Challenges of business models for sustainability in startups

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 11 October 2022

Issue publication date: 4 November 2022

This study aims to analyze the challenges startups face in implementing business models for sustainability. In particular, the research question of this study is: How do the challenges faced by startups affect business models for sustainability in the context of an emerging country?

Design/methodology/approach

Startups are increasingly incorporating ways to thrive in a competitive environment with innovative sustainable business models, a key factor for competitive advantage and corporate sustainability. This paper analyses startups’ challenges in adopting business models for sustainability through a case study in two startups, using the sustainable value exchange matrix (SVEM) tool through workshops, to carry out the diagnosis of these challenges.

The barriers and challenges of business models for sustainability in startups were found in different categories, where the main barriers are linked to the institutional category, the organizational and the market and sales culture. Thus, the authors concluded that there is a need to reformulate public policies and to have greater participation of the actors involved.

Research limitations/implications

The main limitation of the research is the number of case studies (only two), which makes it difficult to generalize the results.

Practical implications

The research presents two major contributions. First, through the case studies, it is possible to verify that the barriers and challenges in business models for sustainability have relevance for startups. The second contribution is the adaptation of SVEM in conducting the debate by incorporating the barriers and challenges in value creation and delivery system.

Social implications

This study contributes to the business models for sustainability literature to better understand the challenges startups face in practice and can serve as insights to help overcome them. As this is an empirical study, the information gathered can help create metrics and public policies to achieve the United Nations sustainable development goals.

Originality/value

The present research has as originality the analysis of the challenges in startups in implementing business models for sustainability and their relationships with the value proposition, capture and creation, as well as and delivery (adapted to the challenges found in the literature) applying the SVEM tool proposed by Morioka et al. (2018).

  • Business model innovation
  • Sustainable development goals
  • Sustainable entrepreneurship
  • Circular economy

Nunes, A.K.d.S. , Morioka, S.N. and Bolis, I. (2022), "Challenges of business models for sustainability in startups", RAUSP Management Journal , Vol. 57 No. 4, pp. 382-400. https://doi.org/10.1108/RAUSP-10-2021-0216

Emerald Publishing Limited

Copyright © 2022, Andressa Kelly da Silva Nunes, Sandra Naomi Morioka and Ivan Bolis.

Published in RAUSP Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Entrepreneurs who pursue business sustainability link their commercial success directly to achieving positive effects for the natural environment and humanity, thus creating value for a wide range of stakeholders ( Freudenreich, Lüdeke-Freund & Schaltegger, 2020 ). Business efforts are expected to be combined with other societal actors (governments, civil society, etc.) according to the 17 sustainable development goals – SDGs ( Morioka, Bolis, Evans & Carvalho, 2017 ). Business challenges are issues that repeatedly appear as impediments to successful business models. As such, they must be resolved to enable a paradigmatic shift toward innovation and sustainability ( Todeschini, Cortimiglia, Callegaro-de-Menezes & Ghezzi, 2017 ).

Sustainable systems are challenging because of the wide range of environmental, economic and social factors that must be considered throughout the system’s life cycle ( Fiksel, 2003 ). As research delimits business models from the perspective of three elements: value proposition, value creation and delivery system and value capture ( Richardson, 2008 ), companies are initially invited to think about behavior, responsibility and corporate performance; to define their resources to frame the main activities; and finally, to analyze the stakeholders and their economic context ( Bocken, Rana & Short, 2015 ; Svensson & Wagner, 2011 ).

Research on business models for sustainability demonstrates that the process is iterative, with sustainability objectives gradually integrated into stakeholders’ priorities ( Baldassarre, Calabretta, Bocken & Jaskiewicz, 2017 ). The research gap is because it is an emerging topic that needs studies to empirically analyze the barriers associated with business models for sustainability as well as the effectiveness of related strategies ( Hueske & Guenther, 2021 ). Therefore, it is essential to continue studying the relationship between organizational commitment to sustainability and its effective implementation and performance ( Silvestre & Fonseca, 2020 ).

How do the challenges faced by startups affect business models for sustainability in the context of an emerging country? Based on the gaps, this research aims to analyze the challenges startups face in implementing business models for sustainability. The sustainable value exchange matrix (SVEM) tool proposed by Morioka, Bolis and Carvalho (2018) will be used for this aim.

Despite possible semantic differences between the terms “barriers” and “challenges,” the present research considers the two terms synonymous, as literature uses both. For example, Bocken and Geradts (2020) , Hueske and Guenther (2021) , Laukkanen and Patala (2014) and Salim, Stewart, Sahin and Dudley (2019) mention barriers to sustainable business models , while Todeschini et al. (2017) , Geissdoerfer et al. (2018) and Morioka et al. (2017) use challenges .

Section 2 discusses business models for sustainability and the challenges of implementing business models for sustainability in startups based on the exploratory literature. Section 3 discusses the research method (case study), selection of startups and application of the SVEM, proposed by Morioka et al. (2018) . The SVEM aims to critically analyze the mutual benefits of the stages of value proposition, value creation and delivery and value capture with the stakeholders. Furthermore, with this tool, we aim to assess the challenges of startups and how they can become more sustainable by making explicit decisions to connect their business model elements to address the barriers to sustainable development. Section 4 presents the results and discussions obtained through the application of SVEM. Finally, Section 5 summarizes the conclusions of our analysis and its relation to other research streams and suggests possible avenues for future research.

2. Theoretical framework

2.1 business model for sustainability.

Several studies have examined business models from the perspective of their three elements: value proposition, value creation and delivery system and value capture ( Richardson, 2008 ). A brief explanation of each element in the context of sustainability is presented below. The customer value proposition supports a business model’s logic, data and other evidence, integrating a viable revenue and cost structure for the company delivering that value ( Teece, 2010 ). The challenge of developing offerings (products and services) that can create value for customers and contribute to global sustainable development is considered high for companies ( Selberherr, 2015 ). Economic viability is a requirement for the business model for sustainability ( Boons, Montalvo, Quist & Wagner, 2013 ; Morioka & de Carvalho, 2016 ). However, business goals should be aligned with social and environmental values, following the triple bottom line – TBL approach ( Elkington, 1997 ).

The principle of reflexivity is interesting to define the value proposition and support organizations. It can be defined as a continuous consideration of environmental, economic and social aspects of corporate sustainability, which should be constantly observed to achieve the goals and analyze the power of all organizational actors ( Schneider, 2015 ). This principle supports organizations, critically analyzes their role in society and reinforces the formation of the value proposition of business models for sustainability ( Boons et al. , 2013 ).

The study conducted by Morioka et al. (2017) in the analysis of multiple case studies converging to business models for sustainability resulted in the value proposition being composed of two levels: tangible and intangible . The tangible level is ensured by the products and services offered by the organization, whereas the intangible level of the value proposition represents the business purpose, combining entrepreneurial vision and personal values and beliefs. Therefore, delimiting a value proposition is fundamental in the business model for sustainability because incorporating a sustainability mission in the company’s strategy and values directly affects corporate behavior, responsibility and performance ( Svensson & Wagner, 2011 ).

The value creation and delivery system is the second element of business models for sustainability and serves to delimit the organization’s main activities: supply chain and logistics, operations, marketing and sales, innovation (design, research and development), human resources, corporate governance and organizational culture ( Morioka et al. , 2017 ). Several logics within the literature address how business models for sustainability create and deliver value. Some examples are corporate social responsibility (CSR) as a bridge between TBL pillars toward the business model for sustainability ( Govindan, Kannan & Shankar, 2014 ), business for the sharing economy ( McLoughlin et al. , 2009 ) and circular economy ( Salim et al. , 2019 ; Tura et al. , 2019 ).

Value capture, the third element of business models for sustainability, refers to aspects of the business model related to the capture of economic, environmental and social value by different stakeholders ( Lashitew, van Tulder & Muche, 2020 ) and tends to consider the financial flow captured by the organization ( Richardson, 2008 ). In general, the ultimate goal of value appropriation is to maximize shareholder value through decisions related to pricing, customer acquisition, market development and cost management, among others ( Lashitew et al. , 2020 ). However, the value captured by stakeholders tends to be often intangible. Thus, business models for sustainability face the challenge of measuring the value captured or destroyed by their existence. Value capture by stakeholders who have not (directly) contributed to value creation is referred to as value diversion ( Lepak, Smith & Taylor, 2007 ).

Sustainable business model innovation is a change in how a company operates to create positive impacts or reduce negative consequences for the environment and society. This article aims to explain which paths a company can follow when implementing a sustainable business innovation process aligned with the SDGs ( Ferlito & Faraci, 2022 ). The UN SDGs bring support for implementing business models for sustainability. To deepen the integration of the SDGs into business operations and stakeholder engagement, corporations’ need strategy, governance and operation ( Devalle et al. (2020) . Promoting network empowerment due to explicitly highlighting the contribution to the SDGs will require improvements in stakeholder-level governance and, in many cases, will also require changes in the existing institutional logic of actors ( Giacomarra, Crescimanno, Sakka & Galati, 2019 ).

A research stream is focused on proposing tools to help organizations with business model innovation for sustainability, as they need to create a sustainable value proposition ( Minatogawa et al. , 2022 ). Studies have proposed practical tools to support sustainable capability integration, such as the business model canvas ( Osterwalder & Pigneur, 2010 ), the three-layer business model canvas ( Joyce & Paquin, 2016 ), the evolutionary processes of sustainable entrepreneurship ( Schaltegger, Lüdeke-Freund & Hansen, 2016 ), the value ideation process ( Geissdoerfer, Bocken & Hultink, 2016 ) and the sustainability-driven service innovation (SOSI) ( Calabrese, Forte & Ghiron, 2018 ).

In the early stages, Bocken et al. (2015) proposed the value mapping tool, using structured workshop-based brainstorming sessions to surface both positive and negative value deployed from the organization using a multi-stakeholder perspective ( Silvestre, Fonseca & Morioka, 2022 ). Another particularly suitable tool is the SVEM ( Morioka et al. , 2018 ), which seeks to instigate discussions of corporate sustainability innovation based on face-to-face interactions between academics and practitioners with brainstorming/workshop support, in addition to conducting a diagnosis of the organization’s value proposition, value creation and delivery system and value capture.

2.2 Challenges of business models for sustainability in startups

The ability to quickly and successfully switch to new business models is an important source of sustainable competitive advantage and a key lever for improving the sustainability performance of organizations ( Geissdoerfer et al. , 2018 ). However, the aforementioned author’s research found that many business model innovations fail, and despite the importance of the topic, the reasons for failure are relatively unexplored in academic works; in the context of startups, they are yet to be implemented.

Inigo and Albareda (2019) point out that companies can engage in four main organizational changes in innovating for sustainability: seeing new social and environmental regulations as an opportunity; making their value chains sustainable (operations and life cycle assessment); designing sustainable products and services; and developing sustainable business models (finding new ways to deliver and capture value).

There is a threefold problem in sustainable business model innovation: the first is that meetings and workshops on business model innovation occur, but the ideas are not followed up; the second is that even though there are promising sustainable business model concepts, they are still not implemented; the last is that most implemented business models, especially at their inception, fail over time in the market ( Geissdoerfer et al. , 2018 ).

Table 1 was divided into the contexts of the challenges found in the literature that fall into different categories such as institutional, organizational culture, marketing and sales, supply chain, operations and logistics, innovation and research and development. These categories were an adaptation of SVEM, which frames the following categories in the value creation and delivery system: supply chain and logistics, operations, marketing and sales, innovation, research and development, organizational culture and corporate governance ( Morioka et al. , 2018 ). These dimensions are interconnected, and business models for sustainability depend on balancing all of them, as a lack of performance in one can harm the others.

The discussion in the literature of barriers is in different segments of companies following business models for sustainability, e.g. the renewable energy sector ( Engelken, Römer, Drescher, Welpe & Picot, 2016 ; Salim et al. , 2019 ), circular economy ( Tura et al. , 2019 ; Vermunt, Negro, Verweij, Kuppens & Hekkert, 2019 ) and fashion industry ( Todeschini et al. , 2017 ). Most studies mention barriers in general terms and lack conceptual clarity on how barriers may differ across various business models for sustainability ( Vermunt et al. , 2019 ).

External barriers are considered external forces that prevent companies from developing their business model for sustainability. They were also divided into two categories: institutional and market and sales. Category 1 refers to social norms and rules that impact business models for sustainability, such as regulations (considered “hard” institutions) and social values, habits and traditions (considered “soft” institutions) ( Crawford & Ostrom, 1995 ). Lack of strict legislative pressure and economic incentives are seen as the main barriers to the business models for sustainability of technological orientation, whereas, in the social category of market and sales, the main challenge is the lack of consumer or customer acceptance and economic incentives to those of social orientation ( Laukkanen & Patala, 2014 ). In the context of market and sales, Vermunt et al. (2019) pointed out challenges related to efficient interaction with stakeholders, which is given by the lack of involvement of stakeholders in decision-making.

supply chain, operations and logistics;

organizational; and

innovation, research and development (Hoffman, 1999).

The factors related to organizational culture are linked to the company’s internal decision-making; when the company becomes flexible to new changes and empowers employees to be protagonists, it may be innovation-oriented ( Morioka et al. , 2017 ).

There are differences in the types of barriers encountered between business models. Research by Vermunt et al. (2019) shows that companies with a product-as-a-service model mentioned mainly internal organizational and financial barriers (88 and 63%, respectively), and external market and institutional barriers (63 and 50%, respectively) but did not mention supply chain barriers. Firms with a product life extension model encountered mainly external supply chain and market barriers (70 and 80%, respectively). Most companies with the resource recovery model mentioned supply chain barriers (67%), followed by institutional barriers (56%) and market barriers (50%). Regarding knowledge and technology barriers, 44% mentioned them ( Vermunt et al. , 2019 ).

3. Research method

To analyze startups’ challenges in implementing sustainability business models, this research will adopt the case study method. This method is appropriate to specify research questions until reaching their closure, in addition to checking whether validation occurs with the exploratory literature, allowing theory building through the combination of previous publications and the data collected about the organizations ( Eisenhardt, 1989) . The steps for the core case studies adopted in this study follow the sequence proposed by Eisenhardt (1989) and are described below to ensure replicability and increase research reliability ( Yin, 2001 ).

This research was divided into three stages ( Figure 1 ), according to Yin (2001) :

definition and planning;

preparation, collection and analysis; and

analysis and conclusion.

The first stage focuses on the part of the exploratory literature review, the research question described earlier in this paper and the elaboration of the case selection criteria. According to Eisenhardt (1989) , defining the research question allows the researcher to specify the type of organization to be addressed and the type of data to be collected.

3.1 Case definition and planning

Case selection was conducted with two companies named “Company A” and “Company B.” Both companies are located in Brazil, an emerging country that faces institutional gaps and sustainability paradoxes, requiring greater empirical evidence ( Jabbour et al. , 2020 ). Company A is focused on solutions related to the environment, also serving as a consultant and in the development of products such as composting. In terms of services, it offers solid waste management plans, water allocation, rainwater harvesting and licensing, among others. Company B is in the phase of consolidating the minimum viable product on the market. Its product is an automated waste collector that automatically separates recyclable waste, without the need for human collection.

to be a startup company;

to be concerned with environmental and social issues, expressing the need to minimize society’s challenges to sustainable development; and

the workshop participants had to be the company’s chief executive officers (CEOs), given their high hierarchical level and the support required to conduct the research, as indicated by Voss, Tsikriktsis and Frohlich (2002) .

3.2 Data preparation and collection

The second stage consists of developing the research protocol, conducting Case studies 1 and 2, and finally, the case study report. According to Yin (2001) , the protocol is one of the main tactics to enhance the reliability and validity of the case study research as well as the procedures and general rules that should be followed when using the instrument and instructions for collecting other empirical evidence. A fundamental strength of data collection for a case study is the opportunity to use multiple sources for obtaining evidence, allowing for triangulation. Such methods may include interviews and workshops that can strengthen the validity of the research ( Voss et al. , 2002 ).

In this research, a workshop was conducted with each company. A guiding script drove the workshops using the SVEM ( Morioka et al. , 2018 ). Before the application of SVEM, improvements were made to adapt the research instrument, with a researcher expert in business models for sustainability, to refine and make the understanding easier in the reality of challenges found in Table 1 . After the initial contact, the workshops with the startups were conducted in July 2021 with a CEO from Company A and two CEOs from Company B, which lasted 1 h and 21 min and 1 h and 9 min, respectively.

(A) value proposition: delimitation of the business reason for existence;

(B) value capture: value captured by stakeholders;

(C) value creation and delivery system (in the case of this article, this stage was adapted and is related to the challenges): practices, capabilities and resources ( Figure 2 ); and

(D) critical analysis of the matrix.

During the application of SVEM, delineations of relevant contextual factors were considered, from which the more specific sustainability challenges can be derived. Step (A) serves as an input to delineate the business purpose and competitive advantage, which is made tangible by the organization’s offerings, e.g. its products and services ( Morioka et al. , 2018 ). Step (B) starts with naming the key stakeholders and seeks to identify the key sustainable value captured by each stakeholder. Step (C) names key business processes, i.e., participants are asked to point out the key practices, capabilities and resources required for each process ( Morioka et al. , 2018 ). In the case of this paper, Step (C) was adapted by making a relationship between SVEM and the barriers found in the literature in Table 1 .

Steps A, B and C seek to promote a description of the main aspects that represent the organization, initiating some reflections during execution. In the end, we expect to provoke more profound reflections by pointing out the contribution of business model innovation guidelines for sustainability (Step D) for an organization that can become more sustainable by making explicit decisions to connect its business model elements as a tool to address the challenges of sustainable development ( Morioka et al. , 2018 ).

3.3 Data analysis and report generation

The third stage of the research consists of analysis to produce analytical conclusions involving the description of the cases. After this, a comparison of the challenges with the literature was carried out to analyze whether existing theories match the empirical findings to strengthen them at a higher conceptual level ( Voss et al. , 2002 ). As for the recording of workshops, two synthesizing reports were prepared with transcribed information from the interviews with the CEOs, resulting in four pages for each company. Subsequently, both reports were forwarded to the respective interviewees to validate and ensure information accuracy.

Then, the analysis of the transcribed data occurred through content analysis of the workshops. This technique consists of a systematic and objective research method to make replicable and valid inferences from data in their context to build a model, conceptual system or category that describes a broad phenomenon ( Elo & Kyngäs, 2008 ). At this point, it is noteworthy that the results of the empirical study were compared with the literature review findings, thus enabling data triangulation and discussion.

4. Results and discussion

Business models can involve different organizational contexts ( Morioka et al. , 2017 ). Therefore, this section is divided into three sub-sections: (4.1) analysis of the value proposition of the case studies; (4.2) analysis of sustainable value capture by stakeholders; and finally, (4.3) discussion of the barriers to value creation and delivery practices affecting business models for sustainability, derived from the theoretical foundation (Section 2.2).

4.1 Value proposition

Company A is an environmental consulting startup whose work mainly focuses on a solid waste management plan, water and sewage treatment and environmental licensing. Company B proposes technology solutions in more sustainable projects and develops services through one of its projects, the “LISA - Lixeira Inteligente Seletiva.” It automates the separation of solid residues. In addition to forwarding them to companies that perform recycling, it also supports recyclable materials collectors.

The debate on the company’s value proposition started with the contribution of startups to the SDGs of the United Nations Organization, the company’s purpose, its main products and services and its competitive advantage. The companies’ value proposition corresponding to each category is summarized in Table 2 . The 17 2030 United Nations Goals can be considered a call to action for society actors, including organizations ( Morioka et al. , 2017 ).

Therefore, it is possible to see that startups have common points concerning the SDGs, considering that they contribute to sustainable cities and communities (11), responsible consumption and production (12) and action against global climate change (13). Therefore, startups’ relationship with the SDGs can be based on their value proposition; for example, the products and services offered by the company are able to contribute to one of the SDGs, even if it is on a small scale.

Below, we list examples mentioned by companies regarding their contribution to the SDGs. Company A: “(SDG 6 - drinking water and sanitation), we carry out projects for systems to capture and use rainwater.” Company B: “(SDG 11 - sustainable cities and communities), as we contribute to the reduction of waste sent to landfills and this also impacts on (SDG 13 - action against global climate change), as it reduces the emission of greenhouse gases.”

The results show that the value proposition is composed of the company’s purpose, encompassing the value that is delivered to customers with social and environmental responsibility, in addition to the economic one. For Company A, evidence of this is: “To deliver services and projects to our clients and partners with social and environmental responsibility on an ongoing basis.” For Company B, this value delivered to customers is given by its product’s contribution to the circular economy, as evidenced in the following line: “Encourage our customers, by purchasing our product, to send waste that would previously go to landfills, to recyclable material cooperatives.” The tangible level of this delivered value is evidenced by products and services such as rainwater harvesting and home composting (A) and advertising in app advertisements and integration with other apps (B).

Data collection showed personal narratives and insights to build the company’s value proposition regarding economic, environmental and social values. Both organizations focus on the social, environmental and economic pillars. A sustainable business opportunity can derive from an environmental problem, adding social and economical solutions ( Morioka et al. , 2017 ). One of the common roles of both companies is to make environmental information available to society, government and customers. Evidence of this are the sentences: “Environmental responsibility, in addition to social issues. Deliver socio-environmental responsibility to our customers and partners continuously. […] Try to show that customers can generate economic value to their companies through environmental licensing. The licensing can be positive so that these regularized customers can get loans with banks” (Company A). “In addition to our product, we intend to build a platform to serve our clients with various resources. One of them would be to integrate our platform with the National Solid Waste Information System (SINIR), and the companies that would have the data of this waste stored in the cloud would directly communicate with SINIR since they need to account for this waste.”

As expected, the competitive advantages pointed out by the case studies depend on the sector in which they operate. The competitive advantages mentioned include an innovative approach to problem-solving and knowledge in the environmental area, as well as meeting deadlines: “Competitive advantage with deadlines, we are able to ensure delivery within the deadlines” (Company A). “We see our project as a very broad field, we were able to identify few companies in Brazil with our product and there is little exploration. Waste generation is high and we can’t recycle even 4% of it. Our product would help in this regard, but everything that is new needs to prove its worth” (Company B).

4.2 Value capture

Several stakeholders capture the value created by the case studies’ business models for sustainability: shareholders/investors, customers, employees, suppliers/stakeholders, society, environment, government, competitors, universities, organizations with similar interests and indirect stakeholders. In addition, the startups studied present indirect stakeholders represented by a person or group.

In terms of the financial value captured by the companies studied, income generation is a point in common for both. Clients of startup A capture value from their customers through regularization, licensing and environmental awareness. Those of Company B can benefit by obtaining greater control of their waste and through scoring apps “place your waste and score,” in addition to waste management and green marketing itself ( Table 3 ).

Mechanisms to be used by companies to ensure the value captured by society were mentioned, such as environmental education through lectures (Company A) and by reward systems for the user in exchange for benefits (Company B). For the environment, points such as improvements are highlighted through reports and in the very performance of Company A: “We were able to make groups of people gardening, giving lectures on how to compost at home, applying environmental awareness daily. And the encouragement of selective collection and assistance in the logistics of forwarding the collection of solid waste”; Company B: “Environmental education, through the application and gamification, which is precisely a reward system, obtaining discounts on energy bills, in supermarkets.”

In the case of the value captured by the government, there is the aid in environmental licensing (Company A), which minimizes the environmental impacts through the conditions established in the licenses. There is also urban cleaning for municipalities (Company B), by allocating part of waste to collectors of recyclable materials, in addition to generating information on waste to the municipality on platforms such as SINIR (National Information System on Solid Waste Management) and optimizing the collection of recyclable materials collectors. This is a measure that also generates social value by increasing customer satisfaction for using more sustainable products. As mentioned by both companies, sharing knowledge is necessary to strengthen the network and thereby enable companies to obtain a better market view.

4.3 Value creation and delivery system – challenges and barriers

Several aspects were pointed out during data collection to affect the business model. This topic addresses a vision based on the challenges and barriers found in the literature. Companies indicated that the presence of competitors increases market awareness and the understanding of the solution proposed by the business models for sustainability. Consequently, it can increase market demands to demonstrate product quality when remanufacturing, promoting education and culture on remanufacturing and circular economy ( Table 4 ).

The proper functioning of regulatory structures in business models for sustainability has great relevance ( Laukkanen & Patala, 2014 ). The startups reported different views regarding the institutional scope. Company A perceives the government as absent and legislation as hindered by changing laws or by customers hiring only because it is a legal requirement to obtain reports or environmental permits. Company B mentions that the institutional category is not a barrier for them, as they receive incentives from the “Centelha” program (deriving from the public policy), which facilitates taxation mechanisms. For example, “Centelha” includes the company in the Simples Nacional , a simplified taxation system so that companies pay fewer taxes, favoring its services.

Different forms of businesses, e.g. social businesses, cooperatives and collectives, are not well supported by regulators ( Laukkanen & Patala, 2014 ). This occurs with Company A, in terms of the lack of technologies provided by environmental agencies in the transaction of the licensing processes. Another barrier related to the lack of regulatory incentives for Company A is the risk of change in legislation on environmental licensing, which can be evidenced in the statement, “There is a new project to make licensing basically optional. To keep our business active, it is necessary that there is an obligation for the entrepreneur to have engineering projects in the licensing part.”

The absence of government incentives, in general, is emblematically revealed in the following statement of the interviewee from Company B: “I don’t see tax incentives, I don’t see project incentives. I don’t see incentives from other agencies, for instance, the Regional Engineering and Agronomy Council (CREA), at least not for the environmental area.” In fact, in the Brazilian scenario, double taxation of recyclables is an example of the lack of institutional incentives that should not be ignored. Reductions in taxes on the marketing of recycled materials and products made with them are absent; however, they could be an effective incentive for manufacturers to use more recycled materials, encouraging the entire production chain ( Haro-de-Rosario, Gálvez-Rodríguez, Sáez-Martín & Caba-Pérez, 2017 ).

As for organizational culture, the barrier arises for companies to delimit their proposition for the next generation. In general, this question was interpreted as relating to their financial affairs, in the short term, which suggests the difficulty in staying true to the core values of sustainability, consistent with sustainable strategic objectives ( Todeschini et al. , 2017 ; Zott, Amit & Massa, 2011 ). The challenge of this category for Company A is related to short-term investments, as the startup is focused on growing as fast as possible. The only medium-term investment is made in internet platforms, such as Google Ads, which is still incipient.

Another point raised was the lack of ethics amongst competitors, which hinders relations with the municipality’s city hall, indicating that the focus on value creation and on value capture encompasses activities beyond the company’s own borders ( Zott et al. , 2011 ). Therefore, Company B is concerned with recyclable material because this requires a partnership effort with local recyclable material cooperatives. However, to get to this collaborative relationship, it is first necessary to map these cooperatives and their constraints, which limits the startup’s scalability and growth to other regions, suggesting a typical challenge, converging with previous studies ( Todeschini et al. , 2017 ; Zott et al. , 2011 ).

Regarding the market and sales, the investment with social media is a challenge for Companies A and B, it is related to attracting customers. Consumers or customers appreciate cheaper prices than sustainability aspects, a “disposable” culture is created, where it is more profitable to produce or buy cheap and short-lived products ( Laukkanen & Patala, 2014 ). For Company B, evidence of the difficulty in attracting customers is highlighted in the speech: “capture investments from other agents, get more people in society to know the company and help in the dissemination of the product.”

In the category of innovation, research and development , continuous training and academic research for developing new solutions in Companies A and B, fundamental for companies’ growth, are also challenging in the sense of continuously maintaining innovations: “We use software, but then we went after other tools for more visual work. One of our members is in his doctorate, and developing research is of great value, but it requires a lot of his time” (Company A). “What we are working on today would already be an innovation, and every innovation involves research. The challenge is to reconcile, in terms of time, the Master’s degree activities with the company’s activities” (Company B). Thus, one notices that both startups have difficulty prioritizing and reconciling time, because at least one company’s CEO has other academic activities. What is divergent from what we found in literature, i.e. problems in lack of knowledge and technology ( Vermunt et al. , 2019 ).

Finally, in as much as the supply chain is concerned, challenges are related to public agencies with failures in the monitoring system due to the lack of technology updates (A) and for (B) the products that are being discarded might not be the materials that the waste pickers recycle, and there is a concern as to the functioning of the process. Another point for Company B would be possible problems with suppliers of the boards to manufacture their product: “The services that we consume from other suppliers, are well available in the market, but a possible problem could be with the raw material of the boards in the manufacturing of our product.” Supply chain dependencies were found to be problematic also in Vermunt et al. (2019) research, mainly caused by the limited number of suppliers of circular materials. As the circular economy is still in its infancy, few suppliers are already producing biodegradable or recyclable materials ( Vermunt et al. , 2019 ).

5. Conclusion

This study explores the existing research gap concerning the emerging theme and the need for empirical studies insofar as it empirically analyzes the barriers found in the literature associated with business models for sustainability and the relationship between organizational commitment, reflected in the value creation and delivery system, to sustainability and its practical implementation.

This research found that the barriers and challenges of business models for sustainability in startups are found in different categories. The existing theoretical frameworks of business models for sustainability were highlighted through the challenges focused on the following categories: institutional and organizational culture, market and sales, innovation, research and development, supply chain, operations and logistics. As the case study method, the SVEM tool ( Morioka et al. , 2018 ) was applied to analyze the value propositions, value capture and the value creation and delivery system related to the categories of the challenges found in the literature.

The barriers linked to the institutional category have a greater impact on Company A, whereas the market and sales category prevails in Company B. This leads to the conclusion that there is a need to reformulate public policies and to have a greater participation of the actors involved. Similarly to what was found in Hueske and Guenther’s research (2021), the barriers related to market and sales are linked to investments and financial return. In other words, the difficulty in making the business model economically viable.

Challenges related to innovation and technology for the product life extension model were reported, for example, by Matsumoto et al. (2016) . In our sample, technological barriers were prominent in continuing education, especially regarding the search for the development of new solutions and continuous improvement in entrepreneurial performance. This can be explained by the fact that at least one of the CEO’s of each company is also pursuing a Master’s/PhD degree in parallel to the entrepreneurial activity.

The challenges found in both startups regarding organizational culture are related to short-termed and are also found in the research of Bocken and Geradts (2020) . While corporations seek to realize immediate profits to satisfy shareholders who demand quick returns, it is said that short-term investment beliefs dominate investment decisions. Meeting this assertion, the sustainable business model involves a broader understanding of value and stakeholders, as it “captures economic value by maintaining or regenerating natural, social and economic capital beyond its organizational boundaries” ( Schaltegger et al. , 2016 , p. 6).

Our research presents two major contributions. First, through the case studies, it is possible to state that barriers and challenges in business models for sustainability have great relevance for startups and collaborate with empirical data to understand the obstacles to business development toward sustainability. This information can help us create metrics and national public policies to achieve the SDGs. The second contribution is the adaptation of SVEM by holding the debate incorporating the barriers and challenges in the value creation and delivery system.

The main limitations of this research are the number of case studies, which was limited to only two, making it difficult to generalize the results. Furthermore, future research should observe whether the conclusions of this research can be replicated in companies from different sectors. It is also suggested that further research should include a more significant number of case studies of startups to compare with the results of this research and others that deal with barriers and challenges in the literature. Another suggestion for future research is to apply other tools found in the literature on business models for sustainability, after diagnosing the challenges.

business model startup thesis

Research framework

business model startup thesis

Barriers/challenges of sustainable business models

*CREA – Regional Council of Engineering and Agronomy

Source: Authors (2021)

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Acknowledgements

This work was carried out with the support from the Coordination for the Improvement of Higher Level Personnel (CAPES) through the grant of a scholarship from the Postgraduate Program Production and Systems Engineering/UFPB and from the Foundation for Research Support of the State of Paraíba (FAPESQ), UFPB Grant Term 046/2021 and FAPESQ Grant Agreement: 3216-2021.

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AI Startup Business Models

Key Characteristics and Directions for Entrepreneurship Research

  • Research Paper
  • Open access
  • Published: 13 December 2021
  • Volume 64 , pages 91–109, ( 2022 )

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  • Michael Weber   ORCID: orcid.org/0000-0003-1528-1856 1 ,
  • Moritz Beutter   ORCID: orcid.org/0000-0002-4538-738X 1 ,
  • Jörg Weking   ORCID: orcid.org/0000-0002-5288-240X 1 ,
  • Markus Böhm   ORCID: orcid.org/0000-0003-2859-5651 2 &
  • Helmut Krcmar   ORCID: orcid.org/0000-0002-2754-8493 1  

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We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action.

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Artificial intelligence: how leading companies define use cases, scale-up utilization, and realize value

Michael Grebe, Marc Roman Franke & Armin Heinzl

Avoid common mistakes on your manuscript.

1 Introduction

Artificial Intelligence (AI) inarguably creates large waves of excitement in business and research alike. AI refers to a broad suite of techniques (Russell and Norvig 2016 ) that gives machines the ability “to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, […] and even demonstrating creativity” (Rai et al. 2019 , p. iii). AI technology might serve as an external enabler (Davidsson et al. 2020 ) that offers manifold opportunities for entrepreneurship (Chalmers et al. 2020 ; Obschonka and Audretsch 2020 ). Indeed, we can observe the rapid emergence of AI startups that apply AI technology as a key element to their product or service. For instance, the database Crunchbase ( https://www.crunchbase.com ) lists over 27,900 startups related to “Artificial Intelligence” as of September 2021. Popular examples include the research-driven venture OpenAI or the business automation venture UiPath. Those AI startups attract a significant and growing interest of investors and venture capital firms, as evident in the staggering amount of investment into AI startups (OECD 2018 ) and the perceived frequency of intriguing news headlines (e.g., Microsoft’s $19.7 billion acquisition of health AI company Nuance (Wilhelm and Heim 2021 )).

Regardless of the current hype, it will be indispensable for those startups to find an appropriate business model to ensure their long-term performance and survival (George and Bock 2011 ; Böhm et al. 2017 ). The business model represents the focal business logic of a firm (Teece 2010 ) and is essential to the successful commercialization of any technology (Chesbrough 2010 ). Recent research suggests that AI startups employ novel or different business models. Economists have predicted that the use of AI technology and its unique capabilities will lead to new products, services, and business models (Brynjolfsson and McAfee 2017 ; Makridakis 2017 ). Furthermore, Information Systems (IS) scholars have noted significant challenges to the successful value creation from AI (Jöhnk et al. 2021 ; Benbya et al. 2020 ). Hence, different key activities and partnerships might be required in the business model. However, one could also argue that AI technology is not new (Stone et al. 2016 ) and has been used in business models for a long time already, which questions the novelty of AI startup business models. For example, while data is essential to the value creation from AI (Jöhnk et al. 2021 ), the use of data in business models has long been recognized in research (e.g., Hartmann et al. 2016 ). Moreover, many business models, such as those of digital platform providers (Hein et al. 2020 ), have already implicitly used AI technology at the core of their business (Gregory et al. 2020 ). Hence, the question arises whether AI startups employ novel or different business models, and if so, how they differ from common IT-related business models.

Clarifying these potential differences would contribute to our fundamental understanding of AI startup business models. A fundamental understanding of a phenomenon is essential for any research stream to support theory development and testing (Gregor 2006 ; Rich 1992 ). For example, a descriptive analysis of AI startup business models would help to structure the diverse landscape of AI startups and reveal a clear set of categories that can further be studied. It would also provide insights into how AI, a different technology to traditional IT (Ågerfalk 2020 ; Berente et al. 2021 ), impacts startup business models in ways that potentially challenge our current theoretical underpinnings. In addition, a fundamental understanding of AI startup business models is highly relevant for practitioners, for example, when developing new business models using AI technology, or when evaluating and investing in AI startups.

However, extant research on AI startup business models is in its infancy, and studies investigating AI-related business models are scarce (e.g., Garbuio and Lin 2019 ; Armour and Sako 2020 ). Hence, our current understanding of the characteristics of AI startup business models is limited; and the question of what potentially differentiates them from common IT-related business models remains to be answered. Consequently, more research on AI startup business models is considered a priority for the field (Obschonka and Audretsch 2020 ). To address this gap, we ask the research question: What are the differences between AI startup business models and common IT-related business models?

To examine this research question, we (1) identify the key characteristics of AI startup business models and (2) distill the distinctive aspects against the background of prior research on IT-related business models. To that end, we first build a business model taxonomy for AI startups following the taxonomy development method proposed by Nickerson et al. ( 2013 ). Such an analytical approach is particularly valuable for novel and unstructured phenomena (Gregor 2006 ), such as AI startup business models. To develop the taxonomy, we build a case base of 100 AI startups randomly drawn from Crunchbase, a database for startups, which we further triangulate with other data sources (Yin 2017 ). In an iterative development process, we combine empirical findings from our sample of 100 AI startups with prior theoretical concepts from literature. The taxonomy of AI startups follows the conceptual representation of a business model (Massa et al. 2017 ). We further apply the resulting taxonomy to the sample of 100 AI startups and perform a hierarchical cluster analysis to derive four archetypal business model patterns. These patterns represent common instantiations of AI startup business models in practice. Against the background of prior studies on IT-related business models, we ultimately discuss the distinctive aspects of AI startup business models and propose directions for future entrepreneurship research.

We contribute to a growing research stream concerned with AI in entrepreneurship (Chalmers et al. 2020 ; Obschonka and Audretsch 2020 ) and research on IT-related business models (Veit et al. 2014 ; Steininger 2019 ). First, we address how AI startup business models differ from common IT-related business models to shed light on the impact of AI technology on startup business models. Second, our descriptive analysis allows us to derive promising directions for future research on AI in entrepreneurship. Third, we provide one of the first comprehensive analyses of AI startup business models. Our taxonomy and patterns reveal the key characteristics of AI startup business models and their common instantiations, which can serve as a springboard for future research. As Rich ( 1992 , p. 758) put it, “organizational classification provides the basis for strong research by breaking the continuous world of organizations into discrete and collective categories well suited for detailed analysis.” For practice, the taxonomy and patterns can be used as structured tools to support venture creation and business model innovation using AI technology. Moreover, they provide insights into a complex and diverse AI startup landscape, assisting investors and venture capitalists in their activities.

2 Background

The background section of this study is threefold: First, we clarify the term “Artificial Intelligence” and describe recent developments. Second, we take a closer look at research on IT-related business models and highlight imporant findings in this area. Third, we present related work that has investigated the influence of AI technology on business models.

2.1 Artificial Intelligence

AI refers to a broad and long-established research field in computer science (Stone et al. 2016 ). The AI research field never had a clear definition, but rather had the creation of intelligent machines as a common goal in mind (Stone et al. 2016 ). Machine intelligence can be interpreted as machines thinking or acting rational, or thinking or acting like humans (Russell and Norvig 2016 ). Therefore, it is typically associated with machines performing functions such as perceiving, learning, reasoning, problem-solving, and demonstrating creativity (Rai et al. 2019 ). Throughout the years, AI researchers have developed a plethora of techniques and methods, including machine learning, deep learning, knowledge-based reasoning, natural language processing (NLP), computer vision, and robotics (Stone et al. 2016 ). We summarize these under the term AI technology. In recent years, AI has gained renewed momentum thanks to advances in machine learning, computational processing, and the vast availability of data (Ågerfalk 2020 ; Berente et al. 2021 ; Haenlein and Kaplan 2019 ). Machine learning is an AI technology that enables machines to improve automatically through experience, which is often accomplished by analyzing patterns in existing data (Jordan and Mitchell 2015 ). Thereby, an information system is basically able to create its own rules (Ågerfalk 2020 ). An important subset of machine learning is deep learning, which uses multiple processing layers to learn from data at multiple levels of abstraction (LeCun et al. 2015 ). Recent breakthroughs in deep learning have caused significant improvements in many areas of AI including speech recognition, object detection, and medical drug discovery (LeCun et al. 2015 ).

2.2 IT-related Business Models

When using the term business model, we refer to the conceptual representation of a business model, as suggested to clarify by Massa et al. ( 2017 ). Various definitions for the business model have emerged over time (Wirtz et al. 2016 ). Above all, the business model describes the business logic of a firm (Teece 2010 ). It describes the value proposition that is offered, how the value is created and delivered to the customers, and how revenue is generated and captured (Teece 2010 ). The business model is often conceptualized by its constituting components or building blocks, for example, the customer segment or the revenue stream (Remane et al. 2017 ; Osterwalder and Pigneur 2010 ). In IS research, the business model is considered the missing link between strategy, processes, and IT (Veit et al. 2014 ). Therefore, it is widely used as a lens to study how IT alters existing and creates new business models, including those of startups (e.g., Spiegel et al. 2016 ; Hartmann et al. 2016 ). Following the framework proposed by Steininger ( 2019 ), IT can facilitate the operations of startups, serve as mediator at the customer interface, and be the product or service itself. In this study, we investigated startups that use AI technology as a core component of the offered product or service.

Prior research investigated IT-related business models in various contexts and found a plethora of ways IT can alter existing and enable new business models (Veit et al. 2014 ; Bock and Wiener 2017 ). Examples include the servitization of industrial products using the Internet of Things (Weking et al. 2020c ), the disintermediation of transactions through distributed ledgers (Chong et al. 2019 ), or the creation of multi-sided digital platforms (Täuscher and Laudien 2018 ; Floetgen et al. 2021 ). Within IT-related business model research, one stream is concerned with data-driven business models (Wiener et al. 2020 ). As AI, big data, and analytics can be seen as “three different, although related beasts” (Ågerfalk 2020 , p. 2), we expect to find overlapping characteristics regarding the business model. Wiener et al. ( 2020 ) distinguish three archetypes of business models: data users , data suppliers , and data facilitators . Data users use big data to streamline their operations or to create new products or services. Data suppliers collect and sell data to other firms. Data facilitators enable other firms to use big data analytics, for example by providing the necessary infrastructure or analytics as a service (Hartmann et al. 2016 ). We will later discuss how AI startup business models potentially differ from common IT-related business models.

2.3 Related Work on Artificial Intelligence and Business Models

Extant research linking AI with the business model concept has predominately focused on the impact of AI technology on internal processes of value creation. As such, AI can be used to automate operations, create insights for decision-making, and provide new means for engaging with customers and employees (Davenport and Ronanki 2018 ; Borges et al. 2021 ). For example, in the legal industry AI technology can increase the efficiency of operations by taking over routine tasks and assisting humans with non-routine tasks (Armour and Sako 2020 ). Here, especially the use of NLP is expected to play a major role, because it enables the automated analysis of documents (Brooks et al. 2020 ). As another example, in the healthcare industry AI technology is used to increase the quality of services, for example, supporting the detection of diseases like cancer (Valter et al. 2018 ). In contrast, Canhoto and Clear ( 2020 ) point to novel risks introduced into the business model when using AI technology. For example, value creation might be negatively influenced when AI solutions make wrong or biased decisions.

In addition to its impact on operations, AI technology can enable new products and services (Davenport et al. 2020 ; Borges et al. 2021 ). However, following Borges et al. ( 2021 ), we found that extant research thus far lacks a thorough examination of AI technology’s potential to enable new products and services. Specifically, research on the underlying business models used to commercialize these products and services is scarce. Therefore, Garbuio and Lin ( 2019 ) conducted a comprehensive study of AI startups in the healthcare industry as a rare example. They found that AI startups target multiple value areas, including solutions for patient lifestyle management, patient safety, or operational efficiency of healthcare providers (Garbuio and Lin 2019 ). They distinguish between two business model archetypes: startups that provide information and startups that aim at connecting multiple parties. Furthermore, they identified three delivery models employed by AI startups: the platform model (or multisided market business model), software as a service, and platform as a service (Garbuio and Lin 2019 ). In their study on the industrial Internet of Things, Ehret and Wirtz ( 2017 ) recognize the potential to offer new services in combination with AI technology, for example, using sensor data for predictive maintenance. Hence, traditional business models involving physical machines are complemented with data-based analyses to create new value propositions.

In conclusion, research has just started to investigate AI-related business models. Much focus has been put on AI technology’s potential to enhance internal operations. In contrast, business models with AI technology as a core component of the product or service remain mostly unstudied. Therefore, we currently do not know how the business models employed by AI startups potentially differ from common IT-related business models. However, this would contribute to our fundamental understanding of AI startup business models. Therefore, we address this research question in this study.

3 Research Method

To address our research question, we (1) identify the key characteristics of AI startup business models and (2) distill the distinctive aspects against the background of prior research on common IT-related business models. First, we build a case base containing 100 AI startups (Yin 2017 ). Second, we develop a business model taxonomy of AI startups using the method proposed by Nickerson et al. ( 2013 ), which reveals the key characteristics of AI startup business models (cf. Sect.  4.1 ). Third, we perform a hierarchical cluster analysis to derive four archetypal business model patterns, which gives us additional insights into common instantiations of AI startup business models (cf. Sect.  4.2 ). Against the background of extant research on IT-related business models, we ultimately distill the distinctive aspects of AI startup business models and provide directions for entrepreneurship research (cf. Sect.  5 ).

3.1 Building a Case Base

To gain empirical insights into the subject of our research, we created a case base of AI startups (Yin 2017 ). We used Crunchbase to identify the startups, because it is one of the world’s largest databases of new ventures. Crunchbase has been widely used in research and serves as a valuable source to identify startups (e.g., Spiegel et al. 2016 ; Weking et al. 2020b ). On 22 October 2020, we extracted all startups from Crunchbase that used the terms "Artificial Intelligence" or "Machine Learning" in their description. We found that other AI technologies such as deep learning, NLP, computer vision, and robotics were also covered with these terms. Using four selection criteria, we reduced the sample to startups aligned with the purpose of our research question (cf. Table 1 ). We filtered for startups that have a stable operating status and received over 1 million USD funding. This threshold was found useful after initial data exploration, because it eliminated many startups from the sample that had underdeveloped products or services, unclear and unestablished business models, or were already dead. In addition, we filtered for startups founded after 2010, as we wanted to include startups founded during the recent uptake of AI technology (Haenlein and Kaplan 2019 ). This initially led to a sample of 8076 AI-associated startups, which we imported into Microsoft Excel. From this sample, we randomly drew 100. For this, we used the random function of Microsoft Excel to generate a number between 1 and 8076. We validated the resulting startups in more detail for website and information availability. We then assessed whether AI technology was a core element of the business model. We only considered startups that use AI technology as a core component of their product or service, following the business model framework proposed by Steininger ( 2019 ). For every startup excluded at this stage, we redrew another startup until the case base contained a sample of 100 AI startups that meet all criteria. Table 5 in the Appendix shows the final list of startups considered in this study. We used multiple data sources to collect detailed information on each startup. Following Amshoff et al. ( 2015 ), we included (1) websites, (2) industry portals such as Crunchbase, (3) whitepapers, and (4) investment interviews. On average, we used 3.8 data sources per startup. The diversity of data sources allowed for data triangulation, which helps to address potential bias from one source (Yin 2017 ).

3.2 Taxonomy Development

We used the taxonomy development method proposed by Nickerson et al. ( 2013 ) to develop a business model taxonomy of AI startups. This method allowed us to systematically combine prior theoretical concepts with empirical insights from our case base. Furthermore, the application of this method reduces the likelihood of adopting arbitrary dimensions and aims to increase the usefulness of the resulting taxonomy (Nickerson et al. 2013 ). This method has been widely used in IS research before, for example, to develop other business model taxonomies (e.g., Remane et al. 2017 ; Weking et al. 2020b ).

The first step of the method is to define the meta-characteristic, which should be “the most comprehensive characteristic that will serve as the basis for the choice of characteristics in the taxonomy” (Nickerson et al. 2013 , p. 343). To classify AI startups, we used the conceptual representation of a business model (Massa et al. 2017 ) as the meta-characteristic. Following that, we looked for any dimension or characteristic that describes an element of the business model of an AI startup, which includes the value proposition, value creation, value delivery, or value capture (Teece 2010 ; Gassmann et al. 2014 ). The second step comprises the definition of ending conditions for the taxonomy development. For this, we build on the objective and subjective ending conditions proposed by Nickerson et al. ( 2013 ). First, we must have considered a representative sample of AI startup business models. Second, we require the dimensions and characteristics of the taxonomy to be mutually exclusive and collectively exhaustive to describe AI startup business models. Third, every characteristic must at least occur once at an object from the sample. Fourth, no dimensions or characteristics must have been added, deleted, or modified during the last iteration of taxonomy development. Fifth, we add subjective ending conditions, in that we require the taxonomy to be concise, robust, comprehensive, extendible, and explanatory (Nickerson et al. 2013 ).

The next steps are to develop the taxonomy iteratively. Before every iteration, one must choose between the conceptual-to-empirical and the empirical-to-conceptual approach (Nickerson et al. 2013 ). The conceptual-to-empirical approach is recommended if the researchers are already familiar with the domain of interest. Building on our initial conceptual understanding, we first chose this approach to derive the initial dimensions and characteristics of the taxonomy. First, we added the dimensions of the business model canvas (Osterwalder and Pigneur 2010 ), namely key partners , key activities , key resources , customer relationships , channels , customer segments , cost structure , and revenue streams . The business model canvas is widely accepted in research, compromises the key dimensions of a business model, and is generally applicable to all contexts. Hence, it serves as a promising starting point to structure a new field of business models. Second, we added AI-related dimensions, namely data structure (Hartmann et al. 2016 ), data ownership (Hartmann et al. 2016 ), AI technology (Russell and Norvig 2016 ) , and additional technology (Weking et al. 2020c ). Using 25 startups from our case base, we examined and evaluated the conceptually derived dimensions and characteristics, which resulted in an initial taxonomy.

Following the first iteration, we further developed the taxonomy using the empirical-to-conceptual approach. This approach suggests deriving common characteristics from objects that are similar and can be grouped (Nickerson et al. 2013 ). For each iteration, we first drew a subset of AI startups from our case base. Two of the authors then independently analyzed, compared, and grouped the startups given the taxonomy. Next, we discussed and merged our findings to add, delete, or modify dimensions and characteristics. After each iteration, we checked the previously defined ending conditions, increased our sample of AI startups, and started the next iteration. After three additional iterations, this procedure resulted in adding, deleting, and modifying multiple dimensions and characteristics. Figure  1 outlines the development of dimensions for the taxonomy.

figure 1

Iterative development of dimensions for business model taxonomy (own illustration)

After the fourth iteration, we now considered all 100 AI startups and again evaluated the taxonomy based on the previously defined ending conditions (Nickerson et al. 2013 ). The taxonomy was mutually exclusive and collectively exhaustive and allowed us to classify all 100 AI startups from the sample. Each characteristic was attributed to at least one AI startup in the sample. Furthermore, we did not have to add, delete, or modify any of the dimensions and characteristics. This also suggested that we had analyzed a reasonably representative sample of AI startups. We further discussed whether the taxonomy was sufficiently concise, robust, comprehensive, extendible, and explanatory within the research team, which ultimately concluded in an affirmation. Therefore, all previously defined objective and subjective ending conditions were met and the taxonomy development terminated.

3.3 Application of the Taxonomy and Pattern Development

We further applied the resulting taxonomy to derive business model patterns. Thereby, we go beyond the mere identification of key characteristics and reveal common instantiations of AI startup business models in practice. Patterns are popular artifacts in business model research (Remane et al. 2017 ; Weking et al. 2020a ), because they represent an abstraction from proven real-world business models that is useful for both research and practice (Amshoff et al. 2015 ). For example, business model research could use such patterns to create a typology (Doty and Glick 1994 ) that links the patterns to certain outcomes (e.g., venture growth). In practice, business model patterns can be directly implemented to support business model innovation (Remane et al. 2017 ; Gassmann et al. 2014 ).

We performed a quantitative cluster analysis (Ketchen and Shook 1996 ) on our sample of 100 AI startups to derive the patterns. We followed the four steps proposed by Sarstedt and Mooi ( 2014 ) to perform the cluster analysis. First, we selected the variables used for clustering (Sarstedt and Mooi 2014 ). As an outcome of the taxonomy development process, we had already classified all 100 AI startups using the dimensions and characteristics of the taxonomy. We removed the dimensions continuous learning , data type , and customer charge , because we did not have enough reliable information consistently available for all startups. We then transformed the eight dimensions into dichotomous dummy variables. Second, we selected a clustering approach. We decided for hierarchical agglomerative clustering using the Ward method, because it allows for a stable analysis even for smaller sample sizes (Sarstedt and Mooi 2014 ). In addition, the Ward method is applicable when there is no information about the optimal cluster size. Third, after having applied the Ward method, we determined the number of clusters. We analyzed the distance where the objects are combined, which is a useful metric for deciding on the number of clusters (Sarstedt and Mooi 2014 ). We selected the cutoff at which the combination of clusters or objects would occur at a maximum distance. This procedure resulted in four clusters (Fig.  2 ). Table 5 in the Appendix shows the cluster assignment for each startup.

figure 2

Dendrogram with clustering results (own illustration, created with RStudio)

In the fourth step, we validated the clusters to ensure meaning and usefulness (Ketchen and Shook 1996 ). We first made sense of the resulting clusters by analyzing the absolute and relative occurrences of characteristics across clusters and calculating the standardized mean difference of the relative occurrences within one cluster compared to the total sample (cf. Table 6 in the Appendix, cf. Table A.1 in the online Appendix for full results). This allowed us to interpret and understand the respective business model pattern that each cluster potentially represents. Thereby, we could derive four business model patterns that from our perspective represent useful abstractions. Furthermore, we validated the performance of the clustering. We manually assigned all 100 AI startups to the four clusters based on our qualitative assessment. We then compared our assignment with the result from Ward’s method to test the logic and the applicability of the clustering. The assignment was correct in 84% of the cases. Thus, we could demonstrate external heterogeneities between the clusters and internal homogeneities. We conclude that the four clusters, and patterns respectively, are meaningful and valid.

The results section of this study is twofold: First, we present the resulting business model taxonomy of AI startups and depict each dimension and characteristic in more detail. Second, we present the four archetypal business model patterns of AI startups and provide illustrative examples for each pattern.

4.1 Business Model Taxonomy of AI Startups

The resulting taxonomy consists of 11 dimensions and 39 characteristics and is based on the conceptual representation of a business model (Massa et al. 2017 ). Each combination of characteristics across the dimensions results in a new instantiation of an AI startup business model. The taxonomy is shown in Table 2 . In the following, we will describe each dimension and characteristic in more detail.

Regarding value proposition, we found that AI startup business models can be classified by the two dimensions core AI value and continuous learning . First, the core AI value describes the value that is created by the respective AI solutions that AI startups employ as part of their product or service. We found that these solutions either aim to analyze vast amounts of data, including mostly unstructured data, to create cognitive insights , to analyze streams of data for monitoring & anomaly detection , to provide interactive process & task support for humans, or to automate tasks through autonomous robots & bots . For example, the startup Zebrium analyzes log files of various platforms and detects anomalies in real-time. As another example, the startup Osaro offers industrial robots with computer vision to automate packaging tasks. Second, continuous learning describes whether or how the respective AI solutions are capable of learning from new data over time. Thereby, the respective AI solution might become more accurate over time as part of the value proposition. Whereas some AI solutions are improved at the provider side in the form of central learning & updates to the customer base, other AI solutions are learning at the customer side without further interference by the provider. However, this feature is sometimes not provided by AI startups.

Regarding value proposition, we found that AI startup business models can be classified by four dimensions: primary AI technology , data type , data source , and hardware provision . First, primary AI technology describes the AI technology that is most essential to the startups’ employed AI solution, both from a functional and marketing perspective. We can classify these AI technologies by “conventional” machine learning (includes shallow and deep machine learning for numerical or mixed data), natural language processing (includes analysis and generation of documents, texts, and speech), computer vision (includes analyses and generation of images and videos), and robotics (includes individual robotic components and autonomous vehicles). While the latter three types of AI technology typically rely on machine learning themselves, they also involve other or additional components that go beyond “conventional” machine learning, such as the lemmatization of textual data or sensors and actuators for robotics. Hence, we found this to be a meaningful and useful classification scheme. Second, the data type describes whether an AI startup predominately processes well-structured numeric/sensor data , textual/document data (excluding conversations), natural language data (including spoken language), visual data (including videos), or mixed data types. Third, the data source describes where the data used for training the AI solution originates from. Following prior research (e.g., Hartmann et al. 2016 ), we found that the data can either be self-generated at the startup side, be acquired from external data providers, collected from publicly available sources, or provided by the customer. In the latter case, we found a useful distinction between the data being customer provided on demand , or the data being customer transmitted continuously . For example, the startup SuperAnnotate uses batches of customer data that are provided on demand, whereas the startup Axonize offers a platform that constantly analyzes customer data. Fourth, hardware provision describes whether a startup also produces and offers specific hardware components as part of the business model, such as robotic components, drones, or cameras. For example, the startup Elemental Machines offers a data analytics platform and a broad range of sensors for data collection.

Regarding value delivery, we found that AI startup business models can be classified by four dimensions: delivery mode , level of customization , customer , and industry scope . First, the delivery mode describes how the value is delivered to the customer. Startups either offer software applications in diverse formats (e.g., web, desktop, mobile; on-premise, software-as-a-service), programmable interfaces on the code level (e.g., application programmable interfaces, software development kits, platform-as-a-service), or simply the base technology without having a software application or programmable interfaces (e.g., code pieces and specific algorithms). For example, the startup Hugging Face offers rich application programmable interfaces for NLP. In contrast, some startups do not provide software or hardware to their customers; but, instead, they solely provide the AI-produced output . For example, the startup Cyclica does not offer its technology directly to its customers. Instead, they provide AI-produced outputs for new drug discovery. Second, the level of customization describes how the startups’ product or service can be configured and tailored to serve individual customer needs. Startups either deliver standardized products/services without further customization, the option for tailoring/individualization through parameterization or custom model training, or the option for full customization (e.g., in the case of fully programmable interfaces). Third, the customer describes whether the startups’ product or service is targeted and sold to business customers ( B2B ), private consumers ( B2C ), or both . Fourth, the industry scope describes whether the startups’ product or service is bound to a specific industry ( industry focused ), or whether it addresses customer needs across industries ( industry agnostic ). For example, the startup Notable provides a solution for the healthcare context, whereas the startup Wisdom AI is offering a customer service solution that can be used across industries.

Regarding value capture, we found that AI startup business models can be classified by the dimension customer charge . AI startups either offer their products and services free of charge , as part of a subscription-based or transaction-based model, or as a one-time payment . For example, the startup Fakespot provides a plugin that is free of charge, whereas the startup Kubit offers diverse subscription plans for their solution.

4.2 Archetypal Business Model Patterns of AI Startups

We identified four archetypal business model patterns of AI startups (Table 3 ). All 100 AI startups of our sample can be assigned to one of the patterns. The salient characteristics that define the patterns can be taken from Table 6 . These are the characteristics that make a pattern unique and different from other patterns. Based on these salient characteristics, we now describe each pattern in more detail, and provide illustrative examples of real-world AI startups from the sample.

Pattern 1: AI-charged Product/Service Provider Startups applying this pattern offer products or services with readily trained AI models embedded at the core of their business models. The solutions are mostly delivered as standardized products and services that do not require further customization. Startups of this pattern typically do not cover entire workflows, but offer a solution for one specific task case within an industry, for example, detecting forbidden items at airports (e.g., Synapse Technologies). The solutions are mainly sold to other business customers. Because the products and services are rather standardized, startups in this pattern are also able to serve private consumer needs in some instances. An example of this pattern is the startup Overjet. The solution allows dentists to upload X-ray images of a jaw and check them for malposition. Overjet enables a faster analysis for doctors and patients and ensures a more objective cost claim for insurance companies. Another example is Alegion, which offers a software service that supports manual data labeling by suggesting salient image sections in videos.

Pattern 2: AI Development Facilitator Startups applying this pattern focus on facilitating AI development for their customers at the core of their business model. Startups of this pattern offer application programmable interfaces or software development kits that can be used for AI development. In addition, some startups offer no-code workbenches, where businesspeople with little IT know-how can develop new AI solutions (e.g., build-your-own chatbot). In this pattern, NLP is often the dominant AI technology. Perhaps, NLP-based solutions, such as chatbots, can barely be standardized and require strong customization to the customer’s specific context and individual requirements. Startups of this pattern target business customers across industries and often use subscription-based models for value capture. An example of this pattern is Mindsay, a startup that offers a comprehensive solution for customer service. Their solution is composed of easily configurable chatbots, real-time chat support, and process analytics components. Another example is the startup BotXO. The startup offers a platform to develop fully customized chatbot solutions.

Pattern 3: Data Analytics Provider Startups applying this pattern focus on the integration and analysis of vast amounts of data within their business model, including internal and external data sources. The provided solutions offer comprehensive data analyses to support well-informed decision-making, for example by continuously monitoring operations, uncovering hidden patterns, or making predictions for the future. To that end, the data is typically analyzed using conventional machine learning approaches. For data integration, the solutions often require initial tailoring at the customer. However, the solutions typically connect well with widely used information systems. Startups of this pattern predominately target business customers and employ transaction-based or subscription-based revenue models. As an example, the startup Kubit integrates customer information with external data to detect anomalies and predict customer retention and profitability. Another example is Falkonry. The startup offers a solution that integrates sensor and machine data to predict machine operating states. The necessary hardware, such as sensors, is not offered by the startup itself and is therefore not part of the business model.

Pattern 4: Deep Tech Researcher Startups applying this pattern research and develop innovative niche solutions at the frontiers of AI technology as the core of their business model, for example, in the areas of robotics, autonomous driving, and medical drug discovery. Startups of this pattern are often research-led with the aim of driving their AI models and algorithms to perfection. They do not offer standardized or easily customizable solutions for mass markets, but rather deliver the complex base technology that can be implemented and customized by their business customers. Therefore, those startups are not maintaining a stable revenue stream, but, instead, often rely on external funding. In the case of robotics, startups also work on the respective hardware components as part of their business model. As an example, the startup Syrius Robotics develops robots that autonomously transport goods in warehouses and supply production workers with materials. Another example is Cerenion, which develops a software solution to analyze, monitor, and quantify the functioning of the brain based on brain activity.

5 Discussion

We currently observe the rapid emergence of startups that use AI technology as part of their products or services. While AI startups receive much interest from venture capitalists and investors, they also need to find a stable business model to ensure long-term performance and survival. In this study, we raised the question of whether the business models of AI startups differ from common IT-related business models. To investigate this research question, we developed a business model taxonomy of AI startups, which reveals the key characteristics of AI startup business models. We further applied the taxonomy and performed a cluster analysis to identify four archetypal business model patterns of AI startups: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher . Against the background of extant research on IT-related business models, we were able to distill the key distinctive aspects of AI startup business models. Overall, we conclude that AI startup business models share noticeable overlaps with common IT-related business models. For example, they employ similar approaches to value delivery and value capture to those already known from common IT-related business models, such as software-as-a-service or subscription-based revenue models. However, AI startup business models also depart from common IT-related business models in certain aspects. Specifically, we found (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. In the following, we will elaborate on these distinctive aspects and propose promising directions for entrepreneurship research on AI. Table 4 summarizes potential future research questions. Thereafter, we will discuss the limitations of our research and our contributions to theory and practice.

5.1 New Value Propositions Through AI Capabilities

While certain value propositions are well known from research on data-driven business models (e.g., decision support or anomaly detection), we observe that AI technology offers additional capabilities that widen the scope for applying IT to meet new customer needs and ease their pains. In particular, AI startups shift the application of IT toward the domain of knowledge and service work, where human workers are either supported in accomplishing their tasks, or substituted through the automation of robots and bots (Coombs et al. 2020 ). For example, in certain specific tasks, such as fraud detection or disease diagnosis, AI technology can outperform its human counterparts (Brynjolfsson and McAfee 2017 ). Given these enhanced capabilities, the question arises how and when AI startups might be able to challenge existing industries, especially those that are knowledge and service work dominant. For example, an AI startup that offers a solution to automate customer support might successfully challenge traditional call center business models due to reduced personnel intensity and enhanced scalability. Prior advances in digitalization have already shown that the use of emergent technologies, such as big data analytics, enables new business models that can disrupt traditional industries (Loebbecke and Picot 2015 ).

While these AI capabilities open new opportunities, they also imply the need to increasingly consider ethical aspects, both when replacing human workers and when using AI solutions for critical decisions, such as personnel recruitment decisions (Köchling et al. 2021 ). Interestingly, our analysis did not reveal that these ethical aspects are key characteristics of the business models of AI startups. For example, we would have assumed that AI startups promote the adherence to ethical standards or the algorithmic transparency of their products and services in an effective way. Perhaps such an advertising is not required, as most AI startups serve business customers instead of private customers. These business customers are then responsible for communicating ethical aspects to their customers. However, given the importance of ethics for AI solutions (Buxmann et al. 2021 ), we encourage research to investigate the potential role of ethics in AI startup business models.

5.2 Different Roles of Data for Value Creation

While data often plays a vital part in common IT-related business models (e.g., Wiener et al. 2020 ; Hartmann et al. 2016 ), we identified different roles of data for value creation in AI startups. For most AI startups, we see that data is an important element of the value creation. This does not come surprisingly, as most of the current upswing of AI is happening thanks to the application of machine learning and the vast availability of data (Haenlein and Kaplan 2019 ). On the one hand, AI startups analyze or help to analyze data to generate insights or detect anomalies. On the other hand, however, we see the data being used in a different and new way. Especially in the pattern AI-charged Product/Service Provider , we observe that data is not analyzed to create insights; instead, data is used to train models that are then readily embedded in products and services. For example, a computer vision algorithm is trained to detect certain diseases, which then can be transferred and applied across hospitals. Here, the value is delivered by a readily trained model instead of providing the means for new data analysis.

Given the important role of data for most AI startups, data acquisition becomes an important part of the business model, as evident in our taxonomy ( data source and data type ). Similar to previous findings, we can state that AI startups can leverage data in various types and from various sources as part of their value creation, such as self-generated data, external customer data, or publicly available data (Bock and Wiener 2017 ; Hartmann et al. 2016 ). To gain access to more exclusive data, we see some AI startups form close relationships with industry partners, for example to obtain real-world data from manufacturing. For entrepreneurship, the question arises how AI startups potentially follow different strategies to access or gather data. And, in turn, how digital entrepreneurship ecosystems (Elia et al. 2020 ) might foster data to facilitate entrepreneurial action. These questions should be examined against the background of extant research on data-driven business models (e.g., Wiener et al. 2020 ).

Despite the importance of data to some AI startups and the common assumption that AI is data intensive, we argue that not all AI startup business models are equally dependent on data. For example, certain machine learning techniques used as primary AI technology require substantially less data (Benbya et al. 2020 ), or some AI startups are leveraging publicly available data for value creation. Future research needs to further explore the essentiality of data for AI startups and its implications in various contexts. When and in what contexts do AI startups not heavily rely on data? Given a high data essentiality in a specific context, what does the possession of rare or scarce data imply for the valuation of a startup? For this, it will be indispensable to take a more nuanced perspective on AI in entrepreneurship to account for the different AI techniques (Stone et al. 2016 ) and application contexts.

5.3 Impact of AI Technology on the Overall Business Logic

Our taxonomy and patterns reveal that AI startup business models are strongly technology-centered, which led us to examine how AI, a different technology compared to traditional IT, impacts the overall business logic. We identified many technical dimensions and characteristics in our taxonomy (e.g., continuous learning, primary AI technology , data source ) that seemingly overshadow other aspects, such as the target customer or revenue model. AI startups are mostly focused on giving their business customers access to complex AI technology that is otherwise too difficult and costly for these to develop (Jöhnk et al. 2021 ). Our patterns revealed different archetypes on how this technical complexity is mastered and delivered: by means of providing products and services with pre-trained AI models ( AI-charged Product/Service Provider ), facilitating development with customizable and programmable interfaces ( AI Development Facilitator ), providing solutions for data analytics ( Data Analytics Provider ), and researching and developing basis AI technology ( Deep Tech Researcher ).

This focus on mastering the technical complexity raises interesting questions for future research into entrepreneurship. One aspect certainly is how AI startups manage to obtain access to in-depth technical know-how and extensive resources, as other scholars have mentioned previously (Chalmers et al. 2020 ; Obschonka and Audretsch 2020 ). Another aspect is how AI startups can make themselves stand out against competitors. One possible way could be to obtain leadership in the underlying algorithms and their performance. For example, the startup DeepL managed to build a natural language translation software that outperformed tech giants like Google, Facebook and Amazon (Coldewey and Lardinois 2017 ). We would expect that especially startups of the type AI-charged Product/Service Provider and Deep Tech Researcher are likely to follow this direction, as their offering mostly depends on the performance of the AI models. Other potential ways could be the provision of a well usable and comprehensive solution that goes beyond single AI-based features (e.g., covering the whole marketing process), or the provision of a very flexible and customizable solution (e.g., build-your-own chatbot). This discussion opens fruitful avenues for future research: How can AI startups create competitive advantage (e.g., via AI model leadership)? What type of AI technology is easier to replicate than others?

Furthermore, our taxonomy reveals that the continuous learning of AI-based products and services is an interesting mechanism that impacts the overall business logic. The products and services can potentially become smarter over time while in use by the customer, or through federated learning and central updates from the provider, as more data becomes available for AI training. Given this mechanism, an early mover could build a critical customer base first and obtain a competitive advantage through the data that is collected from the customers, because this data then would allow to refine the algorithms and increase the value of the service, which in turn would attract more customers (Gregory et al. 2020 ). Would another startup be able to catch up with a bigger dataset and better algorithms, or maybe compensate this technical disadvantage with better usability or branding? More research is needed to understand the implications of continuous learning and data network effects in the context of entrepreneurship.

5.4 Limitations and Extensions

Our research comes with limitations. First, taxonomies, in general, can never be fully exhaustive or perfect (Nickerson et al. 2013 ). However, we were able to ensure the appropriateness and usefulness of the taxonomy by following the structured and proven method proposed by Nickerson et al. ( 2013 ). Nevertheless, we do recognize that our taxonomy likely needs to be reviewed and extended in upcoming years since the field of AI is evolving fast (Stone et al. 2016 ). Second, we used Crunchbase for startup identification, which relies on self-reported information. Consequently, we could not identify all startups that use AI technology as an important element of their business model, as some might refrain from reporting the use of AI technology explicitly. Nevertheless, we are confident that our sample featured enough startups to capture the diversity of the underlying business models. Third, our taxonomy and patterns were mainly built with AI startups from North America and Europe, as Crunchbase tends to predominately feature Western countries. Therefore, our results should be treated with caution when applying them to AI startups from other countries. Accounting for national differences, such as data-related regulations (Wiener et al. 2020 ), is beyond the scope of our study.

5.5 Contributions to Theory and Implications for Practice

Our work contributes to a growing research stream of AI in entrepreneurship (Chalmers et al. 2020 ; Obschonka and Audretsch 2020 ) and to research on IT-related business models (Veit et al. 2014 ; Steininger 2019 ). First, we addressed the research question of how AI startups business models potentially differ from common IT-related business models. Using our descriptive analysis as a vantage point (Gregor 2006 ), we were able to distill the distinctive aspects of AI startup business models. We can conclude that while AI startup business models indeed share noticeable overlaps in some aspects, they certainly go beyond common IT-related business models, such as data-driven business models. Second, we further elaborated on these differences and their implications, which enabled us to present promising directions for future research on AI in entrepreneurship. Here, we particularly argue for a more nuanced perspective on AI in entrepreneurship, because our analysis showed that AI startups apply different AI techniques which each have different implications for the business model. Third, we provided one of the first comprehensive analyses of AI startup business models. We revealed the key dimensions and characteristics of AI startup business models and derived respective patterns. Previous business model research has predominately assessed the implications of AI technology to enhance operations as part of the value creation, whereas the overall business model remained mostly unstudied (Garbuio and Lin 2019 ). Our taxonomy and patterns can serve as a springboard for future research, because they represent clearly defined categories that allow for an in-depth examination (Rich 1992 ). For example, one could use the patterns to develop a typology (Doty and Glick 1994 ) of AI startups, which links the patterns to specific outcomes (e.g., venture growth).

Our work has relevant implications for practice. First, our business model taxonomy for AI startups supports entrepreneurs in developing and innovating business models by using AI technology. It serves as a morphological box, meaning that every combination of dimensions results in a new business model. In addition, the four archetypal patterns reveal interesting insights into common instantiations of AI startup business models. They could be considered as current best-practice and may serve as a blueprint for new ventures. Second, our work is also relevant for managers of larger and more established firms. As Hartmann et al. ( 2016 , p.2) note, in comparison with larger firms, “young companies create a rich variety of, presumably, purer business models.” Hence, our investigation might have also revealed opportunities for larger firms, because some elements of AI startup business models could be directly applicable. Third, we support venture capitalists and investors in making more profound decisions regarding AI startups. We help to structure a vast landscape of AI startups and provide the key characteristics of business models to be considered for AI startup evaluation. Given the prevalence of technical dimensions in the business model, we recommend venture capitalists and investors to develop a good technical understanding of AI technology to appropriately evaluate the potential of an AI startup.

6 Conclusion

We currently observe the rapid emergence of startups that use AI technology as part of their products or services. While AI startups receive much interest from venture capitalists and investors, they inevitably need to find a stable business model at one point to ensure long-term performance and survival. On the one hand, recent research led us to suggest that AI startups do employ novel or different business models. On the other hand, we also found compelling arguments that much of what is sold as AI today has been around for a long time already. Because a fundamental clarification would be important for both research and practice, we raised the question of how AI startup business models potentially differ from common IT-related business models. To investigate this research question, we developed a business model taxonomy of AI startups, which revealed the key characteristics of AI startup business models. We further applied the taxonomy and performed a cluster analysis to derive four archetypal business model patterns of AI startups: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher . Against the background of extant research on IT-related business models, we further distilled the distinctive aspects of AI startup business models. We found that (1) AI capabilities open new opportunities for value proposition, (2) data features different roles and is typically—yet not necessarily—important to the value creation, and (3) AI technology impacts the overall business logic in potentially new ways. We further discussed promising directions for future research on AI in entrepreneurship.

We contribute to a growing research stream concerned with AI in entrepreneurship (Chalmers et al. 2020 ; Obschonka and Audretsch 2020 ) and to research on IT-related business models (Veit et al. 2014 ; Steininger 2019 ). First, we distilled the distinctive aspects of AI startup business models to sharpen our understanding of the impact of AI technology on entrepreneurship and business models. Second, we presented promising directions to guide future research on AI in entrepreneurship. Third, we provided one of the first comprehensive analysis of AI-related business models. Our taxonomy and patterns reveal the key dimensions and characteristics of AI startup business models and their common instantiations. Practitioners may use our taxonomy and patterns as tools to support entrepreneurial action. Furthermore, we help to structure a broad and diverse AI startup landscape.

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How To Choose the Right Funding Model for Your Startup Choosing the right funding model for your startup is a pivotal decision that requires careful consideration.

By Kimberly Zhang • Feb 20, 2024

Key Takeaways

  • Whichever method you opt for, aligning the funding model with your startup’s stage, industry financial needs is essential.

This story originally appeared on Under30CEO

Choosing the right funding approach is a critical decision for launching your startup that can shape the trajectory of your business.

In this article, we will explore various funding models available to startups and provide insights on how to make informed decisions based on your unique needs and goals.

Understanding Types of Startup Funding Models

Bootstrapping.

Bootstrapping involves funding your startup with personal savings, revenue generated by the business, or loans from friends and family. While it offers autonomy and control, it comes with the challenge of limited resources and a potentially slower growth trajectory.

Angel Investors

Angel investors are affluent individuals who provide capital for startups in exchange for ownership equity or convertible debt. This funding model not only brings in financial support but often includes mentorship and industry connections.

Related: 12 Things You Need to Understand about the Silicon Valley Model before Using it in Other Markets

Using Security

Some entrepreneurs use security as a means of funding. This can come in multiple forms, including using your property, inventory or other assets as collateral, which can be risky if you cannot repay the finance. Other options include using accounts receivable (or invoice factoring), such as future orders, and borrowing money against these future orders.

Venture Capital

Venture capital firms invest larger amounts of money in startups with high growth potential. Venture capital funding is suitable for businesses with scalability, a strong market opportunity, and a capable team. However, it involves giving up a portion of equity and adhering to rigorous growth expectations.

Crowdfunding

Crowdfunding platforms like Kickstarter and Indiegogo allow startups to present their ideas to a global audience and collect small contributions from backers.

Kickstarter alone has facilitated over 500,000 projects, raising more than $6 billion from 18.6 million backers, showcasing the impact of crowdfunding on startup funding.

This model not only provides capital but also serves as a marketing tool, generating buzz and interest around the startup.

Related: 12 Key Strategies to a Successful Crowdfunding Campaign

Bank Loans and Traditional Lending

Historically, if you need a loan, you would visit your local bank branch and speak to a bank manager. This has changed significantly over the last few decades towards more private institutions which may offer more favourable terms and faster funding.

Through the likes of Funding Circle, MT Finance, Iwoca and Swoop, new businesses are able to access capital much quicker and raise significant amounts, even as much as £500,000 or £1 million. However, note that you may need to be trading for a minimum period of time, e.g., 6 months or 2 years, and have regular revenue.

Factors to Consider When Choosing a Funding Model

  • Stage of Your Startup: The stage of your startup plays a crucial role in determining the most suitable funding model. Bootstrapping might be ideal for early-stage ventures, while later stages may benefit from venture capital to fuel rapid growth.
  • Business Model and Industry: The nature of your business and industry can influence the choice of funding. Some high-growth industries may be more attractive to venture capitalists, such as biotechnology, while other new businesses, such as in consumer goods, may find success through crowdfunding or angel investment.
  • Financial Need: Evaluate the specific financial needs of your startup. Consider factors such as initial capital requirements, operating expenses, and potential expansion plans. This assessment will guide you toward a funding model that aligns with your financial goals.
  • Risk Tolerance: Assess your risk tolerance as an entrepreneur. While venture capital might bring substantial funding, it also involves relinquishing control and adhering to aggressive growth targets. Bootstrapping, on the other hand, offers autonomy but requires a higher risk tolerance due to limited resources.
  • Timeframe for Results: Consider the timeframe within which you expect to see results. Venture capital may provide rapid injections of capital for quick scaling, while crowdfunding campaigns might take time to build momentum. Bootstrapping offers a gradual approach but may result in slower growth.

How To Choose The Right Funding Option For Your Startup

Thoroughly research each funding model, understanding its advantages, challenges, and success stories within your industry. Networking becomes incredibly important, so take time to consult with industry experts, mentors or advisors who have experience in your field. Their insights can provide valuable perspectives on the most suitable funding model for your startup.

Also consider a diversified approach by combining multiple funding sources. For instance, a mix of angel investment, crowdfunding and bootstrapping might provide a well-rounded and resilient financial foundation.

Choosing the right funding model for your startup is a pivotal decision that requires careful consideration of various factors. Whichever method you opt for, aligning the funding model with your startup's stage, industry financial needs is essential.

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Tripathi, N. (Nirnaya). "Initial minimum viable product development in software startups:a startup ecosystem perspective." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526224176.

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Cukier, Daniel. "Software startup ecosystems evolution: a maturity model." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-20062017-151018/.

Aka, Bouame Donald Magloire. "Challenges and opportunities for private investment funds in Western Africa's startup ecosystem." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123570.

Egbringhoff, Andrea. "Entrepreneurship in China : Small Batch Production of Consumer Goods." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188821.

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FRANK, ERIK SIMON. "Corporate Innovation: A Case Study of the Corporate Incubation Process." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240276.

Tsagkidis, Panagiotis, and Gabriele Blomkvist. "Stay ahead of the competition : How the perception of Competitive Intelligence influences the way Swedish startups are dealing with international competition." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414564.

Calatrava, Castagnetti Ruperto Andrés, and Zelati Alberto Coti. "Understanding Entrepreneurial Leadership that supports local entrepreneurship." Thesis, Uppsala universitet, Industriell teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-364792.

Cottin, Arredondo Randall Ismael, and Enzo Garry. "The Venture Capital behavioral bias and the ecosystem investment flows : A comparative quantitative study about the relationship between Venture Capitalist's drivers and their investment behavior in Stockholm and Silicon Valley." Thesis, Umeå universitet, Företagsekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-137128.

Granhed, Anna, and Hanna Söderlund. "The Paradox of User Perceived Performance : An Empirical Study on User Experience in a Digital Platform Ecosystem." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300895.

Hiltunen, M. (Marja). "Business ecosystems and startup development." Master's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201705101758.

Pinto, Felipe de Matos Sardinha. "A construção de um modelo de acompanhamento da evolução de startups digitais em contexto de aceleração: o caso Start-Up Brasil." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/12/12142/tde-21022018-110337/.

Duarte, Kenia Soares. "Top management team influence on early internationalization strategy and decision of moving location to startup ecosystems." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/22789.

Bertin, Clarice. "Driving factors for symbiotic collaborations between startups and large firms in open innovation ecosystems." Thesis, Strasbourg, 2020. https://publication-theses.unistra.fr/restreint/theses_doctorat/Bertin_Clarice_2020_ED221.pdf.

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SPRINGER, JULIAN, and MIKO KINNUNEN. "Value creation through digital services in start-up support organisations." Thesis, KTH, Industriell Management, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239721.

Asokan, Aravind. "Effectiveness of the University Entrepreneurial Eco-System in the Growth of Entrepreneurship and Threshold Capability Development of Students." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41975.

Johansson, Johan. "The Role of Big Data Facilitators in the Business Ecosystem : Drivers, Barriers and Value offered." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69589.

Stenbom, Agnes. "Understand That Everything is Different and be Humble to the Task : An Exploratory Study on Establishment Challenges for Swedish Micro-Sized Tech Businesses in NYC." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231439.

Marquês, Joana Maria das Neves. "Ecossistemas de empreendedorismo : análise da Área Metropolitana de Lisboa." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/13028.

Piqué, Huerta Josep Miquel. "Understanding the urban development and the evolution of the Ecosystems of Innovation." Doctoral thesis, Universitat Ramon Llull, 2019. http://hdl.handle.net/10803/665076.

Halm, Lisa, and Oscar Mörke. "Exploring the interplay of the entrepreneurial process and the incubation process." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388776.

Regnér, Susanna, and Johanna Wasberg. "Implementering av stadsgrönska och ekosystemtjänster i urbana miljöer : från start till mål." Thesis, Högskolan Väst, Avdelningen för Matematik, Data- och Lantmäteriteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-14496.

Almeida, João Nuno Barreiros. "Porto’s startup ecosystem : how are tech startups shaping the development & growth of Porto’s ecosystem?" Master's thesis, 2018. http://hdl.handle.net/10400.14/26370.

Águeda, André Filipe Pelicano. "Interconnectivity between Ecosystem Builders and Investor Groups in European Startup Ecosystems." Master's thesis, 2016. http://hdl.handle.net/10362/18268.

Parracho, André Rodrigues. "The Portuguese startup ecosystem : key success factors on the entrepreneurial ecosystem." Master's thesis, 2017. http://hdl.handle.net/10400.14/21833.

Castro, José Pedro Freire Vieira de. "Do accelerated ventures learn what really matters? : an exploratory study of the Portuguese Ecosystem." Master's thesis, 2017. http://hdl.handle.net/10400.14/22204.

Lin, Hung-Chun, and 林宏駿. "Startup Fever: The Development and Evolution of the Entrepreneurial Ecosystem–A Case Study on the Taipei Metropolitan Area." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wx9t5p.

Caldeira, Martim Lino da Cruz. "Does “The Lean Startup” increase startups’ chances of success in Lisbon? : introduction to a “Leanness” scale." Master's thesis, 2018. http://hdl.handle.net/10400.14/25331.

Miguel, Pedro José Bernardo. "The european entrepreneurial ecosystem and its events : Beta-I case." Master's thesis, 2018. http://hdl.handle.net/10400.14/25929.

Gonçalves, Marisa Alexandra Abreu. "Understanding the Trends of European Startup Ecosystems." Master's thesis, 2016. http://hdl.handle.net/10362/20020.

MARCHENA, JOSE RICARDO LIZAMA DE, and 李瑞奇. "FinTech Ecosystem in Mexico. Opportunities and Risks for Startups." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/n6h6x7.

Tatrik, Kreet. "Entrepreneurship ecosystems in the examples of Portugal and Estonia." Master's thesis, 2019. http://hdl.handle.net/10071/19095.

Hernández, José Miguel Salazar, and 沙米格. "Analyzing the Technology Start-up Ecosystem in Taipei, Taiwan." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/gw433m.

Mota, Pedro Filipe Esteves. "Are the new startup ecosystems able to overcome the geographic concentration on venture capital investments?" Master's thesis, 2020. http://hdl.handle.net/10362/114469.

Carvalho, Ana Clara Caleiro Coelho de. "Digital Startups Accelerators: Characteristics and Evolution Trends." Master's thesis, 2016. http://hdl.handle.net/10362/20023.

Lin, Shih-Han, and 林士涵. "Case study research in ITRI entrepreneurial ecosystem and start-up." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8gmwqt.

Cunha, Luiza Mara Pereira. "Contribution of startups incubators and government agencies to the advancement of the entrepreneurial ecosystem in Portugal." Master's thesis, 2021. http://hdl.handle.net/10400.1/17667.

Kang, Li-Yun, and 康瓈云. "Interaction between Startups and Entrepreneurial Ecosystem from Dynamic Capability Perspective - A Case Study of Viscovery and Perkd." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/945222.

Duarte, David João Queimado Vaz Ponte. "Model for evaluation of less-matured digital business ecosystems." Master's thesis, 2018. http://hdl.handle.net/10362/42447.

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  1. Business models of start-ups and their impact on the sustainability of nascent business

    Business models of start-ups and their impact on the sustainability of nascent business Journal of Entrepreneurship and Sustainability Issues 8 (4):29-52 DOI: 10.9770/jesi.2021.8.4 (2)...

  2. PDF Getting to Now: Entrepreneurial Business Model Design and Development

    The purpose of this thesis is to uncover and examine the processes that start-up entrepreneurs go through while designing and then developing their business models. This is done with the intent of deciphering the kind of development that might ultimately lead to a unique or innovative business model.

  3. PDF A Study of Factors Influencing on Start-up Business: Failure and Success

    The objectives of the research are: 1.1 To analyses the factors influencing on previous startup success and failure. 1.2 To figure out what skills the recent entrepreneurs were well-prepared on and what skills were lacking. 1.3 To select the most possible factors and apply them in the business field.

  4. Challenges of business models for sustainability in startups

    1. Introduction 2. Theoretical framework 3. Research method 4. Results and discussion 5. Conclusion Abstract Purpose This study aims to analyze the challenges startups face in implementing business models for sustainability.

  5. PDF Business Model Innovation in Start-ups

    A qualitative case study of Business Model Innovation in the context of Technology Start-ups in Sweden MASTER THESIS WITHIN: General Management (JGMT26 - S19) NUMBER OF CREDITS: 15 ECTS PROGRAMME OF STUDY: Engineering Management AUTHORS: Alexandre Sixel, Canan ÖZTÜRK TUTOR: Tomasso Minola JÖNKÖPING May , 2019 Business Model Innovation

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    First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher.

  7. PDF The Business Model of Start-Up—Structure and Consequences

    (Booming Berlin2016, p. 9) on the Berlin start-up scene assumes that with a functioning business model, the start-up will develop into a growing enterprise over a period of up to five years. Sedlacek and Sterk(2014), in their research study, are attentive to start-ups like a significant source

  8. PDF MASTER THESIS

    MASTER THESIS The Business Model Innovation Process and its Importance to Micro-foundations: A Dynamic Capabilities Perspective Author: Karin Lara Anna Dierichsweiler ... and new firms (often high tech, start-up, and e-businesses) reach concrete results, many incumbents do not generate satisfactory profits or competitive advantages from BMI ...

  9. PDF Strategic Management: Business Model Canvas for Start-Up Company

    1 Introduction The main idea of thesis is to look at the company from different angle, in order to understand actual factors, which Kakunpala has as a Start-Up Company.

  10. PDF PhD-Thesis AN EXPLORATION OF THE BUSINESS MODEL CONCEPT'S

    application of business model thinking. It is shown that SMEs are in a different business model development stage from start-ups and large firms, having different business model needs. The necessity of a framework assisting managers in the creation and diversification of revenue streams is proposed.

  11. PDF Lean Startup as a Tool for Digital Business Model Innovation: Enablers

    This thesis examines lean startup as a tool for digital business model innovation in established companies. The purpose of this part is to familiarize the reader with the research field by introducing and bridging the main topics of business models and business model innovation with digital technologies and lean startup.

  12. PDF Start-up Company Analysis and Theories for Internationalization

    1 Abstract Purpose - The rise of e-commerce and software startup companies have significantly changed the Internet and business playground. HVO Finland Oy is a young startup company, operating in software industry providing a health care site for manual therapy businesses.

  13. Digital Commons @ NJIT

    Start-up and open innovation Gonxhe Tali New Jersey Institute of Technology Follow this and additional works at: https://digitalcommons.njit.edu/theses Part of the Business Administration, Management, and Operations Commons, and the Operations Research, Systems Engineering and Industrial Engineering Commons Recommended Citation

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    dustry-wide solutions. Through profound cooperation, start-up companies can scale their busi-ness models, as well as share risks with partner companies. Keywords: business model, business model innovation, ecosystem theory, circular economy, strategy The originality of this thesis has been checked using the Turnitin OriginalityCheck service.

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    4.2 The Business Model 52 4.3 Competitiveness and Agility 69 4.4 International Business Activities 82 5. Analysis 91 5.1 Cleantech-as-a-Service 91 5.2 Agile Natives 95 5.3 Characterizing an Agile Internationalization 99 5.3.1 A Virtualized Expansion 99 5.3.2 The entrepreneur's role towards digital international business

  16. PDF Implementing new ways of working model for a business ...

    The objective of this thesis is to create an effective work model for a business transformation project team by applying elements of Agile, Lean Startup and Design Thinking frameworks. The main research question aimed to clarify how to effectively implement a project work model by applying those frameworks. Further sub-questions

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    1. Freemium business model. The freemium model lets users access the base application or service for "free" before enticing them to upgrade to a "premium" license to unlock advanced (often necessary) features. You likely recognize this model through your company's use of startup marketing tools and software.

  18. (PDF) Early stage startup development: the critical ...

    The dissertation "Early Stage Start-up Development: The Critical Success Factors' Perspective" aims to understand the success of start-up development as the early growth stage is dominated by...

  19. PDF Technological innovations and business model innovations

    Business model theory (Osterwalder & Pigneur 2010). 22 Figure 6. The Main Elements of a Business Model (Boston Consulting Group, 2009). 24 Figure 7. List of components of the business model (Schön, 2012) 25 Figure 8. The four components of a business model (Afuah, 2003). 26 Figure 9. The importance of a business model (Teece, 2010). 28 Figure 10.

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    This thesis examines business model innovations in the context of the energy transition by investigating emerging start-up business models. The implemented research methodology in this thesis consists of a systematic literature review and an investigation of empirical data of 15 European energy start-ups.

  21. How To Choose the Right Funding Model for Your Startup

    Stage of Your Startup: The stage of your startup plays a crucial role in determining the most suitable funding model. Bootstrapping might be ideal for early-stage ventures, while later stages may ...

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    Method: To achieve the objective, two multivocal literature reviews and multiple empirical studies were conducted to examine: a) the elements in a startup ecosystem, b) initial MVP development in software startups, and c) the effects of startup ecosystem elements on the initial MVP development phase.

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    505 likes, 7 comments - wpcareyschool on September 11, 2023: "Over this past summer, Sara Sroka '24 completed a marketing internship with Starbucks in Seattl..."