Firstly, there remains a notable gap between actual enrollments and sanctioned capacities in engineering programs. The All India Survey of Higher Education (AISHE) reports a 10.3% decline in undergraduate engineering enrolments from 2016 to 2021, with vacancy rates of 40% to 50% becoming prevalent (AICTE, 2021; Government of India, 2021). This discrepancy suggests a disconnect between educational capacity and the actual demand for engineering positions.

Secondly, the employment landscape for engineering graduates in India has deteriorated. The phase termed ‘jobless growth,’ where economic expansion did not lead to increased employment, has transitioned to ‘job-loss growth,’ directly leading to job losses (Kannan and Raveendran, 2019; Kathuria and Krishna, 2021). Recent reports indicate declining employment rates among Indian engineering graduates, with less than 40% securing immediate employment after graduation (SHL India, 2019; Wheebox, 2020; AICTE, 2021; Statista, 2022). This high unemployment rate, despite robust corporate demand, highlights a significant skills gap. The India Skills Report and subsequent studies have consistently underscored this misalignment between educational outcomes and job market needs (Wheebox, 2018, 2020; Statista, 2022).

This situation has led to negative stereotypes and misconceptions about the Indian engineering field, impacting motivation and reducing interest among potential students (Gupta, 2012, 2019). Consequently, there is a negative public perception of the profession and concerns among graduates about their societal standing (Gupta, 2012; Pavai Madheswari and Uma Mageswari, 2020).

India produces a vast number of engineering graduates, but its per capita engineer count and low ranking on the Engineering Index reveal gaps in quality and employability compared to countries like Russia, the EU, Japan, China, and Brazil (World Bank, 2013; Centre for Economics and Business Research, 2016a). Research highlights a significant perception that many Indian engineering graduates lack critical competencies, including technical skills and soft skills (Blom and Saeki, 2011; Loyalka et al., 2014; Honnutagi et al., 2016). This perception restricts employment opportunities and results in lower starting salaries for engineering graduates (Kolmos and Holgaard, 2019; SHL India, 2019; Tilak and Choudhury, 2021).

These trends raise critical questions about the effectiveness of the skills that Indian engineering graduates possess. Research by Shekhawat (2020), Sinha (2021), and Roy and Roy (2023) indicates that the primary cause of unemployment among these graduates is the lack of perceived employable skills. Academic performance and career trajectories in engineering are significantly influenced by students’ psychological and behavioral beliefs (Zimmerman, 2000a; Wigfield and Cambria, 2010). Students’ aspirations and attitudes towards their careers shape their skills acquisition and employment perspectives (Becker, 2010; Crawley et al., 2014; Chan et al., 2019). Recent studies in the Indian context highlight perceived ability as crucial in reinforcing engineering education outcomes, particularly in fostering problem-solving abilities and persistence (Venugopal and Singh, 2020).

To mitigate these impacts, aspiring engineers must be self-reliant and improve their awareness and perceptions of engineering education. Self-efficacy is a crucial part of engineering education (Venugopal et al., 2020), significantly influencing academic performance and persistence. Students with high self-efficacy engage deeply with complex tasks and persist through challenges, leading to academic success (Bandura, 1989; Lent and Brown, 2019). Success in engineering as an academic career, largely depends on the self-efficacy of aspiring and graduate engineers. Addressing self-efficacy issues is vital, as the quality and quantity of engineers significantly influence a nation’s economic performance.

This study aims to explore the disparity in beliefs about academic career among students throughout their engineering educational trajectory. By examining beliefs at three critical stages—entry, progression, and completion—the study seeks to understand how these perceptions influence academic career choices and success. Specifically, it will:

This research will assess the relationships and potential mediating influences among these stages while considering individual student characteristics within the Indian context. The ultimate goal is to bridge the gap between high demand and declining interest in engineering education, revitalizing its appeal and relevance for future generations.

This study refers to an academic career self-efficacy (ACSE) rather than work / occupation related career self-efficacy. An academic career self-efficacy (ACSE) refers to an individual’s belief in their ability to successfully perform academic tasks and achieve academic goals. This is different from work or occupation-related career self-efficacy, which focuses on confidence in performing job-related tasks and succeeding in a professional career.

Self-efficacy, defined by Bandura (1986) and elaborated by Labib et al. (2021), refers to an individual’s confidence in their ability to achieve specific goals or tasks. This belief system evolves through an interplay of cognitive, social, motivational, and behavioral factors, making self-efficacy a dynamic and inherently subjective construct. Over the past decades, self-efficacy has been identified as a strong predictor of career trajectories, influencing career choice, performance, and persistence (Hackett, 1995; Zeigler-Hill et al., 2020). It is particularly significant in social cognitive career theory (SCCT), where it is shaped by mastery experiences, vicarious experiences, social persuasion, and emotional states (Bandura, 1977). Notably, self-efficacy is not an assessment of one’s inherent abilities but rather a belief in one’s capacity to effectively utilize their skills to achieve goals (Bandura, 1977; Betz and Hackett, 1981).

The concept of academic career self-efficacy (ACSE), introduced by Betz and Hackett (1981), applies self-efficacy to academic and career-related beliefs and behaviors. This domain-specific adaptation, sometimes referred to as academic self-efficacy, is crucial in understanding and addressing the challenges specific to engineering education (Elias and MacDonald, 2007; Honicke and Broadbent, 2016; Gerçek and Elmas-Atay, 2022). Engineering self-efficacy, in particular, relates to students’ beliefs in their ability to navigate the rigorous academic challenges characteristic of the engineering field (Concannon and Barrow, 2009). This form of self-efficacy significantly impacts academic persistence, academic performance, and career choices within engineering (Lent et al., 2005; Raque-Bogdan and Lucas, 2016; Lent and Brown, 2019; Kenny and Claudius, 2020). It is vital to note that within this study, “CSE” primarily refers to self-efficacy related to academic matters, distinct from occupational self-efficacy.

Extensive educational research has underscored the pivotal role of Academic Career Self-Efficacy (ACSE) in shaping engineering students’ academic and career trajectories. ACSE influences a broad spectrum of outcomes, including students’ career choices, establishment of career goals, engagement in academic tasks, and their perseverance in completing these tasks (Van Dinther et al., 2011). This impact is supported by motivational and behavioral theories which suggest that students’ academic progress is heavily dependent on their belief in their ability to overcome various academic challenges throughout their educational journey.

Research has led to the development of several models based on Western studies on academic ACSE, which integrate psychological, cognitive, and environmental factors to explain disparate career outcomes across different cultural contexts (Betz and Hackett, 1986; Niles and Sowa, 1992; Lent et al., 1994). These models have effectively delineated the complex interactions between ACSE and various career-related behaviors and developments (Schunk, 1991; Hackett et al., 1992; Zimmerman et al., 1992; Pajares, 1996; De Vos and Soens, 2008; Hulleman and Harackiewicz, 2009). They have been instrumental in linking academic CSE to critical outcomes such as career performance and persistence (Lent and Hackett, 1987; Multon et al., 1991; Lent et al., 2008), employability (Rothwell et al., 2008; Gbadamosi et al., 2015; Praskova et al., 2015; Wang et al., 2022a; Yang and Zhang, 2022), skills development (Kenny and Claudius, 2020), career satisfaction (Rothwell and Arnold, 2007; De Vos and Soens, 2008; Duffy et al., 2015; Gerçek and Elmas-Atay, 2022), and overall career accomplishment (De Vos and Soens, 2008; Smale et al., 2019).

Empirical studies by Sullivan and Baruch (2009) and Honicke and Broadbent (2016) have further validated that engineering students with higher self-efficacy are more likely to excel academically. The application of academic ACSE has proven to be a valuable tool in attracting a diverse cohort of students and enhancing academic attainment in engineering disciplines (Hofacker, 2014). In summary, the literature firmly establishes the profound impact of self-efficacy beliefs on students’ academic performance, career choices, and pathways within the engineering field (Fouad et al., 2002; Betz and Hackett, 2006; Lent et al., 2008).

The literature has extensively examined the utilization of self-efficacy in all aspects of human existence, encompassing both professional pursuits and educational endeavors across diverse cultures (Betz and Hackett, 1981; Zimmerman, 1995; Betz, 2000). However, the literature review identifies several notable research gaps in the field of academic ACSE, particularly in the context of engineering education and within the Indian cultural context. These research gaps can serve as potential avenues for future research:

Promotion of engineering as a career: Investigating how self-efficacy beliefs contribute to creating an inclusive environment within engineering professions could be crucial. This area of research could explore how individuals from diverse backgrounds and with varying self-efficacy levels can contribute to the co-creation of a supportive engineering community. In summary, these identified gaps underscore the need for more nuanced and culturally sensitive studies on CSE, emphasizing the entire career trajectory. By addressing these gaps, researchers can enhance understanding of how self-efficacy influences entrance into, progression within, and accomplishment in engineering and related fields, ultimately contributing to more effective career development strategies.

Based on the research objectives, a literature review followed by a research gap, and the exploitation of ACSE as a construct, the present study seeks to answer the following research questions to examine students’ academic CSE throughout engineering graduation trajectory representing three career segments. These research questions are designed to provide a comprehensive understanding of the dynamics of academic CSE in engineering education and how it relates to students’ academic career segments.

The initial phase of conceptual framework focuses on students’ ACSE at the point of entry into their engineering programs. At this stage, students are faced with crucial challenges and decisions (National Academy of Engineering, 2018) that have a lasting impact on their engineering journey. Several factors come into play during the beginning stage of an engineering education journey, the ACSE undergoes a transformative process influenced by an array of factors discussed below.

The amalgamation of these factors guides students toward either embracing engineering or seeking alternative career paths. Hence, this career segment signifies the crucial juncture where ACSE begins to take root. Furthermore, these factors signify mastery and vicarious experience, social conviction, and emotional condition that have proven to be a potent stimulus for this particular segment. This particular segment, which is titled ‘ACSE at career entry’, has been formulated using scale items that have been predominantly adopted from Western literature. This is due to the scarcity of validated scale items specifically designed for the Indian context at present (refer to Table 1).

Self-efficacy in engineering education is a dynamic concept that evolves throughout an engineering student’s academic journey. A critical phase where self-efficacy plays a pivotal role is during academic progression.

Numerous studies highlight the significant relationship between self-efficacy and academic performance (Elias and MacDonald, 2007; Honicke and Broadbent, 2016). Self-efficacy beliefs drive students to set challenging goals, persevere in the face of difficulties, and maintain high levels of effort and commitment, contributing to improved academic performance (Hackett et al., 1992).

The final phase of the engineering education journey involves the transition of students into alumni (recent graduates). This phase is characterized by alumni expressions about their campus experiences, their engagement in student endeavors, and their contributions as donors (Alnawas and Phillips, 2015). Academic self-efficacy during this phase is crucial, particularly concerning academic career reflections and expressions, which typically occur upon the successful completion of engineering education (Hirschman, 1974).

The importance of entry-related ACSE lies in its predictive value for students’ career choices, outcomes, and aspirations (Nauta et al., 1998). Students’ beliefs in their abilities to pursue and succeed in an educational program significantly influence their interest and commitment to that program (Jackson and Wilton, 2017). Research consistently shows that entry-related CSE predicts students’ performance in acquiring skills (Rothwell et al., 2009; Itani and Srour, 2016; Wang et al., 2022a; Yang and Zhang, 2022). As students progress through their engineering education, the positive impacts of early self-efficacy continue to resonate (Gbadamosi et al., 2015; Dražić et al., 2018). In the Indian context, students’ beliefs about their career prospects upon entering an engineering program also influence their employability skills (Wheebox, 2023).

Career segment 1 → Career segment 2

As students progress through their engineering programs, their ACSE about engineering skills plays a pivotal role in shaping their career reflections after academic completion (Rothwell and Arnold, 2007; De Vos and Soens, 2008). Academic experiences, career success, satisfaction, and loyalty contribute to the broader concept of career reflections post-academic completion (Baruch, 2001; Harvey, 2005, 2010; Yorke, 2006). A strong sense of ACSE regarding engineering skills is closely linked with satisfaction in one’s chosen field (Dacre Pool and Qualter, 2013). Moreover, employability directly influences behaviors and intentions post-graduation, including those aimed at assisting future generations (Borraz-Mora et al., 2020).

Career segment 2 → Career segment 3

Direct Connection: Students’ self-efficacy at the entry point of their educational program directly impacts their subsequent career reflections and behaviors upon completion (Ahuja, 2016; Roy et al., 2018). Students who enter their engineering program with high career self-efficacy are more likely to exhibit positive career reflections, such as satisfaction, upon academic completion (Lent et al., 2007, 2015).

Indirect Connection: Research suggests that the direct relationship between entry-related ACSE and post-graduation ACSE is mediated by ACSE during academic progression (Rothwell and Arnold, 2007; De Vos and Soens, 2008; Duffy et al., 2015; Wilkins-Yel et al., 2018). Entry-related ACSE impacts ACSE during academic progression, which in turn influences post-graduation ACSE.

Career segment 1 → Career segment 3

Career Segment 1 → Career Segment 2 (Indirect, Mediating Effect) → Career Segment 3

Incorporating students’ traits as moderators adds a valuable dimension to understanding the relationships among ACSE across different career segments in context to India (Geetha Rani, 2019; Gupta, 2019). Traits such as gender, social caste, native place, major enrolled in, academic grades, and campus placement opportunities can significantly influence self-efficacy and career-related behaviors.

Career segments; second and third are dependable constructs, whereas career segment one is an independent construct of the model, as presented in Figure 1. Due to its efficiency and applicability for gathering primary data from a large population, the quantitative survey approach was preferred (Kotler et al., 2016) with recent engineering graduates (alumni) as a single point of contact.

As this study belongs to full spectrum of engineering trajectory, it became imperative to take into account the experiences of students who have been exposed to all three segments of academic career paths. Recent graduates are the only output indicators in a position to comment on whether or not the engineering profession has met their career goals and expectancies (Saunders-Smits and de Graaff, 2012; Alnawas and Phillips, 2015) in regard to their academic progression and completion. Consequently, in this study recently engineering graduated students (recent alumni) who were about to enter into their occupational career (job market) were selected as the subject group (population) purposefully for this investigation. They firmly provided valuable insights into their academic aspirations, progression and career experiences.

A selection of thirteen engineering institutions located in North Maharashtra, India was made from a sampling frame consisting number of engineering institutions, based on their accessibility and convenience. The nominated participants were recent graduates who had completed their graduation in the recent academic year 2019 from these institutions. Contacts of 2,449 graduates including their email addresses and WhatsApp numbers, were obtained by formally requesting the data from these institutions. Consent was received from these institutions to use the provided data for the self-report survey. The survey specifically targeted individuals aged between 21 and 24 years, ensuring diversity in terms of demographic characteristics, geographical locations, social status, and academic backgrounds as exhibited in Table 2.

To enhance the flexibility of participation in terms of time and location, and to facilitate a prompt response from the student participants, an online self-report survey was conducted using the Google Form tool over the internet (Kolb, 2008). The decision to use an online survey was informed by its ability to automatically collect responses and the convenience it offers to both researchers and participants (Vasantha Raju and Harinarayana, 2016).

In early March 2020, a link to the survey questionnaire, created through the Google Form tool, was distributed to the identified contacts of recent engineering graduates via email and WhatsApp. After a 15-day data collection period, the availability of the Google Form questionnaire link was terminated. During the first half of March 2020, out of the initial pool of 2,449 sample graduates, a total of 573 (N = 573) responses were received within the fifteen-day timeframe, resulting in a response rate of 23.4%. It is important to note that no missing data was reported due to non-response to the questions. All survey respondents provided written authorization before responding to survey, indicating their consent to participate in the survey and allowing for the survey data to be made public. The responses were automatically collected and stored in the researcher’s Google account.

The data for this study was collected in March 2020, just before the COVID-19 pandemic’s full impact. The four-year reporting delay is due to pandemic disruptions. Despite this, the data remains applicable as it provides a baseline for comparing pre- and post-pandemic trends in ACSE among engineering students. The core issues of self-efficacy, employability skills, and career choices have persisted, making the findings relevant. Additionally, these insights can inform strategies to address challenges in engineering education caused by the pandemic.

The use of a self-reported survey offered several advantages, primarily the elimination of potential bias that could arise from a surveyor’s influence or interpretation. Additionally, it enabled the researchers to maximize the number of participants who could respond within the set timeframe. It’s important to note that, since this survey involved human participants, it was reviewed and approved by the Institutional Ethical Committee to ensure ethical research practices.

To address research question RQ₁, a Confirmatory Factor Analysis (CFA) analysis was conducted to assess the significance and accountability of scale items included in Sections I, II, and III in contributing to their respective constructs (career segments). This analysis aimed to assess the factor structure of the scale items and their alignment with the corresponding career segments (constructs). It’s important to note that no specific hypothesis were formulated for this stage of the analysis, as the question primarily focused on understanding the accountability of structure of the data for corresponding constructs.

To validate the hypotheses outlined in H₁, H₂, and H₃, and to arrive at conclusions for research question RQ₂, CFA was performed first to validate the structural relationships. Reliability and validity tests were also performed to ensure the robustness of the measurement models. Then, Structural Equation Modelling (SEM) was employed. SEM allowed for the confirmation of the conceptual relationships between the career segments, as well as the examination of potential mediation effects. SEM was confirmed with the related fitness indices. Sections I, II, and III collectively contributed to the validation of H₁, H₂, and H₃ by assessing the relationships between these career segments. Furthermore, in conjunction with the personal characteristics of the graduate engineers, these sections played a crucial role in the validation of hypothesis H₄, as well as in responding to research question RQ₃.

The statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS 29.0). Specifically, SPSS was used for data processing, descriptive statistics, and initial exploratory analyses. For the more complex SEM analyses, IBM AMOS (version 29.0) was employed. AMOS facilitated the confirmation of the relationships and mediation effects within the structural model. Additionally, AMOS was utilized to perform multiple group comparisons, enabling the identification of potential moderating effects of students’ personal traits on the relationships between career segments. These analyses involved pairwise parameter comparisons to evaluate the impact of individual characteristics on the model’s outcomes.

Descriptive analysis was performed as the first stage of the statistical analysis through SPSS to determine the profile of the survey respondents, as presented in Table 2.

Table 2 provides an overview of the survey participants’ characteristics, including their personal traits. These traits were used to categorize and analyze the respondents based on their demographic, geographic, and socioeconomic backgrounds.

The social caste group division was based on educational and cultural advancements (Bertrand et al., 2010). In India, the caste system is complex and diverse, with scheduled tribes, scheduled castes, and special backward castes considered historically disadvantaged groups (lower caste), while other backward castes and general castes tend to enjoy greater social and educational privileges (upper caste) (Subramanian, 2015; Malish and Ilavarasan, 2016). Accordingly, panticipants were categorized into upper caste and lower caste groups.

Considering India’s diverse linguistic, religious, and cultural landscape, individuals’ beliefs and perspectives can vary based on their local context. Place of origin was categorized as rural or urban, with rural areas often defined by panchayat governance and urban areas encompassing tehsil and municipal governance (Bhagat, 2005; Census Commissioner of India, 2011).

Engineering majors were categorized based on their curriculum and practicality in the industry (Tilak J. B., 2020). Computer allied majors included fields such as computer science, information technology, and electronics, while the rest were classified as non-computer allied majors.

The grading system used in this study adhered to the assessment and evaluation criteria of the respective boards or universities. Grading criteria can vary between institutions, and this classification ensured consistency in the analysis (SCRIBD, 2009).

CFA was conducted to evaluate the precision of the constructed indicators and assess the factor structure associated with the three identified constructs (Segments S1, S2, and S3) (Field, 2013; Kline, 2015). The study had predetermined assumptions and hypotheses, confirming the existence of these constructs and their corresponding factor structures: S1 ← (S1-1 to S1-10), S2 ← (S2-1 to S2-14) and S3 ← (S3-1 to S3-5). Henceforth, CFA was directly conducted to confirm or reject the hypothetical paths specified in the conceptual model (Barrett, 2007).

Prior to conducting CFA, data were screened for normality, with skewness values ranging from −0.067 to −0.636 and kurtosis values ranging from −0.943 to −1.335. All these values exhibited normality, ensuring the validity of further analysis (Kline, 2015). Further statistical tests, such as the Kaiser-Meyer-Olkein value (0.98), Bartlett’s Test of Sphericity (p-value < 0.001), and communalities above 0.5, confirmed excellent sample adequacy for all three constructs (S1, S2, and S3) (Vogt and Johnson, 2011). The sample size of 573 exceeded the minimum ratio of 20 (samples to the number of variables) required for robust analysis (Floyd and Widaman, 1995; Maxwell Scott, 2000; Creswell, 2012). Additionally, the sample size met Hoelter’s critical N criteria (N = 436, p < 0.01) for Structural Equation Modeling (SEM).

Factor analysis was separately performed for each construct (S1, S2, and S3) with Eigenvalues greater than 1. The included scale items (factor structure) for ACSE at career entry (S1), during academic progression (S2), and post-completion (S3) explained 77.33%, 92.3%, and 92.62% of the variance, respectively. All factor structures provided strong conceptual foundations and support for their inclusion in their respective constructs (refer Table 3). The response to the research answer RQ₁ is provided as all three segments have been accounted strongly by the virtue of their factor structure (Fornell and Larcker, 1981; Hair, 2009).

Reliability was assessed using Cronbach’s alpha, which showed excellent internal consistency with values above 0.90 (Churchill, 1979). Item-to-total correlations were examined, with strong values > 0.6 (Fornell and Larcker, 1981; Briggs and Cheek, 1986), further confirming the reliability of the scale items.

Convergent validity was established through construct reliability (CR) and average variance extracted (AVE), with CR values ranging from 0.971 to 0.991, exceeding the minimum 0.80 threshold (Fornell and Larcker, 1981; Bagozzi and Yi, 1988) (refer Table 3). AVE values for the constructs (S1 = 0.773, S2 = 0.923, S3 = 0.926) were above 0.50, indicating good convergent validity (Fornell and Larcker, 1981). Additionally, CR values were higher than the AVE values for all three constructs, confirming convergent validity (Hair, 2009; Hair et al., 2010) (refer Table 4).

Discriminant validity was also established, with the square roots of the AVEs (S1 = 0.879, S2 = 0.960, S1 = 0.962) exceeding the respective inter-construct Pearson correlations (S1 → S2 = 0.637, S2 → S3 = 0.903, S1 → S3 = 0.602, p < 0.001) (Bagozzi, 1981; Fornell and Larcker, 1981; Hair et al., 2010). These results indicated that the constructs manifested distinct characteristics from one another, ensuring discriminant validity (refer Table 4).

Based on the conceptual framework, the three constructs characterizing career segments derived from CFA were linked by the hypothetical paths depicted in Figure 1. To evaluate the structural model, SEM was performed using IBM SPSS Amos 29 software. The SEM involved a total of 63 variables, comprising 29 observable and 34 unobserved variables, with 32 exogenous variables and 31 endogenous variables. The SEM results indicated that the model with a sample size of 573 was overidentified and recursive.

The SEM output, as presented in Figure 2, displayed values without modification of indices. Modifications were applied only to some error variances identified by AMOS through the modification indices tab. These modifications aimed to establish connections to reduce discrepancies and enhance the model’s fit (Schreiber, 2008). These adjustments were made while ensuring that the underlying hypotheses remained unaffected.

The residual variables, identified with modification indices equal to or greater than 30 (Ho, 2014), were interconnected to yield the final measurement model with improved fit indices. After modification, the chi-square (χ2) result remained significant, indicating a good fit for the hypothesized model (χ2 = 3115.63, df = 1071, p < 0.001, χ2/df = 2.9, TLI = 0.963, CFI = 0.967, RMSEA = 0.041).

The R² values for the endogenous variables S2 (ACSE during academic progression) and S3 (ACSE post-graduation completion) were 0.42 and 0.84, respectively. These values indicated moderate and strong strength of estimation, respectively (Cohen et al., 2013; Hair et al., 2013).

The fitness metrics, as shown in Table 5, for the measurement model in this investigation align with diverse fitness metrics suggested for SEM, substantiating the credibility of the associations among variables (Teo, 2014). SEM also established the direct and indirect effects among the underlying constructs, as revealed by the findings of this study (refer Table 6).

The ACSE structural model revealed a significant and substantial impact of career segment S1 on career segment S2, with an accountability of 42%. This impact was positive, denoted as S1 → S2, with a path coefficient (β) of 0.65. When career segment S1 increases by one standard deviation (SD), career segment S2 increases by 0.65 SD (B = 0.69, S.E. = 0.04, C.R. = 17.86, p < 0.001).

This result validates and, therefore, accepts the hypothesis 1 (H₁) stating that students’ entry-related ACSE (S1) is positively associated with their ACSE during academic progression (S2) (refer to Table 6). In other words, ACSE related to aspiration, expectation, outcome values, and motivation at the entry level significantly impacts ACSE during academic progression aligned with engineering skills. This finding emphasizes the importance of engineering skills in students’ academic progression.

This result is consistent with prior research by Mamaril et al. (2016) and Zimmerman (2000b), who found that engineering ACSE related to program utility and task value significantly influences students’ performance in engineering skills. It also aligns with previous studies that demonstrated how ACSE related to choice decisions and expectancy are influencing factors for ACSE perceived throughout academic learning (Hackett et al., 1992) and performance (Park and Park, 2020; Labib et al., 2021) and skills (Itani and Srour, 2016).

The SEM results indicated that career segment S3 was significantly and directly impacted, with 84% of its variance explained by career segment S2. There was a positive influence of S2 on career segment S3, denoted as S2 → S3, with a path coefficient (β) of 0.88. In practical terms, as career segment S2 increases by 1 SD, career segment S3 increases by 0.92 SD (B = 0.92, S.E. = 0.03, C.R. = 30.167, p < 0.001).

This result provides strong support for hypothesis H₂ (refer to Table 6). It suggests that students’ ACSE (S2) regarding the engineering skills they acquire during their academic progression has a significant and positive impact on their ACSE (S3) post-graduation completion expressions and reflections, including their experience, satisfaction, and loyalty to their engineering studies.

This finding aligns with research by (Bandura and Locke (2003) and (Berntson et al., 2010), which emphasize that performance attainment and perceived experience lead to varying levels of career completion reflections. This finding is in line with studies that have reported a positive correlation between self-perceived engineering skills and career satisfaction (Dacre Pool and Qualter, 2013) and between engineering skills and indicators of career success such as voice and loyalty (Berntson et al., 2010).

Examining the relationship between career segments S1 and S3, it was found that career segment S1 has a direct and significant impact on career segment S3, indicating a positive association (S1 → S3, β = 0.05, B = 0.055, S.E. = 0.027, C.R. = 2.028, p < 0.05). Furthermore, an additional significant impact was observed indirectly, mediated by career segment S2 (β = 0.58, p < 0.001). Consequently, career segment S3 is significantly and positively influenced by career segment S1 both directly and indirectly through the mediating role of career segment S2, with a total standardized estimate (β = 0.63), as detailed in Table 6.

These results provide support for hypothesis H₃, which posits that students’ entry-related ACSE has both a direct positive connection with ACSE in post-academic completion behaviors and an indirect connection via the mediating effect of ACSE during academic progression.

The expressions and reflections that mark the completion of one’s career, such as academic satisfaction (Borrego et al., 2018) and perceived experiences (Breeding, 2008), can serve as persuasive influences operating through vicarious means. This influence not only imparts knowledge and information to prospective students but also offers guidance and support to existing students (Hofacker, 2014; Borrego et al., 2018). Similar findings have been reported by Sheldon and Kasser (2001), Duffy et al. (2015), and Gerçek and Elmas-Atay (2022), who observed positive relationships between ACSE perceived at career entry and expressions of satisfaction post-graduation completion. According to this study, improved academic performance in terms of engineering skills may have met students’ expectations about their careers, thus influencing their perspectives on career completions. Consequently, engineering skills play a significant role in facilitating the transition from the commencement of a career to its successful completion.

Moderation involves examining whether the relationship between an independent variable and a dependent variable is influenced by a third variable known as the moderator variable (Swanson and Holton, 2005). Moderation helps assess heterogeneity in data (Strasheim, 2014), and it can impact both the direction and strength of the relationship between two variables. In this study, the author used the subgroup methodology in AMOS, which is considered a robust technique for analyzing categorical moderation (Lowry and Gaskin, 2014). Multiple groups were moderated through a series of pairwise parameter comparisons, which aimed to assess differences in the parameters of hypothetical paths (S1 → S2, S2 → S3, S1 → S3) connecting the constructs. These comparisons were designed to investigate the moderating effect of categorical variables (students’ traits) on the hypothetical paths. The presence of moderation was evaluated based on critical ratios that indicate differences akin to z-scores at a 95% confidence level (Singh and Sharma, 2016; Feder and Niţu-Antonie, 2017). To identify the presence of moderation, a threshold value of z > 1.96 was used (Gao et al., 2008).

CFA model was assessed for the moderating influence of seven independent categorical variables representing students’ individual characteristics through pairwise parameter comparisons. The results of these analyses are summarized in Table 7.

This study represents a pioneering effort to comprehensively examine self-efficacy within the context of an engineering career. It builds upon Bandura’s self-efficacy theory, adapting it to encompass three distinct career segments. Each of these segments—entry into engineering education, progression throughout academic development, and post-graduation completion reflections—has been rigorously analyzed, confirming the robustness of study’s measurement constructs within the Indian context.

The mean scores for each segment (S1 μ¯ = 3.3119, S2 μ¯ = 3.5846, S3 μ¯ = 3.4639) underscore the prominence of self-efficacy in terms of engineering skills development during the academic progression. These findings not only validate study’s research questions but also affirm the significance of considering engineering skills in the context of academic progression within engineering education.

Through Structural Equation Modeling (SEM), this study unearthed compelling relationships between these career segments. Of note, the combined influence of ACSE at the commencement of engineering education (direct and indirect) and ACSE throughout academic progression (direct) had the most substantial impact on ACSE post-graduation completion. Furthermore, results of this study affirm the mediation role of ACSE during academic progression between the entry-level ACSE and the post-graduation ACSE.

Thus, this study not only underscores the importance of ACSE across the various stages of an engineer’s professional journey but also validates the presence of mediation processes among these ACSE segments. It has thus successfully fulfilled the necessary requirements for extended intervention/mediation between the segments of self-efficacy (Lent et al., 2013; Lent and Brown, 2019; Bandura, 2021; Wang et al., 2022b). These findings add depth to author’s understanding of how self-efficacy evolves and shapes behavior throughout an engineer’s career.

This study also investigated the moderating effects of various student characteristics, such as gender, social caste, enrolled majors, campus placement, and engineering grades, on the relationship between career segments.

Interestingly, female students, those enrolled in computer-related majors, and those not selected through campus placements reported higher ACSE regarding engineering skills, attributing this to their entry-level ACSE in terms of aspirations, goals, motivation, and outcome value. Conversely, engineering grades played a moderating role in amplifying the link between ACSE during academic progression and post-graduation ACSE. Students with higher grades demonstrated greater academic experiences, satisfaction, and loyalty, driven by their engineering skills development during academic progression. Additionally, social diversity, specifically social caste, also emerged as a moderator. Students from upper castes showed greater expressions and reflections in terms of academic experience, satisfaction, and loyalty, primarily due to their entry-level ACSE.

These findings demonstrate the influence of individual characteristics and academic achievements on the development and transformation of self-efficacy across different career segments. Recognizing these moderating effects is essential for engineering institutions to tailor support and interventions to the specific needs of their diverse student populations.

Present study highlights the strong connections between career segments, with the most significant impact observed in the context of ACSE post-graduation completion. By exploring the link between academic performance and post-graduation completion behaviors, model of this study sheds light on how these ACSE segments collectively predict co-creation behaviors.

Notably, certain scale items, such as career experiences, satisfaction, assistance to future and existing students, and financial assistance, emerged as significant contributors to the development of ACSE post-graduation completion.

Findings of this study revealed that the factor structure, shaped by vicarious and social persuasion, as well as emotional states, plays a more vital role in establishing ACSE post-graduation completion compared to mastery experience, in alignment with Bandura (1977). These sources have a profound impact with several far-reaching effects. Firstly, they enhance the reputation of the engineering profession and instill enthusiasm in individuals considering engineering careers. Secondly, they facilitate co-creation, transmitting the importance of the engineering profession to future generations of potential students and motivating current students to excel.

Drawing from insights of Bansal et al. (2004), individuals who possess higher self-efficacy and satisfaction with a service are more likely to endorse it through verbal persuasion and affective commitment. In the realm of students as service customers, as they transition into active participants (Lusch et al., 2007), their expressions and reflections become instrumental in value co-creation (Prahalad and Ramaswamy, 2004), resulting in ‘win more - win more’ outcomes (Ramaswamy and Ozcan, 2014). The collective influence of these expressions and reflections, as indicated in study’s investigation, also extends to word-of-mouth recommendations for engineering programs and related offerings (McKee et al., 2006).

A comprehensive review conducted by Iskhakova et al. (2017) underscores the significance of alumni loyalty, which contributes to attracting new enrollments, securing funding, and supporting existing and aspiring students. In the Indian context, Micari and Pazos (2016) have highlighted the role of alumni interactions in fostering self-efficacy and enhancing satisfaction among aspiring engineers. Through their shared experiences and intentional behaviors (Hofacker, 2014), alumni have the power to transfer their intellectual capabilities to the next generation of engineering students. Consequently, alumni involvement facilitates the process of value co-creation, benefiting both students and institutions (Dollinger et al., 2018).

Given that engineering identity and value are significantly influenced by peer’s beliefs themselves (National Academy of Engineering, 2018), career experiences and reflections (Rehman et al., 2022), play a pivotal role in cultivating loyalty (Lin and Tsai, 2008), and shaping beliefs (Hutchison et al., 2006; Chen et al., 2023). This investigation, through its unique positioning, has the potential to foster the co-creation of self-efficacy among both prospective and current engineering students.

As this study has shown and as emphasized by Zeldin and Pajares (2000), alumni, as effective sources of self-efficacy, offer unique advantages through the co-creation of self-efficacy within engineering institutions. Firstly, this exploration into co-creation provides a foundation for enhancing organizational competitiveness while reducing marketing expenditures. Moreover, it furnishes additional evidence for the utilization of students’ word-of-mouth (Palma et al., 2018), outreach programs (Fogg-Rogers and Moss, 2019), Alumni-Alma Mater connections (Natarajan, 2002) and social media engagement (Kowalik, 2011). Furthermore, co-creation has the potential to challenge and transform engineering stereotypes, such as ‘muscularity,’ ‘richer’s game,’ ‘superior culturist,’ ‘chilly climate,’ ‘technical,’ and ‘tough and hard.’ The model proposed in this study possesses significant potential to operate independently, facilitating a student-as-partner approach. These unique outcomes accentuate the pivotal role of self-efficacy in co-creating ACSE, marking a significant milestone in the history of career self-efficacy.

The findings of this study have significant implications for the field of engineering education and its potential to enhance the career trajectories of aspiring engineers. Here are the key implications:

Exploiting Bandura’s theory of self-efficacy: The study highlights the potential of applying Bandura’s theory of self-efficacy in engineering education. By doing so, institutions can improve students’ beliefs about entering the field, enhance their engineering skills, and promote their reflections and expressions as future engineers. This can contribute to a more robust and confident engineering workforce.

Empowering aspiring engineers: Aspiring engineering students can benefit immensely from understanding the profound impact of their self-efficacy before entering their educational journey. They can mitigate the influence of misinterpretations, misconceptions, and stereotypes about engineering careers by looking up to role models, engaging with alumni, and participating in outreach programs offered by institutions. With 80% of India’s aspiring generation interested in engineering careers (Centre for Economics and Business Research, 2016b), this research holds significant implications for empowering these future engineers, including a substantial female presence in the field. It can help fulfill their aspirations and contribute to India’s vision of becoming a global leader.

Engineering skills as academic advancement: The research offers a thorough examination engineering skills as an academic progression, encompassing its significance in entrepreneurial ventures and employability gain. This highlights the crucial proficiencies that engineers need to succeed in their careers. Students are required to manage and build their own skills since skills cannot be taught but can be acquired. Rather than relying solely on formal skill-enhancing training, institutions should encourage students to participate in extracurricular and co-curricular activities. Skills are acquired through hands-on experiences, and these activities can help students build their skills. Further this will enable the institutions to cut down their cost on skill training programs. Thus, this investigation serves as the basis for bolstering students’ self-efficacy pertaining to engineering skills by means of extracurricular, co-curricular and extension activities.

Encouraging career expressions and reflections: The third career segment focuses on career expressions and reflections following graduation. It provides valuable insights for prospective students, motivating them to pursue engineering careers. Existing students can find inspiration to excel academically. Institutions should facilitate role models, career counseling programs, and alumni engagement initiatives to support this segment.

Promoting participation in social media: Educators and institutions can enhance their presence on social media platforms like Facebook, Instagram, Twitter, and LinkedIn to foster the co-creation of self-efficacy. Social media can be a powerful tool for connecting with students and alumni, sharing success stories, and promoting the engineering profession (Murphy and Salomone, 2013). Engineering institutions will be in greater position in terms of their financial stability and enrollments if they involve its alumni students to express and reflect their academic experience through social media platforms.

Contributing India’s initiatives and economy: This study, by examining the realm of self-efficacy, possesses the potential to allure a multitude of aspiring engineers while simultaneously enhancing their caliber.

In India, a multitude of progressive undertakings are currently underway, their triumph largely hinging upon the numerical strength and expertise of engineers. This study is poised to play a crucial role in initiatives like Make in India, Digital India, Smart Cities, and Skill India (Puri and Misra, 2017; Verma, 2018) along with revolution of Industry 0.4 (Sony and Aithal, 2020).

It aligns with India’s Education Policy’s goal of achieving a gross enrolment ratio of 50% by 2035 (MHRD India, 2020). It can help address the anticipated shortage of engineering graduates by 2028 and contribute to India’s economic growth (NASSCOM, 2023). The research additionally serves as the important foundation for addressing the challenges of 4Es; education, employability, employment, and entrepreneurship (Sabharwal, 2013), and further dignifies towards enhancing India’s total built asset wealth in the years to come (Centre for Economics and Business Research, 2016b).

In summary, this study offers a comprehensive exploration of self-efficacy in engineering education, providing valuable insights and recommendations for institutions, educators, and aspiring engineers. It has the potential to shape the future of engineering education in India and contribute to the nation’s growth and development.