.78, pointing to a reliable factor capturing Barriers to Adoption. Component 4 (PEDT1–PEDT3) shows strong loadings (0.60–0.86), reflecting Perceived Effectiveness of Digital Technologies. Component 5 (PAE1–PAE5) has loadings from 0.63 to 0.82, indicating a strong grouping around Perceived Advantages and Enablers. Overall, the high loading values suggest well-defined, internally consistent factors.

iii) Descriptive and Normality Test Statistic

A normality test evaluates whether a sample of data follows a normal distribution. Several statistical tests, including the Student’s t-test, one-way ANOVA, and two-way ANOVA, require a normally distributed sample population. The study's model uses skewness and kurtosis to assess data normality

Table 5: Descriptive and Normality Test Statistics

Source: Values compiled through SPSS Software

According to Hair et al. and Byrne (2010) define normal data is defined as having a skewness of -2 or +2 and a kurtosis of -7 to +7. The normality test findings show that most variables in the study meet acceptable skewness and kurtosis limits, indicating an approximate normal distribution

iv) Collinearity Diagnostics

Substantial relation could be found in two or more independent variables in a regression model that is covered under the statistical phenomenon of multicollinearity, showing a strong linear relationship between the predictor variables. This issue impacts regression analysis by making it difficult to exactly compute the effects of each independent variable on the dependent variable.

Table 6. Multicolinearity Diagnostics

Source: Values compiled through SPSS Software

A tolerance near 1 indicates low multicollinearity, while a tolerance near 0 indicates a potential concern. The variance inflation factor is the reciprocal of tolerance. The VIF measures how much the variance of the coefficient estimate is increased by multicollinearity. According to Jamal (2017), VIF values above 5 or 10 indicate poorly estimated regression coefficients due to multicollinearity. The table shows that all VIF values are less than 10, indicating that multicollinearity may not be a significant issue for this model. Potential Emerging Digital Technology has the greatest VIF (2.711), suggesting stronger multicollinearity compared to other variables.

v) Hypothesis Testing

H1: There is a significant positive relationship between the awareness of emerging digital technologies and the level of sustainable entrepreneurship.

Table 7: Model Summary

Source: Values compiled through SPSS Software

The model summary in regression analysis is critical for understanding the outcomes of your regression model, the relationships between variables, and the overall fit of the model. A value of R 0.689 shows a moderate to high positive relationship between the Independent Variable (awareness and use of emerging digital technologies) and the Dependent Variable (sustainable entrepreneurship). An R² of 0.475 indicates that awareness and use of emerging digital technologies account for approximately 47.5% of the variation in sustainable entrepreneurship levels. An Adjusted R² of 0.471 is slightly lower than the R². A standard error of 0.68605 indicates the average distance that the observed values deviate from the regression line.

Table 8: Anova Table

Source: Values compiled through SPSS Software

The ANOVA table determines the overall significance of the regression model. The F-test score of 121.226 indicates that the model is statistically significant. The p-value (Sig.) of 0.000, less than the 0.05 threshold, indicates that the model accurately predicts the dependent variable. The regression model indicates the association between awareness and usage of digital technology and the level of sustainable entrepreneurs.

Table 9:  Coefficients

Source: Values compiled through SPSS Software

 The Coefficients table provides comprehensive data on the model's predictors. The coefficient of the constant term has 1.382 and a p-value of 0.000, indicating no statistical significance at the 0.05 level. For every one-unit increase in "AWARENESS_USAGE" (independent variable), the dependent variable (e.g., level of sustainable entrepreneurship) is expected to increase by 0.647 units.  The standardized coefficient (Beta) is 0.689, showing a strong positive effect. The t-value of 11.010 and the p-value of 0.000 indicate that the relationship between awareness and usage, and the level of sustainable entrepreneurship is statistically significant. Based on the statistical significance of the awareness and usage variable (p < 0.05), we reject the null hypothesis and accept the alternative hypothesis H1. There is a considerable positive association between awareness of emerging digital technologies and sustainable entrepreneurship. It is possible that boosting entrepreneurs' awareness and expertise of digital technologies will lead to more sustainable business operations.

H2: Barriers and challenges in adopting digital technologies negatively affect sustainable entrepreneurship.

Table 10: Model Summary

Source: Values compiled through SPSS Software

The regression model shows that barriers and challenges are a significant predictor of Sustainable Entrepreneurship, accounting for approximately 56.9% of the variance in the dependent variable. The high R value suggests a significant positive association between Barrier Challenges and Sustainable Entrepreneurship. The estimate's relatively low standard error shows that the model's predictions are reasonably reliable. R is the correlation coefficient between the observed values of the dependent variable (Sustainable Entrepreneurship) and what was predicted from the regression model. An R-value of 0.754 indicates a high positive correlation between the predictor (Barrier Challenges) and the dependent variable. The Barrier Challenges predictor comprises 56.9% of the variance in Sustainable Entrepreneurship (R²=0.569). This indicates that the model has moderate to strong explanatory power. The Adjusted R² value is 0.566, slightly lower than the R² value. This suggests that even with more predictors, the model may still have significant explanatory power. A standard error of 0.62151 indicates that the observed values of Sustainable Entrepreneurship differ from the predicted values by approximately 0.62 units. Lower standard error values suggest that the model fits the data effectively.

Table 11: Anova Table

Source: Values compiled through SPSS Software

The ANOVA table demonstrates that the regression model is statistically significant, which means that the predictor(s) account for a significant portion of the variation in the dependent variable. The regression sum of squares equals 68.366. This number indicates the total variance in the dependent variable (Sustainable Entrepreneurs) that is explained by the independent variable (Barrier Challenges). The residual sum of squares equals 51.762. This value shows the total amount of variance in the dependent variable that is not explained by the model's independent variables. The total Sum of Squares is 120.127. This is the combination of both the value and the explanation of the total variability of the dependent variable (Sustainable Entrepreneurs). The F-value = 176.985. A high F-value shows that the model explains a considerable percentage of the variance in the dependent variable compared to the residual variance. The p-value for the F-statistic is less than 0.05, suggesting that the overall regression model is statistically significant. This means there is substantial evidence that the independent variable (Barrier Challenges) accounts for a significant amount of the variance in the dependent variable (Sustainable Entrepreneurs). The hypothesis suggests that entrepreneurs' challenges in adopting digital technology may limit their ability to pursue sustainable entrepreneurship effectively.

Table 12:  Coefficients

Source: Values compiled through SPSS Software

The coefficient table in regression analysis shows the estimated correlations between the independent variables (predictors) and the dependent variable. The constant (B = 1.022) is the intercept of the regression line, representing the predicted mean value of sustainable entrepreneurs with zero barriers and obstacles. The unstandardized coefficient for barriers and challenges (B =.777) predicts that a one-unit rise in barriers and challenges leads to a 0.777-unit positive change in sustainable entrepreneurship. This indicates a significant negative impact of barriers and challenges. The coefficient has a Standard Error of 0.058, which represents the predicted variation. Smaller standard errors suggest more exact estimates of the coefficient. The Standardized Coefficient (Beta) of 0.754 indicates a significant impact of barriers and challenges on sustainable entrepreneurship, as measured by standard deviations. The t-statistic of 13.304 tests the null hypothesis of no effect (coefficient equal to zero). The high t-value and p-value of 0.00 imply a statistically significant association between barriers and challenges in the adoption of sustainable entrepreneurship. If digital adoption is hindered, entrepreneurs may find it difficult to adopt innovative, sustainable practices effectively.

H3: The potential of emerging digital technologies significantly enhances sustainable entrepreneurship.

Table 13: Model Summary

Source: Values compiled through SPSS Software

The table analysis of the R-value of 0.694 indicates a moderate to significant relationship between the variables (including emerging digital technologies) and sustainable entrepreneurship. The R-squared value of 0.481 suggests that the model accounts for about 48.1% of the variance in sustainable entrepreneurship. This implies that the predictors, which include the potential for emerging digital technologies, have a significant influence on the dependent variable. The adjusted R-squared of 0.477, which is close to R-squared, shows that the model is a good fit and does not overfit the data with insignificant variables. The standard error of 0.68206 represents the average distance the observed data are from the regression line. The model's predictions are more accurate in the lower standard error. Based on the model summary, the predictors, which include emerging digital technologies, appear to have a significant effect on sustainable entrepreneurship.

Table. 14: Anova Table

Source: Values compiled through SPSS Software

The ANOVA table shows that the model explains the variation in sustainable entrepreneurship with a regression sum of squares of 57.790. As indicated by the Total Sum of Squares (120.127), a high regression sum of squares relative to the residual sum of squares (62.337) indicates that the model accounts for a sizable portion of the variance in the dependent variable. The F-statistic of 124.225 measures the model's overall significance. The model is statistically significant, as evidenced by its high value and p-value of 0.000, which is below the generally accepted threshold of 0.05.

Table 15: Coefficients

Source: Values compiled through SPSS Software

The coefficient table indicates whether the hypothesis is accepted or not. While the independent variable is zero, the dependent variable's expected level is displayed by the regression line's intercept, which is the constant (B = 1.343). The unstandardized coefficient for the .658 (B =0.658) indicates that for every one-unit rise in the practices of sustainable entrepreneurship, the potential of emerging digital technology should grow by 0.658 units. This suggests that the 0.658 potential of emerging digital technologies has a significant positive impact on sustainable entrepreneurship. The potential of emerging digital technology coefficient has a standard error of 0.059, which represents the predicted variation. Smaller standard errors suggest more exact coefficient estimations. The Standardized Coefficient (Beta) of 0.694 indicates a significant impact of digital technology on sustainable entrepreneurship, as assessed in standard deviation units. The t-statistic of 11.146 tests the null hypothesis that the coefficient is zero, suggesting no influence. The statistically significant association between the potential of emerging digital technology and sustainable entrepreneurship is indicated by the high t-value and p-value (0.000).

H4: Entrepreneurs with a positive perception and attitude towards emerging digital technologies are more likely to engage in sustainable entrepreneurship.

Table. 16: Model Summary

Source: Values compiled through SPSS Software

Another Model Summary from a Regression Analysis. This summary includes crucial data for evaluating the regression model's effectiveness, with "Perception Attitude Entrepreneurs" serving as a predictor for the dependent variable, which is most likely "sustainable entrepreneurship" in this scenario. The value of R is 0.782. It shows a substantial positive association between the independent variable (Perception Attitude Entrepreneurs) and the dependent variable (sustainable entrepreneurship). The association becomes stronger as the R value approaches 1. The value of R-squared is 0.611. It suggests that the model using the predictor "Perception Attitude Entrepreneurs" accounts for around 61.1% of the variance in the dependent variable (sustainable entrepreneurship). This is a rather high percentage, showing good explanatory power. The adjusted R-squared value is 0.608. This value is slightly lower than the R-squared value, showing that the model takes into consideration the number of predictors and that adding more variables would not significantly improve the model. The standard error of the estimate is 0.59060. This statistic determines the average distance between actual data points and the model's predicted values. A lower standard error suggests a better fit between the model and the data.

Table 17: Anova Table

Source: Values compiled through SPSS Software

 The ANOVA table shows the results of a regression analysis, with "Sustainable Entrepreneurs" as the dependent variable and "Perception Attitude Entrepreneurs" as the predictor (independent variable). The Regression Sum of Squares (SS): 73.387. This value reflects how much variance in the dependent variable (Sustainable Entrepreneurs) can be explained by the predictor (Perception Attitude Entrepreneurs). The total sum of squares (SS) is 120.127. This represents the overall variation in the dependent variable. It is the sum of regression and residual sums of squares. The F-statistic of 210.395 assesses the overall significance of the model. The model is statistically significant, as indicated by the large F-value and p-value of 0.000. The ANOVA table demonstrates that the model is very significant, indicating that "Perception Attitude Entrepreneurs" has a significant effect on "Sustainable Entrepreneurs." The model explains a considerable percentage of the variance in the dependent variable, as evidenced by the high F-statistic and significant p-value.

Table 18 Coefficients

Source: Values compiled through SPSS Software

The coefficients table contains the details of the regression equation used to predict the dependent variable "Sustainable Entrepreneurs" using the independent variable "Perception Attitude Entrepreneurs". The Coefficients table provides further information. The constant (B = 0.903) reflects the regression line's intercept, indicating the predicted degree of sustainable entrepreneurship when the entrepreneur's attitude is zero. According to the unstandardized coefficient for perception on entrepreneur attitude (B = 0.767), changes in entrepreneur attitude by one-unit lead to a 0.767-unit change in the level of sustainable entrepreneurship. The entrepreneur's attitude coefficient has a standard error of 0.053, which represents the predicted variation. Smaller standard errors suggest more exact coefficient estimations. The Standardized Coefficient (Beta) of 0.782 indicates a significant impact of entrepreneur attitude on sustainable entrepreneurship, as evaluated in standard deviation units. The t-statistic of 14.505 tests the null hypothesis that the coefficient is zero, suggesting no influence. The statistically significant association between entrepreneur attitude and degree of sustainable entrepreneurship is indicated by a high t-value and accompanying p-value (0.000).

1.7 Findings of the study

Table 19: Summary of Findings