Visualizing and managing value creation through integrated reporting practices: a dynamic resource-based perspective

Abstract

The article builds on the current debate on how accounting tools can assist top management teams to manage their resources, while communicating a variety of data and information about value creation to their stakeholders. Within this debate, the study focuses on a recent tool for corporate reporting, the Integrated Reporting (〈IR〉), and investigates its utility to support the development of a holistic model for managing strategic resources to create value. To operationalize the 〈IR〉 according to this perspective, the article combines 〈IR〉 with the Dynamic Resource-Based View (DRBV) of the firm on the basis of their common idea that strategic resources are interconnected and have to be managed with the collaboration of all stakeholders in order to inform governance actions and create value with a holistic perspective. For the two case studies analyzed, the information provided by the two organizations’ 〈IR〉 is specifically organized and re-framed using “resource mapping”, which is a DRBV-based visual and analytical technique representing the causal relationships between resources and governance actions. In this way, we not only aim to re-organize and visualize existing information into a new form, but we also expect to describe and communicate the dominant logic in the business and the leverage points where the value creation process lies, supporting the usefulness of Integrated Reporting as a management and governance tool.

Appendix

1.1 Method to build resource maps from 〈IR〉

The process of developing the resource maps follows the stages described below.

The first step entailed searching within the documents for key 〈IR〉 -related concepts (e.g., capitals, business model, value creation process, business activities, outputs and outcomes) and, more in detail, identifying the two organizations’ capitals/resources, classified according to the indication of the 〈IR〉 framework (IIRC 2013a, c). In our case, we reviewed in detail the four annual Integrated Reports (analyzed separately for SASOL and ENI), focusing on all sections of the reports explaining the two organizations’ business model, value creation process and key performance indicators (KPIs); these three elements represent the fundamental concepts at the core of any integrated report. This step also entailed identifying and visually reviewing any representation, table or matrix related to the key guiding principles listed by the IIRC’s framework (IIRC 2013a): strategic focus and future orientation; connectivity of information; stakeholder relationship; materiality; conciseness; reliability and completeness; consistency and comparability. The analysis in this phase of our research was conducted following the guidelines and suggestions provided by several contributions in the field of content analysis for qualitative research and, more specifically, when applied to sustainability and ESG-related reporting (Milne and Adler 1999; Guthrie et al. 2004; Guthrie and Abeysekera 2006). In broad terms, content analysis is “a research technique for the objective, systematic and quantitative description of the manifest content of communication” (Berelson 1952); moreover, content analysis is also “a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use” (Krippendorff 2004, p. 21). Notably, the technique can be used with either qualitative or quantitative data (Elo and Kyngäs 2008, p. 107) and provides powerful support for researchers who aim to analyze large volumes of data in a systematic fashion with relative ease (Stemler 2001, p. 137). Therefore, content analysis allows organizing “the text of writing into various groups or categories based on selected criteria” (Guthrie et al. 2004, p. 287).

The second step of the process required exploring and treating the information retrieved according to a coding technique that involved, as suggested by Kim and Andersen (2012, p. 315), “discovering concepts and their relationships from raw data and iteratively working with the concepts and relationships to allow theories to emerge from the data”. Specifically, we applied two (out of the three) typologies of coding suggested by Kim and Andersen (2012, pp. 315–316): first, open coding was used to break down data into smaller pieces-words, phrases, sentences and paragraphs and to define (sub)systems boundaries and identify key variables; secondly, axial coding was used to find relationships among the concepts, variables and codes previously identified. The objective of coding was to confirm linkages between the components of the resource maps as well as other components not related with resources such as external factors or activities.

Then, we proceeded to the next stage of our research process, using the software Vensim (Eberlein and Peterson 1992) to develop the resource map for each organization accordingly with the steps outlined below (see Table 5) (Kunc and Morecroft 2009; Kunc and O’Brien 2017).

More details are provided below.

Table 5 Methodology to develop a resource map for 〈IR〉

• 1. Lay out the resources (boxes) In order to identify resources in the integrated report, there is a set of questions: what are the capitals identified in the report? How is this capital built? Does the capital have a long-term life in the organization? Is this the basic unit of analysis or can we identify another accumulation process defining the capital? For example, “Chemical plants” (from SASOL’s map) have a long-term life, are tangible and there is no other accumulation process. Notably, for all of the different categories of capitals (see IIRC 2013a), each report specified the name of the resources either owned or managed by the organization. Therefore, this allowed us to start building the resource map, beginning with the identification of the resources at the core of the two organizations’ business models.

• 2. Identify the processes (flows) responsible for building or eroding resources The information collected has to be codified in order to recognize and represent the processes causing the resource growth or decrease, i.e. inflows and outflows. For example, “New buildings and equipment” (from ENI’s resource map) is an investment (inflow) increasing the resource “Buildings and other equipment”, or “Investment” is an outflow reducing the organization’s “Liquidity reserves”, which is a resource.

  As an example, Fig. 6 portrays the key resources and the main governance actions for SASOL, respectively visualizing the variables belonging to the “Manufactured capital” and the “Financial Capital” and their trade-offs and relationships. In terms of the flows between the resources of the manufactured capital, they can be associated with flows of energy between the different assets.

  Fig. 6

  

  SASOL’s resource map based on the stocks of Manufactured capital and Financial capital as indicated in the organization’s 2015 and 2016 integrated reports

  Particularly, the simple map represented in Fig. 6, identifies the SASOL’s key stocks of capital (resources), main actions (flows), and also value creation outputs and outcomes for its stakeholders (concepts in red color). Different colors serve to make the resource map more intelligible. The names of the resources (rectangles) are aligned to the stocks of capital as indicated by the organization’s integrated reports, and the names of the flows (arrows with little valves) indicate the processes responsible for accumulation and depletion of the resources (as identified in the previous steps). We applied the same method also to build an initial simple resource map for ENI.

  It is to note that an integrated report usually provides a verbal description of the stocks and their flows, and that most of the variables and actions which are mentioned and described in such reports are expressed in monetary terms. We subsequently connected resources through a number of flows, re-framing the information provided by the reports into a different form. In this regard, dimensional consistency between resources is not strictly ensured in this work, since the resource maps are primarily aimed at providing a qualitative and quite aggregate visualization of the complex hierarchy of resources and connections existing in the business domain which is under investigation and is described by the integrated reports. Should the maps be converted into simulation models, this would definitely require further analysis and modeling effort.

• 3. Identify capabilities Capabilities 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 stakeholders. The questions to identify capabilities are: what processes originate from resources? Where this activity comes from? The capabilities discovered in the integrated reports are presented in the resource maps using variables and not boxes.⁴ For example, “Technology and patents” (resource) boost “Technological upgrade” (capability) which in turn sustains “Fracking” (flow) thus increasing “Hydrocarbon (gas) reserves” (resource) (see Fig. 3).

• 4. Portray relationships (direct and indirect) and polarities (positive and negative)To design the resource map basing on the integrated report data, we also need to represent the causal links in the organization. They are depicted through the use of connectors (lines) which contain the direction of the linkage and the type of linkage, e.g. (from Fig. 3), “Financial capital” (resource) generates new “Investments” (outflow) that sustain “Research and development expenditures” (inflow) that eventually develop “Technology and patents” (resource). The type of linkage indicates a positive impact—an increase in A increases B, or a negative one,—an increase in A decreases B. For example, “Investments” have a negative impact on “Financial capital” because “Investments” decrease initially the amount of “Financial capital”. However, “Investments” have a positive impact on “Technology and patents” because they support “Research and Development expenditures”, which generate patents. A critical aspect to consider is the evidence to uncover these relationships: a verbal description of the linkage (explicit) or an inference from existing data or comments in the report (implicit) through the content analysis process performed at the beginning.

  See examples in Table 6, which also provides a rather general description of the concept of “polarity”.

  Table 6 Examples of causal relationships (from ENI’s resource map)

  Identifying causal relationships and defining the polarity to be assigned to the linkages were not always straightforward. To this aim, we relied on the information explicitly provided by the documents (e.g., maps describing business processes or verbal descriptions of the business activities) and on the coding technique applied in the previous step of the research design. In this regard, it is to stress again that a resource map developed in accordance with the methodology we outline in this study, is in some way the “representation” of the business domain as perceived by the organizations and their managers. This representation is conveyed through the information, data, graphs, and descriptions included in the organizations’ integrated report which eventually reflects the underlying managers’ mental models.

• 5. Include additional external or unmanageable effects If the 〈IR〉 stresses the existence of further (external or unmanageable) events affecting the capitals, these events have to be included in the resource map, specifying the type of the effect produced (negative or positive).

• 6. Identify feedback loops (reinforcing and balancing) The resource mapping is finished with the identification of the feedback processes between resources and flows. A feedback process consists of a circular relationship between a set of concepts (or parts of a system), e.g. A affects B, then B affects C and ultimately C affects A determining a circular relationship between A-B-C. They are recognized and labelled as either reinforcing (positive, generating growth) or balancing (negative, inducing stagnation).⁵ This last step is also fundamental to explain trade-offs among resources/capitals, and correctly portraying value creation patterns, as emerged from the analysis of the integrated reports.

Subsequently, we analyzed the structure of feedback loops responsible for determining value creation patterns.

As example is provided by Fig. 5, already presented in this study, which depicts two feedback loops identified in the ENI’s resource map.

The first one, the positive (or reinforcing) feedback loop can be traced circularly as follows:

• (1) An increase in Brand & Reputation management (activities),

• (2) increases ENI brand + Reputation,

• (3) increases Customers and Suppliers Satisfaction,

• (4) increases Products delivered to market,

• (5) increases Availability of energy sources and green products,

• (6) increases stakeholders engagement + projects for local development + strategic partnership,

• (7) increases Strength of relationship with stakeholders,

• (8) decreases Environmental and social impacts (blow-out risk, fight against corruption, wellness and satisfaction of ENI’s people and local communities,…) (the polarity of this link is a “–”), which in turn

• (9) increases Brand & Reputation management (activities), thus closing the feedback loop.

The second one, the negative (or balancing) feedback loop, connects the following variables:

• (1) An increase in stakeholders engagement + projects for local development + strategic partnership,

• (2) increases Strength of relationship with stakeholders,

• (3) reduces Environmental and social impacts (blow-out risk, fight against corruption, wellness and satisfaction of ENI’s people and local communities,…),

• (4) which feedbacks to the starting variable, decreasing stakeholders engagement + projects for local development + strategic partnership, and closing the loop.

Notably, the analysis centered on the structure of feedback loops can be carried out extensively for any variable (resources, outcomes, and capabilities) in the map.

We also underline that the two resource maps were refined with the addition of a number of colored boxes, used to group together variables (either resources, flows or auxiliaries) pertaining to the same category as indicated by the IIRC Framework (IIRC 2013a, c). These boxes can be considered sub-systems (see Sterman 2000, pp. 99–102 about the use of subsystem diagrams) and constitute the general architecture of the 〈IR〉-based resource maps, even though at a quite aggregate level.

As an additional methodological note related to this study, we underline that data and information included in the 〈IR〉 under investigation were separately analyzed by each one of the three researchers involved in the research with the aim to reduce potential “researcher effects” (Miles et al. 2013, p. 296). The outputs of the three autonomous data processing were subsequently compared in order to derive the resource maps presented in this article. Notably, the resource maps we present in this article are 〈IR〉-based ones: for this specific reason, we used the categorization of the capitals/resources in 6 main typologies (as presented in the Sect. 2.2.) to group together “similar” resources, capabilities and flows within the maps.

As mentioned in Sect. 3 of this study, the last step of our research design entailed analyzing the resulting resource maps in terms of issues related to causal ambiguity and dynamic complexity in the business. To this aim, we employed SDM_Doc software (Martinez-Moyano 2012).

Notably, this last step allowed generating a number of descriptive statistics about the structure of the two resource maps, and identifying interesting information about the importance of each variables portrayed in the maps. Specifically, this stage allowed clarifying which are the Key Value Creation Spots for the two organizations, that is to say those resources or value outcomes affected by dense feedback processes that are controlled by the management of the organizations or their stakeholders. Tables 3 and 4 in the main body offer the evidence to identify those KVCS. As we emphasized in the article and previously literature stressed (e.g., Groesser and Schaffernicht 2012), density is a measure that supports analysts in describing a “property” of a model, and subsequently, it facilitates a deeper understanding of the conceptual structure of that specific model. More in detail, in our study density was regarded as the concept of accumulation of feedback loops over a variable that can determine its importance in terms of control or in terms of ambiguity due to the confounding effects of multiple feedback loops. It is to stress again that a high level of density associated to one specific variable, if on hand clearly points to that variable as a relevant one in the managers’ view, on the other hand it does not necessarily entail loop dominance or specific dynamics emerging from the model. This is where transforming an IR-based resource map into a quantified simulation model may help to expand the analysis, investigate the managers’ mental models and test the accuracy of the information provided by the organizations’ integrated reports more in-depth.