The Influence of Content Presentation on Users’ Intention to Adopt mHealth Applications: Based on the S-O-R Theoretical Model

Round 1

Reviewer 1 Report

Thank you to the authors for their efforts in writing this paper. Although the topic is interesting, I have some concerns as follows:

1. The main reference for deriving the concept of content presentation (Qiu et al. (2016) [67]) is not from reputable journal. Even, no papers have cited it.

2. It is not clear, how did the authors come up to the three dimensions of content presentation in m-health application. I do understand, the authors referred from the works of Qiu et al. (2016) [67] and the ICA classification, however, they classified the content presentation dimensions in different terms and purpose.

3. I also didn't get the meaning of each content presentation dimension, as it seems that the concepts are very broad in their scopes. I do understand, the authors for example, refer to "Tuncer, İ. The relationship between IT affordance, flow experience, trust, and social commerce intention: An exploration using the SOR paradigm. Technol. Soc. 2021, 65, 101567. 619 " for platform information, however, they used the concept of "visibility" to point how the platform can make the product attribute more visible to the consumers (detailed picture, etc). The same also applies for other dimension such as "relational information", where the authors used indicators such as "Health knowledge is important to me" which is not relevant to the relational information".

4. As my previous concerns, I feel the indicators used were not reflecting the intended concept. For example:

- "Health knowledge is important to me" is not relevant for representing the relational information. Given the indicators are not appropriate, the instrument is incorrect, thus the research results are also not sound.

- What are the differences between intention to participate and recommend? Both variables share the same indicator "recommend/share". For example, in the variable "willingness to participate" you used "I will forward and share information about mHealth", which is actually a form of recommendation (willingness to recommend).

5. There are some problems with the statistical computation used in this research.

- Please report the common method bias test

- The values of loading factors and AVE somehow are bellow accepted standard (loading factors is > 0.7 and AVE > 0.5). Some references allow loading factor > 0.5, however, the values of AVE should above 0.5 (whereas some of your AVE  values < 0.5).

6. The discussion is shallow. The authors should put the context of their findings into body of literature.

Author Response

Dear Reviewer,

We would like to thank you for your valuable comments. We have revised the paper according to your comments and suggestions. Changes in the revised manuscript are marked in red. The responses to your comments are detailed below:

Reviewer 1:

Thank you to the authors for their efforts in writing this paper. Although the topic is interesting, I have some concerns as follows:

Q1. The main reference for deriving the concept of content presentation (Qiu et al. (2016) [67]) is not from reputable journal. Even, no papers have cited it.

Response: Thank you very much for your kind reminding. We agree with your comment about Qiu et al. (2016) [67]. Our idea about the dimensions of content presentation indeed comes from Qiu et al. (2016) published on a Chinese journal. However, in order to facilitate readers’ understanding and traceability of content presentation, we have now replaced Qiu et al. (2016) and supplemented 4 articles on content presentation from reputable journal (pp.4, in red). The newly added references are listed below:

[1] Ren, X.; Zhai, Y.; Song, X.; Wang, Z.; Dou, D.; Li, Y. The application of mobile telehealth system to facilitate patient information presentation and case discussion. Telemedicine and e-Health. 2020, 26, 725-733.

[2] Nguyen, M.H.; Smets, E.M.; Bol, N.; Loos, E.F.; Van Weert, J.C. How tailoring the mode of information presentation influences younger and older adults’ satisfaction with health websites. Journal of health communication. 2018, 23, 170-180.

[3] Guo, F.; Yang, Y.; Gao, Y. Optimization of visual information presentation for visual prosthesis. International journal of biomedical imaging. 2018, 2018.

[4] Fisher, K.C.; Haegeli, P.; Mair, P. Impact of information presentation on interpretability of spatial hazard information: lessons from a study in avalanche safety. Natural Hazards and Earth System Sciences. 2021, 21, 3219-3242.

Q2. It is not clear, how did the authors come up to the three dimensions of content presentation in m-health application. I do understand, the authors referred from the works of Qiu et al. (2016) [67] and the ICA classification, however, they classified the content presentation dimensions in different terms and purpose.

Response: Thanks to the reviewer’s insightful concerns. We have removed Qiu et al. (2016) and Lovejoy and Saxton (2012) from the manuscript. The platform information presentation is adapted from Quintero Johnson et al. (2017) and Veltri et al. (2020), the guidance information presentation from Wang et al. (2020), and relational information presentation from Heycke & Gawronski (2020) and Córdova et al. (2019) (pp.4, in red). The newly added references are listed below:

[1]Quintero Johnson, J.M.; Yilmaz, G.; Najarian, K. Optimizing the presentation of mental health information in social media: the effects of health testimonials and platform on source perceptions, message processing, and health outcomes. Health communication. 2017, 32, 1121-1132.

[2] Veltri, G.A.; Lupiáñez-Villanueva, F.; Folkvord, F.; Theben, A.; Gaskell, G. The impact of online platform transparency of information on consumers’ choices. Behavioural Public Policy. 2020, 1-28.

[3] Wang, Z.; Bai, X.; Zhang, S.; He, W.; Zhang, X.; Yan, Y.; Han, D. Information-level real-time AR instruction: a novel dynamic assembly guidance information representation assisting human cognition. The International Journal of Advanced Manufacturing Technology. 2020, 107, 1463-1481.

[4] Heycke, T.; Gawronski, B. Co-occurrence and relational information in evaluative learning: A multinomial modeling approach. Journal of Experimental Psychology: General. 2020, 149, 104.

[5] Córdova, N.I.; Turk‐Browne, N.B.; Aly, M. Focusing on what matters: Modulation of the human hippocampus by relational attention. Hippocampus. 2019, 29, 1025-1037.

We hope you find that the source of three dimensions of content presentation in mHealth APPs is clear in this revision.

Q3. I also didn't get the meaning of each content presentation dimension, as it seems that the concepts are very broad in their scopes. I do understand, the authors for example, refer to "Tuncer, İ. The relationship between IT affordance, flow experience, trust, and social commerce intention: An exploration using the SOR paradigm. Technol. Soc. 2021, 65, 101567. 619 " for platform information, however, they used the concept of "visibility" to point how the platform can make the product attribute more visible to the consumers (detailed picture, etc).

Response: Thank you very much for your comments. We have now revised the manuscript accordingly and added a graph (see Figure 1). In this study, content presentation is an approach to attract its users to the relative activities by the mHealth providers. In the field of IT, information disclosures can be categorized as presentation format and information content. We focus on the effect of the information content on users’ intention to adopt mHealth APPs. Therefor, we classify the information content as platform information, guidance information, and relational information. Among them, platform information includes the basic information about the platform and medical-related information which help users understand the advantages or service features of the platform and medical services. Guidance information refers to the information which guides visitors to participate in the marketing activities of mHealth APPs. Relational information is mainly used for communication and maintenance of relationships with users.

Figure 1 Classification of content presentation

We hope you find that the current statement is clear and complete.

Q4. The same also applies for other dimension such as "relational information", where the authors used indicators such as "Health knowledge is important to me" which is not relevant to the relational information". As my previous concerns, I feel the indicators used were not reflecting the intended concept. For example:

- "Health knowledge is important to me" is not relevant for representing the relational information. Given the indicators are not appropriate, the instrument is incorrect, thus the research results are also not sound.

- What are the differences between intention to participate and recommend? Both variables share the same indicator "recommend/share". For example, in the variable "willingness to participate" you used "I will forward and share information about mHealth", which is actually a form of recommendation (willingness to recommend).

Response: Many thanks for your valuable comments. Sorry for the mistakes. Regarding the item “Health knowledge is important to me”, we realize that we used the wrong word “Health knowledge” in this context, instead, it should be “Dissemination of health knowledge is important.” (pp.22, in red). The relational content of presentation could help users acquire health knowledge and intensify their connection to the mHealth APPs. The dissemination of health knowledge on the platform could help users better understand the products and service of the mHealth, boosting their awareness and attachment to the mHealth APPs.

As for the second part of the comment, we have compared the differences between willingness to participate and recommend. Willingness to participate refers to users’ personal subjective judgments about the possibility of seeking for online medical care through mHealth APPs. Willingness to recommend refers to the behavior of existing users to recommend a product to new users. Chen and Tsai (2007) proposed a concept of intention to adopt that includes willingness to recommend as “a customer’s judgment of the likelihood of purchasing the same product or recommending it to others”. In the mHealth context, we defined willingness to recommend as a user’s willingness to recommend the mHealth application to other users (pp.6-7, in red). As for “I will forward and share information about mHealth”, we are sorry for the mistake. The correct sentence should be “I would like to utilize the mHealth application, and also share this to others for their benefit” (pp.22, in red).

Q5. There are some problems with the statistical computation used in this research.

- Please report the common method bias test

- The values of loading factors and AVE somehow are bellow accepted standard (loading factors is > 0.7 and AVE > 0.5). Some references allow loading factor > 0.5, however, the values of AVE should above 0.5 (whereas some of your AVE  values < 0.5).

Response: Many thanks for your careful review. According to Fornell and Larcker, if AVE is less than 0.5 but combined reliability is higher than 0.6, the convergent validity of the construct is still adequate (Fornell & Larcker, 1981). In this study, although Average Variance Extracted (AVE) was 0.4, the CR values of other variables were all greater than 0.6 (0.68~0.90), which met the internal consistency requirements of each factor, indicating that the questionnaire had good reliability. The two relevant references are listed below:

[1] Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research. 1981, 18, 39-50.

[2] Lam, L.W. Impact of competitiveness on salespeople's commitment and performance. Journal of Business Research. 2012, 65, 1328-1334.

Q6. The discussion is shallow. The authors should put the context of their findings into body of literature.

Response: Thank you again for your careful review. We added several papers and compared them with our findings (pp.13-14, in red). The newly added references are listed below:

[1] Cheng, H.F.; Wang, R.; Zhang, Z.; O'Connell, F.; Gray, T.; Harper, F. M.; Zhu, H. Explaining decision-making algorithms through UI: Strategies to help non-expert stakeholders. In Proceedings of the 2019 chi conference on human factors in computing systems. 2019, May, 1-12.

[2] Sullivan, Y.W.; Kim, D.J. Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments. International Journal of Information Management. 2018, 39, 199-219.

[3] Alshurideh, M.; Salloum, S.A.; Al Kurdi, B.; Monem, A. A.; Shaalan, K. Understanding the quality determinants that influence the intention to use the mobile learning platforms: A practical study. International Journal of Interactive Mobile Technologies. 2019, 13(11).

We hope you find that the current discussion is clear and complete.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper investigates the influence of content presentation on users’ intention to use mhealth apps. The topic is valid and up-to-date.

 

Prior work:

 

Serial references (e.g., [21-24] or [9-12]) are not precise. It would be good to add a few words about each of these publications, respectively.

 

Methods:

 

Regarding the methodology, the authors base their work on the Stimulus-Organism-Response (S-O-R) model, with the content presentation of mHealth APPs being regarded as an external stimulus (S), the user’s internal experience being regarded as an organism with internal cognition (O), and the users’ intention to adopt is the behavioral response (R). Although not new, the chosen model suits the aims of the research. 

 

Figure 1 presents the adopted research model, but it's missing the hypotheses (H1-H11). In my opinion they should be added to the figure, as edge labels.

 

The questionnaire used in the survey is attached in the appendix.

 

The data were obtained from 230 valid questionnaires, which is a sufficient number. However, information is missing on the sampling process, it is only stated that the “snowball” method was used, but not explained who were the initial respondents recruited, and how the snowballing process progressed.

 

Results:

 

Although data on Gender, Age, Education etc. were gathered, they were not exploited in any way. And it would be quite valuable to find out whether there were significant differences between males and females, young and old users, or those having and not having higher education.

 

Figure 2 could be improved with adding hypotheses (H1-H11) to labels, as with Fig. 1.

 

In the discussion of limitations, the lack of random sampling is ignored, and this is a serious limitation.

 

Technical remarks:

 

There are missing spaces in some places, e.g.:

[21-24].Studies => [21-24]. Studies

[45].Cao => [45]. Cao

 

There are supefluous spaces in some places, e.g.:

2.2. 2 => 2.2.2

 

Author Response

Dear Reviewer,

We would like to thank you for your valuable comments. We have revised the paper according to your comments and suggestions. Changes in the revised manuscript are marked in red. The responses to your comments are detailed below:

Reviewer 2:

The paper investigates the influence of content presentation on users’ intention to use mhealth Apps. The topic is valid and up-to-date.

Prior work:

Serial references (e.g., [21-24] or [9-12]) are not precise. It would be good to add a few words about each of these publications, respectively.

Response: Many thanks for your helpful suggestions. We have rewritten this part. We elaborated on the studies on the content presentation of mobile APPs in detail. We also analyzed the influence between content presentation and users’ intention behavior (pp.2, in red). To provide more supporting sources to our study, several recent pertinent studies have added into the manuscript as listed below:

[1]           Wrisberg, A. Investigating the Effect of Information Presentation Alternatives on Email Pervasiveness and on Awareness of, Attitudes Towards, and Willingness to Seek and Recommend Help for Mental Illness in South Africa. 2022.

[2]           Zhang, D.; Yoon, S. Social media, information presentation, consumer involvement, and cross-border adoption of pop culture products. Electronic Commerce Research and Applications. 2018, 27, 129-138.

[3]           Van Berkel, N.; Goncalves, J.; Russo, D.; Hosio, S.; Skov, M.B. Effect of information presentation on fairness perceptions of machine learning predictors. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021, May, pp. 1-13.

[4] Al-Hazmi, N.M. Social Networks Apps and their Role in Tourism Marketing in the Kingdom of Saudi Arabia. International Journal of Interactive Mobile Technologies. 2021, 16(20).

[5]  Roosan, D.; Li, Y.; Law, A.; Truong, H.; Karim, M.; Chok, J.; Roosan, M. Improving medication information presentation through interactive visualization in mobile Apps: human factors design. JMIR mHealth and uHealth. 2019, 7, e15940.

[6]  Daly, L.M.; Boyle, F.M.; Gibbons; K., Le, H.; Roberts, J; Flenady, V. Mobile applications providing guidance about decreased fetal movement: Review and content analysis. Women and Birth. 2019, 32, e289-e296。

Methods:

Regarding the methodology, the authors base their work on the Stimulus-Organism-Response (S-O-R) model, with the content presentation of mHealth Apps being regarded as an external stimulus (S), the user’s internal experience being regarded as an organism with internal cognition (O), and the users’ intention to adopt is the behavioral response (R). Although not new, the chosen model suits the aims of the research.

Response: Thank you very much for your time handling our submission.

Figure 1 presents the adopted research model, but it's missing the hypotheses (H1-H11). In my opinion they should be added to the figure, as edge labels.

Response: Many thanks for your helpful suggestions. We have now added the hypotheses (H1-H11) to the figure in the manuscript accordingly (see Figure 1).

Figure 1 Research Model

The questionnaire used in the survey is attached in the appendix.

The data were obtained from 230 valid questionnaires, which is a sufficient number. However, information is missing on the sampling process, it is only stated that the “snowball” method was used, but not explained who were the initial respondents recruited, and how the snowballing process progressed.

Response: Many thanks for your careful review. We have added an explanation of the snowballing process in the 3.2. Sample selection and data collection (pp.8, in red). This study used snowball sampling method to collect questionnaires randomly. First, the questionnaire was designed and formed a link or QR code on a professional questionnaire website (Questionnaire Star). Then, 30 respondents known to the authors of the paper, including civil servants, medical workers, students, teachers, freelancers and retirees, were randomly selected and invited to fill in the questionnaire, while they were asked to send the questionnaire link to their WeChat friend and invite their friends and relatives to fill in the questionnaire. In addition, to guarantee the reliability and validity of the questionnaire, there is a screening question at the beginning of the questionnaire: “Have you ever used mobile medical APP?” If you answered “no”, the respondent does not need to finish the questionnaire.

We hope you find that he “snowball” method is clear and complete.

Results:

Although data on Gender, Age, Education etc. were gathered, they were not exploited in any way. And it would be quite valuable to find out whether there were significant differences between males and females, young and old users, or those having and not having higher education.

Response: Many thanks for your careful review. We double checked this issue, and found that the demographical data, such as gender, age, and education, were exploited in the 4.1. Reliability and validity test (pp.8-9, in red). The results indicate that the participants have the ability to effectively fill in the questionnaire, which ensures the effectiveness of data collection to a certain extent.

Figure 2 could be improved with adding hypotheses (H1-H11) to labels, as with Fig. 1.

Response: Many thanks for your clear guidance. We have added the hypotheses (H1-H11) to the figure  in the manuscript accordingly (see Figure 2).

Figure 2 Structural equation test results (standardized path coefficient model)

Note: The dashed line indicates that the path relationship did not pass the hypothe-sis test.

In the discussion of limitations, the lack of random sampling is ignored, and this is a serious limitation.

Response: Thank you for your careful review. Our study used snowball sampling method to collect questionnaires randomly. At the same time, we have added the method of “scenario experiment + questionnaire random sampling survey” can be further combined to investigate the effect of the content presentation of mHealth APPs on intention of adoption of different user groups in the discussion of limitation (pp.15-16, in red).

Technical Remarks:

There are missing spaces in some places, e.g.:

[21-24].Studies => [21-24]. Studies

[45].Cao => [45]. Cao

There are supefluous spaces in some places, e.g.:

2.2. 2 => 2.2.2

Response: Thank you again for your careful review. We have now revised the manuscript accordingly (pp.2-5, in red).

We highly appreciate the opportunity of resubmitting our manuscript, and hope you will find that the revised manuscript satisfactorily addresses your concerns.

Author Response File: Author Response.pdf

Reviewer 3 Report

Thank you for the opportunity to review this paper. Moving away from TAM/UTAUT and derivatives, this paper applies an alternative theory (S-O-R) to develop and apply a new model to evaluate different factors that impact adoption and use of mHealth apps, focussing on ‘content presentation’.   

The greatest difficulty with the paper in its current form is related to context. At a minimum, I would strongly urge the authors to consider the following suggestions:

a)      Insert into the Introduction a brief description and summary of TAM and UTAUT, and their application (in the global healthcare literature) to explain adoption and use of mHealth (and its associated apps). Be sure to reference this literature using appropriate and current references.

Rationale: Within the healthcare sector there is a wealth of literature related to adoption of mHealth (and related apps) that focusses on use of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) model and their derivatives. This has not been spoken about in this paper, and the current research must be placed in context and in relation to it.

b)      Following this, insert a brief segment noting the acknowledged limitations of TAM and UTAUT (and derivatives), and that the literature calls for alternate approaches to explain adoption and use of mHealth (and its associated apps). Indicate that this paper offers just such an alternative approach, using another theory and model and by focussing on three aspects of ‘content presentation’ of mobile apps.

Rationale: This provides (particularly for those in the healthcare sector) a valid reason for the research.

[The above items should require no more than a paragraph to accomplish].

c)       Insert in the Introduction (perhaps around page 2, first paragraph) a sentence or two noting that much research ignores the commercial nature of many apps, which is inappropriate. Healthcare as a whole is a business and entities that develop apps must promote and explain their products through the ‘content presentation’ of their apps, making this research of importance.

Rationale: Those in healthcare often skirt around or ignore this fundamental fact, and pointing out the reality may help place the paper in better context.

d)      Endeavour to seek and replace many of the existing references with more current references that appropriately support the text.

a.       Rationale: Currently about ~80% of the cited references were published during or prior to 2017. In such a dynamic field as mHealth adoption and use, five years is a long time.

e)      Consider the other comments / suggestions made below, which will help in terms of the content and presentation of the paper (see the attached annotated file).

General Comments:

1.       It should be clearly stated in the Methods that the study uses Structural Equation Modelling, providing a suitable reference and a brief description of its utility included in a sentence or two. Currently the uninitiated are presented with SEM approaches and terms in the Results (‘Empirical analysis’) section with no prior explanation.

2.       The Materials and Methods section strays into presentation of ‘results’ (see below), but this may be excusable given the format used for the paper (i.e., ‘Empirical analysis’ rather than ‘Results’).

“….316 questionnaires were collected through a “snowball” method, of which 230 were valid questionnaires, with an effective rate of 72.8%. In addition, from the demographical data of the collected questionnaires, it is found that most of the mHealth APP users are young and well-educated, with 156 under the age of 40, accounting for 67.8%, and 162 with bachelor degree or above, accounting for 70.4%, which shows that users with higher education have a friendly attitude towards mHealth APPs and are more likely to seek for online treatment.”

3.       There are several places where insertion of a line space would make presentation of the paper much easier to read. These locations have been identified.

Specific Comments:

1.       In Table 4, I would recommend replacing the term ‘Not Support’ with ‘Fail’ as used in the text. The current presentation is very awkward and potentially misleading, seeming to say ‘support’ everywhere!

2.       The Conclusion should be shortened. Currently it repeats much of the information from the Discussion.

3.       Limitations should be dealt with separately (in the Discussion, perhaps as a distinct sub-section) and not in the Conclusion.

Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

We would like to thank you for your valuable comments. We have revised the paper according to your comments and suggestions. Changes in the revised manuscript are marked in red. The responses to your comments are detailed below:

Reviewer 3:

Thank you for the opportunity to review this paper. Moving away from TAM/UTAUT and derivatives, this paper applies an alternative theory (S-O-R) to develop and apply a new model to evaluate different factors that impact adoption and use of mHealth Apps, focussing on ‘content presentation’.

The greatest difficulty with the paper in its current form is related to context. At a minimum, I would strongly urge the authors to consider the following suggestions:

Q1. Insert into the Introduction a brief description and summary of TAM and UTAUT, and their application (in the global healthcare literature) to explain adoption and use of mHealth (and its associated Apps). Be sure to reference this literature using appropriate and current references.

Rationale: Within the healthcare sector there is a wealth of literature related to adoption of mHealth (and related Apps) that focusses on use of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) model and their derivatives. This has not been spoken about in this paper, and the current research must be placed in context and in relation to it.

Response: Many thanks for your constructive suggestions. We have added a description of the TAM and UTAUT in the Introduction (pp.2-3, in red). Firstly, we elaborated the interpretation of TAM and UTAUT in the mHealth field on a broader scale. Then, TAM model and some other models, such as UTAUT, UTAUT2, Dual-factor Model, TPB, TRA, and Privacy Personalization Paradox, have been described(see Figure 1). At the same time, we have cited 9 more references from the global reputable journals (pp.19-20, in red). The newly added references are listed below:

[1] Octavius, G.S.; Antonio, F. Antecedents of intention to adopt mobile health (mHealth) application and its impact on intention to recommend: An evidence from Indonesian customers. International journal of telemedicine and applications. 2021, 2021.

[2] Hajesmaeel-Gohari, S.; Khordastan, F.; Fatehi, F.; Samzadeh, H.; Bahaadinbeigy, K. The most used questionnaires for evalu-ating satisfaction, usability, acceptance, and quality outcomes of mobile health. BMC Medical Informatics and Decision Making. 2022, 22, 1-9.

[3] Samsuri, A.S.; Hussin, S.M.; Badaruddin, M.N.A.; Arifin, T.R.T.; Zainol, S. S.; Mohamad, Z.Z. Antecedents of User Satisfaction and Continuance Usage of Mobile Health Applications: A Study on MySejahtera Apps in Malaysia. Asian Journal of Behavioural Sciences. 2022, 4, 91-105.

[4] Byrd IV, T.F.; Kim, J.S.; Yeh, C.; Lee, J.; O'Leary, K.J. Technology acceptance and critical mass: Development of a consolidated model to explain the actual use of mobile health care communication tools. Journal of Biomedical Informatics. 2021, 117, 103749.

[5] Semiz, B.B.; Semiz, T. Examining consumer use of mobile health applications by the extended UTAUT model. Business & Management Studies: An International Journal. 2021, 9, 267-281.

[6] Wu, P.; Zhang, R.; Zhu, X.; Liu, M. Factors Influencing Continued Usage Behavior on Mobile Health Applications. In Healthcare (Vol. 10, No. 2, p. 208). MDPI. 2022, January.

[7] Nezamdoust, S.; Abdekhoda, M.; Rahmani, A. Determinant factors in adopting mobile health application in healthcare by nurses. BMC Medical Informatics and Decision Making. 2022, 22, 1-10.

[8] Nadal, C.; Sas, C.; Doherty, G. Technology acceptance in mobile health: scoping review of definitions, models, and measurement. Journal of Medical Internet Research. 2020, 22, e17256.

[9] AlBar, A.M.; Hoque, M.R. Patient acceptance of e-health services in Saudi Arabia: an integrative perspective. Telemedicine and e-Health. 2019, 25, 847-852.

Figure 1 Technology acceptance model-based model and their constructs

Q2. Following this, insert a brief segment noting the acknowledged limitations of TAM and UTAUT (and derivatives), and that the literature calls for alternate approaches to explain adoption and use of mHealth (and its associated Apps). Indicate that this paper offers just such an alternative approach, using another theory and model and by focussing on three aspects of ‘content presentation’ of mobile Apps.

Rationale: This provides (particularly for those in the healthcare sector) a valid reason for the research.

[The above items should require no more than a paragraph to accomplish].

Response: Thank you again for your helpful suggestions. We have also added acknowledged limitations of TAM and UTAUT (pp.2-3, in red). Although TAM, UTAUT and their extended models are useful, many efforts are needed to improve their explanatory power. Regarding predictions of user behavioral intention, TAM is easy to be applied, while SOR is developed and widely used to explain the behavior of individuals under the stimulation of internal and external factors. Therefore, we analyzed the relationship among content presentation, users’ internal experience and users’ intention of adoption, and constructed the effect model from the perspective of the S-O-R theoretical model.

Q3. Insert in the Introduction (perhaps around page 2, first paragraph) a sentence or two noting that much research ignores the commercial nature of many Apps, which is inappropriate. Healthcare as a whole is a business and entities that develop Apps must promote and explain their products through the ‘content presentation’ of their Apps, making this research of importance.

Rationale: Those in healthcare often skirt around or ignore this fundamental fact, and pointing out the reality may help place the paper in better context.

Response: Many thanks for your clear guidance. We have now added some sentences in the manuscript accordingly by reviewer’s suggestions (pp.2, in red). The newly added sentences are listed below:

“……Healthcare as a whole is a business and entities in which the developed Apps must promote and explain their products through the content presentation. The content presentation of mHealth Apps is an important carrier for realizing medical services and has a great influence on users’ attitude, adoption intention and behavior [6-8]. ……”

Q4. Endeavour to seek and replace many of the existing references with more current references that appropriately support the text.

Rationale: Currently about ~80% of the cited references were published during or prior to 2017. In such a dynamic field as mHealth adoption and use, five years is a long time.

Response: Many thanks for your careful review. We have removed and added some references (pp.16-18, in red), of which 3 papers from the journal of Sustainability are cited (pp.17-18, in red). The newly added references are listed below:

[1] Vo, V.; Auroy, L.; Sarradon-Eck, A. Patients’ perceptions of mHealth Apps: meta-ethnographic review of qualitative studies. JMIR mHealth and uHealth. 2019, 7, e13817.

[2] Floch, J.; Vilarinho, T.; Zettl, A.; Ibanez-Sanchez, G.; Calvo-Lerma, J.; Stav, E.; Haro, P.; Aalberg, A.; Fides-Valero, A.; Bayo Montón, J. Users’ Experiences of a Mobile Health Self-Management Approach for the Treatment of Cystic Fibrosis: Mixed Methods Study. JMIR mHealth and uHealth. 2020, 8: e15896.

[3] Cudjoe, J.; Gallo, J.J.; Sharps, P.; Budhathoki, C.; Roter, D.; Han, H.R. The role of sources and types of health information in shaping health literacy in cervical cancer screening among African immigrant women: a mixed-methods study. HLRP: Health Literacy Research and Practice. 2021, 5, e96-e108.

[4] Sayed, W.S.; Noeman, A.M.; Abdellatif, A.; Abdelrazek, M.; Badawy, M.G.; Hamed, A.; El-Tantawy, S. AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications. 2022,1-31.

[5] Babel, F.; Kraus, J.; Miller, L.; Kraus, M.; Wagner, N.; Minker, W.; Baumann, M. Small talk with a robot? The impact of dialog content, talk initiative, and gaze behavior of a social robot on trust, acceptance, and proximity. International Journal of Social Robotics. 2021, 13, 1485-1498.

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The cited literature clearly indicates that our paper well fits the scope of this journal.

Consider the other comments / suggestions made below, which will help in terms of the content and presentation of the paper (see the attached annotated file).

GENERAL COMMENTS

Q1. It should be clearly stated in the Methods that the study uses Structural Equation Modelling, providing a suitable reference and a brief description of its utility included in a sentence or two. Currently the uninitiated are presented with SEM approaches and terms in the Results (‘Empirical analysis’) section with no prior explanation.

Response: Thank you very much for your comments. We have supplemented the Structural Equation Modelling in the Materials and methods (pp.8-9, in red). In this study, SPSS25.0 and AMOS24.0 were used as analysis tools to test the reliability and validity of the sample data, and the least squares PLS structural equation model was used to test the hypothesis. Structural Equation Model (SEM) ) is a multivariate statistical technique for testing hypotheses regarding the influences among interacting variables. There are mainly two calculation methods: Maximum Likelihood Estimation (MLE) and Partial Least Square (PLS). Since PLS can predict the weights and factor loadings of the hypothesized relationships in the model to the greatest extent, this study uses the structural equation model of PLS for hypothesis testing.

Q2. The Materials and Methods section strays into presentation of ‘results’ (see below), but this may be excusable given the format used for the paper (i.e., ‘Empirical analysis’ rather than ‘Results’).

“….316 questionnaires were collected through a “snowball” method, of which 230 were valid questionnaires, with an effective rate of 72.8%. In addition, from the demographical data of the collected questionnaires, it is found that most of the mHealth APP users are young and well-educated, with 156 under the age of 40, accounting for 67.8%, and 162 with bachelor degree or above, accounting for 70.4%, which shows that users with higher education have a friendly attitude towards mHealth Apps and are more likely to seek for online treatment.”

Response: Many thanks for your constructive comments. We have changed and moved this part in the manuscript and polished the language to make the transitions from one topic to the next more logical and structured.

“……316 questionnaires were collected, of which 230 were valid questionnaires, with an effective rate of 72.8%. We used the snowball sampling method to collect questionnaires randomly.. ……” This part has been rewritten in the Materials and Methods section (pp.8, in red).

“from the demographical data of the collected questionnaires, it is found that most of the mHealth APP users are young and well-educated, with 156 under the age of 40, accounting for 67.8%, and 162 with bachelor degree or above, accounting for 70.4%, which shows that users with higher education have a friendly attitude towards mHealth Apps and are more likely to seek for online treatment.” This section was moved to in 4.1. Reliability and validity test of the empirical analysis (pp.9, in red).

Q3. There are several places where insertion of a line space would make presentation of the paper much easier to read. These locations have been identified.

Response: Much appreciated to the reviewer’s kind reminding. We have now revised the manuscript accordingly. We have inserted a line space before and after the Figures and Tables. The whole paper has been modified to make it easier to read.

SPECIFIC COMMENTS

Q1. In Table 4, I would recommend replacing the term ‘Not Support’ with ‘Fail’ as used in the text. The current presentation is very awkward and potentially misleading, seeming to say ‘support’ everywhere!

Response: Many thanks for your careful review. We have replaced the term ‘Not Support’ with ‘Fail’ in Table 4 (pp.12, in red). The Table 4 looks more concise and pretty (As shown in Table 4 below).

Table 4 Structural equation model test results

Hypothesis	Standardized Path Coefficient	Standard Error	T Value	Conclusion
H1: Platform Information Presentation→Perceived Value	0.49***	0.146	4.387	Support
H2: Platform Information Presentation→Trust	0.48***	0.159	3.481	Support
H3: Guidance Information Presentation→Perceived Value	0.20*	0.104	1.987	Support
H4: Guidance Information Presentation→Trust	0.10	0.096	0.945	Fail
H5: Relational Information Presentation→Perceived Value	0.19*	0.061	2.559	Support
H6: Relational Information Presentation→Trust	0.13	0.057	1.699	Fail
H7: Perceived Value→Trust	0.08	0.101	0.654	Fail
H8: Perceived Value→Willingness to  Participate	0.59***	0.075	5.377	Support
H9: Perceived Value→Willingness to  Recommend	0.21**	0.076	2.763	Support
H10: Trust→Willingness to Participate	0.23**	0.071	2.512	Support
H11: Trust→Willingness to Recommend	0.52***	0.097	6.065	Support

Q2. The Conclusion should be shortened. Currently it repeats much of the information from the Discussion.

Response: Many thanks for your valuable comments. After careful revision, we reduce the words of the main conclusion from 232 to 132. Some sentences have been removed, and only findings related to the study are reserved (pp.15-16, in red). The whole conclusion has been more concise and logical.

“This study creatively constructs a comprehensive model to explain the influence of content presentation of mHealth Apps on users’ willingness to participate and recommend in terms of cognitive behavior. ……However, the effect of perceived value on trust is insignificant. This research could provide valuable information and suggestions to boost users’ intention to adopt mHealth Apps.”

Q3. Limitations should be dealt with separately (in the Discussion, perhaps as a distinct sub-section) and not in the Conclusion.

Response: Many thanks for your helpful suggestions. We have now revised the manuscript accordingly. We have presented limitations as a distinct sub-section (e.g.: 5.3. Limitations and future research) in the discussion (pp.15, in red).

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Thanks to the authors for their efforts in revising this paper. Although there have been some improvements, I still have some minor comments on this paper:

1. I ask the authors to carefully stated the research gaps of this paper. For example, the authors said "However, only a few studies have explored users’ trust in mHealth platforms, medical personnel, and medical services". Please check again your literature search, you will find a lot papers discussing the impact of trust in m-health setting.

2. I'm also still confused on how the authors adapting the references for developing their research model regarding information content. For example, Quintero Johnson et al. and Vetri et al did not define the three dimensions of information content as in this research, thus how did you adapt the references to suit the context of this research?

3. Why did you only define the content information only into three dimensions? I recognized some critical information are not there, for example, related to doctors. One of the main considerations in using m-health is doctor reputation.

4. Why did you use snowball sampling (as you just mentioned this in this version)? What are the risks of having such sampling and how did you address such risks?

5. You mentioned that you want to investigate the mediating roles of perceived value and trust. However, you never presented the results of mediating test results.

6. As consequence of point 5, you also need to discuss it in this paper.

Overall, I'm not happy with the revision of this paper. 

Author Response

Dear Reviewer,

We would like to thank you again for your 2^nd round valuable comments on our revisions. We have revised the paper according to your comments and suggestions. Changes in the revised manuscript are marked in red. The responses to your comments are detailed below:

Reviewer 1:

Thank you to the authors for their efforts in writing this paper. Although the topic is interesting, I have some concerns as follows:

Q1. I ask the authors to carefully stated the research gaps of this paper. For example, the authors said "However, only a few studies have explored users’ trust in mHealth platforms, medical personnel, and medical services". Please check again your literature search, you will find a lot papers discussing the impact of trust in m-health setting.

Response: Thank you very much for the reviewer’s insightful comments. We have now revised the manuscript accordingly. Just as the reviewer mentioned, there were plenty of works discussing the impact of trust in mhealth setting in some pertinent research. For example, Alam et al. (2020) pointed out that trust had an positive influence on users’ behavioral intention in Bangladesh. Octavius & Antonio (2021) also indicated that initial trust in mHealth platform had a statistically significant effect on the intention to adopt mHealth products based on the TAM model. Different from these research, our study specifically focused on exploring the effect of the users’ trust on the content presentation of platform, guidance, and relational in mHealth Apps, which also covers the research gap of the thorough investigation of “the users’ trust in mHealth platforms, medical personnel, and medical services”.

Q2. I'm also still confused on how the authors adapting the references for developing their research model regarding information content. For example, Quintero Johnson et al. and Vetri et al did not define the three dimensions of information content as in this research, thus how did you adapt the references to suit the context of this research?

Response: Many thanks again for the reviewer’s valuable comments. Following your question, we have improved the explanations of the three dimension definitions and adoptions. Among the three dimensions of the information presentations, the definition of platform information presentation is similar to the mental health information presentation in the research of Quintero Johnson et al. (2017) and the information transparency of the online platforms in the research of Veltri et al. (2020). Those two definitions could help users better understand the advantages and service features of the platform products. The concept of the second dimension, the guidance information presentation, comes from the research of Wang et al. (2020), which is a critical factor to guide users to participate in the marketing activities of applications. The third dimension, i.e., the relational information presentation, is originated from the works of Heycke & Gawronski (2020) and Córdova et al. (2019). This dimension is mainly used for the communication and relationship maintenance with users. Moreover, Heycke et al. (2020) and Córdova et al. (2019) both stated that the relational information was beneficial to deepen the users’ cognitive level to the brand. The three dimensions of information presentations and their supporting references are listed in the following table.

Table 1 Three dimensions of information presentations and their supporting references.

No.	Dimensions	Related References
1	Platform Information Presentation	Quintero Johnson et al. (2017), Veltri et al. (2020)
2	Guidance Information Presentation	Wang et al. (2020)
3	Relational Information Presentation	Heycke & Gawronski (2020), Córdova et al. (2019)

Q3. Why did you only define the content information only into three dimensions? I recognized some critical information are not there, for example, related to doctors. One of the main considerations in using m-health is doctor reputation.

Response: Basically, we categorized the content information into three dimensions based on an influential and valuable research from Qiu et al. (2016). The original concept was published on the Chinese Journal of Library and Information Service. To elaborate and broaden this novel concept, we added 5 more literature reviews in our manuscript. With regards to the context and the focus of our study, the three general dimensions have been selected as the major structures of our model. Admittedly, we agree with your comment that doctor reputation is a main consideration in using mhealth. It should also have been added as an independent variable to investigate the adoption behavior of the mHealth users. It could be listed as one of our future works to add the doctor reputation as a critical variale into the model of the mHealth users’ intention to adopt so that we would be able to predict and explain users’ mHealth adoption behavior more precisely and comprehensively.

Q4. Why did you use snowball sampling (as you just mentioned this in this version)? What are the risks of having such sampling and how did you address such risks?

Response: Based on the reviewer’s question, we have provided the following explanation. As China's mobile medical service is not widely applied, it is difficult to find respondents with the conventional sampling methods. In terms of using the snowball sampling method, this study first randomly selects a group of survey objects. After interviewing these selected respondents, they are encouraged to share such surveys with other potential respondents via the Wechat App (the top-one application in China with more than 1.2 billion users, and can be viewed as a combination of Twitter and Facebook). In this way, the amount of samples is like a snowball from small to large. Therefore, the snowball sampling cost is relatively low, while its feasibility is strong, and the sampling effect is also acceptable.

The main risk of snowball sampling is that the survey objects are often limited to a group of people with similar life attributes, which may cause the problem of insufficient sample representation. In response to this problem, this study selected 30 respondents who have diverse occupations, including civil servants, medical workers, students, teachers, freelancers, and retirees. This ensures the diversity and representativeness of the selected samples. Therefore, snowball sampling through Wechat App ensures the diversity of samples to a certain extent and also has the randomness of sampling.

Q5. You mentioned that you want to investigate the mediating roles of perceived value and trust. However, you never presented the results of mediating test results.

Response: According to Baron et al. (1986) judgment method of mediating variables, to test whether the mediating effect exists, it must first judge the significance of the path relationship between the independent variable and the outcome variable. Then, test whether the independent variable and the outcome variable are significantly related. On the premise that the independent variable and the outcome variable are significantly related when the direct effect and the indirect effect exist at the same time, the mediating variable plays a part of mediating role. If the indirect effect exists, the direct effect does not exist, and the mediating variable plays a complete mediating role.

Accordingly, the intermediary role of perceived value and trust is tested, and the test results are shown in Table 1. Specifically, perceived value plays a partial mediating role between the presentation of platform information content and participation intention. Trust plays a partial mediating role between the presentation of platform information content and recommendation intention. Perceived value plays a partial mediating role between the presentation of guiding content information and participation intention. Perceived value plays a partial mediating role between the presentation of related information content and participation intention.

Table 1. Mediating variables effect test results.

IV	MV	DV	IV→DV	IV+MV→DV			Test Results
				IV→MV	IV→DV	MV→DV
Platform Information Presentation	Perceived Value	Willingness to Participate	0.441**	0.595***	0.314*	0.276***	Partial mediating
Platform Information Presentation	Perceived Value	Willingness to Recommend	0.553**	0.595***	0.695***	0.104	Not significant
Platform Information Presentation	Trust	Willingness to Participate	0.441**	0.489**	0.314*	0.095	Not significant
Platform Information Presentation	Trust	Willingness to Recommend	0.553**	0.489**	0.695***	0.345***	Partial mediating
Guidance Information Presentation	Perceived Value	Willingness to Participate	0.268**	0.245*	0.171*	0.276***	Partial mediating
Guidance Information Presentation	Perceived Value	Willingness to Recommend	0.409**	0.245*	0.063	0.104	Not significant
Guidance Information Presentation	Trust	Willingness to Participate	0.268**	0.077	0.171*	0.095	Not significant
Guidance Information Presentation	Trust	Willingness to Recommend	0.409**	0.077	0.063	0.345***	Not significant
Relational Information Presentation	Perceived Value	Willingness to Participate	0.364**	0.148*	0.099*	0.276***	Partial mediating
Relational Information Presentation	Perceived Value	Willingness to Recommend	0.293**	0.148*	0.013	0.104	Not significant
Relational Information Presentation	Trust	Willingness to Participate	0.364**	0.091	0.099*	0.095	Not significant
Relational Information Presentation	Trust	Willingness to Recommend	0.293**	0.091	0.013	0.345***	Not significant

Note: IV stands for independent variable，MV stands for mediating variable，DV stands for depedent variable；***p<0.001，**p＜0.01，*p<0.05.

Q6. As consequence of point 5, you also need to discuss it in this paper.

Response: The presentation of information content will significantly affect users' adoption intention through the intermediary of perceived value and trust. It can be seen that perceived value and trust play a very important role in the willingness of users to adopt mobile medical apps. Compared with users searching for medical information through offline hospitals, the presentation of mobile medical app information is conducted in a virtual network environment, with uneven information quality, and users cannot truly feel and judge the reliability and effectiveness of medical information.

As medical information is different from other types of product information, false or exaggerated medical information will damage users' property and endanger users' life and health. Under such circumstances, users will have a cautious or skeptical attitude towards medical information, which further highlights the importance of users' value perception and trust in the information contained in the adoption intention of mobile medical apps. This will significantly affect users' participation intention and recommendation intention.

Author Response File: Author Response.pdf

Reviewer 3 Report

My thanks to the authors for considering and responding to the suggestions made. In general I am satisfied that the responses are adequate and have enhanced the paper and placed it in better context. The more up to date referencing is a significant improvement.

I am uncertain of the absolute need for the new Figure 1, but it is fine if the authors believe it is of value.

I would comment about the darkness of the objects in Figure 4. This was not the case in the first version I reviewed. If left as it is now, when reproduced in the journal the content of the boxes may not be clearly visible to readers. I would recommend reverting to (as before) no shading at all.

Author Response

Dear Reviewer,

We would like to thank you for your valuable comments again. We have revised the paper according to your comments and suggestions. Changes in the revised manuscript are marked in red. The responses to your comments are detailed below:

Reviewer 3:

My thanks to the authors for considering and responding to the suggestions made. In general I am satisfied that the responses are adequate and have enhanced the paper and placed it in better context. The more up to date referencing is a significant improvement.

Q1. I am uncertain of the absolute need for the new Figure 1, but it is fine if the authors believe it is of value.

Response: We adopted Figure 1 to make some framework induction and structural summary to the influencing factors which significantly affect the users’ mHealth adoption behaviour. Moreover, the Figure 1 could also be used to exhibit the novelty and importance of this research among the existing research works.

Q2. I would comment about the darkness of the objects in Figure 4. This was not the case in the first version I reviewed. If left as it is now, when reproduced in the journal the content of the boxes may not be clearly visible to readers. I would recommend reverting to (as before) no shading at all.

Response: Thank you very much for the reviewer’s kind reminding. We have removed the shading of the figure and make it more clear as shown in the attachment and in the revised manuscript as well.

Author Response File: Author Response.pdf

Round 3

Reviewer 1 Report

Thanks for the revision. I have no more requests on this paper.