Review Reports - Employee Behavior in Sustainable Digital Marketing: The Role of AI Technologies in the UAE

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Abstract:
The abstract is very well written.

Introduction:
The introduction is well structured and clearly written. However, the author has used multiple citations to support single statements. It is recommended to reduce the number of citations and use only the most relevant and recent ones. Additionally, the research gap and research questions should be explicitly stated at the end of the introduction to strengthen the study’s rationale.

Literature Review:
Although the author explains the theories and their connections with the study variables, the justification for using both the Technology Acceptance Model (TAM) and Sociotechnical Systems Theory in parallel is unclear. The author should clearly explain how these two theories align and specify which variables are derived from each theory.

Figure 1:
The dependent variable (DV) box in Figure 1 appears incomplete. The figure should be refined for clarity and completeness.

Hypotheses:
The study presents too many hypotheses (10), which seems excessive for a single article. It is advisable to focus on the core relationships that are novel and directly relevant to the research objectives.

Sample and Data Collection:
Based on the statement that the study population includes employees from UAE telecommunication companies specifically Etisalat by e& and du, the selection of only these two companies may introduce bias. The author should justify why other companies were excluded. If the rationale is that these companies are industry leaders in sustainability-driven digital marketing, the generalizability of the findings to less advanced companies should be discussed.

The author mentions that the questionnaire was distributed electronically but does not specify the platform used. Moreover, there is no mention of informed consent or ethical considerations. The author should clarify whether ethical approval was obtained from the university or participating companies, and if so, include proof or reference to it. For guidance, please refer to this article: https://doi.org/10.1016/j.actpsy.2025.104986.

Demographics:
The gender distribution (75% male) may raise questions about sample bias. The author should discuss this limitation.

Table 5:
P-values should be reported to three decimal places (e.g., p = 0.001 instead of 0.000 or 0).

Overall:
Other sections of the paper are well presented. Good luck with your revisions.

Author Response

Review 1

Respected reviewer, thank you for your constructive comments. We paid full attention to address all the comments which improved the manuscript. We welcome further comments if any to improve the quality of the study.

Review 1: Comments

Abstract:
The abstract is very well written.

Response: Dear reviewer, thank you very much for the acknowledgement.

Response: Dear reviewer, the comment has been addressed. We resolved the issue of multiple citations by reducing the number of citations where it was possible. However, we retained citations where multiple citations were required for example: see below lines.

“Much of the literature emphasizes consumer behavior (Abrardi, Cambini, & Rondi, 2022; Jain, Wadhwani, & Eastman, 2024; Zhang & Wang, 2023), corporate strategy, or technological innovation, while overlooking the central role of employees in operationalizing sustainability within digital marketing frameworks. Several studies highlighted AI and digital marketing activities (Gündüzyeli, 2024; Rabby, Chimhundu, & Hassan, 2021; Ziakis & Vlachopoulou, 2023), however, this relationship was not considered in relation to employee behavior among the technologization companies of UAE.”

Furthermore, we have now expanded the theoretical contribution section by integrating a new paragraph (see end of introduction) that clarifies how the study extends the Technology Acceptance Model (TAM) and Sociotechnical Systems Theory to the context of sustainable digital marketing. The revised text explains how AI adoption, smart distribution channels, and AI trust collectively shape employee behavior toward sustainability, thereby offering a novel theoretical lens that connects technology adoption and human behavior within sustainability-oriented organizations.

Literature Review:

Although the author explains the theories and their connections with the study variables, the justification for using both the Technology Acceptance Model (TAM) and Sociotechnical Systems Theory in parallel is unclear. The author should clearly explain how these two theories align and specify which variables are derived from each theory.

Response: Thank you for highlighting this important point. We have revised the Theoretical Framework section to explicitly justify the integration of TAM and Sociotechnical Systems Theory. Please see the last paragraph of section 2.1 Theoretical Framework of the Study. It is also given below for your review:

“This study significantly advances the understanding of sustainable digital marketing by integrating AI adoption and employee behavior within the frameworks of the Technology Acceptance Model (TAM) and Sociotechnical Systems Theory. While prior studies have separately explored AI or sustainability in marketing, this research uniquely demonstrates how AI-driven mechanisms influence employee behavioral alignment toward sustainability goals. By confirming the mediating role of smart distribution channels and the moderating effect of AI trust, this study extends behavioral theory into a digital sustainability context. The findings emphasize that technological transformation alone cannot achieve sustainability without corresponding behavioral adaptation among employees, thus bridging the gap between technology-driven innovation and human-centric sustainability outcomes.”

Figure 1:

The dependent variable (DV) box in Figure 1 appears incomplete. The figure should be refined for clarity and completeness.

Response: Dear reviewer, the comment has been addressed. Please see the complete dependent variable (DV) box in Figure 1.

Response: Dear Reviewer, thank you very much for your constructive comment. Although this study proposes ten hypotheses, their inclusion is theoretically and empirically justified to comprehensively explain the multilayered relationships among AI adoption, smart distribution channels, sustainable employee intention, AI trust, employee behavior, and sustainable digital marketing performance. The framework integrates both direct and indirect effects to uncover not only the primary pathways but also the underlying mechanisms such as mediation and boundary conditions such as moderation. Each hypothesis represents a distinct theoretical linkage derived from the Technology Acceptance Model and Sociotechnical Systems Theory, ensuring conceptual coherence rather than redundancy. The extended model thus provides a holistic understanding of how technological and human dimensions interact to drive sustainability outcomes in digital marketing environments.

Sample and Data Collection:

Based on the statement that the study population includes employees from UAE telecommunication companies specifically Etisalat by e& and du, the selection of only these two companies may introduce bias. The author should justify why other companies were excluded. If the rationale is that these companies are industry leaders in sustainability-driven digital marketing, the generalizability of the findings to less advanced companies should be discussed.

Response: Dear Reviewer, thank you very much for your constructive comment. We revised the section: 3.2 Population and Sampling. The study focuses on employees from Etisalat by e& and du because these two organizations represent the largest and most technologically advanced telecommunication firms in the UAE, accounting for nearly the entire national market share. Both companies are industry leaders in AI-driven and sustainability-oriented digital marketing practices, making them highly suitable for examining the proposed framework. The decision to exclude smaller firms was intentional, as many have not yet implemented comparable AI-based or sustainability initiatives. In addition, following limitation is added to the limitation section as fifth limitation and future direction: The study focuses on Etisalat by e& and du which may limit the generalizability of the findings to less technologically mature firms. Future research could extend this model to emerging or smaller enterprises to validate whether similar behavioral patterns exist in less advanced organizational settings.

Response: Dear Reviewer, the comments have been addressed. See the revised section: 3.4 Data Collection, paragraph 1. The electronic questionnaire was distributed via a secure online platform (Qualtrics/Google Forms) and included an introductory information sheet which participants had to acknowledge before proceeding. This sheet detailed the study’s purpose, estimated completion time, voluntary nature, confidentiality of responses, and the right to withdraw at any time without penalty. Furthermore, we add the Institutional Review Board Statement and Informed Consent Statement before references. Consent is also added to methodology.

Demographics:
The gender distribution (75% male) may raise questions about sample bias. The author should discuss this limitation.

Response: Dear Reviewer, the comments has been addressed. We add to the limitation section as sixth limitation:

The sample of the study shows a gender imbalance, with 75% male respondents, reflecting the male-dominated structure of the UAE telecommunications sector. However, this may still introduce sample bias, as gender can influence perceptions of AI adoption and sustainability behavior. Therefore, the findings should be interpreted cautiously, and future studies should aim for more balanced gender representation to enhance generalizability.

Table 5:
P-values should be reported to three decimal places (e.g., p = 0.001 instead of 0.000 or 0).

Response: Dear Reviewer, p-value in Table 5. Direct Effect and Moderation and Table 6. Indirect Effect is already shown as three decimals. However, where it is reported “0”, it is actually zero. We cannot change “0” to another value.

Overall:
Other sections of the paper are well presented. Good luck with your revisions.

Response: Dear reviewer, thank you very much for the acknowledgement.

Reviewer 2 Report

Comments and Suggestions for Authors

Employee Behavior in Sustainable Digital Marketing: 2 The Role of AI Technologies in UAE

Comments and Suggestions for Authors

The paper explores the mediating and moderating roles of smart distribution channels (SDC), sustainable employee intention, and AI trust in linking AI adoption to employee behavior and sustainable digital marketing in the UAE telecommunications sector. The topic is contemporary and relevant to sustainability and digital transformation studies. However, several areas require significant refinement before publication.

Theoretical and Conceptual Clarity

The manuscript applies the Technology Acceptance Model (TAM) and Sociotechnical Systems Theory, yet the integration between the two is not clearly articulated. The theoretical framework feels mechanical rather than conceptually innovative. Please clarify why these theories together provide a novel understanding of AI adoption in sustainable marketing, and how employee behavior specifically extends TAM or sociotechnical reasoning. Explicitly mapping each hypothesis to the theoretical logic will enhance coherence.

Originality and Contribution

While the topic is relevant, the study’s contribution remains limited. Similar frameworks connecting AI adoption, employee behavior, and sustainability have been published recently in Sustainability and Administrative Sciences. To strengthen originality, emphasize what new insight your model offers—perhaps cultural specificity of the UAE context, or the dual role of employee trust and AI ethics. A clearer problem gap statement is needed at the end of the introduction.

Literature Review

The literature review summarizes numerous sources but lacks critical synthesis. Please:

Discuss contradictions or gaps in past findings rather than listing them.
Include seminal and recent works (2023–2025) on AI trust, digital ethics, and employee sustainability behavior beyond MDPI sources.
Explain how your constructs differ from closely related variables (e.g., digital capability, green HRM).

Methodology

The methodology is adequate but needs further rigor:

Clarify the sampling logic: why Etisalat and du employees best represent the population and how bias was minimized.
Discuss common-method variance (CMV) checks, as data are self-reported and cross-sectional.
Provide more details on instrument validation, translation procedures (if any), and ethical approval.
Justify why PLS-SEM was preferred over CB-SEM and confirm whether all fit indices met accepted thresholds.

Results and Interpretation

Although results are statistically sound, the discussion section merely restates the coefficients. Please interpret why certain relationships (e.g., sustainable employee intention → behavior) were nonsignificant. Relate findings to theory—could cultural or organizational dynamics in UAE explain this? Use the discussion to generate theoretical insight rather than repeating numerical outcomes.

Practical and Theoretical Implications

The implications section offers general advice to managers but lacks depth. Please:

Distinguish theoretical contributions (extension of TAM, sociotechnical integration) from managerial implications.
Provide actionable recommendations for policy or HR practices in UAE organizations (e.g., trust-building strategies in AI adoption).

Language and Presentation

The paper requires thorough English editing. There are recurrent grammatical issues (“technologization companies,” “farmwork of the study”) and stylistic redundancies. Improve academic tone and ensure concise paragraphing. Figures and tables should be clearly referenced in the text and formatted consistently according to MDPI standards.

Limitations and Future Research

The limitations section is adequate but should acknowledge the potential endogeneity of self-reported perceptions. Future research could employ multi-source or longitudinal data and explore other sectors to enhance generalizability.

Overall Assessment

The manuscript addresses a relevant topic but currently reads as confirmatory research with modest theoretical novelty. It demonstrates solid empirical work but needs major revision in conceptual framing, critical synthesis, and presentation before being suitable for publication.

Comments on the Quality of English Language

The manuscript is written in understandable English; however, the overall quality of language requires substantial improvement to meet academic publication standards. There are frequent grammatical, syntactic, and stylistic issues that affect readability. Sentence structures are sometimes awkward, and word choices occasionally obscure the intended meaning (e.g., “technologization companies,” “farmwork of the study”). Transitions between paragraphs are abrupt, and several sections contain repetitive phrasing (e.g., “AI adoption has a positive effect on…”).

The authors are encouraged to:

Seek professional English editing to correct grammar, punctuation, and sentence flow.
Simplify overly long or redundant sentences for clarity.
Use consistent academic terminology (e.g., use “framework” instead of “farmwork,” “organizations” instead of “companies” when referring to institutions).
Ensure that verb tenses are consistent and technical terms are properly defined.

Author Response

Review 2

Comments: Review 2

Top of Form

Employee Behavior in Sustainable Digital Marketing: 2 The Role of AI Technologies in UAE

Comments and Suggestions for Authors

Response: Dear reviewer, thank you very much for the acknowledgement.

Theoretical and Conceptual Clarity

Response: Dear reviewer, thank you for this constructive suggestion. We have revised the Theoretical Framework section: Theoretical Framework of the Study. See last two paragraphs. We have revised the Theoretical Framework section to explicitly justify the integration of TAM and Sociotechnical Systems Theory. Furthermore, the added paragraph explains that employee behavior serves as the bridge connecting individual-level technology acceptance (from TAM) with organizational-level system optimization (from Sociotechnical Theory). This integration provides a novel contribution by extending TAM to include sustainability-oriented behavioral outcomes and expanding Sociotechnical reasoning to account for AI-driven human adaptation in digital marketing contexts.

Originality and Contribution

Response: Thank you for this valuable comment. We have revised the Introduction. Please see the newly added third paragraph in introduction and last paragraph of introduction. The clarification given in revised section clarifies strengthens of the study’s theoretical and contextual uniqueness and provides a clearer articulation of the research gap and contribution.

Literature Review

The literature review summarizes numerous sources but lacks critical synthesis. Please:

Discuss contradictions or gaps in past findings rather than listing them.
Include seminal and recent works (2023–2025) on AI trust, digital ethics, and employee sustainability behavior beyond MDPI sources.
Explain how your constructs differ from closely related variables (e.g., digital capability, green HRM).

Response: Thank you for these constructive suggestions. We have revised the Literature Review, see newly added section after hypothesis 9 and 10 to incorporate a critical discussion of inconsistencies in prior studies and to cite recent, non-MDPI sources. The updated section now contrasts positive and negative findings on AI adoption and employee sustainability behavior, integrates the emerging discourse on AI trust and digital ethics, and explicitly differentiates our constructs from similar concepts such as digital capability and green HRM. These enhancements strengthen the theoretical clarity and originality of the study.

Methodology

The methodology is adequate but needs further rigor:

Clarify the sampling logic: why Etisalat and du employees best represent the population and how bias was minimized.

Response: Dear reviewer, the comment has been addressed, see the first paragraph of 3.2 Population and Sampling. The study focuses on employees from Etisalat by e& and du because these two organizations represent the largest and most technologically advanced telecommunication firms in the UAE, accounting for nearly the entire national market share. Both companies are industry leaders in AI-driven and sustainability-oriented digital marketing practices, making them highly suitable for examining the proposed framework.

Discuss common-method variance (CMV) checks, as data are self-reported and cross-sectional.

Response: Dear reviewer, the comment has been addressed. See the second paragraph of section: 3.4 Data Collection. Because this study relied on self-reported and cross-sectional data, steps were taken to minimize and assess common-method variance (CMV). First, procedurally, respondent anonymity was assured, question wording was simplified, and predictor and criterion variables were separated within the questionnaire to reduce response bias. Second, full collinearity VIF values were below the threshold of 3.3, confirming that common-method bias did not significantly influence the results. These combined procedural and statistical remedies enhance the robustness and validity of the findings.

Provide more details on instrument validation, translation procedures (if any), and ethical approval.

Response: Dear reviewer, the comment has been addressed. A panel of three academic experts and two industry professionals reviewed the questionnaire for clarity, relevance, and alignment with the UAE telecommunications context. Minor modifications were made to adapt wording and terminology for local understanding without altering construct meanings. Since the respondents were proficient in English, no translation or back-translation was required; however, a pilot test with 30 employees was conducted to assess reliability and comprehension. Furthermore, ethical approval is added before references.

Justify why PLS-SEM was preferred over CB-SEM and confirm whether all fit indices met accepted thresholds.

Response: Dear reviewer, the comment has been addressed. See revised second paragraph of section: 3.4 Data Collection. Additionally, this study employed PLS-SEM because it is more suitable for complex models with multiple mediation and moderation paths and when data may not meet multivariate normality assumptions

Results and Interpretation

Response: Dear Reviewer, we have substantially revised the Discussion section (last paragraph) to interpret the nonsignificant path between sustainable employee intention and employee behavior. The added paragraph links this result to cultural and organizational factors in the UAE, where hierarchical structures may limit employees’ behavioral expression of sustainability intentions. The discussion now provides a deeper theoretical explanation using TPB and Sociotechnical Systems Theory, generating new insight into how contextual and systemic factors mediate the intention–behavior gap in sustainable digital marketing.

Practical and Theoretical Implications

The implications section offers general advice to managers but lacks depth. Please:

Distinguish theoretical contributions (extension of TAM, sociotechnical integration) from managerial implications.
Provide actionable recommendations for policy or HR practices in UAE organizations (e.g., trust-building strategies in AI adoption).

Response: Thank you for this helpful feedback. Comment has been addressed and we revised the section: 5.1 Implication of the Study to clearly separate theoretical and practical implications. Theoretical implications now emphasize the extension of TAM and integration of Sociotechnical Systems Theory, while practical implications provide concrete, context-specific recommendations for HR practices, trust-building in AI adoption, and policy interventions in the UAE. These revisions enhance both academic contribution and managerial applicability.

Language and Presentation

The paper requires thorough English editing. There are recurrent grammatical issues (“technologization companies,” “farmwork of the study”) and stylistic redundancies. Improve academic tone and ensure concise paragraphing. Figures and tables should be clearly referenced in the text and formatted consistently according to MDPI standards.

Response: Thank you for this comment. We addressed the comment by proofreading the article. Furthermore, we reviewed all the Figures and tables and make it consistent with the MDPI standards.

Limitations and Future Research

Overall Assessment

Response: Thank you for this feedback. We addressed all the comments given above to improve the quality.

Comments on the Quality of English Language

The authors are encouraged to:

Seek professional English editing to correct grammar, punctuation, and sentence flow.
Simplify overly long or redundant sentences for clarity.
Use consistent academic terminology (e.g., use “framework” instead of “farmwork,” “organizations” instead of “companies” when referring to institutions).
Ensure that verb tenses are consistent and technical terms are properly defined.

Response: We sincerely appreciate the reviewer’s detailed feedback regarding language quality. The entire manuscript has undergone English editing to improve grammar, sentence structure, and overall readability. Redundant and awkward phrasing has been removed, transitions between paragraphs have been smoothed, and terminology has been standardized. Verb tenses have been made consistent, and technical terms have been refined to align with academic conventions. These revisions substantially enhance the clarity, coherence, and professional tone of the manuscript

Reviewer 3 Report

Comments and Suggestions for Authors

Article: Employee Behavior in Sustainable Digital Marketing: The Role of AI Technologies in UAE

A brief summary

The objective of the research is to examine the role of artificial intelligence adoption in sustainable digital marketing through smart distribution channels, employee intention, and behavior.

Introduction

In general, I appreciated the way the authors constructed the Introduction section. My only concern refers to the last paragraph, which briefly mixes elements of the general objective, methodology, and research justification. I recommend that the authors separate these points into distinct paragraphs to clearly define each one. Specifically: state the central focus of the study (objective), explain how the research question is addressed (methodology, including the mediation effect in the model), and, finally, emphasize the theoretical and practical importance of the study (justification).
Lastly, I recommend including a short final paragraph outlining the structure of the article — about five lines are sufficient to describe succinctly the content of each section.

Methodology

In Section 3.3, the authors state that they adapted items from several empirical studies to measure their constructs. However, they do not specify the motivation for making these adaptations instead of directly using the original items. This is not an error, but rather a methodological choice that should be properly justified.
For the construct Trust in Artificial Intelligence presented in Table 1, only two items are used for measurement. This limited number of indicators may pose statistical constraints, which the authors do not discuss.
In Section 3.4, the authors appropriately justify the use of a five-point Likert scale in the questionnaire. However, “originality” and “respondent frustration” do not appear to be the most appropriate justifications. For example, what would distinguish the choice of a five-point scale from a seven-point scale? This methodological rationale would better support their decision.
I do not understand why Table 2 (Descriptive Statistics) is presented in the Methodology section rather than in the Results/Data Analysis section. Once this table is introduced, it generates interpretive information that directly affects the model’s outcomes. This represents a content issue in the current version of the paper. Moreover, the statistics in this table are not discussed, and there are relevant aspects—especially regarding variability—that should be addressed.

Data Analysis

As the items and constructs employed in this study were adapted from previously validated scales, the evaluation steps — factor loadings, composite reliability (CR), average variance extracted (AVE), and discriminant validity (HTMT) — collectively represent a Confirmatory Factor Analysis (CFA). Therefore, I recommend that the authors explicitly use this concept to highlight the confirmatory nature and logical consistency of the structural equation modeling procedures adopted.
At the beginning of Section 4.2, the authors claim that PLS-SEM is the most appropriate method for testing direct, indirect, and moderating relationships. However, both PLS-SEM and CB-SEM are suitable for such purposes. The authors should clearly justify why PLS-SEM is the preferred approach.
The explanations accompanying Table 2 merely repeat the table’s content. It would be more meaningful to discuss how variables such as level of experience, education, or job position might influence the model’s dynamics.
Is the mediation effect partial or full? This clarification is important for the interpretation of the model’s results.
The R² values are not discussed, even though they could provide useful insights into the explanatory power of the models.
Although the moderating effects are presented correctly, the practical meaning of the interaction (AI trust × sustainable employee intention → employee behavior) is not explained.
Two important measures are missing: internal collinearity (VIF), which assesses redundancy among constructs; and the combined f² and q² statistics, which indicate effect size and predictive relevance. I strongly recommend including and discussing these statistics within the modeling context.
The section would benefit from a theoretical synthesis based on the models. I encourage the authors to consolidate and succinctly summarize the results of all hypotheses.

Discussion and Conclusion

I encourage the authors to reflect on the distinct purposes of the Discussion and Conclusion sections. The Discussion should connect the study’s results with existing evidence in the literature, while the Conclusion should address the research justification and highlight the theoretical and practical insights of the study, thereby filling the identified gaps from both perspectives.

Limitations and Future Directions

Although the authors make an effort to discuss the study’s limitations, important aspects remain unacknowledged. These include potential biases inherent to questionnaire-based data collection procedures, sampling bias, the absence of a statement regarding the use of Instructional Manipulation Checks (IMCs) to ensure data quality, and the lack of recognition of limitations intrinsic to the PLS-SEM method. Furthermore, the authors should note that the results represent a specific moment in time, without addressing the spatial and temporal dynamics of the phenomenon under study.

Author Response

Review 3

Review 1: Comments

Article: Employee Behavior in Sustainable Digital Marketing: The Role of AI Technologies in UAE

A brief summary

The objective of the research is to examine the role of artificial intelligence adoption in sustainable digital marketing through smart distribution channels, employee intention, and behavior.

Introduction

In general, I appreciated the way the authors constructed the Introduction section. My only concern refers to the last paragraph, which briefly mixes elements of the general objective, methodology, and research justification. I recommend that the authors separate these points into distinct paragraphs to clearly define each one. Specifically: state the central focus of the study (objective), explain how the research question is addressed (methodology, including the mediation effect in the model), and, finally, emphasize the theoretical and practical importance of the study (justification).

Response. Dear reviewer, thank you for this constructive comment. We have thoroughly revised the last paragraph of the Introduction to improve clarity and organization. The revised version now presents three distinct components: (1) a concise statement of the study’s objective, (2) a clear description of the methodological approach including the mediation and moderation effects, and (3) a focused explanation of the theoretical and practical justification of the research.

Lastly, I recommend including a short final paragraph outlining the structure of the article — about five lines are sufficient to describe succinctly the content of each section.

Response. Dear reviewer, comment has been addressed and we added a new paragraph in the end of introduction. The remainder of this article is structured as follows. Section 2 presents the theoretical framework and development of hypotheses. Section 3 outlines the research methodology, including data collection and analysis procedures. Section 4 reports the empirical results, while Section 5 discusses the findings in light of existing theories. Finally, Section 6 concludes the study by highlighting theoretical and practical implications, limitations, and directions for future research.

Methodology

In Section 3.3, the authors state that they adapted items from several empirical studies to measure their constructs. However, they do not specify the motivation for making these adaptations instead of directly using the original items. This is not an error, but rather a methodological choice that should be properly justified.

Response. Dear reviewer, thank you for this constructive comment. The section 3.3 is revised by adding that the measurement items used in this study were adapted rather than directly adopted from prior empirical studies to ensure their contextual relevance to the UAE telecommunications industry and the study’s sustainability focus.

For the construct Trust in Artificial Intelligence presented in Table 1, only two items are used for measurement. This limited number of indicators may pose statistical constraints, which the authors do not discuss.

Response. Dear reviewer, thank you for this constructive comment. The comment has been addressed and further explanation has been added. The construct trust in AI was measured using two key indicators adapted from established scales. Although having only two items may limit measurement depth, both indicators demonstrated high reliability and convergent validity (factor loadings > 0.70; AVE > 0.50), confirming their adequacy for structural modeling. The decision to retain two items was based on the conceptual parsimony of the construct and the need to reduce respondent fatigue given the model’s complexity.

In Section 3.4, the authors appropriately justify the use of a five-point Likert scale in the questionnaire. However, “originality” and “respondent frustration” do not appear to be the most appropriate justifications. For example, what would distinguish the choice of a five-point scale from a seven-point scale? This methodological rationale would better support their decision.

Response. Dear reviewer, thank you for this constructive comment. The comment has been addressed by adding more explanation. Furthermore, the selection of a five-point scale was based on methodological and cognitive considerations. Compared with seven-point scales, the five-point format reduces respondent cognitive load and enhances response reliability, particularly in organizational surveys involving diverse educational and cultural backgrounds such as those in the UAE.

I do not understand why Table 2 (Descriptive Statistics) is presented in the Methodology section rather than in the Results/Data Analysis section. Once this table is introduced, it generates interpretive information that directly affects the model’s outcomes. This represents a content issue in the current version of the paper. Moreover, the statistics in this table are not discussed, and there are relevant aspects—especially regarding variability—that should be addressed.

Response. Dear reviewer, we added the Table 2 in data analysis section and removed from methodology. Explanation is also added. Based on the 300 valid responses, the data statistics are reported in Table 2. It presents the descriptive statistics for all study variables, including mean values, standard deviations, and distribution characteristics. The results indicate moderate mean scores across constructs, suggesting balanced perceptions among respondents regarding AI adoption, employee behavior, and sustainability orientation. The standard deviations show acceptable variability, confirming that responses were not clustered and providing sufficient dispersion for reliable analysis. These descriptive insights offer an initial understanding of data trends and support the suitability of the dataset for subsequent PLS-SEM analysis.

Data Analysis

As the items and constructs employed in this study were adapted from previously validated scales, the evaluation steps — factor loadings, composite reliability (CR), average variance extracted (AVE), and discriminant validity (HTMT) — collectively represent a Confirmatory Factor Analysis (CFA). Therefore, I recommend that the authors explicitly use this concept to highlight the confirmatory nature and logical consistency of the structural equation modeling procedures adopted.

Response. Dear reviewer, comment has been addressed and we remodified the section and used Confirmatory Factor Analysis (CFA), instead of measurement model.

At the beginning of Section 4.2, the authors claim that PLS-SEM is the most appropriate method for testing direct, indirect, and moderating relationships. However, both PLS-SEM and CB-SEM are suitable for such purposes. The authors should clearly justify why PLS-SEM is the preferred approach.

Response: Dear reviewer, the comment has been addressed. See revised second paragraph of section: 3.4 Data Collection and Statistical Tool. This study employed PLS-SEM because it is more suitable for complex models with multiple mediation and moderation paths and when data may not meet multivariate normality assumptions

The explanations accompanying Table 2 merely repeat the table’s content. It would be more meaningful to discuss how variables such as level of experience, education, or job position might influence the model’s dynamics.

Response: Dear reviewer, Table 2 is based on the Statistics data which does not discussed the as level of experience, education, or job position. By following your recommendation, we added the Table 2 in data analysis section and removed from methodology. Now Table 2 is converted to table 3.

Is the mediation effect partial or full? This clarification is important for the interpretation of the model’s results.

Response: Dear reviewer, the comment has been addressed. We added a new paragraph above Table 6. Indirect Effect. The results indicate a partial mediation effect of both SDC and Sustainable Employee Intention in the relationship between AI Adoption and Employee Behavior. Although AI adoption directly influences employee behavior, the indirect effects through SDC and sustainable intention remain significant, confirming that these mediators explain part but not all of the relationship. This suggests that while AI systems directly shape employee behavior, their full impact unfolds through improved distribution mechanisms and enhanced sustainability motivation, reflecting both technological and human dimensions of organizational adaptation.

The R² values are not discussed, even though they could provide useful insights into the explanatory power of the models.

Response: Dear reviewer, the comment has been addressed. We added a new paragraph below Table 6. Indirect Effect to report R² value.

Although the moderating effects are presented correctly, the practical meaning of the interaction (AI trust × sustainable employee intention → employee behavior) is not explained.

Response: Dear reviewer, the comment has been addressed. It is reported in the paragraph given below Table 5. Direct Effect and Moderation. AI trust strengthens the relationship between sustainable employee intention and employee behavior. Thus, AI trust has key contribution to enhance employee behavior towards digital marketing activities.

Two important measures are missing: internal collinearity (VIF), which assesses redundancy among constructs; and the combined f² and q² statistics, which indicate effect size and predictive relevance. I strongly recommend including and discussing these statistics within the modeling context.

Response: Dear reviewer, the comment has been addressed. We added a new paragraph above Table 6. To further assess the effect size and predictive relevance of the structural model, f² and q² statistics were computed. The f² values for the main paths ranged between 0.12 and 0.36, indicating small to medium effect sizes, while the q² values (ranging from 0.18 to 0.29) confirmed that all endogenous constructs possess satisfactory predictive relevance. These results demonstrate that the inclusion of SDC, Sustainable Employee Intention, and AI Trust meaningfully enhances the explanatory power of the model beyond statistical significance, thereby supporting the robustness of the proposed relationships. Furthermore, full collinearity VIF values are now reported in methodology.

The section would benefit from a theoretical synthesis based on the models. I encourage the authors to consolidate and succinctly summarize the results of all hypotheses.

Response: The comment has been addressed, and we added a paragraph before section: 5. Discussion and Conclusion. Overall, the findings provide strong empirical support for the proposed theoretical framework grounded in the TAM and Sociotechnical Systems Theory. Most hypotheses were confirmed, demonstrating that AI adoption positively influences both smart distribution channels and sustainable employee intention, which in turn enhance employee behavior and sustainable digital marketing outcomes. The significant mediating effects of SDC and sustainable intention confirm the dual pathways, technological and behavioral—through which AI adoption drives sustainability. The moderating role of AI trust further underscores the human dimension, reinforcing that employee confidence in AI strengthens the model’s predictive power. Collectively, these results validate the integration of TAM and STS, showing how individual acceptance and system-level alignment jointly advance sustainable performance in digital marketing contexts.

Discussion and Conclusion

I encourage the authors to reflect on the distinct purposes of the Discussion and Conclusion sections. The Discussion should connect the study’s results with existing evidence in the literature, while the Conclusion should address the research justification and highlight the theoretical and practical insights of the study, thereby filling the identified gaps from both perspectives.

Response. Dear reviewer, the comment has been addressed, and we added a separation section of conclusion. We added the 6.1 Implication of the Study and 6.2 Limitations and Future Directions in conclusion section.

Limitations and Future Directions

Although the authors make an effort to discuss the study’s limitations, important aspects remain unacknowledged. These include potential biases inherent to questionnaire-based data collection procedures, sampling bias, the absence of a statement regarding the use of Instructional Manipulation Checks (IMCs) to ensure data quality, and the lack of recognition of limitations intrinsic to the PLS-SEM method. Furthermore, the authors should note that the results represent a specific moment in time, without addressing the spatial and temporal dynamics of the phenomenon under study.

Response: Dear reviewer, the comment has been addressed and we added a more limitations in last part of section: 6.2 Limitations and Future Directions. The self-reported, cross-sectional survey may involve response and sampling biases, and results represent only a specific point in time. No Instructional Manipulation Checks (IMCs) were included, which future research could use to enhance data reliability. The PLS-SEM approach also has inherent constraints, such as limited model fit comparison. Moreover, findings are context-specific to UAE organizations; future studies should apply longitudinal or cross-country designs to explore temporal and spatial variations in AI-driven sustainability behavior.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The figures still require improvement. Some parts of the text within the figure are cut off and not fully visible, which affects readability and completeness. Please review and adjust the layout to ensure that all words and labels are clear and properly aligned.

Additionally, it is unclear why the authors chose to test the effect of AI trust directly on employee behavior, and again between smart distribution channels and sustainable environments. The conceptual justification for these connections is not adequately explained, especially since the figure suggests multiple direct and indirect paths that are not theoretically supported or stated in the hypotheses.

I recommend revising both the conceptual framework and hypotheses to ensure alignment between the proposed model, theoretical background, and the relationships actually tested.

Author Response

Comments and Suggestions for Authors

Response: Dear reviewer, thank you very much for your constructive comment. In my opinion you are talking about the Figure 2. Confirmatory Factor Analysis (CFA) and Figure 3. Structural Model, because all other Figures are very much clear. Figure 2 and Figure 3 are also clear, for instance, the information required in Figure 2 is factor loading shown in outer model and Figure 3 shows path analysis which shows inner model. Furthermore, all values given in these Figures are also reported in respective Tables for better clarity. Therefore, if you need to clarify any values, you can review respective Tables. Additionally, we cannot edit these Figures, because these are software generated (output of Smart PLS) Figures and we cannot edit them.

Response: Dear Reviewer, thank you very much for your constructive and thoughtful comment. In this study, AI Trust has been conceptualized as a moderating variable. When examining the moderation effect of AI Trust, Employee Behavior serves as the dependent variable, while Smart Distribution Channels and Sustainable Employee Intention function as the independent variables. To accurately assess the moderating role of AI Trust: specifically whether it strengthens or weakens these relationships, it is necessary to estimate the direct effect of AI Trust on Employee Behavior. The beta coefficient of this relationship is essential for interpreting the nature and magnitude of the moderation effect. Accordingly, Figure 4, which illustrates the moderation effect of AI Trust between Sustainable Employee Intention and Employee Behavior, cannot be developed or interpreted without first establishing this direct relationship.

Additionally, in your comment, you referred to “sustainable environments.” However, this construct is not included as a variable in the present study. We believe you may be referring to Sustainable Employee Intention, which, along with Smart Distribution Channels, constitutes the independent variables, while AI Trust operates strictly as a moderating variable. The theoretical justification for these relationships is provided in Section 2.1, Theoretical Framework of the Study.

We sincerely appreciate your valuable feedback, which has helped us further clarify and strengthen the manuscript.

Moreover, the hypotheses section does not clearly include or support these types of relationships. The logic of testing direct moderation between AI trust and employee behavior is inconsistent with the proposed model, which seems to imply a serial mediation process. This inconsistency creates confusion. I recommend revising both the conceptual framework and hypotheses to ensure alignment between the proposed model, theoretical background, and the relationships actually tested.

Response: Dear reviewer, thank you very much for your constructive comment. The logic of testing moderation between AI trust and employee behavior is reported in section 2.5 Moderation Effect of Artificial Intelligence (AI) Trust. See the hypotheses developed section of following hypotheses:

Hypothesis 9. AI trust moderates the relationship between SDC and employee behavior.

Hypothesis 10. AI trust moderates the relationship between sustainable employee intention and employee behavior.

We would like to respectfully clarify that although we reported the beta value for the relationship between AI Trust and Employee Behavior, we did not conceptualize or test this path as a direct effect within the proposed model. The beta value was obtained solely to facilitate the interpretation of the moderation effect. Therefore, revising the conceptual framework or hypotheses is not feasible, as these elements are already consistent with the theoretical foundations presented in Section 2.1, Theoretical Framework of the Study. The hypotheses, conceptual framework, and underlying theory are fully aligned with the intended scope and structure of the research.

Revising the conceptual model or hypotheses at this stage would require substantial modifications to the entire manuscript, including the literature review, analytical procedures, results, discussion, and conclusion sections. Such extensive changes would fundamentally alter the original focus and contribution of the study. For these reasons, we believe the current structure remains theoretically sound, methodologically coherent, and reflective of the study’s intended objectives.

We sincerely appreciate your careful review and thoughtful suggestions.

Reviewer 2 Report

Comments and Suggestions for Authors

The topic is timely and important, linking AI adoption, employee behavior, and sustainable digital marketing in the telecom sector.
The theoretical framework is clear, combining technology and human factors in a logical way.
The research design and data analysis (PLS-SEM) are appropriate and statistically sound.
The findings are meaningful and well explained, with clear links back to the research questions.
The paper offers practical recommendations for managers and policymakers, and clearly states its limitations and future research directions.

Author Response

The topic is timely and important, linking AI adoption, employee behavior, and sustainable digital marketing in the telecom sector.

The theoretical framework is clear, combining technology and human factors in a logical way.

The research design and data analysis (PLS-SEM) are appropriate and statistically sound.

The findings are meaningful and well explained, with clear links back to the research questions.

The paper offers practical recommendations for managers and policymakers, and clearly states its limitations and future research directions.

Response: Dear reviewer, thank you for your encouraging and detailed feedback. We are grateful for your recognition of the improvements made in the revised version and your acknowledgement that the earlier comments have been fully addressed and justified. We also appreciate your positive remarks regarding the timeliness and importance of the topic, as well as the clarity of the theoretical framework that integrates both technological and human dimensions. Your constructive evaluation has been instrumental in strengthening the overall quality and contribution of our manuscript.

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors,
The submitted version is considerably better than the initial one. The comments have been addressed and justified appropriately.
Best regards.

Author Response

Comments and Suggestions for Authors

Dear authors, the submitted version is considerably better than the initial one. The comments have been addressed and justified appropriately. Best regards

Response: Respected reviewer, thank you for your positive and constructive feedback. We appreciate your acknowledgement that the revised version has improved considerably and that the previous comments have been appropriately addressed and justified. Your insights have greatly contributed to enhancing the clarity and overa