Analyzing Visitor Behavior to Enhance Personalized Experiences in Smart Museums: A Systematic Literature Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors should address the comments in the attached file.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Areas for Development after review

1. Theoretical Framework Enhancement

The manuscript would benefit from a stronger theoretical framework that connects visitor behavior analysis to established theories in cognitive psychology, learning sciences, and human-computer interaction. Consider:

• Incorporating relevant theories such as Falk and Dierking's Contextual Model of Learning in museums
• Discussing how personalization technologies align with or challenge existing theoretical models
• Developing a conceptual framework that illustrates the relationships between visitor behaviors, technologies, and desired outcomes

2. Methodological Considerations

While the review methodology is sound, several enhancements could strengthen its rigor:

• Include a more detailed discussion of potential biases in the literature selection process
• Consider conducting a meta-analysis of quantitative findings across studies where possible
• Provide a more detailed quality assessment of included studies, particularly regarding sample sizes and methodological limitations

3. Ethical and Privacy Implications

The manuscript mentions privacy concerns but could develop this critical area further:

• Add a dedicated section on ethical considerations in visitor behavior tracking
• Discuss GDPR and other relevant regulations that impact data collection in museums
• Propose ethical guidelines or frameworks for implementing personalization technologies
• Address potential biases in AI systems and how these might affect diverse visitor populations

4. Economic Feasibility Analysis

As correctly identified in the implementation challenges section, the economic feasibility of these technologies is a significant limitation:

• Expand the discussion on cost-benefit analysis for different museum sizes
• Provide case studies of museums that have implemented these technologies with different budgets
• Develop a tiered approach to technology implementation based on institutional resources
• Consider open-source or low-cost alternatives to expensive personalization systems

5. Visitor Diversity and Accessibility

The review could place more emphasis on how personalization technologies address the needs of diverse visitor populations:

• Evaluate how current technologies accommodate visitors with disabilities
• Discuss cultural differences in visitor engagement and how personalization can be culturally responsive
• Address multilingual capabilities of personalization systems
• Consider age-appropriate personalization for different visitor demographics

6. Future Research Directions

While the manuscript identifies some future trends, this section could be expanded:

• Provide a more detailed research agenda with specific questions that need addressing
• Discuss emerging technologies not yet widely implemented in museums (e.g., brain-computer interfaces, emotion AI)
• Consider interdisciplinary research opportunities between museum studies, computer science, and cognitive psychology
• Address the need for longitudinal studies to assess long-term impacts

7. Practical Implementation Guidelines

To increase the practical impact of this review, consider adding:

• A decision framework to help museum professionals select appropriate technologies
• Implementation guidelines based on museum size, type, and resource availability
• Best practices for visitor data collection and management
• Evaluation metrics for measuring the success of personalization initiatives

This manuscript represents a valuable contribution to smart museum studies, offering a comprehensive review of visitor behavior analysis and personalization technologies. By addressing the suggested areas for development, the authors can enhance the theoretical grounding, methodological rigor, and practical impact of their work. The expanded discussion of ethical considerations, economic feasibility, and visitor diversity would also increase the relevance of this review for museum professionals across various institutional contexts.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This systematic review explores methodologies and technologies for analyzing visitor behavior and enhancing personalized experiences in smart museums, examining 33 studies from 2015 to 2024. It highlights key technologies like AI and geospatial tracking, identifies challenges such as privacy concerns, and suggests directions for future research.

However, the authors should address the following issues:

• The paper does not offer sufficient depth in discussing the ethical challenges associated with data collection and user consent, which are crucial for the implementation of personalized systems.
• More attention is needed on the ethical implications of using technologies like AI, biometric sensors, and geofencing, which collect sensitive data from museum visitors.
• Several studies referenced in the paper have small sample sizes (e.g., 10 participants), which limits the generalizability of the findings.
• The paper could benefit from a more extensive evaluation of studies with larger and more diverse participant groups to ensure broader applicability of the findings.
• The paper heavily focuses on quantitative data and methodologies (such as statistical and data analysis, and AI/ML), which might neglect the qualitative aspects of visitor behavior, such as emotional responses and cognitive engagement.
• More qualitative methods, such as in-depth interviews or ethnographic studies, could provide valuable insights into the personal experiences of museum visitors.
• Some sections of the paper, particularly those detailing methodologies and technologies, repeat similar information. This repetition could be streamlined to improve the flow of the paper and avoid redundancy.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

i am happy with the revisions.