Digital Competences and Their Impact on Employability in the Tourism Sector—An Applied Study

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study is based on a qualitative analysis conducted on a small panel of companies (114 firms). Neither the structure of the questionnaire nor the types of questions administered are presented, this makes extremely difficult for a reviewer to give a review. A brief and somewhat assertive list of research hypotheses is provided within the literature review section. No explicit research questions are formulated; rather, the paper relies on considerations that are subsequently validated through an ad hoc questionnaire.
Figure 1, introduced at the beginning of the methodology section, consists of a set of acronyms that are not explained in the main text. A brief list of definitions appears only after the paper’s conclusion.
Moreover, although it is stated that the questions posed to respondents are grouped according to the research hypotheses, no further clarification is provided, leaving the reader unable to systematically link the hypotheses with the questionnaire structure, or to assess whether the proposed questions are indeed suitable for validating the hypotheses.
The panel of SMEs surveyed remains very limited both in size and geographical representation; therefore, the findings should be interpreted in light of these significant constraints.
Furthermore, the study lacks a discussion of future research directions as well as a critical reflection on the study’s limitations by the authors themselves.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Is the survey sample representative of the target population under study in the geographical area?  This is not indicated.  If the findings are intended to have wide application then the sample framework needs to be articulated and be truly representative.  This is missing from the study and is a weakness of the paper.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Review Report

 

This manuscript presents an empirical study examining the impact of digital competencies and second-language proficiency on various dimensions of employability among employees in small and medium-sized tourism enterprises in Quindío and Valle del Cauca, Colombia. The topic is both timely and socially relevant, particularly in the context of accelerating digital transformation in the tourism sector. The use of PLS-SEM is methodologically appropriate, especially given the moderate sample size, and the study demonstrates commendable efforts in validating the measurement and structural models, including the assessment of reliability and convergent/discriminant validity.

However, I would like to highlight critical limitations that warrant further consideration.

First, while the manuscript reviews a range of prior studies related to digital competencies, employability, and second-language proficiency, the overall literature review appears to be somewhat limited in scope and depth. Key conceptual elements are introduced with appropriate references, yet the discussion often remains descriptive and lacks integrative analysis or comparative synthesis among the cited works. Moreover, the review does not sufficiently incorporate recent research trends, particularly those addressing artificial intelligence (AI) as a growing component of digital competence. Although the authors acknowledge the absence of AI as a limitation, relevant frameworks such as OECD’s AI skill guidelines or the EU’s DigComp 2.2 are notably absent. Additionally, while the context of the tourism industry is partially addressed, the theoretical linkage between digital transformation in tourism and workforce development could be further strengthened. A more comprehensive and up-to-date literature review would enhance the theoretical grounding and relevance of the study.

Second, although the study emphasizes digital competencies, it does not incorporate artificial intelligence (AI)-related competencies, which are increasingly recognized as a central component of digital transformation—particularly in the tourism industry, where AI applications (e.g., chatbots, recommendation systems, and automated booking platforms) are becoming widespread. While the authors acknowledge the omission of AI as a limitation, it is addressed only briefly. Given the prominence of AI in shaping workforce requirements in the digital economy, its exclusion from both the conceptual framework and the empirical analysis undermines the study’s explanatory power and practical relevance. A more robust discussion of why AI competencies were not included and how future research might integrate them would strengthen the academic rigor and foresight of the study.

Third, the study focuses primarily on explanatory power (R²) without addressing the predictive power (Stone–Geisser’s Q²) of the proposed model. One of the distinguishing strengths of PLS-SEM lies in its ability not only to model complex relationships but also to assess how well the model predicts outcomes in new or unseen data. The absence of predictive relevance analysis, such as Stone-Geisser’s Q² or PLSpredict procedures, limits the model’s generalizability and reduces its practical utility in guiding future interventions or training programs. Incorporating such predictive assessments in future studies would considerably enhance the robustness and applied value of the findings.

To summarize, while the manuscript has certain limitations, it nonetheless offers a meaningful contribution to the ongoing discussion surrounding digital competencies and employability in the service industry. It lays a solid groundwork for subsequent studies. Future research that incorporates emerging skill sets—particularly those related to artificial intelligence—and employs more rigorous predictive analyses could further elevate the academic depth and practical implications of this research domain.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The work is fine for me

Reviewer 3 Report

Comments and Suggestions for Authors

The revised manuscript is appreciably strengthened and demonstrates a commendable effort to address the earlier round of reviewer feedback. Nevertheless, omitting a predictive-relevance evaluation—specifically, the Stone–Geisser Q² statistic or the PLSpredict procedure—remains a notable methodological gap when considered against the study’s stated aims.

The article seeks not only to establish the causal nexus between digital competences and employability but also to furnish actionable insights for workforce development, organizational training initiatives, and managerial decision-making within the tourism sector. Such objectives move the contribution well beyond theoretical exposition and underscore its intention to inform practice and policy.

In view of this applied orientation, the assessment of predictive validity is indispensable rather than discretionary. Metrics such as Q² and PLSpredict are integral to determining whether the model retains explanatory power with out-of-sample data—a prerequisite for ensuring external relevance and practical utility. Incorporating these analyses would achieve closer alignment between the methodological framework and the paper’s pragmatic objectives, thereby enhancing the study’s robustness and generalizability.

I therefore respectfully recommend that the authors undertake and report the Q² and/or PLSpredict analyses. Should it prove impracticable to incorporate these tests in the current revision, the manuscript’s limitations section ought to articulate more explicitly the methodological consequences of their absence.

Addressing this point would enable the paper to fulfil its declared purpose more completely and would materially strengthen its contribution to both scholarly discourse and applied practice. 

Author Response

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Author Response File: Author Response.docx