The Impacts of Remote Work and Attitudinal Shifts on Commuting Reductions in Post-COVID Melbourne, Australia

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: The description of the data acquisition process in this paper is too simplistic. Can the authors provide a more detailed explanation of how the survey was designed? Especially regarding specific measures to ensure sample representativeness and attract respondents to participate in the survey.

Response 1: The explanation below was added to the paper in line  193-203

The survey, which takes 15-20 minutes to finish, was conducted by The Online Research Unit. It was dis-tributed to 2,327 panel members, resulting in 775 eligible respondents who completed and submitted the survey. To ensure that our sample accurately reflected the population of Greater Melbourne, we established quotas based on geographic and demographic characteristics derived from employment records in the 2021 Census. Greater Melbourne is divided into 8 statistical areas, each representing different geo-graphic and demographic segments. Our goal was to maintain representativeness across these diverse are-as. The survey company took several steps to ensure the survey was both engaging and accessible, offering clear incentives to attract participants from various areas and demographic groups. To boost response rates, particularly in underrepresented areas, they employed tailored outreach strategies, including multiple rounds of reminders and targeted communication. Additionally, the company followed up with respondents after the initial survey to clarify any issues and gather missing answers, ensuring the data collected was as complete and accurate as possible.

Comments 2: Why did you choose factor analysis and cluster analysis? Have you considered other statistical methods? If so, please discuss why they are not suitable for this study.

Response 2 The discussion below was added to the paper in line  249-258

Factor analysis is a technique used to model the covariation among a set of observed variables by identify-ing one or more underlying latent constructs. These constructs, such as satisfaction or comfort, are unob-servable but theoretically justifiable entities, making them "latent" because they cannot be directly meas-ured or observed. Given that our study focuses on understanding the latent factors influencing work-ing from home and commuting behavior, factor analysis was employed as it is particularly well-suited for dealing with latent variables. Furthermore, factor analysis is particularly valuable as a dimension reduc-tion technique in situations where interpretation is important. Unlike many other methods, which often provide limited insight into how the transformed features were created or what they represent, factor anal-ysis offers contextual information about the contribution of each input feature to each factor. This addi-tional context allows for a deeper understanding of what the transformed features represent and how they relate to the original data

Comments 3: The author selected several variables as factors to be considered in factor analysis, but how were these variables determined? Have you considered other factors that may affect remote work and commuting behavior?

Response 3 The variables selected for the factor analysis in our study were determined through a thorough review of existing literature on remote work and commuting behaviour. We identified key factors that have been frequently studied and validated in previous research. These variables were chosen based on their relevance and significance as highlighted in the literature.

Regarding variables related to mode choice, the literature is well-established, emphasizing the importance of factors such as efficiency, reliability, comfort, cost, and safety. These factors were deemed critical in determining the mode of transportation and were thus included in our analysis.

In addition to the factors included in our factor analysis, we acknowledge that other variables could influence remote work and commuting behavior. For future research, it would be valuable to consider additional factors such as Mental and Physical Health, and Job Satisfaction

Comments 4:  Through calculations, the authors obtained some results and conclusions from the model, but from a statistical perspective, whether these results and conclusions will be limited by the sample under investigation. In other words, how can we ensure the reliability of the conclusions derived from this article?

Response 4                                 Line 506-514

Sample Representativeness: Our sample is representative of the Greater Melbourne population, enhancing the external validity and generalizability of our findings.

Appropriate Statistical Tests: We employed appropriate statistical tests, including Bartlett's test of sphericity to assess the suitability of the data for factor analysis. The significant result from Bartlett’s test (chi-square statistic of 3507.8716 with a p-value of 0.000) confirms that the correlation matrix is significantly different from the identity matrix, indicating that our data is appropriate for factor analysis.

Bayesian Information Criterion (BIC): BIC was used to determine the optimal number of factors, balancing model fit with complexity to avoid overfitting and ensure a robust model.

Smallest Class Percentage: We ensured that the smallest class percentage was adequately represented to validate that the factor structure is meaningful and not unduly influenced by insignificant classes

Comments 4: What specific policy recommendations can be provided to transportation decision-makers and urban planners based on the research results of this article? How should these issues be further explored in future research?

Response 5          Extra lines were added 741-747 and 757-761

Policies should prioritize maintaining the flexibility and reliability of work-from-home (WFH) arrangements, as expanding public transport should not come at the expense of these benefits. Doing so could undermine the positive impact on reducing car congestion. To reduce car usage, it is essential to focus on initiatives and policies that make car commuters more willing to switch to public transport by improving its reliability, efficiency, and convenience, making it a viable and attractive alternative to driving. Additionally, public transport must be perceived as reliable and efficient as car travel, requiring investments in service quality, such as reducing delays and increasing frequency, to make it a competitive option. For future research, it is essential to explore public transport more extensively. This should include investigating factors such as the number of transfers required, crowding levels, and the reliability of different modes like trains, trams, and buses. By understanding these aspects in greater detail, researchers can identify the best areas to target for improvements, ultimately making public transport a more attractive and competitive option for current car commuters.

Comments 6: Given the changes in the COVID-19 pandemic, how to evaluate the validity and timeliness of current data? How to ensure that the research results and conclusions can be applied to the future, as well as other cities?

Response 5

COVID-19 was indeed a significant shock to the system, with its impact varying greatly across different cities and countries. In Australia, for instance, only Melbourne and Sydney experienced the full severity of COVID-19 restrictions, which led to a greater appreciation for working from home among their populations. This localized experience means that the results observed in Melbourne, such as the prioritization might not be directly applicable to other cities, even within Australia.

However, there is a degree of commonality in commuting behavior that suggests certain trends might emerge in other cities if working from home were to become more popular. For example, the importance of work-life balance and time savings is likely to resonate broadly, as these factors are universally valued. Yet, the emphasis on saving travel costs, which has become particularly pressing in Melbourne due to the current high cost of living, might not hold the same priority in the future or in other cities with different economic conditions.

This context-sensitive understanding acknowledges that while certain findings from Melbourne can provide insights, their applicability elsewhere must be considered cautiously, with attention to local conditions and future developments.

Comments 1: The text in the manuscript has not been arranged using the template for the Sustainability journal.

Response 1: The manuscript now has been formatted using the template for Sustainability journal  

Comments 2: he citations in the text, for example, from the line 124, are not correct. Please check and revise the citation one by one in the text. The author can follow the instruction given in the journal template to fix this issue.

Response 2  The in-text citation were fixed to follow the format of Sustainability Journal

Comments 3: Please follow the journal's requirements and modify the format of all figures and tables

Response 3 Figures and tables headings were updated to follow journals requirements.

Comments 1: Line 118- How did you do the literature review? Did you use ChatGBT? Please add all the relevant information. How many articles? How did you choose the articles?

Response 1: Thank you for your comment. To clarify, we did not use ChatGPT or any other AI tools for developing the literature review. There was a systematic approach employed. The review was conducted through extensive research on the topic, focusing on finding and including relevant information that would solidify our research. We reviewed a range of academic articles related to commuting behavior, remote work, and public transport efficiency. This approach ensured that the included literature provided valuable context and supported the findings of our study.

We appreciate your feedback and hope this provides clarity on our methodology and literature selection.

Comments 2: Line 153- Despite what? You have positive elements before and you highlight positive elements

Response 2  It is now corrected as there was a redundant sentence.

Comments 3: Line 175- were conducted, in order to evaluate

Response 3 Corrected.

Comments 4: The references in text appear in 2 ways: both with names and with number. Only one style can be used. Please modify.

Response 4 Corrected.

Comments 5: please move Table 2 after the information about it.

Response 5 Moved.

Comments 6: Line 278- was employed.

Response 6 Corrected.

Comments 7: For figure 2 what is not applicable ? What do you refer by that? Please add that information

Response 7 Thank you for your question. The "Not Applicable" option in Figure 2 was included to distinguish between two distinct groups. Specifically, it separates individuals who can potentially work from home but do not do so from those whose jobs are not flexible enough to allow for any remote work. This distinction helps to clarify the responses by indicating that some individuals may have the option to work from home but choose not to, while others may not have the flexibility to work from home at all. Including this option ensures a more accurate representation of the data regarding remote work flexibility and preferences.

Comments 8: Line 504- the writing must start under the table

Response 8 Corrected.

Comments 9 Line 678, 680- please explain this much sooner in the paper, preferably when it first appears

Response 9   in line 600 of the original manuscript there was a mention to this aspect. However the discussion link this to other related results.

Line 600 “Figure 18 reveals that the cluster of variables associated with reduced PT trips to work includes those who work in the CBD, those who are currently using PT to commute, and those who have a positive experience with PT, as represented by the factor "PT Reliability" (F_2_Pos_Pt)”

Comments 10: Reference 13 is not written correctly

Response 10 Corrected.

Comments 1: Can you please argue why you assigned your research to white-collar employees?

the number of white collar employees in times less than Blue-collar employees. So you have to explain and prove your selection. Just saying that blue-collar worker shall be in workplace physically not enough

Response 1: Thank you for your question regarding the focus on white-collar employees in our research. To clarify, our study did not exclusively include only white-collar employees. We designed our research criteria to encompass all individuals who met the specific conditions relevant to our study, rather than limiting it to any single occupational group.

The selection criteria for our survey were defined in the context of transport modeling, particularly focusing on the ability to work from home under extreme circumstances, such as COVID-19 restrictions. This criterion was crucial for understanding the impact of remote work on commuting behaviors. Figure 1 in our paper illustrates the filtering process we used to select participants. Specifically, we excluded individuals who answered "no" in the initial screening steps but included everyone else who met the criteria related to remote work capabilities and commuting patterns.

Our approach aligns with the methodology described in Reference [1], which details the justification for using such criteria to examine the effects of remote work on commuting behaviors. This reference outlines the rationale behind including individuals based on their ability to work from home and the implications for transport modeling.

We appreciate your feedback and hope this explanation clarifies our approach to participant selection.

Comments 2: you have to write full description of CBD for the first time in the article.

Response 2  Corrected

Comments 3: It is quite strange to make a research based on 1 month results (August 2019 and August 2023). it is impossible to validate the research based on this. You shall compare at least 3 months in different seasons (spring, automn/winter and Summner)and in difference 3-5 years (2018, 2019 before pandemic and 2022, 2023 and possibly 2024 after pandemic.

Response 3 Thank you for raising this important point regarding the timeframe of our data. We have now replaced the table with a figure that shows the public transport patronage from 2018 to 2024.

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Comments 4: The average  from table 1 is  27.8(32+27+25+27+28 / 5 = 27.8)

Please check

Response 4 Corrected.

Comments 4: Too difficult to understand this chart. Un-logic to have 3% at the location of 80%. Please redesign it.

Same for next one.

Response 4 Redesigned.

Comments 5: I can not find anything about the impact of WFH on the quality of the employees performance and the quality of performed work.

Comments 6: What are the effects of WFH on employee psychology? Many social psychology researchers have proven that the number of divorces/suicides has increased several times during the lockdown period

Response 5 Unfortunately our study is more concerned with the impact of working from home on commuting mode choice. However, we have included factors related to well-being that measure happiness, life fulfilment, life satisfaction and stress which we believe should cover some of the adverse effects that mentioned above. As for the productivity and employee performance we did have questions that of some relevant few examples below

1-It is more challenging to ensure  team members stay productive and accountable when working remotely

2-Working from  home boosts my productivity more than working from the office

3- I find it easier to manage my team's tasks and priorities remotely