ABSTRACT
Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.
ABSTRACT
In order to grow the food the world needs, there is a pressing need to gain a more detailed understanding of how innovative solutions can be incorporated into the agricultural supply chains, particularly within production, for environmentally, economically, ethically and socially viable food production. Despite a number of innovative solutions available, many challenges in agricultural supply are still prevalent, with researchers to date largely focusing on these challenges in isolation, as opposed to exploring the relationships held between these challenges. Thus, supported by Circular Economy, Agriculture, Industry 4.0 literature and expert opinions, agricultural supply chain challenges are modelled and analysed using ISM methodology to help uncover 12 agricultural challenges which ultimately impede goods moving within the supply chain. Findings discovered that the Unproductive Workers and Pesticide Hazards are the key drivers of agricultural challenges. The ISM Hierarchical model elucidates research propositions and a parsimonious model for future research.
ABSTRACT
[...]through their quantitative decision-making method of analytical hierarchy process, the study identifies technological advancements and data security as amongst the most important factors that may impact the effectiveness and efficiency of big data usage within public sector contexts. [...]public and private sector data sets are processed with an advanced big data-oriented A.I. feature selection algorithm, to identify characteristics of a firm (e.g. resources, capabilities, practices, etc.) as well as its external environment that affect (positively or negatively) its resilience to economic crisis. The research teases out, how approx. 15,000 open data consumers expressed doubt relating to data sources in terms of its availability, interoperability, and interpretation. [...]scant metadata and documentation released by government agencies, as well as limited people having domain-specific expertise to leverage open government assets, were identified as key challenges. [...]this study proposes a data signal framework that explains uncertainty about open data within the context of control and visibility.
ABSTRACT
Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people's well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people's personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.