Introduction

As economic globalization and digital transformation deepen, the demand for workforce in the global labor market is undergoing fundamental changes (Liu et al., 2024). Within this evolving context, engineering education is increasingly recognized as a strategic field for cultivating a workforce capable of responding to new and complex industrial needs. Traditionally, engineering education has emphasized practice-oriented curricula aimed at developing students’ professional skills. However, the rapid emergence of technologies such as artificial intelligence, big data, and smart manufacturing has redefined the skill sets required in modern workplaces, necessitating not only specialized technical expertise but also digital and interdisciplinary competencies (Luengo-Aravena et al., 2024).

This paradigm shift has prompted growing calls for educational reform, requiring engineering education to move beyond siloed skill development and instead foster integrative, multi-skilled workforce (Cabellos et al., 2024). Reforming engineering curricula is not only critical for enhancing individual employability but also for boosting societal productivity and driving sustainable economic growth. Yet, significant challenges remain. Many engineering programs still struggle to effectively integrate digital skills with traditional professional training, resulting in fragmented learning experiences and limited applicability in real-world, interdisciplinary contexts (Cattaneo et al., 2025).

In curriculum design, professional skill development often dominates, while digital competencies are treated as peripheral or supplementary (Weisberg and Dawson, 2024; Cattaneo et al., 2022). Even in institutions that have initiated digital curriculum integration, implementation barriers, such as outdated course content (Hiim, 2023), insufficient digital pedagogical proficiency among instructors (Saikkonen and Kaarakainen, 2021), and the absence of coherent instructional frameworks (Cattaneo et al., 2025)—have hindered the formation of meaningful synergies between digital and professional skill development. This fragmented approach weakens students’ ability to transfer and apply knowledge across domains, undermining their vocational adaptability and overall competence.

Moreover, task motivation has emerged as a critical but underexplored factor in engineering education. It shapes students’ engagement, persistence, and capacity to solve complex problems (Mendoza et al., 2023; Bernecker and Ninaus, 2021). Prior research demonstrates that task motivation not only influences learning outcomes but also mediates the relationship between skill acquisition and vocational performance (Alt, 2023; Luengo-Aravena et al., 2024; Lindfors et al., 2022; Valle et al., 2021). Nevertheless, few studies have systematically examined how task motivation interacts with digital and professional skills to jointly affect vocational competence.

To address this gap, the present study introduces the theoretical framework of Digital–Professional Integration (DPI), which conceptualizes the interconnected roles of digital skills, professional skills, and task motivation in cultivating engineering students’ vocational competence. Situated within the interdisciplinary evolution of engineering education (Bordogna, 1997), this research aligns with the CDIO (Conceive–Design–Implement–Operate) model, which emphasizes systems thinking, data analysis, and project-based learning (Crawley et al., 2007). Specifically, we focus on students in tourism management, a vocational field increasingly requiring the application of engineering-related digital methods to solve practical problems, thus extending engineering education theories into interdisciplinary, application-oriented domains.

Employing Fuzzy Set Qualitative Comparative Analysis (fsQCA), this study explores how diverse configurations of skills and motivational factors lead to high levels of vocational competence. Compared to traditional linear statistical models, fsQCA offers a robust means of capturing complex, nonlinear interactions and identifying multiple effective pathways (Chuah et al., 2021). The findings contribute both a theoretical lens and empirical validation for rethinking curriculum design in engineering education. By advocating an integrated, interdisciplinary skill development model, this research offers practical guidance for implementing innovative, high-impact approaches to workforce development in engineering education (Broo et al., 2022).

Conceptual model

This study proposes the Digital–Professional Integration (DPI) framework (see Fig. 1), grounded in two complementary theoretical perspectives: Self-Determination Theory (SDT) (Ryan and Deci, 2024) and Multiple Intelligences Theory (MIT) (Gardner and Hatch, 1989). SDT emphasizes the role of intrinsic motivation in learning, particularly in complex tasks where internal drivers—such as interest, curiosity, and self-worth—enhance persistence and engagement more effectively than external rewards. MIT, in contrast, asserts that individuals possess multiple forms of intelligence—linguistic, logical-mathematical, spatial, interpersonal, intrapersonal, musical, bodily-kinesthetic—which are activated in different learning contexts, particularly when the basic psychological needs of competence, autonomy, and relatedness (central to SDT) are met.

Fig. 1
Fig. 1
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DPI Conceptual Model.

In the context of engineering education, digital and professional skills should not be seen as separate domains. Digital skills refer to the technical capabilities required in modern workplaces—such as information processing and online collaboration (Loh et al., 2025)—while professional skills refer to domain-specific competencies such as market research, customer communication, and copywriting. For example, information processing (IP) tasks primarily activate students’ logical–mathematical and spatial intelligences. As students complete these tasks and receive feedback, they develop a sense of competence, enhancing their autonomy. This motivational state can facilitate a transition into tasks requiring professional skills, such as client communication (CC), which in turn activates interpersonal intelligence and fosters relatedness. When all three SDT needs—competence, autonomy, and relatedness—are simultaneously fulfilled, intrinsic motivation is strengthened. Students re-engage with digital tasks at a deeper level, creating a bidirectional activation between digital and professional skills. This reciprocal interaction fosters a dynamic co-evolution of skillsets—analogous to a double helix model—in which core intelligences are activated in alternating or concurrent cycles, resulting in integrated and synergistic development.

SDT further identifies self-efficacy—a belief in one’s capacity to execute tasks—as a critical motivational mechanism (Bandura, 1986). Higher levels of self-efficacy enhance students’ willingness to engage in and transfer between digital and professional domains, thereby strengthening their overall vocational competence. Therefore, task motivation in this study encompasses both affective components (e.g., interest, intention, self-worth) and cognitive appraisals such as self-efficacy. While MIT provides a cognitive lens for understanding the diversity of human abilities, SDT offers a motivational framework for how these abilities are activated and integrated in learning. The integration of these two theories allows us to conceptualize not only what abilities should be cultivated but also how motivation drives the process of their coordination.

Based on the DPI framework and its theoretical underpinnings, we propose the following hypotheses:

Hypothesis 1: Digital skills and professional skills exhibit a synergistic effect in the context of engineering education.

Hypothesis 2: The effective integration of digital and professional skills significantly enhances students’ vocational competence.

Hypothesis 3: Task motivation, particularly improvements in self-efficacy, significantly facilitates the integration process and amplifies its impact on vocational competence.

Methods

Sample

The participants in this study were 36 students from the tourism management program at a higher vocational institution in China. Their ages ranged from 17 to 19 years, with an average age of 18 years (standard deviation = 0.9). All students voluntarily participated and were informed about the purpose, data collection, and procedures of the study. We conducted a one-month digital skills training program, which covered topics such as information processing, data analysis, and the use of online collaboration tools, aimed at enhancing students’ digital skill levels. All students completed a pre-test before the training, which recorded their initial levels of task motivation, digital skills, and professional skills. A post-test was administered at the end of the training to observe the skill changes resulting from the program and to provide reliable baseline data for the subsequent fsQCA analysis.

Instruments

This study adopts a pretest–posttest experimental design to examine the impact of digital–professional integration (DPI) on both skill development and task motivation. Data were collected using a combination of structured questionnaires and performance-based assessments to capture changes in students’ digital skills, professional skills, and motivational states throughout the training intervention.

Task motivation was assessed using a validated questionnaire administered before and after the training program. The instrument consists of two sections. The first collects demographic information (e.g., age and gender) to contextualize the sample. The second section comprises 20 items across four dimensions—Interest, Value, Self-Efficacy, and Intention—with five items per dimension. These dimensions align with key constructs of task motivation in the conceptual model.

All items were adapted from well-established, psychometrically validated scales, and rated on a five-point Likert scale (1 = Strongly disagree, 5 = Strongly agree). The Interest subscale was adapted from the Interest/Enjoyment component of the Intrinsic Motivation Inventory (IMI) (McAuley et al., 1989). The Value subscale was derived from the Expectancy–Value Task Value Scale (Wigfield and Eccles, 2000), covering aspects of utility, importance, and perceived cost. The Self-Efficacy and Intention subscales were adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al., 2003), with the intention scale specifying a three-month action horizon. To better reflect the educational context, two additional items—“willingness to recommend” and “commitment to continuous improvement”—were incorporated into the intention dimension (Items 4 and 5), without altering the original construct structure. All items were contextualized to DPI learning scenarios, with minor wording adjustments that preserved the positive/negative framing and theoretical alignment of the source instruments.

This study was conducted within the domain of smart tourism, an application-rich context for exploring interdisciplinary skills. Digital and professional competencies were assessed through performance-based tasks aligned with international and national standards. For digital skills, two competencies were selected from UNESCO’s Digital Literacy Global Framework (Law et al., 2018): Information Processing (IP) and Online Collaboration (OC). These correspond to core requirements in engineering education, namely systematic, data-driven decision-making and collaborative problem-solving.

For professional skills, task design followed the National Teaching Standards for Tourism Management Majors, and included three competencies: Market Research (MR), Copywriting (CW) and Customer Communication (CC). Each competency was assessed through a specific task. In the IP task, students used Python to clean and visualize seasonal big data on scenic area tourist flows, and produced a technical analysis report. The OC task required students to form teams and collaboratively design a scenic route optimization plan using Ding Talk, including task allocation, project tracking, risk alerts, and a submitted Gantt chart. In the MR task, students conducted field research using questionnaires and interviews, and submitted a 1500-word market demand analysis. The CC task involved simulating a B2B client pitch, producing a 10-minute roadshow video, and participating in a Q&A defense. For the CW task, students drafted and published an 800-word, multilingual promotional tweet—with embedded images—on the WeChat official account.

All tasks were evaluated using a dual-rater scoring system, with independent assessments by a course instructor and an industry expert. Final scores were averaged across the two raters. Assessment criteria were based on a collaboratively developed rubric. For example, in the IP task, Data preprocessing completeness accounted for 30%, Visualization accuracy and readability for 40%, and Logic and structure of the technical report for 30%. Inter-rater reliability was verified using Cohen’s Kappa, with all five tasks exceeding the threshold of 0.80, confirming high consistency and validity in evaluation outcomes (Cohen, 1960).

Data analysis

The enhancement of vocational competence is a multifaceted process shaped by the interplay of various factors. Traditional statistical techniques often fall short in capturing the complex, nonlinear relationships among these multidimensional conditions. To address this methodological challenge, this study adopts fsQCA, which offers distinct advantages in exploring configurational causality and interaction effects across multiple variables (Thai and Wang, 2020). Unlike linear regression models that isolate independent effects, fsQCA enables the identification of causal pathways—combinations of conditions that are jointly sufficient for an outcome to occur. This approach is particularly suited for uncovering how different levels and combinations of task motivation, digital skills, and professional skills collectively contribute to the improvement of vocational competence. While fsQCA 3.0 (Kusa et al., 2021) is commonly used for standard fuzzy-set analyses, this study employed a customized Python-based analytical framework. This allowed for greater flexibility in processing both pre-test and post-test data, enabling dynamic tracking of changes and refined calibration of set membership scores.

The data analysis consisted of three key stages. First, fuzzy set calibration was performed by converting pre-test and post-test data on task motivation, digital skills, and professional skills into fuzzy-set membership scores, which were categorized into high, medium, and low levels based on percentile thresholds. This enabled the quantification of condition intensity and its contribution to outcome configurations. Second, a necessity analysis was conducted to determine whether any individual condition—such as high self-efficacy or advanced information processing—was consistently present in cases of high vocational competence, thereby indicating its role as a necessary condition. Third, a sufficiency analysis was conducted by constructing a truth table to evaluate all possible combinations of the three core conditions. Boolean minimization was applied to simplify these combinations into the most parsimonious causal pathways.

Results

Reliability and validity

To evaluate the reliability and validity of the measurement instrument, we assessed factor loadings, Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) for each construct. Table 1 reports the results related to convergent and discriminant validity, as well as internal consistency. All Cronbach’s alpha values ranged from 0.882 to 0.931, and all CR values exceeded the recommended threshold of 0.70, indicating strong internal consistency and construct reliability (Fornell and Larcker, 1981). Furthermore, AVE values for all constructs were above 0.50, providing evidence of satisfactory convergent validity (Segars, 1997). Inter-construct correlations ranged from 0.463 to 0.657, all of which were lower than the square root of the corresponding AVE values, thereby confirming discriminant validity (Chin, 1998).

Table 1 Structure of the questionnaire and its reliability, validity and correlations.
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In addition, model fit indices from structural equation modeling supported a good overall model fit: χ²/df = 3.157 ( < 5), RMSEA = 0.039 ( < 0.05), CFI = 0.937 ( > 0.90), TLI = 0.946 ( > 0.90), and SRMR = 0.038 ( < 0.05). Collectively, these results demonstrate that the measurement model possesses strong psychometric properties, with robust reliability, convergent validity, and discriminant validity.

Descriptive statistics and difference analysis

Our study assessed the changes in students’ professional skills, digital skills, and task motivation before and after the digital skills training (e.g., Fig. 2). To quantify the training effect, we first conducted descriptive statistics and difference analysis on all the measured dimensions. Table 2 presents a comparison of the pre-test and post-test scores for various professional skills (e.g., market research, customer communication, copywriting), digital skills (e.g., information processing and online collaboration), and task motivation dimensions (interest, efficacy, value, and intention). After the digital training, the means for all dimensions significantly increased. For example, the average score for market research increased from M = 85.81 (SD = 6.30) to M = 89.14 (SD = 4.78), and information processing rose from M = 86.17 (SD = 4.35) to M = 92.81 (SD = 3.09). Paired-sample t-tests indicated that these changes were statistically significant (p < 0.001).

Fig. 2
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Mean scores across constructs (pre and post).

Table 2 Descriptive statistics of measurement metrics (pre and post).
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To further evaluate the training effect, we also calculated Cohen’s d effect size to measure the magnitude of change for each dimension. The effect sizes were interpreted as follows: 0.2 represents a small effect, 0.5 a medium effect, and 0.8 or above a large effect. The results (as shown in Table 3) revealed that the effect sizes for most dimensions were above 1.0. For instance, the effect sizes for copywriting (d = 1.73), information processing (d = 1.76), and vocational competence (d = 2.10) were all large, suggesting that the digital skills training had a significant overall impact on students’ skills and motivation.

Table 3 Statistical analysis of constructs: effect sizes and distribution.
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We calculated skewness and kurtosis. The Q-Q plot (shown in Fig. 3) indicates that the overall distribution of data before and after training closely approximates a normal distribution. The gray area marks the ±1.5 reasonable deviation ranges from the theoretical reference line, with data points lying within this range, indicating that our data distribution characteristics meet expectations. After training, the fit of the data distribution with the theoretical normal distribution improved, and the deviations in skewness and kurtosis significantly decreased, further optimizing distribution.

Fig. 3
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Q–Q plots of skewness and kurtosis (pre and post).

Fuzzy-set qualitative comparative analysis

The primary objective of our study was to identify the key skill combinations that contribute to the improvement of students’ vocational competence through fuzzy set qualitative comparative analysis (fsQCA). This analysis process involves three steps: fuzzy set calibration, necessity analysis, and sufficiency analysis, to reveal which combinations of skills in the integration of digital and professional skills (Digital-Professional Integration) contribute to significant improvements in students’ vocational competence.

Calibration of fuzzy sets

We performed three-level fuzzy set coding on the improvements in professional skills, digital skills, and task motivation, which clearly reflects the high, medium, and low levels of ability improvement for the participants. Specifically, the data were divided into three coding intervals: High (above the 67th percentile, coded as 1, indicating full membership in the fuzzy set), Mid (above the 33rd percentile but below the 67th percentile, coded as 0.5, indicating partial membership in the fuzzy set), and Low (below the 33rd percentile, coded as 0, indicating non-membership in the fuzzy set). These percentile thresholds were chosen based on the data distribution to better capture the different levels of skill improvement. After calibration, we generated a fuzzy set dataset containing the indicators of task motivation, professional skills, and digital skills, laying the foundation for subsequent necessity and sufficiency analyses.

Analysis of necessary conditions

At this stage, we conducted necessity analysis on the professional skills, digital skills, and task motivation to investigate whether these factors are necessary conditions for improving vocational competence. Both positive and negative conditions were included in the analysis to comprehensively assess the potential impact of each variable on vocational competence. According to the criteria of Fiss (2011) and Schneider and Wagemann (2010), consistency values exceeding 0.9 are considered necessary conditions. As shown in Table 4, the consistency and coverage result for both the positive and negative conditions did not exceed 0.9, indicating that no single condition can independently serve as a necessary condition for vocational competence.

Table 4 Details of the necessity analyses.
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Based on Ragin’s (2009) explanation, coverage is used to measure the extent to which a condition or combination of conditions explains the outcome. Typically, a coverage value exceeding 0.5 indicates that the condition has some explanatory power for the outcome. As shown in Fig. 4, the coverage for the positive conditions was all above 0.5, reflecting a broad explanatory range for these conditions in the outcome variable. In contrast, the coverage for the negative conditions was generally lower, indicating limited contribution to explaining vocational competence. Combining the results of consistency and coverage, we conclude that although the positive conditions show some explanatory power in terms of coverage, they did not meet the consistency threshold for necessity. This further suggests that these conditions may be more important in sufficiency analysis rather than in necessity analysis. Therefore, students’ vocational competence is influenced by the interplay of multiple factors, rather than by the independent effect of a single variable.

Fig. 4
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Consistency and coverage analysis of necessary conditions.

Configurational analysis of sufficient conditions

Following the necessity analysis, a truth table was constructed to represent all theoretically possible configurations of the nine causal conditions included in the study. Given that the number of possible combinations is calculated as 3⁹ (since each condition was calibrated into three fuzzy-set levels: high, medium, and low), the resulting truth table contained 19,683 rows, each representing a unique configuration. The initial truth table was then refined by applying filtering criteria. Based on the recommendation by Ragin and Davey (2016), we adopted a consistency threshold of 0.75, which serves as an approximate benchmark for identifying configurations that are sufficiently associated with the outcome. To eliminate configurations that are subsets of others and ensure analytical parsimony, we ultimately retained 12 configurations, each coded with a value of 1 for the outcome variable—vocational competence (VC)—as shown in Table 5.

Table 5 Truth table for configurations of high vocational competence (partial).
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Next, we evaluated the consistency and coverage of each retained configuration. Path-level consistency values ranged from 0.80 to 0.92, all exceeding the 0.75 threshold, confirming the robustness and reliability of the solution. The overall solution consistency reached 0.89, indicating that 89% of cases matching at least one configuration also exhibited high levels of vocational competence, thus affirming the sufficiency of the identified pathways. The solution coverage was 0.83, suggesting that the retained configurations collectively explain 83% of all high-VC cases. The sum of raw coverage was 0.964, which is 0.134 higher than the solution coverage, indicating that approximately 13.9% of cases were covered by more than one configuration—a phenomenon known as “double-counting.” After adjusting for overlap, the sum of unique coverage aligned precisely with the solution coverage value. Notably, Path 1 exhibited a raw coverage of 0.158, accounting for 19% of the model’s explanatory power, and thus emerged as the most representative and influential configuration in explaining high vocational competence. Given the complexity and potential asymmetry in the causal relationships among conditions, and in light of prior research yielding mixed conclusions, we chose not to impose directional hypotheses regarding the linkages between specific causal conditions and the outcome variable.

We used a heatmap (Fig. 5) to illustrate the variable levels of the 12 pathways. The x-axis represents the key variables, and the y-axis corresponds to the identified pathways (Condition 1 to Condition 12). The color intensity indicates the variable levels, with darker colors representing higher values. Among the 12 retained pathways, we found that market research (MR) and information processing (IP) abilities were consistently at higher levels in most pathways (e.g., Conditions 1, 2, 3, 6, and 9). MR appeared at high levels in up to 10 pathways, highlighting its critical role in enhancing vocational competence. Specifically, for students in the tourism management program, market research skills are of great significance in analyzing market demand, designing service plans, and enhancing decision-making capabilities. IP, as a digital skill, appeared 9 times, underscoring its irreplaceability in modern professional environments.

Fig. 5
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Heatmap of Conditions and Variables.

Moreover, task motivation factors, particularly self-efficacy, also played a significant driving role. Self-efficacy appeared in 7 of the 12 pathways, indicating its indispensable role as a psychological drive-in students’ skill application and task completion. Multiple pathways also demonstrated the synergistic effect between digital skills and professional skills. For instance, Path 7 shows that even in the absence of information processing (IP), the combination of professional skills (such as MR, CC, CW) and online collaboration (OC) can compensate for this deficiency, validating Hypothesis 1.

Visualization and validation of configurations

To further validate the results of the sufficiency analysis, we used Boolean set theory to categorize the nine indicators into three distinct dimensions—digital skills, professional skills, and task motivation. This allowed us to construct a Venn diagram to visually display their interactions in achieving vocational competence. As shown in Fig. 6, the intersection area between digital skills and professional skills (Area 3) confirms that these two conditions jointly provide a sufficient foundation for the development of vocational competence, thus validating Hypothesis 2. This conclusion aligns with the theoretical foundation of the Digital-Professional Integration (DPI) framework, in which the integration of digital skills and professional skills constitutes the key mechanism for vocational competence development.

Fig. 6
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Venn diagram of skills and motivation.

Furthermore, the introduction of task motivation significantly enhanced the overall model (Area 4), generating a synergistic effect among the three conditions. The core area (Area 4) indicates that when task motivation is combined with digital skills and professional skills, vocational competence reaches its optimal state, surpassing the outcomes achievable by skill integration alone. This not only strengthens the conclusions of the sufficiency analysis but also highlights the critical role of task motivation as a complementary condition in the DPI framework, thus validating Hypothesis 3.

Discussion

This study employed fsQCA to identify 12 distinct causal pathways leading to enhanced VC among students. Among them, Paths 1–3 demonstrated consistency scores above 0.90, collectively accounting for 41.6% of the overall solution coverage. These high-weight configurations were characterized by elevated levels of IP within digital skills and MR within professional skills, accompanied by at least medium-to-high levels of task motivation, particularly self-efficacy. The heatmap (Fig. 5) further supports this finding, revealing that MR and IP functioned as core conditions in 10 and 9 configurations, respectively.

Drawing upon MIT, MR tasks primarily activate interpersonal and linguistic intelligences, while IP tasks stimulate logical–mathematical and spatial intelligences. These diverse cognitive modalities demonstrate a synergistic interaction when applied to authentic, interdisciplinary learning tasks. Notably, Path 7 presents a compelling exception: despite low proficiency in digital skills (IP), high competence in the three professional skills (MR, CC and CW), combined with strong self-efficacy, resulted in high VC. This illustrates a compensatory effect, wherein high task motivation and professional skill mastery can offset deficiencies in digital capabilities. In this scenario, students first experience Competence and Relatedness in professional tasks, which then catalyze Autonomy—empowering learners to engage more effectively with digital skill development, such as OC. This finding empirically supports the “double-helix” interaction described in the DPI framework (Fig. 1), where digital and professional skills reinforce one another through reciprocal activation.

While Gardner and Hatch (1989) posited that multiple intelligences operate in parallel, our findings suggest that, within interdisciplinary domains such as tourism management, these intelligences may operate hierarchically and sequentially. Digital intelligences, encompassing logical–mathematical and spatial dimensions, are often activated first during data collection and visualization, serving as a foundational layer. Subsequently, interpersonal and linguistic intelligences are engaged during program interpretation and communication, facilitating value co-creation. This reflects a progression from foundational cognition to applied intelligence.

In terms of motivational dynamics, the three core psychological needs outlined in SDT—Competence, Autonomy, and Relatedness—were found not to be fulfilled simultaneously, but rather in a cyclic and cumulative sequence. Our data suggest that these needs are alternately satisfied through repeated engagement with digital–professional task cycles. The fulfillment of Competence and Relatedness enhances Autonomy, which in turn promotes deeper re-engagement with increasingly complex tasks—forming a hierarchical motivational loop. This process aligns with the organismic self-enhancement mechanism proposed by Ryan and Deci (2024), and underscores the importance of examining psychological need satisfaction within specific skill domains and task structures.

Building on these findings, the study offers practical guidance for the development of Digital–Professional Integrated Modules (DP-IMs). Before curriculum implementation, instructors are encouraged to construct a “Position–Skill–Intelligence Matrix”, aligning occupational requirements with professional standards and mapping these to corresponding intelligences. This matrix should be visualized and shared with students to foster clear learning expectations and self-regulation. Subtask design should embed timely feedback (to promote Competence), autonomous topic selection (to enhance Autonomy), and peer evaluation (to build Relatedness), thereby facilitating authentic C–A–R learning environments. For instance, in the IP task, students generate real-time visual reports, reinforcing perceived competence. In the MR task, learners independently select research topics, encouraging autonomous engagement. In the OC task, collaborative progress is transparently managed via Ding Talk, with built-in peer evaluation mechanisms.

Moreover, school–enterprise partnerships should be leveraged to integrate real-world scenarios into teaching. Authentic data and client briefs from scenic areas can be embedded in course projects to bridge the gap between academia and industry. A dual-mentor model, involving both academic instructors and industry professionals, should be established to co-develop evaluation rubrics that reflect real-world performance standards. Continuous data monitoring and instructional adjustment are also essential. Teaching teams should record student performance data in real time and use Python-based fsQCA scripts on a monthly basis to track changes in path coverage. For example, if the combined weight of key elements such as “IP–MR–Efficacy” declines, instructors should adapt task distributions or introduce new learning resources to ensure that curriculum design remains responsive to students’ developmental needs.

Conclusion

This study empirically validates the effectiveness of the DPI framework in the context of engineering education by employing fsQCA. The findings indicate that both professional skills (e.g., market research) and digital skills (e.g., information processing) play a pivotal role in enhancing students’ vocational competence. Moreover, self-efficacy, as a key component of task motivation, significantly facilitates the integration of these skill domains. These results provide not only theoretical support for the DPI framework but also practical implications for curriculum design and pedagogical strategies in modern engineering education. The successful integration of digital and professional skills is essential for developing versatile, adaptive professionals equipped to meet the evolving demands of the increasingly digital labor market. Beyond improving vocational competence, such integration offers a pathway for systemic innovation in engineering education, promoting interdisciplinary learning, real-world problem solving, and responsiveness to technological change. As digital transformation continues to evolve, engineering education must proactively explore new models for multi-skill integration, ensuring that future graduates are equipped with the hybrid competencies required by the 21st-century workforce.

Despite the contributions of this study, several limitations must be acknowledged. The research sample was limited to students in tourism management from a vocational institution in Shanghai, thus excluding core STEM disciplines such as mechanical, electrical, or computer engineering. The generalizability of the findings is therefore constrained. Future studies should apply and test the DPI framework across diverse disciplines and regional contexts to assess its broader applicability and adaptability. In addition, the training program spanned only one month, and as such, the study does not capture the long-term impacts of digital–professional skill integration on vocational development. Future research should adopt longitudinal designs and apply time-series fsQCA methods to explore the durability of learning outcomes and track changes in configurational patterns over time. Such research would contribute to a more comprehensive understanding of how skill integration evolves and sustains itself within educational and workplace environments.