First establish what this efficiency comparison can answer

The draft proposes using public annual reports and patent information to study firms' innovation efficiency. It also recognizes limits in data definitions and interpretation. The main issues now are a research question that remains too broad, uncertainty about access to key data, and incomplete support from the literature.

R&D inputs themselves enter the DEA score calculation, so a correlation between inputs and scores cannot be read directly as an effect of the inputs. Define the scope of a descriptive comparison first, then decide on the sample, indicators, and subsequent analysis.

Title: Make the research subject and analytical scope clear at a glance

The current title names Japanese manufacturing, R&D investment, and innovation efficiency, but “関係” does not say what will be compared. Readers might expect a causal explanation, while the actual plan calls for measuring relative efficiency and making descriptive comparisons.

Keep the industry and research topic, but make listed firms, patent output, or relative efficiency explicit in the title. Use a subtitle for public data and DEA so the main title is not crowded with methods jargon.

Suggested Japanese title: 日本の上場製造業企業における研究開発資源と特許産出の相対効率分析

Cover and contents: Align the order of sections

The title appears twice at the start of the draft. The contents omit the research schedule, while the research purpose and limitations in the body are crowded into adjacent material. This makes the hierarchy hard to follow.

Set the final section structure before checking the contents. One possible order is background and prior research, research purpose, data and methods, limitations and feasibility, expected contribution, research schedule, and references. Match every contents entry to its heading in the body.

Completion check: Every section can be found from the contents, and heading levels, order, and numbering agree.

Background and prior research: Derive your question from a comparison of studies

This section begins with the point that more R&D input does not necessarily mean more efficient use of resources. The research concern is clear. The two cited works address financial performance and firm performance that includes patents, respectively; the draft also notes that their outcome measures differ.

Next compare the research subjects, indicators, and methods, and explain why these differences merit further study. Add research directly about innovation efficiency in manufacturing, then state what comparison or measurement basis this plan would contribute.

Completion check: Readers can follow what prior research explains, what remains open, and how this plan responds.

Research purpose: Align the question, measures, and interpretable results

The draft defines inputs, outputs, and relative efficiency, and explains that inputs are part of the score calculation. It still needs to clarify whether the main question concerns the distribution of efficiency or the descriptive association between input scale and the score.

Choose one primary question and make the charts answer it directly. If you retain a comparison of inputs and scores, describe it as a within-sample observation; do not reinterpret an input used to construct the efficiency score as an independent input effect.

Completion check: One sentence states the subjects, comparison, and scope of the conclusion; the title, methods, and contribution use the same framing.

Data and methods: Ground feasibility in verifiable sources

The draft already outlines checks for years, units, firm coverage, and missing data; keep these. It still needs specific explanations of whether R&D personnel are disclosed, how many comparable firms each industry contains, and why the output orientation and returns-to-scale specification were chosen.

Spot-check a few prospective firms and record the sources and definitions of R&D spending, personnel, and patents. Then use methods literature and the preliminary findings to explain the groups, indicators, model specification, and response to an inadequate sample.

Completion check: Each variable has an actual source, and you can tell before analysis which firms are comparable and which groups can be calculated.

Research limitations: Let the boundaries shape analytical decisions

The draft accounts for lags in inputs, changes in patent definitions, and a response to an inadequate sample. It also states that the design will not be changed in response to statistical significance. These points show research judgment.

Distinguish the primary analysis, sensitivity analyses planned in advance, and later exploratory work. If the sample is too small, personnel data are lacking, or firm matching is unreliable, specify whether you will narrow the subjects, change indicators, or limit the work to organizing data and descriptive analysis.

Completion check: Each major limitation has an actionable response.

Expected contribution: Say what the results can add to knowledge

Defining indicators, documenting the sample, and comparing efficiency are more defensible outputs than assuming a positive association. But “organizing measurement conditions” does not yet say what this work adds beyond existing studies.

After reading more literature, choose one contribution you can deliver: for example, clarify when a type of public data is comparable, or compare relative efficiency among similar firms. Practical uses should likewise match the judgments the results can support.

Completion check: The scholarly addition is grounded in a literature comparison and practical uses in the analysis; absent or reversed associations remain interpretable.

Research schedule: Make preliminary data checks a condition for proceeding

The sequence from checking sources to primary analysis, sensitivity analysis, and interpretation is sound. Also explain when “after starting” begins and how you will adjust the schedule if key data are unavailable.

Keep the eight-month staged plan. Before calculation, add three checkpoints: disclosure of personnel data, matching firms to patents, and sample size by group. Move rules for statistical definitions into the methods section or cross-reference them there.

Completion check: Each stage has a deliverable, prerequisites, and a fallback when data are insufficient.

First separate three matters: source, evidence, and citation style

The draft lists two studies and their DOIs, which can be used to locate the sources. You now need to check separately whether the bibliographic details are complete, whether the studies support the claims in the body, and whether in-text citations correspond to the reference list.

The two existing studies concern financial performance and the performance of electricity and gas firms, respectively. They alone cannot establish a research gap in manufacturing innovation efficiency. The most pressing additions are support for the research question and the choice of method.

Source 1: DEA performance study of electricity and gas firms

The draft uses this study to show that patent counts can be included as an output measure in DEA. The publisher's first page and abstract can confirm that use, but the study concerns electricity and gas firms and cannot by itself establish that manufacturing samples are comparable.

The original reference omits the authors, volume, and article number. Verifiable details are Toshiyuki Sueyoshi and Mika Goto; 2021; Energies, volume 14, article 1705; DOI: 10.3390/en14061705.

As you read, record how the patent measure is defined and what inputs and outputs it is combined with. Then explain why this plan uses a different industry and set of measures.

Source 2: Study of R&D intensity and financial performance

The draft cites this study for the claim that the direction of the association differs between mechanical and electrical equipment industries. Because this is a finding, return to the paper to check the sample, definition of financial performance, methods, and location of the conclusion.

The author publication record at Institute of Science Tokyo can verify Mika Goto and Toshiyuki Sueyoshi; 2008; R&D intensity and financial performance; IEMC - EUROPE 2008 proceedings, pages 235–239; DOI: 10.1109/IEMCE.2008.4617977. The original reference omits the authors and conference publication details.

So far, only the bibliographic record has been checked. Compare the specific industry finding against the paper itself and record the page or table number before deciding which summary to retain.

Finally, reconcile in-text citations with the reference list

The body currently cites sources by shortened title and year, while the reference list lacks authors. First follow the school's required style. If you use an author–year style, names and years in the body must match the reference list exactly.

Check each point: Is every study mentioned in the body in the reference list? Is every listed source actually used in the body? Do author order, year, title, journal or conference, volume and pages or article number, and DOI match the source?

When citing a finding, keep reading notes with the corresponding page, table, or paragraph. Finding a citation, reading an abstract, and checking the full-text conclusion are different stages; document which you completed before updating the body.

Research question

“The relationship between inputs and efficiency” is broad, and the inputs also enter the efficiency score.

Specify the subjects and years, and limit conclusions to descriptive associations within the sample.

Logic of the argument

The distinction between inputs and outcomes provides a sound route to measuring efficiency, but differences among cited studies do not yet adequately establish a research gap.

Explain what each existing study answers and how the proposed primary analysis addresses the question left open.

Support from literature

Two studies using different outcome concepts provide a starting point, but they cannot alone support the choice of innovation efficiency and DEA specification.

Verify complete bibliographic details and read studies directly addressing manufacturing innovation efficiency, patent measures, and methodological choices.

Methods and data

The draft has plans for checking definitions, handling missing data, and sensitivity analysis, but coverage of key data and group sample sizes still need preliminary checks.

Check R&D personnel data, matches between firms and patent holders, and the numbers of observations and indicators before fixing the scope of calculation.

Supervisor fit

No target professor was selected for this review, so there is no basis to assess this item.

Once the application target is set, check how the research question and methods connect to the professor's specific work.

Research significance

Measurement conditions and the scope of interpretation could constitute a contribution, but the plan must explain what it adds to existing research.

Define the addition using newly reviewed literature, and limit practical uses to conclusions supported by the efficiency comparison.

Consistency of writing style

The main text uses a plain style and maintains a generally cautious tone.

Use “だ・である調” consistently, and distinguish planned work, expected results, and work already completed through tense and wording.

Format and citations

The correspondence between contents and body, and the completeness of the references, still need checking.

Check heading levels and numbering one by one, then fill in authors, publication sources, and other required details from the original records.