Entrepreneurial commitment describes the character and attitude of an individual, shown by their dedication and resolve in executing ideas for business. The theoretical foundation of EC is a psychological theory derived from Meyer and Allen’s (1991) commitment model, which assesses the psychological variables motivating individuals to engage in their entrepreneurial roles (Yangailo & Qutieshat 2022). The concept refers to the psychological attachment and sense of responsibility experienced by individuals in managing a business, confronting obstacles and making decisions.

Research in entrepreneurial psychology seeks to identify the primary motivating factors that influence entrepreneurial intention. The integrative model indicates that EC delineates individual characteristics (Dushnitsky & Matusik 2019) and discovers internal and external motivating factors that may impact entrepreneurial intention (Snihur, Zott & Amit 2021). The EC model specifically examines three elements from previous organisational commitment theories, including the works of Allen and Meyer (1990), Meyer, Becker and Vandenberghe (2004) and Mowday (1998). The individual’s involvement in their work is primarily a result of the positive attitude they receive; they are persuaded that the tasks they perform are ethical and reasonable (Bekmezci, Orçanlı & Fırat 2022).

Several recent studies classify the EC model into dimensional constructs to affirm entrepreneurial goals and performance. Entrepreneurial commitment is the result of a dimensional construct developed by Tasnim (2015) based on the development of multidimensional constructs by MacKenzie, Podsakoff and Jarvis (2005), namely affective commitment (AC), normative commitment (NC) and continuous commitment (CC); all three are hierarchical reflective latent constructs. So, AC is reflected by entrepreneurial passion (EP), value (Val) and personality (PER). Meanwhile, NC reflects internalised norm (NM) and righteousness and responsibility (RR), and, finally, CC reflects investment (INV) and lack of alternatives (LA).

Technological readiness indicates an individual’s willingness to adopt and take advantage of technology to pursue various objectives, including business interests. This concept is a mental state that arises from the internal factors that function as either drivers or inhibitors in an individual’s decision to utilise the expanding technology. Entrepreneurial technology readiness (ETR) is an individual’s readiness, comfort and enthusiasm in adopting and integrating technology and business activities (Parasuraman 2000). Technological readiness has become an essential factor in the progress of modern entrepreneurship today, be it the use of devices, platforms and digitalisation in all lines of business.

According to Parasuraman and Colby (2015), technological readiness in entrepreneurship involves significant aspects that can affect an individual’s intention to adopt and take advantage of technological innovations. The concept of planned behaviour implies that an individual’s purpose in adopting new technology will affect their attitudes, beliefs and experiences as entrepreneurs (Bernardus et al. 2020; Mfazi & Elliott 2022). Parasuraman (2000) was later developed by Astuti and Nasution (2014) to identify a model that is recognised to measure technological readiness among entrepreneurs, namely innovation (individual tendencies to adopt technology), optimism (positive views and beliefs that technology can offer improvements, control, flexibility and efficiency in life), discomfort (fears and worries felt when dealing with technology) and insecurity (feelings of scepticism regarding the ability to work and face technology-based situations). It is essential to understand technological readiness in entrepreneurship research to emphasise the involvement of micro, small, and medium enterprises (MSME) individuals in adopting and embracing information technology (Suhartanto & Leo 2018). Therefore, technological readiness in the downstream development process can be predicted to develop further with the stabilisation of new technology.

The relationship between EC and entrepreneurial technological readiness among college students has become attractive in recent years. Because the entrepreneurial landscape is getting wider, coupled with the development of modern technology, it builds the passion and ability of the younger generation as the next innovators (Cassia, Minola & Paleari 2011). The literature highlights EC as the dedication, perseverance and resilience of entrepreneurs in attaining business success (Yoganandan & Dinesh Kumar 2021). Commitment serves as the primary motivator for entrepreneurs to deal with challenges and leverage opportunities. Conversely, commitment is crucial, while technological readiness assesses an individual’s capacity to effectively leverage technological advancements in business operations (Dutot & Horne 2015; Neumeyer, Santos & Morris 2020).

Entrepreneurship has been an increasing phenomenon in the creative sector, enabling many possibilities for investigation. The creative industry is closely intertwined with product advancements and marketing techniques, necessitating entrepreneurs to leverage technology media to accomplish business goals (Harding et al. 2020). The use of technology by a business, whether large, medium or small scale, has multiplied. Technology has brought customers and entrepreneurs into limitless interactions, and customers are faced with increasingly modern products and services since entrepreneurs adopted technology in the sales and service process (Parasuraman 2000; Zhu & Luo 2021).

Technological readiness in entrepreneurship contains not only technical skills and knowledge in implementing innovative technological instruments and platforms but also an attitude and readiness to adopt innovative technology and information in business operations (Farradinna, Herawati & Mulyani 2021; Nacu & Avasilcai 2013; Trivedi, Oza & Savalia 2010). The intersection of entrepreneurial drive and technological proficiency is one of the important topics in higher education today, as it forces educators and policymakers to understand how best to support the next wave of young entrepreneurs (Gupta & Pathak 2018; Yi & Duval-Couetil 2018). Particular researchers emphasise that enhanced technological readiness levels exhibit greater adaptability to change, capitalise on superior chances and maintain a positive mindset when confronting problems (Kumi et al. 2024; Subrahmanya 2022). This is recognised because of the capacity to leverage and manage existing data and employ accessible resources, whether technologies or platforms, to enhance student achievement in business planning (Kraus et al. 2022; Martins, Shahzad Xu 2023).

A relationship between commitment and technological readiness among entrepreneurs can significantly influence the success of potential entrepreneurs (Linton & Xu 2021). Young entrepreneurs exhibiting elevated technical readiness may be better prepared to face and negotiate the problems posed by innovative financial markets (Martín–Rojas, Morales & Bolívar–Ramos 2013; Subrahmanya 2022). Particular academics predict that technology and business significantly influence entrepreneurial competencies (Zhang 2015). Strong EC (Ardelean 2021), proficiency and precision in contemporary technology (Gupta & Bose 2022) and integrating innovations are anticipated to enhance the possibility of enduring and sustained achievement. By comprehending the interplay between dedication and technological preparation, academics may devise and execute superior programmes that fulfil the requirements and ambitions of young entrepreneurs. Based on the explanation above, we propose the following hypothesis for this study:

This study developed a non-experimental quantitative methodology, focusing on exploring the relationship between two variables without employing any manipulation process. This research methodology applies scores collected through surveys, frequently employed in social research that examines multiple data points, which are then interpreted through statistical analysis. This study evaluates the impact of EC and technology readiness on students.

Our survey design was chosen to collect data using questionnaires distributed directly and online. We asked several students who had taken the entrepreneurship courses to answer their level of agreement with some statements from the variables of EC and entrepreneurial technological readiness; a total of 409 students had taken entrepreneurship subject. The participants were selected from multiple universities in the Riau province, such as Universitas Pasir Pangaraian, Universitas Islam Riau and Universitas Muhammadiyah Riau. The sample components were chosen through purposive sampling methods. A personal survey employing a five-point Likert scale, ranging from strongly agree to disagree, was provided to gather substantial primary data.

The analysis in this study uses the partial least squares structural equation modelling (PLS-SEM) model method using Smart-PLS software version 3.2.8 (Ringle, Sven & Jan-Michael 2015) to test the interaction between ETR and EC. Based on the opinion of Hair, Ringle and Sarstedt (2011), running an analysis with the help of PLS-SEM can predict linear constructs and their indicators at once.

The assessment of model quality explains the predictive power of the structural model run by predicting the model fit index R square, Q square, quality assessment of each block of variables and evaluation of the quality of the structural model.

Ethical clearance to conduct this study was obtained from the Universitas Islam Riau Faculty Research Ethics Committee (Ref. No. ER-2025/A-15).

Based on the analysis of reliability and internal validity, it shows that Cronbach’s alpha for technological readiness and EC is above the threshold of 0.7, especially composite reliability (CR); this can be seen in Table 2, namely for ETR, CR = 0.975, with a reasonably high Cronbach’s alpha internal consistency value of 0.973; meanwhile, entrepreneurship commitment shows CR = 0.859, with Cronbach’s alpha = 0.859. The Cronbach’s alpha value has proven the internal consistency of the data, thus indicating a consistent and valid value in these data.

The alpha coefficient in the Smart-PLS approach is used to complete the verification of construct convergent validity. We clean the variables from indicators with a threshold < 0.7. To compare the square root of the average variance extracted (AVE) value with the correlation of latent variables, it is necessary to understand that each construct must be higher than its highest correlation with other constructs (Hair et al., 2017). The square root of AVE for reflective constructs is known to be greater than the correlation between constructs with other constructs in the path model.

Table 3 shows that the measurement is discriminant; the AVE value of a construct is more significant than its squared correlation from other constructs (Fornell & Larcker 1981). We examine the AVE, which moves from 0.594 to 0.866 for both variables (technology readiness and EC).

The reliability of all observed items is indicated by the outer loading model involved in the given construct. The outer loading value represents the relationship between the reflective measurement model, whose value must be more than 0.705 (Hair et al. 2011).

Discriminant validity, as given in Table 4, shows how far the characteristics of the construct are unique and different from others (Hair et al. 2011). The achievement of discriminant validity is carried out by external loading of the self-construct, which must be low and high (Chin 2010).

This section explains the results of the structural model evaluation and predicts the results of the formulated hypothesis. The results of the bootstrapping algorithm from the PLS approach are used, where bootstrap replication n = 500. Chin (1998) states this method is more effective because of the newer resampling method. Thus, to test the significance of the structural relationship, it is necessary to carry out a bootstrap procedure that can estimate precision (standard error, confidence interval, etc.), which is more flexible and powerful and can improve the reliability and accuracy of the estimate. The results are shown in Table 5, displaying the β-value coefficient, t-statistic and p-value. PLS and regression analysis have similarities in determining the path and standard β coefficient, where each hypothesised model path is tested. The greater the β value, the more substantial the effect on the endogenous variable. The t-statistic values are used to assess significance, with thresholds of > 2.58 for α = 1%, > 1.96 for α = 5%, and > 1.65 for α = 10%.

The analysis results allow us to validate the hypothesis of this study. Statistical tests reveal a significant positive correlation between entrepreneurial technological readiness and EC in students. Thus, this hypothesis is confirmed (β = 0.624; t = 19.408, p < 0.05). This shows that entrepreneurial technological readiness significantly influences students’ EC.

Although this study has provided new insights into the relationship between technological readiness and EC among university students, its limitations must be considered. The sample was limited to university students, so the findings cannot be generalised widely to other regions or institutions. Geographical and socio-cultural contexts and opportunities for access to technology influence students’ perceptions and behaviours towards entrepreneurship and technology.

In addition, the quantitative approach using a questionnaire that focuses on perceptions may cause subjective bias in some individuals, especially in the measurement of variables of technological readiness and EC, because the analysis depends on the interpretation of respondents and the circumstances responded to at a certain time. Future research, to overcome the limitations, is suggested to use mixed methods to gain a deeper understanding related to technological readiness and EC. Longitudinal research is recommended to observe changes in student behaviour and attitudes towards entrepreneurship over a certain period, especially in the current era of digital disruption.

This research has practical implications for higher education institutions and professionals. Practical implications in the design of an entrepreneurship programme underscore theoretical and motivational dimensions to enhance students’ preparedness for involving technology as an integral component of the entrepreneurial process. Therefore, a more adaptable approach to education for digital growth will probably make students more determined to pursue a career as an entrepreneur, which is a positive for the environment.