Table 1.

Methodology to develop an IR-based DRBV simulation model

Steps of the processSynthetic explanationAdditional information
Layout the resources/capitalsIdentify the key resources/capitals included in the <IR> reports and visualize them as stocksStocks are represented as boxes (suggesting each box/stock is holding its content)
Identify the processes (flows) responsible for building or eroding resourcesThe information collected has to be codified to recognize and represent the processes causing the resource growth or decrease, i.e. inflows and outflowsFlows are either inflows or outflows. Inflows are represented by an arrow pointing into a stock (adding to it). Outflows are represented by arrows pointing out of the stock (subtracting from it)
Valves control the flows
Clouds represent the sources and sinks of the flows
Identify capabilitiesCapabilities originate from either a single resource or from a set of related resources Capabilities can build other resources, generate value by attracting customers or generate activities influencing external stakeholdersThe capabilities discovered in the integrated reports may be presented in the resource maps using stock or auxiliary variables. In this study, we used auxiliary variables
Portray relationships (direct and indirect) and polarities (positive and negative)This entails representing the causal links in the organization, specifying their direction and assessing their polarityCausal links are depicted through the use of connectors (lines), which contain the direction of the linkage and the type of linkage, that indicates a positive impact – an increase in A increases B or a negative one, – an increase in A decreases B
Identify feedback loops (reinforcing and balancing)The resource mapping is finished with the identification of the feedback loops between resources and flows. A feedback process consists of a circular relationship between a set of concepts (or parts of a system)A feedback loop is formed when two or more variables are circularly connected, e.g. A affects B, then B affects C and ultimately C affects A determining a circular relationship between A-B-C
Feedback loops are recognized and labeled as either reinforcing (positive and generating growth) or balancing (negative and inducing stagnation)
Formulate the simulation modelAfter building the map, parameters have to be estimated, causal relationships among variables are to be specified and initial conditions need to be setInitial values and parameters, as well as quantitative relationships, are derived from the organization’s integrated reports. They may be immediately available (e.g. the initial value of a stock could be provided by the organization’s report) or derived thanks to the information retrieved from those reports
Simulate and calibrate the simulation modelThe model is simulated to generate dynamics. At the same time, the model is simulated under specific conditions to gain confidence in its reliabilitySimulation is performed using specific computer software. The simulation is carried out in reference to a specific time horizon (how far in the future should the model consider?)
The simulation results are analyzed to assess if the model reproduces dynamics adequate to the issue and domain under investigation
Perform sensitivity analysis and scenario testingThe model is tested performing sensitivity analysis. Specific scenarios can be specified and exploredSensitivity analysis entails testing the dynamics generated by the model given the uncertainty in parameters. In reference to integrated reports, this is relevant for testing the effects of risk factors and FLI
Scenarios are particularly useful to understand the effects generated by changing (or new) environmental and market conditions
Develop policy evaluation and formulate recommendationsThe IR-based DRBV simulation model is eventually used to gain policy insightThis last step entails assessing the results of the simulation model to gain policy insights, subsequently redesigning rules and strategies
Source: Adapted from Kunc and Morecroft, 2009 and Barnabè et al., 2019 

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