The Business Model Ontology - a proposition in a design ... - HEC

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Knowledge of the Problem Domain processes that are placed on the value chain. The authors have applied this method to a case in the media industry that broadcasts advertisements in the form of video and/or interactive content and wants to measure efficiency in order to offer personalized information through a TV set-top box in the consumer's household. Papakiriakopoulos and Poulymenakou found that the framework and methodology helped highlighting several issues corresponding to the four elements of their model, namely coordination, customer value, competition and core competencies. 3.1.7 Business Model Evaluation and Indicators A last, but nevertheless very important and challenging field of business model research concerns the definition of indicators, business model measurement and evaluation. A number of authors have written on this question, attacking the problem from different angels (Hamel 2000; Gordijn 2002; Afuah and Tucci 2003). Hamel (2000), for example, talks of the wealth potential of a business model that covers four factors. Firstly, it questions to which extent the business concept is an efficient way of delivering customer benefits. Secondly, it covers the extent to which the business concept is unique. Differentiation is of immense importance because the more similar business models, the less probable are chances for above-average profits. Thirdly, it analyzes the degree of fit among the elements of the business concept. Finally, it questions the extent to which the business concept exploits profit boosters that have the potential to generate above-average returns. Under profit boosters Hamel understands increasing returns, competitor lock-out, strategic economies and strategic flexibility. Afuah and Tucci (2003) appraise business models on three levels. Namely profitability measures, profitability predictor measures and business model component attribute measures. The first level embraces earnings and cash flows, two frequently used indicators by analysts. If a firm's earnings or cash flows are better than those of competitors, this would mean that it has a competitive advantage. The second level comprises profit margins, revenue market share and revenue growth. Again, a firm has a competitive advantage if these measures indicate a better performance than competitors. The third and capital level provides benchmark questions for each of Afuah and Tucci's business model components (cf. section 3.1.3.1. and see Table 14). Component of Business Model Customer Value Scope Pricing Revenue Source Connected Activities Benchmark Questions Is customer value distinct from that competitors? If not, ist the firm's level of value higher than that of competitors? Is the firm's rate of increase in customer value high relative to that of competitors? Is the growth rate of market segments high? Is the firm's market share in each segment high relative to that of competitors'? Is potential erosion of products high? If so, in what segments? Is the quality-adjusted price low? Are margins and market share in each revenue source high? Are margins and market share in each revenue source increasing? Is the firm's value in each source of revenue distinctive? If not, is the level of value higher than that of competitors? What is the extent to which activities: Are consistent with customer value and scope? Reinforce each other? Take advantage of industry success drivers? Are consistent with the firm's distinctive capabilities? Make the industry more attractive for the firm? 38
The Business Model Ontology - a proposition in a design science approach Implementation Capabilities Sustainability Is the quality of the team high? To what extent are the firm's capabilities: Distinctive? Inimitable? Extendable to other product markets? Has the firm been able to maintain or extend its lead in its industry? Table 14: Appraising a Business Model: Component Measures (Afuah and Tucci 2003) The probably most advanced proposition for evaluating business models is outlined by Gordijn (2002) and is part of their e 3 -value method. They propose studying the economic feasibility of an e-business idea in quantitative terms by creating a profit sheet and assessing the value of objects for all actors involved. This is possible because their method is highly actor-, network- and value-centered and focuses and the value exchanges among business model participants (cf. 3.1.5). The authors admit that this evaluation serves for building confidence in an e-business idea rather than calculating precise profit estimations, which would be unrealistic. Further, Gordijn introduces an additional confidencebuilding step through the elaboration of “whati-if” scenarios. This helps stakeholders understand the sensitivity of e-business models with respect to its parameters, such as financials, future trends or customer behavior. 3.2 ONTOLOGIES The second field of research that has influenced this dissertation is ontologies, particularly in IS. I outline some of the existing work in the following lines. The term Ontology (with a capital "O") has its origin in philosophy and denotes the philosophical discipline that deals with the nature and the organization of reality, contrary to Epistemology, which deals with the nature and sources of our knowledge (Guarino and Giaretta 1995). In this thesis I use the term ontology (with the lowercase "o") to which I refer to with Gruber's (1993) definition as an explicit specification of a conceptualization. And I refer to a conceptualization as an intentional semantic structure which encodes the implicit rules constraining the structure of a piece of reality (Guarino and Giaretta 1995). In order to clarify what an ontology is Ushold and Gruninger (1996) refer to a quote from the SRKB (Shared Re-usable Knowledge Bases) electronic mailing list that nicely summarizes its signification and the various forms and contexts it arises in. "Ontologies are agreements about shared conceptualizations. Shared conceptualizations include conceptual frameworks for modeling domain knowledge; content-specific protocols for communication among inter-operating agents; and agreements about the representation of particular domain theories. In the knowledge sharing context, ontologies are specified in the form of definitions of representational vocabulary. A very simple case would be a type hierarchy, specifying classes and their subsumption relationships. Relational database schemata also serve as ontologies by specifying the relations that can exist in some shared database and the integrity constraints that must hold for them." After emerging in the artificial intelligence community and being applied to knowledge engineering ontologies are increasingly used in IS and IT. The main goal of applying ontologies in IS is the development and implementation of an explicit account of a shared understanding in a given subject area in order to solve a problem (Ushold and Gruninger 1996). Furthermore, Moschella (2002, p.25) describes IT ontologies as "logical supersets that combine metadata, taxonomies and semantics into formal systems that can be encoded in software, enabling diverse Web applications to truly understand one another". He also indicates that after the IT industry standardization efforts have focused on hardware, software and communication technologies the frontier is constantly shifting towards information itself. Moschella (2002, p.25) believes this will "require the systematic management of business terms and their usage". The main set of problems this shift addresses can be found in a text by 39
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