38 Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 Performance Indicators for Business Models: The Current State of Research Montijn van de Ven1, Paola Lara Machado2, Alexia Athanasopoulou3, Banu Aysolmaz4, Oktay Turetken5 Abstract Organizations need to evaluate new and existing business models to innovate their business logic and remain competitive. One way to carry out this evaluation is through business model performance in- dicators. Performance indicators for business models can support organizations in quantifying their business model objectives, monitoring business model performance during and after implementa- tion, and benchmarking their business model against competitors. However, the current literature lacks a complete picture of performance indicators that can be used to evaluate business models and monitor their performance. Therefore, we conducted a semi-systematic literature review to an- alyze which performance indicators are referred to in the academic literature related to business models. We provide an overview of the current state of research on this topic and discuss possible directions for further research. Keywords: Business Models, Performance Indicators, Literature Review Please cite this paper as: van de Ven, M., Lara Machado, P., Athanasopoulou, A., Aysolmaz, B., and Turetken, O. (2023), Performance Indicators for Business Models: The Current State of Research, Journal of Business Models, Vol. 11, No. 1, pp. 38-57 1–5 Eindhoven University of Technology, Department of Industrial Engineering and Innovation Sciences, PO Box 513, 5600 MB Eind- hoven, The Netherlands ISSN: 2246-2465 DOI: https://doi.org/10.54337/jbm.v11i1.7177 Introduction To stay competitive in today ’s dynamic business environment, organizations increasingly focus on innovating the way they do business. In this regard, the business model functions as a useful conceptual tool to represent, analyze, and innovate an organiza- tion’s business logic (Osterwalder, Pigneur and Tuc- ci, 2005). As a result, the business model concept has gained increasing interest in both academia and practice (Johnson, Christensen and Kagermann, 2008; Fielt, 2014; Wirtz et al., 2016; Massa, Tucci and Afuah, 2017). In this study, we consider business models as “the design or architecture of the value creation, delivery, and capture mechanisms” of an or- ganization (Teece, 2010, p. 172). Although organizations need to rethink and adapt their business model continuously, business model https://doi.org/10.54337/jbm.v11i1.7177 Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 3939 innovation is a major challenge for most organiza- tions (Frankenberger et al., 2013). They are faced with several challenges throughout the innovation pro- cess, including identifying change drivers and managing the implementation of the new business model through pilots and experimentation (Frank- enberger et al., 2013). To reduce uncertainty during the innovation process, organizations need to evalu- ate new and existing business models (Gilsing et al., 2022). One possible way to carry out this evaluation is through performance measurement, for which or- ganizations can use business model performance indicators (Heikkilä et al., 2016; Gilsing et al., 2021). Performance indicators are measurable constructs that enable organizations to monitor the extent to which their objectives are fulfilled (Lebas and Euske, 2007). In the context of business models, organiza- tions need to use performance indicators to formu- late measurable objectives related to the expected performance of a new business model (Heikkilä et al., 2014; Gilsing et al., 2021). Moreover, organiza- tions can use business model performance indica- tors to monitor the performance of an organization’s business model during and after its implementation (di Valentin et al., 2013) or benchmark the business model performance of the organization against that of competitors (Afuah and Tucci, 2003; Montemari, Chiucchi and Nielsen, 2019). While existing literature focuses mainly on devel- oping methods and frameworks for representing business models, less attention has been paid to identifying performance indicators for monitoring business model performance (Burkhart et al., 2011; Nielsen et al., 2018). A few studies present catalogs of performance indicators to support organiza- tions in selecting and defining indicators for their business models. However, these catalogs mainly cater to a specific domain or context, such as elec- tronic business (Dubosson-Torbay, Osterwalder and Pigneur, 2002) and networked organizations (Heik- kilä et al., 2016). To the best of our knowledge, no structured review of business model performance indicators currently exists in the literature. The main objective of this paper is to review busi- ness model performance indicators referred to in the academic literature to depict the current state of research and discuss future research directions in this field. To fulfill this objective, we conducted a semi-systematic literature review following the guidelines of Snyder (2019) and classified the identi- fied indicators. We contribute to the existing body of knowledge by providing an overview of performance indicators for business models and categorizing them into a catalog consisting of relevant business model dimensions. The catalog can support organi- zations that are in the process of selecting and con- cretizing performance indicators for their business models to adopt and tailor these indicators for their specific business context and needs. The remainder of this paper is structured as follows. First, we describe the methodological approach used to identify performance indicators in the lit- erature. Next, we present our key insights regarding the categorization and frequency of the identified indicators. Finally, we discuss the key insights about the review and present our conclusions and possible directions for further research in the last section. Methodological approach We conducted a semi-systematic literature review following the guidelines of Snyder (2019). Accord- ingly, our review process comprised four main steps: design, conduct, analyze, and structure and write (Snyder, 2019). First, we defined the objective of our review (as depicted in Section 1) and established a review protocol that all authors followed during the literature search and selection process. To find relevant studies, we specified the following search string: “business model*” AND (“performance indica- tor*” OR “performance measure*” OR “performance metric*” OR “KPI*”). We included the terms (key) per- formance indicator, measure, and metric in the search string as these are often used interchangea- bly in the literature (Neely, Gregory and Platts, 2005). In this paper, we adopt the definition of Lebas and Euske (2007) and use the term ‘performance indica- tor’, as it is most commonly used in the performance measurement literature (Neely, Gregory and Platts, 2005). In addition, we decided only to include stud- ies that (1) adopt a non-trivial definition of business Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4040 models, in line with our interpretation as outlined in Section 1, (2) present clearly defined business model performance indicators, measures, or metrics, and (3) are published in academic venues, such as jour- nals, conference proceedings, or academic book chapters. We conducted our search in the following digital li- braries that publish research studies on business models: Web of Science, Scopus, and AIS eLibrary. Next, we performed a title, abstract, and keyword search using the specified search string in the se- lected libraries. This search resulted in an initial set of 879 studies published between 1988 and Decem- ber 2021. In the next step, we excluded 236 duplicates from the initial set and conducted a title, abstract, and keyword screen on the remaining studies. We excluded 423 studies based on this initial screening, after which we read the full text of the remaining 220 papers. Finally, we selected 18 studies that were rele- vant based on our inclusion criteria. We used Google Scholar to snowball back and forth on the selected studies, which allowed us to find an additional 13 rel- evant studies. As a result, our final set consists of 31 publications (15 journal articles, 12 conference pa- pers, and 4 book chapters) that present performance indicators for business models. The initial results of this literature review have been reported in Van de Ven et al. (2022). Appendix I presents the selected publications resulting from the literature review. Next, we performed several review iterations on the selected papers to extract and categorize the indi- cators. This iterative process resulted in an unstruc- tured set of 951 performance indicators, including duplicates. When specified in the paper, we also extracted the way in which the indicators were oper- ationalized, for example, through a qualitative ques- tion or mathematical formula. Qualitative questions are used to measure performance in a subjective way (e.g., on a Likert scale), while mathematical for- mulas are used to calculate performance indicators objectively based on quantitative data. 16 of the 31 selected studies did not present a clear operation- alization for the proposed indicators. In the next step, we defined the initial conceptual di- mensions of the catalog. Since the Business Model Canvas (BMC) (Osterwalder and Pigneur, 2010) is the most widely used framework to represent business models in both research and practice (Massa, Tucci and Afuah, 2017), the nine building blocks of the BMC were selected as the initial catalog dimensions: Val- ue Propositions, Customer Relationships, Customer Segments, Channels, Key Activities, Key Resources, Key Partners, Revenues Streams, and Cost Struc- ture (Osterwalder and Pigneur, 2010). Moreover, we adopted the term ‘business model pillar’ (Osterwal- der, Pigneur and Tucci, 2005) to describe the me- ta-dimensions of the catalog, and categorized the initial nine BMC dimensions into the business model pillars ‘Frontstage’, ‘Backstage’, and ‘Profit Formu- la’ (Osterwalder et al., 2020). The Frontstage pil- lar (Osterwalder et al., 2020) includes performance indicators related to the value proposition that the organization offers to its customers (i.e., products and services), the relationships that the organiza- tion establishes and maintains with customers, the different customer segments and their characteris- tics, and the channels used to deliver the value prop- osition (i.e., communication, distribution, and sales). Next, the indicators categorized in the Backstage pillar (Osterwalder et al., 2020) are concerned with the performance of key activities performed by the focal organization to deliver value to the customer, the resources required to perform these activities, and the network of partners that the organization re- lies on. The third pillar, the Profit formula (Osterwal- der et al., 2020), contains indicators related to the value capture mechanisms of the business model, including its revenue streams resulting from the de- livery of the value proposition, and costs associated with performing activities, acquiring resources, and collaborating with partners. Subsequently, we iteratively categorized the iden- tified indicators in the selected business model di- mensions. In this step, we merged similar indicators and rephrased them into more general terms. Exam- ples of two specific indicators are ‘(website-related) conversion rate’ (Heikkilä et al., 2016) and ‘premium conversion rate’ (Nielsen, Lund and Thomsen, 2017). These two indicators were merged into the more general indicator ‘conversion rate’. The authors fre- quently met to align on the tentative categoriza- tion of the indicators. We discovered that several Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4141 indicators presented in the literature were related to the profitability of business models during this itera- tive process of categorization and synthesis. To ac- count for profit-related indicators mentioned in the literature, we added the new dimension ‘Profitability ’ to the Profit Formula pillar. We also identified indi- cators related to market performance (for example, shareholder expectations) and the environmental sustainability and societal impact of business mod- els. We added these categories as two distinct di- mensions to the catalog, ‘Market’ and ‘Sustainability & Society ’, respectively, and categorized them in a new pillar called ‘Environment’. The Environment pil- lar includes indicators related to a business model’s ‘contextual logic’ (Lüdeke-Freund et al., 2017), which refers to the larger stakeholder environment in which the business model is embedded. During this phase, we also adapted and refined the operationalizations of the indicators. We attempted to define the operationalizations as close as possible to the original definition and context of the selected publications. If an indicator’s operationalization was not provided in the original publication, we looked for appropriate definitions in the literature and dis- cussed them to reach an agreement. Our final step was to reorder and refine the indica- tors in the catalog until all authors agreed on the final form. This required several meetings until an agreement about the synthesis and categorization of the indicators was reached. Key Insights To analyze the business model performance indi- cators referred to in the academic literature, we extracted the performance indicators related to business models from selected publications and categorized them. The final catalog consists of 215 performance indicators for business models, includ- ing an operationalization for each indicator. An ex- cerpt of the catalog is presented in Appendix II. The indicators are categorized along four pillars and 12 dimensions relevant to business models (Table 1). Figure 1 presents the number of identified indica- tors per business model pillar and dimension. It shows that the majority of indicators are related to the Profit formula pillar of business models (73 in- dicators), while the Frontstage pillar (69 indicators) and Backstage pillar (51 indicators) also cover many indicators. According to these numbers, the major- ity of indicators in the literature are aimed at these three original pillars of the Business Model Canvas (Osterwalder et al., 2020). However, we discovered only 22 indicators related to the Environment pillar of business models. As such, performance indica- tors related to the environment of business models appear to be overlooked in the current literature. Furthermore, the number of identified performance indicators varies greatly across business model di- mensions. Figure 1 shows that the Cost Structure dimension has the highest number of indicators (N=31). This number could be explained by the fact that costs are important in evaluating the business case of new business models (Turetken et al., 2019) and controlling the performance of an existing busi- ness model (Wirtz, 2020). The Channel performance dimension accounts for the second-highest number of indicators, with a total of 28 performance indica- tors, and is part of the Frontstage pillar, which has the second-highest number of indicators. These numbers align with the argument by Wirtz et al. (2016) that an organization’s customer interface de- sign is critical to the success of a business model. At the same time, only a few indicators were discovered related to the environmental and societal context of business models (six indicators, respectively), de- spite the growing interest in evaluating these con- textual dimensions of business model performance (Schaltegger, Hansen and Lüdeke-Freund, 2016; Lüdeke-Freund et al., 2017; Turetken et al., 2019; Or- tuño and Dentchev, 2021). A few performance indicators were frequently re- ferred to in the business model literature. The most used performance indicators for business models are ‘Product or service quality’ (part of the Value proposi- tion dimension) and ‘Customer satisfaction’ (Customer relationships dimension), which both appeared in 14 studies. The second-most used performance indica- tors are ‘Perceived customer benefit’ and ‘Satisfaction of customer needs’, both part of the Value proposition dimension, which were mentioned in 13 studies. Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4242 Table 1. Business model pillars Business model dimensions Focus of performance indicators Frontstage Value proposition performance Product and service performance, perceived customer value, price-related performance Customer relationship per- formance Customer acquisition, customer satisfaction, and relationship-building performance Customer segment perfor- mance Performance of different customer segments, custom- er characteristics, and behavioral performance Channel performance Communication, distribution, and sales channel performance, including the performance of marketing and post-purchase customer support Backstage Key activity performance Development, production, service provision performance Key resource performance Performance related to physical assets, financial resources, intellectual resources, human resources Key partner performance Performance of the partner network related to relationships, outsourcing, knowledge sharing Profit formula Revenue stream perfor- mance Financial performance regarding sales and recurring fees Cost structure performance Fixed and variable costs incurred by the company to deliver the value proposition Profitability performance Value capture performance related to profit margins Environment Market performance Strategic positioning and shareholder-related performance Sustainability & Societal performance Environmental sustainability performance, societal impact, and non-economic environmental or societal costs and benefits Table 1: Business model dimensions and corresponding pillars. Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4343 Discussion and Conclusions This paper reviews the academic literature to ana- lyze the performance indicators related to business models. To this end, we conducted a semi-system- atic literature review, resulting in a sample of 31 relevant studies. Based on the identified indicators in the selected literature, we developed a catalog consisting of 215 performance indicators, catego- rized into four business model pillars (Frontstage, Backstage, Profit formula, and Environment) and 12 dimensions relevant to business model performance (Value proposition, Customer relationships, Custom- er segments, Channels, Key activities, Key Resourc- es, Key partners, Revenue streams, Cost structure, Profitability, Market, and Sustainability and Society). A number of performance indicator catalogs for busi- ness models are presented in the literature (e.g., Dubosson-Torbay, Osterwalder and Pigneur, 2002; Heikkilä et al., 2016). However, we discovered that more than half of the identified studies in our re- view did not present a clear operationalization (i.e., question or formula) to measure and calculate the suggested indicators. Thus, existing research of- ten fails to provide specific guidance for concretely measuring business model performance indicators. We aim to go beyond the state-of-the-art by providing a catalog of 215 business model performance indica- tors, including an operationalization for each indica- tor. Our research thereby responds to the multiple calls in the literature to investigate performance in- dicators for monitoring business model performance (Burkhart et al., 2011; Nielsen et al., 2018). Business professionals who aim to select and spec- ify performance indicators for the business models of their organization can use the catalog. The indica- tors can be modified to fit a particular organization and business context. The additional key contribu- tion of our work compared to existing catalogs is that we provide an explicit operationalization for most of the indicators that can be used to meas- ure the performance of existing or novel business models. It can serve as a starting point for selecting Figure 1: Number of performance indicators per business model pillar and dimension Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4444 indicators for each dimension of an organization’s business model, which can be further concretized based on its specific context and needs. As with any research endeavor, our work is subject to limitations. First, as the catalog developed in this study is still conceptual, future research should fo- cus on empirically evaluating the structure of the catalog. Researchers can apply the catalog to im- prove and validate its applicability in different busi- ness settings. Secondly, during the review process, we found that authors of existing studies use and in- terpret the terms performance indicator, measure, and metric in different ways. Because we interpret- ed these different terms as synonyms in this study, there may have been some subjectivity involved in the process of reviewing papers and categorizing the identified indicators. We mitigated this by ac- tively involving different authors of this paper in all research steps and by iteratively developing the cat- egorization and synthesis of indicators. Based on our findings, we outline several possi- ble future research directions. First, our research showed that the Profit formula pillar of business models has received the greatest attention in terms of the number of performance indicators. The other business model pillars (i.e., Frontstage, Backstage, and Environment) need greater focus by researchers in order to identify relevant indicators and formal- ize their operationalizations. Second, we found that existing studies contain very few indicators dedi- cated to the environmental sustainability and so- cietal performance of business models. Therefore, future research can investigate what indicators are relevant to these emerging dimensions related to the contextual logic of business models, which are quickly becoming important (Schaltegger, Hansen and Lüdeke-Freund, 2016; Lüdeke-Freund et al., 2017; Turetken and Grefen, 2017; Ortuño and Dentch- ev, 2021). Third, researchers can evaluate the valid- ity and utility of the catalog by conducting empirical case studies with business model professionals in various business settings. Fourth, future research can investigate how the catalog can be used during different phases of the business model innovation and management process (Wirtz, 2020; Taran, Boer and Nielsen, 2021; Lara Machado et al., 2022) and how the performance indicators are possibly evolv- ing during the development of the business model over time (Heikkilä et al., 2016). Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4545 References Afuah, A. and Tucci, C.L. (2003) Internet business models and strategies: Text and cases. 2nd edn. New York: McGraw-Hill. Burkhart, T., Krumeich, J., Werth, D. and Loos, P. (2011) ‘Analyzing the Business Model Concept – A Comprehen- sive Classification of Literature’, in ICIS 2011 Proceedings. Dubosson-Torbay, M., Osterwalder, A. and Pigneur, Y. (2002) ‘E-business model design, classification, and meas- urements’, Thunderbird International Business Review, 44(1), pp. 5–23. Available at: https://doi.org/10.1002/ TIE.1036. Fielt, E. (2014) ‘Conceptualising Business Models: Definitions, Frameworks and Classifications’, Journal of Business Models, 1(1), pp. 85–105. Frankenberger, K., Weiblen, T., Csik, M. and Gassmann, O. (2013) ‘The 4I-framework of business model inno- vation: A structured view on process phases and challenges’, International Journal of Product Development, 18(3–4), pp. 249–273. Available at: https://doi.org/10.1504/IJPD.2013.055012. Gilsing, R., Turetken, O., Grefen, P., Ozkan, B. and Adali, O.E. (2022) ‘Business Model Evaluation: A Systematic Review of Methods’, Pacific Asia Journal of the Association for Information Systems, 14(4), p. 2. Available at: https://doi.org/10.17705/1pais.14402. Gilsing, R., Wilbik, A., Grefen, P., Turetken, O., Ozkan, B., Adali, O.E. and Berkers, F. (2021) ‘Defining business model key performance indicators using intentional linguistic summaries’, Software and Systems Modeling, 20, pp. 965–996. Available at: https://doi.org/10.1007/S10270-021-00894-X. Heikkilä, M., Bouwman, H., Heikkilä, J., Solaimani, S. and Janssen, W. (2016) ‘Business model metrics: an open repository ’, Information Systems and e-Business Management, 14(2), pp. 337–366. Available at: https://doi. org/10.1007/s10257-015-0286-3. Heikkilä, M., Solaimani, S., Soudunsaari, A., Hakanen, M., Kuivaniemi, L. and Suoranta, M. (2014) ‘Performance estimation of networked business models: case study on a Finnish eHealth Service Project’, Journal of Busi- ness Models, 2(1), pp. 71–88. Johnson, M.W., Christensen, C.M. and Kagermann, H. (2008) ‘Reinventing your business model’, Harvard busi- ness review, 86(12), pp. 57–68. Lara Machado, P., van de Ven, M., Aysolmaz, B., Athanasopoulou, A., Ozkan, B. and Turetken, O. (2022) ‘Bridging Business Models and Business Processes: A Systematic Review of Methods’, in MCIS 2022 Proceedings. Lebas, M. and Euske, K. (2007) ‘A conceptual and operational delineation of performance’, in A. Neely (ed.) Busi- ness performance measurement: unifying theories and integration practice. Cambridge: Cambridge University Press, pp. 125–139. Lüdeke-Freund, F., Freudenreich, B., Schaltegger, S., Saviuc, I. and Stock, M. (2017) ‘Sustainability-Oriented Business Model Assessment—A Conceptual Foundation’, Analytics, Innovation, and Excellence-Driven Enter- prise Sustainability, pp. 169–206. Available at: https://doi.org/10.1057/978-1-137-37879-8_7. Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4646 Massa, L., Tucci, C.L. and Afuah, A. (2017) ‘A critical assessment of business model research’, Academy of Man- agement Annals, 11(1), pp. 73–104. Available at: https://doi.org/10.5465/annals.2014.0072. Montemari, M., Chiucchi, M.S. and Nielsen, C. (2019) ‘Designing Performance Measurement Systems Using Business Models’, Journal of Business Models, 7(5), pp. 48–69. Available at: https://doi.org/10.5278/ojs.jbm. v7i5.1905. Neely, A., Gregory, M. and Platts, K. (2005) ‘Performance measurement system design: A literature review and research agenda’, International Journal of Operations and Production Management, 25(12), pp. 1228–1263. Avail- able at: https://doi.org/10.1108/01443570510633639. Nielsen, C., Lund, M. and Thomsen, P. (2017) ‘Killing the balanced scorecard to improve internal disclosure’, Journal of Intellectual Capital, 18(1), pp. 45–62. Available at: https://doi.org/10.1108/JIC-02-2016-0027. Nielsen, C., Lund, M., Thomsen, P.P., Kristiansen, K.B., Sort, J.C., Byrge, C., Roslender, R., Schaper, S., Mon- temari, M., Delmar, A.C.P., Lorenzo, S., Paolone, F., Massaro, M. and Dumay, J. (2018) ‘Depicting a performative research Agenda: The 4th stage of business model research’, Journal of Business Models, 6(2), pp. 59–64. Ortuño, C.A. and Dentchev, N.A. (2021) ‘We need transdisciplinary research on Sustainable Business Models’, Journal of Business Models, 9(2), pp. 72–86. Available at: https://doi.org/10.5278/JBM.V9I2.3573. Osterwalder, A. and Pigneur, Y. (2010) Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons. Osterwalder, A., Pigneur, Y., Smith, A. and Etiemble, F. (2020) The Invincible Company: How to Constantly Rein- vent Your Organization with Inspiration From the World’s Best Business Models. John Wiley & Sons. Osterwalder, A., Pigneur, Y. and Tucci, C.L. (2005) ‘Clarifying Business Models: Origins, Present, and Future of the Concept’, Communications of the Association for Information Systems, 16. Available at: https://doi. org/10.17705/1cais.01601. Schaltegger, S., Hansen, E.G. and Lüdeke-Freund, F. (2016) ‘Business Models for Sustainability: Origins, Pre- sent Research, and Future Avenues’, Organization & Environment, 29(1), pp. 3–10. Available at: https://doi. org/10.1177/1086026615599806. Snyder, H. (2019) ‘Literature review as a research methodology: An overview and guidelines’, Journal of Busi- ness Research, 104, pp. 333–339. Available at: https://doi.org/10.1016/J.JBUSRES.2019.07.039. Taran, Y., Boer, H. and Nielsen, C. (2021) The Business Model Innovation Process: Preparation, Organization and Management. 1st edn. London: Routledge. Available at: https://doi.org/10.4324/9781003215097. Teece, D.J. (2010) ‘Business models, business strategy and innovation’, Long Range Planning, 43(2–3), pp. 172– 194. Available at: https://doi.org/10.1016/j.lrp.2009.07.003. Turetken, O. and Grefen, P. (2017) ‘Designing service-dominant business models’, in Proceedings of the 25th European Conference on Information Systems (ECIS). Guimarães, Portugal, pp. 2218–2233. Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4747 Turetken, O., Grefen, P., Gilsing, R. and Adali, O.E. (2019) ‘Service-Dominant Business Model Design for Digi- tal Innovation in Smart Mobility ’, Business and Information Systems Engineering, 61(1), pp. 9–29. Available at: https://doi.org/10.1007/s12599-018-0565-x. di Valentin, C., Emrich, A., Werth, D. and Loos, P. (2013) ‘Architecture and Implementation of a Decision Support System for Software Industry Business Models’, in AMCIS 2013 Proceedings. van de Ven, M., Lara Machado, P., Athanasopoulou, A., Aysolmaz, B. and Turetken, O. (2022) ‘Key Performance Indicators for Business Models: A Review of Literature’, in ECIS 2022 Research Papers. Wirtz, B.W. (2020) Business Model Management. 2nd edn. Cham: Springer International Publishing (Springer Texts in Business and Economics). Available at: https://doi.org/10.1007/978-3-030-48017-2. Wirtz, B.W., Pistoia, A., Ullrich, S. and Göttel, V. (2016) ‘Business Models: Origin, Development and Fu- ture Research Perspectives’, Long Range Planning, 49(1), pp. 36–54. Available at: https://doi.org/10.1016/j. lrp.2015.04.001. Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4848 Appendix I - Selected Publications Resulting from the Literature Review ID Year Authors Title Source title Type 1 2003 Afuah A., Tucci C. Internet Business Models and Strategies McGraw-Hill Book chapter 2 2018 Augenstein D., Fleig C. Towards increased busi- ness model comprehension - Principles for an advanced business model tool ECIS 2018 Proceedings Conference paper 3 2017 Batocchio A., Minato- gawa V.L.F., Anholon R. Proposal for a method for business model perfor- mance assessment: Toward an experimentation tool for business model innovation Journal of Technology Management and In- novation Article 4 2003 Bouwman H. Designing metrics for busi- ness models describing mobile services delivered by networked organizations Workshop on concepts, metrics & visualization, at the 16th Bled Conf. Conference paper 5 2004 Bouwman H., Van den Ham E. Business models and e- metrics, a state of the art E-Life after the Dot.com Bust Book chapter 6 2012a Di Valentin C., Emrich A., Werth D., Loos P. Conceiving Adaptability for Business Models: A Litera- ture-based Approach CONF-IRM 2012 Pro- ceedings Conference paper 7 2012b Di Valentin C., Werthe D., Loos P., Weiblen T. Quantifying the Quality of Business Models Int. Conference in Human-Oriented and Personalized Mecha- nisms, Technologies and Services. Conference paper 8 2017 Díaz-Díaz, R., Muñoz, L., Péréz-Gonzalez, D. The Business Model Evalu- ation Tool for Smart Cities: Application to SmartSan- tander Use Cases Energies Article Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 4949 ID Year Authors Title Source title Type 9 2002 Dubosson-Torbay M., Osterwalder A., Pigneur Y. E‐business model design, classification, and measure- ments Thunderbird Interna- tional Business Review Article 10 2021 Gilsing R., Wilbik A., Grefen P., Turetken O., Ozkan B., Adali O.E., Berkers F. Defining business model key performance indicators using intentional linguistic summaries Software and Systems Modeling Article 11 2010 Heikkilä J., Tyväinen P., Heikkilä, M. Designing for performance - a technique for business model estimation Proceedings of EBRF 2010 Conference paper 12 2016 Heikkilä M., Bouwman H., Heikkilä J., Solaimani S., Janssen W. Business model metrics: an open repository Information Systems and e-Business Man- agement Article 13 2014 Heikkilä M., Solaimani S., Soudunsari A., Ha- kanen M., Kuivaniemi L., Suoranta M. Performance estimation of networked business mod- els: case study on a Finnish eHealth Service Project Journal of Business Models Article 14 2008 Johnson M.W., Chris- tensen C.M., Kager- mann H. Reinventing Your Business Model Harvard Business Review Article 15 2013 Kastalli I.V., Van Looy B., Neely A. Steering manufacturing firms towards service busi- ness model innovation California Management Review Article 16 2007 Khoshalhan F., Kaldi A. Skills brokerage perfor- mance measurement through BSC Int. Conf. on Computer and Information Tech- nology Conference paper 17 2010 Kijl B., Boersma, D. Developing a business model engineering & experi- mentation tool–the quest for scalable 'lollapalooza conflu- ence patterns' AMCIS 2010 Proceed- ings Conference paper Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5050 ID Year Authors Title Source title Type 18 2021 Kostin, K.B., Stein- biss, K., Petrinovic, O. Determining the KPIs of the German engineering indus- try based on the evaluation of contemporary business models Strategic Management Article 19 2016 Kriegel J., Auinger K., Reckwitz L., Schmitt-Rüth S., Weissenberger S., Tuttle-Weidinger L. AAL service performance measurement cube - key criteria for AAL new service development Proceedings of eHealth2016 Conference paper 20 2017 Lüdeke-Freund, F., Freudenreich, B., Saviuc, I., Schalteg- ger, S., Stock, M. Sustainability-Oriented Busi- ness Model Assessment—A Conceptual Foundation Analytics, Innovation, and Excellence-Driven Enterprise Sustainability Book chapter 21 2020 Minatogawa V.L.F., Franco M.M.V., Ram- passo I.S., Anholon R., Quadros R., Durán O., Batocchio A. Operationalizing business model innovation through big data analytics for sus- tainable organizations Sustainability Article 22 2019 Montemari, M., Chiuc- chi, M.S., Nielsen, C. Designing Performance Measurement Systems Us- ing Business Models Journal of Business Models Article 23 2018 Mourtzis D., Papathe- odorou A.-M., Fotia S. Development of a key perfor- mance indicator assessment methodology and software tool for product-service sys- tem evaluation and decision- making support Journal of Computing and Information Science in Engineering Article 24 2017 Nielsen C., Lund M., Thomsen P. Killing the balanced score- card to improve internal disclosure Journal of Intellectual Capital Article Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5151 ID Year Authors Title Source title Type 25 2001 Palanisamy, R. Evolving internet business model for electronic com- merce using flexible sys- tems methodology Global Journal of Flex- ible Systems Manage- ment Article 26 2015 Rodríguez-Rodríguez R., Alfaro-Saiz J.-J., Verdecho M.-J. A performance-based sce- nario methodology to assess collaborative networks busi- ness model dynamicity Working Conference on Virtual Enterprises Conference paper 27 2022 Stalmachova K., Chi- noracky R., Strenitze- rova M. Changes in Business Models Caused by Digital Transfor- mation and the COVID‐19 Pandemic and Possibilities of Their Measurement—Case Study Sustainability Article 28 2021 Udo Y., Ishino Y. Two-Stage Lean Startup Model for Subscription Busi- ness KES International Con- ference Conference paper 29 2020 Wirtz B.W. Business model manage- ment: Design - instruments - success factors Springer Book chapter 30 2014 Yu C.-C. Developing value-centric business models for mobile government EGOV 2014 Conference paper 31 2006 Yu C.-C. A hybrid modeling approach for strategy optimization of E-business values BLED 2006 Proceedings Conference paper Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5252 Appendix II - Catalog of performance indicators for business models (excerpt) Business model pillars Business model dimensions Performance indicators Operationalization Frontstage Value proposition Perceived cus- tomer benefit Extent to which the product or service is bet- ter than current alternatives of competitors (qualitative scale from high to low) which can be measured based on various dimensions (e.g., security, protection of privacy, skills or learning provided, comfort, ease of use of the service, brand image, trust) and scales (e.g., Customer Effort Score, CSE) Satisfaction of customer needs • Extent to which the product or service meets the requirements or needs of the customer (qualitative scale from high to low) • Number of customer requirements satis- fied divided by total number of require- ments requested by the customer (e.g., performance according to service-level agreement) • Number of additional and value added ser- vices offered on top of the main product or service offering Product diversifi- cation • Number of different products or services, • Number of different product or service categories • Percentage of specific type of products (e.g., fresh products) of total product portfolio Customer relationships Conversion rate Number of conversions of free customers to paying customers divided by total number of interactions per time period Customer satis- faction • Customer Satisfaction Index (CSI) • Satisfaction barometer Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5353 Business model pillars Business model dimensions Performance indicators Operationalization Frontstage Customer relationships Recommendation ratio or willing- ness to refer • Net Promotor Score (NPS) (i.e., willingness of customers to recommend the service to their friends) • Number of referrals divided by total num- ber of customers per time period Customer segments Profitable cus- tomers Number of customers that are profitable di- vided by total number of customers Online customers Number of customers who order products or service online / Total number of customers Average order size or customer expenditure • Average amount of money a customer spends in one transaction • Average amount spend by a customer per purchase multiplied by the purchase fre- quency over a certain time period Channels Website perfor- mance • Average number of page-views over a cer- tain time period • Number of click-throughs on the website divided by the number of times the web- site is shown to the customer • Ease of finding and navigating through the website (qualitative scale from high to low) • Average time to load a web page • Maximum number of users logged in at the same time on the website On-time delivery • Number of on-time deliveries divided by total number of deliveries • Percentage of late deliveries Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5454 Business model pillars Business model dimensions Performance indicators Operationalization Frontstage Channels Sales performance • Number of companies contacted by the commercial department over a certain time period • Number of deals closed with companies by the commercial department over a cer- tain time period • Time to first proposal • Average sales per sales person (monetary value) • Number of sales orders received but not completed yet) Backstage Key activities Process throughput Number of completed cases per time period (e.g., customer complaints) Product or ser- vice development speed or time-to- market • Average time from idea to prototype (i.e., development time of new product or ser- vice concept) • Time from product development to prod- uct or service placement on the market (i.e., product or service launch) Production per- formance • Time to produce a single product (i.e., completion time) • Number of products that are built-to-or- der per time period Key resources System architec- ture or Informa- tion Technology (IT) infrastructure performance • 24-7 availability and downtime • Response time (e.g., API response) • Number of help desk calls per time period • Number of disaster recoveries per time period • Mean time between failures • Data security or integrity • Number of applications • Extensibility of applications • Percentage of service providers' data base visits • Percentage of cross-system collaboration (i.e., interoperability of systems) Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5555 Business model pillars Business model dimensions Performance indicators Operationalization Backstage Key resources Internal col- laboration perfor- mance • Number of units and departments involved in the business model • Number of organizational layers involved • Number of different roles and responsi- bilities Workforce size • Number of employees • Number of Full-time equivalent (FTE) em- ployed Key partners Partner network control or co- ordination • Type of coordination (Middle, high, none) • Centrality of specific actors in value ex- change Vertical integra- tion of activities • Degree of co- or outsourcing of activities (e.g., logistics, manufacturing) • Owned activities compared to outsourced activities Partner collabo- ration and inno- vation • Number of new projects started with part- ners per time period • Percentage of cross-unit or organization- al collaboration • Improvement of the degree of collabora- tive innovation per time period Profit formula Revenue streams Volume or value of traded goods • Number of products and/or services sold per time period • Value per product multiplied by total num- ber of products traded per time period" Sales growth Net sales of the prior period minus net sales of the current period, divided by net sales of the prior period (Premium) sub- scription revenue Revenue from customers through recurring (premium) fees multiplied by number of time period intervals (often regular intervals, e.g., weekly, monthly, or annually) Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5656 Business model pillars Business model dimensions Performance indicators Operationalization Profit formula Cost structure Personnel costs Average costs per working hour, total salary costs Operating ex- penses (OPEX) Direct costs of goods sold and other operating expenses over a certain period of time Sales and mar- keting expenses • Total expenses made to market and sell products and services • Total costs of sales (e.g., distribution costs, marketing costs, wages, commis- sions)" Profitability Return on invest- ment (ROI) Profit divided by total capital (i.e., efficiency of the total capital) Net profit margin Revenue minus cost, divided by revenue Earnings Before Interest and Taxes (EBIT) Annual net profit plus or minus taxes and inter- est (operating profit excluding tax and interest) Environment Market Positioning Extent to which business model is affected by competitive forces from (qualitative scale from high to low): rivalry, customers, complemen- tors, suppliers, potential new entry, substi- tutes (Porter's Five Forces) Earnings per share (EPS) Net income minus preferred dividends, divided by outstanding shares Shareholder value Total (monetary) value delivered to the equity owners of a company due to management's ability to increase sales, earnings, and free cash flow Journal of Business Models (2023), Vol. 11, No. 1, pp. 38-57 5757 Business model pillars Business model dimensions Performance indicators Operationalization Environment Sustainability & Society Unit energy con- sumption All energy consumed in a production cycle divided by production quantity Wastage degree Scrap quantity divided by planned scrap quan- tity Non-economic benefits Non-economic aspects of the business model that are beneficial to society and the envi- ronment (e.g., development goals related to knowledge development, innovation produc- tivity, creativity, social cohesion)