There are several managerial implications of our study. We discuss them below.
Balanced redundancy. Conventional business model logic focuses on efficiency through
the elimination of redundant resources in order to create and capture value. However, such a conceptualization of the business model is applicable when the business model has stabilized and the focus is on the exploitation of an opportunity. The emphasis of such a business model is to gain efficiency from economies of scale and scope. However, our study shows that a balanced level of redundancy is necessary when there are economies of innovation. Firms that are able to create and stretch their resources in order to develop some redundancies, or specifically cater for some overlapping resources, are better able to morph their business models and enable them to evolve. Such a capability is useful in new firms as well as in established firms. In a new firm the initial planned customer value proposition is unlikely to create a profitable and sustainable business model but requires refinement over time. In established firms, the changing
43
technological and competitive landscape might require continuous evolution of the business model in order to serve new and emerging customer value propositions.
Requisite variety. Our study shows that designing the business model in order to enhance
the variety of sources of information is critical to enabling new opportunities to be identified.
The multiple sources of variety help in two ways. First, they enable discrepancies to be reconciled and, hence, opportunities to be identified within components of the business model system. Second, the multiple information source enables the firm to connect the different components to see the architectural or overall picture of how to evolve the business model further. Our study shows that managers should build up the capabilities of the senior team to acquire and assimilate detailed knowledge of each component of the business model. The senior team should also be able to step back and develop the capability to see the bigger picture by examining relations between components in order to develop a deeper understanding of the workings of the business model. Such a capability plays to the part–whole structure of systems thinking.
Cognitive discretion. The challenges of business model evolution are both cognitive and
economic. The business model is a cognitive conception (Doz and Kosonen, 2010; McGrath, 2010; Teece, 2010) and therefore, managers have to change the mental model of their business from one to another to enable business model evolution. The business model is also simultaneously an economic one because it is a description of the underlying routines and architecture of the business (Casadesus-Masanell and Ricart, 2010; Zott and Amit, 2010).
Business model evolution requires changes in the cognitive, as well as economic, aspects. Our study has implications for managers in terms of how to effect cognitive change in the business model. The cognitive change can be realized on a variety of fronts, such as an adaptive or
44
aspirational search. Our study shows that management should encourage the cognitive discretion of its senior team by giving them the freedom to perceive and construct idiosyncratic and semiotic worlds. Such a policy by management enables better opportunity identification and also business model evolution. Studies have shown that both deductive and inductive logic are useful in effecting innovations (Dunbar, Garud and Raghuram, 1996). Deductive logic enables incremental improvements to existing business models, while inductive logic enables analogical reasoning for more radical evolutions. Our study shows how cognitive discretion enables managers to use both deductive and inductive logic to identify opportunities in order to enable business model evolution. Table 1 provides a summary of the findings and implications.
Insert Table 1 here.
6. Conclusion
Understanding the organizational capabilities that enable business models to evolve is particularly important from both research and managerial dimensions. While conclusions drawn from one case study inevitably require some caveats, our research highlights how the business model can be seen as a complex system and, hence, systems thinking provides a preliminary understanding of how business models evolve. In doing so, we provide insights into how organizational capabilities based on balanced redundancy, requisite variety and cognitive discretion, and the associated mechanisms, can facilitate business model evolution. This framework may be useful as a tool to explore more generally how organizations need to develop dynamic capabilities in order to explore and exploit opportunities in nascent markets with significant ambiguity.
Scholars have also emphasized the importance of rational design, experimentation, as well as cognitive framing, as the basis to alter an existing business model and assemble a new
45
one (Martins et al., 2015). Modularization can be seen from the perspective of a cognitive activity in order to simplify a complex system such as a business model by articulating it as a model of interconnected elements (Aversa et al., 2015). Business model evolution could arise as a result of the process of changing the business model elements, their linkages and their order, initially through a cognitive process, which could subsequently translate into actual practice.
Such a process could be the result of cognitive framing, rational design, as well as experimentation. We show that organizational capabilities such as balanced redundancy, requisite variety and cognitive discretion enable such business model evolution.
There are several possible extensions to this study. First, which other principles from systems theory can be applied to business model evolution? In particular, are there other characteristics of physical, biological and organizational systems that could be applied to achieving a better understanding of the capabilities needed to enable business model evolution?
Second, what are the boundary conditions that should be applied to these organizational capabilities in order to stimulate business model evolution? Third, how do these organizational capabilities interact with one another to encourage the evolution of business models? Fourth, does the application vary for business models related to developing new products as opposed to services? In particular, do business models that are designed for delivering products require a different set of organizational capabilities to enable business model evolution compared to business models that deliver services as their key proposition? Finally, what can we learn from systems thinking in order to develop a deeper understanding of how business models linked to one another as an ecosystem might evolve?
Moreover, there are limitations to our study. First, the extension of the theory is being developed from a single case study. The study itself is based on a single case with its attendant
46
context such as country- and industry-specific issues. Hence, the study needs to be extended to consider other case contexts in order to verify the robustness of the findings. Second, although we acknowledge that the business model transcends the firm to include other collaborative organizations, our focus has been on the capabilities of the single focal firm. Further studies should address inter-firm capabilities and how a systems thinking approach could help to provide a deeper understanding of such dynamic capabilities. Acknowledging these limitations and possible extensions, we argue that our study provides a useful framework for understanding how firms can develop the relevant organizational capabilities in order to evolve the business model to deliver the customer value proposition profitably.
Acknowledgements: The author would like to thank Sriya Iyer for her insightful comments on the agricultural markets and the Indian economy. The author would also like to thank the Editors and two anonymous referees for their helpful comments on earlier versions of this article.
47 Figure 1: Data structure
Data structure
Table 1: Summary of findings and business model evolution
Organizational capabilities Implications for business model evolution Balanced redundancy – refers
to cases where the functions of one element can be performed by another element, either partially or fully.
Firms that are able to create and stretch their resources in order to develop some redundancies in a balanced manner, or specifically cater for some overlapping resources, are better able to morph their business models and enable them to evolve.
Requisite variety – refers to the extent to which
Enables discrepancies to be reconciled and, hence, opportunities to be identified within components of the business model system. The multiple information enables the firm to connect the different components to see the architectural or overall picture of how to evolve the business model further.
Cognitive discretion – refers to the freedom to perceive and construct idiosyncratic
Enables the use of deductive logic by using more rational approaches and inductive logic, using metaphors/analogies to transform the cognitive aspects of the business model.
First-Order Concepts Second-Order Themes Aggregate Dimensions Information from customers at different points in time Incompatibilities between different information sources Aggregating information to create new information
Looking at logical implications of actions and outcomes Causation driven with focus on discovery
48 meaning.
49 Appendix 1A – Balanced redundancy
First-order concepts
Quotes
Slack resources ‘We built some slack resources within the content team because of the unpredictable nature of the content we needed to develop.’ (Content manager)
‘We had among our staff some overlaps in skills. However, we felt that such overlaps in resources are needed in order to be flexible and responsive when it came to experimenting with new models.’ (Operational manager)
Resource-stretching
‘If we wanted photos for marketing and promotion, we would create a contest for employees to participate and provide the photos from the field rather than pay an agency a lot of money to do so.’ (Finance officer)
‘We learnt that staff out in the field were happy to spend their free time in the evenings doing some extra work which helped them earn some additional income.’ (Human resources officer)
Limited
experiments and fail fast
‘There is often a tendency to want to conduct many experiments simultaneously to work out which is the best way forward. This pressure was particularly evident when many of our initial initiatives were not paying dividends. However, we often had to force ourselves to prioritize the initiatives in order to focus on the key ones and to have a list of what to do next in case they fail.’ (Sales officer)
‘Managing the cash inflow and outflow was a major driver of how much resources we could have to develop the business.’ (Finance officer)
Keep up employee motivation
‘As we were conducting new experiments, we wanted to ensure that staff did not lose focus on the current initiatives. Therefore, we were open with the staff about new experiments that were being conducted through periodic communications such as town hall meetings. These communication sessions brought out any uncertainties and rumors that might have demotivated our staff. It also became imperative to us that we did not overstretch our resources in conducting these experiments.’
(Management team member)
‘The venture capital providers were of the opinion that putting some constraints on resources would encourage creativity. The constraints acted as a way for employees to motivate themselves and find work arounds creatively.’ (Management team member)
50
‘Everyone had a different view about what opportunities to pursue. For example, the view from the marketing team was often different from the operations team in charge of delivering the service. At first this was confusing but such difference in views enabled us to better understand the existing opportunities from the market.’ (Strategy officer)
‘In some states such as Punjab the concept of a door-to-door sales person was less acceptable and hence this made us rethink as to what should be our key customer value proposition.’ (Operations officer)
Information from customers at different points in time
‘The sales team at some of the major fertilizer companies would meet the farmers through the life cycle of the crops and hence were in a good position to provide crop advisory services in order to enhance the sale of fertilizers.’ (Sales officer)
‘Some farmers wanted not only local market prices but also national prices because traders come from big cities to buy crops but we were not able to provide them with our focus on localized service.’ (Sales officer) Incompatibilities
between different information sources
‘We realized that merely providing the information on SMS to farmers was the antibiotic and not the vitamin. We needed to also provide the vitamin for the farmers.’ (Marketing officer)
‘We moved from a crop-centric information provider to a farmer-centric information provider as we understood the issues better.’ (Strategy officer)
Aggregating information to create new information
‘In a sense, the last mile connectivity was not great. We needed to more efficiently connect the small farmers with the market and enable the transaction rather than merely providing the prices for the crops.’
(Technology officer)
‘We needed to provide national prices and the only way we could make this work economically is to aggregate demand and supply through a platform and provide a transactions based service.’ (Technology officer)
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‘We initially thought that we would disintermediate the middleman that was serving as the conduit of price dissemination between the mandi (market place) and the farmer. However, we later realized that we need to work with these middlemen by enabling them and making them part of the beneficiaries of the service.’ (Management team member)
‘The rapid growth of smartphones had implications for the business opportunities for us. We were increasingly collecting more and more information about the farmers based on our transactions business model.
Hence, we looked at ways to monetize the value of this information.’
(Strategy officer) Causation-driven
with a focus on delivery
‘Farmers were rather impatient with the sometimes long waiting time from calling the free phone call center number. This made us realize that more immediacy is required which a smartphone is better able to provide through a library based system of providing access to information.’ (Sales manager)
‘Farmers could access information on a smart phone as and when required rather than being delivered to them via text messages when the information was ready.’ (Sales manager)
Drawing on analogies and metaphors
‘It was clear that we needed a new business model. However, our challenge was to change the mindset of the team to look beyond a business model based on selling information and transactions. We had many discussions in the Board meetings about other related businesses such as Netflix, Google, Apple and other information-intensive business models.’ (Management team member)
‘We studied many other successful information service firms globally and often had heated debates about not only what is similar about our business to the comparative set but more importantly what is different so that we can design our proposition accordingly.’ (Content officer)
Effectuation-driven with a focus on creation
‘Our close study of other information providers made us realize that we do not need to own all the content in order to deliver a suitable customer value proposition. We can rely on others to deliver but we needed to create the really novel propositions internally.’ (Content officer)
‘We were aware that we would be one of the first in the world to create a community of farmers with an end-to-end solution of their needs via a mobile smartphone. We had to be creative in developing the proposition as we needed to learn from our customers in order to monetize the service.’ (Marketing officer)
52 References
Achtenhagen, L., Melin, L., Naldi, L., 2013. Dynamics of business models – Strategizing, critical capabilities and activities for sustained value creation. Long Range Planning 46, 427–442.
Afuah, A., Tucci, C.L., 2001. Internet business models and strategies: Text and cases. New York: McGraw-Hill.
Ashby, W.R., 1952. Design for a Brain: The Origin of Adaptive Behavior. London: Chapman and Hall.
Aversa P., Furnari, S., Haefliger, S. 2015. Business model configurations and performance: A qualitative comparative analysis in Formula One racing, 2005-2013 Industrial and Corporate Change, 24(3), 655-676
Aversa, P., Haefliger, S., Rossi, A., Baden Fuller, C., 2015. From Business Model to Business Modeling: Modularity and Manipulation. Advances in Strategic Management 33, 151–185.
Baden-Fuller, C., Morgan, M.S., 2010. Business models as models. Long Range Planning 43 (2–
3), 156–171.
Baden-Fuller, C., Mangematin, V., 2014. Business models: A challenging agenda. Strategic Organization 11 (4) 418–427.
Baldwin, C.Y., Clark, K.B, 2000. Design Rules, Volume 1, The Power of Modularity.
Cambridge MA: MIT Press.
Bertalanffy, L. von., 1950. An outline of general systems theory. British Journal of Philosophy of Science 1, 139–164.
Berthon, P., Pitt, L., Nel, D., Salehi-Sangari, E., Engstrom, A., 2008. The biotechnology and marketing interface: Functional integration using mechanistic and holographic responses to environmental turbulence. Journal of Commercial Biotechnology 14(3) 213–224.
Burke, W. 2014. Organizational change: Theory and practice. Sage.
Cabrera, D., Cabrera, L., Powers, E., 2015. A unifying theory of systems thinking with Psychological applications, Systems Research and Behavioral Science 32, 534–545.
Casadesus-Masanell, R., Ricart, J.E., 2010. From strategy to business models and onto tactics.
Long Range Planning 43 (2–3), 195–215.
DaSilva, C., Trkman, P., 2014. Business Model: What It Is and What It Is Not. Long Range Planning 47, 379–389.
53
Demil, B., Lecocq, X., 2010. Business model evolution: In search of dynamic consistency. Long Range Planning 43 (2–3), 227–246.
Desyllas, P., Sako, M., 2013. Profiting from business model innovation: Evidence from Pay-As-You-Drive auto industry. Research Policy 42, 101–116.
Dos Santos, J.F.P., Spector, B., Van der Heyden, L., 2015. Towards a theory of business mdoel change. In: Foss, N., Saebi, T. (Eds.), Business Model Innovation: The Organizational Dimension. Oxford University Press, Oxford.
Doz, Y.L., Kosonen, M., 2010. Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning 43 (2–3), 370–382.
Dunbar, R.L.M., Garud, R., Raghuram, S., 1996. A frame for deframing in strategic analysis.
Journal of Management Inquiry 5, 23–34.
Eisenhardt, K.M., Furr, N.R., Bingham, C.B. 2010. Micro-foundations of performance:
Balancing efficiency and flexibility in dynamic environments. Organization Science 21 (6), 1263–1273.
Fligstein, N., 1996. Markets as Politics: A Political-Cultural Approach to Market Institutions.
American Sociological Review 61 (4), 656–673.
Geroski, P., 1998. Thinking creatively about markets. International Journal of Industrial Organization 16 (6), 67–695.
Gioia, D.A., Corley, K.G., Hamilton, A., 2012. Seeking qualitative rigor in inductive research:
Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31.
Henderson, R.M., Clark, K.B., 1990, Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms, Administrative Sciences Quarterly 35, 9–30.
Humphreys, A., 2010. Megamarketing: The creation of markets as a social process, Journal of Marketing 74, 1–19.
Johnson, W., Christensen, C., Kagermann, H., 2008. Reinventing your business model. Harvard Business Review 86 (12), 50–59.
Kafri, R., Springer, M., Pilpel, Y., 2009. Genetic redundancy: new tricks for old genes. Cell 136, 389–392.
Kast, R.E., Rosenzweig, J.E., 1972. General systems theory: Applications for organization and management. Academy of Management Journal 15, 447–465.
54
Katz, D., Kahn, R.L., 1978. The social psychology of organizations. New York, Wiley.
Leih, S., Linden, G., Teece, D. 2015. Business model innovation and organizational design. In:
Foss, N., Saebi, T. (Eds.), Business Model Innovation: The Organizational Dimension. Oxford University Press, Oxford.
Lubik, S., Garnsey, E., 2016. Early business model evolution in science-based ventures: The case of advanced materials. Long Range Planning, 49, 393-408.
Martins, L.L., Rindova, V.P., Greenbaum, B.E., 2015. Unlocking the hidden value of concepts:
A cognitive approach to business model innovation. Strategic Entrepreneurship Journal 9, 99–
117.
McGrath, R.G., 2010. Business models: A discovery driven approach. Long Range Planning 43, (2–3) 247–261.
Midgley, G., 2003. Systems Thinking. Sage Publications: Thousand Oaks, CA.
Mullins, J., Komisar, R., 2009. Getting to Plan B: Breaking through to a Better Business Model.
Harvard Business Press, Boston MA.
Orton, J.D., Weick, K.E., 1990. Loosely Coupled Systems: A Reconceptualization. Academy of Management Review 15 (2) 203–223.
O'Reilly, C., Tushman, M.L., 2013. Organizational ambidexterity: Past, present and future.
Academy of Management Perspectives 27(4), 324–338.
Pratt, M.G., 2009. For the lack of a boilerplate: Tips of writing (and reviewing) qualitative research. Academy of Management Journal 52 (5), 856–862.
Rindova V.P., Kotha, S., 2001. Continuous morphing: competing through dynamic capabilities.
Academy of Management Journal 44 (6), 1280–3.
Rumelt, R. 2011. The Perils of Bad Strategy. McKinsey Quarterly 1, 30–39.
Seelos, C., Mair, J., 2007. Profitable business models and market creation in the context of deep poverty: A Strategic View. Academy of Management Perspectives 21 (4), 49–63.
Siggelkow, N., 2002. Evolution towards fit. Administration Science Quarterly 47 (1), 125–159.
Simon, H.A., 1962. The architecture of complexity. Proceeding of the American Philosophical Society 106, 467–482.
Sosna, M., Trevinyo-Rodriguez, R.N., Velamuri, S.M., 2010. Business model innovation through trial and error learning. Long Range Planning 43 (2–3), 383–407.