The rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation.

Airbnb is a home-sharing mobile platform that enables tourists and local hosts to connect. Airbnb mobile app allows hosts to list down the space available for rent and allow tourists to look for accommodations that are often more localized. Historical cities are often engaging tourists using traditional hotel reservation methods such as phone reservations or booking by travel agents, however, given digital disruption more local businesses are adopting technology in their business. This motivated the current study to assess the rate of peer-to-peer technology pervasiveness in historical city tourism, particularly for hotel booking using Airbnb app. This research investigates the factors that predict tourists’ behavioural intention to use Airbnb app when they travel to the historical city of Malacca in Malaysia by adopting the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model and by critically evaluating the exhaustiveness of the theory to measure tourists’ use of a mobile app. Partial Least Square analysis was used to test the research model, construct path model and to validate research hypotheses. The results from a sample of 200 tourists who visited and stayed in Malacca show that price value and social influence have high significant positive influence on their behavioural intention to use Airbnb app when booking accommodation in the historical city. In contrast, hedonic motivation and habit were found not significant, potentially due to the short-term and occasion-specific usage of Airbnb app not forming enjoyment in the process of usage, which raises concerns on applicability and relevance of UTAUT2 theory to measure user adoption of a mobile app for tourism. A peculiar finding in the study is the significant negative influence of facilitating condition to behavioural intention, for which we explain a plausible cause in the paper.

In order to meet the rising global demand for food and to ensure food security in line with the United Nation’s Sustainable Development Goal 2, technological advances have been introduced in the food production industry. The organic food industry has benefitted from advances in food technology and innovation. However, there remains skepticism regarding organic foods on the part of consumers, specifically on consumers’ acceptance of food innovation technologies used in the production of organic foods. This study measured factors that influence consumers’ food innovation adoption and subsequently their intention to purchase organic foods. We compared the organic foods purchase behavior of Malaysian and Hungarian consumers to examine differences between Asian and European consumers. The findings show food innovation adoption as the most crucial predictor for the intention to purchase organic foods in Hungary, while social lifestyle factor was the most influential in Malaysia. Other factors such as environmental concerns and health consciousness were also examined in relation to food innovation adoption and organic food consumerism. This paper discusses differences between European and Asian organic foods consumers and provides recommendations for stakeholders.

This study investigates and determines the most crucial factors that impact electronic commerce (EC) adoption among home-based business (HBB) owners in an emerging economy i.e. Malaysia and developed economy i.e. Singapore. An empirical survey research was conducted and 150 home-based business owners participated from Malaysia and Singapore respectively. The research is descriptive and causal in nature, interviews and data collection from both countries were conducted between June 2017 to April 2018. Data analysis was done using statistical software SPSS version 24 and SmartPLS 3.0. Findings reveal HBB owners' IT knowledge, risk perception and online trust are significant predictors of their EC adoption and online trust is found to be the most important factor that contributes to EC adoption collectively for both countries. HBB owners from both countries are found to have different sets of ranking for EC adoption drivers as indicated by the importance-performance map analysis chart. The governments' role in encouraging HBB ownership in the wake of Industry 4.0 can be enhanced in both countries. HBB Owners could be empowered with the right training, awareness and policy that is HBB friendly. Especially in Malaysia, risk perception is still a significant hindrance for HBB owners to engage in EC. This study pioneers in comparative EC adoption research between a developed and an emerging economy who are neighboring each other and share rich history together. The results assist in understanding the dynamics of EC in this region as well as pave paths for further research inquiries, especially with the advent of Big Data and increasing numbers of HBB owners in both countries powered by Social Media.

The recession in India and the UK peaked in 2017 due to the implications of new policy initiatives. The outbreak of the COVID-19 pandemic at the beginning of 2020 intensified the crisis, causing a drastic decline in aggregate demand and output. India and the UK have resorted to monetary and fiscal stimulus packages to face the economic crisis. This study investigated the inflation–unemployment dynamics during the recession and COVID-19 times in India and the UK. Using a generalized additive model (GAM), the results of this study revealed that the recession had given way to stagflation in India. In contrast, in the UK, it has led to a more severe recession in the short-run. During the downturn, policy initiatives aggravate the recession and eventually turn to stagflation in India due to inflation caused by the weak supply side. However, in the UK, the policy initiatives during this downturn pushed the economy into a deeper recession due to reduced demand. The outbreak of the COVID-19 pandemic has had a similar recessionary impact on both economies. A time horizon based recovery plan is suggested to help the economies recover from stagflation and even deeper recession. This framework could enable policymakers to choose the right path of recovery within the shortest possible time.