Evaluating Public Library Services in Taiwan through User-Generated Content: Analyzing Google Maps Reviews

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors use the Google maps reviews for the assesment of public libraries.  The work could potentially be useful but I see a number of issues with the paper.

1. There is no clear indication what you wanted to achieve in your research, how would you use the results for. I think the authors should rethink and prdvuselg define the obectives of the research. Maybd how do some metrics on Google maps compare to other methids and thus what is their credibility?

2. It is well known that the people are  more likely to present extreme opinions, either very high or very low, how does it affect your research? You only show the average of reviews but not their distribution. So is the,, lets say, average of 4.1 result of many reviews around 4, 4.2 or maybe a number of low reviews like 2 or 3 and many 5 star reviews?

3. The keyword analysis shows the high usage of words like reading or book, books but then it seems obvious if writing about libraries. It can be predicted without any analysis at all. 

4. You show numbers of reviews,  but without indication of a given city population this numbers are useless.  

5 The paper is mosty descriptive, lacks some ilustration, how the SN you define looks like? 

6 While writing about out and in degrees show the example of a grsph structure.

Author Response

1. There is no clear indication what you wanted to achieve in your research, how would you use the results for. I think the authors should rethink and profusely define the objectives of the research. Maybe how do some metrics on Google Maps compare to other methods, and thus, what is their credibility?

 

Response: The suggestion to clarify the research objectives is appreciated. This study explores the potential of user-generated content on Google Maps for evaluating public library services. The research seeks to compare these metrics with traditional methods to assess their credibility and provide actionable insights for service improvement. The introduction will be revised to clearly define these objectives and the intended use of the results. (rewritten paragraph in p2, 70-75)

 

Rewritten Paragraph:

This study aims to evaluate public library services through user-generated content on Google Maps. The goal is to leverage the detailed feedback provided in online reviews to gain insights into user perceptions and identify areas for service enhancement. Additionally, the study compares these digital metrics with traditional evaluation methods to assess their credibility and utility. It ultimately provides actionable recommendations for library administrators and policymakers to improve service delivery and user experience.

 

2. It is well known that people are more likely to present extreme opinions, either very high or very low. How does this affect your research? You only show the average of reviews but not their distribution. So is the, let's say, an average of 4.1 results of many reviews around 4, 4.2, or maybe a number of low reviews like 2 or 3 and many 5-star reviews?

Response: It is acknowledged that extreme opinions can influence the overall average ratings. An analysis of the distribution of review ratings will be included to address this. This will help illustrate whether the average rating results from a balanced distribution or is skewed by extreme scores, providing a more comprehensive understanding of user satisfaction.

 

Rewritten Paragraph:

Extreme opinions can skew average ratings. To address this, the study conducted a distribution analysis of the review ratings to show the spread of scores. This analysis revealed whether the average rating of 4.1 results from a balanced distribution or is influenced by extreme scores. Understanding this distribution provides a more comprehensive view of user satisfaction and highlights potential areas for targeted improvements.

 

 

3. The keyword analysis shows the high usage of words like reading or books, but then it seems obvious if writing about libraries. It can be predicted without any analysis at all.

 

Response: While some keywords like "reading" and "book" are expected in library reviews, the analysis also revealed significant insights into less obvious themes and user concerns. These findings will be emphasized to demonstrate the value of the keyword analysis beyond the predictable terms.

 

Rewritten Paragraphs:

While keywords such as "reading" and "book" are expected in the context of library reviews, the keyword analysis also uncovered less obvious themes and user concerns. These insights provide a deeper understanding of user experiences and expectations, highlighting specific areas libraries can focus on to enhance user satisfaction. The findings are emphasized to demonstrate the broader value of the keyword analysis.

 

4. You show numbers of reviews, but without indication of a given city population, these numbers are useless.

 

Response: The number of reviews should be contextualized with the population of each city. This information will be added to the analysis to provide a clearer understanding of review engagement relative to the city population.

 

Rewritten Paragraphs:

The number of reviews alone may not provide sufficient context for understanding user engagement. Therefore, the study contextualized the review numbers with the population size of each city. This approach allows for a more accurate interpretation of review engagement, indicating how actively residents in each city participate in reviewing their local libraries on Google Maps.

 

5 The paper is mostly descriptive and lacks some illustration; how does the SN you define look like?

 

Response: To enhance the descriptive nature of the paper, illustrations and visual representations of the social network analysis will be included. This will help readers better understand the network structures and relationships discussed in the manuscript.

 

Rewritten Paragraphs:

To enhance the descriptive sections of the paper, illustrations and visual representations of the social network analysis are included. These visual aids help readers better understand the network structures and relationships identified in the analysis, providing a clearer and more engaging presentation of the findings. In discussing out-degree and in-degree centrality, examples of graph structures are provided to illustrate these concepts. These examples help to concretize the discussion, showing how nodes (libraries) and edges (connections between keywords) form the network. This visualization aids in understanding the significance of these metrics within the context of the social network analysis. In Figure 1, the Social Network Analysis diagram is presented. The larger the node, the higher the Degree Centrality, indicating a greater connection with the outside world. This demonstrates that some many overlapping services or nodes are of significant interest to users.

 

6 While writing about out and in degrees, show a graph structure example.

 

Response: Examples of graph structures will be provided to illustrate the concepts of out-degree and in-degree centrality. This will make the discussion of these metrics more concrete and accessible.

 

Rewritten Paragraphs:

In discussing out-degree and in-degree centrality, examples of graph structures are provided to illustrate these concepts. These examples help to concretize the discussion, showing how nodes (libraries) and edges (connections between keywords) form the network. This visualization aids in understanding the significance of these metrics within the context of the social network analysis.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

To better understand public library patronage in Taiwan's six main cities, this study investigates the public library service evaluation domain using user-generated information on Google Maps. Through a mixed-methods research methodology, this work combines Social Network Analysis with Google Maps review content analysis to define public attitudes and pinpoint areas in which public libraries should improve their services. The authors classified and analyzed the terms from Google Maps evaluations of public libraries under the three main LibQUAL+ dimensions: Library as Place, Information Control, and Effect of Service. According to the authors, the results highlight a common desire for clean, comfortable, roomy, and quiet library settings that support reading and learning, as well as a need for excellent services, effective administration, and distinctive cultural offerings.

The paper is well-structured and clear and the bibliography is up-to-date and relevant to the subject. The language is simple and understandable. The flow of speech is smooth. The discussion part, in which the writers should compare their results to those of other relevant surveys, is one element I think is missing. Additionally, sections 4.4 and 4.5 where the Social Network Analysis is discussed are hard to follow. I cannot understand how this process was performed and how the authors came to these conclusions. I would like to see some related graphs and the process presented step by step. There is no explanation and information about Table 2 given in the text. 

Moreover, I would like to see how the keywords were extracted from the comments. Did they use any text-mining tool? How did they end up in these keywords?

Finally, I would think about arguing about subjectivity and objectivity in online reviews. The advantages and disadvantages of internet reviews should be mentioned when the entire study relies on such information.

For example, there is this related research: https://doi.org/10.1002/jcpy.1382

Comments on the Quality of English Language

Minor changes in english language, mostly in lengthy sentences.

Author Response

1. To better understand public library patronage in Taiwan's six main cities, this study investigates the public library service evaluation domain using user-generated information on Google Maps. Through a mixed-methods research methodology, this work combines Social Network Analysis with Google Maps review content analysis to define public attitudes and pinpoint areas in which public libraries should improve their services. The authors classified and analyzed the terms from Google Maps evaluations of public libraries under the three main LibQUAL+ dimensions: Library as Place, Information Control, and Effect of Service. According to the authors, the results highlight a common desire for clean, comfortable, roomy, and quiet library settings that support reading and learning, as well as a need for excellent services, effective administration, and distinctive cultural offerings.

 

Response:

Thank you for your detailed and positive feedback.

 

2. The paper is well-structured and clear and the bibliography is up-to-date and relevant to the subject. The language is simple and understandable. The flow of speech is smooth. The discussion part, in which the writers should compare their results to those of other relevant surveys, is one element I think is missing. Additionally, sections 4.4 and 4.5 where the Social Network Analysis is discussed are hard to follow. I cannot understand how this process was performed and how the authors came to these conclusions. I would like to see some related graphs and the process presented step by step. There is no explanation and information about Table 2 given in the text.

 

Response:

Thank you for your positive feedback regarding the paper's structure, clarity, and relevance. We acknowledge the need for a detailed discussion of our results with other relevant studies. Additionally, we recognize that sections 4.4 and 4.5 on Social Network Analysis need clarification. We have supplemented it with relevant SNA graphs to illustrate our methodology and findings.

 

Revised Paragraph:

 

To enhance the descriptive sections of the paper, illustrations and visual representations of the social network analysis are included. These visual aids help readers better understand the network structures and relationships identified in the analysis, providing a clearer and more engaging presentation of the findings. In discussing out-degree and in-degree centrality, examples of graph structures are provided to illustrate these concepts. These examples help to concretize the discussion, showing how nodes (libraries) and edges (connections between keywords) form the network. This visualization aids in understanding the significance of these metrics within the context of the social network analysis.

 

3. Moreover, I would like to see how the keywords were extracted from the comments. Did they use any text-mining tool? How did they end up in these keywords?

 

Response:

Thank you for your question regarding the keyword extraction process. This study developed a comprehensive keyword list based on the content and items of library services. Using this keyword database, the review content underwent word segmentation analysis to generate the keyword results.

 

Revised Paragraph:

 

The keyword extraction process in this study was systematic and thorough. A comprehensive list of keywords was developed based on the content and items of library services, ensuring coverage of all relevant aspects of user experience. Using this keyword database, text-mining tools, specifically natural language processing (NLP) techniques, were applied to perform word segmentation analysis on the Google Maps review content. This approach enabled the accurate identification of the most frequently mentioned terms. The keywords were then categorized under the three LibQUAL+ dimensions: Library as Place, Information Control, and Affect of Service, ensuring a detailed and structured analysis of user feedback.

 

4. Finally, I would think about arguing about subjectivity and objectivity in online reviews. The advantages and disadvantages of internet reviews should be mentioned when the entire study relies on such information. For example, there is this related research: https://doi.org/10.1002/jcpy.1382

 

Response:

Thanks for your valuable feedback. This study acknowledges the importance of discussing the subjectivity and objectivity inherent in online reviews. We have revised the paragraph to address these aspects, emphasizing the influence of linguistic characteristics on the perceived helpfulness of reviews and the potential advantages and disadvantages of relying on internet reviews for evaluating public library services. This response and revision incorporate the content and findings from the referenced article to effectively address the comment.

 

Revised Paragraph:

Online reviews are a critical source of information for consumers, significantly influencing their decisions. Reviews rated as more helpful tend to exert a stronger influence. A previous study found that linguistic subjectivity and objectivity positively affect review helpfulness. However, combining subjective and objective sentences in the same review can reduce its perceived helpfulness due to the increased complexity and the effort required for processing (Park, Song, & Sela, 2023). In the context of public library services, the advantages of using internet reviews include access to real-time, diverse perspectives and detailed user experiences that traditional survey methods may not capture. However, there are also disadvantages, such as the potential bias introduced by extreme opinions and the challenge of interpreting mixed subjective and objective feedback. These factors must be considered to ensure a balanced and comprehensive evaluation of library services, leveraging the strengths of online reviews while mitigating their limitations.

 

Reference

Park, S. K., Song, T., & Sela, A. (2023). The effect of subjectivity and objectivity in online reviews: A convolutional neural network approach. Journal of Consumer Psychology, 33(4), 701-713.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Introduction is well written, however I would strongly suggest to add references to key statements. For instance, a study conducted in Barcelona is mentioned but the reference to that study is missing.

 

Additionally, this study is specifically conducted in Taiwan. I should be stated it in the title.  I suggest to enhance the introduction with contextual information and official statistics around public libraries in the target country accordingly.

 

In my opinion, Section 2 is not a literature review  but rather an overview of background concepts. It is honestly weak and doesn’t provide an actual overview of the relevance of Online Social Networks in context.

 

Although the article is overall clear and well-written, I would suggest to review the key terminology. For example “This study adopts a mixed-methods research design” can be simply replaced with “This study adopts mixed-methods”.

 

The dataset explored should be clearly described and detailed, in terms of size and dimensionality. In 3.1, rather than “Data Collection”, it is probably more opportune to state “Input Dataset” as no process is described. More in general, Section 3 is not informative and needs major improvement to capture the actual methodology and approach. It should be detailed to assure transparency. Metrics adopted for analysis should be clearly stated.

 

The analysis conducted is honestly too simple. We invite authors to conduct a proper rigorous statistical analysis.

Author Response

1. The introduction is well-written. However, I would strongly suggest adding references to key statements. For instance, a study conducted in Barcelona is mentioned, but the reference to that study is missing.

 

Response:

Thank you for your feedback. We agree that adding references to key statements will strengthen the introduction. We have now included citations to support the mentioned study and other key points.

 

Revised Paragraph:

Google Maps is a web platform that allows users to review businesses and services, including libraries. Given the impact of online reviews on the reputation of companies and institutions, it is crucial to understand how library users share and interpret their experiences on these platforms. Understanding these reviews can provide valuable insights into patrons' perceptions of public libraries and inform the development of effective strategies to address their feedback and manage their suggestions and complaints(Borrego & Comalat Navarra, 2021).

 

2. Additionally, this study is specifically conducted in Taiwan. I should be stated it in the title. I suggest to enhance the introduction with contextual information and official statistics around public libraries in the target country accordingly.

 

Response:

Thank you for your suggestion. We will revise the title to state that the study is conducted in Taiwan.

 

Revised Title:

Evaluating Public Library Services in Taiwan Through User-Generated Content: Social Network Analysis of Google Maps Reviews

 

 

3. In my opinion, Section 2 is not a literature review but rather an overview of background concepts. It is honestly weak and doesn’t provide an actual overview of the relevance of Online Social Networks in context.

 

Response:

We appreciate your feedback. We acknowledge that Section 2 needs to be strengthened to provide a comprehensive literature review. We have revised this section to include relevant studies on the use of Online Social Networks for public service evaluation, emphasizing their relevance in the context of public libraries.

 

Revised Paragraph:

When a computer network connects people or organizations, it forms a social network. The study of these computer-supported social networks has received significant attention, comparable to the research on human-computer interaction, online per-son-to-person interaction, and computer-supported communication within small groups [11]. Various online social networks (OSNs) have rapidly developed on the Internet. Previous studies have analyzed different aspects of these OSNs, primarily focusing on the formation and evolution of the networks and the propagation of information within them [12]. Social networks enable users to connect with friends, discover new acquaintances, and share UGC, including videos, documents, blogs, and photos [13].

 

4. Although the article is overall clear and well-written, I would suggest to review the key terminology. For example, “This study adopts a mixed-methods research design” can be replaced with “This study adopts mixed-methods”.

 

Response:

 

Thank you for your suggestion. We will review and simplify the key terminology throughout the article to enhance clarity and readability.

 

Revised Paragraph:

 

This study adopts mixed-methods to evaluate public library services in Taiwan's six major cities. By integrating quantitative analysis of star ratings and review volumes with qualitative analysis of review content, we aim to provide a comprehensive understanding of user satisfaction. This approach combines the strengths of both methodologies to offer a nuanced perspective on public library service quality.

 

5. The dataset explored should be clearly described and detailed, in terms of size and dimensionality. In 3.1, rather than “Data Collection”, it is probably more opportune to state “Input Dataset” as no process is described. More in general, Section 3 is not informative and needs major improvement to capture the actual methodology and approach. It should be detailed to assure transparency. Metrics adopted for analysis should be clearly stated.

 

Response:

Thank you for your feedback. We agree that Section 3 needs to be more detailed and transparent. We will revise this section to provide a clear description of the dataset, including its size and dimensionality, and to outline the methodology and metrics used for analysis.

 

Revised Paragraph:

3.1. Dataset Description

This study adopts mixed-methods to evaluate public library services in Taiwan's six major cities. By integrating quantitative analysis of star ratings and review volumes with qualitative analysis of review content, we aim to provide a comprehensive understanding of user satisfaction. This approach combines the strengths of both methodologies to offer a nuanced perspective on public library service quality. The dataset for this study consists of over 60,000 Google Maps reviews of public libraries in Taiwan's six major cities: Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The reviews span a three-year period from 2021 to 2023. Each review includes a star rating, review text, date of submission, and reviewer information.

 

6. The analysis conducted is honestly too simple. We invite authors to conduct a proper, rigorous statistical analysis.

 

Response:

 

We appreciate your feedback and understand the need for a more rigorous statistical analysis. We have expanded the analysis section to include detailed descriptive statistics and Table 1. to enhance the rigor of our study. The table has been updated to include extensive data, such as the population of six cities. This allows the study to gauge the enthusiasm of local residents in responding to public libraries. For instance, the response rate for public libraries in Tainan City is notably high at 0.75. Additionally, the proportion of five-star and single-star evaluations across different programs is relatively balanced. This indicates that the residents of Taiwan highly appreciate the services provided by public libraries.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

I do appreciate the effort to improve the paper.

However, in my opinion, the analysis conducted still presents some significant limitation. I would suggest an additional effort to deeply discuss the results according to multiple perspectives. For instance, I'm not sure I have completely understood  the meaning of Figure 1 (Keyword analysis? Knowledge graph? Other?), which seems to be valuable.  

Concerning "Social Network Analysis of Google Maps Reviews" that appears in the title and is somehow the underlying philosophy, I guess the analysis is simply on Google Maps Reviews. I would suggest to simplify the title replacing with "...: analysing Google Maps Reviews" and to explain in the paper that the analysis of such user-generated content has been performed on a resulting network.

Authors claim the use of mixed methods. In the specific context of the paper, it would be advisable to avoid ambiguities and clearly define the scope of qualitative analysis that, if I have well understood, is limited to the analysis of the comments associated with the reviews, while no additional interviews/survey has been conducted by authors (?).     

Last but not least, at this stage is critical to improve the dialog with literature, add proper references and discuss the results accordingly. 

Author Response

Electronics-3018316 –Evaluating Public Library Services in Taiwan Through User-Generated Content: Analyzing Google Maps Reviews

 

Thank you for the valuable feedback on our manuscript. We have revised the discussion to provide a deeper interpretation of the findings and clarified Figure 1. The title has been simplified to "Evaluating Public Library Services in Taiwan Through User-Generated Content: Analyzing Google Maps Reviews." We have defined the scope of our qualitative analysis, confirming it is limited to review comments without additional interviews or surveys. Additionally, we have enhanced the dialogue with the literature by adding proper references.

 

Reviewer Comment:

However, in my opinion, the analysis conducted still presents some significant limitations. I would suggest an additional effort to deeply discuss the results according to multiple perspectives. For instance, I'm not sure I have completely understood the meaning of Figure 1 (Keyword analysis? Knowledge graph? Other?), which seems to be valuable.

 

Response:

Thank you for your feedback. We acknowledge the need for a deeper discussion of the results from multiple perspectives. We have revised the discussion section to provide a more comprehensive interpretation of the findings. Additionally, we have clarified the meaning of Figure 1, explaining its significance in the context of our study.

 

Revised Paragraph:

Figure 1 represents a Social Network Analysis (SNA) of keywords extracted from Google Maps reviews of public libraries. This SNA diagram illustrates the relationships and interactions between keywords extracted from Google Maps reviews of public libraries in Taiwan. The nodes in the diagram represent individual keywords or concepts derived from the reviews. At the same time, the edges signify the connections between these keywords, indicating their co-occurrence within the same reviews. The size of each node reflects its Degree Centrality, which denotes the number of connections a node has; larger nodes signify higher frequency and more connections with other keywords. Different colors may be used to distinguish various categories or themes of keywords, such as library services, facilities, user experiences, etc. Closely connected nodes form clusters, representing groups of keywords frequently appearing in the reviews, revealing specific areas of interest or concern among library users. The largest nodes in the diagram represent the most frequently mentioned keywords, likely indicating key aspects of library services that users care about most. The connections between different clusters highlight overlapping areas of concern, suggesting that multiple aspects of library services are interrelated from the users' perspective. By analyzing this social network analysis diagram, one can identify library users' primary themes and concerns, understand the interrelationships between different aspects of library services, and pinpoint key areas for improvement based on user feedback. The diagram prominently features six large nodes representing the six public libraries studied in Taiwan, with their larger size indicating a high degree of connectivity. At the center of the diagram are the shared service items and focal points common to these six libraries: noise, reading, reading rooms, sustainability, and environmental hygiene. The outer areas of the diagram depict service items specific to individual public libraries, which are not commonly shared or identical across all libraries and may include unique services characteristic of each library. The numerous shared items in the network and many peripheral nodes demonstrate that Taiwan's public libraries have common issues and service items while developing their unique features.

 

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Reviewer Comment:

Concerning "Social Network Analysis of Google Maps Reviews" that appears in the title and is somehow the underlying philosophy, I guess the analysis is simply on Google Maps Reviews. I would suggest simplifying the title, replacing it with "...: analyzing Google Maps Reviews" and to explain in the paper that the analysis of such user-generated content has been performed on a resulting network.

 

Response:

Thank you for your suggestion. We agree that simplifying the title will enhance clarity. We have revised the title accordingly to clarify that the analysis was performed on the resulting network of user-generated content from Google Maps reviews.

 

Revised Title:

"Evaluating Public Library Services in Taiwan Through User-Generated Content: Analyzing Google Maps Reviews"

 

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Reviewer Comment:

Authors claim the use of mixed methods. In the specific context of the paper, it would be advisable to avoid ambiguities and clearly define the scope of qualitative analysis that, if I have well understood, is limited to the analysis of the comments associated with the reviews, while no additional interviews/survey has been conducted by authors (?).

 

Response:

Thank you for pointing out the need for clarity regarding our mixed-methods approach. We confirm that our qualitative analysis is limited to the content of the comments associated with the reviews, and no additional interviews or surveys were conducted. We have revised the methodology section to clearly define the scope of our qualitative analysis.

 

Revised Paragraph:

This study employs a mixed-methods approach, integrating quantitative and quali-tative analyses. The quantitative analysis examines the star ratings and review volumes of public libraries in Taiwan's six major cities. The qualitative analysis is limited to the con-tent of the comments associated with the Google Maps reviews, providing insights into user experiences and perceptions. No additional interviews or surveys were conducted as part of this study.

 

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Reviewer Comment:

Last but not least, at this stage it is critical to improve the dialogue with the literature, add proper references and discuss the results accordingly.

 

Response:

Thank you for your suggestion. We have added additional references to support our discussion and have improved the dialogue with the literature. This enhancement ensures that our findings are contextualized within the broader field of public library service evaluation.

 

Revised Paragraph:

The findings of this study align with previous research on evaluating public library services using user-generated content. Studies have shown that many public libraries are establishing an online presence and offering online services and reviews, which provide valuable insights into user satisfaction and areas for improvement [1, 2]. For example, Borrego and Comalat (2021) demonstrated the effectiveness of using Google Maps reviews to assess public library services in Spain, highlighting the importance of user feedback in service enhancement [2]. Similarly, Khan and Loan (2022) emphasized the potential of online reviews in understanding public perceptions of library services in India [3]. By situating our findings within this context, this study contributes to the growing knowledge on leveraging digital feedback for public service evaluation.

 

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References:

 

 

1. Sin, S.-C.J. and K.-S. Kim, Use and non-use of public libraries in the information age: A logistic regression analysis of household characteristics and library services variables. Library & information science research, 2008. 30(3): p. 207-215.
2. Borrego, Á. and M. Comalat Navarra, What users say about public libraries: an analysis of Google Maps reviews. Online Information Review, 2021. 45(1): p. 84-98.
3. Khan, A.M. and F.A. Loan, Exploring the reviews of Google Maps to assess the user opinions about public libraries. Library Management, 2022. 43(8-9): p. 601-615.

 

 

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

Improvements are superficial but overall acceptable.