How does entrepreneurial orientation and SDG orientation of CEOs evolve before and during a pandemic

A social media competitive intelligence framework for brand topic identification and customer engagement prediction

PURPOSE

The COVID-19 pandemic has changed customer social media engagement behavior, which challenges the establishment of effective marketing strategies to strengthen digital communication with customers and leads to new opportunities for social media competitive intelligence analytics. This study presents a new social media competitive intelligence framework that incorporates not only the detection of brand topics before and during the COVID-19 pandemic but also the prediction of customer engagement.

DESIGN/METHODOLOGY/APPROACH

A sector-based empirical study is conducted to illustrate the implementation of the proposed framework. We collected tweets generated by 23 leading American catering brands before and during the pandemic. First, we used Amazon Comprehend and Latent Dirichlet allocation (LDA) to extract sentiments and topics behind unstructured text data. Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.

FINDINGS

The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. Pre-pandemic topics primarily included "Food and lifestyle", "Promotion", "Food ordering", "Food time", and "Food delivery". During the pandemic, the topics expanded to include "Social responsibility" and "Contactless ordering". For predicting customer engagement, the performance metrics show that Random Forest and C5.0 (C50) are generally the best-performing models, with Random Forest being particularly strong for "Likes" and "Retweets", while C50 performs best for "Replies".

ORIGINALITY

This framework differentiates itself from existing competitive intelligence frameworks by integrating the influence of external factors, such as the COVID-19 pandemic, and expanding the analysis from topic detection to customer engagement prediction. This dual focus provides a more comprehensive approach to social media competitive intelligence.

Sustainable Development Goals in the COVID-19 Pandemic: A Narrative Review

The present narrative review aimed to analyze the impact of the COVID-19 pandemic on the sustainable development goals (SDGS). This information would allow a better comprehension of the actual state of the SDGS and a more efficient programming in future interventions. To achieve the objective of the study, a consensual and critical review was carried out using both primary sources, such as scientific articles, and secondary sources, such as bibliographic indexes, web pages and databases. The Sustainable Development Goals are a global call to action to end poverty, close the gender gap, protect the planet, and improve the lives of people around the world. In 2015, the United Nations General Assembly adopted 17 goals as part of the 2030 Agenda for Sustainable Development, which sets out a plan to achieve the goals in 15 years. However, the COVID-19 pandemic crisis has been a turning point in the achievement of these goals, due to all its consequences at the political, economic, and socio-cultural levels. This review can be used as a guide for future research and reviews in order to understand the status of each of the SDGs and what actions have been taken and proposed in the aftermath of the pandemic in recent years.

Vinculum of Sustainable Development Goal Practices and Firms’ Financial Performance: A Moderation Role of Green Innovation

The 2030 Agenda for Sustainable Development (SDGs) has been established to alter our world by addressing the challenges faced by humanity in order to promote wellbeing, economic prosperity, and the protection of the environment. The SDGs provide a holistic and multi-dimensional approach to development compared to conventional development plans that focus on a limited range of dimensions. As a result, linkages between the SDGs may result in differing outcomes. This research is the first to investigate the direct relationship of environmental and social SDGs with firms’ financial performance and the moderating role of green innovation. Data from 67 companies from five continents (Europe, Australia and New Zealand, Asia, North America, and Africa) and their top five blue-chip firms were collected through content analysis. Generalized least squares (GLS) were used to test for direct relationships. The results showed a positive correlation between environmental SDGs and the negative significance of social SDGs on firms’ financial performance. However, mixed findings regarding the moderation variable green innovation over SDGs and firms’ financial performance were found. The new findings extend the SDG literature and provide empirical evidence to practitioners and policymakers.

Novel Insights in the Leadership in Business and Economics: A Post-Coronavirus Update

Our world is evolving at an incredibly enormous speed and what was impossible three years ago is now a reality. The concept of leadership and leaders has also undergone profound transformations. Moreover, the recent COVID-19 pandemic caused a digital surge in the ways economic life, business, or education are perceived or conducted. The pandemic proved that small and large businesses, industries, and the whole economies can be suddenly upended by massive technological shifts. Hence, there is a need for a theoretical research update in leadership in business and economics that would bring new insights into this topic and define its place within the context of Sustainable Development Goals (SDGs). Our paper presents the novel insights for the leaders and the leadership concept in business and economics from various approaches and angles of view in the light of the COVID-19 pandemic with a focus on sustainable leadership and organizational resilience. It aims at outlying the theoretical background of leadership in business and economics after the pandemic and bringing up interesting and recent leadership case studies from all around of the world. Moreover, this paper aims as sharing the valuable insights into what it means to be a sustainable leader in business and economics, why leaders are needed, and how to become one. The main criteria of this research and its instrumentation include both the theoretical discussion based on the literature review and analysis and the empirical analysis that supports these theoretical provisions. The paper features an empirical model that assesses how business and economic leaders are searching for new ways of work and personal development during and after the COVID-19 pandemic using the own data from the 400 respondents collected in the Czech Republic and Russia. We found that the pandemic enhanced the emotional creativity of business and economic leaders and made them to invest and engage more into using new digital technologies and fundamentally altering the old ways of managing and governing their respective companies and institutions. Our results might provide valuable food for thought both for academics working on various angles of leadership, as well as for entrepreneurs and businesspeople who want to receive recent updates on the topic of leadership to use them in their daily work.

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Financial markets across the globe have seen rapid volatility and uncertainty owing to scary and disruptive impacts of COVID-19 pandemic. Mayhem wrecked by frequent lockdowns, curfews, emergencies, etc. has stoked the high quantum of chaotic movement in equity markets and resulted in perplexed investor behaviour. It, therefore, is of paramount practical relevance to measure predictability of market fear at such a crucial juncture of time. Market fear can effectively be measured in terms of implied and historic volatility of equity markets. The present study chooses India VIX and 20-day rolling standard deviation of NIFTY returns to account for implied and historic volatility respectively during the ongoing COVID-19 timeline. Pertinent macroeconomic constructs, technical indicators and Google search volume index on meaningful keywords have been chosen as raw explanatory features for inspecting predictability. Boruta feature selection methodology has been used in a supervised manner to select significant features. State-Of-The-Art machine and deep learning algorithms namely Gradient Boosting (GB), Extra Tree Regression (ERT), Deep Neural Network (DNN), Long Short Term Memory Network (LSTM) are then used on processed feature set to scrupulously evaluate the quantum of predictability of said assets. The integrated predictive frameworks have been subjected to a battery of numerical and statistical checks to draw inferences. Additionally, Explainable AI frameworks have been used to analyse the nature of influence of respective features. Findings indeed suggest that despite exhibiting high degree of volatile traits, both India VIX and historic volatility can be predicted utilizing the proposed architectures effectively and serve practical actionable insights.