Entrepreneurial knowledge and digital entrepreneurial intention mediated by entrepreneurial alertness among Indonesian polytechnic students

Advances in digital technology and the impact of the Covid-19 pandemic have resulted in significant growth in digital entrepreneurship in Indonesia. Apart from contributing to a country's economic development, digital entrepreneurship also has the potential to overcome youth unemployment. However, student participation rates remain low, possibly due to a lack of entrepreneurial knowledge. Furthermore, differences in entrepreneurship education approaches offered by universities result in differences in entrepreneurial knowledge among students. This research aims to analyze the factors that influence digital entrepreneurship intentions among polytechnic students in Indonesia. The survey was conducted with a sample of 268 polytechnic students throughout Central Java, using purposive and proportional random sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research finds the influence of entrepreneurial knowledge on digital entrepreneurial intentions and entrepreneurial alertness. Entrepreneurial alertness influences the relationship between entrepreneurial knowledge and digital entrepreneurial intention. These findings can inform strategies to foster digital entrepreneurship among Indonesian polytechnic students by focusing on increasing entrepreneurial knowledge and entrepreneurship alertness to accelerate the growth of digital entrepreneurship among the younger generation. This is an open access article under the CC BY-SA license.


INTRODUCTION
Digital entrepreneurship is characterized by the development and deployment of novel, technologydriven products, services, and business models, which often disrupt traditional industries and create new markets (Antonizzi & Smuts, 2020).It encompasses a wide range of activities, from e-commerce and digital marketing to app development and data analytics, presenting unique opportunities and challenges for entrepreneurs, investors, and policymakers (Baierl et al., 2019;Barigozzi & Tedeschi, 2019).
Digital entrepreneurship is increasingly being recognized as a viable and important solution to economic and societal issues, such as unemployment and rural poverty, given its potential to drive economic growth, job creation, and innovation (Chang & Chen, 2020;He, 2019;Kraus et al., 2019;Leonidou et al., 2020).Globally, the proliferation of digital technologies and the advancement of Information and Communication Technology (ICT) have facilitated the emergence of new entrepreneurial opportunities and business models, transforming traditional industries and fostering economic growth globally (Mafimisebi & Ogunsade, 2022).The development of digital technology has been a key driver in growth of digital entrepreneurship, particularly in Indonesia, where this trend has been further amplified by the Covid-19 pandemic (Purbasari et al., 2021;Tayibnapis et al., 2018;Zamzami et al., 2022).This surge in digital entrepreneurship has not only impacted existing businesses but also presents a vital opportunity to address unemployment among university students (Belitski et al., 2022;Masenya, 2021;Sudarmo, 2021).
The Covid-19 pandemic has drastically influenced the global entrepreneurial landscape, catalyzing a marked surge in digital entrepreneurship (Modgil et al., 2022).This shift has been primarily triggered by the new norms of social distancing and remote work, which have compelled businesses to adapt to digital models or risk becoming obsolete (Belitski et al., 2022).The pandemic has also highlighted the importance of digital entrepreneurship in promoting youth employability (Masenya, 2021).With conventional job markets severely affected by the crisis, young people are increasingly turning to digital entrepreneurship as a means to secure their livelihoods and contribute to economic recovery (Masenya, 2021).Indonesia's digital economy has shown resilience and dynamism during the Covid-19 crisis, opening up new avenues for economic activity and job creation, especially for university students (Muafi et al., 2021;Sudarmo, 2021).The Indonesian government's commitment to improving digital infrastructure and providing support to SMEs has further paved the way for digital entrepreneurship to thrive (Tayibnapis et al., 2018;Sulhan, 2021).
Digital entrepreneurship has emerged as a viable solution for job creation and economic growth in Indonesia (Muafi et al., 2021).The potential of digital entrepreneurship to mitigate youth unemployment is substantial, with students being empowered to create their own job opportunities rather than relying on traditional employment avenues (Dvouletý et al., 2018;Khan, 2023).It also provides a unique opportunity for university students to gain practical experience and develop digital skills relevant to the current market (Permatasari & Anggadwita, 2019).To stimulate university students' interest in digital entrepreneurship, it is important to incorporate relevant digital skills into the curricula of entrepreneurship education programs (Permatasari & Anggadwita, 2019;Putro et al., 2022).Such programs can help students develop the competencies needed to navigate the digital business landscape and utilize digital tools for business innovation and growth (Ngoasong & Kimbu, 2019).Furthermore, creating a robust entrepreneurial ecosystem, complete with financial resources, mentorship, and networking, is vital to the success of digital entrepreneurs (Purbasari et al., 2021;Widiasih & Darma, 2021).
Indonesia has been grappling with a high level of unemployment among university graduates, which poses a significant challenge to the country's socio-economic development (Prasetyo & Novita, 2022); Sudarmo, 2021).Several data has cited the low employment rate among university students in Jakarta.For example, Based on data from the Central Statistics Agency (BPS), as of August 2019, the number of unemployed university graduates reached 5.67% of the total workforce of around 13 million people.Although the percentage fell compared to August 2018, which was 5.89%, the figure was above the national unemployment average of 5.28%.A report by The Jakarta Post, which cites data from the Ministry of Manpower, the unemployment rate among university graduates in Indonesia was 7.92% in February 2021 (The Jakarta Post, 15 April 2021).
The high level of unemployment in Indonesia, especially for university graduates, present a significant to the nation's economic growth and development.challenge for the government and the Indonesian people, especially in the workforce.The development of university students into entrepreneurs, particularly in the digital sector, can help address this issue by creating new job opportunities and contributing to the country's innovation and competitiveness (Astuty et al., 2018).Entrepreneurship has been identified as a potential solution to this unemployment problem, as it can create jobs and stimulate economic growth (Dencker et al., 2021;Masenya, 2021).However, there is a relatively low participation in entrepreneurship among university students in Indonesia (Putro et al., 2022;Widiasih & Darma, 2021).This low participation can be attributed to various factors, including the lack of entrepreneurial education, limited access to resources, and cultural perceptions about entrepreneurship (Permatasari & Anggadwita, 2019;Purbasari et al., 2021).There is a pressing need to boost interest in entrepreneurship among Indonesian university students to address the unemployment issue and capitalize on the potential of entrepreneurship to contribute to the country's economic development (Putro et al., 2022;Sudarmo, 2021).This can be achieved through incorporating entrepreneurship education into university curricula, providing mentorship and resources, and fostering a supportive entrepreneurial ecosystem (Permatasari & Anggadwita, 2019;Purbasari et al., 2021).
The current digital entrepreneurial landscape in Indonesia, shaped by technological advancements and the Covid-19 pandemic, presents a promising but complex arena for economic development and employment generation.However, to fully harness this potential, a nuanced understanding of the relationship between entrepreneurial knowledge and digital entrepreneurship is needed.Empirical research on this relationship could reveal key insights into how entrepreneurial education can be optimally designed and delivered to foster digital entrepreneurship among university students, who are among the most tech-savvy and adaptable segments of the population.Such research could guide the development of curricula that balance theoretical understanding with practical skills, equipping students with the knowledge and tools they need to thrive in the digital business landscape (Permatasari & Anggadwita, 2019;Putro et al., 2022).Hence, the call to action for this research is not only timely but also vital for Indonesia's continued development in the digital age.
The digital age necessitates an overhaul of entrepreneurship education and training to enhance competencies related to digital business, such as those in the field of Information, Communication, and Technology (ICT).This is because the majority of contemporary businesses tend to utilize online media, often referred to as e-commerce, and the rise of startups has been largely driven by technological trend innovation (Nugraha & Wahyuhastuti, 2017;Sheung, 2014).ICT has become an integral part of everyday life, serving functions in information delivery, education, and entertainment.However, its role as a double-edged sword is apparent, offering both benefits and disadvantages depending on how it is utilized.In terms of education, when used proportionally as a tool for entertainment, education, and information, ICT can be highly beneficial.Its role in influencing strategic decisions in digital entrepreneurship has been established, highlighting the importance of digital competence in enhancing entrepreneurial skills (Ngoasong, 2018).
The purpose of this study is to gain understanding of the relationship between entrepreneurial knowledge, entrepreneurial alertness and digital entrepreneurial intention among polytechnic students in Indonesia.The researchers hope that this study will contribute to the the knowledge expansion of the issue discussed, as well as becoming a reference for future research.

METHOD Population, Sample Size, and Sampling Technique
In this study, the target population was polytechnic students in Central Java and have taken entrepreneurship courses.The sample was selected from students enrolled in Informatics study programs at four Polytechnics in Central Java, Indonesia, namely the Harapan Bersama Polytechnic, Semarang State Polytechnic, Indonusa Surakarta Polytechnic, and Pratama Mulia Surakarta Polytechnic.These four polytechnics were chosen as they represent a large population and a wide coverage area.
The sampling procedure involved determining the number of Informatics study program in Polytechnics in Indonesia that offer Informatics technology study programs with a sample size of 760 students.Subsequently, student samples were determined based on proportional distribution of questionnaires at each polytechnic, with a minimum of 200 students.There were 760 final semester students in the selected Informatics study program in Polytechnics in Central Java.The sample size was also calculated using the Slovin formula, and qouta sampling techniques were applied for each of the four selected polytechnics.Therefore, the calculated sample size was 268 students.The sampling technique used in this study quota sampling based on the percentage distributed across the four selected polytechnics.

Measurement of Constructs
The aim of this research is to propose a model that captures the relationship between entrepreneurial knowledge and digital entrepreneurial intention, mediated by entrepreneuroal alertness.In this study, one independent variable, which is the entrepreneurial knowledge and two dependent variables, which are the entrepreneurial alertness and digital entrepreneurial intention were analyzed.The following section describes the measurement of the variables involved in this present study.The measurement of the variables considered in this study is described in the following section.

Dependent Variables
In this study, one dependent variable were considered, namely the digital entrepreneurial intention.Entrepreneurial intention refers to the desire of individuals to undertake or create new digital busisness by exploiting business opportunties and taking risks (Linan et al., 2011).In this study, six indicators adopted from Linan and Chen ( 2009).The indicators measure the construct through affirmative statements showing various aspects of intentions.These indicators have been widely used by other researchers (such as, Lu and Wang, 2018;Neneh, 2020).These indicators were measured using a 7-point Likert scale, ranging from 1 = Strongly Disagree; 2 = Disagree; 3 = Somewhat Disagree; 4 = Neither Agree or Disagree; 5 = Somewhat Agree; 6 = Agree, and 7 = Strongly Agree.

Independent Variable
This study only considered one independent variable, which is entrepreneurial knowledge was considered.In this study, entrepreneurial knowledge refers to the conceptual and analytical understanding of the multi-functional and multi-faceted process of entrepreneurship (Roxas, 2014).It is the knowledge that entrepreneurial individual acquires through formal or informal learning to conduct a business.To measure entrepreneurial knowledge, six indicators adapted from Roxas (2014) were used, and they were measured using 7-point Likert scale ranging from 1 = Strongly Disagree; 2 = Disagree; 3 = Somewhat Disagree; 4 = Neither Agree or Disagree; 5 = Somewhat Agree; 6 = Agree, and 7 = Strongly Agree.

Mediating Variables Entrepreneurial Alertness
Entrepreneurial alertness in this study is based on the definition advocated by Kirzner (1979), which refers to a person's skills and cognitive ability to identify new opportunities that are often overlooked by others.Other studies used Tang et al. ( 2012) are (Li, Wang, & Liang, 2015) and (Pratomo, Siswandari, & Wardani, 2021).For this study, 13 indicators adopted from Tang et.al (2012), were used to measure the contructs.Following Tang et al. (2012), entrepreneurial alertness in this study is viewed as a multidimensional construct consisting of three categories scanning and search, association with connection, and valuation and judgement.These indicators have been used widely by other researchers (Adomako, Danso, & Boso, 2018;Awwad & Alaseer, 2021) Pratomo, Siswandari & Wardani, 2021).These indicators were measured using 7-point Likert scale, ranging from 1 = Strongly Disagree; 2 = Disagree; 3 = Somewhat Disagree; 4 = Neither Agree or Disagree; 5 = Somewhat Agree; 6 = Agree, and 7 = Strongly Agree.

Pilot Study: Validity and Reliability test
To test the validity of the data using confirmatory factor analysis (CFA) using SPSS 23 software, where each question item must have a factor loading > 0.50.Confirmatory Factor Analysis (CFA) must be fulfilled, because it is one of the requirements to be able to analyze the model with Structural Equation Modeling (SEM).The results can be explained through a rotation component matrix which assumes that all indicators are independent variables which are a function of an invisible factor (latent variable).The value of the convergent validity rotation component matrix can be shown by the highest factor loading.The validity test results for all variables have a loading factor above 0.5, and are extracted perfectly, this means that each question is valid.To test the internal reliability of the data collected, Cronbach's alpha reliability coefficients were computed using SPSS software.Although a Cronbach's alpha value of 0.70 is typically considered acceptable for internal consistency, values as low as 0.60 can be tolerated for research with small sample sizes ( (Ponterotto & Ruckdeschel, 2008).The results show that all three constructs have high internal consistency, as all the Cronbach's alpha values are above the acceptable threshold of 0.70.The highest Cronbach's alpha value is for entrepreneurial alertness (0.951), followed by Entrepreneurial knowledge (0.945), Digital entrepreneurial intention (0.935),.These results indicate that the items included in each construct measure the same underlying concept consistently and reliably, providing strong evidence for the validity of the survey instrument used in the study.As such, this questionnaire were used for the main data collection.

RESULTS AND DISCUSSION Respondents Background
Table 1 shows the demographic information of the respondents such as Gender group, School Name, and Location.Classification of respondents based on gender was 40.3% male.Respondents were belonged to Polytechnic of Harapan Bersama group with 62.7%.However, Tegal comes the highest group among the respondent with 62.7%.

Assessment of Measurement Model
The research model for the present study was tested through Structural Equation Modelling (SEM) techniques using Partial Least Square (PLS) method.The SmartPLS software version 3.2.8 has been used as a software tool to carry SEM data analysis for assessment of measurement and structural models in this study.The assessment of measurement model was based on reliability and validity estimates which were measured through indicator loading, internal consistency, discriminant, and convergent validity values.Composite reliability (CR) and Average Variance Extracted (AVE) values for each latent variable was used to determine convergent validity.Fornell-Larcker criterion and cross loadings values determined discriminant validity.These measures are explained in the next sections.To sum up the reliability and validity measures, the tests conducted to analyse data confirmed that the measurement model of the present study is valid and can be further considered to assess the parameters of structural model.Table 2 provides the summary the values of each construct's items factor loadings, CR, and AVE values.

Internal Consistency
The internal consistency among items was used to determine reliability of a scale used in a research instrument.For this purpose, Cronbach's alpha is the most commonly used measure to determine internal consistency which determines the reliability of a scale (Hair et al., 2013).The values of 0.8 or 0.9 in advance stages of a research indicate the internal consistency reliability of a measurement model, while values below 0.6 indicate low reliability.The higher Composite Reliability (CR) value indicates higher consistency of items.For the present study, both Composite Reliability (CR) and Cronbach Alpha (CA) values are greater than 0.6 and 0.7 as shown in Table 2. Here, all three variables have composite reliability values above 0.90, indicating high consistency in measuring the latent constructs.These results suggest that the items within each variable are closely related and are measuring the intended construct consistently, which strengthens the overall reliability and validity of the survey instrument used in the study.

Indicator Reliability
Indicator reliability was assessed through factor loading or outer loading values of each item.The indicator value of an item of 0.70 and above is considered acceptable and good.However, if an indicator's value is less than 0.40, it must be considered for elimination of that indicator.While, indicator's value between 0.40 and 0.70 should be considered for elimination if it increases the CR (composite reliability) and AVE (average variance extracted) values.Cronbach's alpha test results in Table 2 obtained a value above 0.7, meaning that all instruments have high reliability.

Convergent Validity
For the present study, the convergent validity was measured by the value of average value extracted (AVE) as suggested by Waddock & Graves (1997).An AVE value of 0.50 and above showed the acceptable convergent validity.Table 2 contains the values of AVE for the convergent validity of the constructs used in the present study.Since, all the values satisfied the minimum threshold value (0.50) of AVE, thus it showed the acceptable convergent validity for measurement model of the present study.

Discriminant Validity
There are two methods to measure discriminant validity of the constructs, criterion and cross-loadings of the construct items (Fornell & Larcker, 1981).In the first method, the value is obtained when the square root of AVE of a construct is greater than its correlation with other constructs.In the second method (Cross-Loading) value indicates that the items loadings are higher of their respective construct and compared to the other constructs.These values indicate the discriminant validity of a measurement model.The values of discriminant validity were obtained through running algorithm function in Smart PLS software.The result given in Table 3 shows the values estimated through Fornell-Larcker criterion for measuring discriminant validity.

Assessment of structural model
The structural model in SEM is used to analyze the hypothesized relationships between the proposed variables.It is imperative to provide conclusive evidence that supports theoretical significance of the model indicated by structural model (Chin, 1998).Generally, the following four criterions are used to establish the significance of the proposed relationships among variables.Coefficient of determination ( 2 ) is used to determine the goodness of structural model (Hair et al., 2013).It is further explained that the value of  2 determines the amount of variance in dependent variable caused by independent variable(s).Thus, the higher value of  2 is desirable.The value of  2 in the range of 0.02 -0.12 are considered lower, 0.13 -0.25 are moderate, and 0.26 or greater are substantial (Cohen, 1988b).However, Hair et al. (2013) stated that the appropriate value of  2 depends upon the framework of the research study.The next sections will describe the results which were used to validate the structural model of this study.The validity of the structural model, was determined by assessment of multicollinearity, coefficient of determination ( 2 ), effect size ( 2 ), and path coefficients.In addition to this, mediating relationship, as proposed in the present study was also assessed through these measures.

Multicolinearity
The problem of multicollinearity occurs when there is a higher correlation between two or more independent variables of a research model.In order to measure a potential multicollinearity between the variables in this study, the variance of inflation factors (VIF) values were tested.According to Hair et al. (1995), the VIF values indicate the existence of collinearity level among independent variables are based on tolerance of 0.20 or lower and 10 or higher VIF values.The VIF values measured for the independent variables in present study are given in Table 4. Since all the values of VIF with respect to the independent variables used in the present study are within acceptable limit (0.20 -10.00), therefore it can be confidently stated that there is no multicollinearity issues found among the variables of present study.

Path Coefficients
The structural model of research study is also evaluated through path coefficients.The values of path coefficients are useful to indicate the strength and significance of the relationship between two latest variables.In SmartPLS, there is technique called "bootstrapping" is used to obtain values for evaluation of relationships (paths) between independent and dependent variables.Moreover, t-statistics and p-values are assessed to verify the significance of all the paths exist between these variables.According to Hair et al. (2013), when the empirically measured statistical t-value is greater than the critical value, the coefficient is considered significant at a specific confidence level.For the present study, t-value of 0.95 is used at a significance level of 0.05 (Hair et al., 2013).Further explained that nonparametric statistical test called bootstrapping is carried by PLS-SEM in order to measure the significance of estimated path coefficients.Moreover, they stated that the values of coefficients are ranged between -1 and +1.Thus the values of path coefficients close to +1 indicated strong relationship, while values of coefficient close to -1 show weak relationships.The results of the empirically measured t-values, p-values and path coefficients values between variables in the present study is described below.The acceptance or rejection of hypothesis were based on path assessments at a significance level of 0.05.As shown in Table 5, it can be concluded that 3 hypotheses can be accepted.Hypotheses 1, 2, and 3 tested the direct relationships between EK and DEI, EK and EA, and EA and DEI, respectively.

Coeffcient of Determination (𝑹 𝟐 )
Coefficient of determination ( 2 ) value is used to explain the amount of variance in dependent variable caused by the independent variables.The higher  2 values indicates the predictive ability of the structural model.However, the strength of  2 values depends upon the complexity of research model and type of discipline (Hair et al., 2013).For example,  2 values for endogenous latent variables are assessed as follows: 0.26 (substantial), 0.13 (moderate), 0.02 (weak) (Cohen, 1988b).On the other hand,  2 values should be equal to or greater than 0.10 in order for the variance explained of a particular endogenous construct to be deemed adequate.Based on the results from PLS algorithm analysis, as shown in Table 6, 49.8 % of the variance in DEI was explained by EK and EA.Furthermore, 45.0% of the variance in EA was explained by EK.Overall, the findings illustrate that all  2 values exceeded the cut-off value of 0.02.The model, therefore, provides an adequate predictive power for the DEI.

Effect Size (𝒇 𝟐 )
The fitness of proposed research model of the present study was evaluated through the value of  2 , which explains the effect size.According to (J Cohen, 1988), the value of  2 is measured by increase in  2 relative to proportion of variance of the endogenous variable which remains unexplained.He further explained that values of  2 between the ranges of 0.02-0.14are considered weak effect, 0.15-0.34are moderate, and 0.35 and above show strong effect.For the present study, model fitness through  2 was measured and the values obtained for each path are presented in Table 7.  7, it is suggested that entrepreneurship knowledge has a weak relationship with digital entrepreneurial intention.Entrepreneurship knowledge has a strong relationship with entrepreneurial alertness, and a moderate relationship between entrepreneurial alertness and digital entrepreneurial intentions.

Evaluation of Mediating Variables
The first step for mediating analysis is to verify the significance of direct and indirect effects values through path coefficients, t-statistics, and p values.The next step is to calculate the VAF (variance accounted for) value to establish the strength of mediation (full, partial, or no mediation).For this purpose, VAF is estimated by dividing indirect effect over total effect.In this study, one hypotheses that tested the mediation role of entrepeneurial alertness on the relationship between entrepreneurial knowledge and digital entrepreneurial intention (H4).The results of the indirect effect test showed significant results with a p value of 0.000 <0.05 with a VAF value of 0.599 or 59.9%.This shows that the entrepreneurial alertness variable partially mediates the relationship between entrepreneurial knowledge and digital entrepreneurial intention.This suggests that the role of these mediator variables should be considered in entrepreneurial education and training programs, as they can potentially increase the effectiveness of such programs in promoting digital entrepreneurial behaviour (digital entrepreneurial intention).
H3 was accepted indicating a positive impact of entrepreneurial alertness on digital entrepreneurial intention.This finding aligns with the work of Saptono et al. (2020) who argued for the importance of an entrepreneurial mindset, which can be likened to alertness, in students' entrepreneurial preparation.
In this present study, hypotheses (H1) were accepted, suggesting that entrepreneurial knowledge does have significant relationship with digital entrepreneurial intention (H1).This finding supports with the study by Al-Mamary and Alraja ( 2022 2019) study, which identified a positive relationship between entrepreneurial knowledge, skills, competencies, and performance among micro-enterprises in Malaysia, and the findings by Karyaningsih et al. (2020), who found a significant influence of entrepreneurial knowledge on entrepreneurial intentions.This discrepancy could be due to different cultural contexts, emphasizing the need for further investigation.

Mediation of Entrepreneurial Alertness on the Relationship between Entrepreneurial Knowledge and Digital Entrepreneurial Intention
To achieve research objective 3, one hypotheses have been tested focusing on the mediating effect of entrepreneurial alertness on the relationship between entrepreneurial knowledge and digital entrepreneurial intention.Based on the results from hypotheses H4 provide insights into the complex interplay between various factors in shaping digital entrepreneurial intention (DEI).Specifically, they suggest the partial mediation of entrepreneurial alertness (EA) in the relationships between entrepreneurial knowledge (EK), and EI.This suggests the importance of considering indirect pathways through which these variables may influence one another.Regarding H4 (EK→EA→EI), the study's findings echo the results of studies by Hussain et al. ( 2021) and Karyaningsih et al. (2020), which highlighted the critical role of entrepreneurial knowledge in shaping entrepreneurial intentions.This aligns with Farani et al. ( 2017) research, which emphasized the role of entrepreneurial knowledge in shaping students' career intentions to start a new digital business.The partial mediation effect in this hypothesis further emphasizes the role of entrepreneurial alertness as a mechanism through which entrepreneurial knowledge translates into entrepreneurial intentions.

Model of the Relationship among Entrepreneurial Knowledge between Digital Entrepreneurial Intention Mediated Entrepreneurial Alertness
In the present study, the model investigates the relationships between entrepreneurial knowledge and digital entrepreneurial intention, mediated by entrepreneurial alertness among polytechnic students in Indonesia.Figure 1 presents the model of the relationship between entrepreneurial knowledge and digital entrepreneurial intention mediated by entrepreneurial alertness.

Figure 1. Model of Digital Entrepreneurial Intention
The model, as shown in Figure 1 demonstrates the relationships between entrepreneurial knowledge and digital entrepreneurial intention, with mediating variables of entrepreneurial alertness, presents a complex picture of the process by which knowledge might translate into intention within the entrepreneurial context.The model shows that entrepreneurial alertness mediates the relationship between entrepreneurial knowledge and digital entrepreneurial intention.This aligns with the recent expansion of entrepreneurial alertness as an additional factor influencing entrepreneurial intentions (Mir et al., 2022).In the context of fostering digital entrepreneurs among polytechnic students in Indonesia, these findings highlighted the need for an integrated approach.This should not only focus on imparting entrepreneurial knowledge but also on fostering entrepreneurial alertness for entrepreneurship (Darmanto et al., 2022;Triyono et al., 2023).By addressing these factors, policymakers and educators can better promote digital entrepreneurship and contribute to the growth of Indonesia's digital economy (Triyono et al., 2023;Wibowo & Narmaditya, 2022).

CONCLUSION
This research provides valuable insights into digital entrepreneurial intentions in Indonesia, highlighting the importance of entrepreneurial knowledge (EK) and entrepreneurial alertness (EA) as mediating variables.
The study highlights the need for an educational and policy framework that supports the development of entrepreneurial knowledge and fosters a conducive environment for proactive entrepreneurial actions.The findings highlight the need for a comprehensive theoretical framework in this area, as the digital landscape continues to evolve.However, the research has limitations, such as a cross-sectional design, a sample primarily consisting of Polytecnic students, and a specific geographical and cultural context.Future research could benefit from a longitudinal design, examining these relationships in more diverse samples, and including nonstudents, existing entrepreneurs, and individuals from various demographic backgrounds to provide a more comprehensive picture of digital entrepreneurial intentions and actions.
), Akhter et al. (2022) and Alferaih (2022) who found a strong positive relationship between attitude and intention in the context of digital entrepreneurship and a study by Hussain et al. (2021) and Firman and Putra (2020) which highlighted the role of personal attitude in developing entrepreneurial intentions.H1 was accepted, support with the results of studies by Omar et al. (2021) and Roxas et al. (2008), which emphasized the importance of embedding entrepreneurial knowledge in the curriculum.This outcome also support with Al Mamun et al.'s (

Table 2 .
Summary of the assessment model

Table 6 .
Coefficient of Determination

Table 7 .
2 Values for Each Path