Socially Responsible Investing as a Competitive Strategy for Trading Companies in Times of Upheaval Amid COVID-19: Evidence from Spain

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Abstract

Sustainable and responsible investing (SRI) is a strategy that seeks to combine both financial return and social good. The need to create and preserve SRI represents a key argument in investment decision-making, which leads other firms and investors to make strategic decisions beyond financial logic, based on environmental, social, and governance (ESG) factors. Within this framework, this paper aims to further clarify the understanding of potentially profitable strategies for firms during a global crisis such as a pandemic. Both primary and secondary data were gathered, and descriptive analyses were conducted. In Spain, several IBEX-35 companies announced donations amid the COVID-19 crisis. First, companies were classified into two groups based on donations made. For this, we searched for ESG online news. Then, profitability records amongst companies were identified and compared. In the trading session after the announcements, we found 12 of the 35 companies that made donations had a higher performance index of more than 2 and 3 points over the companies that did not make donations. With a weekly perspective, the difference was 91 and 60 basis points, respectively. These results suggest that in times of upheaval, investors base their strategy on ESG factors, contributing to the emerging literature on individual motives of SRI. Second, by conducting a survey and collecting data from 575 Spanish citizens, we conclude that after this crisis, people’s perceptions towards corporate social responsibility (CSR) will change, affecting consumption preferences in those companies that exhibited socially irresponsible or unsupportive behaviour. Hence, the reputation of firms, their social image, and social trust will play an important role in the near future.

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Predictive model assessment in PLS-SEM: guidelines for using PLSpredict

Galit Shmueli, Marko Sarstedt, Joseph Hair … (2019)

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure. The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses. The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies. Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment. This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses. This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.