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This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign institutional investors (FII). These subcategories include resource use, emission reduction and innovation under the environmental pillar, workforce, human rights, community and product responsibility under the social pillar and management, shareholders and CSR strategy under the governance pillar.

A machine learning technique known as “topic modeling” is used to analyse the current literature on ESG. To investigate the correlation between ESG scores and their subcategories with the investment decisions made by FII and to address concerns regarding multicollinearity and overfitting, a penalty-based regression model is employed.

The findings indicate that FIIs invest in firms with higher emission reduction and innovation scores under the environmental indicator. Additionally, firms with high human rights, community and product responsibility scores under the social indicator category have a positive relationship with FII investors. All subcategories of governance indicators, such as corporate social responsibility (CSR), strategy, shareholders and management scores, also positively impact FII investment. Of the three indicators, i.e. ESG, non-promoter FIIs give maximum weightage to governance indicators.

Since ESG is a contemporary topic, the findings on the relationship between different categories of ESG on FII investment will support managers in their FII investment. Also, the study will help the government frame policy decisions on ESG.

Previous studies have explored the impact of the overall ESG indicators on FII investments, but they have not specifically studied the influence of sub-indicators within these categories on investment decisions. By addressing this gap, the study enhances stakeholder theory by identifying and prioritizing the various subcategories of ESG indicators that impact FII investment decisions.

The purpose of this paper is to study the interaction of timing cues (active deals vs upcoming deals) and brand familiarity on consumers’ purchase intention.

The research design of this paper is based on one experiment conducted in a behavioural experimental laboratory and two experiments in Mechanical Turk. The data received from these experiments were analysed using one-way ANOVA technique and PROCESS models.

Results across three experiments show that consumers prefer upcoming deals over active deals when brands are of low familiarity. However, the effect is attenuated for brands with high familiarity. Further, the paper proposes and shows that the effects of timing cues and brand familiarity interactions on purchase intention are explained by temporal distance. Finally, results show that need for cognition moderates the effects.

The research results may vary when size of discounts is mentioned. There is a need to study various other moderators such as product type, self-other overlap and regulatory focus.

The paper offers managerial strategies and insights that can be adopted by the online retailing industry. Paper offers practical implications for brands, especially for ones with low familiarity.

This paper adds to the literature by studying new timing cues to understand the behaviour of consumers in retailing industries.