Integration of Intellectual Capital in Financial Reporting: Understanding Key Performance Indicators through Business Model Disclosure
Anna Khasyanova PhD student Dottorato di ricerca in Economia Aziendale e Management XXX ciclo 2014/2017 Università degli Studi di Pisa
Francesco Giunta Professore ordinario, supervisor Dipartimento di Scienze per l'Economia e l'Impresa Università degli Studi di Firenze
Content
Introduction………4 Chapter 1. Theoretical Background. Integration of Key Performance Indicators and
Business Model in financial reporting………...…12
1.1. Key Performance Indicators as a tool of IC measurement………...12 1.1.1. The increasing demand for disclose of non-financial information and relevant KPIs………...13 1.1.2. Integration of KPIs in financial and business reporting. Implications and current issues. ………...…16 1.1.3. The challenges of the KPIs identification and the possible resolutions..…….………. 19 1.2. Business Model: the value creation storytelling bridging the communication gap…………23 1.2.1. The notion of Business Model in the modern literature and academic research…..…23 1.2.2. Business Model conceptualisation: creating a common language through
ontologies……….…25 1.2.3. Business Model in business reporting: the new unit of analysis……….29 1.2.3.1. The call for a standardised Business Model reporting framework ………29 1.2.3.2. The International Integrated Reporting <IR> Framework (IIRC, 2013)………..32 1.2.3.3. The <IR> Framework shortcomings and the following critique…………35 1.2.4. Linking Key Performance Indicators to Business Model: the importance of entanglement……….36 1.3. The research question development…….………..…..39
Chapter 2. Research design. Evaluation of the Key Performance Indicators disclosure quality through the Business Model disclosure analysis………...…44
2.1. Description of the sample………....44 2.2. Business Model disclosure analysis. Identification of the value drivers by Business Model disclosure content analysis………..………...46 2.3. Key Performance Indicators disclosure analysis. Linking the disclosed key performance measures to the value drivers………..65 2.3.1. Selecting the key performance measures out of the frond-end of the annual reports………....65 2.3.2. Matching the disclosed key performance indicators to the value drivers……...…66 2.4. Evaluation of the Key Performance Indicators disclosure quality. Construction of the Disclosure Index………69 2.5. Revealing the factors of the Key Performance Indicators Disclosure quality……...……….74
Chapter 3. Discussion of the Results………..75
3.1. State-of-the-art of the KPI and Business Model disclosure practises. An explorative analysis………..75
3.2. The KPI disclosure quality factors………….……….……….81
3.2.1. Data………..…81
3.2.2. Summary output………...83
3.2.3. Regression model testing………...…..85
Concluding Remarks……….……..91
Introduction
"What you measure is what you get." (Kaplan and Norton, 1992)
Knowledge and Intellectual Capital (IC) have become the new source of corporate development and, undoubtedly, the commercial success of companies is now determined by continuous innovation, the development of new technologies, and skills and knowledge of the employees rather than by tangible assets such as plants and equipment. Competitive advantage increasingly involves value creation processes that rely on IC assets not recognised in the financial statements (Beattie et al, 2004; Lev, 2001; Beattie and Smith, 2013; Brabazon, 1997; Holland, 2006; Johanson et al., 2001; Litan and Wallison, 2000). Intellectual capital (also widely termed as ‘knowledge capital’) has not yet found its commonly agreed definition. The term ‘intellectual capital’ is frequently used together with such terms as ‘intangibles’, ‘knowledge assets’, ‘intellectual property’ etc., and there is some confusion concerning how they differ one from another. In a broader sense IC of a company can be considered as an asset (intangible in its nature) which represents a collection of all informational resources that a company has at its disposal that can be used to gain profits, acquire new customers, create new products and make other business improvements. Many companies refer to IC as the unique source of their competitive advantage. Indeed, there are some indications that companies that manage their own IC outperformed other companies (DTIDC, 1997; Bornemann et al., 1999; Johanson et al., 1999). For example, Skandia in its IC report defines intellectual capital as “the possession of knowledge, applied experience, organisational technology, customer relationships and professional skills that provide Skandia with a competitive edge in the market”. In other words, IC is a broader notion that comprises the elements recognised and allowed as assets on balance sheets in accordance to the accounting standards, such as intangible assets or intellectual property, as well as other intangibles that can not be recognized as assets in traditional financial statements, however, are crucial for the value creation of a company.
In the E-based economies companies with almost no physical assets such as Google or eBay, or companies like Apple, Microsoft and Coca-Cola, have their stocks more highly rated than many global industrial companies and companies, operating in heavy industries. Traditional financial reporting was developed for manufacturing companies with mostly ‘hard’ assets, and rooted in the periodic reporting of aggregated, historical, financial information (Beattie, 1999). In recent years, the nature of business has changed fundamentally and, hence, the accounting numbers are, by themselves, inadequate to serve the needs of the market and no longer satisfy users (Beattie et al., 2004) since they represent lagging financial performance indicators and are not value drivers, per se (Dempsey et al., 1997). A reporting framework with non-financial measures
alongside financial measures is needed (Sveiby, 1997). By incorporating non-financial indicators into their measurement systems, many firms sought to create a wider set of measures that capture not only firm value, but also the factors leading to the creation of value in the business. Investors are cognizant, to some extent, of these accounting deficiencies and therefore rely primarily on non-financial information (Amir and Lev, 1996).
The research of Bouwman et al. (1995) showed that in deciding to invest in a company, analysts looked at more qualitative, future-orientated, non-financial information. The study of Dempsey et al. (1997) also reported that financial analysts recognise the value of many non-financial measures as leading indicators of long-term non-financial success – analysts use, or are interested in using, a broad range of non-financial information in addition to financial measures. The research findings, based on the survey of Belgian financial analysts, suggest that financial analysts who use more forward-looking information and more internal-structure information offer more accurate forecasts (Orens and Lybaert, 2007).
Thereon, a number of prior research was devoted to the investigation of the linkage between the company’s value and its non-financial performance indicators. The studies took two approaches to examination of those possible linkages and documentation of the relevance of non-financial information: (1) establishing a direct link between non-non-financial measures and equity values (Kim and Taylor, 2014; Lev and Zarowin, 1999; Brown et al., 1999; Francis and Schipper, 1999; Amir and Lev, 1996; Callen et al., 2010; Coram et al., 2011; Riley et al., 2003; Ittner and Larcker, 1998; Elzahar et al., 2015), and (2) demonstrating the link between non-financial measures and future financial performance, indicating that non-financial information should be useful for investors and creditors (Nagar and Rajan, 2001; Banker et al., 2000; Behn and Riley, 1999; Ittner and Larcker, 1998; Brancato, 1995).
Despite a certain lack of consensus (i.e. differences among the various studies caused by different sample industries, life cycle stage of the sample firms, types of non-financial information items used and sample size), overall academic research suggests that non-financial performance measures are relevant for predicting future financial performance and valuing corporate equity (AAA, 2002). Assuming market efficiency, rational investors incorporate non-financial measures in their equity values only if these measures are both relevant and reliable predictors of future performance.
Although the importance of traditional model of financial reporting can not be devalued, economic actors have to admit that traditional reporting represents and communicates only a part of value creation process. The growing proportion of intangible assets to tangible ones provokes significant differences between book and market values of companies due to a failure of traditional financial statements to capture the value of intangible assets. The reporting ‘value gap’ between
more traditional financial accounting measures of value (such as book value on the one hand and market capitalisation on the other) suggests a need to go beyond conventional accounting (ICGN, 2009; Lev, 2001; IFAC, 1998; Husin et al., 2012; Ittner and Larcker, 1998; Cordon, 1998). Despite certain intangibles as intellectual property (patents and trademarks) and acquired items such as goodwill are already on balance sheets, the major portion of intangibles that generate value is not yet recognised in the traditional accounts.
Ignorance of internally created intangible assets can provoke damaging consequences at both micro and macro levels. At the micro-firm level poor understanding of the value generation process can lead to inefficient resource allocation. The future business opportunities can be missed out because the company’s managers do not fully understand the potential of the IC assets. Further, on a macro level, the lack of information that users can acquire through ‘official’ channels would push investors to rely on rumours and speculations which, in turn, might lead to anomalous market behaviour and stock volatility. A misallocation of resources on a macro level in terms of market investments might occur. On the contrary, relevant and timely additional non-financial information helps investors to assess the future financial prospects of the company (e.g. future earnings), therefore reduces the risk associated with the company that, in turn, leads to a lower cost of capital. For example, Leif Edvinsson, former corporate director for intellectual capital at Swedish financial services company Skandia AFS, claims that the company achieved 1% reduction in the cost of capital due to the company’s ability to measure and report its IC. The findings of Orens et al. (2009) show that the extent of IC disclosure are positively associated with firm value, it is associated with lower information asymmetry, lower implied cost of equity capital and lower rate of interest paid.
To make value creation process be properly understood, intangible assets should be taken into consideration even though it is not always possible to assign monetary values to most of them. According to FASB (2001), improved business and financial reporting of the ‘new economy’ requires attention to recognition of internally generated intangible assets in financial statements and improved measures of those assets, expanded and systematic use of non-financial performance metrics and expanded use of forward-looking information. Moreover, the recent legislation (FRC, 2010; Companies Act, 2006; DTI, 2002; G100, 2003; EU Directive, 2014-95) forces listed companies to publish an account of how their intangible assets contribute to the value generation process – communicate “qualitative as well as financial evaluation of performance, trends and intentions”. By requiring to report on the value drivers – key success factors with respect to the company’s future performance both competition-wise and financially – standard setters are intended to rise awareness of the need of companies of all sizes to manage and communicate the value of their business beyond that captured by financial numbers alone.
Holland (2006) points out that the problems of categorisation and measurement of IC and intangibles have become acute since knowledge assets have become an increasingly important issue in corporate valuation. There are different approaches aimed at identifying and classifying IC components. Numerous IC classifications and taxonomies were developed by academic authors as well as policymakers, and, notwithstanding certain peculiarities, a significant overlap is observed (Hall, 1992; Sveiby, 1997; Edvinsson and Malone, 1997; Stewart,1997).
The most common approach to the components of IC implies breaking down IC into three elements: Human, Structural (Organizational) and Relationship (Customer) capital (Figure 1). That kind of approach was also applied by MERITUM Project (2002) and by Organisation for Economic Cooperation and Development (e.g. OECD, 1999) in the various reports devoted to the topical issues of Intellectual Capital, Intellectual Assets, Value Creation and Measuring and Reporting on IC.
As presented in the Figure 1, IC embraces all kinds of intangibles, either formally owned or used, or informally deployed and mobilised (MERITUM Project, 2002). Moreover, IC is rather more than simply the sum of knowledge resources and capitals of the firm, it is about how to let the knowledge of a firm work for it and have it create value (Roberts, 1999).
Figure 1. Conceptualisation of Intellectual Capital
Realising the potential of knowledge assets and their influence on financial performance investors need to understand what drives value (ICGN, 2009) and claim companies to be transparent and report on all the value drivers1 of their performance that unavoidably includes non-financial ones. Thus, the developed IC taxonomies are intended to facilitate companies to visualise their IC and, by doing so, give them an opportunity to communicate IC assets to investors.
1 The notion ‘value driver’ differs from somewhat of a notion ‘growth driver’ which is often connected to share-triggers, whereas a growth driver would be considered to be more long term (Bukh and Nielsen, 2010).
External Capital Human Capital Internal Capital • Management Philosophy • Organisational Culture • Organisational Structure • Service/Product Quality • Management Processes • Information Systems • Intellectual Property • R&D • Company Name/Brand
• Alliances and Partnerships • Licensing/Franchising • Distribution Channels • Favorable Contracts • Community Relations • Customer Relations • Supplier Relations • Financial Relations • Innovation • Knowledge • EEO/Diversity • Education/Training • Learning and Development
• Employee Demographics
DTIDC (1997) states that typical IC reports are used to illustrate the importance of intangible assets to the company and those reports are able to create value for companies.
Despite the growing acknowledgement of the strategic significance of IC, there is limited understanding of how organisations measure, manage and report on their knowledge resources (Guthrie, 2001; Fincham and Roslender, 2003). There is a growing need to provide practical examples of assessing, managing and reporting on IC together with illustrating how companies benefit from doing so and how they may improve their Intellectual Capital Management, Measurement and Reporting (ICMMR) activities and capabilities (Boedker et al., 2005).
There were a number of interesting proposals concerning IC visualisation, measurement and integration into financial reporting. Generally, the methodologies of IC measurement are dichotomous: financial and non-financial. The most accepted financial methodologies are based on: a) market capitalisation of the company (e.g. Tobin’s Q, Market-to-book value, Knowledge Capital Earnings (Lev, 1997) and Investor Assigned Market Value – IAMVe (Standfield, 1998); b) values communicated in the financial report (the most important methods include Economic Value Added - EVA (Stewart, 1997), Human Resource Cost and Accounting (Flamholtz, 1985) and Calculated Intangible Value (Stewart, 1997; Luthy, 1998); c) discounting back future financial flows (e.g. Total Value Creation – TVCe (Anderson and McLean, 2000), The Value Explorer (Andriessen and Tiessen, 2000), Intellectual Asset Valuation (Sullivan, 2000) and Inclusive Valuation Methodology – IVM).
The most popular non-financial methods of measuring IC include: IC – Index (Roos and Roos, 1997), Skandia Navigator (Edvinsson and Malone, 1997), Balanced Scorecard (Kaplan and Norton, 1992), Intangible Asset Monitor (Sveiby, 1997), Intellectual Capital Accounts (DATI, 2001, 2003), IC Audit Model (IFAC, 1998), Value Chain Scoreboard (Lev, 2001), The Invisible Balance Sheet (Arbetsgruppen Konrad, 1989; Sveiby, 1997), The Resourse Matrix (Lusch and Harvey, 1994; Sveiby, 1997) and MERITUM Project (2002). Non-financial methodologies are based on quantitative methods (which are not financial), identification of various IC components and applying a classification. This leads to the identification of indicators. The incorporated sets of indicators document the growth in stocks of intellectual capital, combining financial and non-financial information (Fincham and Roslender, 2003; Starovic and Marr, 2003).
Some companies, typically large high-tech companies, have already implemented various IC measurement tools and techniques (Coloplast, Skandia, HP, BT Group, Royal Dutch/Shell), whereas others still consider IC report being an optional extra. One of the instruments that has been suggested as a tool for identifying, managing and reporting on IC and intangibles is the Intellectual Capital Statement (DMSTI, 2003; Zambon, 2003). As Nielsen et al. (2017) state “IC reports are representations of actual value creation”. Key actors in the financial reporting supply
chain as well as academic research have released the guidelines and recommendations on how to report on intangibles, suggesting that business reporting should become more comprehensive, with greater disclosure of forward-looking information, more background description and enhanced analysis of the critical factors influencing performance – value drivers (AICPA, 1994; CICA, 2002; MERITUM Project, 2002; ASB, 1993, 2003; DTI, 2002; AAA, 2002; IIRC, 2013; EU Directive, 2014-95; EFRAG, 2013; ICAEW, 2009; ICAS, 2015; PwC, 2016; Di Piazza and Eccles, 2002; Chatterjee, 2013), however, all have certain limitations and many suffer from a lack of practical testing. It is noted that the various IC reporting frameworks that have been proposed have not been widely adopted, the relevant information being highly diverse, company-specific and subject to change. It was concluded that the development of a comprehensive, ‘joined-up’ model was a ‘pipe dream’ (ICAEW, 2009).
On the other hand, reporting on internally created intangible assets is arguably equally important for all companies. Apparently, intangibles might play different role and have different fraction in the value generation process. Thus, companies’ managers should employ their skills and expertise in measurement and control of intangibles in order to be able to place them into the value creation process map and communicate it adequately to investors. It is noted that measuring and controlling intangibles is challenging for managers – “the belief that managers have sophisticated internal systems to measure and value intangibles is a myth” (Lev, 2001). The KPMG survey of non-executive directors in 2003 showed that more than 60% of them didn’t consider themselves to be very knowledgeable about non-financial performance indicators. On the contrary, financial performance was at the top, with 94% of respondents saying this was an area where they were most knowledgeable. That was explained by the fact that “information provided by executives is mainly financial”. Current situation, therefore, should appeal to the accountants since its them who have a direct access to the performance data of the companies and they are responsible for communicating right information to the right people.
In addition, there is an indication of a substantial difference between the types of information found in companies’ annual reports and the types of information demanded by the market. User needs research has shown that there is a demand for non-financial information (business reporting package to incorporate greater discussion of the critical drivers of business success, especially intangible assets), more non-financial performance indicators related to these critical business drivers, and more forward-looking information that is not generally being met (Coleman and Eccles, 1997; Mavrinac and Siesfeld, 1997; Eccles et al., 2001; Eccles and Kahn, 1998; Epstein and Palepu, 1999; AICPA, 1994; Beattie and Pratt, 2002). Effective strategic and operational decision-making is based on the information being relevant, timely and robust – and that means it has to consist of more than just financial numbers.
Alongside the user needs research, the annual reports’ surveys witness the differences of non-financial information reporting policies among companies. Nowadays, companies are making serious efforts at disclosing more and more non-financial information focused on value creation process and value drivers of the business, and there is an evidence that companies are progressing differently in doing so (ICAS, 2015, Deloitte, 2015; PwC, 2013, 2015; Black Sun and IIRC, 2014, 2015; PwC, 2014; ASB, 2009; PwC, 2006). For example, the ASB Survey (2009) found that in 32% of investigated annual reports there was no explicit identification of non-financial key performance indicators, although few companies disclosed some non-financial measures, leaving users to guess if the measures were ‘key’. The key performance indicators were clearly relevant to the business and the purpose of the measure was easily understood – for example, measuring customer satisfaction when customer retention is a key element of strategy – just in 30% of the investigated annual reports. 94% of the sample, however, provided some financial key performance indicators. 32% of the sample did not disclose any non-financial key performance indicators, despite the Companies Act (CA, 2006) requirement to do so where ‘necessary’ and ‘appropriate’. As for non-financial key performance indicators, companies looking to improve should seek to measure the key drivers of their business. Possibly because the CA specifically mentions employee and environmental non-financial key performance indicators, many companies have chosen peripheral disclosures of KPIs in these areas over the more important key drivers of their business (ASB, 2009).
Considering the discussed above growing importance of reporting on non-financial information, its documented value relevance and ability to forecast future financial performance of companies, alongside the active encouragement of policymakers to externally communicate the value drivers of the business in addition to financial statements, it is noted that companies’ reporting policies with respect to non-financial information, non-financial performance measures and their role in the value creation process differ significantly among each other and the major progress should be made towards incorporating such information in financial reporting. The present research, therefore, seeks to understand what factors can explain the differences in the disclosure quality of non-financial information and performance indicators that drive companies’ value. Addressing such a research purpose, however, would require determining the meaning of the term ‘quality’ with respect to performance indicators disclosure, as well as developing of a method that allows to measure the disclosure quality of the value-driving performance indicators.
The findings of the study and the method developed for the purpose of performance indicators disclosure quality measurement contribute to the debate concerning the disclosure quality of non-financial information and its measurement, and might be used by academics, policymakers and auditors when evaluating the quality of business reporting and tracking its
progress over time. Besides, the research findings could be useful for investors when giving an evaluation of the quality of the company’s intangible assets management, as well as for companies’ managers themselves when making interventions into their performance indicators disclosure policies.
The thesis is structured as follows. Chapter 1 represents the theoretical background and the overview of the prior research required for the research question elaboration. Chapter 2 discusses the methodology and methods applied to address the research purpose, including the description of the sample. The research results are presented in the Chapter 3. The section Concluding Remarks summarises the outcomes of the research, indicates the limitations of the study and questions for further research.
Chapter 1. Theoretical Background. Integration of Key Performance Indicators and Business Model in financial reporting
1.1. Key Performance Indicators as a tool of IC measurement
As it was pointed out in the Introduction, non-financial methodologies of measuring IC are based on quantitative non-financial methods, which imply application of IC classification and identification of indicators that drive the company's value. When speaking about performance indicators that drive value, academics, policymakers, and other economic actors usually refer to the term Key Performance Indicators (KPIs). Boulton et al. (2000) answering the question of how intangible assets can be valued claim that “most likely, key performance indicators will be required”. The authors propose their definition of KPIs that are the factors that drive success in creating value using one or another class of assets. In other words, a key performance indicator measures whatever drives the creation of value for any specific asset. CCI (2004) defines a KPI as the measure of performance that is critical to the success of an organisation. EBRC (2010) refers to KPIs as to measures, often non-financial, that are leading indicators of business performance and that cover a broad range of resources and processes. The UK Companies Act (2006) defines KPIs as the “factors by reference to which the development, performance or position of the business of the company can be measured effectively”.
KPIs indeed have found its wide application in managerial practises. Companies’ managers are increasingly using KPIs to run their businesses, e.g. the Balanced Scorecard (EBRC, 2010). Black Sun and IIRC research (2014) states that “quantitative indicators of performance, such as key performance indicators (KPIs), are particularly useful in expressing targets and managing performance against targets”. CCI (2004) highlights that a KPI system must be used to drive improvement since system without outcomes creates work without creating any benefit.
Undoubtedly, in strategic planning it is important to discuss KPIs (Lannon, 2014). Key performance indicators represent a type of measurement and they are crucial for managers to understand what is happening in their business. There is an important deviation applied to KPIs – a key performance indicator can represent a leading or a lagging measure.
Lag Indicators are after-the-event measurement, essential for charting progress but useless when attempting to influence the future (easy to measure but hard to improve). To influence the future, a different type of measurement is required, one that is predictive rather than a result. On the contrary, Lead Indicators are in-process measures that are predictive. However, lead indicators are always more difficult to determine than lag indicators. They are predictive and therefore do not provide a guarantee of success (hard to measure and easy to influence).
What has become clear over years of research is that a combination of lead and lag indicators result in enhanced business performance overall. When developing a business
performance management strategy and system, it is always a good practice to use a combination of lead and lag indicators. The reason for that is obvious: a lag indicator without a lead indicator will give no indication as to how a result will be achieved and provide no early warnings about tracking towards a strategic goal. Equally important, however, a lead indicator without a lag indicator may provide an understanding of business activities but it will not provide confirmation that a business result has been achieved. There is a cause and effect chain between lead and lag indicators, both are important when selecting measures to track toward business goals.
Although it is not the same, the classification of lagging and leading measures in highly correlated with the financial and non-financial performance indicators. The financial performance indicators capture financial performance, financial position, or cash-flows, either presented as an absolute value or as an index. Usually, four categories of financial performance were used to classify the financial indicators: 1) growth measures; 2) profitability measures; 3) leverage measures; and 4) liquidity and cash flow measures (Matsumoto et al., 1995). Non-financial performance indicators are indicators which are not based on conventional accounting figures. The non-financial indicators were classified into four categories, referring to the main multidimensional performance models: 1) market measures; 2) internal processes measures; 3) innovation measures; and 4) environmental and social measures (Lynch and Cross ,1991; Kaplan and Norton, 1992; Wright and Keegan, 1997). Thus, a parallel could be made between lagging and financial performance indicators being ‘output’ oriented indicators, and leading and non-financial performance indicators being ‘input’ oriented indicators.
1.1.1. The increasing demand for disclose of non-financial information and relevant KPIs Currently, the increasing interest in non-financial leading performance indicators is observed from both managerial and external stakeholders’ side. On the one hand, key financials are important for business management decisions and, on the other hand, key non-financial indicators make it easier for investors to understand how well the company is performing, stakeholders become increasingly interested and the company’s reputation enhances (Gazdar, 2007). As PwC Review (2016) underlines, the KPIs managers include in the strategic report have a powerful impact on reader perceptions of the quality of management and of the management information used to run the business. Jenkins Report (AICPA, 1994) calls leading indicators “existing conditions that provide insight into the future”. In reporting historical financial performance, financial statements focus on lagging indicators and not leading non-financial indicators of future financial success (ECAEW, 2003; Brennan and Connell, 2000; Lev, 2000, Ittner and Larcker, 1998). According to Eccles et al. (2001) and Lev (2000) current financial reporting is often seen as insufficient for e.g. analysts’ decision making due to a historical, static
focus and a limited source of information for valuing the ability of the companies to obtain future revenues. Having a reference to the non-financial KPIs investors can have a better sense of the company’s overall performance, since non-financial indicators usually reflect realms of intangible value, such as R&D productivity, that accounting rules refuse to recognize as assets (Ittner and Larcker, 2003).
Case studies by Fisher (1995) and Brancato (1995) have identified that companies managers believed that, compared to key non-financial indicators, traditional accounting measures (1) are too historical and ‘backward-looking’, (2) lack predictive ability to explain future performance, (3) reward short term or incorrect behaviour, (4) are not actionable, providing little information on root causes or solutions to problems, (5) do not capture key business changes until it is too late, (6) are too aggregated and summarised to guide managerial action, (7) reflect functions, not cross-functional processes, within a company, and (8) give inadequate consideration to difficult to quantify ‘intangible’ assets such as intellectual capital (Ittner and Larcker, 2004). These inferences from the managerial side became premises for the standard setters and other actors of the business reporting supply chain to raise an awareness concerning the importance of non-financial information and KPIs in decision making process:
- “We are also encouraging the private sector to develop key performance indicators (KPIs), on an activity and industry basis, that would capture important aspects of a company’s activities that may not be fully reflected in its financial statements or may be non-financial measures. In our view, KPIs are likely to provide investors with an enhanced understanding of company performance, so this is a fruitful area for encouraging further uniformity and disclosure” (ACIFR, 2008);
- “In addition, the strategic report should include an explanation of the main trends and factors affecting the entity; a description of its principal risks and uncertainties; an analysis of the development and performance of the business; and an analysis using key performance indicators”
“Where relevant, linkage to and discussion of key performance indicators (KPIs) should be included in any descriptions given in order to allow an assessment of the entity’s progress against its strategy and objectives” (FRC, 2014).
- “Shall include in the management report a non-financial statement containing information to the extent necessary for an understanding of the undertaking's development, performance, position and impact of its activity, relating to, as a minimum, environmental, social and employee matters, respect for human rights, anti-corruption and bribery matters, including: (a) a brief description of the undertaking's business model; (e) non-financial key performance indicators relevant to the particular business” (EU Directive 2014-95);
- “The key performance indicators or narrative about qualitative factors…will enable stakeholders to better assess sustainable business practices and the quality and variability of a company’s cash flows and profitability” (EBR, 2005);
- “The Committee believes that disclosure of performance measures would:
1) Provide leading indicators about a company's future. Because of changes in the business environment and within companies, predicting a company's financial future is not merely an extrapolation of trends in a company's financial past. And because those changes are accelerating, the financial past may be an ever-weaker indicator of a company's financial future. Users are forever searching for better leading indicators of performance — indicators about existing conditions that provide insight into a company's future performance. Since future performance is often a function of how well a company performs key activities, performance measurements are often superior leading indicators of a company's performance.
2) Provide insight into the nature of a company's business. Operating statistics often describe a company's activities in more tangible and understandable terms than do financial measures (AICPA, 1994);
- “Quantitative indicators, such as KPIs, can help increase comparability and are particularly helpful in expressing and reporting against targets”.
“An integrated report contains qualitative and quantitative information about performance: • …;
…
• KPIs that combine financial measures with other components … or narrative that explains the financial implications of significant effects on other capitals and other causal relationships … may be used to demonstrate the connectivity of financial performance with performance regarding other capitals” (IIRC, 2013);
- “Authentic and consistent reporting means using relevant KPIs” (PwC, 2016).
- “Use key performance indicators that are linked to strategy and facilitate comparisons” (ICGN, 2009).
Current surveys and questionnaires also show that investors admit the increased importance of non-financial KPIs and realise their advantages in predicting future financial performance relatively traditional financial measures (PwC, 2014; Eccles et al., 2001; Eccles and Mavrinac, 1995, Beattie et al., 2004; Amir and Lev, 1996; Dempsey et al., 1997). User needs research has shown that there is a demand for non-financial information (business reporting package to incorporate greater discussion of the critical drivers of business success, especially intangible assets), more non-financial performance indicators related to these critical business drivers, and
more forward-looking information that is not generally being met (Coleman and Eccles, 1997; Mavrinac and Siesfeld, 1997; Eccles et al., 2001; Eccles and Kahn, 1998; Epstein and Palepu, 1999; AICPA, 1994; Beattie and Pratt, 2002). The research of Beattie and Pratt (2002) proves that there is a need for more information that is forward-looking and non-financial in nature. The authors conducted a research where four groups (professional users, private shareholders, listed companies’ finance directors and auditors of listed companies) were asked to (1) rank the 11 broad topic categories and (2) rate the usefulness of each individual information item for investment decision making. In descending rank order, the top five broad topic categories were: financial; broad objectives and strategy; MD&A; background; and value drivers – growth and innovation. 1.1.2. Integration of KPIs in financial and business reporting. Implications and current issues
The awareness of investors about the importance of KPIs, an empirical evidence of their equity value relevance and ability to predict the future financial performance underpin the external stakeholders’ request for rigorous approach towards reporting on non-financial information and KPIs. To help users with analysing trends affecting a business, the AICPA Committee's model (1994) calls for a summary of key financial and non-financial data on a consolidated basis as well as for each industry segment. The final report of the Special Committee stated that in order to meet users’ changing needs, business reporting must “(1) focus more on factors that create longer term value, including non-financial measures indicating how key processes are performing” and must “(2) better align information reported externally with the information reported to senior management to manage the business” (AICPA, 1994).
At the international level, first, in 2003, the SEC released a guideline which emphasises that “companies should identify and discuss key performance indicators, including non-financial performance indicators, that their management uses to manage the business and that would be material to investors” (SEC, 2003). In 2010, the IASB issued a practice statement entitled “Management Commentary” according to which companies preparing their financial statements under the International Financial Reporting Standards (IFRS) are required to disclose in the Management Commentary the “performance measures and indicators (both financial and non-financial) that are used by management to assess progress against its stated objectives.” (IASB, 2010).
Furthermore, The Financial Accounting Standards Committee believes that non-financial performance measures should be judged against the same criteria as financial performance measures, namely, the characteristics of relevance, reliability, and comparability espoused in Statement of Financial Accounting Concepts No. 2, Qualitative Characteristics of Accounting Information (AAA, 2002). The three similar criteria for KPIs being reported by companies were
also identified by Lev (2001). The author claims that the specific indicators included in the scoreboard should satisfy the following three criteria to assure maximal usefulness:
1. The indicators should be quantitative. Qualitative aspects of the value chain (e.g., employee work practices, patent cross-licensing) may be provided in an annex to the scoreboard;
2. The measures should be standardised (or easily standardisable), meaning that they can be compared across firms for valuation and benchmarking purposes;
3. Most important, the measures should be confirmed by empirical evidence as relevant to users, generally by establishing a significant statistical association between the measures and indicators of corporate value (e.g. stock return, productivity improvement).
The more that non-financial information becomes integrated into companies’ decision making processes, and their external reporting, the greater the need for users to place trust in these KPIs when making informed decisions (ICAS, 2015). The increasing application of KPIs in managerial purposes in turn provoked the necessity of certain tools that aimed at controlling the assurance of the companies’ KPIs by audit boards and committees. The ‘validity’ criteria refers to the extent to which a metric succeeds in capturing what it is supposed to capture, while ‘reliability’ refers to the degree to which measurement techniques reveal actual performance changes and do not introduce errors of their own (Ittner and Larcker, 1998).
ICAS Guidelines (2015) also admits the importance of such KPIs criteria as reliability, consistency and completeness. The aim of the guidance is to provide insights to audit committees/boards on the source of assurance that might be attributable to each of their reported KPIs and to help them form a view on the relevance and degree of reliability that can be placed on each. This, in turn, will enable them to critically assess whether the existing source of assurance is optimal in responding to the needs of users and other key stakeholders, and how they communicate this. The Assurance Matrix method (ICAS, 2015) suggests five illustrative sources of assurance that might be obtained over each KPI which audit committees/boards may wish to tailor, along with their descriptions, to the individual company. The completed matrix is intended to provide a clear, concise, complete and understandable means of communicating the assurance the audit committee/board has obtained over each KPI.
The criteria of reliability, consistency and comparability of KPIs are considered as the main barriers towards achieving the standardised reporting framework for non-financial performance measures. According to Nielsen (2004), the lack of consistency in the choice of indicators and the lacking reliability of present measures are a significant basis for the inability to benchmark information across companies even in the same industry. The performance measures when are not comparable with other companies’ measures make them difficult to use for decision-making. Standardisation would be a means of making benchmarking possible, in turn increasing reliability
and usefulness, and minimising uncertainty (Nielsen, 2004).
However, despite all the reasonable claims for mandating and standardising disclosures of performance indicators, such an approach is likely to be difficult because of significant differences in metrics across firms and industries, costly verification and disclosure enforcement, and considerable related proprietary costs (Skinner, 2008).
Nonetheless, academics and policymakers seek to give companies certain recommendations concerning the improvements that should be made when reporting on non-financial performance measures. According to ASB (2009) the quality of KPIs reporting can be achieved when “each KPI disclosure includes definition, purpose, comparatives, commentary on targets, etc.” IIRC (2013) states that the ability of the organisation to create value can best be reported on through a combination of quantitative and qualitative information. In addition to the quantitative indicators, such as KPIs, the qualitative information considered to be relevant includes an explanation of: a) measurement methods and underlying assumptions; b) the reasons for significant variations from targets, trends or benchmarks, and why they are or are not expected to reoccur (IIRC, 2013). Elzahar et al. (2015) in their research claim that a clear indication of the KPIs’ qualitative characteristics (e.g., definition, calculation method, purpose for disclosing, and motivation of why the disclosure should be useful to users of the annual report) is essential for the understanding of the nature of a firm’s business and value creation model. More informative disclosures of leading indicators allow financial analysts to make full use of these indicators in their forecasts (Simpson, 2010).
ASB (2009) highlights that disclosure which includes definition, purpose, comparatives, commentary on targets for each KPI is an example of the best practice. Deloitte Survey (2015) also recommends companies’ managers to provide information that enables shareholders to understand each KPI used in the strategic report. For example, the definition and calculation method; purpose; and the source of underlying data for KPIs may all be useful to shareholders. The results of Deloitte Survey (2015) showed that 64% of the companies surveyed clearly defined their KPIs and explained the calculation method, while 56% clearly set out the purpose of each measure. However, only 22% of companies disclosed the source of underlying data for some or all of the non-financial KPIs they presented.
It can be argued that the amount of disclosure might not be an exact indicator of disclosure quality (Beattie et al., 2004). ASB (2006) and DEFRA (2012) indicate that what matters in KPIs reporting is quality and not quantity. According to the ASB Survey (2009) a number of companies were reporting too many KPIs: in some case “there were too many KPIs to all be key – for example, one company listed 68 measures throughout the report and there were several others with close to 20”. Coherently, Mouritsen et al. (2001) in their research found that the number of indicators used
in the individual intellectual capital statements range from 5–6 to more than 50. CCI KPI Report (2004) suggest companies to limit the number of indicators to about 8-12 since the application of any system will become very difficult if there are too many measures and too much data to collect.
Empirical research shows that the state-of-the-art of the KPI reporting practices are a subject to improvement. Dempsey et al. (1997) point out that the availability of some of the key indicators is limited, especially measures of product quality and customer satisfaction. Elzahar et al. (2015) found that although the quality of KPIs disclosure increases over time, the quality levels for KPIs disclosure are still very low, which indicates that many UK firms do not follow the ASB’s corresponding guidelines and this should have an appeal to regulators.
Currently, an important objective for many organisations is improving the relevance, usefulness and quality of data for intangible assets and capitals other than financial capital (Black Sun and IIRC, 2014). Standard setters and researchers provide standard definitions and calculation formulas for the standard KPIs (EBRC, 2010; Boulton et al., 2000; DTIDC, 1997; MERITUM Project, 2002; Husin et al., 2012; Gazdar, 2007), and many companies are involved in implementing KPI systems, however, sometimes without understanding their place within the Rethinking Construction Agenda (CCI, 2004). The FRC report (2007) concludes that “many companies are providing a good deal of information on measures and indicators, but improvements can be made in identifying their KPIs, both financial and non-financial.”
1.1.3. The challenges of the KPIs identification and the possible resolutions
The choice of key performance indicators is one of the most critical challenges facing organisations. Companies are continuously struggling with the selection of the key performance measures that are really key for the commercial success of their business. Performance measurement systems play a key role in developing strategic plans, evaluating the achievement of organisational objectives, and compensating managers (Ittner and Larcker 1998).
PwC in their Guide to KPIs (2006) tried to give companies’ managers a hand at answering the question “What makes a performance indicator ‘key’?” According to the Guide, the starting point for choosing which performance indicators are key to a particular company should be those that the Board uses to manage the business and that allow to assess progress against the stated strategies. ICGN (2009) similarly states that an indicator is likely to be important and relevant to strategy if it is used by the board in monitoring the company’s performance in achieving its strategy, and if it is, therefore, likely to affect board decisions. In addition, the KPIs will to a degree be conditioned by the industry in which a company operates. For example, a company in the retail industry might use sales per square foot and customer loyalty as key performance indicators, whereas an oil and gas company might opt for measures of exploration success, such as the value
of new reserves. Presently, a number of support mechanisms are available to management disclosure designers. One possibility is to study online KPI libraries offering a wide variety of examples of different KPIs. Some of these libraries contain up to 17,000 different KPIs (Baroudi, 2010). However, as Marr (2012) notes, the large number of available KPIs, while being a source of inspiration, proportionally generate a great deal of confusion for many managers, ultimately leading to a great deal of “hit or miss” in the choice of management disclosures (Nielsen et al., 2017).
The ability of establishing precise connections and causal links and relationships between knowledge resources, competences, intellectual capital, etc., and the value creation of an organisation, has been in the interest of the business and academic communities for a long time (Bukh and Nielsen, 2010).
The business model configurations are viewed to be valuable for understanding business performance and, therefore, important for managers to understand (Teece, 2010) and to measure (Montemari and Nielsen, 2013) the principal value drivers, and are a potentially powerful tool for the generation of internal management disclosures (Nielsen et al. 2017). Via business model approach it is possible to identify causal loops that depict linkages between key performance measures and financial results (Bell et al., 1997) and that links combinations of assets to value creation (Boulton et al., 2000). In the framework of ICS by Mouritsen et al. (2003b), the identification of management disclosures was set out as an iterative process combining the construction of a knowledge narrative, identification of management challenges, and, from the latter, identifying a set of managerial activities and KPIs (Nielsen et al., 2017).
Recently, an interesting proposal regarding the identification, validation, and benchmarking of management disclosures by expanding upon the business model QUANT database was released by Nielsen et al. (2017). This database offers a value driver platform with related clusters of KPIs connected to each business model configuration as a starting point for management’s disclosure – whether internal or external. The identification of a business model configuration automatically provides an overview of the relevant value drivers for the company. These value drivers are then used as the backbone for identifying management disclosures because the business model QUANT database provides suggestions for KPIs on the basis of each distinct business model configuration. Considering the levels of abstraction (Massa and Tucci, 2013), the abstraction level of business model configurations was viewed by authors as a more privileged level of analysis for the identification of internal management disclosures than that of other abstraction levels, emphasising that the business model configuration level analysis will ensure that companies do not end up with the same KPIs due to lack of knowledge of the management team identifying these KPIs.
Thus, Nielsen et al. (2017) in their essay argue that a valid starting point for external IC disclosure identification is to first identify relevant internal management disclosures. Once these have been identified, these relevant internal management disclosures, chosen on behalf of their association with the business model configuration with which a given organisation chooses to compete, can then be compared over time, benchmarked with other organisations, and finally used as a privileged basis for IC disclosure choices (Nielsen et al., 2017).
Although not referring to ‘business model configurations’, Bray (2002) in the paper developed for KPMG expressed his shared opinion that one of the attributes in relation to the KPIs chosen should be business model coverage. The KPIs should include both outcome measures (financial and non-financial) and performance driver measures (in relation to core and support business processes, management and people, technology and infrastructure). As Bray notes, many organisations now report non-financial as well as financial outcome measures, but little progress has been made in reporting performance driver KPIs. The problem of lacking KPIs’ comparisons to budget, peer and competitor dimensions in KPI reporting is also emphasised in the paper.
Another similar approach, the Strategic-Systems Auditing (SSA) framework was proposed earlier by Bell et al. (1997) in their special monograph written for KPMG. The SSA model is based on an analysis procedure that departs in the strategic analysis of the external forces affecting the company and the markets on which it operates, along with an analysis of its alliances, products, and customers. Next, an analysis of the business processes regarding strategic management processes, core business processes, and resource management processes leads to a so-called Entity Level Business Model and the identification of key business performance measures. Finally, the first two steps lead to the actual business performance measurement including the identification of performance KPI’s according to the four dimensions: financial, market, process, and resource.
The Business Model approach to identification and selection of key performance measures raises a number of potential research questions. Although establishing the firm's business model prior to selecting measures has the advantage of sharpening strategic focus and organisational priorities, it can be difficult to establish the reliability and predictive validity of the multiple measures in the business model without having done a great deal of measurement and analysis in the first place. Moreover, there is no guarantee that a business model based on current measures and competitive environments will be relevant to the choice of performance measures if there are major shifts in the firm's environment. Management should reflect on whether the KPIs chosen continue to be relevant over time. Strategies and objectives develop over time, making it inappropriate to continue reporting on the same KPIs as in previous periods. ICGN (2009) also emphasises the fact that companies should disclose indicators that are comparable over time, unless circumstances change and they cease to be appropriate. Therefore, reason for, and nature
of, changes in KPIs and how they are measured and reported should be clearly explained (PwC, 2006). More successful companies have attacked this problem by choosing their performance measures on the basis of causal models, also called value driver maps, which lay out the plausible cause-and-effect relationships that may exist between the chosen drivers of strategic success and outcomes (Ittner and Larcker, 1998).
Despite certain critique addressed to the business model approach to KPIs’ identification, academic authors mostly agree that with the business model captured, understood, and described it is easier to identify the indicators of the executive information system (Osterwalder et al., 2005), which will measure progress towards the desired strategic outcomes and the performance, comprising a balance of financial and non-financial measures across the whole business model (Bray, 2002). Managers are encouraged to articulate a model of how their business creates value so that they can report this externally and identify value drivers and related KPIs that will also be reported externally as well as internally (ICAEW, 2003).
However, despite well-developed arguments for disclosure of non-financial information and evidence that companies are disclosing more and more information, there are also indications that disclosure is ineffective (Nielsen, 2010). Various studies of investors’ and analysts’ information demands (Eccles and Mavrinac, 1995; Bouwman et al., 1987) indicate a substantial difference between the types of information found in companies’ annual reports and the types of information demanded by the capital market (Eccles et al., 2001). This information gap is partly due to an increased demand for non-financial information, i.e., concerning the company’s strategy and competencies, and its ability to motivate the staff, increase customer satisfaction, etc. However, this information gap may also be due to a lack of understanding of business models and of proper communication between company management and the capital market (Bukh, 2003). For example, Bukh and Nielsen (2010) found that from the analysts’ perspectives, value creation is only thought of in terms of attaining revenues and thereby boosting profits and other shareholder value measures. An important insight from that study was, therefore, that the financial analysts have grave difficulties in distinguishing between the companies’ business model and the model by which the payment of revenues are allocated between end users and reimbursing organisations. Moreover, most of the analysts initially had great difficulties in expressing not only what the business model of the case company was, but also what a business model in itself was. That raises a question of presence of a so widely discussed reporting gap, or rather an understanding gap (Nielsen, 2010).
In this respect, Nilsson et al. (1999) suggest that the main objective of applying a business model approach is to bridge the communication gap between management and external stakeholders, as shared models become a platform for creating common understanding. Thus, the
business model is perceived as a management technology that helps management communicate and share its understanding of the business logic to external stakeholders (Fensel, 2001).
1.2. Business Model: the value creation storytelling bridging the communication gap
The complementary and conditional nature of the value of non-financial measures seen in value relevance and predictive ability studies, raise the issue of whether companies should use an integrated framework to report on financial and non-financial measures. Such an integrated framework could disclose specific non-financial performance measures and provide a description of the firm's business model in the context of these measures and how these measures map into firm value (AAA, 2002).
1.2.1. The notion of Business Model in the modern literature and academic research Yet, despite all the ink spilt and words spoken, business models are still relatively poorly understood (Linder and Cantrell, 2000), particularly as a research area. Indeed, Zott et al. (2011) found that only 30% of all academic papers concerned with business models explicitly state what a business model is. The concept of ‘business model’ (BM) has no established theoretical grounding in economics or in business studies (Teece, 2010). Linsmeier (2011) notes that there is neither commonly agreed definition of business model in financial reporting. Definitions abound, with most overlapping only partially. Some authors suggest very brief and abstract definitions of business model saying that “it is simply a description of how a firm does business” (Richardson, 2008). Rappa (2006) sees BM as the method by which an organisation sustains itself, namely, generates revenue - the business model discussion explains how an organization makes money by indicating its position in the value chain. Teece (2010) and Fielt (2013) suggested their pretty similar definition stating that business model articulates the value logic of an organisation and provides data and other evidence that demonstrates how a business creates and delivers customer value. Boulton et al., (2000) define a business model as “the unique combination of tangible and intangible assets that drives an organisation’s ability to create or destroy value”.
Many other definitions can be reconciled by emphasising common features, namely, inputs, activities, processes, value chain, financial performance (e.g., revenue generation, cash flow) and outcomes. For example, according to KPMG report Rethinking the Business Model (2006) BM is the mechanism by which a business intends to generate revenue and profits. It encompasses the components such as value proposition, market segment, revenue generation model, cost structure and value chain. Boulton et al. (2000) use the term Value Dynamics that accurately represents the asset classes which create value in the New Economy (physical assets – land, buildings, equipment, inventory; financial assets – cash, receivables, debt, investment,
equity; organisational assets – leadership, strategy, structure, brands, know-how, systems, processes, knowledge, intellectual property; customer assets – customers, channel, affiliates; employee/supplier assets – employee, suppliers, partners). The authors believe that “in its purest form, a business is simply a collection of assets (tangible and intangible) which are connected and leveraged by the organisation using technology” and “every company needs to create a business model that links combinations of assets to value creation”.
According to Chesbrough and Rosenbloom (2002) the business model takes technological characteristics and potentials as inputs and converts them – through customers and markets – into economic outputs. IFAC (2009) defines BM as “how an organisation takes resource inputs and generates value for stakeholders. It includes an organisation’s objectives, revenue streams, strategy, operations and other functions”. According to Nielsen (2005) business model represents the platform, which connects resources, processes and the supply of a service which results in the fact that the company is profitable in the long term.
The Technical Collaboration Group (TCG, 2013), established by International Integrated Reporting Council (IIRC) to prepare the Background Paper for <IR> before the release of International Integrated Reporting (<IR>) Framework in 2013, carried out a review of business model literature (a range of professional and academic articles, websites and blogs) established as a starting point for the development of related guidance for the Framework. Despite considerable variation in business model definitions, several recurrent themes can be identified. As Figure 2 shows, nearly two-thirds of the articles drew an explicit link between the business model and an organisation’s ability to make money and drive financial performance. More than half viewed the organisation’s inputs – the resources and capabilities (or capitals) on which it relies – as a key component of the business model. The majority of references also considered actions or activities – the very mechanics of the business – to be within the business model scope. These activities contribute to the quality or uniqueness of the organisation’s offerings. Just over half of the references considered the business model as also including how an organisation creates value, outcomes or impacts for its customers and other stakeholders.
Figure 2. Business Model Components According to an External Literature Review (TCG, 2013).
Notably, common terms used are: inputs (assets, resources), processes or activities (technologies, competencies, relationships, value creation), outputs (financial outcomes and value delivery). On the basis those BM definitions synthesis, the most commonly accepted definition of business model has evolved within the International <IR> Framework (IIRC, 2013) that defines business model as “the chosen system of inputs, business activities, outputs and outcomes that aims to create value over the short, medium and long term.”
Taking into account numerous definitions of what business model is, Shafer et al. (2005) argue that all business model concepts should encompass four generic categories: (1) strategic choices; (2) value creation in the form of activities performed and the company’s ‘value proposition’; (3) mechanisms of value capture; and (4) the value network. Thus, despite differences in content, there is a set of issues that business model theorists broadly agree a business model ought to cover (Tweedie et al., 2017).
1.2.2. Business Model conceptualisation: creating a common language through ontologies Moving on from simply elaborating a definition of a business model, Osterwalder et al. (2010) get somewhat closer to the goal of identifying the ‘how’ of the business model because they place the value proposition at the centre of the model as an aligning feature between infrastructure interrelations such as competences, partner network and value configuration, and customer interrelations such as customer relationships, distribution channel, and target customers. A business model is a conceptual tool containing a set of objects, concepts and their relationships with the objective to express the business logic of a specific firm. Therefore, it must be considered which concepts and relationships allow a simplified description and representation of what value is provided to customers, how this is done and with which financial consequences (Osterwalder, et al., 2005). Based on the literature synthesis leading to the nine building blocks Osterwalder, et al. (2005) proposed the following definition for business models: “A business model is a conceptual tool that contains a set of elements and their relationships and allows expressing the business logic of a specific firm. It is a description of the value a company offers to one or several segments of customers and of the architecture of the firm and its network of partners for creating, marketing, and delivering this value and relationship capital, to generate profitable and sustainable revenue streams”.
A conceptualised business model helps business model designers to modify certain elements of an existing business model (Petrovic et al. 2001). The need for conceptualisation of BM let to the proposition of business model meta-models in the form of reference models and ontologies. The main idea of identifying the domains, concepts and relationships addressed in the business model field is to create a common language. That is, creating a reference model shared
among a specific community of practice or creating a more formal ontology of the business model domain. In this context an ontology can be understood as an explicit specification of a conceptualisation (Gruber, 1993) and would define the terms, concepts, and relationships of business models. The most successful BM ontologies were developed by Petrovic et al. (2001) (an extension modification of Wirtz’s model), Richardson (2008), Teece (2010), Osterwalder et al. (2005) and Baden-Fuller and Mangematin (2013) that demonstrated pretty much of overlap: Ø From Petrovic et al. (2001) point of view, a business model can be divided into seven
sub-models:
1. Value Model – Describes the logic of what core product(s)/service(s)/experience(s) are delivered to the customer and other value-added services derived from the core competence; 2. Resource Model – Describes the logic of how elements are necessary for the transformation process, and how to identify and procure the required quantities;
3. Production Model – Describes the logic of how elements are combined in the transformation process from the source to the output.
4. Customer Relations Model – Describes the logic of how to reach, serve, and maintain customers. It consists of the following sub-models:
• Distribution Model – The logic of behind the delivery process
• Marketing Model – The logic behind reaching and maintaining customers • Service Model – The logic behind serving the customer;
5. Revenue Model – Describes the logic of what, when why, and how the company receives compensation in return for the products;
6. Capital Model – Describes the logic of how financial sourcing occurs to create a debt and equity structure, and how that money is utilised with respect to assets and liabilities, over time; 7. Market Model – Describes the logic of choosing a relevant environment in which the business operates.
Ø Richardson (2008) proposed the Business Model Framework that based on aggregation of the numerous researches regarding BM components:
1. The value proposition – what the firm will deliver to its customers, why they will be willing to pay for it, and the firm’s basic approach to competitive advantage:
• The offering
• The target customer.
