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Academic scholars have tested students’ entrepreneurial intention (SEI) through the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT). Still, the link between entrepreneurial intention and career is missing in previous studies. An extensive literature review developed the rationale that existing theories in the entrepreneurial discipline have limitations in linking entrepreneurial intention with career. This research is conducted to develop a comprehensive model for the relationship between entrepreneurial university support, student entrepreneurial intention, behavior and career. Stimulus-Organism-Behavior-Consequence (SOBC) paradigm from organizational behavior research is borrowed to entrepreneurship literature.

The cross-sectional data was collected from Pakistani university students enrolled in business incubators. A sample of 100 responses was tested with a partial least square–structural equation modelling approach.

The study established that by the underpinning of SOBC, entrepreneurial university support influences students’ entrepreneurial intention. It is also found that the students’ entrepreneurial intention strongly influences their entrepreneurial behavior, leading them to entrepreneurship careers.

The policies influencing students’ entrepreneurial intention and behavior can be developed using the SOBC paradigm. Higher education institutions can improve students’ entrepreneurial intentions and behavior to lead them to entrepreneurship careers.

This research introduced the SOBC paradigm in entrepreneurial intention and behavior literature. SOBC underpinning explored a new dimension of entrepreneurial intention and behavior literature.

This paper aims to present a dynamic model for strategic and personalized decision-making in human resources (HR), using data mining techniques to enhance corporate social sustainability (CSS). The focus is on the interconnectedness of employee engagement (EE), enablement and the quality of work life.

The proposed model integrates various HR data, including demographic information, job specifications, payment and rewards, attendance and absence, alongside employees’ perceptions of their work-life quality, engagement and enablement. Data mining processes are applied to generate meaningful insights for senior and middle managers.

The study implemented the model within a production organization, revealing that factors influencing EE and enablement differ based on gender, marital status and occupational group. Performance-based rewards play a significant role in enhancing engagement, regardless of the reward amount. Factors such as “being recognized for competency” influence engagement for women, while payment has a greater impact on men. Engagement does not directly influence the quality of work life, but subcomponents like perceived transparency and the organization’s processes, particularly the “employee performance evaluation system,” improve work-life quality.

The findings are specific to the studied organization, limiting generalizability. Future research should explore the model’s effectiveness in different cultural and organizational settings.

The proposed model provides practical implications for organizations that enhance CSS. Organizations can gain insights into factors influencing EE and enablement by using data mining techniques, enabling informed decision-making and tailored human resource management practices.

This research addresses the societal concern regarding the impact of business activities on sustainability. Organizations can contribute to a more socially responsible and sustainable business environment by focusing on work-life quality and EE.

This paper offers a dynamic model using data mining and machine learning techniques for sustainable human resource management. It emphasizes the importance of customization to align practices with the unique needs of the workforce.