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Financial ratios are often used to classify firms into different clusters of financial performance. This study aims to classify firms using financial ratios with advanced techniques and identify the transition matrix of firms moving clusters during the COVID-19 period.

This study uses compositional data (CoDa) analysis based on existing clustering methods with transformed data by weighted logarithms of financial ratios. The data include 66 listed firms in Vietnam’s food and beverage and fishery sectors over a three-year period from 2019 to 2021, including the COVID-19 period.

These firms can be classified into three clusters of distinctive characteristics, which can serve as benchmarks for solvency and profitability. The results also show the migration from one cluster to another during the COVID-19 pandemic, allowing for the calculation of the transition probability or the transition matrix.

The findings indicate three distinct clusters (good, average and below-average firm performance) that can help financial analysts, accountants, investors and other strategic decision-makers in making informed choices.

Clustering firms with their financial ratios often suffer from various limitations, such as ratio choices, skewed distributions, outliers and redundancy. This study is motivated by a weighted CoDa approach that addresses these issues. This method can be extended to classify firms in multiple sectors or other emerging markets.

This study aims to focus on the impact of COVID-19 on the Spanish wine sector and the financial resilience of Spanish wineries in the period 2019–2020.

The data set contains 355 limited companies of the Spanish wine sector which were active in the period 2019–2020. The explanatory variables used are size and age of the company, exports, subsidies and gender distribution in the workforce. The financial statements of the companies are treated as compositional data, using log-ratios for asset structure, leverage, margin, turnover and debt maturity. The first-difference estimator is used for the panel-data model relating the differences in the log-ratios between 2020 and 2019 to the explanatory variables.

In average terms, margin and turnover have significantly worsened between 2019 and 2020, while debt maturity has increased. A larger firm size, a greater age, a higher share of women in the workforce and subsidies have made wineries more resilient between 2019 and 2020.

To the best of the authors’ knowledge, this is the first financial statement analysis of the impact of COVID-19 in the winery sector.

First, this article seeks to establish a conceptual model for campus information systems for students, in order to make their comparison possible for strategic management purposes. Second, it seeks to test this conceptual model in a fieldwork on Spanish higher education institutions, in order to relate information system's characteristics with other organizational features.

The conceptual model was based on socio‐technical information systems and knowledge management literature. A holistic vision of higher education student's information needs was kept into account. Data about Spanish universities were gathered by means of structured visits to institutional websites and structured interviews with undergraduate students. Data were analyzed with multivariate statistical techniques.

From multivariate analysis, a classification was obtained for Spanish universities into three clusters with homogeneous characteristics, in the sense of informational and organizational aspects. These clusters are discussed in temporal terms and with some individual examples.

Campus information systems need to be broadly characterized for strategic information management purposes. The proposed and tested model is a step to address their characterization.

This user centered information systems conceptual framework is set up to obtain a global vision of new information and learning electronic environments in campus. Its application in a fieldwork on Spanish universities offers new insights about informational and organizational features in higher education.