The purpose of the current study is to investigate the role of the Islamic financial system in recovery post-COVID-19 and the way Fintech can be utilized to combat the economic reverberations created by COVID-19. The global financial crisis of 2008 has established the credentials of the Islamic financial system as a sustainable financial system which can save the long run interests of the average citizens around the world while adding value to the real economy. The basic ethical tenets available in the Islamic financial system make it more suited and readymade to fight the economic aftershocks of a pandemic like COVID-19. The basic principles of ethical Islamic finance have solid connections to financial stability and corporate social responsibility within the wide-reaching business context. With the emergence of Financial technology (Fintech) it has provided a missing impetus to the Islamic financial system to compete on equal ground with its conventional counterpart and prove its mettle. The study uses discourse analysis along with the content analysis to extract content and draw a conclusion. The findings of the study indicate that COVID-19 pandemic has provided the opportunity for the social and open innovation to grow and finance world have turned to open innovation to provide a speedy, timely, reliable, and sustainable solution to the world. The findings of the study provide significant implications for governments and policy makers in efficient application of Fintech and innovative Islamic financial services to fight the economic consequences of the COVID-19 pandemic.

With the advent of the Internet and other digital technologies, contemporary businesses from all sectors are using social media for communication with consumers to engage them meaningfully with a brand. However, the use of social media for corporate social responsibility (CSR) communication is relatively new to the existing literature. Likewise, the impact of CSR communication through social media (CSR-S) on consumer emotions and behavior is, to date, underexplored. To address this, the present research aims to test the relationship of CSR-S on brand admiration and consumer purchase intention. The study proposes a direct relationship between CSR-S and purchase intention with a mediating effect of brand admiration. The data were collected from the banking consumers of Pakistan through a self-administered questionnaire. The authors distributed 800 questionnaires and received 463 questionnaires useful for data analysis, so the present research study response rate was around 59%. The data were analyzed using the structural equation modeling (SEM) technique in AMOS. The results revealed that CSR-S is positively related to purchase intention (β = 0.233). The results further showed that brand admiration partially mediates this relationship (β = 0.079). The survey respondents confirmed that their bank’s CSR communication helps enhance their purchase likelihood and their feelings of admiration for their bank. These findings will help policymakers at banking institutions better understand the importance of CSR communication on different social media platforms to achieve consumer-related outcomes.

The purpose of the paper is to assess the artificial intelligence chatbots influence on recruitment process. The authors explore how chatbots offered service delivery to attract and candidates engagement in the recruitment process. The aim of the study is to identify chatbots impact across the recruitment process. The study is completely based on secondary sources like conceptual papers, peer reviewed articles, websites are used to present the current paper. The paper found that artificial intelligence chatbots are very productive tools in recruitment process and it will be helpful in preparing recruitment strategy for the Industry. Additionally, it focuses more on to resolve complex issues in the process of recruitment. Through the amalgamation of artificial intelligence recruitment process is increasing attention among the researchers still there is opportunity to explore in the field. The paper provided future research avenues in the field of chatbots and recruiters.

Tourism and hospitality have been recognized as leading economic sectors globally. Before the outbreak of COVID-19, it was estimated that the tourism and hospitality sector was growing by around 4% each year. Although the economic-efficiency-led hypothesis of the tourism and hospitality sector is strong, there is another perspective related to tourism and hospitality. That is, tourism and hospitality are not as “green” as they were supposed to be. Indeed, this sector is known for its outsized carbon footprint. It is estimated that, if not managed efficiently, the GHG contribution of the tourism sector will grow in the future. Specifically, the hotel business accounts for 1% of total global greenhouse gas emissions (GHG), which is huge. Responding to these significant issues, this study investigates the relationship between the corporate social responsibility (CSR) activities of a hotel enterprise and employees’ pro-environmental behavior (PEB). The mediating role of environmental-specific transformational leadership (ESTFL) and the moderating role of green perceived organizational support (GPOS) were also tested in the above relationship. The data were collected by the employees through a self-administered questionnaire. The hypothesized relations were statistically investigated by using structural equation modeling (SEM). The findings revealed that CSR activities of a hotel not only influence employees’ PEB directly, but the mediating role of ESTFL was also significant. At the same time, the conditional indirect role of GPOS was also confirmed. This study offers different theoretical and practical insights, which have been discussed in detail.

The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.