A managerial mental model to drive innovation in the context of digital transformation

ABSTRACT

Industry 4.0 is transforming how businesses innovate and, as a result, companies are spearheading the movement towards ‘Digital Transformation’. While some scholars advocate the use of design thinking to identify new innovative behaviours, cognition experts emphasise the importance of top managers in supporting employees to develop these behaviours. However, there is a dearth of research in this domain and companies are struggling to implement the required behaviours. To address this gap, this study aims to identify and prioritise behavioural strategies conducive to design thinking to inform the creation of a managerial mental model. We identify 20 behavioural strategies from 45 interviewees with practitioners and educators and combine them with the concepts of ‘paradigm-mindset-mental model’ from cognition theory. The paper contributes to the body of knowledge by identifying and prioritising specific behavioural strategies to form a novel set of survival conditions aligned to the new industrial paradigm of Industry 4.0.

KEYWORDS:

• Strategic cognition
• mental models
• industry 4.0
• digital transformation
• design thinking

1. Introduction

There is a growing interest in the ‘fourth industrial revolution’ or ‘Industry 4.0’, which is considered to be a new industrial paradigm driven by the introduction of new methods and technologies that are fundamentally changing the nature and context of work (Elizabeth, Traavik, and Wong 2020; Fareri et al. 2020). Technologies, such as robotics, artificial intelligence (AI), the internet-of-things (IoT), machine learning, blockchain, and additive manufacturing are being introduced in organisations in an effort to make processes more efficient, transform user experiences, and enhance innovation efforts (Hanelt et al. 2020). In response to the new industrial paradigm, companies are leading the movement towards ‘Digital Transformation’, a process that ‘aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies’ (Vial 2019, 118). Although a significant body of literature emerging in the area of enabling effective digital transformation initiatives, our understanding is still limited in at least three fundamental ways.

First, technology is not a silver bullet. The changes caused by digital transformation have implications not only for a company’s technological resources but also for how it innovates (Fareri et al. 2020). For instance, as explained by Verganti, Vendraminelli, and Iansiti (2020), Netflix uses AI to find new patterns in users’ tastes to make decisions about how the user experience is shaped. In this way, instead of the data being derived from a typical innovation process, a machine is learning from user data and making decisions about how the product could be personalised (Verganti, Vendraminelli, and Iansiti 2020). Insights gained from analysing vast amounts of data derived from customer interactions with digital channels are transforming the innovation process (Dremel et al. 2017). Decisions in innovation processes have previously been taken by humans only. Consequently, companies now have to align their internal competencies to enable employees to drive innovation in an environment that leverages user data that is constantly being collected by technologies (Marion and Fixson 2020). Accordingly, the success of digital transformation relies on understanding the new set of conditions that have to be fostered on an organisational level in order to drive new behaviours that support innovation. Therefore, research should be extended to improve our understanding of behavioural competencies to ensure that innovation is effective in the face of digital disruption.

Second, when shifting the focus from technology to new behaviours that enable innovation in the context of digital transformation, scholars are now referring to innovation approaches such as design thinking. However, research in the domain is still in its infancy. Design Thinking is a creative human-centred approach that has attracted increased interest from academics and practitioners, moving from being a buzzword to becoming a widely established practice for facilitating innovation in digital contexts (Przybilla et al. 2021). Evidence supports the view that design thinking plays an important role in achieving a more human-centric digital transformation due to its ability to support stakeholders engaging in learning loops during the design process while also ensuring user-centredness (Magistretti, Ardito, and Messeni Petruzzelli 2021; Magistretti, Tu Anh Pham, and Dell’Era 2021). However, we still do not know which specific design thinking behaviours that are conducive to human-centric digital transformation should be encouraged (Nagaraj et al. 2020; Russell et al. 2020; Pham, Magistretti, and Dell’Era 2021). In response to these calls, our purpose is to identify and prioritise behavioural competencies to help companies establish innovative behaviours in the context of digital transformation.

Third, while scholars agree on the relevance of developing design thinking competencies for a human-centric digital transformation, the move away from existing organisational mental models to a new one is very challenging (Rajiv, Hambrick, and Jer Chen 2007). This is because teams tend to rely on inherited habits and ways of thinking (e.g. organisational memory in the form of routines) to solve problems. Consequently, companies struggle to understand that important structural changes are required for changing the value creation process – how firms make decisions, how they reorganise themselves, and how they innovate. In an attempt to investigate how organisations can change existing organisational mental models that enable innovation in the context of digital transformation, scholars are now referring to the extensive body of knowledge that focuses on strategic cognition (Russell et al. 2020). Strategic cognition pays particular attention to the belief that top management can shape employees’ behaviours and enable collective cognition aligned to an organisation’s strategy and objectives. In particular, research on how management can enable individuals to develop a mental template conducive to the development of particular behaviours has been addressed for decades (Lin and McDonough 2014). The work of Russell et al. (2020) is among the first to develop a cognitive model for digital transformation; however, the authors focus on individuals’ perception, feelings and emotions towards digital transformation initiatives and not on innovative behaviours. In fact, research into how managers can create a shared organisational mental model (Grewatsch and Kleindienst 2018) that facilitates new innovative behaviours in the context of digital transformation is underdeveloped (Ceipek et al. 2021a). This is particularly important, however, since those in charge of digital transformation who understand how to nurture these conditions will be more effective than those who do not (Elizabeth, Traavik, and Wong 2020). This underlines the need to understand how theoretical concepts from cognition research can be used to facilitate the establishment of new innovative behaviours in the context of digital transformation.

To address these shortcomings, this study aims to identify and prioritise behavioural strategies related to the design thinking approach to inform the creation of a managerial mental model that can facilitate change aligned with the new industrial paradigm. The following research question guided this study: How can we identify and prioritise design thinking behaviours to inform the creation of a managerial mental model to meet the challenges posed by Industry 4.0?

We conducted and analysed 45 interviews with industry professionals and educators to comprehensively identify design thinking behavioural strategies and their underlying actions, which led to the identification of 42 behavioural strategies covering four dimensions (strategy & vision, culture & environment, employee & competencies, and data & structures). To ensure reliability, we performed internal consistency tests, identifying closely related items that measure or represent each of the four dimensions. As a result, 20 behavioural strategies were deemed reliable, and 50 practitioners were asked to rank them in order of importance using a 5-point Likert scale. These 20 behavioural strategies informed the creation of a managerial mental model. In this paper we argue that design thinking concepts, in combination with cognate elements from strategic cognition, enable individuals, teams, and organisations to overcome existing organisational mental models to develop a new mental model that allows companies to engage in learning that is conducive to the new industrial paradigm.

This paper contributes to theory by expanding the knowledge of cognition research to include the attributes and behaviours associated with the design thinking mindset, aimed at increasing our understanding of how to support managers in changing existing behaviours and establishing a shared organisational mental model that can drive creative responses in the context of digital transformation. Additionally, new and tangible insights for managers are provided which include the identification and prioritisation of design thinking behaviours to enable the changes required by the Industry 4.0.

The remainder of this paper is organised as follows. Chapter 2 discusses the phenomenon of the new industrial paradigm of digital transformation, the relevance of design thinking to digital transformation, and the current body of research about mental models. Chapter 3 describes our research design and the data analysis. Chapter 4 presents and discusses our conceptual model. Chapter 5 concludes with a discussion of the implications of our findings for strategic cognition and digital transformation research.

2. Theoretical background

The increasing spread of new digital technologies is disrupting how companies innovate. Scholars are now concerned with investigating theoretical concepts that can be leveraged to support companies developing new innovative behaviours. In the next sections, we provide a theoretical discussion of relevant changes that are required due to digital disruption, and how leveraging design thinking behaviours in combination with elements from socio-cognitive research can be used to deal with the necessary adaptions.

2.1. The new industrial paradigm arising from digital transformation

The new industrial paradigm and its technologies, notably artificial intelligence and the internet-of-things, open up new opportunities for organisations. As the material world becomes increasingly digitised, digital technology is incorporated into objects that previously were purely physical. For instance, in product-intense industries such as automotive and industrial manufacturing, the so-called smart products and production facilities afford novel opportunities for how organisations create value in smart services that leverage the properties and in particular the data obtained by smart products (Porter and Heppelmann 2015). However, due to the changing nature of digital transformation, these systems strongly affect work practices, competencies and routines, requiring people to shift gears and to focus on the design and consideration of the recursive relationship between work practices and the enabling technology innovations (i.e. big data technologies, AI, and IoT) (Nambisan et al. 2017; Ceipek et al. 2021b)

Digital transformation projects often fail due to the inherent disruption it causes to activities, processes, and capabilities (Correani et al. 2020). Such failure is often blamed on key aspects of change management being overlooked, such as how employees respond to the required changes in their way of working (Correani et al. 2020). Also, new value is created in an organisation not merely by the digital technologies alone (Kane 2014), but through their application in a specific context, which enables the discovery of new ways of working and value creation (Vial 2019). Therefore, in order to ensure the appropriate establishment and use case of digital technologies in organisations, we need to investigate how the required transformation of an organisation’s practices and routines can be implemented effectively.

However, research at the crossroad of digital transformation and innovation management still lies in its infancy (Nambisan et al. 2017; Danneels and Frattini 2018; Usai et al. 2021). In particular, scholars are concerned that a company’s socio-cognitive inertia to reframe innovative behaviours might inhibit innovators from perceiving the possibilities of the digital disruption (e.g. the digital augmentation of organisations’ normal operating procedures) and development of new competencies (e.g. socio-cognitive sensemaking) that are required to fully leverage the provided opportunities (Wiener, Saunders, and Marabelli 2020; Nambisan et al. 2017; Russell et al. 2020; Ceipek et al. 2021b). Accordingly, successful digital transformation strategies depend on not only enabling innovation actors to develop new cognitive frames but also understanding how these different frames can be fostered on an organisational level (Verganti, Vendraminelli, and Iansiti 2020).

Overall, the transition to digitisation is a process that is at once complex, intricate and difficult to manage. In our study, we argue that establishing specific mental models that inherently contain design thinking as a core concept can help to facilitate the adaptation and transformation of collective innovative behaviours.

2.2. The importance of design thinking to drive a creative reaction in the context of digital transformation

Rooted in Simon’s (1969) cognitive understanding of design, design thinking is a human-centred approach to creative problem solving, which highlights the importance of problem-framing (and reframing) by expanding the problem and solution space through empathy, collaborative abduction and iteration (Simon 1969). Since the design firm IDEO has popularised the term ‘Design Thinking’, and many frameworks have been developed (Buchanan 1992; Brown 2008; Kelley and Kelley 2013), design thinking has become a well-established approach in the innovation management community (Pietro, Perks, and Beverland 2018; Liedtka and Jaskyte 2020; Nakata and Hwang 2020; De Paula, Cormican, and Dobrigkeit 2021).

Design thinking is typically founded on a few key principles, or attributes. In an effort to understand the attributes that serve as foundation for the design thinking mindset, Pietro et al. (2018) derived the following ten attributes from 104 articles (1985–2017), nine influential books, and three models: 1) creativity and innovation, (2) user-centeredness and involvement, (3) problem solving, (4) iteration and experimentation, (5) interdisciplinary collaboration, (6) ability to visualise (7) adopting a gestalt view, (8) abductive synthesis, (9) tolerance of ambiguity and failure, and (10) blending analysis with intuition. Each principle is related to a distinct design capability from Simon’s analysis of the role of design (Simon 1969), namely, problem-framing, empathy, collaborative abduction and iteration. For instance, being user-centred and engaging in interdisciplinary collaboration for the continual generation of solutions is referred to as collaborative abduction, whereas embracing ambiguity and tolerance of failure is concerned with problem-framing. Additionally, iteration refers to the ability of performing analysis, synthesis and visualisation of concepts through experiments until a desirable outcome is reached.

Due to design thinking’s focus on the capabilities that companies should enact to innovate (De Paula, Dobrigkeit, and Cormican 2019), design thinking is making valuable contributions to companies’ digital transformation path. For instance, it has been empirically linked to product usefulness and novelty (Nagaraj et al. 2020), and to a higher level of trust and collaboration in cross-functional teams (Appleyard, Enders, and Velazquez 2020). Additionally, it is claimed to promote higher levels of empowerment in teams (and hence, project performance; Roth et al. 2020), higher quality prototyping, and more innovative business models (Przybilla et al. 2021). Together, these studies confirm that design thinking is a valuable approach for facilitating innovation in the face of the new industrial paradigm.

A recent study has focused on identifying managerial practices that allow companies to use design thinking for value creation in a digital context (Magistretti et al. 2019). Surprisingly, we know very little about the specific behaviours that managers should foster to leverage design thinking in a digital context (Lynch et al. 2021). From a strategic perspective, while Klenner, Gemser, and Oswald Karpen (2021) are among the first to identify behavioural practices associated with design thinking, they focused on effectuation theory and entrepreneurial innovation. Additionally, scholars have analysed design thinking from the lens of dynamic capability in order to identify the specific capabilities that are required by effective digital transformation (Magistretti, Ardito, and Messeni Petruzzelli 2021; Magistretti, Tu Anh Pham, and Dell’Era 2021). From a process perspective, Pham, Magistretti, and Dell’Era (2021) developed a process model that illustrates the role that design thinking can play in shaping big data-based innovation processes, while Przybilla et al. (2021) drew on 21 projects for their identification of the opportunities and challenges that arise when design thinking is used to develop digital solutions. However, none of these studies adopted a cognition perspective to identify the specific behaviours that companies should foster if they want to use design thinking to stimulate innovative behaviours in a digital context.

To identify behavioural strategies conducive to design thinking, it is fundamental to understand some of the philosophical stances underpinning the implementation of design thinking in management settings. Rylander Eklund, Anna, and Amacker (2021) have argued that, due to the management field’s own cognitive tendencies, Simon’s ideas (Simon 1969) are unconsciously utilised for creative problem solving; indeed, companies attempt to establish beliefs and norms aimed at creating a shared mental state and process through practices and behaviours to foster empathy, collaboration, abduction and iteration (Rylander Eklund, Anna, and Amacker 2021). Evidence from industry has been found in studies investigating companies, e.g. Deutsche Bank (Vetterli et al. 2016), in the financial services sector, and information technology companies, such as Samsung (Chang, Kim, and Joo 2013), IBM (Clark and Smith 2008), and SAP (Carlgren, Elmquist, and Rauth 2016). However, very little is known about how to holistically support companies to identify and prioritise specific behaviours that relevant stakeholders should adopt to establish a shared organisational mental model. In light of that, our study aims to address the following question: How can we identify and prioritise design thinking behaviours to inform the creation of a managerial mental model to meet the challenges posed by Industry 4.0?

Overall, the 10 design thinking attributes mentioned above (Pietro, Perks, and Beverland 2018) are especially evident in activities related to the cognitive tasks required to developing empathy, collaboration, abduction and iteration. Together, these attributes characterise the design thinking mindset and underpin the development of our managerial mental model combining the design thinking mindset and related behaviours that are needed to facilitate change aligned with the new industrial paradigm. The next section provides a detailed analysis of research on mental models.

2.3. Mental models in a digital transformation context

Our study is based on social cognition research (Tripsas and Gavetti 2000; David, Stubbart, and Ramaprasad 2001; Russell et al. 2020; Elizabeth, Traavik, and Wong 2020), which shows that, when people are faced with complexity and environmental uncertainty, they rely on simplifying strategies. Applied to a managerial context, the research field of strategic cognition investigates how cognitive structures and processes develop in an organisation and how they relate to decision-making, strategies, and intra-organisational dynamics, such as transformation and change. This school of thought is rooted in the bounded rationality of human actors, and their limited cognitive abilities, in an organisational setting (Barnard and Simon 1947).

Cognition and related mental models are likely to play a large role in any digital transformation or related change process (Tripsas and Gavetti 2000; Russell et al. 2020). Scholars have produced evidence demonstrating the impact of mental models on strategic decision-making (Eggers and Kaplan 2009) and firm performance (David, Stubbart, and Ramaprasad 2001). Special emphasis has been given to the positive role of mutual or shared mental models in collective performance (Burtscher et al. 2011). The shift towards digital transformation relies heavily on the mental representation and cognitive processes of the corporate actors involved, and on an understanding of how those representations relate to each other – in particular in ambiguous situations. According to Elizabeth, Traavik, and Wong (2020), organisations that are able to create favourable conditions for the transformation process will be more successful in helping their employees engage in the process and develop creative responses.

The problem is that it can be extremely difficult to recognise and change mental models, especially when the intention is to change existing individual perspectives about a shared mental organisational model (Rajiv, Hambrick, and Jer Chen 2007). In their longitudinal analysis, Ceipek et al. (2021b) show that top management teams with rigid mental models inhibit investments in IoT innovations in a digital transformation context, and the authors encourage managers to establish mechanisms that mitigate rigid mental models. Applied to entrepreneurs in small and medium sized enterprises (SMEs), Liang et al. (2018) come to a similar conclusion by showing how changing mental models as part of managerial cognition renewal can explain how SMEs successfully drive forward digital transformation.

Seeking an explanation for the challenges in changing mental models, one can think of a nested framework in which paradigm, mental model, mindset, behavioural strategy and observable behaviour are all intertwined through constant alignment and reciprocal reinforcement (Argyris and Schon 1974). Based on Argyris and Schon (1974), the overarching paradigm can be described as managers’ mental models, their corresponding mindsets and behavioural strategies as ‘espoused theories’ – i.e. practices that an individual claims to follow. In contrast, ‘theories in use’, i.e. theories that are inferred from action, can be linked to observable or tangible strategies in a managerial context. In relation to these contrasting theories, mental models are aligned with the dominant paradigm. Conforming to the established paradigm and mental models, managers develop their own attitudes (mindset) which, by being implemented, influence the establishment of behavioural strategies for employees to follow. The behavioural strategies then become observable strategies. In this hierarchical chain, mental models, mindsets, behavioural strategies, and observable strategies constantly interact with each other, reinforcing the overarching paradigm. This powerful interconnectedness facilitates transformation initiatives. To successfully lead digital transformation, then, managers have to understand how to leverage a shared, functional mental model that can help foster creative responses to digital disruption.

3. Research method

Adopting an interpretative research paradigm and an empirical qualitative methodology allows us to explain complex dependent real-world phenomena in their social, or organisational, embedded contexts (Eisenhardt 1989). We performed a wide range of field interviews across several sectors to gain a better understanding of how professionals employ design thinking (Strauss and Corbin 1990). Field interviews help to capture deep insights from (socially constructed) context-specific environments (McGrath 1964). As a result, we acknowledge the multidimensional nature of design thinking, as well as the fact that we are investigating an emerging phenomenon with undefined boundaries. Interviews are an effective data collection approach for understanding complicated phenomena in real-world contexts. This was also in line with our broader goal of developing a managerial mental model that could be used in a variety of settings.

3.1. Interview selection and background

Using snowball sampling to find relevant experts, we collected data from a diverse set of perspectives, i.e. professionals both in industry and in academia (Myers and Newman 2007). We purposefully selected our informants on the basis of the following criteria. First, in order to gather a holistic view on the innovative behaviours in organisations, we targeted a wide range of key industries that play a relevant role in the context of Industry 4.0, namely, healthcare, automotive, chemistry, banking, and insurance (Wolf 2019). Second, for our informants in education, we chose academics who are actively educating and helping organisations to successfully adopt design thinking, such as educators from the Stanford D-school. Third, we systematically collected and analysed our empirical data until ‘no new data appear[ed]’ (Morse 2003, p.1123) in line with our interpretive research approach (Klein and Myers 1999). Specifically, after reaching this theoretic saturation we stopped adding additional informants after having reached a coherent picture of the most relevant design thinking behavioural strategies, from the perspective of industry and academic leaders, relevant to Industry 4.0. This resulted in a set of 45 representative interviews. An overview of our interviewees’ background and their codes can be found in our online shared folder.¹

3.2. Data collection

For the purpose of the study, a semi-structured interview approach was adopted, which enabled us to better understand the phenomenon of interest, namely the development of a managerial mental model, which helps to address the challenges of the new industrial paradigm. The interviews were conducted via video call, phone call, or in-person. We used NVivo to support our coding and data analysis. The data collection process took place between 2016 and 2018. Good practice for qualitative research guided our data collection and analysis (Gioia, Corley, and Hamilton 2013; Cathy, Lehmann, and Myers 2010), i.e. constant comparison, iterative conceptualisation, scaling up, and theoretical integration. Selected interview quotes can be found in Table A1, following procedures from Gioia, Corley, and Hamilton (2013).

3.3. Data analysis and model development

Recognising the nascent stage of the topic, we adopted a predominantly inductive, interpretive approach for the identification of behavioural strategies (Walsham 2006). We did not impose any a priori theory on our data or to test a theoretical framework (Gioia, Corley, and Hamilton 2013). Rather, we collected and analysed data iteratively, shifting between qualitative data and theoretical concepts. We began our initial analysis by writing memos after each interview or meeting, to reflect on what we had learned (Walsham 2006). Our data analysis consisted of open coding, selective coding and theoretical coding (Cathy, Lehmann, and Myers 2010).

To execute open coding, we read the transcripts and assigned a code to each line of text following the principle of constant comparison. We then grouped our open codes into higher level selective codes, using selective coding as ‘a process of scaling […] codes into those dimensions that are important for the research problem’ (Cathy, Lehmann, and Myers 2010, 49) to achieve iterative conceptualisation. This process led to the identification of 42 behavioural strategies. To perform internal consistency tests, we transformed the 42 behavioural strategies into survey items and assessed their reliability using Cronbach’s Alpha. All strategies that had Cronbach’s alpha above 0.7 were deemed reliable. This resulted in a set of 20 behavioural strategies. Finally, to assess the importance of these strategies, a new cohort of 50 practitioners were asked to rank the 20 behavioural strategies in order of importance using a 5-point Likert scale.

Finally, we scaled up and theoretically integrated our findings to theories in the research field (Cathy, Lehmann, and Myers 2010). In line with Gioia, Corley, and Hamilton (2013), we paid particular attention to nascent themes in the existing literature, which built the foundation and helped to frame our model. For instance, we borrowed concepts from social cognition research (i.e. paradigm, mental model, mindset; e.g. Barnard and Simon 1947; Tripsas and Gavetti 2000; Levinthal 2011), as well as current knowledge on the design thinking mindset attributes (Pietro, Perks, and Beverland 2018). These concepts were integrated into our model in accordance with the behavioural strategies identified from the interviews to build a coherent managerial mental model. An iterative process of relating our behavioural strategies to the body of knowledge allowed us to distil a coherent and robust managerial mental model for effective digital transformation.

4. A managerial mental model to enable creative responses to digital transformation

Our managerial mental model has five concepts from cognition research as its theoretical foundation, namely, paradigm, mental model, mindset, behavioural strategy and observable behaviour (Argyris and Schon 1974). According to the theory of Argyris and Schon (1974), mental models (in alignment with the established paradigm) influence the development of behavioural strategies that are intended to manifest certain observable behaviours. Observable behaviours represent actions performed by stakeholders of the organisation that can be seen and measured. Once the observable behaviours are successfully implemented, they reinforce the mindset. The theoretical concepts are in constant alignment and reinforcement with each other and their interconnectedness facilitates transformation efforts. To investigate industry practice from the perspective of Argyris and Schon’s (1974) theory, the authors’ theoretical concepts were seen in the context of the overarching paradigm of Industry 4.0. The goal was to create a managerial mental model (see Figure 1) that can support a creative reaction as part of a company’s digital transformation.

Figure 1. Managerial mental model.

The proposed mental model aligns the design thinking mindset and related behavioural strategies and observable behaviours. 20 behavioural strategies were derived from the ‘real’ practice in the industry identified in our interviews.

The behavioural strategies were organised into four areas – strategy & vision, culture & environment, employees & competencies, and data & structure – which take into account relevant organisational structures that determine the nature of creative reaction. For the mindset, we adopted 10 design thinking attributes that are well-established in the literature and in the management community and which comprehensively capture the current knowledge and conceptualisation of design thinking (Pietro, Perks, and Beverland 2018). The observable behaviours were also identified from the interviews and are presented in Table 1.

Table 1. Overview of application areas, behavioural strategies, and observable behaviours.

Application Areas	ID	Behavioural Strategy	Means of importance (1–5)	Observable Behaviours
Strategy & Vision	F.1	Secure (top) management support	4.34	Managers provide vision and guidance Individuals are empowered to make decisions independently
	F.2	Focus on long-term internal capability building	3.20	Companies tend to build internal capabilities that focus on improving stakeholder involvement, and/or achieving higher customer-centricity
	F.3	Have clear metrics/KPIs specific for design thinking	2.72	Companies tend to measure number of prototypes, development time, development cost, and/or team morale and employee engagement
Culture & Environment	F.4	Nurture a culture of experimentation, failure and feedback	4.54	Teams share stories about failures and learnings Teams are encouraged to conduct experiments with the user
	F.5	Embed design thinking within a larger cultural and mindset change	4.20	Managers foster a user-centred culture (rather than technology-centric) Companies encourage a more problem-solving mindset to overcome a technology-centred culture
	F.6	Be able to handle ambiguous situations and complexity	4.12	Managers create an environment of trust and psychological safety Managers cede control to enable teams to feel comfortable in ambiguous environments
Employees & Competencies	F.7	Ensure that design thinking workshops end with actionable results	4.22	Managers align workshops with organisational strategy and vision Companies see workshops only as a starting point
	F.8	Provide blended learning and training on design thinking	3.98	Companies implement different training formats (e.g. train-the-trainer) Companies prefer developing internal competencies over buying in external consultancies
	F.9	Provide sufficient resources for design thinking training	3.94	Companies allocate dedicated space for teams to co-create Companies allocate dedicated time for practice
	F.10	Establish collaborative initiatives with key partners	3.72	Companies seek for more Industry/University collaboration because it tends to achieve positive results
Data & Structure	F.11	On-board stakeholders and users/customers early	4.50	Managers highlight the importance of involving the user rather than focusing on technology
	F.12	Provide access to users/customers	4.50	Employees are encouraged to involve different users (e.g. early adopters) Companies seek to foster empathy to create more user engagement
	F.13	Create cross-disciplinary teams	4.46	Managers encourage collaboration from different departments to enhance collective creative problem-solving
	F.14	Apply design thinking in the early phase of innovation	4.34	Teams are encouraged to apply design thinking mindset early to frame and reframe problems
	F.15	Test multiple prototypes	4.26	Companies tend to either use high-fidelity prototypes, or manually simulate the experience that the technology would provide
	F.16	Get tangible fast in the project	3.76	Teams have a a high focus on prototyping Team build prototypes early to quick conduct user testing
	F.17	Adapt the design thinking process to the organisational context	3.66	Teams are able to adapt techniques for user research, prototyping and testing to the industry sector context
	F.18	Conduct professional user research	3.64	Techniques that foster empathy are normally used Managers encourage teams to often engage with the users
	F.19	Set up a dedicated innovation space `	3.62	Teams use open spaces for collaboration and idea sharing Individuals use private spaces for concentration and individual work
	F.20	Augment design thinking with data analytic	3.10	Companies seek for a combination of user engagement and data analytics Managers encourage a problem – solving mindset

Additionally, in an effort to support managers to prioritise these behavioural strategies, we asked each practitioner to rank them in order of importance within their respective dimensions (see Table 1). The next section discusses the components of the model in detail and the behavioural strategies as they were coded in Table 1. In particular, we focus on the behavioural strategies and observable behaviours in alignment with the design thinking mindset attributes. For a more nuanced discussion of the design thinking mindset attributes, please refer to Miche Liang et al. (2018).

4.1. Strategy & vision

This dimension aims to create awareness in top management about the kind of behavioural strategies that should be implemented. Moreover, it intends to show employees the direction the company is pursuing and what they can expect from it. For example, it is fundamental to show that leadership is committed (F1) to supporting behaviours that enable creativity and innovation. Management support should be grounded in serious interest, long-term commitment and openness to change. As confirmed by a Design Thinking Coach, ‘Three years back, he [CEO] saw that our company needs to change, that we need to get more innovative.

So, he built up a completely new department which is called digital solutions and one team of this department is design thinking.’ (Design thinking coach, #11).

Additionally, leaders must offer vision and inspiration to empower employees. As one interviewee mentioned, ‘In the future, the companies who are going to embrace and who are going to win in design are those that are already empowering their organisation to make decisions independently rather than being centrally-driven.’ (Head of growth and strategy, #1). Moreover, leaders have to show that they understand that changes from an existing mental model requires time and therefore their focus should be on long-term capacity building (F2). Companies tend to follow one of two different paths – either they invest in the capacity to explore external (customers) and achieve radical innovations, or they improve the internal (culture and processes) and achieve incremental innovations.

The third most important condition for a successful strategy to achieve sustainable improvement is that it contains clear metrics specifically for design thinking (F3). Some companies started to measure how many prototypes were developed and the time it took to develop them, as one interviewee stated, ‘How many concepts or prototypes were developed before you got to the final one? (…) how fast were you able to work before’ (Design thinking coach, #32). However, the majority of interviewees mentioned that they are now using more and more non-traditional metrics, such as team morale and employee engagement. As another interviewee pointed out, ‘measuring [things] like cross-departmental inefficiencies and how communication works between departments’ (Creative director, #2). This is in line with Liedtka and Jaskyte (2020) who suggest that, due to the exploratory and transformative nature of design thinking, managers should look beyond traditional financial metrics to find adequate metrics that can capture its real value. For instance, the results of the work of Roth et al. (2020) on the effects of design thinking on the empowerment of team members are promising in this regard. Similarly, our interviewees mentioned that financial metrics cannot entirely capture the value of design thinking. As one interviewee explained, ‘You can try to measure [design thinking], but the recognition of the impact has to come from individuals experiencing the concept […], it is more about whether they have a serious interest in reflecting on the way they work and on innovation’ (New work consultant, #8).

4.2. Culture & environment

This dimension represents the core values that are necessary to enable a culture that can support employees to develop new innovative behaviours. Our findings suggest that the most important cultural factor is nurturing a culture of experimentation, failure and feedback (F.4). As one interviewee pointed out, ‘you have to have this failure culture and feedback culture of course. And that people are not driven by their own egos, but to have in mind the customer.’ (Senior principal consultant, #12). Companies reported that they created an environment where employees can share their failures and especially learnings with the community. As another interviewee mentioned: ‘We do a reflection and they share stories of failure and learning from implementing design thinking in the company and it did or didn’t work, aiming to build an internal community and a network.’ (New work consultant, #8).

Our findings show that a rigid engineering and technology-centric corporate culture is often a threat to enable a creative response to digital disruption (F5). Several interviewees reported that projects often start already with a technology in mind instead of investigating what the real underlying need for it is. As one interviewee pointed out, ‘If you have a blockchain project in a blockchain research group and you look at financial streams a different technology might be suitable but it will not be pursued because it is the blockchain group.’ (Senior key expert consultant, #16). It has been suggested that embedding a problem-solving mindset instead of ‘forcing technology into a context’ opens up more opportunities to deliver value to the customer. As an interviewee said, ‘when that [problem solving] mindset becomes part of your culture, you definitely see differences in creativity’ (Design thinking coach, #33).

Moreover, our findings highlight the importance of handling ambiguous situations and complexity (F6) to the framing and reframing of problems, which is in line with previous research (Kane et al. 2015; Pietro, Perks, and Beverland 2018). To do that, management should develop specific characteristics, such as ‘reducing the level of control’, in order to create an environment of trust and psychological safety. By providing a ‘safe environment’, teams can frame and reframe complex situations that they face when dealing with data. As one interviewee mentioned, ‘Technology is just an enabler, but the technology is never the reason why an idea is great.’ (Venture architect, #9).

4.3. Employees & competencies

Providing proper training for employees is critical to facilitate a change in their behaviours. The interviews revealed that organisations are experimenting with a variety of education and learning approaches ranging from one-off workshops to internal academies (F8). However, many interviewees pointed out that only doing workshops is not enough, the learnings must end with actionable results that facilitate change in the organisational culture (F7). As our findings show, ‘running a design thinking workshop doesn’t really change the style or the culture of the company, but it might be just a super small step.’ (New work advisor, #20). Because of that, companies are now experimenting with more internal sources of training, such as organisational learning programs and online material for self-learning. As one interviewee mentioned, ‘It is up to the individual, but the company offers various training support, if you want an education in a certain field, you can apply for it.’ (Junior digital business model creator, #10).

The allocation of sufficient resources for design thinking training (F9) has shown to be fundamental to enable a creative response to digital disruption. As one interviewee said, ‘Design thinking is not a magic pill, but a useful approach that can be applied by capable and willing people equipped with the necessary resources.’ (Senior key expert consultant, #16). One of the most important resources identified was time. Especially in organisations where training was offered on top of regular working hours, lack of time – as opposed to lack of interest – was perceived to be the biggest limiting factor, often leading to frustration. As one interviewee said, ‘Time is the worst enemy at this point because I am pretty sure that people want to be trained and start adopting this, but they don’t have the time. And you need to learn, you need to learn by doing’ (Design team lead, #17).

Finally, establishing collaborative initiatives with key strategic partners (F10) – such as research institutes and other companies – tends to support employees in their transformational path. As one interviewee mentioned, ‘[…] for the past years, I have been focusing on strategic partnerships with large healthcare enterprises, ministries of health and governments or large healthcare institutions.’ (Senior design director, #18). The literature has also highlighted that promoting university/industry collaboration is highly desirable (Jussila et al. 2020).

4.4. Data & structures

Organisations that were most successful in applying design thinking to foster a creative response to digital disruption were often those that tailored and customised design thinking to their existing structures and processes (F17), (F19). Our findings reveal that 10 out of the 20 identified behavioural strategies that address this are applied to capturing user data and leveraging organisational structure.

The most critical behavioural strategy in this dimension is concerned with the onboarding of users early on in the development process (F11). The intention here is that, early on in the process (F14), teams can empathise with the user through cross-functional collaboration (F13) to conduct proper problem-solving, which in turn encourages employees to conduct professional user research (F18). This is particularly important as some interviewees mentioned that companies tend to give them a technology and ask them to create innovation without actually having access to the user (F12). This shows that interviewees are aware that, to create solutions in an iterative way, they need to build a good relationship with users to leverage user engagement and data analytics (F20). In this way, empathy was perceived to be valuable in projects involving multiple internal and external stakeholders.

Additionally, creating prototypes and testing them with users is fundamental to gather feedback for further development (F15). Testing results helps to reformulate the problem and thus enables an iterative cycle (F16). Interviewees reported challenges in testing prototypes that use AI. While some of them claimed to have addressed the challenge by developing high fidelity prototypes to test basic assumptions about their ideas, others preferred to develop low-fidelity prototypes and manually simulate the experience of using AI.

5. Discussion

This study proposes a hybrid model that leverages design thinking and cognition research to foster specific behavioural strategies in combination with cognitive elements to simplify transformational strategies in the Industry 4.0 setting.

The theoretical implications of our study are many. First, our findings extend the discourse about top management’s role in shaping new behaviours (Grewatsch and Kleindienst 2018) by proposing them to act as ‘cognizers’ (Volberda, Foss, and Lyles 2010) who are able to reduce the complexity of digital transformation by fostering shared mental models that directly influence organisation’s work practices and routines underlying innovation processes. Commenting on the role of top management, interviews said that, to cope with the changes imposed by the new industrial paradigm, companies need to be decentralised and employees empowered to make decisions. Accordingly, we posit that a set of beliefs on how to innovate in a digitally disrupted environment can, if leveraged by management and shared throughout the organisation, evolve as a functional mental model aligned with changes towards the new industrial paradigm. Our findings provide support for the conceptual premise that understanding how socio-cognitive theory influences digital transformation is fundamental to any theory of innovation management (Nambisan et al. 2017).

Second, while the literature has focused on the effect of individuals’ perception in the effectiveness of digital transformation (Russell et al. 2020), we argue that not only the innovator’s cognition but also the innovator’s social system of collectives are fundamental to tackle the changing nature of digital transformation. In particular, our findings provide a starting point to discuss how design thinking behaviours when integrated with the theory of Argyris and Schon (1974) can support managers to explore a mental model that considers the innovator’s social system. By doing that, we provide new insights to academia by proposing a combined view of design thinking, a social science approach, with strategic cognition research, a strategic approach. Thus our study answers the call made by Verganti, Vendraminelli, and Iansiti (2020) to explore what changes of competencies are required by the new industrial paradigm. Finally we convey the message on the value that hybrid models and intertwined views can have in the pursuit of innovation and digital transformation (Magistretti, Tu Anh Pham, and Dell’Era 2021).

Our study also offers managerial implications. First, our model provides guidance for companies to identify what competencies, for the development of new innovative behaviours, are recommended to be fostered in alignment with the needs of the Industry 4.0 setting. Having a more detailed understanding of what skills are required helps companies to design training programs and to give employees the opportunity to upskill and reskill. Second, many of our interviewees emphasised the importance of being less technology-centric and more focused on problem solving through empathy and sensemaking. Interviewees emphasised that empathy shapes the reframing of the direction ‘given’ by the technology and keeps it fluid until it can be articulated in a more fruitful direction. Although having the necessary skills to leverage new technologies is extremely relevant, a common view amongst interviewees was that solutions should not start under the premise of the technology itself but rather through a deep understanding of the user needs through empathy and reframing techniques. Third, the interviewees emphasised that managers should foster a culture of rapid iterations and encourage cross-functional teams to enable employees to test what they think they are learning from the data when creating meaning.

Therefore, based on what our interviewees deem to be important, we advise managers that the key pillars of design thinking – user empathy, collaboration, iterations, and reframing – have potential to support organisations in their process of problem solving and making sense of the data in order to have a more human-centric digital transformation. The key pillars enable employees to engage in learning loops that gradually weaken the grip of existing cognitive biases, as design thinking enables cognitive bias reduction (Liedtka 2015), which in turn can lead to more appropriate inferences about problems and solutions. Our findings provide new insights into the role of design thinking in ensuring a more human centric digital transformation. Thus, we extend the work of Magistretti, Tu Anh Pham, and Dell’Era (2021) and Magistretti, Ardito, and Messeni Petruzzelli (2021) by not only identifying specific behaviours that are required by the changing nature of digital transformation but also by prioritising the behaviours based on data arising from the industry.

The reported results enrich the discourse on how the new industrial paradigm is affecting work environment and competencies of employees. Therefore, we also extend the work of Fareri et al. (2020) by providing a more detailed understanding of innovative behaviours that can be used to shape competencies in order to be resilient to change. Overall, we advise managers to use the proposed mental model to identify and foster meaningful behavioural patterns in an effort to pursue a digital transformation that is more valuable for humans.

6. Conclusion

Our study proposes a managerial mental model that uses the design thinking mindset and related behaviours to support managers prepare their organisations for Industry 4.0 by creating the conditions that drive a creative reaction in their employees. The model can be used to influence innovative behaviours in employees, fostering behaviours that are important for making sense of and shaping their responses to digital transformation initiatives that involve a high degree of complexity and ambiguity.

Despite the value that our model offers, it has clear limitations. For instance, it does not consider the individual cognition – thoughts, beliefs and emotions – about a proposed digital transformation. Considering Beck’s (1997) claim that individuals will react differently depending on their cognition, it is expected that some of them will be more susceptible to a change of an existing mental model than the others. Therefore, individual cognition is likely to play a fundamental role in any transformational strategy as shown in Russell et al. (2020). Accordingly, future studies should consider the role of people’s thoughts, beliefs, and emotions in facilitating – or even hindering – the company’s strategic choice related to change of an existing mental model. For instance, if the debate is to be moved forward, findings from a longitudinal study seen from the perspective of other organisational learning theories, such as absorptive capacity, is crucial to understand how organisations ‘break’ unsuitable dominant management logics and are able to absorb new knowledge and develop new mental maps (Volberda, Foss, and Lyles 2010).

From a theoretical perspective, our study makes a timely and necessary contribution to the current digital transformation debate. First, we expand the knowledge of strategic cognition by positioning the theory proposed by Argyris and Schon (1974) in the context of digital transformation. Second, we identified design thinking behaviours from industry practice and integrated them into the conceptual framework of Argyris and Schon (1974) in an effort to advance knowledge in organisational mental models. Third, we propose how paradigms, mindsets and behaviours can be intertwined with respect to fostering an innovative mental model in the face of digital disruption.

In addition, this paper contributes to practice by supporting policymakers and managers to ensure that employees have the necessary skills for the digital economy. In particular, the identified strategic behaviours can be used to drive policies for a new set of survival conditions that are aligned with the new industrial paradigm of Industry 4.0. In particular, the model enables the creation of new policies based on an analysis of which direction companies across many industry sectors are adopting to seamless transition to a more digitised way of working. The model can also help managers to re-allocate their resources to sustain resource efficiency and increase the possibility that the seamless transition to digitalisation is implemented effectively and successfully in their organisation. Further, it sets the tone for the upcoming wave of Industry 5.0, which is promising to be more focused on human creativity.

Acknowledgments

The research for this project was supported by the Hasso Plattner Design Thinking Research Program (HPDTRP) and by the ERCIM ‘Alain Bensoussan’ Fellowship. We also would like to thank Vanessa Ladino for her contribution to the design of the graphic.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to containing information that could compromise the privacy of the research participants.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ https://drive.google.com/file/d/14DDje9M_YbQEr7MVwcKWFkR1In0Z1ML0/view?usp = sharing.

Appendix

Table A1. Main data sources and use.

Quotes	First-order codes	Aggregated dimensions
‘(…) it only really works if you have top management supporting this, without the support, it wouldn’t be possible to implement it into our normal work life’ (Design thinking coach, #11)	Secure (top) management support	Strategy & Vision
‘They [board level] wanted to take this seriously. The idea of the program was to have an internal network of people from different hierarchies that can then spread the mindset and the approach in their projects and build up the culture.’ (New work consultant, #8).	Focus on long-term internal capability building
‘Some companies don’t want to do design thinking because they don’t see the immediate bottom line impact and that’s an ongoing question that I think people have raised since design thinking has become even more popular’ (New work consultant, #36).	Have clear metrics/KPIs specific for design thinking
‘You have to have this failure culture and feedback culture of course. And that people are not driven by their own egos, but to have in mind the customer’ (Senior principal consultant, #12)	Nurture a culture of experimentation, failure and feedback	Culture & Environment
‘It is really the question how you translate what you learned, the processes, methods and mindsets to more people. I would say the only way is to work iteratively. To address the next product, the next department, and this way, step-by-step, according to a quick-win approach […]. You cannot achieve this in one go with a change management initiative’ (Innovation manager, #21).	Embed design thinking within a larger cultural and mindset change
‘When it comes to digital, it depends on your competencies. (…) Whenever you have high uncertainty and you don’t know in which direction to go, you could go in a million directions, but you need to look at it and here design thinking is an efficient way’ (Senior manager digital strategy, #19).	Be able to handle ambiguous situations and complexity
‘So they’ll run a 2 or 3 hour workshop that’s an introduction to design thinking. While I don’t think that’s bad we don’t get really excited about that because we don’t think those people in a really quick workshop are able to use those methods over time and they haven’t used it on a real project, they have used it on a learning project’ (Senior researcher, #37).	Ensure that design thinking workshops end with actionable results	Employee & Competencies
‘You have, at least in this case, eight to nine weeks of intensive program combining both getting together, workshop, educating, but also remote support for the teams. I would say that this is a super-efficient way for these people to start understanding the value of customers, the value of a cross-disciplinary team and new ways of working. Again, this has worked as one of the little fires in the organisation, because they always take something back to their own work’ (New work consultant, #4).	Provide blended learning and training on design thinking
‘In the end, it is all about people. If I don’t have a boss who says “I want this [design thinking] and I give resources for this [design thinking training]”, then there will be no changes ’ (Senior key expert consultant, #16)	Provide sufficient resources for design thinking training
‘Because in the old world, the client would come to the consulting firm and say: “This is my problem, solve it for me, tell me the solution”. In the new world, you go to the client and say: “We have a problem, how might we solve this?”. […] We are facilitating the client to make decisions. It is no longer an outsourcing-relationship, but a partnership. And that is a mind shift.’ (Head of growth and strategy, #1).	Establish collaborative initiatives with key partners
‘There are many stakeholders in delivering care. So, we really have to be thinking about a multistakeholder landscape and look at how we build empathy with every single one of these stakeholders and help them understand the role that they play in the context of delivering better patient care, better outcomes’ (Head of design, #24).	On-board stakeholders and users/customers early	Data & Structure
‘But yes definitely users’ involvement in terms of testing and prototyping and being interviewed and observed and … is very, very critical’ (Lecturer, #41).	Provide access to users/customers
‘You need design thinkers, but you also need thinking designers. And that together becomes extreme powerful. I think when you put a group of people together with different perspectives, different knowledge areas, looking at the problem through experiences, that’s great. Where the magic really begins to happen is when you then got a thinking designer to take all of the learnings, taking the insights and then helping craft and change and envision them in new products and services and solutions.’ (Head of design, #24).	Create cross-disciplinary teams
‘It’s also really useful when you want to really focus on the consumer pain points and problems […] And this is very important early on in innovation’ (Head of innovation, #15).	Apply design thinking in the early phase of innovation
‘It is about expertise. Somehow needs to be trained in usability testing. If possible, we try to get teams to go into testing with at least two prototypes’ (Senior key expert consultant, #16)	Test multiple prototypes
‘We found that (…) the development team worked better when you tangible early. And when you have differentiated prototyping phases that you can get inspiration from ’ (Senior key expert consultant, #16).	Get tangible fast in the project
‘We need to turn design thinking into our method, it needs to be a method that works for our organisation and nothing else. Because if you follow it after the book then you have ideas that are impossible and not economic’ (Senior manager digital strategy, #19).	Adapt the design thinking process to the organisational context
‘It’s not asking a customer what they need, it’s we’re saying it to them with empathy and discovering what those unarticulated customer needs are and I think that’s a skill that’s really valuable’ (Adjunct professor, #45).	Conduct professional user research
‘In the design thinking rooms the furniture is flexible, the atmosphere is more casual and creative. We also did a board meeting there as a test and after one hour there was a good result. You have a different ambiance, a different atmosphere, a different approach on a topic and fairly quickly also a different result. This is what is good about what such a space does to you.’ (PMO CCO, #14).	Set up a dedicated innovation space
‘Every design project should have some data and analytics component, so that in the future we can tie up those links’ (Head of growth and strategy, #1).	Augment design thinking with data analytic