The Twin Transformation Butterfly

Organizations need ordinary capabilities to organize recurring processes or to perform efficiently in the market – for example, to manage production processes (Teece 2014). Organizations also require dynamic capabilities to build, conjoin, and configure ordinary capabilities and assets when exploring new markets or products – for example, while creating digital innovations (O’Reilly and Tushman 2013; Piccoli and Ives 2005; Teece et al. 1997; Winter 2003). Once identified, dynamic capabilities can unfold benefits, as they enhance an organization’s potential to (1) identify new developments and opportunities, (2) make timely and market-oriented decisions, and (3) systematically solve problems (Barreto 2010; Teece 2007). In essence, dynamic capabilities are essential for effective organizational transformations, as they initiate change with a view to ensuring competitive advantage (Teece 2007, 2014).

Dynamic capabilities have gained momentum in IS research (e.g., Huber et al. 2022; Steininger et al. 2022; Wade and Hulland 2004). IS scholars have investigated dynamic capabilities in relation to the integration, deployment, and connection of digital technologies combined with other assets, such as human capital (Bharadwaj 2000; Huber et al. 2022; Wade and Hulland 2004). Furthermore, IS research has predominantly applied two complementary perspectives on dynamic capabilities: first regarding their orientation, and second regarding their role in value creation. First, focusing on the orientation of the capabilities, IS research has grouped dynamic capabilities into three different perspectives (Doherty and Terry 2009; Felipe et al. 2019; Wade and Hulland 2004): (1) inside-out dynamic capabilities, which are deployed from the inside of an organization in response to market requirements (e.g., IS infrastructure, technical skills), (2) outside-in dynamic capabilities, which are externally oriented (e.g., market responsiveness, managing external relationships), and (3) spanning dynamic capabilities, which are needed to integrate an organization’s inside-out and outside-in dynamic capabilities (e.g., working partnerships, IS management and planning). Second, dynamic capabilities in the context of IS have been subdivided into primary and support capabilities, differentiating the capability's role in value creation (Huber et al. 2022; Porter 1995). Primary capabilities focus on delivering new product and service solutions, directly influencing value creation. By contrast, support capabilities focus on improving support processes and collaboration, thus indirectly influencing value creation by increasing the effectiveness of primary capabilities.

Following up on existing IS research, this work refers to dynamic capabilities as the organizational ability to (1) scan the environment regarding opportunities and threats, (2) make decisions based on identified opportunities, and (3) transform ordinary capabilities and assets to realize the identified opportunities withstanding rapidly changing environments (Steininger et al. 2022; Teece et al. 1997; Teece 2007). In the following section, we examine digital and sustainability transformation as the two building blocks of twin transformation and present related research on associated dynamic capabilities.

Digital transformation has garnered significant interest among IS researchers and practitioners for several years (El Hilali et al. 2020; Konopik et al. 2021; Kraus et al. 2022). Such transformation is associated with changes in the self-understanding of organizations (Wessel et al. 2021), the nature of strategy (Bharadwaj et al. 2013), and pathways to value creation (Karnebogen et al. 2021). The new value creation is rooted primarily in emergent digital technologies that trigger digital transformation and often promise to harness novel organizational value (Karnebogen et al. 2021). As scholars in the IS field, as well as practitioners, have not yet reached full conceptual and empirical clarity in relation to the digital transformation phenomenon (Markus and Rowe 2023), numerous and diverse definitions have been proposed for digital transformation (e.g., Hartl and Hess 2017; Soluk and Kammerlander 2021; Warner and Wäger 2019). For example, Vial (2019, p. 3) defines digital transformation as “a process that aims to improve an entity” by adopting digital technologies. Digital technologies are an umbrella term for information, computing, communication, and connectivity technologies (Bharadwaj et al. 2013). According to Vial (2019), the adoption process usually triggers significant changes to the properties of the entity involved, which may be a process, a business unit, or another organizational element. Other scholars suggest that digital transformation goes even further, as the adoption of digital technologies impacts value creation and capture, enabling completely new digital business models (e.g., AlNuaimi et al. 2022; Kreuzer et al. 2022; Wessel et al. 2021). This has potential consequences and implications for different organizational actors and environments (Chanias et al. 2019; Kraus et al. 2022). Furthermore, digital transformation is expected to trigger the development of new organizations. This might involve new market entrants and developments within incumbent organizations that aspire to enhance their competitive advantage (Kraus et al. 2022; Osmundsen et al. 2018).

Scholars have investigated the dynamic capabilities required during a digital transformation, such as those needed to establish digital leadership (Konopik et al. 2021), a digital strategy (Keller et al. 2022), or a digital culture (Weritz et al. 2022). Exemplary dynamic capabilities refer to important initiatives that are part of a digital transformation, such as facilitating data collection and analysis during operational processes – for example, by implementing Industry 4.0 (e.g., Santos and Martinho 2019), or the rollout of process automatization (e.g., Kırmızı and Kocaoglu 2022). Other examples include fostering access to innovative digital infrastructure – for example, to green data centers and thus to green cloud computing (Battleson et al. 2016) – and building a stable funding model for this infrastructure to support the progress of digital transformation (Montealegre et al. 2019). Working with innovative digital infrastructure creates the foundation for the development of other dynamic capabilities such as designing modular processes (Battleson et al. 2016).

Acknowledging and complementing existing valuable insights regarding digital transformation capabilities, this study aims to develop an overview of relevant dynamic twin transformation capabilities. To establish common ground, we define digital transformation in line with Vial (2019), Wessel et al. (2021), and Hanelt et al. (2015) as an organizational change process that improves an organization through digital technologies and may lead to profound changes in value creation and the organizations’ identity.

Like digital transformation, sustainability transformation also refers to an organizational change process that represents a paradigmatic shift in terms of multi-dimensional change (Dao et al. 2011; Dyllick and Muff 2016; Lahtinen and Yrjölä 2019). Efforts to achieve sustainability transformation have increasingly captured the attention of researchers and practitioners (Peters and Simaens 2020). This work builds on the concept of Dyllick and Muff (2016), which defines a truly sustainable organization as one that seeks to have a significant positive impact in critical and relevant areas for society and the planet. We chose to build our work on this concept, as the typology adopted by these authors identifies three maturity levels of business sustainability on the basis of three elements: the what (input), the how (process), and the what for (output; Dyllick and Muff 2016). With this outside-in perspective, serving the common good – defined as benefiting society and the planet as a whole – becomes the main organizational purpose (Dyllick and Muff 2016). Sustainability transformation is undertaken by organizations that review and involve the external environment in which they operate. Moreover, sustainability transformation is present in organizations that ask themselves how to overcome pressing challenges by developing new strategies and business models (Bocken et al. 2014; Geissdoerfer et al. 2018). Consequently, sustainability efforts provide opportunities for innovation and new business models (Bocken et al. 2014; Geissdoerfer et al. 2018). This has resulted in increasing attention being devoted to business model transformation for sustainability in the past few years (Abdelkafi and Täuscher 2016). Today, there are many different forms of sustainable business models. One popular business model centers on the circular economy concept, whose primary objective is to minimize resource input (Ortega-Gras et al. 2021; Zeiss et al. 2021).

Scholars have investigated the dynamic capabilities required during a sustainability transformation, such as those needed to introduce sustainable business practices or market strategies (Gimpel et al. 2020; Mousavi et al. 2018), to foster sustainable process innovation (Chiou et al. 2011), or to initiate sustainability training for employees (Wu et al. 2013). Dynamic sustainability capabilities help organizations anchor systematic life cycle management and integrate sustainability practices into their routines (Bianchi et al. 2022). This supports organizations in integrating sustainability aspects in product life cycles (e.g., Yazici 2020). In addition, dynamic sustainability capabilities translate into formalizing structural mechanisms to involve important stakeholders (Montealegre et al. 2019). Embedded structural mechanisms enhance the collaboration between specialized sustainability units and product developers or owners (Dangelico et al. 2017).

As a foundation, we understand sustainability transformation as an organizational change process that is multilayered, complex, and relates simultaneously to environmental, societal, governmental, regulatory, and individual factors (Lahtinen and Yrjölä 2019; Oghazi and Mostaghel 2018; Seidel et al. 2014). The integration of sustainability within an organization involves reshaping strategy and operative processes, with the ultimate goal of reducing cost, increasing competitiveness, supporting durable profitability, and becoming a viable, long-term contributor to society and the business market (Cici and D’Isanto 2017; Dao et al. 2011; Peters and Simaens 2020).

Conceptualizing twin transformation makes it possible to organize the complex phenomena with a common language, thus facilitating communication between researchers and practitioners and serving as a fundamental building block of twin transformation theory (Podsakoff et al. 2016). With a view to identifying fundamental characteristics of twin transformation (Podsakoff et al. 2016), we followed four critical steps of construct development based on Suddaby (2010) and Podsakoff et al. (2016). First, we explained the nature of twin transformation based on prior literature and the outlined digital and sustainability transformation constructs in isolation. Drawing on this justificatory knowledge, we elaborated on the synergetic interplay between these constructs and delineated contextual application areas. Second, we abstracted relevant twin transformation elements into a robust conceptual generalization, providing a new definition of twin transformation that demonstrated its relationship to similar, preexisting constructs. Third, we demonstrated logical consistency of the twin transformation construct in relation to our overall theoretical argument. Although presented in a linear manner above, this process of conceptualizing twin transformation was dynamic and iterative and proceeded to unearth a well-crafted construct that captures essential elements and highlights similarities to and differences from prior research. Throughout the process, we held intense discussions within the author team to ensure a continuous mutual understanding on the progress of conceptualization. Further, we included feedback from external IS researchers, as well as practitioners from organizations that were striving to become twin transformers.

To explore the dynamic capabilities associated with twin transformation, we followed IS literature’s qualitative research genre (Gioia et al. 2013; Sarker et al. 2018) leveraging qualitative empirical data from interviews with practitioners. Qualitative methods are suitable tools for discovering and exploring new research areas (Miles and Huberman 1994). Such methods can be described on the basis of two dimensions (Sarker et al. 2018): (1) the approach to data (data-centric or interpretation-centric) and (2) the process of reasoning (discovery/inductive or confirmation/deductive). Since we treated our interviewees as “knowledgeable agents,” we followed a data-centric approach (Gioia et al. 2013; Sarker et al. 2018). Consequently, our interviewees are hereafter consistently referred to as “key informants” (Kumar et al. 1993). We gave our key informants a voice in the early stages of data gathering, analysis, and result reporting, thus creating opportunities to discover new concepts and interrelations (Gioia et al. 2013). In addition, our process of reasoning was inductive since we treated our data as representative facts and built a framework grounded in data (Bhattacherjee 2012; Sarker et al. 2018).

We conducted 20 one-on-one, semi-structured interviews with digital, sustainability, and innovation managers from 15 different organizations in various industries from February 2022 to June 2022. We deliberately chose an industry-agnostic sample, as digital, sustainability, and twin transformation are certainly spread across industries. Details of the organizations, industries, and key informants’ roles are presented in Supplementary Appendix A. All key informants represent organizations that had conducted a fruitful digital or sustainability transformation and may have considered combining aspects of both single transformations. We argue that digital or sustainability transformations still serve as valuable sources of information and that organizations have gained capabilities from a digital-first or sustainability-first transformation already undertaken. In essence, we observed several organizations that did not consciously aim toward twin transformation, but implicitly tapped into the advantages of digital and sustainability transformation synergies. Some organizations, for example, understood the importance of digitalization for their sustainability transformation (e.g., establishing data transparency for environmental, social, and governance (ESG) reporting) or of sustainability for their digital transformation (e.g., replacing data centers with green cloud solutions).

Having recruited our initial participants via personal networks, we continued with snowball sampling. In some cases, for example, we received contacts during our interviews as the organizations were partly connected to each other (e.g., through ecosystems, supply chain, or personal relations). This produced a somewhat unrepresentative sample, but is a well-established approach that provides access to key informants who may otherwise be difficult to identify or reach (Heckathorn 1997). We kept including organizations until the point at which few or no new insights could be generated by including more data. The interviews were held in German via video conference. They typically lasted between 30 and 45 min (11,67 h in aggregate). With our key informants’ consent, we recorded and transcribed all interviews for subsequent analysis.

Prior to each interview, we provided a clear description of the project and its purpose. Drawing on the dynamic capability literature (Teece et al. 1997), we created an interview guide based on an iterative approach. Although the interviews were conversational, an interview guideline protocol (see Appendix B) covered topics related to our research question, without leading respondents in any direction (Gioia et al. 2013). The first block comprised the general organizational relevance of both digital and sustainability transformation and explained the research topic to our key informants. The second block focused on explicit activities toward twin transformation: Each key informant was asked to name and describe current digital and sustainability initiatives in which they were personally involved. The intention here was to identify organizational factors that stimulated and supported the development of dynamic twin transformation capabilities. The last block was intended to provide space for key informants’ final remarks.

We performed a pattern-identifying technique to gather empirical qualitative insights from the interview data and to cluster text segments into meaningful concepts, themes, and aggregate dimensions (Gioia et al. 2013). Going back and forth between the literature and data, we consistently compared and contrasted new categories as they emerged and discussed their interconnections. The data were analyzed using the MAXQDA qualitative data analysis program. During the analysis, we used memoing by writing down ideas on key informants’ quotes as they came to our minds while coding (Bhattacherjee 2012). The coding process was divided into three stages: open, axial, and selective coding (Corbin and Strauss 1990). An exemplary coding scheme outlining the coding process is presented in Appendix C. Although presented in a linear manner below, our analysis was dynamic and iterative, with the various aspects being addressed simultaneously. We continued coding new data and refining our findings until the point at which additional interviews did not yield any change in the core categories and relationships. In the following section, we provide details on the coding steps (i.e., open, axial, and selective) and how we derived first-order concepts, second-order themes, and aggregate dimensions.

In the first-order analysis, which relied on informant terms close to the original data, we read the interview transcripts thoroughly and highlighted important text passages. After the first 13 interviews, ~ 210 primary codes emerged. As part of the open coding process, we grouped the primary codes according to equivalent content statements from our key informants. We derived 17 first-order concepts and labeled them, while remaining close to the key informants’ actual wording (Gioia et al. 2013). In the course of the coding process, we invariably encountered the issue of different authors interpreting certain informants’ terms differently. If agreement could not be reached on certain codes, we revisited the transcripts, engaged in discussions, and developed mutual understanding and consensual decision rules. As the research progressed, we conducted a further seven interviews. While constantly comparing and evaluating similarities and differences, three additional first-order categories emerged. Next, we reviewed whether all identified capabilities matched our understanding of dynamic capabilities. In doing so, we removed four first-order categories as we classified them as ordinary capabilities. Finally, we derived a total of 16 first-order concepts. Following Gioia et al. (2013), we searched for similarities and differences between the codes (similar to Corbin and Strauss’s (1990) notion of axial coding) and examined related literature. On the basis of existing literature (e.g., the outside-in/inside-out capability taxonomy of Wade and Hulland (2004)), we interpreted our data by asking whether the emerging themes suggested concepts that might assist us in describing and explaining the phenomena we were observing (Gioia et al. 2013). Moreover, we consistently searched for dynamic interrelations and data-to-theory connections – thus alleviating the usual concern that qualitative research frequently does not demonstrate how data relate to theory. Combining the first-order concepts, we grouped four more abstract second-order themes.

As a last step, we performed selective coding by evaluating all themes with the help of relevant literature (Huber et al. 2022; Steininger et al. 2022; Teece 2014). Consequently, we integrated and refined our second-order themes into larger theoretical schemes and distilled the emergent second-order themes even further into two aggregate dimensions.

As the twin transformation construct is new to IS research and is still to be comprehensively conceptualized, we first developed the twin transformation construct as a foundation for deriving twin transformation capabilities. Following Suddaby’s (2010) and Podsakoff’s (2016) guidelines on construct clarity, we built on two underlying constructs, digital transformation and sustainability transformation, in isolation. Drawing on justificatory knowledge on the digital and sustainability transformation literature, we delimited and elaborated on the synergetic interplay between the two perspectives that constitute twin transformation in the following: digital transformation enabling sustainability transformation and sustainability transformation guiding digital transformation.

Digital transformation as an enabler for sustainability transformation can be divided into two parts. First, “sustainability of digital technology” focuses on making digital technologies more sustainable, using concepts such as Green IT (Veit and Thatcher 2023). It aims to design a sustainable IT infrastructure by, inter alia, implementing IT life cycle management (Loeser 2013) or more sustainable infrastructure such as green data centers and sustainability management systems (Parmiggiani and Monteiro 2018; vom Brocke et al. 2013). Second, “sustainability through digital technologies” comprises the positive impact of using digital technologies to create greater organizational sustainability (El Hilali et al. 2020; Seidel et al. 2017). Digital transformation allows for learning about sustainability effects through transparently generating novel data as a foundation to monitor, for example, sustainability thresholds, to predict sustainability scenarios (Ortega-Gras et al. 2021). The Internet of Things can integrate physical objects into networks via sensors. These networks generate large amounts of environmental data that can be processed by systems based on artificial intelligence (AI). These AI-based systems may identify useful patterns otherwise trapped in massive amounts of unstructured data (Padmanabhan et al. 2022). The patterns, in turn, can inform sustainability transformation design decisions, which, if implemented, generate new data streams (Miranda et al. 2022). Thus, digital technologies enable us to simultaneously address complex economic and environmental concerns. This makes them an essential prerequisite for data-driven decision-making (Ortega-Gras et al. 2021), sustainability investments (Speziale and Klovienė 2014), or new business model opportunities (Chanias et al. 2019; Vial 2019).

Sustainability transformation guides digital transformation in two ways. First, sustainability transformation guides the designing of adequate digital transformation solutions to appropriately address technological, organizational, and cultural aspects (Acciarini et al. 2022). Creating sustainable digital transformation solutions (e.g., innovative business models) may profoundly change an organization’s purpose and way of creating value (Hernández-Chea et al. 2021). Rather than remaining self-centered (Bharadwaj et al. 2013; Karnebogen et al. 2021; Vial 2019), sustainability transformation can shift the focus to goals far beyond an organization’s boundaries (Hernández-Chea et al. 2021). Value is created for the organization and the common good (Dyllick and Muff 2016). Organizations start to think holistically and focus on the circularity of their products and services. They try to minimize resource input (e.g., through reusing, recycling, etc.) and improve overall sustainable development (Zeiss et al. 2021). Second, sustainability transformation increases the general acceptance of transformations (Daily and Huang 2001). Since it requires organizational flexibility and a new culture, digital transformation fosters continuous change and causes significant tensions between the “old” and the “new” (Svahn et al. 2017). As a result, digital transformation is often not as successful as it could be, and its complex drivers and effects are poorly understood (Gurbaxani and Dunkle 2019) – particularly by employees whose support for an organizational transformation is key. Sustainability transformation may help to motivate employees to support transformational activities.

As illustrated in Fig. 1, the interplay between digital and sustainability transformation is rooted in a holistic, transformational, and deeply synergetic approach, which is disregarded when focusing on digital or sustainability transformation in isolation. Twin transformation is a multi- and interdisciplinary construct that is not only the responsibility of and driven by the entire top management, but also requires the cooperation of all organizational departments (Dyllick and Muff 2016; Wessel et al. 2021). Twin transformation balances the efforts and rewards of both transformations by considering them on an equal footing, with the potential to lead to a reinvention of the organization, thus impacting the organization’s identity as well as creating value far greater than the sum of each transformation. Consequently, twin transformation extends prior hybrid constructs at the interplay between digital and sustainability transformation, as set out in Appendix D. Serving as a foundation for future twin transformation research in general and the subsequent development of twin transformation capabilities in particular, we present the following definition of the twin transformation construct: A twin transformation refers to a value-adding interplay between digital and sustainability transformation efforts that improve an organization by leveraging digital technologies for enabling sustainability and leveraging sustainability for guiding digital progress.

Extending Baiyere et al.’s (2020) symbolic perspective on organizational transformations, the twin transformation process can be likened to the metamorphosis of a larva into a butterfly. Its strong body holds a butterfly together, so that it relies on all four wings to fly. Similarly, organizations can only “take off” into twin transform once relevant dynamic twin transformation capabilities are developed and implemented in all four capability categories. We present the twin transformation capability framework as a butterfly to provide a clear, illustrative structure of dynamic twin transformation capabilities. Our framework captured in Fig. 2 constitutes the butterfly’s body, holding together two upper primary wings (i.e., primary capabilities) and two lower supporting wings (i.e., support capabilities). In the following section, we first describe our framework’s overarching structure, starting with the butterfly’s body (i.e., the aggregate dimensions) and its wings (i.e., the second-order themes). Second, we explain the associated first-order concepts, namely the dynamic twin transformation capabilities.

Twin transformation capabilities allow for integrating sustainability and digitalization perspectives into organizations on an equal footing. Our butterfly encompasses dynamic twin transformation capabilities, as they are directed towards organizational change processes (Piccoli and Ives 2005; Teece 2007, 2014) and are therefore needed to initiate the twin transformation. Starting from the butterfly’s body, we distinguish between primary and support capabilities (Huber et al. 2022; Porter 1995). The left and right sides of the butterfly work in conjunction with one another, as both “wings” enable each other. Proceeding with the wings, the first two wings are primary capabilities, which directly influence the organization’s value creation by shaping the supply of new products or services. They encompass spanning capabilities that aim for an integrated approach to digital and sustainability transformation, merging aspects from the inside and outside of the organization. Making digital transformation sustainable presents sustainability transformation as a guide to digital transformation. The category captures the dynamic twin transformation capabilities by harnessing digital transformation as a means to attain sustainability targets. By contrast, making sustainability transformation digital refers to digital transformation as an enabler of sustainability transformation. The category includes dynamic twin transformation capabilities that accelerate digital technology integration to an end of leveraging sustainability-focused practices.

The other two categories relate to support capabilities that indirectly influence the organization’s value creation by increasing the primary capabilities’ effectiveness. Support capabilities encompass inside-out capabilities, deployed from inside the organization, and outside-in capabilities, which are externally oriented. Build inside-out capabilities therefore describes the ability to respond to market requirements and opportunities. Build outside-in capabilities refers to the ability to anticipate market requirements through external partnerships. Support capabilities also encompass relevant change management practices such as “enhance joint innovation and strong community networks.”

To face the challenges of today’s complex and dynamic organizational environment, organizations must develop primary and support capabilities in an integrated manner. Specifically, even though the primary capabilities appear to constitute two separate strategic value creation directions, they intertwine and reveal their full potential through links that should be established in real-world contexts. For example, the first-order dynamic twin transformation capability “deploy digital technologies that themselves are sustainable” is interlinked with its direct counterpart “deploy digital technologies to foster sustainability practices.” While digital technologies can be deployed as sustainability solutions – for example, switching to green cloud computing instead of running large and energy-consuming data centers – they can also be deployed to foster sustainable behavior – for example, making use of an app to encourage employees to travel by public transport instead of driving by car. Another example is the interplay between the dynamic twin transformation capabilities “foster sustainability-related, transparent consumer choices” and “foster sustainability impact of products and services through the entire life cycle.” An organization can enable transparent consumer choices only if data on the life cycle of products and services are available, analyzed, and shared with the public. Our twin transformation capability framework comprises 16 dynamic twin transformation capabilities clustered into four overarching wings. Tables 1 and 2 outline each dynamic twin transformation capability in more detail, presenting short explanations and examples from interviews with our key informants.

Our study’s theoretical implications are threefold. By establishing the twin transformation construct and developing relevant dynamic twin transformation capabilities, we (1) lay the foundation for further twin transformation theorizing and empirical work. In particular, our results give a strong impetus for (2) digital transformation research to incorporate sustainability transformation’s guidance and (3) sustainability transformation research to consider digital transformation as an enabler.

First, we complement existing studies on the interplay between digital and sustainability transformation by considering digitalization and sustainability on an equal footing. By compiling descriptive components of twin transformation, our twin transformation construct and capability framework provide a first step towards a theory for analyzing (i.e., type I; Gregor 2006). Our theory addresses the “what is” of this new twin transformation phenomenon, providing knowledge that “colleagues in other disciplines will acknowledge as belonging to the IS discipline” (Winter 2003, p. 6). Thus, it may also present a starting point for interdisciplinary research projects examining twin transformation from a holistic perspective. Theories for analyzing may then be developed further into other types of theories that provide explanatory or prescriptive knowledge (e.g., type II theory for explaining or type V theory for design and action; Gregor and Hevner 2013). By building upon our theoretical foundation, future researchers may empirically assess and evaluate the influence of a sustainability transformation-guided purpose on digital transformation or of digital transformation-enabled insights on the effectiveness of sustainability transformation. Moreover, our work calls for more integrated and interdisciplinary research efforts as major global challenges cannot be mastered by one (sub-)discipline. Consequently, digital and sustainability transformation researcher may increase their interaction with one another in terms of discussions, critiques, insights, and collaboration.

Second, our work on the twin transformation construct and its capabilities has implications for digital transformation research with regard to the macro level (generic elements of the transformation process) and micro level (inner workings) of an organizational transformation (Baiyere et al. 2020; Wessel et al. 2021). Regarding the macro level, our work expands Wessel et al.’s (2021) process model of transformation, calling for a more detailed understanding of the drivers of an organization’s digital transformation agenda and activities. Wessel et al. (2021) focus on technological change as a main driver of digital transformation. Our work suggests taking Wessel et al.’s (2021) findings one step further by considering the organization’s global environment as a relevant driver of digital transformation, in the sense of social and environmental sustainability aspects as part of the environmental context (Dyllick and Muff 2016). Regarding the micro level of the transformation activity, Wessel et al. (2021) conclude that digital technologies (re-)define an organization’s value proposition. Our research supports this finding and, at the same time, extends the relationship with a third player – the impact of organizational sustainability: Organizational sustainability (1) may (re-)defines the value proposition (Bocken et al. 2014), (2) provides a purpose for digital transformation efforts (Crome et al. 2023b), and (3) guides the design of adequate digital transformation solutions to appropriately address technological, organizational, or cultural aspects (e.g., innovative business models; Geissdoerfer et al. 2018).

Third, in our work we build on previous sustainability transformation research by using digital transformation to help solve complicated sustainability transformation issues. Specifically, our work extends Dyllick and Muff’s (2016) widely used Business Sustainability Typology (Dyllick and Muff 2016). This typology distinguishes between three levels of sustainability transformation. Our results imply that a fourth level would be necessary to consider enabling digital transformation perspectives. The concern triggering a (twin) transformation at this level would stem from the digital and sustainability problem spaces – such as globalization calling for digitalization or the climate crisis calling for sustainable development. Moreover, our work has implications for Dao et al.’s (2011) Integrated Sustainability Framework, which was published in the IS domain, is widely used, and has highlighted the role of IT in organizational sustainability efforts. Our research supports and extends their framework in two aspects. First, we identify dynamic twin transformation capabilities that enable sustainability solutions using digital transformation to enhance organizations’ overall sustainability. In doing so, we address Dao et al.’s (2011) call for integrating sustainability into an organization’s core processes, products and services. Second, we cover the sustainability of digital technologies while also encouraging sustainability through digital transformation solutions. Thereby, we build on Dao et al.’s (2011) claim to develop capabilities that promote the use of clean technologies and processes by adding the perspective of making digital transformation sustainable, demonstrating the twin transformation capability “deploy digital technologies that foster sustainability practices.” By applying our dynamic twin transformation capabilities, organizations can combine digital and sustainability transformation efforts in a targeted manner.

The core practical implications of our work are twofold. Our results provide organizations with a new holistic perspective on the interplay between digital and sustainability transformation, thus assisting practitioners in assessing the status quo and creating a twin transformation capability development roadmap.

First, our work provides a holistic perspective for practitioners to understand the construct and gives guidance on how to perform twin transformation. By carefully identifying elements of twin transformation and developing relevant twin transformation capabilities, we provide practitioners with a new strategic view. As one of our key informants stated, "It is certainly an important topic at the strategic level, but not one for which we, as a company, are already clear what the blueprint is or how it is typically done" (I19). We believe organizations have a significant role to play in responding to major global challenges. With our work, practitioners can capitalize on our twin transformation capability framework to understand how to foster twin transformation holistically in their organization. As another key informant put it, “Yes, I think both trends are perceived. The strategic interconnection is also clear. The translation of these perceived trends and entanglements into very concrete actions is something that I think must be concretely formed in the near future” (I19).

As with any research project, our study has some limitations. The first limitation refers to the breadth (i.e., a limited number of interviews) and depth (i.e., in most cases, only one person per organization) of the interviews. Interviews with more key informants per organization and industry would contribute to further contextualized versions of the twin transformation capability framework highlighting certain situational opportunities or constraints that affect the feasibility of certain capabilities, depending on the context. The second limitation of this study is its explorative nature, in the sense that we consulted key informants to identify twin transformation capabilities that had not yet been covered in the literature. Engaging with existing literature on capabilities required for a separate digital or sustainability transformation would help to derive further twin transformation capabilities. The third limitation is that we derived the twin transformation capability framework from a static point of view, and not yet accounting for interdependencies in terms of time or content. The interdependencies of various twin transformation capabilities in terms of time could be investigated – for example, by developing a process view to shed light on the succession of each twin transformation capability.

We conclude that the presented definition of the twin transformation construct, in combination with the outlined dynamic twin transformation capabilities, provide relevant opportunities for future research on understanding the twin transformation interplay. There are various opportunities for future research that could support organizations during their twin transformation. First, the development of a maturity model with the aim to provide prescriptive guidance for an effective twin transformation could constitute a stimulus for further research. This would help organizations to evaluate their current twin transformation maturity and to derive concrete action fields from developing the required twin transformation capabilities. Second, future research could identify and assess related twin transformation readiness or success factors and develop suitable measurement tools. This would enable organizations to pave their way to become twin transformers. Third, scholars could conduct case studies to reveal further empirical insights about twin transformations. Empirical insights provide the foundation for deriving context-specific characteristics of twin transformations and illustrating ordinary twin transformation capabilities. Organizations require ordinary twin transformation capabilities to sustain their twin transformation, which is initialized by dynamic capabilities in the long term.