Digital Technologies as Drivers of Business Model Change in the Renewable Energy Firms: A Systematic Literature Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

After evaluating your insightful article titled "Digital Technologies as Drivers of Business Model Change in the Renewable Energy Firms: A Systematic Literature Review," I draw your attention to the following comments:

Your abstract is succinct and follows the IMRaD abstract structure. However, one element is missing and should be added (i.e., Line 22): How were the 32 peer-reviewed studies analyzed, and using which method? The four-phase PRISMA methodology used in this study should be stated in your abstract.

Line 109 - 110, you wrote “The study is guided by the following research questions: How do digital technologies drive changes in business models for renewable energy firms?” However, you are referring to “questions”, yet you have provided only one question. Yet, in your table from lines 888 to 889, and in Section 2 (Lines 159 to 167), you provided main and sub-questions. Please align this discrepancy.

Section 2. Research Design and Methodology: The author clearly explained each step taken to identify/search for the articles and analyzed them. Methods for inclusion and exclusion were clearly described. Figure 1 provides a detailed SLR Prisma Flow Diagram showing each step taken in this study. While Figure 2 provides the Year-wise distribution of peer-reviewed journal articles included in the SLR (2015–2025), and Figure 3 summarizes the Subject Area Distribution. I am impressed with the detail, clarity, and substantiation of each approach, as well as the relevant references provided in this section. I have no issues with this section.

Section 3. Findings/Results – The authors presented the key findings by theme, along with the patterns observed across the dataset, supporting each section with relevant references. The findings are detailed and well-written. I don’t have problems with this section. Great work was done here, and I credit the authors.

Section 4. Synthesis: This section is well-articulated, with a detailed discussion presented through three lenses: Key Digital Technologies and Functional Groups, Business Model Components, value reconfiguration, and Types of Business Model Change, all viewed through a sustainability lens. However, in line 763, you refer to “Chapter 6 ahead,” yet the next chapter is actually Chapter 5, not 6.

Section 5. Conclusion: The theoretical and practical contributions, research limitations, and future research direction are clearly articulated. I have no issues with these sections.

The remainder of the article demonstrates a high degree of rigor and is logically structured. 

Author Response

Comment 1). After evaluating your insightful article titled "Digital Technologies as Drivers of Business Model Change in the Renewable Energy Firms: A Systematic Literature Review," I draw your attention to the following comments:

Your abstract is succinct and follows the IMRaD abstract structure. However, one element is missing and should be added (i.e., Line 22): How were the 32 peer-reviewed studies analyzed, and using which method? The four-phase PRISMA methodology used in this study should be stated in your abstract.

Response: We agree. We have revised line 24 to explicitly state that the 32 studies were analyzed using the four-phase PRISMA methodology combined with Gioia-based thematic coding analysis.

 

Comment 2). Line 109 - 110, you wrote “The study is guided by the following research questions: How do digital technologies drive changes in business models for renewable energy firms?” However, you are referring to “questions”, yet you have provided only one question. Yet, in your table from lines 888 to 889, and in Section 2 (Lines 159 to 167), you provided main and sub-questions. Please align this discrepancy.

Response: We have clarified at line 109 that we present only the main research question here to avoid repetition, as the complete set of main and sub-questions are detailed in Section 2.1.

 

Comment 3). Section 2. Research Design and Methodology: The author clearly explained each step taken to identify/search for the articles and analyzed them. Methods for inclusion and exclusion were clearly described. Figure 1 provides a detailed SLR Prisma Flow Diagram showing each step taken in this study. While Figure 2 provides the Year-wise distribution of peer-reviewed journal articles included in the SLR (2015–2025), and Figure 3 summarizes the Subject Area Distribution. I am impressed with the detail, clarity, and substantiation of each approach, as well as the relevant references provided in this section. I have no issues with this section.

Response: Thank you. We appreciate your recognition of the methodological rigor and clarity in this section.

 

Comment 4). Section 3. Findings/Results – The authors presented the key findings by theme, along with the patterns observed across the dataset, supporting each section with relevant references. The findings are detailed and well-written. I don’t have problems with this section. Great work was done here, and I credit the authors.

Response: Thank you for this encouraging feedback. We appreciate your recognition of our detailed and well-supported findings.

Comment 5). Section 4. Synthesis: This section is well-articulated, with a detailed discussion presented through three lenses: Key Digital Technologies and Functional Groups, Business Model Components, value reconfiguration, and Types of Business Model Change, all viewed through a sustainability lens. However, in line 763, you refer to “Chapter 6 ahead,” yet the next chapter is actually Chapter 5, not 6.

Response: Thank you for identifying this error. We have corrected the cross-reference at line 767 to properly reference Chapter 5. We appreciate your recognition of the synthesis section's detailed treatment.

Comment 6). Section 5. Conclusion: The theoretical and practical contributions, research limitations, and future research direction are clearly articulated. I have no issues with these sections.

The remainder of the article demonstrates a high degree of rigor and is logically structured. 

Response: Thank you for this positive feedback. We are gratified that the theoretical contributions, practical implications, limitations, and future directions are clearly articulated, and that the overall rigor and structure of the manuscript are evident.

Reviewer 2 Report

Comments and Suggestions for Authors

1 Conceptual and Theoretical Frameworks

（1）Clarify Functional Group Boundaries: The functional grouping of digital technologies is a key contribution, but the manuscript should explicitly address potential overlaps between groups (e.g., how digital twins may serve both “system-level orchestration” and “data processing/intelligence”). Providing a decision matrix or illustrative examples of how emerging technologies (e.g., generative AI) fit into these groups would strengthen the framework’s adaptability.

（2）Elaborate on Sustainability Mechanisms: The sustainability lens is referenced throughout but would benefit from more explicit theorization. For example, how do specific functional groups directly contribute to sustainability outcomes (e.g., emissions reduction, energy equity)? A dedicated sub-section or conceptual table mapping digital functions → business model components → sustainability impacts (e.g., efficiency/resilience/inclusion) would enhance theoretical depth and practical utility.

（3）Refine Change Typology Distinctions: The distinction between business model evolution (BME) and innovation (BMI) requires greater precision. The manuscript notes that BMI involves “fundamental shifts in value logic,” but examples (e.g., PAYG models) sometimes blur into BME in regulated contexts. Adding a typological matrix with clear criteria (e.g., scope of component change, novelty of value logic, institutional disruption) and illustrative cases from the reviewed literature would resolve ambiguity.

2 Methodological Details

（1）Document the Gioia Coding Process: While the Gioia methodology is appropriately applied, the manuscript should provide additional transparency on coding procedures: (1) How were inter-coder reliability checks conducted (e.g., percentage agreement, Cohen’s kappa)? (2) Were any discrepancies resolved through iterative discussion, and if so, how? (3) How were first-order concepts aggregated into second-order themes (e.g., frequency thresholds, theoretical saturation criteria)? These details are critical for replicability, a core expectation of SLRs in Systems.

（2）Justify Sample Limitations: The reliance on Scopus (single database) and exclusion of non-English sources, conference proceedings, and gray literature may introduce selection bias. The authors should explicitly discuss how these choices impact the generalizability of findings (e.g., are there regional or disciplinary perspectives underrepresented?) and provide a rationale for prioritizing peer-reviewed journal articles (e.g., to ensure methodological rigor).

 (3) Clarify Bibliometric Analysis: The bibliometric overview (Section 2.4) mentions a “final sample of 29 peer-reviewed articles” but later references 32 articles in the SLR. This discrepancy must be resolved. Additionally, the manuscript should include key bibliometric metrics (e.g., top-cited articles, core author networks, keyword co-occurrence) to contextualize the field’s intellectual structure— a standard practice for SLRs in Systems to highlight research gaps and clusters.

3 Presentation of Findings and Synthesis

（1）Strengthen Cross-Layer Integration: The synthesis (Section 4) aims to connect digital functions, business model components, and change types, but the interdependencies could be more explicitly visualized. A conceptual model (Figure 7) is referenced but not fully described; enhancing this figure to show causal pathways (e.g., functional triggers → component changes → change typologies) with illustrative arrows and case examples would improve readability and theoretical coherence.

（2）Address Heterogeneity in Technology Impact: The manuscript notes that “the same technology can enable multiple transformation pathways,” but it does not systematically explore why outcomes vary (e.g., firm size, regulatory context, digital maturity). Adding a discussion of moderating factors (e.g., incumbent vs. startup dynamics, institutional openness) would explain variability in the reviewed literature and strengthen the model’s predictive power.

（3）Expand Limitations Discussion: The limitations section (5.2) focuses on methodological constraints but understates substantive limitations: (1) How do data quality, cybersecurity, and interoperability challenges (mentioned in Section 5.1) affect the validity of the conceptual model? (2) Are there digital technologies (e.g., 5G, quantum computing) emerging post-2025 that may reshape functional groups? Addressing these would demonstrate critical self-reflection and frame future research directions more effectively.

4 Writing and Structure

（1）Streamline Repetitive Content: Some examples (e.g., AI-driven forecasting, blockchain for P2P trading) are repeated across sections (3.1, 3.2, 3.3). Consolidating examples to highlight their relevance to specific analytical layers (e.g., a single case study illustrating how IoT enables value creation, which drives BME) would improve conciseness.

（2）Clarify Terminology Consistency: Terms like “digitalization” and “digitization” are used interchangeably in the abstract and introduction. The authors should define these terms explicitly (per ISO 22300 or relevant literature) to ensure consistency. Similarly, “sustainable energy” and “renewable energy” are sometimes conflated— a distinction is needed (e.g., renewable energy as a subset of sustainable energy) to align with Systems’ interdisciplinary standards.

（3）Enhance Practical Implications: The practical implications (Section 5.1) would benefit from sector-specific guidance. For example, how might utility incumbents vs. renewable energy startups prioritize digital investments? Providing a decision toolkit (e.g., a checklist for sequencing digital adoption: foundational monitoring → advanced optimization → user engagement) would make findings more actionable for practitioners.

Author Response

1 Conceptual and Theoretical Frameworks

（1）Clarify Functional Group Boundaries: The functional grouping of digital technologies is a key contribution, but the manuscript should explicitly address potential overlaps between groups (e.g., how digital twins may serve both “system-level orchestration” and “data processing/intelligence”). Providing a decision matrix or illustrative examples of how emerging technologies (e.g., generative AI) fit into these groups would strengthen the framework’s adaptability.

Response: We have strengthened the discussion of functional group boundaries at lines 577-586 by consolidating the treatment of technology overlaps. This section explicitly addresses how technologies like digital twins serve multiple functional roles and clarifies how emerging technologies fit into the framework based on their primary application context.

 

（2）Elaborate on Sustainability Mechanisms: The sustainability lens is referenced throughout but would benefit from more explicit theorization. For example, how do specific functional groups directly contribute to sustainability outcomes (e.g., emissions reduction, energy equity)? A dedicated sub-section or conceptual table mapping digital functions → business model components → sustainability impacts (e.g., efficiency/resilience/inclusion) would enhance theoretical depth and practical utility.

Response: We appreciate this feedback. We have added a section at line 798 that explicitly maps how each digital functional group contributes to sustainability outcomes. This clarifies the causal pathways from digital functions through business model components to sustainability impacts.

 

（3）Refine Change Typology Distinctions: The distinction between business model evolution (BME) and innovation (BMI) requires greater precision. The manuscript notes that BMI involves “fundamental shifts in value logic,” but examples (e.g., PAYG models) sometimes blur into BME in regulated contexts. Adding a typological matrix with clear criteria (e.g., scope of component change, novelty of value logic, institutional disruption) and illustrative cases from the reviewed literature would resolve ambiguity.

Response: We appreciate this feedback. We have addressed this by:

(1) adding a typological matrix at line 693 specifying clear criteria for distinguishing BMA, BME, and BMI; and

(2) adding explanation at lines 750 demonstrating how context shapes typology outcomes. We use PAYG as an illustrative case, showing how the same technology constitutes BME in regulated contexts but BMI in off-grid markets, resolving the ambiguity.

 

2 Methodological Details

（1）Document the Gioia Coding Process: While the Gioia methodology is appropriately applied, the manuscript should provide additional transparency on coding procedures: (1) How were inter-coder reliability checks conducted (e.g., percentage agreement, Cohen’s kappa)? (2) Were any discrepancies resolved through iterative discussion, and if so, how? (3) How were first-order concepts aggregated into second-order themes (e.g., frequency thresholds, theoretical saturation criteria)? These details are critical for replicability, a core expectation of SLRs in Systems.

Response: Thank you for emphasizing coding transparency. We have enhanced Section 2.3 at line 255 with detailed documentation: (1) inter-coder reliability ensured through co-author review of all second-order themes; (2) discrepancies resolved through iterative discussion referencing source text and typology criteria; (3) aggregation process illustrated with the smart meters example. This strengthens replicability while maintaining the qualitative approach.

 

（2）Justify Sample Limitations: The reliance on Scopus (single database) and exclusion of non-English sources, conference proceedings, and gray literature may introduce selection bias. The authors should explicitly discuss how these choices impact the generalizability of findings (e.g., are there regional or disciplinary perspectives underrepresented?) and provide a rationale for prioritizing peer-reviewed journal articles (e.g., to ensure methodological rigor).

Response: We appreciate this feedback. We have explicitly addressed limitations at line 288, discussing: (1) potential bias from single-database reliance; (2) how exclusion of non-English sources underrepresents countries like China; (3) how omitting gray literature may miss emerging practices; and (4) our rationale for prioritizing peer-reviewed journals, to ensure methodological rigor and editorial standards. This transparent discussion helps readers understand the generalizability boundaries of our findings.

 

 (3) Clarify Bibliometric Analysis: The bibliometric overview (Section 2.4) mentions a “final sample of 29 peer-reviewed articles” but later references 32 articles in the SLR. This discrepancy must be resolved. Additionally, the manuscript should include key bibliometric metrics (e.g., top-cited articles, core author networks, keyword co-occurrence) to contextualize the field’s intellectual structure— a standard practice for SLRs in Systems to highlight research gaps and clusters.

Response: Thank you for pointing out the error, we have corrected the number discrepancy. Regarding additional bibliometric metrics: while citation networks and keyword analyses are standard practice, we made a strategic choice to prioritize depth over breadth, our core contribution is the three-layer functional framework and business model analysis. Expanding bibliometric analysis would substantially increase length without strengthening this contribution. The current overview sufficiently establishes field maturation and multidisciplinary character.

 

 

3 Presentation of Findings and Synthesis

（1）Strengthen Cross-Layer Integration: The synthesis (Section 4) aims to connect digital functions, business model components, and change types, but the interdependencies could be more explicitly visualized. A conceptual model (Figure 7) is referenced but not fully described; enhancing this figure to show causal pathways (e.g., functional triggers → component changes → change typologies) with illustrative arrows and case examples would improve readability and theoretical coherence.

Response: We have updated Figure 7 with additional clear typological definitions for BMA, BME, and BMI. We maintained a generalized framework (not case-specific) to ensure broad applicability, while Section 4 provides comprehensive narrative explanations of interdependencies.

 

（2）Address Heterogeneity in Technology Impact: The manuscript notes that “the same technology can enable multiple transformation pathways,” but it does not systematically explore why outcomes vary (e.g., firm size, regulatory context, digital maturity). Adding a discussion of moderating factors (e.g., incumbent vs. startup dynamics, institutional openness) would explain variability in the reviewed literature and strengthen the model’s predictive power.

Response: We address heterogeneity at multiple levels: (1) Section 4 discusses why identical technologies enable different outcomes depending on context; (2) Section 5.2 explicitly identifies moderating factors (firm size, digital maturity, organizational heritage, regulatory context) as underexplored; (3) Research Questions 5-7 specifically target these effects. Rather than systematically explore all factors (requiring primary data), we identify this as a critical frontier for future empirical research.

 

（3）Expand Limitations Discussion: The limitations section (5.2) focuses on methodological constraints but understates substantive limitations: (1) How do data quality, cybersecurity, and interoperability challenges (mentioned in Section 5.1) affect the validity of the conceptual model? (2) Are there digital technologies (e.g., 5G, quantum computing) emerging post-2025 that may reshape functional groups? Addressing these would demonstrate critical self-reflection and frame future research directions more effectively.

Response: We have expanded our limitations discussion at line 907 to address substantive constraints. We now explicitly discuss how data quality, cybersecurity, and interoperability challenges affect the validity of our conceptual model, and we acknowledge that emerging technologies (5G, quantum computing) post-2025 may reshape the functional groups we identified.

 

4 Writing and Structure

（1）Streamline Repetitive Content: Some examples (e.g., AI-driven forecasting, blockchain for P2P trading) are repeated across sections (3.1, 3.2, 3.3). Consolidating examples to highlight their relevance to specific analytical layers (e.g., a single case study illustrating how IoT enables value creation, which drives BME) would improve conciseness.

Response: The repetition of examples (AI-driven forecasting, blockchain) across sections 3.1, 3.2, and 3.3 serves a deliberate analytical purpose: each section demonstrates how the same technology performs different functions across components. This is explicitly discussed in the text, illustrating that digital technologies are multifunctional enablers. Rather than consolidating, the current approach strengthens the framework by demonstrating technological versatility and contextual adaptability.

 

（2）Clarify Terminology Consistency: Terms like “digitalization” and “digitization” are used interchangeably in the abstract and introduction. The authors should define these terms explicitly (per ISO 22300 or relevant literature) to ensure consistency. Similarly, “sustainable energy” and “renewable energy” are sometimes conflated— a distinction is needed (e.g., renewable energy as a subset of sustainable energy) to align with Systems’ interdisciplinary standards.

Response: Terminology consistency addressed and renewable energy explicitly positioned as subset of sustainable energy.

 

（3）Enhance Practical Implications: The practical implications (Section 5.1) would benefit from sector-specific guidance. For example, how might utility incumbents vs. renewable energy startups prioritize digital investments? Providing a decision toolkit (e.g., a checklist for sequencing digital adoption: foundational monitoring → advanced optimization → user engagement) would make findings more actionable for practitioners.

Response: We acknowledge this valuable suggestion. However, providing sector-specific guidance and detailed decision toolkits requires empirical grounding across multiple organizations and contexts. Our single-case qualitative study establishes a foundational framework for understanding digital transformation in energy business models. Developing robust, validated toolkits for utility incumbents versus renewable startups demands multi-case comparative research, which we identify as a critical direction for future work (RQ6).

Reviewer 3 Report

Comments and Suggestions for Authors

Abstract

The abstract should clearly separate the purpose, method, and results of the research.

Emphasize how this study differs from other available studies.

Emphasize the novelty of the research.

Emphasize the practical significance of your research.

 

Introduction

Why is the analysis limited to the renewable energy sector?

Connect theory with practice.

Remove repetitions from the introduction.

Include a paragraph highlighting the novelty of the article.

Include a paragraph demonstrating the gap the article fills.

 

Methodology

There is no quantitative representation (table) for the SLR selection criteria.

You did not use the Scopus database. This may limit diversity.

What tool was used to analyze the data? Include this.

Why is the timeframe 10 years?

 

Results

"Functional grouping" - without empirical verification, it remains speculative.

"Value capture" - no quantification of financial results.

The text explanations for the figures are too short.

A table of criteria is needed for the categories "evolution" and "innovation"; it is unclear.

 

Conclusions and Recommendations

Add practical implications. Currently, these are general.

Add theoretical implications. Currently, these are general.

Provide directions for future research.

The study does not address the role of regulatory or policy frameworks in digital transformation.

Theoretical and practical implications are not clearly separated.

Discuss the limitations of the study.

Precisely provide directions for further research.

Author Response

Abstract

1). The abstract should clearly separate the purpose, method, and results of the research.

Response: We believe our abstract already follows the standard IMRaD structure: Purpose establishes the research gap; Method specifies the four-phase PRISMA systematic review and Gioia-based analysis; Results present key findings on functional technologies and business model change types. This structure clearly separates these elements

 

2). Emphasize how this study differs from other available studies.

Response: We have emphasized our study's differentiation at lines 18-22, uniquely integrating digital technologies, business model components, and change typologies, a framework absent from prior literature

 

3). Emphasize the novelty of the research.

Response: We have emphasized the research novelty at lines 30-33, highlighting the integrative framework combining digital technology functions with business model components and change typologies as absent from prior literature.

 

4). Emphasize the practical significance of your research.

Response: We have emphasized practical significance at lines 33-35, demonstrating how the framework helps renewable energy firms prioritize digital tools.

 

Introduction

1). Why is the analysis limited to the renewable energy sector?

Response: We have clarified the renewable energy sector focus at lines 44-47 and 55-56, explaining that the sector's rapid technological change, and discussing what makes it an ideal setting for investigating digital-driven business model transformation

2). Connect theory with practice.

Response: Our manuscript connects theory with practice throughout, linking the three-layer conceptual framework to practical implications for renewable energy firms in the discussion and conclusion sections.

3). Remove repetitions from the introduction.

Response: We have reviewed the introduction for repetitions. Any repeated points serve to provide necessary context or bridge connections between concepts, supporting logical flow and reader comprehension rather than representing redundancy.

 

4). Include a paragraph highlighting the novelty of the article.

Response: A paragraph highlighting the novelty of the article already exists at lines 117-127, articulating the unique integrative framework combining digital technologies, business model components, and change typologies.

 

5). Include a paragraph demonstrating the gap the article fills.

Response: The research gap is sufficiently covered throughout the introduction and literature review, with explicit articulation of how our study addresses the lack analysis of digital technology impacts on business model component and business model type changes in renewable energy sector.

 

Methodology

1). There is no quantitative representation (table) for the SLR selection criteria.

Response: The SLR selection criteria are detailed in the methodology (lines 180-185) and visually represented in the PRISMA flow diagram (Figure 1), providing both textual and graphical representation of the screening and inclusion process.

 

2). You did not use the Scopus database. This may limit diversity.

Response: We did use the Scopus database as our primary source. The selection rationale and justification for this choice are addressed in Section 2.2, explaining how Scopus provides broad interdisciplinary coverage appropriate for this research

 

3). What tool was used to analyze the data? Include this.

Response: The analytical tool is already covered at line 213-228, specifying that the Gioia methodology was used to analyze and code the data through first-order concepts, second-order themes, and aggregate dimensions. And that manual coding was carried out.

4). Why is the timeframe 10 years?

Response: The 10-year timeframe (2015-2025) is justified at lines 176-179, reflecting standard practice and the period during which digitalization became increasingly central to renewable energy strategy and business model innovation.

 

Results

1). "Functional grouping" - without empirical verification, it remains speculative.

Response: The functional grouping is not speculative; it is systematically derived from manual coding of 32 peer-reviewed articles using the Gioia methodology (Section 2.3) and grounded in recurring patterns across the literature.

 

2). "Value capture" - no quantification of financial results.

Response: Quantified financial outcomes are rarely documented in renewable energy literature. Our focus on value capture mechanisms is appropriate for a systematic literature review; empirical studies with primary data would be needed to quantify financial impacts.

 

3). The text explanations for the figures are too short.

Response: We believe the text explanations for each figure are sufficient, with paragraphs before and/or after each diagram providing adequate context and interpretation for reader comprehension

 

4). A table of criteria is needed for the categories "evolution" and "innovation"; it is unclear.

Response: A table of criteria for BMA, BME, and BMI distinctions has been added at line 693, clearly defining the scope of component change, novelty of value logic, and institutional disruption for each category.

 

Conclusions and Recommendations

1). Add practical implications. Currently, these are general.

Response: Practical implications are addressed starting at line 838, providing specific guidance for renewable energy firms on prioritizing and integrating digital tools for business model reconfiguration.

2). Add theoretical implications. Currently, these are general.

Response: Theoretical implications are addressed starting at line 770, articulating how the study integrates digital technology, business model components, and change typologies into a novel framework absent from prior literature.

 

3). Provide directions for future research.

Response: Future research directions are provided starting at line 925, proposing specific research questions (RQ1-RQ7) addressing moderating factors, regulatory context, integration depth, and empirical validation of the framework.

 

4). The study does not address the role of regulatory or policy frameworks in digital transformation.

Response:  We address regulatory frameworks in Section 5.1 (Practical Implications). Additionally, we acknowledge in Section 5.2 (Limitations) that systematic examination of their moderating effects is beyond our scope and propose this as an important direction for future research (RQ7).

 

5). Theoretical and practical implications are not clearly separated.

Response: Theoretical and practical implications are now clearly separated through formatting.

 

6). Discuss the limitations of the study.

Response: Study limitations are comprehensively addressed in Section 5.2 (Limitations), covering scope constraints (single-case design, specific context), methodological considerations (qualitative approach, coding subjectivity), and theoretical boundaries (moderating effects, regulatory frameworks). These limitations are framed as opportunities for future research.

 

7). Precisely provide directions for further research.

Response: Future research directions are precisely specified starting at line 925 with seven research questions (RQ1-RQ7).

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

1. Theoretical Framework Refinement

• Sustainability Mechanism Depth: While the new section mapping digital functions to sustainability outcomes is a welcome addition, it would benefit from more granular literature grounding. For each functional group (e.g., “Data Capture and Embedded Infrastructure”), explicitly cite 1–2 key studies from the reviewed sample that demonstrate the causal link to efficiency/resilience/inclusion. This strengthens the empirical basis of the framework and avoids overgeneralization.
• Emerging Technology Adaptability: The manuscript notes that emerging technologies (e.g., generative AI, 5G) may reshape functional groups but provides limited guidance on how to classify them. Adding a brief, practical example (e.g., “Generative AI fits primarily in ‘Data Processing and System Intelligence’ for scenario planning but may also support ‘Customer Interface Platforms’ for personalized energy recommendations”) would enhance the framework’s adaptability—critical for a field evolving as rapidly as digital energy.

2. Methodological Rigor

• Inter-Coder Reliability Metrics: While the authors describe co-author review and consensus-building, Systemstypically expects quantitative measures of inter-coder reliability (e.g., Cohen’s kappa, percentage agreement) for qualitative coding in SLRs. Providing these metrics (even for a subset of coded articles) would further validate the thematic analysis and align with the journal’s methodological standards.
• Bibliometric Contextualization: The authors’ strategic choice to prioritize theoretical depth over bibliometric breadth is understandable, but key metrics (e.g., top 3–5 cited articles in the sample, core keyword co-occurrence) would concisely contextualize the field’s intellectual structure. This helps readers identify research clusters and gaps—standard for SLRs in Systems—without unduly expanding the manuscript. A 1–2 sentence summary of core keywords (e.g., “‘blockchain,’ ‘AI,’ and ‘energy-as-a-service’ emerge as central thematic clusters”) or a simplified keyword network figure would suffice.

3. Findings and Synthesis Clarity

• Conceptual Model Visualization: Figure 7 (Conceptual Model of Digital-Driven BM Change) is referenced as a unifying framework but lacks explicit causal pathways. Adding directional arrows (e.g., “Functional Triggers → BM Component Reconfiguration → Change Typology”) and labeling the sustainability lens as a cross-cutting layer would improve readability. Including 1–2 mini-case examples (e.g., “IoT sensors → Value Creation (efficiency gains) → BME”) as callouts in the figure would bridge theory and practice.
• Moderating Factors Integration: The manuscript identifies firm size, regulatory context, and digital maturity as moderators of technology impact but does not integrate them into the conceptual model. Adding a “Moderating Contexts” box to Figure 7 (linked to the causal chain) would make these factors more visible and strengthen the model’s explanatory power—critical for a framework intended to guide future research.

4. Practical and Substantive Limitations

• Actionable Sequencing Guidance: The authors note that sector-specific decision toolkits require additional empirical data, but preliminary, literature-based sequencing guidance (e.g., a 3-step checklist: 1) Deploy foundational monitoring (IoT/smart meters) to build data infrastructure; 2) Integrate AI/ML for forecasting/optimization; 3) Scale user engagement platforms) would enhance practical utility without overreaching. This aligns with Systems’ focus on translating theoretical insights to actionable strategies.
• Substantive Limitation Mitigation: The expanded limitations section addresses data quality, cybersecurity, and emerging technologies, but it would benefit from linking these constraints to future research solutions. For example: “Future work could investigate how blockchain’s immutability features mitigate data quality risks, or how 5G’s low latency might expand the ‘System-Level Orchestration’ functional group.” This frames limitations as opportunities for extension, rather than gaps.

Author Response

Comment 1: Sustainability Mechanism Depth: While the new section mapping digital functions to sustainability outcomes is a welcome addition, it would benefit from more granular literature grounding. For each functional group (e.g., “Data Capture and Embedded Infrastructure”), explicitly cite 1–2 key studies from the reviewed sample that demonstrate the causal link to efficiency/resilience/inclusion. This strengthens the empirical basis of the framework and avoids overgeneralization.

Response 1: We appreciate this suggestion and have revised Section 5.1 so that each digital functional group is now explicitly linked to efficiency, resilience, and/or inclusion with representative supporting studies from our sample.

Comment 2: Emerging Technology Adaptability: The manuscript notes that emerging technologies (e.g., generative AI, 5G) may reshape functional groups but provides limited guidance on how to classify them. Adding a brief, practical example (e.g., “Generative AI fits primarily in ‘Data Processing and System Intelligence’ for scenario planning but may also support ‘Customer Interface Platforms’ for personalized energy recommendations”) would enhance the framework’s adaptability—critical for a field evolving as rapidly as digital energy.

Response 2: We have added a short paragraph in Section 4.1 that shows how emerging technologies such as generative AI and 5G are classified within the existing functional groups, clarifying the framework’s adaptability.

Comment 3: Inter-Coder Reliability Metrics: While the authors describe co-author review and consensus-building, Systemstypically expects quantitative measures of inter-coder reliability (e.g., Cohen’s kappa, percentage agreement) for qualitative coding in SLRs. Providing these metrics (even for a subset of coded articles) would further validate the thematic analysis and align with the journal’s methodological standards.

Response 3: We clarified in Section 2.3 that the Gioia methodology follows established inductive coding protocols emphasizing researcher immersion, peer debriefing, and consensus validation - aligning with prior SLR applications - rather than quantitative inter-coder metrics suited to mechanical coding of larger samples. We note that comprehensive coder immersion across the focused 32-article sample, with full co-author review and retained coding tables

Comment 4: Bibliometric Contextualization: The authors’ strategic choice to prioritize theoretical depth over bibliometric breadth is understandable, but key metrics (e.g., top 3–5 cited articles in the sample, core keyword co-occurrence) would concisely contextualize the field’s intellectual structure. This helps readers identify research clusters and gaps—standard for SLRs in Systems—without unduly expanding the manuscript. A 1–2 sentence summary of core keywords (e.g., “‘blockchain,’ ‘AI,’ and ‘energy-as-a-service’ emerge as central thematic clusters”) or a simplified keyword network figure would suffice.

Response 4: In Section 2.4 (after Figure 3), we added citation analysis naming the top three most-cited articles plus keyword cluster analysis identifying three core themes, providing field context.

Comment 5: Conceptual Model Visualization: Figure 7 (Conceptual Model of Digital-Driven BM Change) is referenced as a unifying framework but lacks explicit causal pathways. Adding directional arrows (e.g., “Functional Triggers → BM Component Reconfiguration → Change Typology”) and labeling the sustainability lens as a cross-cutting layer would improve readability. Including 1–2 mini-case examples (e.g., “IoT sensors → Value Creation (efficiency gains) → BME”) as callouts in the figure would bridge theory and practice.

Response 5: We have updated Figure 7 to depict the sustainability lens as a cross‑cutting layer across the model and, in Section 4.3, added two short illustrative pathways (IoT‑based monitoring → BME; blockchain‑enabled P2P/VPP platforms → BMI) to explain the causal structure more clearly.

Comment 6: Moderating Factors Integration: The manuscript identifies firm size, regulatory context, and digital maturity as moderators of technology impact but does not integrate them into the conceptual model. Adding a “Moderating Contexts” box to Figure 7 (linked to the causal chain) would make these factors more visible and strengthen the model’s explanatory power - critical for a framework intended to guide future research.

Response 6: We agree that factors such as firm size, regulatory context, and digital maturity are important moderators of technology impact. In the revised manuscript we clarify in the Discussion and Future Research sections that these elements are treated as proposed contextual moderators rather than as a separately coded analytical layer, and we explicitly flag them as a key agenda for future empirical work. For this reason, and to avoid overstating our empirical grounding, we keep these moderating factors in the narrative rather than integrating them visually into Figure 7.

Comment 7: Actionable Sequencing Guidance: The authors note that sector-specific decision toolkits require additional empirical data, but preliminary, literature-based sequencing guidance (e.g., a 3-step checklist: 1) Deploy foundational monitoring (IoT/smart meters) to build data infrastructure; 2) Integrate AI/ML for forecasting/optimization; 3) Scale user engagement platforms) would enhance practical utility without overreaching. This aligns with Systems’ focus on translating theoretical insights to actionable strategies.

Response 7: We have expanded the sequencing guidance in Section 5.2 with a four‑step logic grounded in our functional groupings, showing how firms can stage digital investments to advance both business model change and sustainability outcomes.

 

Comment 8: Substantive Limitation Mitigation: The expanded limitations section addresses data quality, cybersecurity, and emerging technologies, but it would benefit from linking these constraints to future research solutions. For example: “Future work could investigate how blockchain’s immutability features mitigate data quality risks, or how 5G’s low latency might expand the ‘System-Level Orchestration’ functional group.” This frames limitations as opportunities for extension, rather than gaps.

Response 8: We have strengthened the link between limitations and future research by adding a short paragraph at the end of Section 5.3 that explicitly connects our main constraints (secondary data, language/database bias, lack of longitudinal evidence) to the specific research questions proposed in Section 5.4.