Subsequent to preparation of an article, authors start to look for a suitable journal to submit. Authors are assumed to select the journals by considering their future expectations regarding the maximization of prospective impact of the study, increasing the probability of acceptance and minimizing the total time consumed until the paper is published. Furthermore, the scope of a candidate journal should be in line with paper’s content. Currently, it is possible to find these journal related facts (such as average waiting times, acceptance rates, impact factor and etc.) on the web pages of the journals. However, the exact effect of these factors, and how to incorporate them into modeling, are yet unclear; further research is required to explore them. On the other hand, we know that Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) can be a useful approach to rank the journal alternatives. We require corresponding weights of factors to obtain a complete decision making model. If the correct weights of these factors for such a TOPSIS model can be estimated, we can understand the magnitude of their corresponding effects. Thereby, we can explain the journal selection decision by using an analytical approach. Therefore, the main purpose of this paper is to find appropriate weights of these factors that can explain why already published papers were submitted to their current journals. To the authors’ knowledge, this paper is the first to search for weight of factors in TOPSIS, where the actual decisions are known a priori. For testing purposes, we create our data set by collecting the already published papers (in year 2019) which has the “environmental risk” term at the title/abstract or keywords. We test different TOPSIS models (with different random weights) for each of the papers and the rank the journal alternatives. If the first, second and the third journal alternative is the actual journal that published the paper, we assume that the model predicts accurately. As a conclusion, the TOPSIS model which predicts the journal for the published papers much more accurately is accepted as the valid decision making model. Inevitably, we have certain assumptions regarding to this model. We assume that the authors are informed about the journal facts and make rational decisions about journal selection.

中文翻译：

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用于理解作者选择期刊选择的 TOPSIS 模型

在准备好文章之后，作者开始寻找合适的期刊提交。假设作者通过考虑他们未来对研究的预期影响最大化的期望、增加接受的可能性和最小化论文发表前消耗的总时间来选择期刊。此外，候选期刊的范围应与论文内容一致。目前，可以在期刊的网页上找到这些期刊相关的事实（例如平均等待时间、接受率、影响因子等）。然而，这些因素的确切影响以及如何将它们纳入建模尚不清楚。需要进一步的研究来探索它们。另一方面，我们知道，通过与理想解决方案相似的偏好排序技术（TOPSIS）可以是一种对期刊替代品进行排名的有用方法。我们需要相应的因素权重来获得一个完整的决策模型。如果可以估计出这些因素对于此类 TOPSIS 模型的正确权重，我们就可以了解它们相应影响的大小。因此，我们可以使用分析方法来解释期刊选择决策。因此，本文的主要目的是找到这些因素的适当权重，以解释为什么已经发表的论文被提交到他们当前的期刊。据作者所知，本文是第一个在 TOPSIS 中搜索因素权重的论文，其中实际决策是先验已知的。出于测试目的，我们通过收集已发表的论文（2019 年）来创建我们的数据集，这些论文在标题/摘要或关键字中带有“环境风险”术语。我们为每篇论文测试不同的 TOPSIS 模型（具有不同的随机权重），并对期刊替代品进行排名。如果第一个、第二个和第三个期刊选择是发表论文的实际期刊，我们假设模型预测准确。作为结论，可以更准确地预测已发表论文的期刊的 TOPSIS 模型被接受为有效的决策模型。不可避免地，我们对这个模型有一些假设。我们假设作者了解期刊事实，并对期刊选择做出理性决定。