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The purpose of this paper is to propose the concept of conviction in online environment. It examines the vital role of conviction and firm’s brand reputation while understanding the impact of social media usage and electronic word-of-mouth (EWOM) on purchase decisions of Generation Y.

Literature review resulted in six constructs – social media usage, EWOM, conviction, firm’s brand reputation and purchase intention and customer loyalty. The authors adopted the concept of conviction from another field of enquiry (organizational learning), conducted a qualitative study and an e-mail survey with post-graduate management students (Generation Y) of a university to examine the impact of social media and EWOM on customer purchase decision. Data were collected and analyzed with the help of structural equation modeling.

Results indicated that impact of social media usage and EWOM on purchase decision is mediated by conviction. Firm’s reputation as brand (perceived by the customer) moderates the relationship between EWOM and purchase intention in a manner that this relationship is significantly stronger if there is more positive brand reputation.

This study validates the concept of conviction in online environment. The purchase decision is defined as purchase intention and loyalty of the customer.

Solar energy is on a rising trend internationally and in India. The government target of 100 GW solar capacity by 2022 from the present 12 GW is providing a major push for growth in India. However technological development and market competitiveness are pushing down the prices of solar power. The CEO of Amplus Solar has to deal with these challenges to ensure faster growth. He is analysing various options such as expanding the market to include customers who may not be as credit worthy, expanding to foreign geographies, diversification into providing energy efficiency and other services, and entering other markets such as Renewable Energy Certificates, carbon trading, etc.

This research study aims to design a novel risk-managed time-series momentum approach. The present study also examines the time-series momentum effect in the Indian equity market. Apart from this, the study also proposes a novel risk-managed time-series momentum approach.

The study considers the adjusted monthly closing prices of the stocks listed on the Bombay Stock Exchange from January 1996 to December 2020 to formulate long-short portfolios. Newey–West t statistics were used to test the significance of momentum returns. The present research has considered standard risk factors, i.e. market, size and value, to evaluate the risk-adjusted performance of time-series momentum portfolios.

The present research reports a substantial absolute momentum effect in the Indian equity market. However, absolute momentum strategies are exposed to occasional severe losses. The proposed time-series momentum approach not only yields 2.5 times higher return than the standard time-series momentum approach but also causes substantial enhancement in downside risks and higher-order moments.

The study's outcomes offer valuable insights for professional investors, capital market regulators and asset management companies.

This study is one of the pioneers attempting to test the time-series momentum effect in emerging economies. Besides, current research contributes to the escalating literature on risk-managed momentum by suggesting a novel revised time-series momentum approach.

This study aims to measure the level of public awareness about the threat of the emerging coronavirus (COVID-19) pandemic among the Omani population. It also aims to investigate the mediating effect of the Omanis’ attitudes and behaviors with underlying conditions of COVID-19.

A cross-sectional study was conducted to collect data via an online survey of Omani citizens and residents from various geographic areas in Oman, 305 responses were received. SPSS and partial least square-structural equation modeling were used for data analysis.

The study revealed that public awareness regarding the COVID-19 pandemic was significantly influenced by people’s perceived risk, information source and health-related knowledge. Further, preventive behavior during the disease spread has a significant direct and indirect impact on their awareness. However, an insignificant mediation effect of public attitude was found between the source of information and public awareness.

This study is limited by the scarcity of related literature in the Omani context. It is recommended that future research complete an in-depth study of public awareness regarding COVID-19, using other constructs and/or other data collection techniques.

This research will provide governmental health authorities and policymakers with a guideline to establish more efficient pandemic containment strategies to control public behavior toward the COVID-19 pandemic and curb viral prevalence.

This research will help in improving prevention measures against COVID-19 are recommended to be more educated through a more effective mechanism to raise public attitude regarding pandemic prevalence positively.

The originality of this research can be drawn from key findings that indicate that people overall gained knowledge about how to deal with the COVID-19 pandemic and the accuracy of information significantly impacts public awareness.

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.