Next Article in Journal
Detecting Anomalies in Financial Data Using Machine Learning Algorithms
Previous Article in Journal
Effective Evaluation of Green and High-Quality Development Capabilities of Enterprises Using Machine Learning Combined with Genetic Algorithm Optimization
 
 
Article
Peer-Review Record

Antecedents and Consequences of the Ease of Use and Usefulness of Fast Food Kiosks Using the Technology Acceptance Model

Systems 2022, 10(5), 129; https://doi.org/10.3390/systems10050129
by Joonho Moon 1, Jimin Shim 2 and Won Seok Lee 2,*
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Systems 2022, 10(5), 129; https://doi.org/10.3390/systems10050129
Submission received: 23 July 2022 / Revised: 20 August 2022 / Accepted: 23 August 2022 / Published: 24 August 2022

Round 1

Reviewer 1 Report

TAM is a useful model for studying consumer behaviour. However, when this model is applied to fast food kiosks, this paper needs to address the uniqueness of fast food kiosks. In other words, is it still applicable? What are the specific characteristics of fast food kiosks when applying this model? Are there any limitations or modifications that we need to consider? This paper needs to address these questions. In addition, different foods may have an impact on the user's intention of use.

Gender balance may be an issue. The summary statistics show that there are significantly more men than women in the sample, which may mislead the results.

Author Response

.

Author Response File: Author Response.pdf

Reviewer 2 Report

Several issues were noticed while reviewing the manuscript, which need further elaboration from the authors.

* The manuscript suffers from several grammatical mistakes. It is therefore required to proofread the manuscript carefully before submitting the final version.

* Please remove the “s” from the word, “models” in the title as the study relies on TAM.

* In the introduction section, you need to specify the logical stance of the problem statement that motivates you to undertake the study.

* The TAM is criticized as an outdated model. Please refer to the following reference in your study to support and justify the use of the model.

- Al-Emran, M., & Granić, A. (2021). Is it still valid or outdated? A bibliometric analysis of the technology acceptance model and its applications from 2010 to 2020. In Recent advances in technology acceptance models and theories (pp. 1-12). Springer, Cham.

* Please restructure section 2 to have factors, their definitions, justifications, and corresponding hypotheses. The references below will give you an idea about the structure.

* In the methodology, what was the sampling technique used?

* As the data were self-reported, how did you handle the common method bias?

* The manuscript would benefit from citing the following reference, specifically in supporting the hypotheses. Please consider it while revising your final version:

- NuriAbdalla, S. A. (2019). Extend of TAM model with technology anxiety and self-efficacy to accept course websites at University Canada West. International Journal of Information Technology and Language Studies3(2), 1-7.

Author Response

.

Author Response File: Author Response.pdf

Reviewer 3 Report

The length of the article is moderate, which is good. The researcher explored the factors of acceptance of fast food kiosks using an extended technology acceptance model. I think this study clearly demonstrates the research process. However, I do not think the literature review is adequate and I question whether it fits the scope of the journal's inclusion; I did not see a literature review for fast food kiosk service systems and the discussion and conclusion section lacks relevance (definitely needs to be added). Checking for convergent and discriminant validity, the data did not show a pass. The study lacks innovation and the contribution is inadequate.

Author Response

.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

None

Author Response

Thanks.

Reviewer 3 Report

If the data analysis does not change, all your changes will not be valid in this round.

1. Discriminant validity fails (the diagonal value should be greater than the horizontal and longitudinal value);

2. Multicollinearity fails (correlation coefficient cannot exceed 0.8).

Please delete invalid samples and recalculate or supplement samples and HTMT.

Author Response

We did data cleaning first, and it was the best outcomes from the multiple times of analysis considering goodness of fit. 

We did several times data analysis.

We acknowledge the points of reviewer. We performed several times of analysis, and the current result was the best considering goodness of fit. 

We address the concerning points of reviewer to the manuscript. 

Back to TopTop