On the manuscript title

The title is the first, and often the only, piece of information that a potential reader—or an editor at the screening stage—receives about the paper. A good title in an economics or management article fulfills two functions simultaneously: it precisely names the phenomenon under study and signals the contribution. When one of these elements is missing, the title becomes interchangeable with dozens of similar articles and loses its ability to attract the specialized attention the journal seeks.

The most recurrent weaknesses we observe are four. The first is generic phrasing, which could describe any study on the topic ("An analysis of the impact of education on economic growth"). The second is the use of empty verbal headers such as "A study on…," "Towards an understanding of…," or "Analysis of the effect of…": the academic reader already assumes the work is a study and that it analyzes something, so these words consume space without adding information. The third is the accumulation of symbols, acronyms, or mathematical notation that force the reader to decode the title before understanding it. The fourth is excessive length: titles spanning three lines usually signal a lack of hierarchy regarding what constitutes the core contribution.

The main recommendation is to conceive the title as a brief informative statement that implicitly answers the question: what does the reader learn from this article that was not known before? Whenever possible, the title should name both the variable of interest and the specific mechanism or context. A subtitle after a colon can help separate the general statement from the empirical anchor.

Weak: "An analysis of the impact of education on economic growth"
Better: "Tertiary education and regional growth: panel evidence from Chile"

Weak: "A study on SMEs and technological adoption using econometric models"
Better: "Public subsidies and the adoption of sustainable technologies in Chilean SMEs: the mediating role of training"

 

On the structured abstract

The RAN abstract is organized into five mandatory fields—Purpose, Methodology, Results, Implications, and Originality—with a 220-word limit. This constraint imposes an economy of language that many authors underutilize: if each field averages around forty words, there is no room for ceremonial phrasing. However, the main difficulty is not length but function. Each field has a specific role that, when clearly fulfilled, turns the abstract into a self-contained miniature of the article; otherwise, it becomes a list of generalities that does not allow the reader to decide whether the paper is worth reading.

The Purpose field must clearly formulate the research question or hypothesis, avoiding the empty formula "the purpose of this study is…". The purpose is not a repetition of the title, but its refinement: what causal, associative, or descriptive relationship is being examined, for which population, and in what context.

The Methodology field must describe the research design, the origin and coverage of the data, and the estimation or analytical strategy. A frequent mistake—one the editorial team emphasizes—is to mention the software (R, Stata, SPSS, Python) as if it were part of the method. Software is a computational tool, not a method. In an econometric paper, the method may be, for example, a panel estimation with fixed effects and clustered standard errors; in a qualitative study, it may be a thematic analysis based on semi-structured interviews.

The Results field must report the direction and magnitude of the main finding, not only its statistical significance. Statements such as "the results indicate significant effects" are uninformative: a specialized reader needs to know whether the effect is 2% or 25%, in which direction it operates, and whether it is concentrated in any subgroup.

The Implications field must go beyond the technical finding and offer its practical, managerial, or policy interpretation.

The Originality field is, in our experience, the weakest and the one that most often leads to desk rejection. Statements such as "the results confirm the existing literature" or "this paper contributes to the literature" are not claims of originality; they are filler. Scientific novelty may lie in new data, an unexplored context, an unidentified mechanism, a methodology not previously applied to the problem, or a result that contradicts prior consensus. The recommendation is to state, in a single sentence, what this article adds that was not previously known.

Weak: Purpose: The purpose of this study is to evaluate the impact of government subsidies on the adoption of sustainable technologies by SMEs.
Better: Purpose: To evaluate whether program X increases the adoption of sustainable technologies in Chilean manufacturing SMEs, and whether this effect depends on prior access to training.

Weak: Methodology: A logistic regression was conducted in R using a sample of 200 SMEs.
Better: Methodology: Logistic regression based on a structured survey of 200 SMEs (2022), with controls for sector, size, and region, and robust standard errors.

Weak: Results: The results indicate that subsidies have a statistically significant effect.
Better: Subsidies increase the probability of adopting sustainable technology by 18 percentage points; the effect doubles when the firm previously accessed training programs.

Weak: Originality: The results confirm the existing literature.
Better: Unlike prior evidence focused on OECD countries, this paper provides the first estimate for South American SMEs and documents training as a mediating channel.

 

On tables

Tables should be readable on their own. A specialized reader rarely reads articles linearly: they typically scan the abstract, tables, and figures first. If a table requires returning to the main text to be understood, it fails its communicative function.

Three elements are critical: the title, which must specify what is reported, the unit of analysis, the period, and the method; the column headers, which must be clear; and the table note, which should include definitions, units, estimation method, type of standard errors, number of observations, significance levels, and data source.

In quantitative economics, consistency in reporting inference metrics (standard errors or t-statistics, but not both interchangeably) and decimal precision is also important.

Weak: Table 3. Regression results
Better: Table 3. Effect of subsidies on the adoption of sustainable technology: logit estimates with robust standard errors. Chilean manufacturing SMEs, 2022

Recommended note: Dependent variable: dummy equal to 1 if the firm adopted at least one sustainable technology in 2022. Robust standard errors in parentheses. Controls: sector, size, region, and firm age. Significance levels: *** p<0.01; ** p<0.05; * p<0.1. Source: own elaboration.

 

On figures

Figures follow the same self-contained logic as tables, with two additional requirements: readability and informative labeling. They must remain understandable when reduced in size, avoid relying solely on color, and clearly specify variables, units, and legends.

The title should be self-explanatory and include the phenomenon, unit of analysis, and period. The note should indicate the data source, define abbreviations, and clarify relevant assumptions.

Weak: Figure 2. GDP evolution
Better: Figure 2. Evolution of regional GDP per capita in Chile, 1990–2020. Series in constant 2015 USD

 

On the discussion

The discussion is where the author demonstrates an understanding of the meaning of their results. A common mistake is to treat it as an extension of the results section.

A strong discussion includes: engagement with the literature, explicit statement of novelty, derivation of implications (theoretical, managerial, or policy), and acknowledgment of limitations.

Stating that "the coefficient of X is positive and significant" is not discussion. Proper discussion interprets, compares, and situates findings within the existing literature.

 

On the use and management of references

RAN sets a maximum of 30 references per manuscript, with two additional criteria: at least 60% must correspond to publications from the last ten years, and at least 15 must be in English and come from indexed sources such as WoS, Scopus, or SciELO. These limits are neither arbitrary nor merely administrative: an oversized bibliography is often a symptom, rather than a cause, of argumentative imprecision. When an author accumulates four or five references to support the same claim, they are often implicitly acknowledging that they do not fully trust any single one of them on its own. In this sense, the editorial restriction acts as an incentive for clarity: it forces authors to decide which reference is actually necessary for each point in the argument.

The guiding question for identifying dispensable references is straightforward: if a reference can be removed without weakening the argument it supports, then it is dispensable. Based on this general criterion, several strategies can reduce the bibliography without impoverishing the content. The first and most effective is the consolidation of grouped citations: when a single argument is supported by three or five references in sequence, retaining the most representative one—the seminal work in the field, the most cited, or the one closest to the study's context—is sufficient to sustain the point. A useful question for deciding which to keep is which reference a well-trained reader in the discipline would cite as the canonical source for that claim; that is almost always the one that should be retained. The second strategy is the replacement of multiple primary studies with a systematic review or meta-analysis that synthesizes them: a single citation to an up-to-date review can advantageously replace five citations to primary studies, and it also signals that the author is familiar with the consolidated state of the field. The third strategy is to eliminate decorative references—those that appear only once, are not discussed, not contrasted, not integrated into the argument, and whose implicit function is to display breadth of reading. If a reference can be removed along with the sentence that contains it without affecting the coherence of the paragraph, both were likely unnecessary.

Other strategies operate at the level of the overall composition of the bibliography and make use of RAN's own criteria as a pruning guide. Prioritizing references indexed with a DOI in WoS, Scopus, or SciELO not only helps meet the requirement of at least 15 references in English, but also identifies as primary candidates for removal gray literature, non-peer-reviewed conference papers, general-content web pages, and non-indexed book chapters, provided they are not essential to a specific argument. The same logic applies to the temporal criterion: when a recent reference exists that is equivalent in content to an older one, retaining the newer source simultaneously helps reduce the total number and meet the 60% threshold for the last ten years, without abandoning seminal literature when it is irreplaceable. Self-citations require particularly careful auditing: they should serve the argument rather than the promotion of one's own work; two or three well-integrated self-citations do not create editorial friction, but a larger number may indicate a problem.

Two final heuristics help close the process. The first is to distinguish between references that support the conceptual framework—typically stable and difficult to reduce, since the foundations of the field are what they are—and references that document prior empirical evidence, where redundancy tends to concentrate and where stricter criteria allow for more substantial cuts. The second is reverse review: once the manuscript is written, read the final reference list one by one and ask what function each entry serves in the body of the text. References without a clear answer to that question can likely be removed without loss. It is also worth recalling that not every statement requires bibliographic support: statements that are part of the common knowledge of the discipline—such as that SMEs are important for the economy, that inflation affects consumption, or that education is associated with higher income—do not need to be cited, and doing so is often one of the most frequent sources of unnecessary bibliographic saturation.

Additional considerations

The introduction should clearly identify the gap in the literature. Keywords should complement, not repeat, those in the title. Balance across sections also matters: disproportion often signals weaknesses in argumentation.

When these elements are addressed jointly, the manuscript is more likely to progress through the editorial process without avoidable formal objections.