Exploratory Listening Through Background Music: Psychological Predictors of Everyday Use
Nawras Kurzom
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsOverall assessment of the Manuscript:
This manuscript addresses a worthwhile topic: the psychological and experiential correlates of everyday background music use among Chinese adults. In its current form, the manuscript has several issues that can be improved before acceptance for publication.
-The main strength of the article is that it asks a clear general question, and uses a broad set of predictors rather than focusing on personality alone. The study also uses multivariate and tree-based models, which are positive features.
-Major comments:
1-the conceptual framing needs sharpening:
the introduction moves repeatedly between background music, environmental music and ambient music, and it also mixes private everyday listening contexts with commercial or public atmospheres. These constructs are not interchangeable, and the outcome measure here appears to reflect self-reported personal use of music while working, studying or engaging in daily activities. The literature review therefore needs to be narrowed and aligned more closeslyt with the actual dependent variable that the authors are examining. As written now, the manuscript overextends from everyday self-use to commercial, environmental, and even therapeutic implications (the design of the study doesn’t support all of these variables)
2-the reporting of the cross-sectional survey design is not yet sufficient:
the manuscript states that data were collected by an online company, but it does not adequately describe the sampling frame, recruitment method, geographic coverage, participant compensation, inclusion/exclusion criteria, missing-data handling…etc.
3-the psychometric section needs stronger justification:
the manuscript provides Chinese validations sources for some measures, but it is not clear that the the use of music inventory background use subscale and the hardiness measure were properly adapted and validated for this Chinese adult sample. This is particularly important because the dependent variable has only five items and a reported Alpha (Cronbach) of 0.62, which is somehow modest for a primary outcome measure.
4-the regression strategy might require some reconsideration:
the manuscript uses stepwise regression as a central predictive tool, but stepwise selection is widely criticized because it can yield unstable variable selection. A regression that is based on a theory (theory-driven hierarchical regression) would be more defensible. The authors should also report internal validation more transparently if they wish to use a predictive framing, and without fully satisfying the reporting expectations, the manuscript might read as an explanatory correlational study.
5-there is a serious result-level inconsistencies that must be resolved before publication:
on p.7 (table 2): fun seeking is shown as negatively correlated with background music use (r=-0.13, p<0.05). but the text describes this association as positive and the abstract also presents it as positive. In a similar way, table2 shows neuroticism as negatively correlated with background music use (r=-0.25, p<0.001). but table 3 reports neuroticism as a positive regression predictor, and the discussion alternates between interpreting neuroticism as negatively related and positively predictive: without properly explaining the discrepancy. The more likely interpretation is that there is a coding or sorting problem, and this should be fixed.
6-the decision-tree analysis is not reported convincingly and may contain major errors:
the method description and interpretation should be corrected. The statistical methods section states that because the target variable was continuous, a “likelihood ratio chi-square statistic” was applied. However, IBM’s CHAID documentation indicates that for a continuous dependent variable, the split statistics is based on an ANOVA F test, and spss reports F values for scale dependent variables. This was one example that should be fixed in relation to the reporting and description of the decision-tree analysis.
7-the decision-tree results themselves appear internally inconsistent an in places implausible. For example, the tree and accompanying text refer to a drive split of (>16.), although the stated scale structure suggests that 16 is the likely maximum possible score for that subscale. The authors need to re-check the raw output and regenerate the entire tree section from the dataset and syntax
8-the presentation of the results section needs some fixing:
Table 1 is labeled as N=322 even though the manuscript repeatedly states N=332. Also, the stepwise regression table is poorly formatted and does not clearly present (cumulative R-squared, adjusted R-squared, the full model F, standard errors or confidence intervals). And other places in the results need several corrections.
9-the discussion might need some reconsideration in the way it interprets the results:
the article moves from a cross-sectional (self-report) association study to claims about implications for music therapy interventions, industrial/commercial applications and broad functional uses of background music. The discussion should be more compatible with the corrected results, especially regarding fun seeking and neuroticism.
10-the manuscript needs a careful edit in general, including the bibliography
-Specific revisions priorities before resubmission:
Before the manuscript can be evaluated again, I strongly recommend that the authors do the following. They should first audit the dataset, scoring syntax, tables, and figure output from the beginning, because the current contradictions suggest either scoring errors or substantial transcription errors. They should then rewrite the conceptual framing so that the paper consistently studies one construct rather than moving across background, environmental, and ambient music. Next, they should improve the methods section with fuller reporting of recruitment, exclusions, missing data, and psychometric adaptation. After that, they should replace or at least justify the stepwise procedure, report multicollinearity diagnostics, and present the regression and tree analyses with complete, internally consistent output. Finally, they should rewrite the abstract, results, discussion, and conclusion so that every interpretation matches the corrected statistics exactly.
Author Response
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files. The revised parts were marked in red, and we included the page and line of the revised part. We appreciate your complimentary comments.
Point-by-point response to Comments and Suggestions for Authors
Comments 1: The conceptual framing needs sharpening: the introduction moves repeatedly between background music, environmental music and ambient music, and it also mixes private everyday listening contexts with commercial or public atmospheres. These constructs are not interchangeable, and the outcome measure here appears to reflect self-reported personal use of music while working, studying or engaging in daily activities. The literature review therefore needs to be narrowed and aligned more closesly with the actual dependent variable that the authors are examining. As written now, the manuscript overextends from everyday self-use to commercial, environmental, and even therapeutic implications (the design of the study doesn’t support all of these variables).
Response 1: We sincerely thank you for this insightful and constructive comment. We agree that the previous version of the introduction did not sufficiently distinguish between background, environmental, and ambient music, and that it extended beyond the scope of the dependent variable. In response, we have revised the manuscript to clarify the conceptual focus and ensure alignment with the outcome measure. Specifically, we have consistently defined background music as music used by individuals in everyday personal contexts and have removed or refined references to environmental, commercial, and therapeutic settings. We have also narrowed the literature review to better reflect self-directed background music use. Thank you again for your valuable feedback. [Line 34-37, 39-41, 485-488, 507-510]
While ambient music has been discussed primarily in relation to shaping the atmosphere of physical spaces (Till, 2017), background music in everyday contexts refers more specifically to music that accompanies ongoing activities and supports individuals’ engagement with their tasks.
In this sense, background music can be understood as a functional and self-directed resource that individuals use to manage their psychological states and task environments in daily life.
Similarly, living alone was associated with higher background music use, implying that music might serve as a psychological and situational resource in solitary contexts, supporting mood regulation and engagement during everyday activities (Schäfer & Eerola, 2020).
Finally, background music use was measured using a brief subscale of the Use of Music Inventory (Chamorro-Premuzic & Furnham, 2007), which might not have comprehensively captured the multifaceted nature of background music use in everyday personal contexts.
Comments 2: The reporting of the cross-sectional survey design is not yet sufficient: the manuscript states that data were collected by an online company, but it does not adequately describe the sampling frame, recruitment method, geographic coverage, participant compensation, inclusion/exclusion criteria, missing-data handling…etc.
Response 2: We appreciate your helpful comment. In response, we have clarified the sampling frame, inclusion criteria, recruitment method, geographic coverage, and participant compensation. We also specified that the survey required responses to all items prior to submission, resulting in no missing data, which is typical in structured online panel surveys. These revisions improve the transparency of the study. [Line 171-178]
Participants were recruited from the agency’s nationwide panel of registered mobile users and completed the survey online on a voluntary basis. Eligible participants were Chinese adults aged between 18 and 65 years, with individuals outside this age range excluded. The survey was structured to require responses to all items before submission, resulting in no missing data in the final dataset, which is typical in structured online panel surveys. Upon completion, participants received a small incentive in the form of reward points equivalent to approximately USD 2, following the agency’s standard compensation procedures.
Comments 3: The psychometric section needs stronger justification: the manuscript provides Chinese validations sources for some measures, but it is not clear that the use of music inventory background use subscale and the hardiness measure were properly adapted and validated for this Chinese adult sample. This is particularly important because the dependent variable has only five items and a reported Alpha (Cronbach) of 0.62, which is somehow modest for a primary outcome measure.
Response 3: We sincerely thank you for your valuable comment regarding the psychometric properties of the measures.
In response, we have strengthened the measurement section by providing additional justification for both instruments. For the background music use subscale, we clarified that although the internal consistency in the present study was modest, previous research with Chinese adult samples has reported higher reliability, and we discussed possible reasons for this discrepancy, including the small number of items and the inclusion of a reverse-scored item.
For the hardiness scale, we specified that it was appropriately translated and applied in Chinese samples and has demonstrated satisfactory reliability in prior studies with similar populations.
In addition, we have incorporated this issue into the limitations section by acknowledging the relatively modest reliability observed in the present study and emphasizing the need for developing more comprehensive and psychometrically sound measures of background music use.
[Line 196-201, 242-246, 510-514]
This study, used the background use subscale of the Use of Music Inventory (UMI) developed by Chamorro-Premuzic and Furnham (2007) to assess the participants’ use of music as a background in daily life. This subscale consists of five items, one of which is reverse scored. Example items include “I enjoy listening to music while I work” and “Music is very distracting so whenever I study, I need to have silence” (reverse scored). Participants responded on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Although the internal consistency of the background use subscale in this study was relatively modest (Cronbach’s alpha = 0.62), previous study using Chinese adult samples has reported higher reliability (e.g., α = 0.84; Zhang & Suh, 2026). The lower reliability observed in the present study may be attributable to the small number of items and the inclusion of a reverse-scored item, both of which are known to attenuate alpha coefficients (Cortina, 1993), and thus can be considered acceptable.
Psychological hardiness was measuring using a brief measure developed by Suh (2022). The scale included 12 items distributed across three subscales: commitment (four items), self-directedness (four items), and tenacity (four items). Each item is rated on a six-point Likert scale ranging from 1 (not at all true) to 6 (very true), with higher scores indicating greater psychological hardiness. This scale was translated and applied to Chinese samples following an appropriate translation procedure (Ouyang et al., 2024), supporting its linguistic and contextual suitability. In addition, previous study with Chinese adult samples has reported satisfactory internal consistency for this measure (Zhao et al., 2025), indicating its reliability in similar population. In this study, Cronbach’s alphas were 0.84 for commitment, 0.80 for self-directedness, 0.72 for tenacity, and 0.92 for the overall scale.
Finally, background music use was measured using a brief subscale of the Use of Music Inventory (Chamorro-Premuzic & Furnham, 2007), which might not have comprehensively captured the multifaceted nature of background music use in everyday personal contexts. In this study, the internal consistency of this subscale was relatively modest, although previous study with Chinese adult samples has reported higher reliability (e.g., Zhang & Suh, 2026). This discrepancy may be attributable to the limited number of items and the inclusion of a reverse-scored item, which can attenuate reliability estimates.
Comments 4: The regression strategy might require some reconsideration: the manuscript uses stepwise regression as a central predictive tool, but stepwise selection is widely criticized because it can yield unstable variable selection. A regression that is based on a theory (theory-driven hierarchical regression) would be more defensible. The authors should also report internal validation more transparently if they wish to use a predictive framing, and without fully satisfying the reporting expectations, the manuscript might read as an explanatory correlational study.
Response 4: We sincerely appreciate your thoughtful and important comment regarding the use of stepwise regression.
We fully agree that stepwise regression has limitations, particularly in terms of model stability and variable selection, and that theory-driven approaches such as hierarchical regression can provide a more robust analytical framework. At the same time, we respectfully note that research on background music use, particularly in relation to multiple psychological variables, remains relatively limited. In this context, the stepwise regression in the present study was intended as an exploratory approach to identify potential factors associated with background music use, rather than to establish a definitive predictive model.
To address your concern, we have revised the manuscript to clarify the exploratory nature of this analysis and have carefully moderated our interpretation of the findings. Specifically, we have avoided predictive claims and reframed the results as identifying potential associations. In addition, we have explicitly acknowledged the limitations of the stepwise approach in the discussion and limitations sections, and we have emphasized the need for future research to adopt theory-driven models, such as hierarchical regression, to validate and extend these findings.
We sincerely appreciate your guidance, which has helped us improve the conceptual clarity and methodological transparency of the manuscript. [Line 132-140, 290-293, 433-437, 504-507]
This study aimed to examine how personality traits, temperament (BAS/BIS), self-efficacy, and hardiness were associated with individuals’ tendency to use background music and to explore factors associated with background music use among Chinese adults. Specifically, this study addresses the following research questions: First, are there significant relationships among the Big Five personality traits, BAS/BIS, self-efficacy, hardiness, and the tendency to use background music among Chinese adults? Second, what variables are identified in the stepwise regression model as exploratory predictors of background music use? Third, what patterns can be identified using a decision tree model to describe variations in background music use?
3.2. Exploratory models for background music use
This study verified the models that explored potential predictors of background music use among Chinese adults. First, multiple regression analysis was conducted using the Big Five personality traits.
Overall, converging evidence from both analytical approaches indicates that background music use is primarily structured by arousal- and emotion-related personality dimensions. The stepwise regression analysis should be interpreted as exploratory, as it was used to identify potential predictors rather than to establish a stable predictive model.
In addition, the use of stepwise regression may involve limitations related to model stability and variable selection. Therefore, the findings from this analysis should be interpreted with caution, and future studies are encouraged to employ theory-driven approaches, such as hierarchical regression, to validate these results.
Comments 5: There is serious result-level inconsistencies that must be resolved before publication: on p.7 (table 2): fun seeking is shown as negatively correlated with background music use (r=-0.13, p<0.05). but the text describes this association as positive and the abstract also presents it as positive. In a similar way, table2 shows neuroticism as negatively correlated with background music use (r=-0.25, p<0.001). but table 3 reports neuroticism as a positive regression predictor, and the discussion alternates between interpreting neuroticism as negatively related and positively predictive: without properly explaining the discrepancy. The more likely interpretation is that there is a coding or sorting problem, and this should be fixed.
Response 5:
We thank you for your careful reading and for pointing out these important inconsistencies in the results. We sincerely apologize for the errors and any confusion they may have caused. We feel both regretful and embarrassed that these inconsistencies were not identified prior to submission.
In response to your comment, we have carefully rechecked the data and corrected the inconsistencies across the manuscript. Specifically, we revised the direction of the association for fun-seeking to reflect its negative correlation with background music use, and we corrected the interpretation of neuroticism to ensure consistency between the correlation and regression results. Corresponding revisions have been made in the abstract, results, discussion, and conclusion sections.
We greatly appreciate your attention to detail, which has helped us improve the accuracy and clarity of our manuscript. [Line 18, 405-412, 440-448, 524-525]
Among temperamental traits, fun seeking showed a small but significant negative relationship.
The positive relationship between neuroticism and background music use suggests that individuals with higher emotional reactivity may be more likely to use background music as a means of managing their affective states. Individuals high in neuroticism tend to experience greater emotional fluctuation and psychological tension, and music may function as an accessible resource for mood regulation in everyday contexts. This interpretation is consistent with previous findings that neuroticism is associated with emotion-oriented uses of music, including coping and affect regulation (Chamorro-Premuzic & Furnham, 2007).
Only the fun-seeking subcomponent of the BAS showed a small but significant negative association. Fun seeking reflects a tendency toward a spontaneous approach behavior and the pursuit of immediate enjoyment and novel stimulation (Carver & White, 1994). This negative association suggests that individuals who are more strongly oriented toward immediate pleasure and novelty may be less likely to use background music in a sustained or functional manner during everyday activities. In other words, background music use in daily life may reflect not only hedonic motivation but also a degree of task-related regulation and environmental structuring, which may not align with a more impulsive fun-seeking tendency.
Fun seeking showed a small but meaningful negative association with background music use, and specific components of hardiness, particularly commitment and self-directedness, also showed meaningful associations.
Comments 6: The decision-tree analysis is not reported convincingly and may contain major errors:
the method description and interpretation should be corrected. The statistical methods section states that because the target variable was continuous, a “likelihood ratio chi-square statistic” was applied. However, IBM’s CHAID documentation indicates that for a continuous dependent variable, the split statistics is based on an ANOVA F test, and SPSS reports F values for scale dependent variables. This was one example that should be fixed in relation to the reporting and description of the decision-tree analysis.
Response 6: We are grateful for your careful and technically insightful comment regarding the description of the decision-tree analysis. We sincerely apologize for this error and feel quite embarrassed that we inaccurately described the statistical procedure.
Upon reviewing your comment, we recognized that our original description was incorrect. As you pointed out, when the dependent variable is continuous, the CHAID algorithm in SPSS employs an F-test based on analysis of variance, rather than a chi-square statistic. We regret this oversight and appreciate you bringing it to our attention.
We have carefully revised the relevant section of the manuscript to accurately describe the analytical procedure. We hope that this correction resolves the issue and improves the methodological clarity of the study. [Line 261-262]
Because this variable was continuous, the splitting procedure was based on the F-statistic derived from analysis of variance.
Comments 7: The decision-tree results themselves appear internally inconsistent an in places implausible. For example, the tree and accompanying text refer to a drive split of (>16.), although the stated scale structure suggests that 16 is the likely maximum possible score for that subscale. The authors need to re-check the raw output and regenerate the entire tree section from the dataset and syntax.
Response 7: We greatly appreciate your careful and critical evaluation of the decision-tree analysis. We sincerely apologize for the errors and inconsistencies in this section, and we feel quite embarrassed that these issues were not identified prior to submission.
Following your comment, we thoroughly re-examined the raw data, analysis procedures, and SPSS output. We found that there were indeed errors in the reporting and interpretation of the decision-tree results, including incorrect split values and inconsistencies involving variables such as drive, neuroticism, and fun-seeking. In response, we have carefully regenerated the entire decision-tree analysis using the original dataset and syntax, and we have revised all related descriptions accordingly. The corrected results now accurately reflect the scale ranges and statistical outputs, and the inconsistencies you pointed out have been fully addressed.
We are truly grateful for your detailed feedback, which has helped us identify and correct these mistakes and significantly improve the accuracy and credibility of our manuscript. [Line 357-359, 368-369, 372-373, and Figure 1]
Participants with drive scores of 7 or lower (Node 9) had an average of 17.40. Those with drive scores between 7 and 11 (Node 10) had an average score of 15.63, and participants with drive scores higher than 11 (Node 11) had an average score of 18.17.
Participants with neuroticism scores of 9 or lower (Node 12) had an average score of 16.43, whereas those with neuroticism scores higher than 9 (Node 13) had an average score of 18.80.
Participants in Node 12 were further classified according to their fun-seeking scores. Those whose fun-seeking scores were 9 or lower (Node 16) had an average score of 18.15. By contrast, those with scores higher than 9 (Node 17) had an average score of 15.50.
Comments 8: The presentation of the results section needs some fixing: Table 1 is labeled as N=322 even though the manuscript repeatedly states N=332. Also, the stepwise regression table is poorly formatted and does not clearly present (cumulative R-squared, adjusted R-squared, the full model F, standard errors or confidence intervals). And other places in the results need several corrections.
Response 8: Thank you for your helpful comments regarding the presentation of the results.
We apologize for the typographical error in Table 1, where the sample size was incorrectly reported as 322; this has now been corrected to 332. In addition, Table 4 has been revised to address your concerns, and now includes cumulative R², adjusted R², standard errors, and overall model fit indices. We have also improved Table 3 by adding standard errors and adjusted R² values.
Corresponding revisions have been made in the main text to ensure consistency with the updated tables. [Line 314-323, 297, Table 4, Table 3]
An exploratory stepwise regression analysis is presented in Table 4. Extraversion was entered at the first step and accounted for a significant proportion of variance in background music use (β = 0.365, t = 7.13, p < 0.001), explaining 13.4% of the variance (R² = 0.134, adj. R² = 0.131, F = 50.86, p < 0.001). The inclusion of neuroticism in the second step significantly increased the explained variance by 4.3% (ΔR² = 0.043), with neuroticism emerging as a significant predictor (β = 0.214, t = 4.26, p < 0.001), resulting in a cumulative R² of 0.179 (adj. R² = 0.174, F = 35.84, p < 0.001). Finally, self-efficacy was added to the model, contributing an additional 5.2% to the explained variance (ΔR² = 0.052), and was also a significant predictor (β = 0.256, t = 4.86, p < 0.001). The final model explained 23.4% of the variance in background music use (R² = 0.234, adj. R² = 0.227, F = 33.40, p < 0.001).
The overall model was statistically significant, F(5, 326) = 14.66, p < 0.001, and explained approximately 18.4% of the variance in background music use (R² = 0.184, adj. R² = 0.174).
Comments 9: The discussion might need some reconsideration in the way it interprets the results:
the article moves from a cross-sectional (self-report) association study to claims about implications for music therapy interventions, industrial/commercial applications and broad functional uses of background music. The discussion should be more compatible with the corrected results, especially regarding fun seeking and neuroticism.
Response 9: Thank you for your insightful comment. We agree that the previous version of the manuscript extended beyond the scope supported by the cross-sectional and correlational design.
In response, we have revised the discussion to ensure that interpretations are more closely aligned with the results and the nature of the data. Specifically, we have moderated the interpretation of key findings, including those related to neuroticism and fun-seeking, and avoided overgeneralization. We have also reduced statements implying direct applications and instead emphasized the exploratory and interpretive nature of the findings.
In addition, we have revised the conclusion and the final sentence of the abstract to avoid overstating implications for interventions or commercial applications. These sections now focus on understanding patterns of background music use and informing future research rather than suggesting direct practical applications. [Line 25-26, 381-382, 412-414, 448-451, 518-519, 533-535]
The results provide useful implications for further research and offer foundational knowledge for understanding background music use in everyday contexts.
This study explored factors that may predict background music use among Chinese adults with the aim of contributing foundational knowledge for future research and providing preliminary insights into music-related practices in everyday contexts.
However, given the correlational nature of the present study, this interpretation should be considered tentative, and further research is needed to clarify the causal role of emotional reactivity in background music use.
However, given the relatively small effect size, this finding should be interpreted with caution, as temperamental approach tendencies may reflect context-specific patterns rather than a central or general determinant of background music use.
Despite these limitations, the present study provides meaningful insights into individual differences in background music use and offers a foundation for future research and for developing hypotheses about how music may function in everyday contexts.
The results provide meaningful implications for future research and offer foundational knowledge for understanding patterns of background music use in everyday life and for informing future studies in applied and real-world contexts.
Comments 10: The manuscript needs a careful edit in general, including the bibliography
Response 10: Thank you for this important comment. We agree that the manuscript requires careful editing for language, style, and bibliographic consistency.
In response, we have revised the manuscript as carefully and thoroughly as possible to improve clarity, readability, and overall presentation. Although most of the authors received their master’s and/or doctoral training in English-speaking countries, we also obtained professional English editing from Editage prior to submission. If necessary, we are willing to seek additional professional editing support during the revision process to further improve the manuscript.
We also recognize the need for careful review of the bibliography. We will continue to check the reference list closely for completeness, consistency of formatting, and possible errors, and we will add or revise references where needed.
We appreciate your comment, which has encouraged us to improve the manuscript more carefully and systematically.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe manuscript is generally understandable, but a language review would bring more clarity to the text.
This article discusses an interesting topic, but it has significant flaws that affect its reliability. The main concern seems to be the presence of internal inconsistencies in the reporting of results, or maybe, they need to be clarified to avoid, eventual, misundersantings. For exemple, the texts says that fun-seeking showed a “positive correlation (r = 0.13, p < 0.05)” (3.1), whereas Table 2 appears to report the association with background music use as negative for fun-seeking. If both are correct, please, clarify. Additionally, in the decision-tree results on page 11, nodes 12 and 13, seems no be correct, since scores “<31” versus “>58”, seems a binary split that is not consistent with the rest of the three. It seem a middle level (>31 to <58) is missing. If not, explain. Between table 1 and table 2, the number of participants sems not consistent (table 1: N=322; table 2: N=332). These issues undermine confidence in the interpretation of the findings.
There are also some problems of clarity and bibliographic consistency. for example Suh (2002) in the text but Suh (2022) in the references; Nadon et al. (2021) is cited in the text but does not appear in the references.
the study has potential, but the manuscript requires substantial revision before it can be assessed positively.
Author Response
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files. The revised parts were marked in red, and we included the page and line of the revised part. We appreciate your complimentary comments.
Point-by-point response to Comments and Suggestions for Authors
Comments 1: This article discusses an interesting topic, but it has significant flaws that affect its reliability. The main concern seems to be the presence of internal inconsistencies in the reporting of results, or maybe, they need to be clarified to avoid, eventual, misunderstandings. For example, the texts says that fun-seeking showed a “positive correlation (r = 0.13, p < 0.05)” (3.1), whereas Table 2 appears to report the association with background music use as negative for fun-seeking. If both are correct, please, clarify. Additionally, in the decision-tree results on page 11, nodes 12 and 13, seems no be correct, since scores “<31” versus “>58”, seems a binary split that is not consistent with the rest of the three. It seem a middle level (>31 to <58) is missing. If not, explain. Between table 1 and table 2, the number of participants seems not consistent (table 1: N=322; table 2: N=332). These issues undermine confidence in the interpretation of the findings
Response 1: Thank you for your careful reading of our manuscript and for pointing out these important issues. We sincerely apologize for the errors and inconsistencies, and we feel quite embarrassed that these were not identified prior to submission.
Following your comments, we carefully re-examined the dataset, statistical outputs, and the manuscript. First, the association between fun-seeking and background music use was incorrectly described in the text; the correct result indicates a negative correlation, and all related descriptions have been revised accordingly. Second, we identified errors in the reporting of the decision-tree results, including incorrect split values for neuroticism, drive, and fun-seeking. We have therefore regenerated the decision-tree analysis and corrected all corresponding descriptions. Third, the discrepancy in sample size between Table 1 and Table 2 has been corrected, as the correct sample size is N = 332.
We greatly appreciate your detailed feedback, which helped us identify and correct these issues and improve the accuracy and clarity of the manuscript. [Line 17, 441, 524-525, 357-359, 368-369, 372-373, Table 1, and Figure 1]
Among temperamental traits, fun seeking showed a small but significant negative relationship
Only the fun-seeking subcomponent of the BAS showed a small but significant negative association.
Fun seeking showed a small but meaningful negative association with background music use, and specific components of hardiness, particularly commitment and self-directedness, also showed meaningful associations.
Participants with drive scores of 7 or lower (Node 9) had an average of 17.40. Those with drive scores between 7 and 11 (Node 10) had an average score of 15.63, and participants with drive scores higher than 11 (Node 11) had an average score of 18.17.
Participants with neuroticism scores of 9 or lower (Node 12) had an average score of 16.43, whereas those with neuroticism scores higher than 9 (Node 13) had an average score of 18.80.
Participants in Node 12 were further classified according to their fun-seeking scores. Those whose fun-seeking scores were 9 or lower (Node 16) had an average score of 18.15. By contrast, those with scores higher than 9 (Node 17) had an average score of 15.50.
Comments 2: There are also some problems of clarity and bibliographic consistency. for example Suh (2002) in the text but Suh (2022) in the references; Nadon et al. (2021) is cited in the text but does not appear in the references.
Response 2: Thank you for your careful observation regarding the issues of clarity and bibliographic consistency. We sincerely apologize for these oversights.
The citation “Suh (2002)” in the text was a typographical error and has been corrected to “Suh (2022)” to ensure consistency with the reference list. In addition, we regret that Nadon et al. (2021) was inadvertently omitted from the references. This has now been added to the reference list as follows: [Line 239, 618-619]
We have also carefully reviewed the entire manuscript to improve consistency and accuracy in citations and references.
Thank you for helping us improve the clarity and quality of our manuscript. Although most of the authors received their master’s and/or doctoral training in English-speaking countries, we also obtained professional English editing from Editage prior to submission. If necessary, we are willing to seek additional professional editing support during the revision process to further improve the manuscript.
Psychological hardiness was measuring using a brief measure developed by Suh (2022).
Nadon, É., Tillmann, B., Saj, A., & Gosselin, N. (2021). The emotional effect of background music on selective attention of adults. Frontiers in Psychology, 12, 729037. https://doi.org/10.3389/fpsyg.2021.729037
Author Response File: Author Response.pdf
