Hardik Bhimani

Bio: Hardik Bhimani is an academic researcher from RMIT University. The author has contributed to research in topics: Perspective (graphical) & Open innovation. The author has an hindex of 1, co-authored 1 publications receiving 87 citations.

Disengagement in Open Innovation: A Cognitive Perspective

Abstract: The central tenet of open innovation (OI) is that useful knowledge is widely distributed. By purposively engaging in knowledge sourcing and sharing (KSS) activities, organisations can create and capture value through collaborative exchange with others. Organising for OI relies on the assumption that individual managers tasked to bring it to fruition will enact behavioural responses conducive to external KSS. However, understanding what characterises and informs managers’ disengagement in OI remains an unresolved challenge. The interactions between managers’ in-role demands in OI and their self-concept, which guide behavioural responses, are under-investigated. Drawing on cognitive dissonance theory (CDT), this article conceptualises the sources of managerial dissonance and situates discussions on underlying influencing mechanisms, culminating towards a cognitive model of disengagement in OI. Bridging OI, psychology and management literature, hypotheses are developed to stimulate investigations into what characterises and influences managers’ disengagement in OI. Managerial implications are discussed to curate approaches that can help manage managerial dissonance in order to attain the desired organisational OI goals.

Utilization of text mining as a big data analysis tool for food science and nutrition.

Abstract: Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.