Operationalizing Embeddedness for Sustainability in Local and Regional Food Systems

Agricultural systems are deeply embedded in social processes and the institutions that govern them. Measuring these processes and understanding the extent of that embeddedness is critical to crafting policy for sustainable agricultural systems. The bulk of measurement in sustainability research, however, focuses on economic and environmental indicators such as farm profitability and water quality. Since policy is most often aimed at what is measured, it tends to focus on issues like price, production, and market access. And while those are important, policies aimed at social issues such as community reciprocity are often outside the scope of policy design. The gap between social measurement and policy is not for lack of care; the importance of social dynamics is well known. Yet due to the difficulty of measuring complex social systems— How does one measure values?—more straightforward economic and environmental measures dominate research and policy. When social systems are measured, as, for example, with the social capital or sustainable livelihoods frameworks, they often do so using economic methodologies and indicators. Such economic-based social indicators are important but focus heavily on outcomes such as poverty or profitability. Accordingly, the complex social processes that lead to such outcomes such as culture, heritage, tradition or generational dynamics are often overlooked. These policy and methodological difficulties present a problem: measurements import the theoretical framing of their intellectual development. Economic methodologies are largely rooted in an atomistic theory of human behavior in which individuals are selfishly motivated by economic gains. While individuals do seek economic success, they are also motivated by social connection, reciprocity, values, and culture. The institutions governing these social processes and the degree to which individuals and businesses are embedded in society are incredibly important, yet poorly understood and measured. This paper outlines a theoretical framing for understanding these complex social processes and develops a methodology for measuring social embeddedness in local and regional agricultural systems. Coined by sociologist Karl Polanyi, embeddedness is the extent to which economic systems like markets are governed by non-economic systems such as culture and social cohesion. While markets and their price and output components are well understood and widely measured, the non-economic institutions like culture and values that support and govern markets have tended to be seen as non-measurable. This has important policy implications for rural agriculture. Accordingly, this paper develops a tool for measuring the social embeddedness of producers and consumers in ten agricultural sectors in Vermont that can be replicated across New England. The tool uses a Likert scale survey designed to understand the degree to which producers and consumers are motivated by self-interest—what we call Instrumentalism—and the extent to which they are market-oriented—what we call Marketness. Survey responses are analyzed using a Factor Analysis to generate Instrumentalism and Marketness scores for each survey respondent on a scale of -1 to 1. The Embeddedness Type Matrix consists of a vertical Instrumentalism axis and a horizontal Marketness axis that together create four quadrants that represent different types of embeddedness: embedded, underembedded, disembedded, and overembedded. Individual consumers and producers are plotted on the matrix based upon their respective Instrumentalism and Marketness scores and yield an embeddedness type given their quadrant. Plotting all producers and consumers of a particular industry on the Embeddedness Type Matrix provides an understanding of the motivations, values, actions, and interactions of the individuals in that industry. This paper provides researchers and policy makers in Vermont and New England with a tool to understand and measure the social aspect of agricultural sustainability in multiple industries. This approach allows for the design of policy aimed at aspects of the food system outside of price, production, and market access alone

Dynamic Personalization of Gameful Interactive Systems

Gameful design, the process of creating a system with affordances for gameful experiences, can be used to increase user engagement and enjoyment of digital interactive systems. It can also be used to create applications for behaviour change in areas such as health, wellness, education, customer loyalty, and employee management. However, existing research suggests that the qualities of users, such as their personality traits, preferences, or identification with a task, can influence gamification outcomes. It is important to understand how to personalize gameful systems, given how user qualities shape the gameful experience. Current evidence suggests that personalized gameful systems can lead to increased user engagement and be more effective in helping users achieve their goals than generic ones. However, to create these kinds of systems, designers need a specific method to guide them in personalizing the gameful experience to their target audience. To address this need, this thesis proposes a novel method for personalized gameful design divided into three steps: (1) classification of user preferences, (2) classification and selection of gameful design elements, and (3) heuristic evaluation of the design. Regarding the classification of user preferences, this thesis evaluates and validates the Hexad Gamification User Types Scale, which scores a person in six user types: philanthropist, socialiser, free spirit, achiever, player, and disruptor. Results show that the scale’s structural validity is acceptable for gamification studies through reliability analysis and factor analysis. For classification and selection of gameful design elements, this thesis presents a conceptual framework based on participants’ self-reported preferences, which classifies elements in eight groups organized into three categories: individual motivations (immersion and progression), external motivations (risk/reward, customization, and incentives), and social motivations (socialization, altruism, and assistance). And to evaluate the design of gameful applications, this thesis introduces a set of 28 gameful design heuristics, which are based on motivational theories and gameful design methods and enable user experience professionals to conduct a heuristic evaluation of a gameful application. Furthermore, this thesis describes the design, implementation, and pilot evaluation of a software platform for the study of personalized gameful design. It integrates nine gameful design elements built around a main instrumental task, enabling researchers to observe and study the gameful experience of participants. The platform is flexible so the instrumental task can be changed, game elements can be added or removed, and the level and type of personalization or customization can be controlled. This allows researchers to generate different experimental conditions to study a broad range of research questions. Our personalized gameful design method provides practical tools and clear guidelines to help designers effectively build personalized gameful systems

Gamification of workplace activities

Gamification—taking game design patterns and principles out of video games to apply them in non-game environments has become a popular idea in the last 4 years. It has also successfully been applied to workplace environments, but it still remains unclear how employees really feel about the introduction of a gamified system. We address this matter by comparing the employees’ subjective perception of gamification with their actual usage behavior in an enterprise application software. As a result of the experiment, we find there is a strong relationship visible. Following up on this observation, we pose the gamification design problem under the assumptions that (i) gamification consists of various types of users that experience game design elements differently; and (ii) gamification is deployed in order to achieve some goals in the broadest sense, as the problem of assigning each user a game design element that maximizes their expected contribution to achieve these goals. We show that this problem can be reduced to a statistical learning problem and suggest matrix factorization as one solution when user interaction data is given. The hypothesis is that predictive models as intelligent tools for supporting users in decision-making may have the potential to support the design process in gamification