Nonaka (2009) emphasizes that in the process of committing to and refining knowledge co-creation, participants learn to identify and remove obstacles to knowledge conversion (e.g. limited resources and capacities, a lack of time to engage in knowledge creation, as well as a lack of mutual trust among stakeholders). Knowledge conversion processes may, therefore, have a knowledge outcome as well as a social practice outcome (Nonaka 2009). While the interactions and relationships between agencies and stakeholder groups is essential to the success of boundary crossing processes, a key function of serious game simulations as boundary objects should therefore be to effectively increase and strengthen the interactions between these agencies and stakeholder groups in a watershed context. Although serious-game simulations, such as Aqua Republica, allow for participants to come together and interact in an informal environment with low stakes (Zhou 2014), it is essential at the same time to get a deeper understanding of the extent to which such boundary objects are capable of facilitating a space where bonds and connections can be formed and relationships forged and strengthened.
4. Interaction and Social Network Analysis
Interaction analysis refers to an interdisciplinary method for the empirical investigation of human-to-human interactions and human-environment interactions (Jordan and Henderson 1995). It pays close attention to verbal and non-verbal interactions as well as the use of artifacts and technology to facilitate such interactions (Jordan and Henderson, 1995). One of the main underlying assumptions of Interaction Analysis is that knowledge and action are fundamentally social in origin, organization and use. Knowledge creation processes cannot be measured solely through surveys, which are more static in nature. Jordan and Henderson (1995) emphasize that expert knowledge is not found within the brain or the mind of individuals, but within “social and material ecologies” that can only be discovered and transferred to other members of a community or network through the analysis of interactions and relationship dynamics.
Interaction analysis can be applied to learning and knowledge co-creation processes. In an interaction analysis frame, learning is ‘a distributed, ongoing, social process’ (Garfinkel, 1967). Serious game simulations then provide an arena where interactions are facilitated allowing for an ideal setting for interaction analyses to occur. Furthermore, the use of technology and artifacts – in the case of this study serious game simulations – allow for the creation of what Jordan and Henderson call a ‘social field’ or what Nonaka and Takeuchi refer to as a ‘Ba’. This social field is a space in which certain activities become either likely, probable, or possible (Jordan and Henderson, 1995). Therefore, by using serious game simulations, a social field is facilitated in which interactions may become more frequent and the exchange of knowledge and information may be facilitated.
Interaction analysis focusses on both verbal and non-verbal interactions. Body language can certainly speak volumes and it is important to take it into consideration. Interaction analysis is also concerned with interactions with technology or artifacts. In terms of this study that would refer to the laptop that players were using to play the game and the information transmitted by the game itself.
With the rise of portable video recording techniques interaction analysis has become easier to practice in a variety of different environments. Furthermore, with the increased ability to store large video files on the cloud, interaction analysis is facilitated by allowing for multiple viewers to have access to the files allowing multiple views of the same interactions.
Another useful method of analysis is social network analysis. With the rise of social network websites such as Facebook and Twitter. More resources are being spent studying other more academic uses for these ubiquitous networks. Within any group social networks exist and the information they can provide can have many uses.
Social Network Analysis (hereafter referred to as SNA) places the focus on the structure of these relationships. It is used to measure both formal and informal relationships to better understand the passages and barriers that either hinder or facilitate the exchange of information and knowledge. It allows this information to be mapped visual using SNA software.
The term social network was first coined in 1954 by John Barnes (Asian Development Bank, 2017). Social networks refer to groups of individuals frequently referred to as nodes in the context of social network analysis. These nodes can refer to individuals or groups (formal or informal) that include shared vision, values, or ideas. These nodes are held together by social contracts (Asian Development Bank, 2017) (i.e. a group of friends informally connected by similar or interests, or government agencies that share jurisdictions and are mandated to work together. Social networks are an “umbrella term” covering many forms and functions.
SNA is being used increasingly within the social sciences specifically in terms of psychology, health and business organization. (Asian Development Bank, 2017). Increasingly it is being used within leadership networks to analyze and ideally strengthen bonds across groups, organizations and related systems. (Asian Development Bank, 2017). By visualizing the bonds and relationships that connect people, researchers can isolate areas bonds that need improvement. Likewise, researchers can investigate the stronger bonds looking for methods to increase connectivity between weaker nodes. Simulation gaming events offer the perfect environment to practice social network analysis. By bringing together multiple stakeholders researchers can study the effects that serious games have on network connectivity.
5. Research Approach
The objective of this study is to determine whether serious game simulations present an effective boundary object to create a space in which interactions and knowledge co-creation can effectively occur. To achieve this objective, and to answer the specific research questions that have been identified earlier in this paper, the following research framework is proposed (see Figure 3).