research paper on business model

Business model innovation: a review and research agenda

New England Journal of Entrepreneurship

ISSN : 2574-8904

Article publication date: 16 October 2019

Issue publication date: 13 November 2019

The aim of this paper is to review and synthesise the recent advancements in the business model literature and explore how firms approach business model innovation.

Design/methodology/approach

A systematic review of business model innovation literature was carried out by analysing 219 papers published between 2010 and 2016.

Evidence reviewed suggests that rather than taking either an evolutionary process of continuous revision, adaptation and fine-tuning of the existing business model or a revolutionary process of replacing the existing business model, firms can explore alternative business models through experimentation, open and disruptive innovations. It was also found that changing business models encompasses modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements in four areas of innovation: value proposition, operational value, human capital and financial value.

Research limitations/implications

Although this review highlights the different avenues to business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain unexplored.

Practical implications

The business model innovation framework can be used by practitioners as a “navigation map” to determine where and how to change their existing business models.

Originality/value

Because conflicting approaches exist in the literature on how firms change their business models, the review synthesises these approaches and provides a clear guidance as to the ways through which business model innovation can be undertaken.

• Business model
• Value proposition
• Value creation
• Value capture

Ramdani, B. , Binsaif, A. and Boukrami, E. (2019), "Business model innovation: a review and research agenda", New England Journal of Entrepreneurship , Vol. 22 No. 2, pp. 89-108. https://doi.org/10.1108/NEJE-06-2019-0030

Emerald Publishing Limited

Copyright © 2019, Boumediene Ramdani, Ahmed Binsaif and Elias Boukrami

Published in New England Journal of Entrepreneurship . 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 may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Firms pursue business model innovation by exploring new ways to define value proposition, create and capture value for customers, suppliers and partners ( Gambardella and McGahan, 2010 ; Teece, 2010 ; Bock et al. , 2012 ; Casadesus-Masanell and Zhu, 2013 ). An extensive body of the literature asserts that innovation in business models is of vital importance to firm survival, business performance and as a source of competitive advantage ( Demil and Lecocq, 2010 ; Chesbrough, 2010 ; Amit and Zott, 2012 ; Baden-Fuller and Haefliger, 2013 ; Casadesus-Masanell and Zhu, 2013 ). It is starting to attract a growing attention, given the increasing opportunities for new business models enabled by changing customer expectations, technological advances and deregulation ( Casadesus-Masanell and Llanes, 2011 ; Casadesus-Masanell and Zhu, 2013 ). This is evident from the recent scholarly outputs ( Figure 1 ). Thus, it is essential to comprehend this literature and uncover where alternative business models can be explored.

Conflicting approaches exist in the literature on how firms change their business models. One approach suggests that alternative business models can be explored through an evolutionary process of incremental changes to business model elements (e.g. Demil and Lecocq, 2010 ; Dunford et al. , 2010 ; Amit and Zott, 2012 ; Landau et al. , 2016 ; Velu, 2016 ). The other approach, mainly practice-oriented, advocates that innovative business models can be developed through a revolutionary process by replacing existing business models (e.g. Bock et al. , 2012 ; Iansiti and Lakhani, 2014 ). The fragmentation of prior research is due to the variety of disciplinary and theoretical foundations through which business model innovation is examined. Scholars have drawn on perspectives from entrepreneurship (e.g. George and Bock, 2011 ), information systems (e.g. Al-debei and Avison, 2010 ), innovation management (e.g. Dmitriev et al. , 2014 ), marketing (e.g. Sorescu et al. , 2011 ) and strategy (e.g. Demil and Lecocq, 2010 ). Also, this fragmentation is deepened by focusing on different types of business models in different industries. Studies have explored different types of business models such as digital business models (e.g. Weill and Woerner, 2013 ), service business models (e.g. Kastalli et al. , 2013 ), social business models (e.g. Hlady-Rispal and Servantie, 2016 ) and sustainability-driven business models ( Esslinger, 2011 ). Besides, studies have examined different industries such as airline ( Lange et al. , 2015 ), manufacturing ( Landau et al. , 2016 ), newspaper ( Karimi and Walter, 2016 ), retail ( Brea-Solís et al. , 2015 ) and telemedicine ( Peters et al. , 2015 ).

Since the first comprehensive review of business model literature was carried out by Zott et al. (2011) , several reviews were published recently (as highlighted in Table I ). Our review builds on and extends the extant literature in at least three ways. First, unlike previous reviews that mainly focused on the general construct of “Business Model” ( George and Bock, 2011 ; Zott et al. , 2011 ; Wirtz et al. , 2016 ), our review focuses on uncovering how firms change their existing business model(s) by including terms that reflect business model innovation, namely, value proposition, value creation and value capture. Second, previous reviews do not provide a clear answer as to how firms change their business models. Our review aims to provide a clear guidance on how firms carry out business model innovation by synthesising the different perspectives existing in the literature. Third, compared to recent reviews on business model innovation ( Schneider and Spieth, 2013 ; Spieth et al. , 2014 ), which have touched lightly on some innovation aspects such as streams and motivations of business model innovation research, our review will uncover the innovation areas where alternative business models can be explored. Taking Teece’s (2010) suggestion, “A helpful analytic approach for management is likely to involve systematic deconstruction/unpacking of existing business models, and an evaluation of each element with an idea toward refinement or replacement” (p. 188), this paper aims to develop a theoretical framework of business model innovation.

Our review first explains the scope and the process of the literature review. This is followed by a synthesis of the findings of the review into a theoretical framework of business model innovation. Finally, avenues for future research will be discussed in relation to the approaches, degree and mechanisms of business model innovation.

2. Scope and method of the literature review

Given the diverse body of business models literature, a systematic literature review was carried out to minimise research bias ( Transfield et al. , 2003 ). Compared to the previous business model literature, our review criteria are summarised in Table I . The journal papers considered were published between January 2010 and December 2016. As highlighted in Figure 1 , most contributions in this field have been issued within this period since previous developments in the literature were comprehensively reviewed up to the end of 2009 ( Zott et al. , 2011 ). Using four databases (EBSCO Business Complete, ABI/INFORM, JSTOR and ScienceDirect), we searched peer-reviewed papers with terms such as business model(s), innovation value proposition, value creation and value capture appearing in the title, abstract or subject terms. As a result, 8,642 peer-reviewed papers were obtained.

Studies were included in our review if they specifically address business models and were top-rated according to The UK Association of Business Schools list ( ABS, 2010 ). This rating has been used not only because it takes into account the journal “Impact Factor” as a measure for journal quality, but also uses in conjunction other measures making it one of the most comprehensive journal ratings. By applying these criteria, 1,682 entries were retrieved from 122 journals. By excluding duplications, 831 papers were identified. As Harvard Business Review is not listed among the peer-reviewed journals in any of the chosen databases and was included in the ABS list, we used the earlier criteria and found 112 additional entries. The reviewed papers and their subject fields are highlighted in Table II . Since the focus of this paper is on business model innovation, we selected studies that discuss value proposition, value creation and value capture as sub-themes. This is not only because the definition of business model innovation mentioned earlier spans all three sub-themes, but also because all three sub-themes have been included in recent studies (e.g. Landau et al. , 2016 ; Velu and Jacob, 2014 ). To confirm whether the papers addressed business model innovation, we examined the main body of the papers to ensure they were properly coded and classified. At the end of the process, 219 papers were included in this review. Table III lists the source of our sample.

The authors reviewed the 219 papers using a protocol that included areas of innovation (i.e. components, elements, and activities), theoretical perspectives and key findings. In order to identify the main themes of business model innovation research, all papers were coded in relation to our research focus as to where alternative business models can be explored (i.e. value proposition, value creation and value capture). Coding was cross checked among the authors on a random sample suggesting high accuracy between them. Having compared and discussed the results, the authors were able to identify the main themes.

3. Prior conceptualisations of business model innovation

Some scholars have articulated the need to build the business model innovation on a more solid theoretical ground ( Sosna et al. , 2010 ; George and Bock, 2011 ). Although many studies are not explicitly theory-based, some studies partially used well-established theories such as the resource-based view (e.g. Al-Debei and Avison, 2010 ) and transaction cost economics (e.g. DaSilva and Trkman, 2014 ) to conceptualise business model innovation. Other theories such as activity systems perspective, dynamic capabilities and practice theory have been used to help answer the question of how firms change their existing business models.

Using the activity systems perspective, Zott and Amit (2010) demonstrated how innovative business models can be developed through the design themes that describe the source of value creation (novelty, lock-in, complementarities and efficiency) and design elements that describe the architecture (content, structure and governance). This work, however, overlooks value capture which limits the explanation of the advocated system’s view (holistic). Moreover, Chatterjee (2013) used this perspective to reveal that firms can design innovative business models that translate value capture logic to core objectives, which can be delivered through the activity system.

Dynamic capability perspective frames business model innovation as an initial experiment followed by continuous revision, adaptation and fine-tuning based on trial-and-error learning ( Sosna et al. , 2010 ). Using this perspective, Demil and Lecocq (2010) showed that “dynamic consistency” is a capability that allows firms to sustain their performance while innovating their business models through voluntary and emergent changes. Also, Mezger (2014) conceptualised business model innovation as a distinct dynamic capability. He argued that this capability is the firm’s capacity to sense opportunities, seize them through the development of valuable and unique business models, and accordingly reconfigure the firms’ competences and resources. Using aspects of practice theory, Mason and Spring (2011) looked at business model innovation in the recorded sound industry and found that it can be achieved through various combinations of managerial practices.

Static and transformational approaches have been used to depict business models ( Demil and Lecocq, 2010 ). The former refers to viewing business models as constituting core elements that influence business performance at a particular point in time. This approach offers a snapshot of the business model elements and how they are assembled, which can help in understanding and communicating a business model (e.g. Eyring et al. , 2011 ; Mason and Spring, 2011 ; Yunus et al. , 2010). The latter, however, focuses on innovation and how to address the changes in business models over time (e.g. Sinfield et al. , 2012 ; Girotra and Netessine, 2014 ; Landau et al. , 2016 ). Some researchers have identified the core elements of business models ex ante (e.g. Demil and Lecocq, 2010 ; Wu et al. , 2010 ; Huarng, 2013 ; Dmitriev et al. , 2014 ), while others argued that considering a priori elements can be restrictive (e.g. Casadesus-Masanell and Ricart, 2010 ). Unsurprisingly, some researchers found a middle ground where elements are loosely defined allowing flexibility in depicting business models (e.g. Zott and Amit, 2010 ; Sinfield et al. , 2012 ; Kiron et al. , 2013 ).

Prior to 2010, conceptual frameworks focused on the business model concept in general (e.g. Chesbrough and Rosenbloom, 2002 ; Osterwalder et al. , 2005 ; Shafer et al. , 2005 ) apart from Johnson et al. ’s (2008 ), which is one of the early contributions to business model innovation. To determine whether a change in existing business model is necessary, Johnson et al. (2008) suggested three steps: “Identify an important unmet job a target customer needs done; blueprint a model that can accomplish that job profitably for a price the customer is willing to pay; and carefully implement and evolve the model by testing essential assumptions and adjusting as you learn” ( Eyring et al. , 2011 , p. 90). Although several frameworks have been developed since then, our understanding of business model innovation is still limited due to the static nature of the majority of these frameworks. Some representations ignore the elements and/or activities where alternative business models can be explored (e.g. Sinfield et al. , 2012 ; Chatterjee, 2013 ; Huarng, 2013 ; Morris et al. , 2013 ; Dmitriev et al. , 2014 ; Girotra and Netessine, 2014 ). Other frameworks ignore value proposition (e.g. Zott and Amit, 2010 ), ignore value creation (e.g. Dmitriev et al. , 2014 ; Michel, 2014 ) and/or ignore value capture (e.g. Mason and Spring, 2011 ; Sorescu et al. , 2011 ; Storbacka, 2011 ). Some conceptualisations do not identify who is responsible for the innovation (e.g. Casadesus-Masanell and Ricart, 2010 ; Sinfield et al. , 2012 ; Chatterjee, 2013 ; Kiron et al. , 2013 ). Synthesising the different contributions into a theoretical framework of business model innovation will enable a better understanding of how firms undertake business model innovation.

4. Business model innovation framework

Our framework ( Figure 2 ) integrates all the elements where alternative business models can be explored. This framework does not claim that the listed elements are definitive for high-performing business models, but is an attempt to outline the elements associated with business model innovation. This framework builds on the previous work of Johnson et al. (2008) and Zott and Amit (2010) by signifying the elements associated with business model innovation. Unlike previous frameworks that mainly consider the constituting elements of business models, this framework focuses on areas of innovation where alternative business models can be explored. Moreover, this is not a static view of the constituting elements of a business model, but rather a view enabling firms to explore alternative business models by continually refining these elements. Arrows in the framework indicate the continuous interaction of business model elements. This framework consists of 4 areas of innovation and 16 elements (more details are shown in Table IV ). Each will be discussed below.

4.1 Value proposition

The first area of innovation refers to elements associated with answering the “Why” questions. While most of the previously established models in the literature include at least one of the value proposition elements (e.g. Brea-Solís et al. , 2015 ; Christensen et al. , 2016 ), other frameworks included two elements (e.g. Dahan et al. , 2010 ; Cortimiglia et al. , 2016 ) and three elements (e.g. Eyring et al. , 2011 ; Sinfield et al. , 2012 ). These elements include rethinking what a company sells, exploring new customer needs, acquiring target customers and determining whether the benefits offered are perceived by customers. Modern organisations are highly concerned with innovation relating to value proposition in order to attract and retain a large portion of their customer base ( Al-Debei and Avison, 2010 ). Developing new business models usually starts with articulating a new customer value proposition ( Eyring et al. , 2011 ). According to Sinfield et al. (2012) , firms are encouraged to explore various alternatives of core offering in more depth by examining type of offering (product or service), its features (custom or off-the-shelf), offered benefits (tangible or intangible), brand (generic or branded) and lifetime of the offering (consumable or durable).

In order to exploit the “middle market” in emerging economies, Eyring et al. (2011) suggested that companies need to design new business models that aim to meet unsatisfied needs and evolve these models by continually testing assumptions and making adjustments. To uncover unmet needs, Eyring et al. (2011) suggested answering four questions: what are customers doing with the offering? What alternative offerings consumers buy? What jobs consumers are satisfying poorly? and what consumers are trying to accomplish with existing offerings? Furthermore, Baden-Fuller and Haefliger (2013) made a distinction between customers and users in two-sided platforms, where users search for products online, and customers (firms) place ads to attract users. They also made a distinction between “pre-designed (scale) based offerings” and “project based offerings”. While the former focuses on “one-size-fits-all”, the latter focuses on specific client solving specific problem.

Established firms entering emerging markets should identify unmet needs “the job to be done” rather than extending their geographical base for existing offerings ( Eyring et al. , 2011 ). Because customers in these markets cannot afford the cheapest of the high-end offerings, firms with innovative business models that meet these customers’ needs affordably will have opportunities for growth ( Eyring et al. , 2011 ). Moreover, secondary business model innovation has been advocated by Wu et al. (2010) as a way for latecomer firms to create and capture value from disruptive technologies in emerging markets. This can be achieved through tailoring the original business model to fit price-sensitive mass customers by articulating a value proposition that is attractive for local customers.

4.2 Operational value

The second area of innovation focuses on elements associated with answering the “What” questions. Many of the established frameworks included either one element (e.g. Sinfield et al. , 2012 ; Taran et al. , 2015 ), two elements (e.g. Mason and Spring, 2011 ; Dmitriev et al. , 2014 ). However, very few included three or more elements (e.g. Mehrizi and Lashkarbolouki, 2016 ; Cortimiglia et al. , 2016 ). These elements include configuring key assets and sequencing activities to deliver the value proposition, exposing the various means by which a company reaches out to customers, and establishing links with key partners and suppliers. Focusing on value creation, Zott and Amit (2010) argued that business model innovation can be achieved through reorganising activities to reduce transaction costs. However, Al-Debei and Avison (2010) argued that innovation relating to this dimension can be achieved through resource configuration, which demonstrates a firm’s ability to integrate various assets in a way that delivers its value proposition. Cavalcante et al. (2011) proposed four ways to change business models: business model creation, extension, revision and termination by creating or adding new processes, and changing or terminating existing processes.

Western firms have had difficulty competing in emerging markets due to importing their existing business models with unchanged operating model ( Eyring et al. , 2011 ). Alternative business models can be uncovered when firms explore the different roles they might play in the industry value chain ( Sinfield et al. , 2012 ). Al-Debei and Avison (2010) suggested achieving this through answering questions such as: what is the position of our firm in the value system? and what mode of collaboration (open or close) would we choose to reach out in a business network? Dahan et al. (2010) found cross-sector partnerships as a way to co-create new multi-organisational business models. They argued that multinational enterprises (MNEs) can collaborate with nongovernmental organisations (NGOs) to create products/or services that neither can create on their own. Collaboration allows access to resources that firms would otherwise need to solely develop or purchase ( Yunus et al. , 2010 ). According to Wu et al. (2010) , secondary business model innovation can be achieved when latecomer firms fully utilise strategic partners’ complementary assets to overcome their latecomer disadvantages and build a unique value network specific to emerging economies context.

4.3 Human capital

The third area of innovation refers to elements associated with answering the “Who” questions. Most of the established frameworks in this field tend to focus less on human capital and include one element at most (e.g. Wu et al. , 2010 ; Kohler, 2015 ). However, our framework highlights four elements, which include experimenting with new ways of doing business, tapping into the skills and competencies needed for the new business model through motivating and involving individuals in the innovation process. According to Belenzon and Schankerman (2015) , “the ability to tap into a pool of talent is strongly related to the specific business model chosen by managers” (p. 795). They claimed that managers can strategically influence individuals’ contributions and their impact on project performance.

Organisational learning can be maximised though continuous experimentation and making changes when actions result in failure ( Yunus et al. , 2010 ). Challenging and questioning the existing rules and assumptions and imagining new ways of doing business will help develop new business models. Another essential element of business model design is governance, which refers to who performs the activities ( Zott and Amit, 2010 ). According to Sorescu et al. (2011) , innovation in retail business models can occur as a result of changes in the level of participation by actors engaged in performing the activities. An essential element of retailing governance is the incentive structure or the mechanisms that motivate those involved in carrying out their roles to meet customer demands ( Sorescu et al. , 2011 ). For example, discount retailers tend to establish different compensation and incentive policies ( Brea-Solís et al. , 2015 ). Revising the incentive system can have a major impact on new ventures’ performance by aligning organisational goals at each stage of growth ( Roberge, 2015 ). Zott and Amit (2010) argued that alternative business models can be explored through adopting innovative governance or changing one or more parties that perform any activities. Sinfield et al. (2012) suggested that business model innovation only requires time from a small team over a short period of time to move a company beyond incremental improvements and generate new opportunities for growth. This is supported by Michel’s (2014) finding that cross-functional teams were able to quickly achieve business model innovation in workshops through deriving new ways to capture value.

4.4 Financial value

The final area of innovation focuses on elements associated with answering the “How” questions. Previously developed frameworks tend to prioritise this area of innovation by three elements (e.g. Eyring et al. , 2011 ; Huang et al. , 2013 ), and in one instance four elements (e.g. Yunus et al. , 2010 ). These elements include activities linked with how to capture value through revenue streams, changing the price-setting mechanisms, and assessing the financial viability and profitability of a business. According to Demil and Lecocq (2010) , changes in cost and/or revenue structures are the consequences of both continuous and radical changes. They also argued that costs relate to different activities run by organisations to acquire, integrate, combine or develop resources. Michel (2014) suggested that alternative business models can be explored through: changing the price-setting mechanism, changing the payer, and changing the price carrier. Different innovation forms are associated with each of these categories.

Business model innovation can be achieved through exploring new ways to generate cash flows ( Sorescu et al. , 2011 ), where the organisation has to consider (and potentially change) when the money is collected: prior to the sale, at the point of sale, or after the sale ( Baden-Fuller and Haefliger, 2013 ). Furthermore, Demil and Lecocq (2010) suggested that changes in business models affect margins. This is apparent in the retail business models, which generate more profit through business model innovation compared to other types of innovation ( Sorescu et al. , 2011 ).

5. Ways to change business models

From reviewing the recent developments in the business model literature, alternative business models can be explored through modifying a single business model element, altering multiple elements simultaneously and/or changing the interactions between elements of a business model.

Changing one of the business model elements (i.e. content, structure or governance) is enough to achieve business model innovation ( Amit and Zott, 2012 ). This means that firms can have a new activity system by performing only one new activity. However, Amit and Zott (2012) clearly outlined a systemic view of business models which entails a holistic change. This is evident from Demil and Lecocq’s (2010) work suggesting that the study of business model innovation should not focus on isolated activities since changing a core element will not only impact other elements but also the interactions between these elements.

Another way to change business models is through altering multiple business model elements simultaneously. Kiron et al. (2013) found that companies combining target customers with value chain innovations and changing one or two other elements of their business models tend to profit from their sustainability activities. They also found that firms changing three to four elements of their business models tend to profit more from their sustainability activities compared to those changing only one element. Moreover, Dahan et al. (2010) found that a new business model was developed as a result of MNEs and NGOs collaboration by redefining value proposition, target customers, governance of activities and distribution channels. Companies can explore multiple combinations by listing different business model options they could undertake (desirable, discussable and unthinkable) and evaluate new combinations that would not have been considered otherwise ( Sinfield et al. , 2012 ).

Changing business models is argued to be demanding as it requires a systemic and holistic view ( Amit and Zott, 2012 ) by considering the relationships between core business model elements ( Demil and Lecocq, 2010 ). As mentioned earlier, changing one element will not only impact other elements but also the interactions between these elements. A firm’s resources and competencies, value proposition and organisational system are continuously interacting and this will in turn impact business performance either positively or negatively ( Demil and Lecocq, 2010 ). According to Zott and Amit (2010) , innovative business models can be developed through linking activities in a novel way that generates more value. They argued that alternative business models can be explored by configuring business model design elements (e.g. governance) and connecting them to distinct themes (e.g. novelty). Supporting this, Eyring et al. (2011) suggested that core business model elements need to be integrated in order to create and capture value ( Eyring et al. , 2011 ).

6. Discussion and future research directions

From the above synthesis of the recent development in the literature, several gaps remain unfilled. To advance the literature, possible future research directions will be discussed in relation to approaches, degrees and mechanisms of business model innovation.

6.1 Approaches of business model innovation

Experimentation, open innovation and disruption have been advocated as approaches to business model innovation. Experimentation has been emphasised as a way to exploit opportunities and develop alternative business models before committing additional investments ( McGrath, 2010 ). Several approaches have been developed to assist in business model experimentation including mapping approach, discovery-driven planning and trail-and-error learning ( Chesbrough, 2010 ; McGrath, 2010 ; Sosna et al. , 2010 ; Andries and Debackere, 2013 ). Little is known about the effectiveness of these approaches. It will be worth investigating which elements of the business model innovation framework are more susceptible to experimentation and which elements should be held unchanged. Although business model innovation tends to be characterised with failure ( Christensen et al. , 2016 ), not much has been established on failing business models. It is interesting to explore how firms determine a failing business model and what organisational processes exist (if any) to evaluate and discard these failed business models. Empirical studies could examine which elements of business model innovation framework are associated with failing business models.

Another way to develop alternative business models is through open innovation. Although different categories of open business models have been identified by researchers (e.g. Frankenberger et al. , 2014 ; Taran et al. , 2015 ; Kortmann and Piller, 2016 ), their effectiveness is yet to be established. Further research is needed to examine when can a firm open and/or close element(s) of the business model innovation framework. Future studies could also examine the characteristics of open and/or close business models.

In responding to disruptive business models, how companies extend their existing business model, introduce additional business model(s) and/or replace their existing business model altogether remains underexplored. Future research is needed to unravel the strategies deployed by firms to extend their existing business models as a response to disruptive business models. In introducing additional business models, Markides (2013) suggested that a company will be presented with several options to manage the two businesses at the same time: create a completely separate business unit, integrate the two business models from the beginning or integrate the second business model after a certain period of time. Finding the balance between separation and integration is of vital importance. Further research could identify which of these choices are most common among successful firms introducing additional business models, how is the balance between integration and separation achieved, and which choice(s) prove more profitable. Moreover, very little is known on how firms replace their existing business model. Longitudinal studies could provide insights into how a firm adopts an alternative model and discard the old business model over time. It may also be worth examining the factors associated with the adoption of business model innovation as a response to disruptive business models. Moreover, new developments in digital technologies such as blockchain, Internet of Things and artificial intelligence are disrupting existing business models and providing firms with alternative avenues to create new business models. Thus far, very little is known on digital business models, the nature of their disruption, and how firms create digital business models and make them disruptive. Future research is needed to fill these important gaps in our knowledge.

6.2 Degrees of business model innovation

Business models can be developed through varying degrees of innovation from an evolutionary process of continuous fine-tuning to a revolutionary process of replacing existing business models. Recent research shows that survival of firms is dependent on the degree of their business model innovation ( Velu, 2015, 2016 ). This review classifies these degrees of innovation into modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements of the business model innovation framework.

In changing a single element, further research is needed to examine which business model element(s) is (are) associated with business model innovation. It is not clear whether firms intentionally make changes to a single element when carrying out business model innovation or stumble at it when experimenting with new ways of doing things. It may also be worth investigating the entry (or starting) points in the innovation process. There is no consensus in the literature on which element do companies start with when carrying out their business model innovation. While some studies suggest starting with the value proposition ( Eyring et al. , 2011 ; Landau et al. , 2016 ), others suggest starting the innovation process with identifying risks in the value chain ( Girotra and Netessine, 2011 ). Dmitriev et al. (2014) suggested two entry points, namely, value proposition and target customers. In commercialising innovations, the former refers to technology-push innovation while the latter refers to market-pull innovation. Also, it is not clear whether the entry point is the same as the single element associated with changing the business model. Further research can explore the different paths to business model innovation by identifying the entry point and subsequent changes needed to achieve business model innovation.

There is little guidance in the literature on how firms change multiple business model elements simultaneously. Landau et al. (2016) claimed that firms entering emerging markets tend to focus on adjusting specific business model components. It is unclear which elements need configuring, combining and/or integrating to achieve a company’s value proposition. Furthermore, the question of which elements can be “bought” on the market or internally “implemented” and their interplay remains unanswered ( DaSilva and Trkman, 2014 ). Casadesus-Masanell and Ricart (2010) argued that “[…] there is (as yet) no agreement as to the distinctive features of superior business models” (p. 196). Further research is needed to explore these distinctive elements of high-performing business models.

In changing the interactions between business model elements, further research is needed to explore how these elements are linked and what interactions’ changes are necessary to achieve business model innovation. Moreover, the question of how firms sequence these elements remains poorly understood. Future research can explore the synergies created over time between these elements. According to Dmitriev et al. (2014) , we need to improve our understanding of the connective mechanisms and dynamics involved in business model development. More work is needed to explore the different modalities of interdependencies among these elements and empirically testing such interdependencies and their effect on business performance ( Sorescu et al. , 2011 ).

It is surprising that the link between business model innovation and organisational performance has rarely been examined. Changing business models has been found to negatively influence business performance even if it is temporary ( McNamara et al. , 2013 ; Visnjic et al. , 2016 ). Contrary to this, evidence show that modifying business models is positively associated with organisational performance ( Cucculelli and Bettinelli, 2015 ). Empirical research is needed to operationalise the various degrees of innovation in business models and examine their link to organisational performance. Longitudinal studies can also be used to explore this association since it may be the case that business model innovation has a negative influence on performance in the short run and that may change subsequently. Moreover, it is not clear whether high-performing firms change their business models or innovation in business models is a result from superior performance ( Sorescu et al. , 2011 ). Further studies are needed to determine the direction of causality. Another link that is worth exploring is business model innovation and social value, which has only been explored in a few studies looking at social business models (e.g. Yunus et al. , 2010 ; Wilson and Post, 2013 ). Further research is needed to examine this link and possibly examine both financial and non-financial business performance.

6.3 Mechanisms of business model innovation

Although we know more about how firms define value proposition, create and capture value ( Landau et al. , 2016 ; Velu and Jacob, 2014 ), what remains as a blind spot is the mechanism of business model innovation. This is due to the fact that much of the literature seems to focus on value creation. To better understand the various mechanisms of business model innovation, future studies must integrate value proposition, value creation and value capture elements. Empirical studies could use the business model innovation framework to examine the various mechanisms of business model innovation. Also, the literature lacks the integration of internal and external perspectives of business model innovation. Very few studies look at the external drivers of business model innovation and the associated internal changes. The external drivers are referred to as “emerging changes”, which are usually beyond manager’s control ( Demil and Lecocq, 2010 ). Inconclusive findings exist as to how firms develop innovative business models in response to changes in the external environment. Future studies could examine the external factors associated with the changes in the business model innovation framework. Active and reactive responses need to be explored not only to understand the external influences, but also what business model changes are necessary for such responses. A better understanding of the mechanisms of business model innovation can be achieved by not only exploring the external drivers, but also linking them to specific internal changes. Although earlier contributions linking studies to established theories such as the resource-based view, transaction cost economics, activity systems perspective, dynamic capabilities and practice theory have proven to be vital in advancing the literature, developing a theory that elaborates on the antecedents, consequences and different facets of business model innovation is still needed ( Sorescu et al. , 2011 ). Theory can be advanced by depicting the mechanisms of business model innovation through the integration of both internal and external perspectives. Also, we call for more empirical work to uncover these mechanisms and provide managers with the necessary insights to carry out business model innovation.

7. Conclusions

The aim of this review was to explore how firms approach business model innovation. The current literature suggests that business model innovation approaches can either be evolutionary or revolutionary. However, the evidence reviewed points to a more complex picture beyond the simple binary approach, in that, firms can explore alternative business models through experimentation, open and disruptive innovations. Moreover, the evidence highlights further complexity to these approaches as we find that they are in fact a spectrum of various degrees of innovation ranging from modifying a single element, altering multiple elements simultaneously, to changing the interactions between elements of the business model innovation framework. This framework was developed as a navigation map for managers and researchers interested in how to change existing business models. It highlights the key areas of innovation, namely, value proposition, operational value, human capital and financial value. Researchers interested in this area can explore and examine the different paths firms can undertake to change their business models. Although this review pinpoints the different avenues for firm to undertake business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain underexplored.

The evolution of business model literature (pre-2000 to 2016)

Business model innovation framework

Previous reviews of business model literature

Reviewed papers and their subject fields

Source of our sample

Business model innovation areas and elements

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REVIEW article

The digital transformation of business model innovation: a structured literature review.

• 1 Department of Management, Ca’ Foscari University of Venice, Venice, Italy
• 2 Department of Management, Lincoln International Business School, University of Lincoln, Lincoln, United Kingdom

This paper has a two-fold aim: to analyze the development of the digital transformation field, and to understand the impact of digital technologies on business model innovation (BMI) through a structured review of the literature. The results of this research reveal that the field of digital transformation is still developing, with growing interest from researchers since 2014. Results show a need for research in developing countries and for more collaboration between researchers and practitioners. The review highlights that the field is fragmented among disruptive technologies, shared platforms and ecosystems, and new enabling technologies. We conclude that digital transformation has impacted value creation, delivery, and capture in almost every industry. These impacts have led to the employment of a variety of new business models, such as those for frugal innovation and the circular economy.

Introduction

The phenomenon of digital transformation (DT) has become very popular in recent years ( Fitzgerald et al., 2013 ; Kane et al., 2015 ). Digital transformation or “digitalization” is “the integration of digital technologies into business processes” ( Liu et al., 2011 , p. 1728). The exploitation of digital technologies offers opportunities to integrate products and services across functional, organizational, and geographic boundaries ( Sebastian et al., 2017 ). As a consequence, these digital technologies increase the pace of change and lead to significant transformation in a number of industries ( Bharadwaj et al., 2013 ; Ghezzi et al., 2015 ), since they have the “power” to disrupt the status quo and can be used to drive technological change ( Bharadwaj et al., 2013 ). Digital technologies have revolutionized the way industries operate ( Dal Mas et al., 2020c ), introducing the concept of “Industry 4.0” or the “smart factory” ( Lasi et al., 2014 ). Digital platforms have created a new way of operating for companies and organizations in a “business ecosystem” ( Presch et al., 2020 ), which has led to changing dynamics in value networks ( Gray et al., 2013 ). Digital technologies have substantially transformed the business ( Ng and Wakenshaw, 2017 ) and society, bringing fundamental changes through the new emerging approaches of the circular and sharing economy.

For strategy researchers, the three characteristics of digital technologies, namely, digital artifacts, digital platforms, and digital infrastructures ( Nambisan, 2017 ) create opportunities for a layered modular architecture and present to firms the strategic choice of following a digital innovation strategy ( Yoo et al., 2010 ). This has drastically changed the nature of strategizing, since many digitized products offer new features and functions by integrating digital components into physical products (digital artifacts), and can simultaneously be a product and a platform (with related ecosystem). In this regard, the literature has coined the term “platfirms” to define those companies relying their business models (BMs) on a web platform ( Presch et al., 2020 ). Moreover, digital infrastructures like data analytics, cloud computing, and three-dimensional (3D) printing are providing new tools for rapid scaling ( Huang et al., 2017 ). Therefore, digitalization blurs the boundaries between technology and management, providing new tools and concepts of the digital environment that are changing dramatically the way firms face new managerial challenges, innovate, develop relationships, and conduct business ( Verma et al., 2012 ; Bresciani et al., 2018 ).

The new digital environment requires firms to use digital technologies and platforms for data collection, integration, and utilization, to adapt to platform economy ( Petrakaki et al., 2018 ) and to find growth opportunities to remain competitive ( Subramanian et al., 2011 ). Besides, recent research shows that firms utilize external venturing modes (e.g., startup programs and accelerators; Bagnoli et al., 2020 ) to develop dynamic capabilities ( Enkel and Sagmeister, 2020 ). Digitalization is therefore seen as an entrepreneurial process ( Henfridsson and Yoo, 2014 ; Autio et al., 2018 ) where firms in pursuit of digital transformation render formerly successful BMs obsolete ( Tongur and Engwall, 2014 ; Kiel et al., 2017 ) by implementing business model innovation (BMI), which is revolutionizing many industries. Indeed, the literature suggests that in designing an appropriate BM, it can be possible to benefit from the potential embedded value in innovation ( Chesbrough and Rosenbloom, 2002 ; Björkdahl, 2009 ). For instance, firms adopting digital technologies consider data streams to be of paramount importance and assign to them a central role in supporting their digital transformation strategies ( Zott et al., 2011 ), in contrast to traditional BMs frameworks ( Pigni et al., 2016 ). For this reason, digital technologies inherently link to strategic changes in BMs ( Sebastian et al., 2017 ) and consequently, the development of new BMs ( Hess et al., 2016 ).

In the digital context, BMs have become a new unit of analysis ( Zott et al., 2011 ) to examine the changing effects of digital technologies on the way firms produce and deliver value through BMI. As the literature suggests, BMI provides opportunities in capturing profits in a system of networked activities ( Zott and Amit, 2010 ; Amit and Zott, 2012 ), and in enhancing firm performance ( Foss and Saebi, 2017 ). The role of the BM is essential in identifying the crucial aspects behind a digital strategy. Indeed, it helps firms in applying the digital lens to innovate their BM to create an appropriate new value ( Berman, 2012 ). However, this process is still evolving ( Ferreira et al., 2019 ) and many questions remain unanswered for entrepreneurs and managers, especially in relation to the integration of digital transformation strategies and business transformation strategies ( Matt et al., 2015 ), in order to realize the “digital business strategy” ( Bharadwaj et al., 2013 ). Indeed, a recent study ( Atluri et al., 2018 ) argues that digital transformation and the opportunities it creates for BMs in every sector are still in the beginning.

Given the increased interest in investigating the relationship between digital transformation and BMI in academia and its importance for practice as well, the purpose of this paper is to understand better what we currently know about the digital transformation of BMI. Specifically, our aim is to review and critique the state of research in the digital transformation of BMI literature, provide a comprehensive, holistic overview of the digital transformation of BMI covering many perspectives, and outline avenues for further research. We adopt Teece (2018) definition of BMs as “mechanisms for creating, delivering, and capturing value” to reflect the value proposition, target segments, value chain organizations, and revenue capture components ( Foss and Saebi, 2017 ). For BMI, we apply the definition by Foss and Saebi (2017) : “designed, novel, and non-trivial changes to the key elements of the business model innovation and/or the architecture linking these elements.” According to this definition, BMI involves changes in the individual components and in the overall architecture of the BM.

From a theoretical perspective, this study contributes to these digitally-enabled types of BMIs, which make the emergence of BMs a promising unit of analysis for undertaking innovation strategies. It also responds to the knowledge gap in the literature and enriches our understanding in the digital transformation of BMs ( Visnjic et al., 2016 ). In addition, the results of this study may help practitioners from a variety of industries who seek guidance to understand how digital transformation of BMI can be achieved through value creation and capture ( Casadesus-Masanell and Ricart, 2010 ). This study may help especially practitioners in incumbent firms, since digital transformation of their BMI is a highly complex process requiring a sequence of interdependent strategic decisions ( Aspara et al., 2013 ; Velu and Stiles, 2013 ).

The paper is organized as follows: the next section explains the method of data collection and analysis used for the structured literature review. This is followed by the results of the study and answering the three research questions addressed in the methodology. The following section focuses on discussing the existing gaps in the literature and avenues for further research. The final section of the paper discusses the conclusions, contribution, and implications for theory and practice.

Methodology

This paper adopts a structured literature review. According to Massaro et al. (2016) , a structured literature review is “a method for studying a corpus of scholarly literature, to develop insights, critical reflections, future research paths, and research questions.” The structured literature review was adopted because “it is based on a positivist, quantitative, and form-oriented content analysis for reviewing literature” ( Massaro et al., 2016 ). This method follows a 10-step process that enables the researcher to “potentially develop more informed and relevant research paths and questions” ( Massaro et al., 2016 ), advancing theory, which is the objective of the literature review ( Webster and Watson, 2002 ).

We wrote a literature review protocol to guide us during the process of reviewing the literature. The protocol-driven approach offers researchers a framework to select, analyze, and assess papers with the aim of ensuring robust and defensible results through reliability and repeatability ( Massaro et al., 2016 ). In the further step, we defined the research questions that aim to bring new insights from the literature review. We identified the following research questions in the protocol document:

RQ1. How has the field of digital transformation developed over time?

RQ2. What is the focus of the literature on the digital transformation of BMI?

RQ3. How has digital transformation facilitated BMI in the literature?

The next step was to determine the type of studies to consider for the review. We decided on the keywords to use to search for articles and the criteria for article selection. Following the keywords used in previous studies in the digital transformation literature, we decided to search using “digital transformation,” “digital disruption,” “technolog* change,” “organis* change,” “disrupt*” and “business model.” As the specific aim of this study is to offer a holistic understanding of the digital transformation of BMI, we purposefully focused on scholarly empirical research that provides insights into how digital transformation is impacting the innovation of BMs. Nodes for coding were determined based on previous systematic literature review (SLR) studies ( Massaro et al., 2015 ; Dal Mas et al., 2019 , 2020a ). According to these studies, nodes examine information related to authors, the time distribution of publications, country of research, the focus of the paper and methodology. We added nodes about industry sectors, the disciplines of the studies, theories used, and potential impact on the value creation, delivery, and capturing process. These nodes were added to gain deeper insights into the development of the field and suggest implications for further advancement. These nodes were integrated into a framework that served for the coding of the papers and the analysis of the results. The framework, with a description of parameters, is provided in Table 1 .

Table 1 . Classifying framework for literature review.

After identifying the keywords and the framework for the study, we started the collection and selection of papers in a multi-staged process. Firstly, we searched in the Scopus database with the defined keywords in the protocol. This first search revealed 215 publications. In a second step, in order to control the quality of articles, we restricted the search to peer-reviewed journals in the Business and Management category that were ranked 3, 4, and 4* in ABS evaluation. With this additional restriction, we did not take into consideration book chapters, book reviews, and conference articles. In this second search, we, therefore, found articles published in peer-reviewed journals from 1996 to 2020, which reduced the number of publications to 126. After collecting all the articles, each paper was checked for the inclusion of keywords in the title, abstract, and keywords, in order to ensure that the articles fit the research objective of the study. The criteria for article inclusion required the existence of string words about both digital transformation and BMs, which were connected by the Boolean operator AND. When screening publications, we found only a few articles about digital transformation, which were published before 2014. Other articles talked about digital transformation or disruptive technologies, but not about the impact or the connection with BMI. The articles which were not focused on both disruptive technologies and BMI were excluded. At the end of the process, 54 articles were excluded, and the final sample of publications included 72 research articles.

We used the NVivo12 software package for the analysis of the final list of papers. The folder with the selected papers was imported into the software. Each article was coded based on the same nodes as specified in the framework in order to reach the aim of the SLR and avoid researcher bias. We created nodes that were related to the bibliographical information of articles, methodology, discipline, the focus of the paper, and theoretical perspectives. These nodes were used to answer the first two research questions of our study. We created another node for the third research question, to code all the impacts of new enabling technologies on BMI.

After having coded all the papers, following the steps of the protocol, the research group shared the coding project among the members in order to verify that the coding complied with the research questions and the framework of the study and to ensure inter-code reliability. Next, analysis of the dataset developed insights and critique in the field of the digital transformation of BMI. Part of the work in this study was intended to advance the knowledge in the field of digital transformation, by highlighting gaps, identifying new avenues for research, and raising new research questions.

RQ1: How Has the Field of Digital Transformation in BMI Developed Over Time?

This section provides an overview of the development in the field of the digital transformation of BMI. It reports the findings related to the descriptive features of this emerging field of research.

Author Demographics

The list of analyzed articles shows that there does not seem to be any author domination in the field in terms of the number of publications. Ghezzi and Li are the only authors who published three papers. Several scholars contributed to the research field with two articles each (Bogers, Bose, Frank, Frattini, Gupta, Mangematin, and Wang). All the other authors have published only once in the field of digital transformation of BMI. Most of the articles are co-authored. The analysis of the 198 authors of the 72 publications reveals that most of the articles were written by academic scholars. There are no articles written mainly by practitioners, and collaboration between practitioners and scholars comprised of just a few of the publications. More specifically, these collaborations were carried out in very new topics such as platform-based ecosystems and intelligent goods in closed-loop systems. This implies a close relationship between the research field and practitioners, despite the wide practitioner-academic divide. This divide can result from paywalls in publications, and would be helpful to hold common conferences, encourage more engagement with practitioners, and provide open-access journals to overcome it. Otherwise, the growing divide between academics and practitioners results in field fragmentation, as subgroups will form on both sides of the divide. Greater collaboration between practitioners and academics is thus needed in the future to shape this field of study ( Serenko et al., 2010 ). These demographics also suggest that four authors in this field of research have remained focused on exploring further aspects of BMI driven by digital transformation. For instance, Ghezzi published about strategy making and BM design in dynamic contexts in 2015 in Technological Forecasting and Social Change, and in 2017, he published in the Journal of Business Research. This trend of republishing after 2 years in a different journal from the first is also demonstrated in articles by Bogers (2016) . The lack of specialization by researchers might also fragment the field further. In the future, more scholars should remain focused on further exploring other aspects of digital transformation impacts on BMI.

Time Distribution of Published Articles

The analysis shows that the first article about the digital transformation of BMs was published in 2009. This article was part of a case study of Kodak ( Lucas and Goh, 2009 ), which missed the digital photography revolution when faced by disruptive technology. As can be seen from Figure 1 below, only five papers were published within the next 4 years (until 2013) after the first paper was published. These first papers dealt mostly with a general understanding of the opportunities and barriers created by disruptive technologies on BMI ( Chesbrough, 2010 ), such as, for example, in the case of latecomers that can capture value through a secondary BM ( Wu et al., 2010 ). Publication on the topic remains poor and scattered until 2013 and research continues to highlight the importance of technological discontinuities in the creation of disruptive BMs and the challenge of dominant industry logics ( Sabatier et al., 2012 ). Only Simmons et al. (2013) studied the role of marketing activities in inscribing value on BMI during the commercialization of disruptive digital innovations in industrial projects. Interesting enough, the production of knowledge is particularly active in 2020, which, at the time of the research, saw the articles published in Scopus as of mid-September. Twenty-one meaningful papers were listed in 2020, considering that the year was not finished yet and several more might be in press, forthcoming, or still to be indexed.

Figure 1 . Journals of the selected articles.

In the past 3 years, there has been a growing number of articles published in this field of enquiry, with 42 out of 72 articles published between 2018 and 2020. The greatest interest in publishing about the digital transformation of BMI was recent, where 53 articles (almost 74% of the total sample) were published since 2017. The gradual increase in publications reflects the need to carry out more research in this field, as the impacts and issues related to digital technologies become apparent in many industries. This is shown in articles published during 2014–2015, which try to explore the effects of digitization on incumbent BMs in more depth. Researchers investigated these effects in the publishing industry ( Øiestad and Bugge, 2014 ), and with a special interest in understanding organizational or sectoral lock-ins in creative industries ( Mangematin et al., 2014 ) and the newspaper industry ( Rothmann and Koch, 2014 ). To overcome the challenges of strategy formulation and implementation in dynamic industries, Ghezzi et al. (2015) suggest a framework for strategic making and BM design for disruptive change.

The analysis again reveals the practitioner-led nature of research in this field. As demonstrated above, the time distribution of the articles highlights the relevance of studies in the field. Over time there has been a continuous change in the researched topics, shifting from the impact of disruptive technology on incumbent BMs to the impact of digital technologies on the BMI of digital start-ups. This implies that the field shows characteristics of pragmatic science, where society benefits from the best combination between the relevance of the topic and the rigor of findings ( Anderson et al., 2001 ). The high concentration of the distribution of publications in recent years reveals both the importance of the topic and the increased interest of researchers in this novel field of enquiry. These insights from the analysis of the distribution of articles inform us about the nascent stage this field of enquiry, with rapid growth in 2014. Serenko et al. (2010) consider three indicators to define field maturity: co-authorship patterns, the role of practitioners, and enquiry methods. According to these indicators, we observe that the publication of multi-authored manuscripts increased after 2014, especially in 2016–2017. We further observe more collaboration with practitioners during the 2016–2018 period. In terms of enquiry methods, as a newly emerging scholarly domain, the articles mainly develop theoretical frameworks, revealing the early stage of the field.

Moreover, addressing the topic of the academic-practitioners divide ( Bartunek, 2007 ), the topic seems ideal as an opportunity to gather academics and professionals working together and create some exchange zones to foster a dialog ( Romme et al., 2015 ). While scholars struggle to find robust data to develop sound theories, managers are the ones who see the potential of disruptive digital technologies and their real-world applications, including new BMs.

Journal Title

We identified the journals in which these articles were published and their distribution in each journal ( Figure 2 ).

Figure 2 . Industry sectors analyzed in the selected articles.

Our analysis shows that a total of 22 journals were captured in this review of literature. The Technological Forecasting & Social Change journal takes the lead for the majority of articles published (23 articles, 32%). The three other journals with a higher number of publications than others are Journal of Business Research, California Management Review, and Technovation. These journals have published seven, six, and five articles, respectively, for a total of 18 articles (25%). The remaining articles were spread over the rest of the journals, and a diverse range of disciplines. This topic seems to be practitioner-led, and with greater relevance recently for businesses, policy makers, and society. This is demonstrated in the Technological Forecasting & Social Change journal, firstly by Sung (2018) , suggesting policy implications regarding Industry 4.0 in Korea. Jia et al. (2016) examine the commercialization efforts of a United Kingdom-based 3D printing technology provider to evaluate the financial viability of innovative BMs.

Country of Research

Part of our analysis was to identify and describe the geographical regions where studies have been conducted. Figure 3 gives a classification of the countries that have been studied in the field of digital transformation of BMI. The left side of the graph includes studies carried out in developed countries, and the right shows developing countries. The results show that most of the research in this field is conducted in developed countries, and within this, the digital transformation of BMI has been studied mostly in the United States and Germany. This concentration of research mainly in these two countries may be the result of governmental efforts, as in the case of German government support for Industry 4.0, or the European Union-funded DIGINOVA digital project for advancing innovation in digital making ( Potstada et al., 2016 ).

Figure 3 . Research methodology of the selected articles.

According to the analysis, other countries in Europe reflecting the same interest in researchers are the Netherlands, Italy, and the United Kingdom, with two publications in each country (except for the Netherlands, which accounts for three articles). In contrast, emerging and Far-East countries are very under-represented, with China publishing two papers, and India and United Arab Emirates with one article each. This implies that emerging and Far-East countries in general are either ignored or poorly analyzed, despite the presence of several digital firms (let us think about the giant multinational companies like Alibaba, Wechat, or Huawei in China). While there may be publications written in languages different than English or in books or journals not indexed on Scopus, more research is needed in these countries to define the boundaries of theorization in the digital transformation of BMI, which will lead to a better understanding of this phenomenon. As Ghezzi and Cavallo (2020) argue, generalization and the relevance of findings depend on the peculiarity of the context under examination. For this reason, a replication of research in other (mature) contexts should be carried out ( Ghezzi and Cavallo, 2020 ). This will overcome the problem of generalizability with a single geographic region ( Simmons et al., 2013 ).

Industry Sectors

In order to enhance our understanding of industry influences on the digital transformation of BMI, we classified the articles according to the industry sectors in which their empirical setting was based. As depicted in Figure 4 , the articles are based in 18 different specific industries, with several papers referring to multiple sectors together, or not identifying one defined field under investigation.

Figure 4 . Disciplines of the selected articles.

The results also indicate an almost equal spread of articles among industries, and that there is no concentration in only a handful of industry sectors. Nevertheless, we can identify two groups of industries that are represented by a higher number of articles: manufacturing (nine articles) and creative industries (six articles). A closer examination of these industries shows that the manufacturing industry mainly dealt with consumer goods manufacturing, while creative industry sectors were represented by the accommodation industry and digital game industry. Most remaining articles were spread across the broad range of industry sectors. The focus on only a few industries can be a limitation for the generalization of findings. There is a need to study other industries, such as design, architecture, advertizing, and the fashion industry ( Mangematin et al., 2014 ), which currently do not appear on our list.

Research Methods

Most studies conducted so far on the digital transformation of BMI have used an exploratory approach ( Figure 5 ).

Figure 5 . Main focus of the selected articles.

These studies aimed at achieving a first understanding of the phenomenon of digital transformation of BMI, which is indicated by the extensive use of qualitative research. This finding relates to the fact that digital transformation is a new phenomenon. Consistent with this, Li (2020) argues that we are facing a methodological challenge in the investigation of new emerging trends since these trends “are still at very early stages of development with limited empirical presence”. For this reason, the author suggests using new research methods such as research prototyping and fictional design.

Few longitudinal studies have been carried out. This creates a need for future longitudinal studies, which will help in better understanding the sharing economy and peer-to-peer platforms ( Akbar and Tracogna, 2018 ). The contributions of these studies mainly consist of offering frameworks and propositions derived from explorative research. There have been no further empirical studies to support or refute the suggested propositions. Few papers investigate the relationship between digital transformation and BMI following an explanatory methodology. A considerable number of papers (eight papers) are conceptual or theoretical viewpoints. These insights suggest that the field of research in the digital transformation of BMI has the potential to be restricted to a single paradigm. The absence of positivist research will prevent the wider acceptance and development of the field.

Disciplines

Most of the research is undertaken in the disciplines of technology and innovation management, general management and strategy, and entrepreneurship. Few studies are from the disciplines of economics, information systems, marketing, and operations ( Figure 6 ).

Figure 6 . Countries analyzed in the selected articles.

This might primarily be because the purpose of our study is too focused and bridges two different topics: digital transformation and MBI. The other reason might be these three disciplines are more concerned with the impact and implications of the phenomenon of DT. The dominance of only a few disciplines relates also to the journals that are interested in publishing on this topic. Since most of the articles have been published in Technological Forecasting & Social Change, California Management Review, the Journal of Business Research, and Technovation, this affects the disciplines that will be covered by research. The low presentation of articles focusing on operations and entrepreneurship is unexpected, however. This suggests that the field of digital transformation of BMI is fragmented between three major discipline areas, and the predominance of single-discipline research is noted. The fragmentation of the field has implications for the conceptualization and research methodology for the progression of the digital transformation of the BMI field.

RQ2: What Is the Focus of the Literature on the Digital Transformation of BMI?

The literature on digital transformation is dispersed between disruptive technologies, shared platforms and ecosystems, and new enabling technologies such as Big Data, the Internet of Things (IoT), Industry 4.0, Cloud computing, and digital fabrication (DF). Disruptive technologies in the literature refer to technologies that have the potential to introduce new product attributes, which could become a source of competitive advantage ( Christensen, 1997 ); while a platform is defined as “any combination of hardware and software that provides standards, interfaces, and rules that enable and allow providers of complements to add value and interact with each other and/or other users” ( Teece, 2018 ). Taken together, the platform innovator(s) and complementors constitute an ecosystem ( Teece, 2018 ).

The majority of research in this field (49 articles, 63%) has focused on understanding the impacts that new disruptive technologies have on industries, identifying the areas of transformation in activities, processes, and BMs. Only few articles focus on understanding how the process of transformation takes place by drawing on different disciplines and theories.

An analysis of articles about disruptive technologies reveals that in earlier years, the literature (2009–2010) was focused on the challenges and opportunities created for incumbent BMs by these technologies. Some of the articles focus on the challenges faced by incumbents when managing radical technological change. As Chesbrough (2010) notes, there are many “opportunities and barriers in business model innovations” from technological advances. For instance, the case study of Kodak identified organization structure and culture as playing a crucial role in overcoming core rigidities to create new value from disruptive technologies ( Lucas and Goh, 2009 ). Rothmann and Koch (2014) took a very divergent perspective, showing that the digital transformation of BMI fails when companies follow the same old strategic patterns and remain path-dependent. From 2013, focus shifted to ways to overcome these challenges. For example, Karimi and Walter (2016) argue that the adoption of a disruptive BM requires firms to give groups autonomy and allow risk-taking and proactiveness. Kapoor and Klueter (2013) suggested overcoming a firm’s inertia associated with prevailing incumbent BMs by investing in research and development through alliances and acquisitions.

Nevertheless, disruptive technologies bring opportunities to firms who understand how environmental changes necessitate BM modifications. Wirtz et al. (2010) argue that the Web 2.0 phenomenon, based on social networking, interaction orientation, user-added value, and customization/personalization serves as a value offering to traditional internet-based BMs (content, commerce, context, and connection). Another opportunity considered in the literature relates to the introduction of disruptive technologies from advanced economies into emerging economies through a second BMI by latecomer firms ( Wu et al., 2010 ). Firms can also use different tactics (compensating, enhancing, and coupling) to reconfigure their value propositions ( Bohnsack and Pinkse, 2017 ). Table 2 summarizes the challenges and opportunities of disruptive technologies, according to some of the contributions analyzed.

Table 2 . Challenges and opportunities of disruptive technologies.

The second most important topic analyzed, as shown in Figure 7 , focused on shared platforms or “platfirms” and ecosystems as new BMs for digital enterprises. Table 3 below summarizes the focus of some of these studies and their findings. We can see that shared platforms and ecosystems are a very recent focus, studied between 2017 and 2018, however, we note that the literature has addressed a number of broad issues which relate to an initial understanding of platforms, starting with their classification into five typologies ( Muñoz and Cohen, 2017 ), and the investigation of the role played by platforms in dealing with disruption ( Alberti-Alhtaybat et al., 2019 ) and BMI ( Gupta and Bose, 2019a ). Our results show that there is an important focus on financial aspects of platforms and ecosystems. For instance, Teece (2018) and Helfat and Raubitschek (2018) focus on aspects of profiting from innovation, while Khuntia et al. (2017) consider the relationship between the evolution of service offerings and the financial viability of platforms. Analysis of the data also indicates a focus on the managerial issues and success factors of these digital platforms. Since digital enterprises operate in a highly dynamic environment, lean startup approaches (LSAs) have been studied within the strategic agility context. LSAs can be employed as agile methods to enable digital entrepreneurs to innovate BMs ( Ghezzi and Cavallo, 2020 ). Piscicelli et al. (2018) identified the success factors of sharing platforms: the identification of a significant market friction, building of a critical mass of users before implementing a correct pricing level and structure, addressing the hurdles of competition and regulation, and positive interaction fostered between users.

Figure 7 . Time distribution of the selected articles.

Table 3 . Focus of literature on shared platforms and ecosystems.

The results shown in Figure 7 indicate that research is also led by recent arising interest in big data ( Urbinati et al., 2018 ), cloud computing ( Nieuwenhuis et al., 2018 ), and closed-loop systems in the circular economy ( Rajala et al., 2018 ). These new enabling technologies allow firms to apply new BMs in support of sustainability issues. The growing intelligence of goods generates novel BMs, which rely on the intelligence of ecosystems within the activities for resources, by shaping closed-loop systems ( Rajala et al., 2018 ). Firms are also engaging more in frugal innovations, allowing them to carry out resource-constrained innovations for emerging markets ( Winterhalter et al., 2017 ).

To conclude, this section develops insights regarding the focus of the literature. The literature that is focused on disruptive technologies advances disruptive innovation theory by proposing culture, organizational structure, and cognitive leadership intentions as important factors affecting company responses to disruptive innovation. However, there is still a missing link in understanding the moderating role of disruptive technologies, based on their digital infrastructure and this requires more research into the conditions and the extent of BM transformations ( Gupta and Bose, 2019a ). The literature also shows that shared platforms and ecosystems, as well as new enabling technologies, are a very recent focus. In contrast to articles about disruptive technologies that focus on challenges and opportunities, articles about shared platforms consider a broad number of issues from typologies to managerial and financial aspects. Nevertheless, the results show that few articles focus on one topic and the focus shifts quickly, leaving topics under-investigated. This finding highlights the need for more research on topics that are under-investigated and represented by only a few studies. The scattered nature of the field might affect the accumulation of knowledge, as studies do not focus on previous findings.

Theoretical Perspectives

Theory development is essential for the proper advancement of knowledge in any field of research ( Kuhn, 1970 ). To develop a better understanding of theoretical perspectives in the field of digital transformation of BMI, we analyzed the articles and determined whether a theoretical perspective was apparent in each. We further analyzed articles that reflected theoretical perspectives and identified whether the theory was an existing one or a new theory. The results of this analysis revealed that the majority of articles (47 articles, 65%) was not based on any discernible theory.

Of the articles with an apparent theoretical perspective, we observed that the majority had adopted theoretical perspectives. Recent contributions (e.g., Vendrell-Herrero et al., 2017; Akbar and Tracogna, 2018 ; Helfat and Raubitschek, 2018 ; Teece, 2018 ) have started questioning and seeking more theoretical frameworks in order to explain and understand the digital transformation of BMI. Interestingly, disruptive innovation theory ( Christensen, 1997 ) was the most popular with five contributions, and other theories were adopted only by single studies. The theory of disruptive innovation was initiated by Christensen (1997) to explain the replacement process of a mainstream innovation by innovations that are cheaper than those on the market and of inferior performance. In this dominant view within the field, which originates from a technological and innovation management perspective, DT is studied at an organizational and individual level of analysis. These researchers incorporate disruptive innovation theory in their studies to show how value generated from technology can be accelerated. For instance, the case study of Kodak ( Lucas and Goh, 2009 ) recognizes culture and organizational structure as crucial elements in creating new value when disruptive technologies are introduced in an industry. Osiyevskyy and Dewald (2015) concentrate on the strategic decisions of managers and argue that responding to ongoing disruption with experimentation depends on a leader’s explorative intentions.

More recent articles that relate the digital transformation of BMI to disruption theory concern topics based on managerial practices of inspiring and managing disruptive innovations in digital entrepreneurships, such as collaborative open foresight ( Wiener et al., 2018 ) and knowledge management ( Alberti-Alhtaybat et al., 2019 ). As Alberti-Alhtaybat et al. (2019) note about the logistic company Aramex that “current study seeks to illustrate their approach to logistics and their mindset regarding disruptive technologies, which is reflected in their particular business model.” Also, for instance, Wiener et al. (2018) argue for collaborative open foresight as a new managerial solution for inspiring disruptive innovations.

We highlight other theoretical perspectives that provide a variety of perspectives on the digital transformation of BMs. Simmons (2013) takes an actor-network perspective to demonstrate that the digital transformation of BMI is a social process facilitated by the negotiation between the network of partners involved. Other researchers use different theoretical perspectives to understand DT of BMI. Akbar and Tracogna (2018) develop their research on transaction cost economics theory to explain the impact of transaction features on the emergence of sharing platforms. Teece (2018) and Helfat and Raubitschek (2018) ground their profit from innovation framework on dynamic capabilities theory. Teece (2018) builds on the recent importance of digital platforms, standards, appropriate regimes, complementary assets, and technologies to show that the mobilization of relevant resources and platform capabilities is an important dynamic ability in managing complements in the ecosystem in order to capture value from it. Similarly, Helfat and Raubitschek (2018) suggest that integrative capabilities are important for designing and orchestrating the alignment of activities and their products with other partners in the ecosystem BMs. Finally, Gupta and Bose (2019a) identify the factors impacting digital transformation of BMs based on affordances theory and attempt to develop a theory of strategic learning for digital ventures, as digital technologies offer firms the potential to develop strategic learning while they adapt continuously to their operating environment. Interestingly, more recent papers ( Gupta and Bose, 2019b ; Trabucchi et al., 2019 ) rely on the business model canvas framework ( Osterwalder and Pigneur, 2012 ) to analyze in-depth the variables of innovation, which lead to competitive advantage and communication with the external stakeholders.

These findings suggest that the digital transformation of BMI was firstly related to disruptive innovation theory in the literature and that recently this trend is appearing again. The only difference is that while previous research addresses digital transformation as an extension of the disruptive theory that brings challenges and opportunities to the BM of incumbents, considering digital transformation a consequence of disruptive innovation, recent research relies on disruptive theory and is more focused on practices and methods to manage and inspire disruptive innovations.

To conclude, these theoretical insights suggest that digital transformation has brought a new conceptualization of BMs and new ways for value creation and capture. According to the transaction cost theory, sharing platforms are dominating as BMs, where the transactions between the parties have resulted in the creation of ecosystems. The creation of ecosystems and sharing platforms has pushed research into disruptive innovation theory to emphasize the commercializing value of disruptive technologies. Simons’ article brings a new perspective to our understanding of digital transformation in companies, taking into consideration the moderating role of social aspects in creating value from digital transformation at a firm level. Further research should investigate which social aspects in the network of actors make more contributions to value creation. We also lack an understanding of how the social relationships of the actors in a network contribute value delivery and capture. This perspective of actor-network theory can be very helpful in studying sharing platforms and ecosystems, outside the boundaries of the firm.

Researchers suggest numerous ways for managing disruptive innovation in ecosystems and among firms – through coordination building ( Teece, 2018 ), the implementation of strategic learning processes and structures ( Gupta and Bose, 2019a ), involvement in collaborative open foresight projects ( Wiener et al., 2018 ), leveraging strategic partnerships through knowledge management ( Alberti-Alhtaybat et al., 2019 ) and using agile methods that enhance strategic agility ( Ghezzi and Cavallo, 2020 ). The digital transformation thus emphasizes not only competition but also collaboration, closing the gap between stakeholders. Referring also to what we discussed previously in the focus of the literature section, digital transformation is enabling companies to work toward issues of sustainability by engaging them in circular and sharing economy approaches. BMs have thus become an open tool for everyday changes related to technological improvements and knowledge management concerning stakeholders and sustainability issues. The digital transformation of BMI now includes technological developments, relationships with stakeholders and sustainability issues in its framework. Our analysis, therefore, suggests that the digital transformation of BMI is a bridge that links the value of strategic innovation management required to solve problems to stakeholders, technology development and sustainability issues, with their opportunities to create and capture value. Further analysis may include the psychological aspects of the various stakeholders, who represent primary actors in the ecosystem, and who may still feature competing interests in the use of digital transformation and its outputs.

This section combines the results of the literature review to understand better the impact of digital technologies on value creation, and the capture and delivery of BMs. In the literature, digital technologies “are regarded to play a critical role in facilitating business model innovations in different sectors” ( Li, 2020 ). New enabling technologies create new ways of doing business for companies and lead to the implementation of new ways of creating, delivering, and capturing value.

Digital Transformation and Value Creation

The value creation sub-component of the BM describes the products and services offered to the customer. The review of the literature shows that digital transformation is enabling companies to create new value in a diversity of ways. We identify below four means of value creation and explain each of them.

First, digital transformation allows firms to create new value through the revision and extension of their existing portfolio of products and services. For example, newspaper and book publishing industries adopted a servitization strategy to offer digital products to customers ( Øiestad and Bugge, 2014 ). This extension of products and services relates specifically to the dematerialization of physical products and the switch from product to service logic. In fact, dematerialization and service logic have impacted the pharmaceutical industry through new approaches such as personalized medicine, nanobiotechnology, and systems biology, providing new therapeutic principles in this industry ( Sabatier et al., 2012 ). Other cases in the literature include firms in the retail industry which have created new value by adding a new BMs through online retailing ( Kim and Min, 2015 ).

Secondly, digital transformation enables firms to understand customer needs better and offer new value propositions in accordance with what they want. One type of value proposition creates high personalization with customers. For instance, novel value propositions can provide a high level of involvement for the customers in value co-creation through additive manufacturing and 3D printing technologies, as in the manufacturing industry ( Bogers et al., 2016 ). High-value creations are also based on new BMs that rely fully on recent technological developments such as smart apps, drones, 3D printing, and crowdsourcing delivery to create new value for customers through new services. The adoption of these digital technologies has transformed companies in the logistics industry into technology enterprises, which sell “transportation and logistic solutions without being encumbered by heavy investments in assets” ( Alberti-Alhtaybat et al., 2019 ). In contrast, other value propositions aim to satisfy only the necessary needs. In this case, firms offer new value propositions and even create new markets by addressing the needs of low-income customers in emerging economies (e.g., resource-constraints innovations in the healthcare industry; Winterhalter et al., 2017 ).

Third, we notice a tendency of some industries, such as financial services, hospitality and automotive services, and healthcare to employ disruptive technologies in their BMs, in order to find solutions for sustainability issues and a sharing economy approach. For instance, the automotive industry is adopting sustainable mobility ( Bohnsack and Pinkse, 2017 ), creating new sources of value by offering a superior product or service (e.g., car-sharing services and mobile applications), or by coupling their products with other services ( Bohnsack and Pinkse, 2017 ). Similarly, embedding the sharing economy approach in the financial services industry is bringing new innovations for processes and services ( Gomber et al., 2018 ), leading to digital banking services, products, and functionality which enhance customer experience ( Gomber et al., 2018 ).

Fourthly, we witness the creation of new value through digital platforms or “platfirms” ( Presch et al., 2020 ) and ecosystems. Digital transformation provides the necessary digital infrastructure for everyone to connect to different actors in networks. For example, in the United States, digital transformation has created new Health Information Exchanges (HIE) organizations, using multi-sided digital platforms to offer information exchange services between different actors in the industry ( Khuntia et al., 2017 ). In the telecommunication industry, the diffusion of data content through mobile devices and the innovation of network infrastructure technology has resulted in a mobile telecommunication ecosystem. In the hotel industry, the emergence of booking platforms ( booking.com ) and sharing platforms (Airbnb) have brought new value propositions to customers, which are cheaper and more authentic.

Digital Transformation and Value Delivery

Value delivery describes the way the activities and processes in a company are employed to deliver the promised value to the customer. The review of the literature reveals a significant change in the way value is delivered in digitally enabled BMs. Digital transformation has challenged core competencies, activities, capabilities, and the roles of firms ( Ghezzi et al., 2015 ; Nucciarelli et al., 2017; Teece, 2018 ).

Firms are first required to examine their core competences to align themselves with the shift to digital formats and servitization ( Øiestad and Bugge, 2014 ). Their new competencies should include knowledge of digital technologies in order to manage relations with customers efficiently and to use the interactivity of digital channels ( Li, 2020 ). Firms should be open to incorporating new disruptive technologies in order to continuously innovate their operations ( Alberti-Alhtaybat et al., 2019 ).

Second, rapid changes in the new ecosystem business environment introduce the need for new capabilities and more emphasis on specific existing capabilities. New capabilities are necessary to deal with changes in the value chain and ecosystem business environment. For instance, in the pharmaceutical industry, firms need to deploy specific assets and capabilities that relate to the orchestration and management of information flows in the network. Previous literature has highlighted the presence of projects relying on new digital technologies (in that case, the blockchain) to distinguish authentic drugs from fake ones ( Dal Mas et al., 2020b ). Integrative capabilities help companies capture value in ecosystems and leverage their assets ( Helfat and Raubitschek, 2018 ). In other industries (e.g., telecommunication) marketing capabilities have to deal with decreased costs and technical abilities to deal with changes in the ecosystem. Firms need to be “agile” and leverage platforms and strategic partnerships.

Third, digital transformation implies a change in the activities and processes of the firm. When firms get involved in projects about sustainability, manufacturers in the automotive industry implement environmentally-friendly processes of manufacturing. This undertaking has led companies and suppliers to collaborate on open innovations projects, such as the “Mobility Scenarios for the Year 2030 – Materials and Joining Technologies in Automotive Engineering” ( Wiener et al., 2018 ). The other example involves processes of frugal innovations in the healthcare industry, which are designed to reduce cost in all value chain activities ( Winterhalter et al., 2017 ).

Fourthly, digital transformation has impacted the role of firms in the industry. The shift in the role of actors in the industry results from the entrance of new players. For example, the entrance of new players (web companies) in the telecommunication industry affects value delivery ( Ghezzi et al., 2015 ).

Digital Transformation and Value Capture

The value capture of the BM involves the revenue model and its financial viability by focusing on revenue streams and cost structures. The literature review suggests that digital transformation creates various new for firms to decrease costs and increase revenue.

Firms capture value by new enabling technologies. Big data provide companies with the means to reduce uncertainty in decision-making ( Urbinati et al., 2018 ) and to optimize processes and increase the efficiency and quality of products and services ( Loebbecke and Picot, 2015 ). These attributes help firms identify new sources of value in other markets and to reduce the costs of adopting BMs over time.

Firms can capture value from superior value propositions. This is demonstrated in industries such as logistics where customers pay for superior service and solutions, or resource-constraint innovations, for the superior quality of a service network. In the pharmaceutical sector, firms capture value through new value propositions for which companies deliver service to patients. In creative industries, premium prices are based on the exclusivity and personalization level of the service offered ( Li, 2020 ).

Digital transformation allows firms to capture value on platforms by leveraging new technologies and improved customer intimacy ( Gomber et al., 2018 ). Research shows that value capture is influenced by the advancement of services provided, however, and transaction-based revenue models are not appropriate revenue models for achieving viability over time.

Future Research Avenues

Based on the results of our literature review, in this section, we discuss the gaps identified in the literature and suggest future research avenues that are relevant for theorizing. We suggest future research avenues, following the previously identified impacts of digital transformation on the new ways of creating, delivering, and capturing value.

Future Research Into Value Creation

Research is needed into understanding how companies should manage the trade-off between the cannibalization of existing products and investing in new advanced services for their customers. It remains unclear how companies can develop numerous value propositions for customers that are personalized and always require the co-existence of existing products and product-centric services. The impacts that adding or extending of BMs have on existing BMs are unclear.

It is essential for the manufacturing industry to understand how manufacturers can manage the customization of products and control the value co-creation process with customers ( Bogers et al., 2016 ). In this avenue of research, it would be necessary to consider also the impact of future technological development on value co-creation; for example, how the combination of digital fabrication and Web 2.0 would create new means of value co-creation.

Further research is needed to identify how new BMs emerge, and how value creation is formed in the creative industries, by researching the different interactions among, for instance, crowdfunding platforms, entrepreneurs, and the crowd. There is a lack of knowledge about the effects that crowdfunding platforms have on value creation activities. It would be useful to understand how the collaborative and competitive dynamics of crowdfunding platforms create value for firms.

It remains unclear how agile practices can help firms to create value from digital technologies and customized services. Future research should also consider the application of agile practices in traditional industries. As firms in traditional industries in the context of ecosystems need to carry out more innovation with other firms, this opens an avenue for further research on how agile practices could become a source of value creation.

There is a need for much more research on understanding the role of single technologies such as the Internet of Things, Cloud computing, artificial intelligence, big data, and the blockchain. The application of these technologies in practice will bring direct knowledge for understanding the dynamics of value creation processes as a source of competitive advantage.

Value creation should also be studied regarding how to create value by generating content from customer data. There is still a call for further research into how firms should exploit all this information through analytics that will help them to design better value propositions for customers, according to their needs.

Value creation for customers should also be analyzed stressing the psychological impacts. New insights and inputs come, for instance, from the healthcare sector in dealing with the recent COVID-19 pandemic, with terminal patients relying only on telemedicine to get in touch with their dear ones ( Ritchey et al., 2020 ; Wakam et al., 2020 ), fostering new possible BMs for firms operating in that field.

Another avenue for further research is to define the boundary conditions under which BMs should be innovated, how often, and how this will impact value creation. Firms learn from the intense and continuous interaction with the high dynamism of the environment and need to undertake changes in the BMI. However, there is still a lack of research defining the boundary conditions driven from the technological advancements that impact value creation in the BMI.

Lastly, it is important to understand the role of new technologies in sustainable issues. It is still unclear how to create new value in the circular economy and from industries where sustainability plays a crucial role, for example, in the retail industry. The link between digital transformation and pro-environmental behaviors of customers, especially from a psychological perspective, appears as a pretty new and promising stream of research ( Yusliza et al., 2020 ).

Future Research Into Value Delivery

There is a need for more research on ecosystems. The recent review shows how roles and interdependencies in the ecosystem change remain unclear. New activities, roles, and capabilities should be identified to enhance our understanding of how firms should orchestrate the new relationships in the ecosystem. Knowing how to develop the abilities to manage the delivery network is essential for key players.

The culture shift to advanced servitization requires more research. This is especially necessary for manufacturing companies that now provide digitally advanced services instead of products. This kind of mental shift is difficult for employees and remains a challenge for companies regarding how its delivery network should be organized. The cultural shift is especially important for distribution channels that call for digital servitization.

More research is also needed on understanding the new capabilities required for manufacturing firms that are involved in digital fabrication. More simulation studies should be carried out to better understand how supply chains will be designed for 3D printing.

There should be more research into identifying the role each technology has in enabling firms with new capabilities and roles. These results will offer a clear idea of the technology they should invest and how it should then be related to new capabilities. The attitude toward the use of technologies has been considered by the literature as a soft skill, rather than a technical one ( Massaro et al., 2013 ; Dal Mas et al., 2021 ; Lepeley, 2021 ). The open debate concerns how much these skills can be learned, or at least fostered. Further investigation is needed to understand how such skills may be empowered through education in order to facilitate delivery and the translation of knowledge. In this regard, psychological aspects related to the attitude toward new technologies may be taken into consideration, following an interdisciplinary perspective.

Future Research on Value Capture

Our results show that investing in digital technologies is costly and undertaking the digital transformation of a firm requires a culture shift. Further studies should investigate how investments in technology relate to the feasibility of revenue models and value capture. Sometimes capturing value from investments in new technologies does not fully exploit the revenue.

Future research should increase our understanding of the value capture of ecosystems, where investments are high. Still, the profits captured by each collaborator actor in the ecosystem are only a fraction of their investment ( Teece, 2018 ).

In the manufacturing industry, the paradigm shift to digital fabrication requires more research into understanding whether value capture is higher for the manufacturer or for the retailer. This can be important in deciding who can invest more in additive manufacturing and 3D printing technologies.

The types of revenue models that should be applied during the evolution of the services are still unclear. There is a need to carry out longitudinal research to explore further the best fit of the revenue models along the lifecycle of the product-centric services ( Khuntia et al., 2017 ).

This paper uses a structured literature review to provide insights into the development of the field of digital transformation of BMI, to understand the impact of digital transformation on BMI and to provide avenues for further research. The review of the literature shows that the digital transformation of BMI is a new field of research with a growth in interest from researchers since 2014. As there is an increased interest from researchers, we expect a growing number of publications in the field. Our results show that this field of research has no dominating authors, implying that few authors remain focused on exploring further aspects of BMI driven by digital transformation. This hinders the knowledge-building process in the field, as only a few authors make use of prior findings to build cumulative knowledge. Indeed, we observe that topics have shifted over time from a focus on incumbents to digital start-ups and from disruptive technologies to new enabling technologies. This reveals the practitioner-led nature of research in this field, although there is a wide divide between academics and practitioners. For this reason, we suggest more collaboration between academics and practitioners, which will help the field to move from an early stage of maturity toward a mature stage. Collaborations may be facilitated by joint forums, think tanks, interventionist research by academics into firms, publications of the main research results in practitioners’ sources like magazines, financial journals, or internet blog posts.

Our results suggest a need for research in developing and emerging countries, especially those from Asia, as they are significantly under-represented, despite their massive contribution to technological solutions. The manufacturing and creative industries dominate research. This raises the need to study other industries such as design, architecture, advertizing, and the fashion industry ( Mangematin et al., 2014 ) and creating more contents in those sectors, like healthcare, which is relying on DT to cope with the several global challenges, including the recent COVID-19 pandemic ( Cobianchi et al., 2020 ; Dal Mas et al., 2020c ; Wang et al., 2020 ). The extensive use of qualitative methodology also suggests that the potential of the field be restricted to interpretive theory building. This calls for more deductive test theory, which might be found if the field involves more interdisciplinary research in the future.

Our review shows fragmentation of the field between disruptive technologies, shared platforms and ecosystems, and new enabling technologies. The focus of research has been mainly on the understanding of impacts that new disruptive technologies have on industries, identifying the areas of transformation in activities, processes, and BMs. Few studies focus on understanding how the process of transformation takes place by drawing on different disciplines and theories. These insights reveal the scattered nature of the field and a quick shift of topics, leaving them under-investigated. Future research should, therefore, be based more on previous findings, thus helping with the accumulation of knowledge and the identification not only of practical gaps but also theoretical gaps.

We suggest that digital transformation has brought a new conceptualization of BMs to the value creation and capture mechanisms. The review of articles provides a variety of theoretical perspectives on the digital transformation of BMs. Disruptive innovation theory is the dominant theoretical perspective, based on which we propose that the digital transformation of BMI is a bridge that links the strategic management of a company’s disruptive innovation required to solve problems with stakeholders, technology development, and sustainability issues to their opportunities to create and capture value. There is a need for further research grounded on theoretical perspectives of dynamic capabilities and actor-network theory.

The results of our study show that digital transformation has impacted value creation, delivery, and capture in almost every industry, although some fields are more investigated than others. Digital transformation enables firms to co-create value with customers through customized manufacturing; through the adoption of servitization strategies and extension of the existing portfolio of products and services; the creation of new value through digital platforms and ecosystems; and finally, allows firms to address solutions to sustainability issues and even address the very specific and particular needs of customers to enhance their experiences. These changes in value creation have required companies to examine their competences, roles, activities, and capabilities. Firstly, firms should possess first-hand knowledge of digital technologies to manage relations with customers efficiently. Secondly, firms should be prepared to shift their roles as new players enter the ecosystem. Thirdly, involvement in sustainability projects, frugal innovation, and circular economy requires a change in activities and processes. Fourthly, integrative capabilities have become necessary for firms to deal with changes in the value chain and ecosystem environment. The adoption of new enabling technologies allows firms to reduce uncertainty in decision-making and capture value from improved customer intimacy and superior service.

To advance research on digital transformation of BMI, we also suggest some future avenues with regard to impacts of digital transformation on value creation, delivery and capture. The identification of these theoretical gaps can be argued to help the advancement of literature on the digital transformation of BMI.

Our study has limitations. Firstly, this paper considers only research published in leading journals, listed in the ABS classification with 3, 4, and 4*. This can be a limitation due to missing results published in other journals that might be relevant for the aim of our study. Secondly, there are some implications from the conclusions of this study. The results are valid only for the specific time period we consider in this study, until September 2020. As we previously saw, since research in the field is experiencing high interest and an increasing number of contributions yearly, future research works could modify our findings. The conclusions derived in this research are based on exploratory research, where sometimes a single case study approach is followed ( Wiener et al., 2018 ), or sharing platforms are evolving over time ( Piscicelli et al., 2018 ) and where IT industry is characterized by short innovation cycles ( Nieuwenhuis et al., 2018 ). Nevertheless, this research into the digital transformation of BMI can provide practitioners with new insights about the phenomenon, and will help them to continually innovate their BMs and remain competitive, as new technologies become more ubiquitous.

Author Contributions

SV and MM conceived the idea of the paper. SV wrote the first draft. EB and FM reviewed and fixed the manuscript. All authors contributed to the article and approved the submitted version.

Research funds come from Ca’ Foscari Institution.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: digital transformation, business model innovation, structured literature review, value creation, value delivery

Citation: Vaska S, Massaro M, Bagarotto EM and Dal Mas F (2021) The Digital Transformation of Business Model Innovation: A Structured Literature Review. Front. Psychol . 11:539363. doi: 10.3389/fpsyg.2020.539363

Received: 29 February 2020; Accepted: 23 November 2020; Published: 07 January 2021.

Reviewed by:

Copyright © 2021 Vaska, Massaro, Bagarotto and Dal Mas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Maurizio Massaro, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

The Business Model: Recent Developments and Future Research

40 Pages Posted: 7 Feb 2011 Last revised: 21 Dec 2011

Christoph Zott

University of Navarra, IESE Business School

Raphael ('Raffi") H. Amit

The Wharton School UPENN

Lorenzo Massa

World Economic Forum; Business School Aalborg University; Business Design Lab

Date Written: February 7, 2011

The paper provides a broad and multifaceted review of the received literature on business models in which we examine the business model concept through multiple subject-matter lenses. The review reveals that scholars do not agree on what a business model is, and that the literature is developing largely in silos, according to the phenomena of interest to the respective researchers. However, we also found emerging common themes among scholars of business models. Specifically, 1) the business model is emerging as a new unit of analysis; 2) business models emphasize a system-level, holistic approach towards explaining how firms “do business”; 3) firm activities play an important role in the various conceptualizations of business models that have been proposed; and 4) business models seek to explain how value is created, not just how it is captured. These emerging themes could serve as catalysts towards a more unified study of business models.

Keywords: business model, strategy, technology management, innovation, literature review

JEL Classification: M00

Suggested Citation: Suggested Citation

Christoph Zott (Contact Author)

University of navarra, iese business school ( email ).

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Raphael H. Amit

The wharton school upenn ( email ).

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Business model innovation: a systematic literature review.

• Sascha Kraus , 
• Matthias Filser , 
• Kaisu Puumalainen , 
• Norbert Kailer , and 
• Selina Thurner

http://orcid.org/0000-0003-4886-7482

Durham University, Durham, United Kingdom

E-mail Address: [email protected]

Corresponding author.

Search for more papers by this author

ZHAW Zürich University of Applied Sciences, Winterthur, Switzerland & LUT University, Lappeenranta, Finland

LUT University, Lappeenranta, Finland

Johannes Kepler University, Linz, Austria

Researching business models (BM) and in specific business model innovation recently received growing attention by academics and practitioners due to increasing global competition and the constant need for adjustment to changing environments among others. Therefore, the main objective of our study is to provide an overview of the state-of-the-art of research on business model innovation by conducting a systematic literature review (SLR). Our review provides a deeper understanding and breakdown of key components of BMI. Likewise, our study identifies organizational, environmental, and societal factors influencing BMI and proposes avenues for future research.

• Business model
• business model innovation
• literature review
• state-of-the art

1. Introduction

Technology and business ideas only have economic value when they are commercialized through the business model (BM) of a company. In that regard, technology itself does not have a measurable economic value. The way a company implements a new technology or innovation successfully, is greatly relative to the firm’s BM. Moreover, innovations can be commercialized in various ways, meaning the identical innovation commercialized in different ways will likely yield two different outcomes. Consequently, BMs are essential for companies and need continuous improvements and adjustments [ Chesbrough ( 2010 )].

Business model innovation (BMI) is considered as one of the main research streams within innovation research and describes how innovations are executed. In terms of practical applicability, BMI can lead to new ways of value creation as a reaction to changes in the environment [ Schneider and Spieth ( 2013 )]. The quantity of scholarly literature on this topic has increased rapidly in the past few years. However, scholars consider the existing literature on BMs as rather ambiguous and highlight the lack of a definition that allows to deepen research in a consistent manner. That is important as research in BMI occurs in different fields such as innovation management, strategic management, and entrepreneurship.

Therefore, this study conducts a systematic literature review (SLR) analyzing 40 selected papers published in highly-ranked journals in order to provide a deeper understanding on BMI. In addition, our study aspires to reveal the state of the art of the research and provides avenues for future research.

2. Theoretical Background

2.1. business models.

The concept of BMs is sector-independent and can be applied to different types of businesses [ Hock-Doepgen et al. ( 2020 )]. In today’s reality, due to globalization and faster changing and competitive. environmental conditions, firms are forced to explore new BM potentials to remain profitable or increase profitability [ Burkhart et al. ( 2011 )]. Accordingly, BMs are facilitators and provide a framework for companies to create and capture value [ Clauss et al. ( 2020 )]. This value however evolves from the novelty, uniqueness and effectiveness of the BM. However, BMs do not represent a single objective value. Novel BMs rather develop from commercialization possibilities, which are realized by a unique setup [ Schneider and Spieth ( 2013 )]. The BM itself does practice two crucial functions: value creation and value capture. The value creation happens at the starting point of matching a customer need with a newly arranged and efficiently setup of resources. The created value is captured from the efficient execution. Therefore, “a better BM often will beat a better idea or technology” [ Chesbrough ( 2007 , p. 12)].

A BM contains several characteristics. First, it articulates the value proposition which deals with the communication of the value that is created for the consumers by offering a certain product or service. Second, a BM detects a market segment, which is represented by the identification of the consumers who can profit from the BM. Third, it creates and spreads the offering of the company in the sense of forming the structure of the value chain of the firm. Fourth, it recognizes the revenue resulted by the offering, which refers to the cost structure, as well as the profit potential of the new product or service. Fifth, a BM investigates in the right relationship between suppliers and customers, but also searches for potential competitors. Finally, it frames a competitive strategy, in terms of achieving and searching for competitive advantage [ Chesbrough ( 2010 )]. Table  1 illustrates various definitions of BM that are cumulated from the literature.

Source : Own elaboration.

Moreover, a BM is not only connected with innovation, new ideas and value creation, but also with the firms’ strategies. In that regard, BMs show a different strategy for solving problems and creating value [ Casadesus-Masanell ( 2010 )]. A BM is not the same as a strategy even though some scholars do not draw a clear-cut course between the definition of a BM and the characterizations of a strategy [ Magretta ( 2002 )]. In todays’ environment, ideas are fast changing and can be quite complex with a high portion of risk. Thus, strategies communicate these changes and risks of a company and form an individual BM for the firm. In addition, to be able to evolve as an efficient strategy, it is essential to consider the uncertainty of the idea. It is crucial to be able to experiment and try different ways when it comes to the implementation of a BM to reach the firms’ aims. It is important that managers of a business recognize the potential for improvements [ McGrath ( 2010 )]. However, a BM is distinctive to a strategy and under certain circumstances these two terms have to be assessed independently. For instance, some firms cannot comprehend how the competition operates on the market. For that reason, before the firm is able to evolve a BM, they have to develop a strategy first. Moreover, BMs often do not consider the real competition when they are developed. BMs describe the different divisions of a firm and how these divisions fit with each other. However, BMs are often not able to see these divisions in a critical way. For that reason, it could be difficult to make improvements. Implementing a sufficient strategy can solve this lack in critical thinking. This strategy can help to gain competitive advantage on the market. When a BM is based on theoretical thinking, the strategy makes the model real and is associated with managing the reality of the firm and its issues and aims [ Magretta ( 2002 )]. It can be said that the strategy of the firm tightly depends on its BM and vice versa. Thus, the BM of a firm can be seen as the reflection of the firms’ strategy [ Casadesus-Masanell and Ricart ( 2010 )].

2.2. Business model innovation

In connection with the rise of new technologies and new product invention, BMs had to be improved and converted to a more focused model, considering innovation in its actions. For this purpose, the term BMI was developed (for an overview of different definitions, see Table  2 ). The implementation of a BMI is crucial when a company wants to generate long-term sustainable competitive advantage, but also wants to explore new ways to organize their business. Besides, a BM is able to balance costs and revenues to generate a sustainable outcome [ Behera ( 2017 )]. In case a BMI is implemented successfully, it will allow the companies to adjust to changes on the market or to even survive on the market. Nowadays, the market is quite dynamic and competitive, which makes it more difficult for companies to resist on the market with its products and strategies. A BMI may involve a modification of an existing or an implementation of a totally new BM. However, its objective is to create value for its stakeholders [Wirtz (2018)]. Based on the fast-changing environment, some factors have to be analyzed when implementing a successful BMI. These factors are the behavior of the competitors of the firm, the outsourcing of activities which do not directly affect the success of a firm, as well as the development of capabilities for risk taking. Especially, risk taking is quite important when a company wants to evolve on a global market efficiently. These factors influence the decisions of a firm when it comes to the invention of new ideas or to the rearrangement of old ideas. The main goal of BMI is the value creation. Moreover, innovation is always a driver for value creation. The value creation is generated through the implementation of a successful BMI. More companies use different BMIs to generate different outcomes and values for stakeholders [ Behera ( 2017 )].

3. State-of-the-Art Review of Current Literature on the Field

The most important literature on the topic BMI was gained within an SLR [see e.g. Kraus et al. ( 2020 )]. Moreover, on the basis of the results of the SLR, a so-called map of knowledge evolves, which should contain all relevant literature and give a significant impression in the current state of research on this topic. This map of knowledge is able to show growing research trends as well as knowledge gaps in the research field. This type of SLR was carried out by various authors in the past and received its popularity in recent research methodology as it ranks the literature according to its quality [ Bouncken et al. ( 2015 )]. In that case, the systematic literature should provide an insight in the current research on the topic BMI. This qualitative research approach was already used by various authors before [e.g. Calabrò et al. ( 2019 ); Demir et al. ( 2020 ); Kraus et al. ( 2020 )].

3.1. Data and method

The literature review is based on a systematic research in the database Web of Science (WoS). Along with the search string “business” AND “model” AND “innovation”, also some further limitations were set such as: only papers, papers in English language and papers which have “Business Model Innovation” in its title should be searched for. This resulted in a number of 287 papers. For the descriptive analysis, the years of publication were focused on. In that regard, the publications per year were examined carefully.

Figure  1 illustrates that the starting years of papers which cover the topic BMI are 2009 and 2010. Before, only few papers refer to this field of study. In recent years, the topic evolved over time and received its most popularity recently when it reached its peak in 2019. For the next steps of the analysis, further indications were carried out according to the citations per year. In that regard, it can be seen in Fig.  2 that most of the citations happened since 2016. For that reason, the focus for the SLR was set on papers published between 2016 and 2019, as the literature review should provide the current state of research on this topic sorted according to their quality. This resulted in a total number of 179 papers.

Fig. 1. Publications per year.

Fig. 2. Citations per year of publications containing the search string in their title (Source: Compiled by the author based on WoS).

Abbreviations: R=rank; TP=total publications; TC=total cited; VHB JQ=VHB Jourqual 3 rating; note that for this analysis only papers published in academic journals were considered focusing on document type article, on language English, and papers published from 2016 to 2019; note that the ranking is developed according to the number of publications of a paper on the topic “business and innovation and model” in its title received.

This number was still too high to result in a qualitatively sufficient outcome for this analysis. For that reason, this number was reduced by the restriction that only journals, which have a VHB Jourqual 3 rating of at least “B”, were considered. It the end, 40 papers, which are illustrated in Table  3 , emerged in that analysis.

Source : Own elaboration based on WoS.

3.2. Results

The research on BMI is undertaken through different approaches by various authors. The following four notions were attributed to four main clusters resulted from the SLR: (1) the role of environmental factors in BMI, (2) the connection of products and services with BMI, (3) the role of organizational aspects in BMI, and (4) the implication of social perspectives in BMI. The first cluster is subdivided into the following two sections: sustainable BMI and new technology. An overview of the top 40 publications resulting from the systematic literature analysis assigned to four appropriate clusters is provided in Table  4 .

3.2.1. Cluster 1: The role of environmental factors in business model innovation

The role of environmental factors in connection with BMI is discussed in the recent academic literature for several times and under different streams. Especially sustainable BMI received its most attention and is applicable to various research fields. Due to the increasing population and the unsustainable behavior of businesses, the need for a sustainability-oriented BMI became crucial [ Baldassarre et al. ( 2017 )]. Moreover, new technologies offer new possibilities for firms to engage with their BMs. In that regard, this cluster section was further divided in cluster 1(a) sustainable BMI and cluster 1(b) new technology.

3.2.1.1. Cluster 1(a): Sustainable business model innovation

Cluster 1(a) places an emphasis on the recent importance of sustainability in business actions. Inigo et al. [ 2017 ] states that stakeholders expect sustainability from businesses. When a business wants to be successful in the long-term, it is essential to consider environmental and social aspects in their organizational activities. In various publications, these two aspects are considered to be connected with each other and also have a huge impact on the organization’s BMI. The concept of BMI is a crucial factor for sustainability. Yang et al. [ 2017 ] mentioned that BMI is less about finding new products or services, it is rather about searching for new ways to create and supply the existing products or services of a firm.

Moreover, Evans et al. [ 2017 ] and Baldassarre et al. [ 2017 ] indicate that BMI refers to how a firm captures value, rather than what they do for capturing value. Whereas, a BM gives details of creating value for the firm’s stakeholders including end users, suppliers, shareholder, government and partner, the sustainable BM also includes the creation of value for the environment and the society. Thus, sustainable BMs consider environmental and social benefits when they capture value. Oskam et al. [ 2018 ] emphasize that a sustainable BM consists of: value proposition (offering of ecological as well as social value), value creation and delivery (how the ecological or social value is created and delivered to the stakeholders of the organization), and value capture (captured capital which is identified as ecological, social, and economic value and do not refer to organizational activities).

França et al. [ 2017 ] also investigate in value proposition. In that matter, the authors emphasize that a value proposition is accordant to an offering of a bundle of products and services. This bundle creates value for a particular customer group. Value for customers can include: newness, new processes, price changes, or brand image. Besides, Lüdeke-Freund [2018] provides an integrative framework that underlines this statement by revealing: sustainability innovation motivates BM which then creates business cases for sustainability. According to Wadin et al. [ 2017 ], BMs can overcome sustainable barriers by introducing: service-based BMs, product-service systems, or servitization.

Yang et al. [ 2017 ] emphasize that most of the studies of BMs are about value proposition, value capture, and value creation. However, there is less attention on the topic of sustainability in connection with BMs. For that reason, Yang et al. [ 2017 ] conducted a research on value uncaptured for sustainable BMI. In that regard, the authors evolved four forms of value uncaptured. These four forms are: value surplus, value absence, value missed, and value destroyed. First, value surplus refers to a value which is not really needed or necessary for the existence of a firm. Moreover, it is a value which is delivered to stakeholders even though the firm does not need to deliver it. For instance, waste of energy or overproduction. Second, value absence is a value that is needed but cannot be provided by the firm as it does not exist. By way of example, a lack of resources or the need of a recycling service for the firm’s products. Third, value missed refers to a value that is not fully exhausted. For example, underutilized assets and resources which could achieve a higher value but do not in the end. Fourth, value destroyed which is a value that has negative effects and a bad outcome for the firm and its stakeholders. It is a quite inefficient BM which might even cause damage to the planet earth, its habitants and its environment. Whereas Yang et al. [ 2017 ] focus on the capture or more likely on the uncaptured value of sustainable BMI, Evans et al. [ 2017 ] evolved three different forms of the actual sustainable value and named them as follows: environmental value forms, social value forms, and economic value forms. Environmental value forms refer to renewable resources, low emissions, and low waste. Social value forms are about equality and diversity, but also well-being of the society and livelihood. Economic value forms relate to profit, return on investments, and business stability.

Baldassarre et al. [ 2017 ] not only mention the need of a sustainable BMI, but also the necessity of user-driven innovation due to the increasing of population and the unsustainable behavior of businesses. The outcome of combining these two concepts should cause a successful and user-centered sustainable value proposition. Additionally, user-driven innovation relates to business opportunities and the deployment of new concepts. Along with user-driven innovation, also design thinking received its attention when it comes to business innovation. In that sense, design thinking is a user-centered innovation approach which passes through three steps including: inspiration, ideation, and implementation. Design thinking is able to find problems and evolve solutions to them. The authors further show that the combination of these two approaches is the key for sustainable value proposition as it unifies economic and environmental objectives. In general, Oskam et al. [ 2018 ] emphasize that the combination of ecological, social, and economic aspects challenged many scholars before.

3.2.1.2. Cluster 1(b): New technology

New technology has been explored by several researches under various aspects. For instance, Karlsson et al. [ 2017 ] underline the importance of BM or specifically BMI when it comes to renewable energy. van Waes et al. [ 2018 ] also indicate energy technologies as opportunity for new ownership values, value chains, or customer relationships. Thus, Karlsson et al. [ 2017 ] reveal that BMI can be a necessary tool in the energy industry as it provides an environmental and social focus rather than a focus on traditional and resource-intensive assets. van Waes et al. [ 2018 ] underline that digitally enabled sharing economy platforms could be seen as new technology as well. Additionally, these sharing economy platforms can make privately owned assets available for rental services which then need a new innovative BM as well. Ciulli and Kolk [ 2019 ] also emphasize that sharing economy can lead to changes of the environmental, social, and economic value creation of the BM of a firm. Thus, sharing economy not only reveals replacements of ownerships, but also opportunities for greater value creation for existing customers or the acquisition of new customers. Sharing economy could evolve efficiency in order to provide equal access to goods and services and new variable employments.

Karlsson et al. [ 2017 ] state that BMI is the key for long-term profitability and sustainable development of firms and society. In that regard, Karlsson et al. [ 2018 ] developed a conceptual 4l-framework which consists of four phases. First, the initiation phase that involve the discovery of the need of an innovation. Second, the ideation phase that searches for solutions and possibilities. Third, the integration phase that elaborates and develops these solutions. And fourth, the implementation phase that considers marketing in its process for promoting these solutions. Moreover, Karlsson et al. [ 2018 ] indicate the dependence of sustainability aspects in these phases and called it BMI process for sustainability.

Conversely, Wells [ 2018 ] enhances that the world is in transition right now and there has to happen changes regarding resource waste and environmental damages in general. The human activity has a great impact on the earth’s ecological, meteorological, and geological systems. In that connection, innovations have to be in accordance with these environmental challenges and social resource constraints. New technologies which are sustainable could be a novel way to generate novel settings and emerge novel solutions to indicate environmentally friendly products and services. Wells [ 2018 ] mentions grassroots energy innovations as an example for new technology under a sustainable setting. Thus, Wells [ 2018 ] highlights that a non-traditional BM is crucial to yield a new technology innovation with low environmental impact. Wells and Nieuwenhuis [ 2018 ] also bring up that technology can not only be seen as a potential for new BMs, but also as a main subject to develop new innovations. Zhao et al. [ 2018a , 2018b ]) investigated in low or zero carbon buildings and BMI. Moreover, zero carbon building is known for its innovativeness and effectiveness when it comes to the reduction of energy consumption and carbon emissions. As a result, BMs can include the following components as a favor for successful sustainable BMIs: product-service systems, closed loop systems, and open innovation platforms along with energy performance contracting. To conclude the section about new technology, Prendeville et al. [ 2017 ] indicate an eco-design dilemma that a firm has when it tries to evolve sustainable technologies in their businesses. The eco-design is not only collaborative, but also systematic and includes a management process for environmentally friendly behavior and actions of a firm. Moreover, the eco-design considers the environmental impacts of packaging, products, processes and services undertaken by the firm. The solution for this dilemma is the right choice of BMI which is conducted by the firm.

3.2.2. Cluster 2: The connection of products and services with business model innovation

When it comes to products and services in connection with BMI, some authors recognize several gaps in this research field. Visnjic et al. [ 2016 ] shed light on the research on the interplay between service BMI and product innovation. In that regard, the authors evolved an examination of two service BMs: the product-oriented model and the customer-oriented model. Service BMI is the result of a servitization strategy. Thus, the servitization strategy includes the offering of additional services combined with the products a firm already offers. In that case, the firm is able to shift its focus from a product-oriented to a service-oriented BM and generate higher competitiveness on the market. Additionally, to the cluster above, technological change is one of the key factors which causes to rethink the firm’s BMs and strategies. Along with the high portion of new technology on the market, it is progressively more difficult to remain its power to compete as a firm. As a result, the firm has to cease existing values and has to generate new superior values. The path of a servitization of a firm starts with a BM which focuses on products only. Then the firm continues by providing product-related services such as repairs or maintenance. At that stage, the BM transfers to a product-oriented BM. Once the firm introduces use-oriented and results-oriented services, the BM shifts to a customer-oriented BM. Naor et al. [ 2018 ] indicate that sustainable business is more likely to shift its offerings from traditional products-only BM to a combination of products and service BM as they prefer functionality rather than ownership. To conclude, Visnjic et al. [ 2016 ] come to the result that the synergy of service BMI and product innovation lead to profits in the short run but in the long-term they generate knowledge losses. In case a firm wants to generate a superior value, they have to be able to overcome the long-term market performance decrease by focusing on the short-term benefits generated.

Supplementary to the cluster that covers environmental aspects in interplay with BMI, Rantala et al. [ 2018 ] investigate in the research field of BMI and its connection with sustainable opportunities. Technologies and services in connection with BMI are a crucial field of study in the scientific literature. In specific, the service sector becomes increasingly more important in terms of economic growth. Moreover, service innovations are a key resource for sustainable development of organizations and societies. Thus, service innovations are quite adjustable to different types of industries. Manufacturing firms, but also service-focused companies are able to implement service innovations and create value. Traditional service innovations are connected with product and process innovations, whereas recent service innovations also focus on new BMs as customer’s interests are changing. Along with Visnjic et al. [ 2016 ], also Rantala et al. [ 2018 ] indicate that the main objective of service innovations lays on the profit provision to the organization in the short run. In that regard, Calabrese et al. [ 2018 ] introduce a tool, namely sustainability-oriented service innovation (SOSI), to discover the main components of a BM in terms of sustainability-oriented service innovations and how a manager can change these components included. When a firm uses such tool, it is able to attract new customers, enter new markets and rise its competitiveness. Thus, within the tool SOSI, the firm is able to engage in new technological, organizational, and social innovations which can be implemented in its BM.

Rosca et al. [ 2017 ] explore sustainable innovation in connection with frugal products and services. In that regard, the results of their analysis include that frugal innovations are able to reintegrate value chains, reengineer products and services, and reconfigure resources. Moreover, frugal innovations offer a wide range of products and services. However, the authors also indicate that BMs with limited products and services show higher value for lower costs and prices. This can be an advantage in terms of reaching a higher number of base-of-the-pyramid customers. Linder and Williander [ 2017 ] deal with circular BM. This kind of BMs focus on cost saving and reduction due to environmental changes. Moreover, the authors take a look on Xerox as a pioneer of product-service offering. Xerox investigated in photocopiers and their remanufacturing.

3.2.3. Cluster 3: The role of organizational aspects in business model innovation

The role of organizational aspects in connection with BMI was considered by several authors. Clauss [ 2016 ] examines a broad literature review of theories and concepts of BMs and BMIs. In that regard, the author evolves three main dimensions and 10 subconstructs of the concept of BMI. Moreover, he provides a conceptual serenity for a better understanding of BMI along with the answer of how a BMI should be interpreted. The three dimensions of BMI are as follows: value creation innovation, new proposition innovation, and value capture innovation. Value creation innovation consists of four subconstructs namely new capabilities, new technology/equipment, new partnerships, and new processes. Whereas the dimension value capture innovation only has two subconstructs (new revenue models and value cost structures), the dimension value proposition innovation has four. These four are the following: new offerings, new customers and markets, new channels, and new customers relationships. Besides, Gebauer et al. [ 2017 ] introduce different types of innovation in connection with base-of-the-pyramid markets. The authors define the following types of BMIs as follows: BM design, renewal, expansion, diversification, and replication. Thus, the base-of-the-pyramid market refers to the four billion people living close to the poverty line. It is a business strategy with its aim to serve these people. However, there are some barriers a firm has to face when it enters that market. The barriers are dependent on the choice of the firm and its type of innovation along with its selection regarding the overall logic, configuration, and the components of its BM.

Spieth et al. [ 2016 ] explore the relationship between BM, BMI, and the strategy of a firm. In that connection, the authors indicate that the strategy of a firm leads to value creation and value assignment. More generally, the relationship between strategy and the BM of a firm can go hand in hand in case different firms offer similar products but with different BMs. In that regard, both firms can attract the same customer group with the same product but also with the same success. Whereas Spieth et al. [ 2016 ] survey the interaction between strategy and BMI, Foss and Saebi [ 2018 ] focus on the problems which may arise when implementing a new BM or BMI. In that case, strategy is an important factor influencing whether a firm has an efficient BM or not. A strategy includes the defining of objectives and goals, the decision on what products and services to offer, and the design of the perception of the firm generally. Moreover, the competitive strategy also includes choices about the organization structure, administrative systems, and policies. Aside from that, von Delft et al. [ 2019 ] underline that firms, which have a globally based focus, have to introduce or rethink their external and internal strategies. Furthermore, the BMI seek for global knowledge in order to generate international competitiveness and to offer new models align with international allowance. Snihur and Wiklund [ 2019 ] use state-of-the-art statistical techniques to explore the external and internal sources of new BMs in established firms. The authors shed light on the multidimensional nature of new innovation types such as BMI. On the one hand, BMI pertain as a catalyst for external sources and strategic renewal. On the other hand, different innovation types require different knowledge which refers to the internal sources of a firm. Hacklin et al. [ 2018 ] also explore the external environment and its effects on the BM of a firm. Moreover, the authors investigate in the research of BM strategies in terms of industry-level forces. The authors analyze the computer and telecommunications industries in specific. In that regard, they investigate in the competitiveness of the BMs of firms which are operating in the same industry. As a result, they conclude that BMs with low degree of value migration along with new strategic opportunities are more efficient when it comes to innovating their BMs. Foss and Saebi [ 2018 ] suggest that the firm should link its properties with its strategic actions in its BMI and BM. Sustainability and innovation can be considered as such strategic action of a firm. Laudien and Daxböck [2016] emphasize that BMI is currently seen as a strategic option to raise awareness and competitiveness of a firm. In that regard, the authors examine BMI processes of average market players in order to show that this kind of market participants do not necessarily pursue BMI. The authors develop four phases which show whether an average market player uses BMI or not. These four phases are as follows: (1) monitoring the BM fit beyond the industry-level, (2) BM development, (3) opening up the BM, and (4) deliberate BMI. As a result, the authors emphasize that BMI is rather an unintended process than a process they really trace.

Besides, Spieth et al. [ 2016 ] also mention the organizational culture on BMI as an important indicator whether a firm is successful with its innovation or not. Thus, some researchers shed light on the research on the phenomenon of BMI. For instance, the prerequisites of BMI, the major parts and processes of BMI, and the key effects evolving from BMI. In this connection, the organizational culture as a part or process of the firm’s BMIs is under examination. Moreover, the organizational culture should lead to an explanation of why and how a firm’s value system assesses its capabilities and resources for BMI. Spieth et al. [ 2016 ] emphasize that the firm’s capabilities are tightly connected with its collective commitment, and its resource fluidity. In that regard, the capabilities in connection with the organizational culture of the firm’s BMI will show the tendency of a firm.

Guo et al. [ 2017 ] analyze the interplay between opportunity recognition and BMI. Opportunity recognition is defined as an individual’s efforts in looking for and recognizing opportunities. Thus, opportunity recognition can be seen as a core player for achieving competitive advantage and superior performance on the market. In that connection, the authors evolve a research on the positive influence of opportunity recognition and the performance of small and medium sized enterprises caused by BMI. While Guo et al. [ 2017 ] indicate opportunity recognition as a term, Schneider [ 2019 ] focuses on opportunities of BMI more generally. In this context, the author evolves a study about how different exogenous conditions impact BMI opportunities for firms. Schneider [ 2019 ] gives an explanation about high levels of exogenous and how that is influencing the ability to recognize signals and opportunities on the market. Firms tend to discover rather environmental threats than opportunities when it comes to their BMI. As a result, firms with a high level of exogenous volatility rather fail to create opportunities for their BMI based on their main competences and valuable resources. Moreover, firms with an exogenous impact are able to detect signals and explore opportunities on the market. Additionally, Sorescu [ 2017 ] emphasizes that big data could be a great opportunity for firms to rearrange or even create a new BM even though research on this topic is incomplete. Schneckenberg et al. [ 2017 ] explore opportunities of businesses related to decision making in BMI. In that regard, the authors mention that value creation mechanisms provide the ability for firms to engage in new opportunities with their BMI by developing new products and services on the market. A successful BMI requires an interplay between value proposition, value creation, and value capture. Coping mechanisms can help for a better understanding of these three configurations.

Karimi and Walter [ 2016 ] investigate in corporate entrepreneurship, disruptive BM adoption, and its performance. The authors mention the importance of digitalization when it comes to sustainable BMs and that various firms struggle by finding the right BM to react to these digital changes. The aim of these firms is to take advantage of the internet and digitalization. In their research, the authors develop five hypotheses which should guide and underline the performance of disruptive BMI adoption. The five hypotheses include the following: autonomy, risk-taking, innovativeness, and proactiveness. Autonomy, risk-taking, proactiveness, and size can be associated with disruptive BMI adoption, whereas innovativeness does not show any coherence with the performance of the BM. The size is ambivalent as it shows a great performance at a low or high level of disruptive BMI adoption, while the performance is only fair at a medium level of disruptive BMI adoption. Futterer et al. [ 2018 ] also explore the performance of BMI and its effectiveness in relation with internal corporate venture. In that connection, the authors research in the following three fields: direct effects of effectuation and causation of BMI, the relationships of these effects and industry growth, and the general effect of BMI on internal corporate venture performance. As a result, effectuation and causation both affect BMI and internal corporate venture can be seen as an entrepreneurial guideline a firm can follow.

3.2.4. Cluster 4: The implication of social perspectives in business model innovation

Dentchev et al. [ 2016 ] recognize the need for further research on the topic social entrepreneurship caused by social transformations in the long run. Moreover, the authors question the similarities and differences among BMs and their aims to reduce social and environmental damages. Thus, another question lays on the financial budget of social enterprises and how they develop such financial resources. Another option for further research is the question about the differences between a BM of a social entrepreneur and a BM of a traditional for-profit BM along with the possibilities to develop and implement a BM for social enterprises. Mongelli and Rullani [ 2017 ] provide a definition for social entrepreneurship or better to be said for social enterprises in general. In that regard, the authors indicate that social enterprises have a hybrid nature, create “blended value”, and use economic and social components. Moreover, social enterprises want to create social impact which is done by indicating an economically sustainable way or environmentally friendly way. Mongelli and Rullani [ 2017 ] as well as Olofsson et al. [ 2018 ] emphasize that for the creation of this social impact, the application of the right BMI is crucial. By doing so, often a conflict arises as social impact and economic logic is sometimes difficult to merge with each other. Along with Dentchev et al. [ 2016 ], also Olofsson et al. [ 2018 ] indicate the importance of BMI when it comes to environmental and social changes of an organization in general. Moreover, Olofsson et al. [ 2018 ] add to Mongelli and Rullani [ 2017 ] that social enterprises focus primarily on social and environmental missions in order to become sustainable in the long-term. However, Olofsson et al. [ 2018 ] also mention that the research in this field is still under progress as most of the publications about social enterprises are focusing on industry sectors, rather than on the social entrepreneurs itself. Additionally, it is still unclear how social enterprises evolve over time and how they are connected with BMI in detail.

4. Discussion and Conclusion

The purpose of this paper was to give an overview of the current state of research on the topic BMI by conducting an SLR. It provides three dimensions evolved from the comparison of the clusters generated according to the results of the literature review: organizational factors, environmental factors, and societal factors. These three dimensions are essential to be considered for future directions of this research field. Moreover, this dimensionalization leads to a better understanding for managers of firms since BMI causes relevant managerial challenges Foss and Saebi [ 2017 ]. It is essential to overcome these managerial challenges, otherwise the firms’ BMI would be less efficient, and the company will not be able to successfully resist on the market on the long run.

These three dimensions were developed by the consideration of the main drivers of BMI, i.e. are products and services. This perception is derived from the results by the SLR, which is discussed under the section implication and illustrated in Fig.  3 : Cluster 1 provides the theories of the BM model concept, which build the core knowledge of BMs. Cluster 2 takes products and services into account, whereby BMI is tightly connected with new products and technologies. Moreover, cluster 2 also indicates that products and services in different forms are closely related to BMI and its organizational structures. Cluster 3 covers the topic of organizational aspects in connection with BMI. This first dimension clearly shows that organizational factors are the core for every BMI and they are absolutely essential for the success of the BM of a company. Cluster 3 deeply investigates in BMI. On that account, the SLR shows that the scholars who recently published articles about that research field focus more on BMI, whereas in past publications, only a few scholars recognized the consideration of the form BMI in their theories. Moreover, the SLR indicates that the scholars have more knowledge about BMI and are more likely to detect further research gaps in this field. This cognition also shows how interchangeable these two terms BM and BMI are being used.

Fig. 3. Factors influencing business model innovation.

The second evaluated dimension concerns with environmental factors. In that dimension, the SLR gives a better understanding and a more detailed overview of the influencing factors reflected in cluster 1, including clusters 1(a) and 1(b). Whereas past researches are more focused on the searching for product innovation and new technologies as well as their influence on BMs, publications nowadays already explored this coherence and even developed a so far new form of BMI namely sustainable BMI. On that account, sustainable BMI received its attention recently. This form of BM became crucial in nowadays businesses as gradually more companies have to consider environmental changes and challenges in their business actions and decisions. Along with the rise in sustainability, also new technologies received their focus as they could lead the company in a totally new direction with its BMI. It can be the case that the company has to develop a totally new BMI according to the external changes an organization might faces.

Finally, the third dimension reflects the societal factors. This dimension is only considered in cluster 4 and therefore evolved from the SLR. Even though societal factors are nothing new or anything that evolved over time, it is surprising that past researches, did not consider this factor in their analysis. For that reason, it is even more important to mention that societal factors such as social transformation and economically changes have an influence on the firm’s BMI as well.

As with any study, also ours entails several limitations. For example, our SLR focuses on articles from the database WoS, which limits the results as other databases might show other outcomes. Future research might therefore want to double check our results by the use of e.g. EBSCO or Scopus as alternatives. The literature review selects only journals which have a VHB Jourqual 3 rating of at least “B”, so that lower ranked publications dealing with the topic have not been recognized. Thus, the SLR also set another restriction, which said that only articles published between 2016 and 2019 have been considered. This restriction rendered the results quite up-to-date and current, but also implicates that publications which might be cited more often and might give deeper understanding of the topic, had been omitted. Besides, the clustering of the articles in different categories is rather the subjective opinion of the author then an objective valuable approach. For that reason, any other author might interpret the results differently and comes to another point of view.

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Sascha Kraus is Professor of Entrepreneurship at Durham University Business School, United Kingdom.

Matthias Filser is Head of the Department of Entrepreneurship at ZHAW Zürich University of Applied Sciences, Switzerland and Visiting Researcher at LUT University in Lappeenranta, Finland.

Kaisu Puumalainen is Professor for Technology Research at LUT University in Lappeenranta, Finland.

Norbert Kailer is Professor of Entrepreneurship at the University of Linz, Austria.

Selina Thurner is a Master’s graduate in Entrepreneurship from the University of Linz, Austria.

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Received 26 June 2020 Revised 12 August 2020 Accepted 13 August 2020 Published: 29 October 2020

A Systems View Across Time and Space

• Open access
• Published: 16 January 2023

Disruptive business value models in the digital era

• Navitha Singh Sewpersadh   ORCID: orcid.org/0000-0002-3219-7974 1  

Journal of Innovation and Entrepreneurship volume  12 , Article number:  2 ( 2023 ) Cite this article

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The coronavirus pandemic illustrated how rapidly the global environment could be disrupted on many levels but also drive an acceleration in others. Business leaders are grappling with dysfunctional business models that are ill-equipped to manage the disruptive environment of growing artificial intelligence. Hence, this study examined the discontinuous shift in the scope and culture of business models by exploring interdisciplinary streams of literature. An integrative review methodology was used in this study to develop theoretical constructs relating to business model innovation in the services sector. Key propositions were an innovation continuum, a responsive business innovation model and value architecture, which inculcates a sustainable value creation proposition and market advantage. Businesses must continuously evolve on the high end of the innovation continuum to reduce the risk of innovation apathy and strategic myopia. A key contribution of this study was the interdependencies in value networks that allow for collaborative working and co-creation of resources, such as crowdsourcing, crowdworking and social media platforms. This study also showed the growing importance of a centre of excellence to function at the forefront of disruptive technologies. A key finding was the need for governance structures to recognise and manage the trade-offs between value drivers, which sometimes may conflict with societal benefits. The integrative review revealed that customer relationship management, global business services and artificial intelligence had not been unified in the extant literature, which makes this paper novel in its contribution to businesses struggling with or opposed to the digital revolution.

Introduction

The evolution of technology has disrupted almost every business globally by continuously transforming, enhancing, and streamlining operational processes and procedures. Digitalisation Footnote 1 is disruptive and brings about discontinuous changes (Paiola & Gebauer, 2020 ), but it is a key element for new value-creation and revenue-generation opportunities for market competitiveness (Kamalaldin et al., 2020 ). Climate change, pandemics, environmental devastation and widening social inequalities have created an abrupt realisation that the existing business models are no longer ‘fit for purpose’. New practices, skills, operational processes, and business models are required to use artificial intelligence Footnote 2 (AI) to create value for customers (Sjödin et al., 2021 ). It is increasingly important for businesses to understand the evolving environment to assimilate for viability in the market and then innovate to gain a competitive advantage. Businesses face pressure to focus on achieving their non-financial goals and not just maximising profits (Rabaya & Saleh, 2022 ). The interconnected elements of environmental, societal and governance (ESG) have provided a catalyst to transform businesses to be more responsive toward the planet and people when pursuing profitability and growth. “The illiterate of the twenty-first century will not be those that cannot read or write, but those that cannot learn, unlearn and relearn” (Toffler, 1970 ). Refining, adapting, revising and reformulating a business model provides businesses with a roadmap for achieving holistic goals by harnessing the strategic advantages of AI technologies.

Digital transformations create new potential for organisations to redefine and optimise their operations by recognising the role of automation Footnote 3 in creating market differentiation and service excellence (Flyverbom et al., 2019 ; Zuboff, 1988 ). The COVID-19 pandemic affected critical business functions across organisations globally, thus serving as an accelerator of digital transformations and the reconfiguration of static business models. The pandemic affected how people operate and customer services are provided, particularly when governments imposed regulated lockdowns to protect human life. According to institutional theory, internal and external pressures (Zucker, 1987 ) accelerate the desire or compulsion to transform an organisation. One such pressure is disruptive digital technology, and the other is the pandemic. The traditional workforce has also been transformed into a blend of humans working collaboratively with AI.

A global survey conducted by Deloitte (2020) found that the largest concern for respondents during the pandemic was the viability of their business models. Some businesses led the business model innovation Footnote 4 , while other companies crumbled. As the contingency theory proposes (Lewin & Volberda, 1999 ), a suitable strategy is required to accomplish a strategic fit with an organisation’s market. Therefore, business model innovation is a key ingredient in underpinning a business resilience strategy, particularly with technological innovation rapidly changing the nature of work. These pressures to innovate in the digital era have widened the gap between innovators and stragglers in the business world. The advantages of conventional business processes that are human reliant are weakening, exposing the fragility of the human capital leverage model, which will be further impacted as AI evolves. Therefore, innovation laggards may fail should they not embrace the principle of accelerating disruptive technologies in their business models. As global economies face unprecedented disruption, a once disruptive business model can become static by becoming complacent or relying excessively on past strategies that may have become outdated. This risk of innovation apathy or myopia motivates businesses to have an agile business model that continually evolves with the disruptive digital era.

A business model is seen as a robust abstract instrument to model a framework for a company’s competitive stance (Hamel, 2000 ) by connecting technical potential with the recognition of economic value (Chesbrough, 2011 ). However, Teece ( 2010 ) argued that approaches to business models are diverse due to the absence of a theoretical grounding in economics or business studies. For this reason, there have been calls for research on business models and value propositions Footnote 5 focusing on market differentiation and industry disruption (Weinstein, 2020 ). Emerging market differentiators are concentrated on labour automation, such as Robotic Process Automation (RPA) and service bots used in Global Business Services (GBS) (OECD, 2007 ; SSON, 2018 ). However, codifiability and digitalisation in the global services literature are absent despite the advantages of the centrality of transaction costs and efficiencies (McWilliam et al., 2019 ). There is an ongoing call for researchers to adapt and extend how AI technologies can be aligned with business (Coltman et al., 2015 ; Santos et al., 2020 ; World Trade Organization, 2019 ). Moreover, a persistent gap exists in academic research regarding the business models using AI for digitalising Customer Relationship Management (CRM) in the global service sector. A necessary first step toward knowledge evolution and model building is a systematic exposition based on theory (Melville et al., 2004 ) and disruptive technology (Parmar et al., 2014 ) that drive an understanding of business model innovation (Teece, 2018 ) to capitalise on business opportunities that overcome pandemic challenges.

With digital servitisation Footnote 6 (Kohtamäki, et al., 2019 ; Vendrell-Herrero et al., 2017 ), the service sector is no longer operating as a separate category, since retailers and manufacturers are entering the service sector with smart services, such as Caterpillar, Michelin, Siemens and Voith Group. They transform their products by embedding software to communicate to the data cloud (Ng & Wakenshaw, 2017 ), which can then be analysed through advanced data analytics for co-created value-added services (Opresnik & Taisch, 2015 ). This study selected the service sector to examine business model innovation, since it is people-centred and an important contributor to the economic environment. A GBS structure was adopted in this study, because it allows the researcher flexibility to incorporate innovative systems with global mobility for the service sector’s offerings. The GBS business model also provides benefits of economies of scale, streamlined processes, superior service quality and scalability of operations through consolidating support functions into a single centre staffed with specialists. This article provides crucial theoretical framing by linking the CRM, GBS and service innovation technologies to business model innovation. This study contributes an innovation continuum, a responsive business innovation model and value propositions focused on market differentiation, service innovation and industry disruption. This study also provided a research agenda to catalyse future research.

Research methodology

This study employed a methodical means of assembling and synthesising previous research (Baumeister & Leary, 1997 ; Tranfield et al., 2003 ) through an integrative review process of experimental and non-experimental research with theoretical and empirical data (Whittemore & Knafl, 2005 ). This study adopted a concept-centric rather than a chronological or author-centric approach (Webster & Watson, 2002 ) due to the inclusion of four streams of literature: GBS, CRM, service innovation and business models.

As Webster and Watson ( 2002 ) envisaged, the research process started with a protocol development to create a defined body of literature for the theoretical development of a responsive business innovation model. The protocol had three phases, as depicted in Fig.  1 . The first phase mitigated the incompleteness risk of the literature review by systematically identifying and reviewing existing databases. While the second phase remedied the overlap from different databases by filtering for duplicates, the final phase focused on creating a consistent structure among all patterns. There was rigorous screening and appraisal of each paper to assess whether its content was fundamentally relevant. A final sample of 79 high-quality articles was selected to build the theoretical constructs for this study. Other articles published by technology or accounting firms in this paper’s literature review and results section were used to establish current market practices. Whittemore and Knafl ( 2005 ) stated that the suppositions of the integrative review could be reported in tabular or diagrammatic form. Since the study intended to develop a theoretical business model in the form of a diagram, a thematic analysis was used to consolidate further and conceptualise higher levels of themes, constructs, patterns and descriptions from articles associated with GBS, CRM, service innovation technologies and business models.

Source: Author

Phases of the integrative review.

Literature review

A theoretical framing is required for constructing a response business model. A business model provides a rationale, design or architecture for strategic choices to create, deliver and capture value (Magretta, 2002 ; Osterwalder & Pigneur, 2010 ) by specifying the structural elements and technology to address the unmet needs and activities of customers (Teece, 2018 ). Accordingly, organisational theory (strategic decision-making), customer relationship management (customer needs), global business service (structure) and service innovation technology provide the grounding for this research.

Organisational theories

The institutional theory provides a multifaceted business outlook on normative pressures from external and internal sources that influence organisational decision-making (Zucker, 1987 ). It determines conventional rules and assumptions (Oliver, 1997 ), whereby conformance to these norms is compensated through improved legitimacy, resources and survival capabilities (Scott, 1987 ). Institutions provide social structures, rules and resources that are important to the service sector. Adopting AI in the service sector differentiates the fourth industrial revolution from the third (Schwab, 2017 ), which triggers adaptive structural processes that progressively change the organisation’s social interaction rules and resources that determine decision efficiency outcomes (DeSanctis & Poole, 1994 ). In the knowledge economy Footnote 7 (Powell & Snellman, 2004 ), greater reliance is placed on the intellectual capabilities of intangible resources as opposed to physical resources for decision-efficiency outcomes.

Extrapolating these theories to the fourth industrial revolution, it is apparent that there are challenges that organisations face to conform to the normative pressures of digital disruption that depend upon each company’s specific circumstances (contingencies). “ A good business model begins with an insight into human motivations and ends in a rich stream of profits ” (Magretta, 2002  pg. 3). Each organisation needs to find a strategic fit within the knowledge economy to gain value-driving opportunities while accelerating its customer-centric initiatives. For this reason, the customer relationship management (CRM) literature provides a framework to delve into human motivations concerning their buying incentives, biases and emotional connections.

Customer relationship management

The core of CRM is understanding customer needs and leveraging that knowledge to increase a firm’s long-term profitability (Stringfellow et al., 2004 ). In the digital era, technology may be leveraged to be customer focused to understand customer needs better. For instance, probing large data sets (big data) may inform CRM strategies (Payne & Frow, 2005 ; Stringfellow et al., 2004 ). Customer data is a rich source of unstructured, voluminous and ambiguous data for further processing through analytics. Data analytics are recommended for managerial strategic decision-making, since it is grounded in evidence rather than perception (IBA Global Employment Institute, 2017 ; McAfee, et al., 2012 ). Knowledge gained from data analytics is essential for building close customer relationships for service differentiation, customer loyalty and value creation.

Irrespective of the industry, the desire to nurture customers is a key success factor driving the need for CRM differentiators to gain a strategic competitive advantage. However, Stringfellow et al. ( 2004 ) criticised knowledge-deficient models developed from superficial customer data (demographics and transactions), since these do not address the functional (purpose-fulfilling) and emotional requirements of customers. They used the study by Schneider and Bowen (1999) to illustrate that decision-making is not dictated by functional needs, since a man may pay double the price to buy a Ralph Lauren polo shirt instead of a similar unbranded polo shirt to fulfil his self-esteem needs. This diversity in customer decision-making illustrates that relational selling may sometimes outweigh value-based selling. Therefore, any customer-centric business model should understand that buyers are not always rational but emotionally guided. For this reason, sales or services can be categorised as value-based to fulfil purpose or relational to fulfil the emotional connections to the product or service.

Global business services

According to OECD ( 2007 ), business services are provided to other businesses instead of customers. Organisations wanting to reduce costs enter the outsourcing market for lower-cost business services. However, within a GBS, various processes and functions are shared and operate unitedly instead of using several shared service centres and dealing with outsourcing vendors independently. The principal objective of GBS is to provide business-to-business services at a reduced fee and at contracted levels of quality that improve practice through lean, cost-competitive, efficient and streamlined processes with an optimised cost structure (Daub et al., 2017 ; OECD, 2007 ; SSON, 2018 ). This goal is achieved by leveraging a range of enablers, including a robust customer interaction framework, standardisation, economies of scale, automation, organisational realignment, labour/robotic arbitrage, implementation of best practices and true “end-to-end” process optimisation (SSON, 2018 ). Thus, companies leverage a GBS model to gain market advantage and operational efficiencies through an agile, focused and leaner service organisation. GBS integrates services that forsake functional silos and transcends to a multifunctional collaborative approach. GBS has an amalgamated delivery model providing “back-office” services to a global customer base, such as accounting, finance, HR, IT and procurement, and increasingly moving to “front office” activities, such as sales, marketing, analytics and reporting (SSON, 2018 ). Currently, businesses are focussed on services related to their digital offerings and the analytics of their customers’ data. Geographical expansion, innovation quest and the adoption of new technologies are important in pursuing profits when competition is rife (Hodgson, 2003 ). GBS, with AI technology, has an opportunity to achieve scalability by integrating its multitude of centres into a single network to expand its range of business across the globe for a competitive advantage.

Most GBS users depend heavily upon intangible assets, particularly technological and service innovations (OECD, 2007 ). GBS centres can integrate automation, virtualisation and analytics, amongst other digital tools and capabilities, into their prevailing processes that provide more effective support to business units (Daub et al., 2017 ). Global organisations, such as Siemens, have incorporated a GBS-type structure into their global multifunctional business model that provides shared services to all Siemens businesses. The two fundamental principles that guide this organisation’s international services centres are customer satisfaction and continuous improvement through innovation (Siemens, 2020 ). For this reason, the GBS-type structure has extended to accounting firms, with their large global networks increasingly centralising certain remote auditing functions through technology and then outsourcing geographic-dependent work to their component auditors. For the longevity of any business, new organisational designs need to evolve that shape human workers, such as service innovation technologies.

Service innovation technologies

The innovation theory proposes that innovations diffuse from early adoption to widespread use (Rogers, 1995 ). However, innovations have a lag effect on their relative advantage (profitability, social prestige, other benefits) over its predecessor. In defining a technology readiness index ranging from innovators to laggards, Rogers ( 1995 ) elaborated on the speed of the adoption being positively related to the perceived benefits, compatibility with the company’s structures, ease of use and trialability (experimental capability). The innovation diffuses at the rate at which an innovation’s results are visible to others (observability). However, the complexity of the innovation is negatively related to the speed of the adoption. Understanding innovation theory is central to constructing or transforming a business model.

The quadruple-helix theory proposes that society can drive the innovation process to design sustainable strategies to achieve social innovations in a green economy (Carayannis et al., 2012 , 2020 ). ESG goals are increasingly being demanded by stakeholders to be incorporated into business models. The focus on ESG has led to traditional business models integrating sustainability while undergoing digital transformation. A sustainable business model delivers multifaceted value to a wider range of stakeholders when compared to the traditional business model (Bocken, et al., 2013 ). Digital technologies allow for strategic planning on economic, social, and environmental performance (Evans, et al., 2017 ). For instance, social network platforms may assist companies in achieving their ESG goals allowing companies to move closer to a green economy. Platforms are technologies that facilitate networking for companies to co-create with stakeholders (Allen, et al., 2009 ). A concept is drawn from the microworking philosophy (Howe, 2008 ), where a large dynamic network enables the organisation to connect with the internal and external environment for co-creation opportunities. Close company–customer collaboration allows for long-term value co-creation (Kamalaldin, et al., 2020 ), where customers co-produce services by providing insights. Types of co-creation opportunities are the wisdom of crowds Footnote 8 (Surowiecki, 2004 ), open innovation Footnote 9 (Chesbrough, 2003 ), crowdsourcing Footnote 10 (Howe, 2008 ) and crowdworking Footnote 11 (Ross, 2010 ). A common feature of these co-creation opportunities is that they all use an open call for knowledge to create innovative solutions. Amazon Mechanical Turk and Uber are examples of the crowdworking philosophy using digital platforms to build networks in the service sector. Leveraging society’s connectivity and responsiveness through platforms facilitates the collaborative designing of personalised products, services and experiences.

Technologies such as RPA and service bots have been widely adopted in the service industry. RPA interacts with the user interface of other computer systems using rule/logic-driven software robots (softbots) that are coded to execute a high volume of repetitive tasks without compromising the underlying IT infrastructure (Deloitte, 2018 ; van der Aalst et al., 2018 ; Willcocks et al., 2015 ). This technology dates to the Eliza programme’s interactive bots that enabled interaction between humans and machines using text-based communication (known as the Turing test) (Turing, 1950 ; Weizenbaum 1966 ). RPA follows prescribed protocols and procedures that increase the speed, accuracy, compliance and productivity of business processes. Footnote 12 Instead of multiple ERP solutions (taking data from one system and inputting it into another system), it is more cost-effective and efficient to integrate RPA into a company’s existing infrastructure and automate processes (van der Aalst et al., 2018 ). However, RPA is on the lower end of intelligent automation, since it uses structured logic and inputs to operate from simple to complex business tasks.

RPA with cognitive automation has allowed softbots to be more useful due to their superior intelligence. Softbots with machine learning Footnote 13 capabilities are designed to mimic human thought and action to manage and analyse big data with greater speed, accuracy and consistency than humans can achieve by leveraging different algorithms and technological approaches (Firstsource, 2019 ). Algorithms do not produce definitive solutions but present probability-based predictions for humans to evaluate and make informed decisions. Table 1 provides a summary of the Softbots.

Softbots are also known as service robots, chatbots, AI bots, AI assistants, virtual assistants or agents, and digital assistants or agents. This study adopts the term service robots, since they are most common in customer support or sales environments, where they are expected to serve customers. For instance, call centre jobs are labour-intensive and employing people’ around the clock’ for one or two late-night phone calls are costly. However, service bots can answer simple queries efficiently and far quicker than a person can. Service bots use Natural Language Processing (NLP) to develop logic from unstructured inputs for human interaction. Service bots with NLP, Natural Language Understanding (NLU) Footnote 14 and Natural Language Generation (NLG) Footnote 15 are distinguished from the greater domain of service bots due to their aptitude to employ language to converse with their clients. Table 2 shows the different types of service bots.

Kiat ( 2017 ) states that service bots can manage CRM quality by handling mundane tasks leaving salespeople to focus on high-value tasks, such as meeting customers and concluding company sales. In general, leads should be attended to within 5 min to convert them to paying customers, which would be achieved with service bots. Other advantages are:

Seamless interface: bots can recall their previous customer interactions and seamlessly verify customer data by linking to social media, so queries are addressed at a speed unmatched by humans. Service bots can also seamlessly transfer complicated cases to human operators, facilitating humans’ foci on higher value customer engagements.

Data enrichment: cost-effectively resolving data leakage problems, since humans often neglect to record key customer information from the various stages of the customer’s purchase process, whereas a service bot would automatically capture the discussion.

Service bots are key differentiators within the IT industry with improved revenue performance and customer value (customer contentment, service delivery and contact centre performance) (MIT Technology Review, 2018 ). Service innovation technologies are employed by renowned brands, such as Amazon, Netflix, Starbucks and Spotify, to name a few. Service bots work reliably and accurately around the clock while maintaining the same competence level without being distracted or fatigued. Service bots also do not have inherent limitations, such as becoming ill, going on strike or requiring leave. In 2019, the banking sector achieved operational cost savings of $209 million from employing service bots. Insurance claims management departments had cost savings of $300 million across motor, life, property and health insurance (Juniper Research, 2019 ). Artificial Solutions ( 2020 ) also reported that Shell attained a 40 per cent decrease in call volume to live agents due to their service bots, Emma and Ethan. They answered 97 per cent of questions correctly and resolved 74 per cent of digital dialogues. Similarly, the service bot Laura is digitally transforming Skoda (a Volkswagen Group’s subsidiary), where customers can discuss their vehicle needs and budget with Laura (Artificial Solutions, 2020 ). Therefore, digitalisation has resulted in customer relationships evolving from transactional to more relational.

Results: theoretical propositions

Several constructs emerged from the thematic analysis of the integrative review for developing a digital business model, reflected in Table 3 .

Using the people, process and technology (PPT) framework (Leavitt, 1964 ), these ten constructs from Table 3 and innovation capabilities are presented in Fig.  2 . This study has added governance to the PPT framework to form the PPTG framework. Governance is imperative for oversight over the value-creating activities (Sewpersadh, 2019a ) to balance the trade-offs from the synergistic benefits of lower costs, increased coordination, greater productivity and value delivery with the ethical and risk concerns over customer data.

PPTG Framework.

In Fig.  2 , people have been expanded to include service bots. Collaboration between service bots, employees and customers are integral for value co-creation. Service bots cost-effectively record customer information from the various stages of their service interactions, allowing for data warehousing. Data warehousing is important for allowing data mining tools and the analysis of critical customer parameters.An ethics and risk officer will play a key governance role in overseeing the principles of fairness and ethics over emerging technologies, such as service bots. Increasingly companies integrate their AI technologies with social media platforms which necessitates the ethics and risk officer to detect, correct and prevent any biases that the service bots learn through the data they collect. For example, service bots may discriminate against customers based on their demographics (Puntoni et al., 2021 ). In 2016, Microsoft launched a service bot called Tay to research conversational understanding. This project failed, because the developers did not anticipate that some Twitter users would teach the bot to make racist, inflammatory and offensive tweets through its Twitter account (Berditchevskaia & Baeck, 2020 ). For this reason, recent studies proposed digital corporate responsibility to guide ethical dilemmas related to AI technology (Lobschat et al., 2021 ). There are also ethical and security risks when service bots impersonate humans (van der Aalst et al., 2018 ), since they may make improper judgements due to contextual changes that may remain undetected, leading to unintended consequences. For instance, service bots may make poor-quality recommendations that do not align with customer interests or may expose customers to vulnerable and risky situations (Mullainathan & Obermeyer, 2017 ). Service bots require service audits to prevent poor service quality outcomes. Service bots also have excessive access and privileges that place them at risk of cyber-attacks. The ethics and risk officer may assist in safeguarding data using surveillance methods to detect intelligent malware. Footnote 16 Research has found that customers are more likely to act unethically and misbehave (LaMothe & Bobek, 2020 ) when interacting with service bots. Therefore, service bots need to be monitored to detect and prevent these infringements.

In Fig.  2 , PPTG is improved with technologies for process value configuration. Technology with people allows for smart analytics on service value capture and optimisation. For example, service staff, key accounts managers and digital developers in Solutioncorp evaluate customer service data to identify priority areas for AI innovation (Sjödin et al., 2021 ). This dispersion of emerging technology gives rise to a disruptive landscape in the knowledge economy, necessitating more R&D and continual business model innovation. The three overarching themes from the constructs presented in Table 3 are innovation, sustainable business models and value creation, which will be discussed further below.

Innovation continuum

The rapid pace of the evolution in technology innovation accelerates the diffusion of innovations (Rogers, 1995 ). The increased R&D in innovation creates a continuum (Fig.  3 ), where companies are not statically classified according to their degree of innovation but rather placed on a continuum. Those businesses that recognise innovations’ relative advantages, compatibility and trialability (Rogers, 1995 ) will move to the higher end of the continuum. Although, a high-innovation company may not remain a disruptor in the market if it becomes complacent or myopic with its innovation strategy and neglects to continuously improve its business processes. This complacency can be explained by the icarus paradox, where success may lead to a path of convergence with an emphasis on the same strategies, which may simplify and desensitise divergent evolving demands (Elsass, 1993 ; Miller, 1990 ). Past successes promote a defensive mindset and overconfidence, resulting in the persistence of the same strategic formulas when executing innovative strategies is the most appropriate response (Sewpersadh, 2019b ) to the market’s changing needs. Thus, this paradox may lead to myopia, complacency and inertia. This complacency leads to a condition of ‘unconscious incompetence’, where the lack of knowledge of the availability of advanced technologies leads to suboptimal decision-making or decision paralysis on deploying such technologies. For this reason, the degree of innovation is bidirectional on the innovation continuum, which allows for the acceleration and deceleration of innovation investment. As business models transition from traditional to transformative ones, eventually evolving into disruptive ones, those with myopic capabilities soon find their business models antiquated. When companies intensify their investment in innovation, they adopt a futurist strategy allowing them to transition up the innovation continuum and challenge complacent companies.

Innovation Continuum.

Rogers ( 1995 ) cautioned that insufficient knowledge, inability to predict consequences or overzealous innovation investments might lead to over-adoption. Also, the complexity or incompatibility of innovations may not be suitable for some businesses, which may jeopardise their positioning on the continuum. For this reason, governance structures, such as a digitalisation committee, are important for moderating the firm’s adoption strategy. This committee will assess the suitability, acceptability, feasibility and sustainability of developing or acquiring innovations. Integrating stakeholder networks in collaborative activities creates trust-based relationships, legitimacy and good governance that allows for the acceptability of innovations. In Fig.  3 , governance optimisation is vital for ensuring value-maximising decision-making concerning value-creating activities for all stakeholders (Sewpersadh, 2019a ).

There could also be a reluctancy to allocate resources for R&D due to a digital paradox (revenue growth is not as expected despite the proven growth potential) (Gebauer, et al., 2020 ). For these reasons, value creation and governance optimisation are unidirectional factors in Fig.  3 and are placed on the high end of the continuum, where disruptive business models operate. Governance is essential to moderate the negative effects of an over-adoption, complex or incompatible innovations and the digital paradox. Good governance is also critical for balancing trade-offs when making strategic decisions. For instance, harmonising the need for legally protected intellectual assets for profit maximisation and sustainability with knowledge sharing to build collaborative networks.

Central to the innovation process is the need for firms to create and acquire “new combinations” of knowledge. Based on the resource-based theory, complementary assets and capabilities are scarce but valuable strategic resources, since they have strong path dependencies that are difficult to imitate (Barney, 1991 ), thus shaping the firm’s competitive advantage in the cooperative network. Since companies compete in a capital-intensive space, with barriers to entry and economies of scale, profits may be achieved with the legal protection of competitive advantages, such as closed innovation. Closed innovation is the internal research within a particular company that is generally protected by patents, so that access to that innovation is controlled by the rightsholder (Chesbrough, 2003 ). Progressively, open innovation has become a way in which key resources are obtained for the development and execution of innovation (Chesbrough, 2003 , 2011 ). Open innovation is a means of sharing costs, ideas, synergies and skills (Chesbrough & Crowther, 2006 ) from value networks to co-create innovation rather than an individual company outlaying capital to conduct R&D from scratch. For this reason, in Fig.  3 , the networking capabilities of a company also follow the direction of its innovation policy due to the collaborative work with extended networks that allow for the acquisition of external knowledge. As innovation diffuses, collaborators within forged networks stimulate newer co-created innovations with superior outcomes.

A significant limitation to knowledge sharing is the disclosure of internal knowledge to external collaborators (Cassiman & Veugelers, 2002 ), commonly referred to as the risk of knowledge leakage (Gans & Stern, 2003 ) or the “paradox of openness” (Laursen & Salter, 2014 ). This paradox describes the fundamental tension between knowledge sharing (value creation) and knowledge protection (value appropriation) in open innovation. Open innovation may increase the imitation tendency of mimetic companies, who benefit from incurring fewer costs and inefficiencies with access to extended networks. Therefore, a company’s position on the continuum and its competitive stance in the industry depends upon its ability to remain at the technological forefront. Consequently, open innovation also poses significant governance challenges to monitoring, controlling, and managing intellectual property rights in enterprise innovation (Graham & Mowery, 2006 ). Hence, risk-averse companies usually have linear business models with a unilateral dependency on internal resources. This tendency to be an information hoarder lends itself to a closed innovation competitive stance. For this reason, the company’s risk strategy must also be considered, since innovation pioneers may be more risk-tolerant than those with more traditional business models. As newer, more revolutionary technologies become available, static business models with poor networks risk being on the low end of the innovation continuum. Companies that have failed to keep at the forefront of technology do not have sustainable business models and may lose their extended networks.

Sustainable business models

The diminishing competitiveness of traditional business models (McGrath, 2010 ) has led to a fundamental rethinking of the firm’s value proposition for new prospects (Bock et al., 2012 ) on refining how an existing product or service is provided to the customer (Velu & Stiles, 2013 ). Reconceptualising structural elements for technology and resource capitalisation to create new activity frameworks and networks aimed at clear value propositions is known as business model innovation (Battistella et al., 2017 ; Hamel, 2000 ; Helfat et al., 2007 ). Therefore, business model responsiveness becomes a critical success factor in addressing challenges in the knowledge economy. A business model’s alignment and coherence should be mutually reinforcing and incorporate a response to the concomitant influence of contextual factors (Dehning & Richardson, 2002 ; Melville et al., 2004 ; Schryen, 2013 ) and lag effects on firm performance (Schryen, 2013 ). The responsive business innovation model, in Fig.  4 is a hybridisation of prior value models with interlinkages to current service technologies employed in the market, including digital platforms, crowdsourcing, blockchain, crowdworking, big data and service bots.

Responsive Business Innovation Model.

Figure  4 ascribes to Santos et al. ( 2015 ), where the model is more about “how is it being done?” than “what is being done? It incorporates an iterative strategy that maps cross-functional relationships between innovations and the underlying activities to be responsive to the evolving economic environment. Large corporates often use share centre services to support their network of firms under a GBS structure. However, with the evolution of AI, the GBS structure can evolve into a digital platform business model. A responsive business innovation model focuses on facilitating interactions across many shared centres by providing a governance structure and a set of standards, so that they operate as one cohesive ecosystem. It is an activity system with interconnected and interdependent activities to satisfy the market’s perceived needs (Foss & Saebi, 2018 ).

The responsive business innovation model enables the acquiring, developing, and integrating of key resources to overcome inertia. Introducing a new business model into an existing organisation is challenging and may require a separate organisational unit to redefine and reconfigure the model. For example, General Electric (GE) experienced business model transformation conflicts when they tried to adopt digital servitisation. There were conflicts between digital and physical service offerings, new ecosystem partnerships and traditional supply chain relationships, digital revenue and product sale models (Moazed, 2018 ). For this reason, positioning a Centre of Excellence (COE) is important, since it can provide the organisational structure, methodology, skills, tools and governance framework for handling the future innovation needs of a large global corporate (SSON, 2018 ). A GBS structure includes a COE for higher level business support and specialist work and thus is incorporated in Fig.  4 . COE comprise of a centralised specialist team to promote collaboration and provide higher value services, resulting in economies of scale. COEs focus on agility, Footnote 17 CRM and talent development while standardising and automating cross-function end-to-end process ownership), resulting in reducing costs and harnessing process efficiency (SSON, 2018 ). Examples of these are procure-to-pay (supply chain and accounting) and hire-to-retire (HR and accounting. The positioning of the GBS is better placed by groups of talent (area of expertise) rather than location, function or lowest costs.

The CRM literature provides a framework to delve into human motivations concerning their buying incentives, biases and emotional connections. For this reason, CRM is at the heart of the business model with AI differentiators (McAfee et al., 2012 ; Payne & Frow, 2005 ; Stringfellow et al., 2004 ) that responds to evolving consumer behaviour and expectations. The deep knowledge of consumers’ emotional and functional needs allows businesses to optimise capital to address those needs. This strategic response to customer needs and experience requires standardisation (lower costs, benchmark service quality) and differentiation (premium service). For instance, businesses could standardise business processes through RPA for efficiency gains but personalise services via service bots for market differentiation.

Service bots are key components of a digital strategy for entities searching for innovative and cost-effective means to build closer customer relationships (Artificial Solutions, 2020 ). With a GBS structure, the service bots may need to be multilingual due to the diversified client base. Furthermore, by integrating with social media (shown in Fig.  4 ), service bots can access clients’ online data and learn their preferences, sentiments, outlooks and proclivities. The data from clients’ online presence are often undervalued, but access to this enables businesses to transcend beyond basic business intelligence. Therefore, the service bot’s initial customer interaction will offer a superior service through seamless verification of personal information (similar to the Facebook sign-up process) and quick information transfer through hyperlinks. A seamless trail of conversations can be achieved whenever users swap from device to device (cross-platform Footnote 18 ), since this practice improves engagement and customer fulfilment (Artificial Solutions, 2020 ). The increased customer engagement means more actionable and enriched data to train service bots to personalise the customer’s experience. In so doing, service bots can service customers more competently and cost-effectively without human error (Artificial Solutions, 2020 ; Kiat, 2017 ).

A limitation of service bots is that humans can notice tone and subtext in a way that a service bot could never master. This disparity calls for cross-functional collaboration between service bots and higher skilled humans, transitioning toward blended workforces. Data-centric CRM harness the potential of big data to focus on not only the functional but also the deeper psychological aspects of buying behaviour (Stringfellow et al., 2004 ). Access to client data is essential for value creation (Paiola & Gebauer, 2020 ) to improve existing services and create novel innovations (Opresnik & Taisch, 2015 ) within the confines of privacy laws. Automating customer interaction with service bots (see Fig.  4 ) allows for a higher degree of message personalisation without increasing personnel costs. In-depth analysis of unstructured conversational data conveys perceptions on what is done well or what can be improved by the business to develop market differentiators for a strategic competitive advantage. Smart analytics, such as sentiment analysis, support businesses in gauging their customers’ mindsets Footnote 19 and analysing the customer’s journey more effectively while remaining within the confines of data safety legislation.

Strategy guides and shapes by including the company’s brand reputation, Fig.  4 . The iterative CRM engagement strategy and value outlook (short, medium and long term) is built from big data collected from the AI-led CRM and crowdsourcing from their networks. This process allows companies to leverage their large network of end-users to inform the co-created products, services and experiences. A large network also provides microwork opportunities through crowd-working platforms for comprehensive support and supplement human labour. However, managing the trade-offs between stakeholders, technology, and societal benefits is important. Stakeholder engagement is essential in identifying key stakeholder requirements for these benefits to occur. Accordingly, business models should recognise and incorporate environmental, social and governance (ESG) goals, whereby trade-offs must be managed. For instance, automation disrupts the human capital leverage model, in which a trade-off exists between harmonising the prospective savings from automation and the human impact of job losses. Due to the escalation of global warming, business models must also incorporate innovative sustainable environmental solutions (Carayannis et al., 2020 ). Therefore, innovations must be expanded beyond service innovations to ESG innovations.

In Fig.  4 , the benefits of using blockchain technology in a business model are also presented. Blockchain represents an endlessly accumulating list of records stored in “blocks” protected using cryptography principles (Arnaut & Bećirović, 2020 ). The peer-to-peer protocol ensures unambiguous and common ordering of all transactions in blocks, a process that guarantees consistency, decentralisation, integrity and auditability (Arnaut & Bećirović, 2020 ; Yuan & Wang, 2018 ). These features make the blockchain’s permanent ledger resistant to data manipulation, which is a value contribution to the company.

Value creation

A business model’s lifecycle involves “periods of specification, refinement, adaptation, revision and reformulation” (Morris et al., 2005  pg.732). The business model’s initial period in the lifecycle has a process of trial and error, where core decision-making delimits the firm’s evolution. For this reason, a value creation cycle is essential to harness a sustainable competitive advantage by continuously refining, adapting, revising and reformulating a business model to counteract the limitation of becoming static. In Fig.  5 , the importance of the continual assessment of the contextual factors, and the suitability thereof, feed into the value creation cycle necessitating the need for change. However, the suitability of this change must be assessed in terms of the company’s contingencies. Research is necessary for informed decision-making on whether the change is incremental versus transformative to reap all the benefits and value that innovations offer. For value creation, the decision-making process should be free from bias and consider the business’s ESG values, goals, and trade-offs. It is also important to be cognisant that there is a time lag before benefits can be realised. A value architecture may also assist in alleviating some of the trade-offs, particularly structuring a digitalisation committee.

Value creation cycle.

The value architecture (Osterwalder & Pigneur, 2010 ), presented in Fig.  6 , allows a responsive business innovation model to capture and create market activation to build the deep, compelling experiences customers desire with service-related products. However, there is a need to balance the trade-offs between conflicting value drivers. For instance, costly R&D may have environmental consequences that conflict with the desire to provide a good return on capital. For this reason, a clear value preposition is the first step in the value architecture. A value preposition is the underlying economic logic explaining how value is delivered to customers at the appropriate cost (Magretta, 2002 ). The building blocks of value proposition, configuration, delivery and capture (Osterwalder & Pigneur, 2010 ; Osterwalder et al., 2005 ) must be considered to develop a sustainable competitive advantage for the organisation (Teece, 2010 ). While the value preposition remains customer centred, the value configuration and capture are focused on relational selling using technological innovations. While the value delivery is focused on efficiency and service optimisation using service innovations.

Value Architecture.

With the global environment moving so swiftly, multidisciplinary research is necessary to condense and intensify business knowledge. This study highlights the need to examine the discontinuous shift in the scope and culture of business models by exploring interdisciplinary streams of literature. An analysis of the recent literature revealed a lack of research fusing automated technologies in the business models of CRM-intensive companies. This study bridged the theoretical frameworks of organisational theories to learn how contingent characteristics influence the design and function of business models. A key contribution was the inclusion of structural elements (GBS, CRM and AI) to design a responsive business innovation model to create, deliver and capture value. It was established that AI-led CRM in a GBS structure yields a greater focus on generating innovative services that satisfy customers’ emerging needs as well as balance ESG goals. Instead of just customers just being consumers, they can be strategic networks to collaborate and co-create outcomes by integrating CRM and AI technologies into a GBS structure.

Global businesses must update their cost focussed models to transcend into the digital age by moving forward on the innovation continuum model and refocussing on customer-centric service innovations to thrive in this evolving environment. An over-reliance on past successful formulae and static business models leads to the eventual demise of AI-complacent companies. A prime example was seen during the COVID-19 pandemic when some businesses adapted swiftly to the enforced lockdowns using more digital avenues of earning revenue, while others failed to advance up the innovation continuum and closed their businesses, resulting in the loss of millions of jobs. The COVID-19 pandemic is not the only crisis faced by the global economy, since there have been other life-threatening epidemics, such as the Zika virus, MERS, Swine flu, SARS, Aids and Ebola. Businesses need to adapt to the ever-changing environment with cognitive flexibility and agility to transform their business in the wake of any crisis. Structures such as the COE may assist companies in averting the risk of unconscious incompetence in respect of evolving AI and place them at the forefront of the innovation continuum for sustained viability. Static business models can use existing digital platforms to enhance their services, enabling them to move up the innovation continuum. These businesses will have collaboration and co-creation opportunities from the large networks on the high end of the innovation continuum.

This article illustrated the benefits of AI, specifically how service bots can assist in creating new and improved business models in business-to-business and business-to-consumer markets with CRM adoption. Since service bots are a market differentiator, businesses at the forefront of service innovation are assured of resilience, even when faced with the threat of a pandemic. Service bots use real-time data to predict and influence customer behaviour, preferences, buying incentives, and spending tendencies. The un-leveraging of the human capital model has accelerated at an unprecedented level amid the COVID-19 pandemic and is foreseen as being at its most impactful in the post-pandemic period. The effects of AI technology on the human capital leverage model vary depending upon humans’ skills set. AI technology is negatively associated with low-skilled workers but significantly positively influences highly skilled workers.

Multinationals have better opportunities than single-country competitors to experiment with various business models in different geographies and then transfer those validated models to all geographies in which they can capture value (Teece, 2014 ). In the digital transformation era, customer-centricity and global marketplace competition, shared services have evolved from outsourcing to in-housing/re-shoring a GBS model for developing a single and consistent approach to providing internal customer services across functions and geographies. For GBS to stay at the forefront of service delivery development and remain competitive, GBS leaders must leverage and scale these new technologies. GBS’s global reach and governance, standardised processes, extended business process ownership and use of consistent operating models and technologies make them ideal candidates for implementing and delivering the aforementioned AI arbitrage benefits for their operations. This study has illustrated the tremendous strides made in AI technologies, whereby AI investment does not comprise resource-depleting disbursements but encompasses intangible assets through which the system autonomously learns and continually advances. These digital avenues provide key market differentiators in customer service.

Management cannot rely exclusively on in-house expertise and needs the benefits of mechanisms, such as crowdsourcing and crowdworking, to create a comprehensive sustainable business model. However, regulators need to be wary of the potential ramifications of crowdsourcing and crowdworking, since opportunistic companies may exploit these platforms for cheap labour. Blockchain must be considered when proposing disruptive models due to its revolutionary potential. As businesses move to scale their digital ingenuities, a focus is placed on the agility to respond to consumers’ evolving tastes with diminishing lag times due to the availability of real-time data.

Inevitable changes in business models are necessary as organisations shift how they create, capture and deliver value. For these reasons, this study developed key value drivers grounded in the theoretical framework. The key findings of this article are the various conflicting trade-offs between value drivers and ESG goals in digital business models that require executives to harmonise. Some examples of these trade-offs were:

the societal impacts of human job losses conflict with the efficiency and cost benefits of cognitive automation,

the utilisation of customer conversational data conflicts with remaining within the confines of data protection legislature,

the cost of software intrusion detection systems to avoid losing confidential data conflicts with the desire to maintain profitability margins,

the cost of innovation R&D conflicts with the desire to provide a good return on capital, and

the standardisation of processes conflicts with the customisation of services to avoid the loss of strategic competitive advantage.

This study identified governance as a key mechanism in managing ethical issues and risks. Concerns about consumer privacy may cause governments to prevent some important innovative developments in global services (World Trade Organization, 2019 ). Data security is a crucial concern for any business due to security risks when handling customers’ personal information. For example, in 2018, Facebook was guilty of invading users’ personal data and giving this information to other large corporations, such as Amazon, Microsoft and Spotify, to increase Facebook’s users and revenue (Dance et al., 2018 ). Although regulatory user protection laws exist, businesses must employ centralised data management with cognitive analytics capabilities, encryptions, independent security audits and codes of practice. Personal identifiable data is a highly valuable commodity in the digital age but is also unsafe, since any data breaches will result in customers losing trust. Kelley ( 2019 ) recommends that a successful security protocol is to program service bots to identify personal and/or sensitive information and treat it accordingly. Systems must be able to anonymise or pseudonymise conversational data, replacing identifiable data with placeholders, so users can still understand the intent for analytics purposes but not know the customers’ identity (Kelley, 2019 ). Despite the challenges of surveillance and privacy issues, digital technologies are increasingly central to people, organisations and societies (Flyverbom et al., 2019 ). For instance, the UK government has invested more than £1 billion into an AI industrial strategy (Berditchevskaia & Baeck, 2020 ), thus, illustrating that some countries have grasped the opportunity to build value-added resiliency into a service delivery model.

Recommendations

Companies should be aware of their business model lifecycle to avoid becoming stagnant. Therefore, it is recommended that they adopt a responsive business innovation model with a value-creating cycle to continuously refine, adapt, revise and reformulate their business model. To achieve this, companies should also have an innovation strategy driving a customer-centric service innovation culture while reducing costs and leveraging the finest skills. Organisations should consider establishing a COE with an innovation leader to be at the forefront of innovative technologies.

The COE would seize, assess and manage cognitive automation technologies for data governance. The COE is vital for providing leadership, driving change, and influencing business strategy and multiple onboard stakeholders across the business. COE’s essential function is driving an automation strategy as follows:

Develop an iterative strategy to extend and expand existing capabilities through automation.

Drive a holistic AI-enabled disruptive operating model, similar to the model proposed in this article, that is cost-efficient and leverages ‘fit-for-purpose’ technology to inspire ‘out-of-the-box’ thinking and nurture an entrepreneurial ethos.

Incorporate and harness a digital platform strategy management that accelerates the rate of digital platforms to realise cost savings and drive resiliency.

Initiate regular consolidating and mapping of business processes to identify areas of duplication and labour-intensive processes for an automation analysis to appraise potential benefits.

Create an AI-intensive GBS with an effective COE to use cognitive automation technologies in customer-centric service delivery.

Ensure CRM focuses on new customer onboarding forms and data-driven methods.

Benchmark against industry and competitors to ensure that the company’s technology has a competitive advantage.

Create consistent and frequent communication channels between COE and those charged with firm governance.

Design a data governance model to determine control and direct the use of data (how and for what purpose).

Create guidelines on data protection, privacy, intellectual property rights and ethical issues in data management.

It is also highly recommended that the public sector employs AI-intensive technologies, specifically RPA and service bots, that can streamline business processes. This sector’s work is extremely labour intensive, which is inefficient and resources depleting, given the recent rise in digital technologies. The large burden placed on taxpayers to supplement the ever-increasing public sector budgets is not met with improved outcomes. Lower level public officials’ mundane and repetitive work, such as capturing information from one system to another, using ineffective reporting templates and manual month-end tasks, are time-consuming, costly and widen the margin for human error. The public sector is also continuously dealing with fraud, tender bribes and schemes that impair its ability to deliver public services efficiently. The employment of digital agents can improve and expedite these laborious, inefficient and frustrating processes and, even more importantly, alleviate fraud to some degree.

Future research agenda

This study focused on the value of service innovation technologies in responsive business innovation models. However, there is an abundance of future research explorations in the list below, which is not exhaustive.

Service bots

Research that empirically tests the customers’ satisfaction journey with digital workers versus human workers, particularly from a customer demographic perspective. For instance, NLP has made strides in making service bots more humanlike. However, there needs to be research that interrogates which customer demographics are more amenable to service bot services and which are not. Furthermore, research needs to be conducted on service bots’ ability to match their customers’ evolving needs.

There should also be studies examining the emotional consequences on customers when their needs are addressed by service bots, particularly from a customer demographic perspective and any potential extensions to service bot biases.

Research examining customers’ concerns over privacy and data leakages and which service bot interactions are more likely to trigger these concerns.

Investigations into the potential impact on the company reputation/brand when faced with negative service bot interactions and biases, amongst others.

Research investigating potential trust or control issues when customers and employees rely on work performed by service bots.

Public service sectors

As with institutional theory, government intervention is also necessary for a functional digital ecosystem concerning infrastructure and access to funding and investment resources. Studies should investigate government funding structures to encourage more innovative R&D.

An appraisal of the public sector’s readiness for the digital transformation of their business model, since automated processes will result in societal benefits of service efficiency and tax savings for citizens.

An empirical study on the suitability of a GBS innovation model for the external audit service. Due to the nature of their service, there is potential for a suitable fit.

Open source/collaborative technologies

An investigation into the use of open innovation systems and collaborative platforms in assisting start-up companies with their digital transformation.

Availability of data and materials

Freely available using online research databases.

Digitalisation or digital transformation is the use of AI technology in the business processes and activities of a company.

AI is distinct from conventional information technology and is defined as the ability to learn, connect, assimilate and exhibit human intelligence.

Automation is defined as the employment of technologies to perform a process or task that reduces human intervention.

Innovation in terms of this research refers to business model, service and technological innovation.

A value proposition enables stakeholders to understand how the business intends to use its strategic resources, which is then mapped to the business model.

The transition from products and add-on services to smart solutions with connectivity, monitoring, control, optimisation and autonomy is known as digital servitisation.

The knowledge economy is defined as production and services based on knowledge-intensive activities that contribute to an accelerated pace of technological and scientific advance as well as equally rapid obsolescence (Powell & Snellman 2004  pg. 201).

Wisdom of crowds uses a wide range of annotators to create large datasets, for example, Wikipedia.

Open innovation is the free flow of knowledge to accelerate internal and external innovation.

Crowdsource is an open call to internet users to get innovative solutions.

Crowdwork is “the performance of tasks online by distributed crowd workers who are financially compensated by requesters (individuals, groups, or organizations)” (Kittur et al., 2013 pg. 1).

Business processes are activities that underly value-generating processes such as transforming inputs to outputs (Melville et al., 2004 ).

Machine learning allows a machine to learn by using algorithms to analyse and draw inferences from patterns in data without direct intervention.

NLU helps bots understand the user by using language objects (such as lexicons, synonyms and themes) in conjunction with algorithms or rules to construct dialogue flows that tell the chatbot how to respond.

NLG enables bots to interrogate data repositories, including integrated back-end systems and third-party databases for information to be used to create meaningful and personalised responses that are beyond pre-scripted responses.

Intelligent malware is AI-based and exploits vulnerabilities by mimicking normal user behaviour to avoid being detected.

Agility can be described as a dynamic process of anticipating or adjusting to trends and customer needs without diverging from the company vision (Fartash et al., 2012 ).

Cross-platforms recognise inter-relationships and complimentary services through different software applications and devices.

Mindset is the attitudes and norms that either inhibit or encourage people’s or firms’ decisions.

Abbreviations

• Artificial intelligence

Chief information officer

Centre of excellence

Coronavirus disease 2019

Enterprise resource planning

Environmental, social and governance

Information technology

Organisation for economic co-operation and development

Natural language understanding

Natural language generation

Natural language processing

Robotic process automation

Research and development

Service robot

Software robots

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A Novel Approach for Forecasting Price of Stock Market using Machine Learning Techniques

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• Abhinay Yadav 1 ,
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• Satyendra Singh 1 &
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In today’s competitive business world, industries strive for rapid growth and leadership. Expanding a business requires additional capital, which can be raised through an initial public offering (IPO), angel investors, or business loans. As a company grows, it becomes difficult for individual investors to sustain operations with their capital alone, necessitating a constant influx of funds. Conducting an IPO not only raises capital but also enhances the company’s reputation and credibility. It can also allow founders or early-stage investors to sell part of their ownership. After an IPO, the company’s shares are publicly traded as stocks, offering various benefits when included in a public investment portfolio. Investing in stocks from different companies enables individuals to accumulate savings and safeguard their wealth against inflation and taxes. However, accurately predicting stock prices is crucial for maximizing investment returns. In this research paper, the main goal is to predict the stock price. To achieve this, a special Hybrid model called LSTM + GRU is used. Two case studies have also been done to support the result. The first case study is done with Tata Motors and the other is with Honda Motors. Different measures, such as RMSE, MAE, and MSE, are used to assess how well the model performs. The results are presented in a visually appealing way, allowing for easy understanding and comparison with other existing models. By conducting this research, our objective is to provide valuable insights into predicting stock prices, helping investors and decision-makers make informed choices

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Department of Information Technology, Rajkiya Engineering College Ambedkar Nagar, Akbarpur Ambedkar Nagar, U.P., 224122, India

Abhinay Yadav, Vineet Kumar, Satyendra Singh & Ashish Kumar Mishra

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Yadav, A., Kumar, V., Singh, S. et al. A Novel Approach for Forecasting Price of Stock Market using Machine Learning Techniques. SN COMPUT. SCI. 5 , 686 (2024). https://doi.org/10.1007/s42979-024-02916-z

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Received : 29 September 2023

Accepted : 19 April 2024

Published : 28 June 2024

DOI : https://doi.org/10.1007/s42979-024-02916-z

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