Convergent Innovation in Food through Big Data and Artificial Intelligence for Societal-Scale Inclusive Growth (BRENNOR JACOBS)

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Dubé, L., Du, P., McRae, C., Sharma, N., Jayaraman, S., & Nie, J. (2018, February). Convergent Innovation in Food through Big Data and Artificial Intelligence for Societal-Scale Inclusive Growth [Electronic version]. Technology Innovation Management Review, 8(2), 49-65.

Economists around the world have reported that wealth is rising unequally, and it is increasingly concentrated in the hands of fewer people. That is not the only problem however, as the benefits of innovation are also being unequally shared. This article looks at convergent innovation in the food industry. The authors objective is to develop the structure and methods for an artificial intelligence digital platform that supports convergent information. Additionally, the authors aim to generate consumer insights on conflicting demand drivers for convergent innovation, by focusing on social media and user generated content.

In this article the authors engage in textual analysis of user-generated content on social media. The authors used this content to gain insight into the behaviour of consumers for convergent innovation. Social media channels are able to put an emphases on consumer needs, and give insight into the roles these needs play for consumers decision making. To do this, big data was utilized to gain insight on consumer packaged goods and product characteristics that appeal to consumers. Because food takes many forms, examining big data allows researchers to examine the connections between “consumers, markets, products, purchase/loyalty intent, and advertising at varying time and location points across physical and digital channels”.

Inclusive growth economics is growth that not only creates economic opportunity, but also ensure equal access to opportunities created for all segments of society. Inclusive projects aim to improve economic situations for low income and marginalized groups. However the difficulty in implementing these strategies comes from a limited share of national and global wealth creation which limits their social and economic impact. Inclusive innovation constraints that prevent impact on a societal scale include the structural divide between poverty alleviation strategies and those that focus on the creation of wealth. This divide is seen between the private sector, which focuses on technological innovation and economic growth; and the government sector which use a “one size fits all” model, which aim to stabilize suitable education, health, and social goods for everyone. The authors also make note of real-world indicators and drivers that effect human behaviour. These drivers cause changes from short-term gratifiers to long-term considerations for oneself and society. Because these drivers substantially effect decision making, creating a platform for food becomes a challenging task. To create a convergent innovation platform requires deep insight into customer behaviour by mining big data to characterize aspects of food choice and behaviour.

The researchers developed a artificial intelligence platform to support convergent innovation in food. The platform utilizes opinions and feelings on different aspects of food from social media users. The AI systematically works in three layers, data collection and management, analysis, and application. In the first layer, the system first acquires data from social media platforms, while simultaneously managing the increasing data input and output for future processing. This data can also be divided into query’s of seed word searches which strongly relate to the food topics being investigated, and in this study 359 seed words were collected for food. The system performed a historical analysis of data collected over 30 days on social media messages (Twitter) and social media streaming (Twitter and Facebook), while data collected from other sites were stored and categorized for future processing.

Next, the system works to crawl through the data and analyzes it, then constructs a food ontology using a processing sequence of immediate results. The system filters spam and expands the search query accordingly. It then draws connections between words and phrases to further categorize discussions on food to analyze them further through opinions gathered through text. A statistical analysis is then performed to analyze word occurrence in the data collected.

The final layer then utilizes the data it has analyzed, and places it into systematic applications to support decision making.

Results from the analysis uncover consumer likes and dislikes about food aspects, as well as the positive and negative drivers of behaviour through regression modelling. The results of this analysis reveal that consumers discuss products and ingredients the most through social media. From a inclusive innovation perspective, it was also found that economic consequence is a strong predictor of consumer global sentiment and highlights the need to make food accessible at the appropriate price.

In order to address problem areas in wealth creation and poverty alleviation, innovation must incorporate ways to create lasting human and environmental health. In order to change these systems, artificial intelligence, big data, and digital technology need to be utilized, and serve as key catalysts for innovation around the world. Because many consumers are self-conflicted with their purchasing habits, innovation is also needed to transform these stakeholder and consumer practices.

Two factors make convergent innovation capable of being implemented at a global scale. First, digital infrastructure, big data, artificial intelligence systems, as well as integrative analytics, improve the capability of bridging operation with policy. The second, integrates cutting-edge scientific knowledge on complex drivers of human behaviour in varying contexts and their linkages to biological and social outcomes, accelerated by the conceptual and methodological development in genomics, neuroscience, and behavioural economics.

In this study there remain limitations to how many levels can be found in each aspect of food, and future research will need to dive deeper into data collection for meaningful insight into behaviour and global sentiment toward these different aspects of food. This analysis will work to inform and develop the convergent innovation in the marketing of food products.

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