From theories to tools

Most studies published in Technovation adopt a theory-driven approach toward empirical phenomena. In this editorial, we call for a more instrumental approach, one that effectively serves the needs of practitioners in the field of technological innovation as well as scholars seeking to increase their impact on innovation practice. In this respect, design science (DS) methodologies have emerged in various adjacent domains such as information systems, operations management, and entrepreneurship. Inspired by Simon ’ s “ The Sciences of the Artificial ” , DS operates at the interface of problem-solving design and explanatory science to create and test solutions as artifacts. Whereas DS work can result in various kinds of artifacts, tools for practitioners are the most promising ones. We first provide several examples of DS work resulting in widely used tools and then identify various challenges in the technological innovation domain that call for tools addressing these challenges. Subsequently, we provide practical guidance on how to prepare manuscripts about designing and testing tools which are likely to have a major impact on practice.


Introduction
The vast majority of studies of technological innovation published in Technovation adopt a theory-driven approach toward empirical phenomena (e.g., Boyson et al., 2022;Pan et al., 2021;Zhang et al., 2022).This theory-driven approach has been very helpful in shaping an academically respected field of inquiry as well as growing the population of scholars involved.Accordingly, Technovation has developed into a flagship journal in the field of guiding and managing technological innovation (Dabić et al., 2021).
In this editorial, we call for a more instrumental approach that would effectively serve the needs of practitioners in technological innovation as well as scholars seeking to increase their impact on innovation practice.In this respect, Herbert Simon (1996)

argued in his monograph
The Sciences of the Artificial that the business and management disciplines, together with disciplines such as architecture and medicine, operate at the interface of problem-solving design and explanatory science to create and test solutions as artifacts.Simon's ideas in this area have inspired the rise of so-called design science (DS) methodologies in domains such as information systems (Hevner et al., 2004;Peffers et al., 2007), operations management (Holmström et al., 2009;Van Aken et al., 2016) and entrepreneurship (Dimov et al., 2022;Romme and Reymen, 2018).In this respect, several impactful theories and tools developed in the entrepreneurship domain-such as effectuation theory (Sarasvathy, 2001(Sarasvathy, , 2003;;Zhang and Van Burg, 2020) and the business model canvas (Osterwalder, 2004;Osterwalder and Pigneur, 2010)arose from doctoral dissertations informed by DS.
Whereas DS work can result in many different types of artifacts (Dimov, 2016;Gilsing et al., 2010;Romme and Reymen, 2018), the most promising ones are tools for practitioners.As such, tools like the earlier mentioned Business Model Canvas provide a highly useful platform for practitioners and scholars alike, comparable to how a scaffold (Janson, 2020) operates for professional workers on construction sites.In general, a tool can be defined as an object that helps someone accomplish a particular task.Accordingly, tools in our field are objects that help professionals create and manage the conditions, processes, and outcomes of any technological innovation effort.
Our call for tools also resonates well with the need to address various grand societal challenges that this planet is facing-such as global warming and the imminent need to stabilize the climate (Matos et al., 2022) which requires systemic changes in extant production and consumption patterns (Engwall et al., 2021); the need to make healthcare systems more resilient and intelligent (Garcia-Perez et al., 2022); and the transformation of the global food and energy systems toward transparent and reliable supply chains (Kumar et al., 2022).The extant body of theoretical work on past and current (technological innovation) strategies, practices and outcomes in these areas needs to be complemented by developing tools that directly serve action.
Moreover, sustained efforts to design tools for new challenges in innovation management can, in turn, also fuel theory development (cf.Gigerenzer, 1991;Holmström et al., 2009;Geissdoerfer et al., 2018) (Romme and Dimov, 2021;Romme and Reymen, 2018).For instance, the business model canvas (Osterwalder and Pigneur, 2010) has informed many theory-driven studies in the area of business model development and adjacent topics (for recent overviews: Gassmann et al., 2020;Shepherd et al., 2022;Zaheer et al., 2019).This may make tool development even more attractive for researchers seeking to make a societal and academic impact.In this editorial, we primarily explore how extant theories can inform efforts to design and test tools and how this type of work can subsequently be published in top-tier journals like Technovation.
In the remainder of this editorial piece, we first provide several examples of DS work resulting in widely used tools and then identify various challenges in guiding and managing technological innovation which call for tool development.Subsequently, we provide practical guidance for preparing manuscripts about designing and testing tools that are likely to significantly impact innovation practice.

Examples of tools arising from studies informed by design science
This section provides several examples of DS work resulting in instrumental tools.In the previous section, we already referred to the toolbox for business modeling developed by Osterwalder.Other examples are Meulman et al. (2018) who designed and tested a tool for entrepreneurs in search of partners for innovation projects, and Suh and Chow's (2021) study that designs and tests a strategy tool for market shaping by ethnic ventures.In the remainder of this section, we outline three other examples more extensively: the tool for prototyping sustainable business models developed by Baldassarre et al. (2020), the machine learning tool for predicting high-growth enterprises developed by Hyytinen et al. (2022), and the Ecosystem Pie Model for mapping and analyzing innovation ecosystems developed by Talmar et al. (2020).Baldassarre et al. (2020) observed that many attempts to create sustainable business models fail because specific ideas for new business models are merely defined 'on paper' and never exposed to any pilot-testing-which is also known as the design-implementation gap.Therefore, they set out to explore how that critical gap may be bridged, by drawing on the literature about business experimentation and strategic design, which provide two approaches that leverage prototyping as a specific way to iteratively implement business ideas early on.Using a DS methodology, Baldassarre and coauthors combine theoretical insights from these two literatures into a tool for setting up small-scale pilots of sustainable business models.They subsequently apply, evaluate, and improve the tool by means of a rigorous process involving nine startups and one multinational company.The result is a tested tool, or normative theory (as Baldassarre and coauthors also call it), for prototyping sustainable business models.This tool invites entrepreneurs piloting a business model prototype to simultaneously consider the desirability (i.e., what users want), feasibility (i.e., what is technically achievable), viability (i.e., what is financially possible), and sustainability (i.e., what is economically, socially, and environmentally acceptable) of a new business model.The tests conducted by Baldassarre et al. (2020) suggest that setting up small-scale pilots (as prototypes) of a new business model is highly functional in bridging the design-implementation gap mentioned earlier.Hyytinen et al. (2022) developed a machine learning tool for predicting high-growth enterprises (HGEs), to help a venture capitalist firm make investment decisions within a fixed size (i.e.budget-constrained) portfolio.They apply a DS approach to develop a procedure based on machine learning, which predicts HGEs 3 years ahead and focuses on decision (rather than statistical) errors, using an accuracy measure relevant to the decision-making context of venture capitalist firms.Hyytinen and coauthors train and test the ML procedure drawing on an extensive data set of Finnish privately-owned limited liability companies (covering the period 2005-2018).They find the tool adheres to the budget constraint and maximizes the accuracy measure: nearly 40% of the HGE predictions are correct.They also conclude that the ML tool performs particularly well where it matters in practice, that is, in the upper tail of the distribution of the predicted HGE probabilities.While the tool was developed for a single venture capitalist firm, its applicability is much broader: other venture capitalists, business angel investors, science and technology parks, and public policymakers are also likely to be interested in this tool because it enhances the predictability of (the absence of) rapid growth in ventures, which in turn can inform private as well as public investment decisions (Hyytinen et al., 2022).
The final example is the tool for innovation ecosystem analysis presented by Talmar et al. (2020), arising from Talmar's (2018) doctoral work that was co-supervised by the two authors of this editorial.Talmar et al. (2020) observed that entrepreneurs seeking to realize a complex value proposition often need input and resources from other actors in a so-called innovation ecosystem, but there is not yet a comprehensive approach that supports them in the process of analyzing and deciding on ecosystem strategy.Talmar and coauthors thus designed the Ecosystem Pie Model (EPM), a visual tool for mapping, analyzing and designing innovation ecosystems.The EPM tool was built from the key constructs (e.g., ecosystem value proposition, resources, value addition and capture, dependence, risk) and the causal relationships between them, reported in the innovation ecosystem literature.This study's novelty thus arises from the synthesis of the extant literature in an instrumental model.In their article published in Long Range Planning, Talmar et al. (2020) merely illustrate the application of the EPM in terms of a single case, without any detailed data from users (e.g., regarding the actual impact on decision-making in the venture team); Talmar (2018) provides these data as well as a detailed description of the DS methodology used.
Overall, these examples of DS-based tools in the broader (innovation) management and entrepreneurship literature demonstrate that this type of work is still rare, given that the vast majority of scholars exclusively adopts a theoretical focus on explaining and modeling extant empirical phenomena.This observation underpins the need for a set of guidelines as to how authors can prepare manuscripts on tools, based on DS, for publication in Technovation.Later in this editorial (section 4), we will respond to this need.

Challenges in technological innovation management
Various challenges in the field of shaping and managing technological innovation call for tools addressing them.In this section, we outline a number of these challenges.This overview merely illustrates the need for evidence-based tools; it is not a complete overview of what practitioners (and scholars teaming up with them) in this highly dynamic field need.
For one, the rise of the Internet of Things (IoT) has been fueled by microchips becoming increasingly smaller and requiring less power as well as the rise of wireless communication devices (Clarysse et al., 2022).A growing body of theory-driven work on the implications of IoT is now available (e.g., Basaure et al., 2020;Huikkola et al., 2022;Leiting et al., 2022;Martens et al., 2022) which implies there is a huge potential for developing impactful tools to be used by IoT professionals and other practitioners facing major dilemmas in, for example, adapting their organizational governance approaches, supply chain configurations, cyber security systems, or business models to the rise of IoT.Such tools will also help to make the nascent body of scholarly knowledge more accessible for practitioners.
A related challenge involves the increasingly pivotal role of digital platforms in many industries (Holmström and Partanen, 2014;Kang, 2022;Ruokolainen et al., 2022), which raises various strategic and operational challenges.These challenges include, for example, how to manufacture products with digital counterparts that contain the digital genes of the physical products (Holmström et al., 2019); the psychological safety of the community using a digital platform (Zhang et al., 2010); Editorial the need for new entrants to continually design and redesign business model components when entering highly regulated non-platformed sectors (Essen et al., 2022); the resilience of incumbent firms and organizations in established industries (Garcia-Perez et al., 2022); the opportunity to consolidate demand from initial users as well as deploy capacity to new users (Holmström and Partanen, 2014); and how these platforms foster the birth, development and growth of new ventures employing digital technologies to shape the evolution of their ecosystem (Zahra et al., 2023).The extant literature in this area has primarily focused on identifying and describing the core elements of (emerging) digital platforms (Cavallo et al., 2022;Kang, 2022;Ruokolainen et al., 2022), rather than evidence-based tools that proactively guide the actual design of these platforms.For example, a generic tool for translating conventional operations into digitalized operations would be very helpful for practitioners; specifically for digital spare parts, such a tool was already developed by Akmal et al. (2022).Such tools will not only help transfer insights and experiences from one setting to other settings, but will also need to specify the boundary conditions under which specific design elements of a digital platform operate.
In a similar way, scholars can move from theories to tools when studying the role and impact of, for example, additive manufacturing (Jiang et al., 2017), digital twin technology (Khajavi et al., 2019), and deep-tech systems in which many different advanced technologies are integrated (Romme, 2022).More broadly speaking, new tools can also address how and when emerging technologies can be effectively commercialized, including which actors should be engaged in doing so (cf., Haessler et al., 2022) and how and when the R&D process should be opened for external stakeholders (cf., Hall et al., 2014;Shaikh and Randhawa, 2022).

How to prepare manuscripts about designing and testing tools
In this section, we provide practical guidance for preparing manuscripts about designing and testing tools that are likely to have a major impact on innovation practice.This guidance involves five points: evaluate whether available tools are likely to be ineffective or incomplete; formulate a problem-solving motivation and research question for the study; consistently implement this research question in how the tool is designed; use established research methods and criteria in testing the tool; and finally, maintain extensive logbooks in iteratively developing and testing the tool.
Evaluate whether available tools (if any) are ineffective or incomplete.This boils down to critically examining the need for any new tool.In many practical situations, existing tools or improvements of these tools are sufficient to solve the problem.However, if the strategic priorities of the focal firm, ecosystem or industry change-such as when sustainability raises to the top of the strategic agenda-the development of new tools is likely be warranted (e.g., Geissdoerfer et al., 2018;Baldassarre et al., 2020).In addition, new tools may be needed when new technologies such as drones and direct digital manufacturing introduce distinctly new generative mechanisms.For example, by exploring (the opportunities arising from) pilot implementations of drones in business operations, Maghazei et al. (2022) observe there is a lack of innovation management tools for Industry 4.0 practices.
Formulate a problem-solving motivation and research question for your study.Following Simon, DS is a science of the artificial that focuses on problem-solving or "how things ought to be"; as such, DS is highly complementary to descriptive-explanatory research that investigates "how things are" (Simon, 1996, pp. 4-5).Studies informed by DS thereby focus on research objects as artifacts, that is, as "intentional materializations that aim to serve or fulfill specific human purposes" (Dimov et al., 2022).This editorial piece focuses on tools as key artifacts arising from DS work.Here, an impactful manuscript informed by DS needs to explicitly adopt a problem-solving (i.e., artifact-oriented) motivation for the main research question and approach (Dimov et al., 2022).In this respect, the primary direction of a study developing tools informed by DS is to develop instrumental knowledge that serves the needs of practitioners, rather than (merely) provide a theoretical account of an empirical phenomenon.For example, Suh and Chow (2021) argue that building an ethnic foothold serves to "empower ethnic ventures' market-shaping over mass customization and customerization and thus allows the ventures to grow fast through their mixed business model combining business-to-business and business-to-consumer" (p.10); subsequently, they adopt a DS approach because "while normal science, driven by a quest for truth, is interested in 'what is' which leads to descriptive knowledge, artificial or design science is about a quest for utility questioning 'what can be' leading to prescriptive knowledge" (p.11).See section 2 for other examples of how one can effectively motivate a DS-based research question and methodology.
Consistently implement the research question in how the tool is designed.The research question needs to be consistently rolled out in how the tool is designed and tested.Many DS-based studies that develop tools start with a systematic review of the literature, to extract key guidelines (or design principles) from the extant body of knowledge (e.g., Romme and Endenburg, 2006;Sagath et al., 2019;Van Burg et al., 2008), but also allow for abductive reasoning on novel ideas to be included in the tool (e.g., Baldassarre et al., 2020;Talmar et al., 2020;Romme and Dimov, 2021).In this respect, idealized design offers a systematic process for going beyond the current body of knowledge, by envisioning novel ideas and solutions (Ackoff et al., 2006;Ozcan et al., 2021).The design phase in developing a novel tool typically also includes so-called alpha-tests, in which the research team explores the pragmatic validity (Holmström et al., 2009;Van Aken et al., 2016) of the tool.Such alpha-tests can be conducted within the design team and/or by engaging lead users closely connected to this team.For example, Meulman et al. (2018) developed an initial set of requirements for an innovation partner search tool in close collaboration with a major European innovation intermediary; lead users within this intermediary later also provided several rounds of feedback on early prototypes of the tool.The key question in this (creative) design phase is to what extent the initial version of the tool performs its core tasks, in terms of criteria such as simplicity, ease of use, robustness, and efficiency (Vom Brocke et al., 2020).
Use established research methods and criteria in (Beta) testing the tool.Finally, it is important to emphasize that adopting a DS approach cannot be an excuse for ignoring established research methods and criteria in how the tool can be beta-tested (cf., Koning et al., 2022).Many established methods such as case studies, focus groups, and experimental designs can be effectively integrated in studies developing tools.For example, Osterwalder (2004) used a case study to test the initial version of the business model canvas.Baldassarre et al. (2020) tested their tool for setting up small-scale pilots of sustainable business models by means of workshop sessions (or focus groups) in which a mix of qualitative research and action research techniques was used.Meulman et al. (2018) adopted a pre-test/post-test experimental setup to test the prototype of their tool for searching innovation partners.Accordingly, commonly used criteria such as reliability and (internal and external) validity also apply to how design scientists collect and analyze data when testing tools (Dimov et al., 2022).
In iteratively developing and testing the tool, maintain extensive logbooks.The design and development of tools in the field of innovation management typically involve many iterations between various research stages (cf.Dimov et al., 2022).It is therefore common practice in DS to maintain extensive logbooks of the various iterations in the research process (McAlpine et al., 2017), thereby enhancing the transparency of the various steps taken and the reliability of the tool developed.A logbook contains reports of each iteration through the DS cycle, providing detailed descriptions of how, when and where data was collected and analyzed, what was learned from the analysis, how the tool was adapted in response to these learnings, and so forth (Dimov et al., 2022).In the final journal article, such an extensive logbook can merely be mentioned or connected to the article via a hyperlink (given Editorial the usual restrictions on the word count of any article).But we strongly recommend authors preparing manuscript on a novel tool to make the entire logbook available to the editor and reviewers, to enhance the transparency and reproducibility of the design and test processes and their outcomes.This is best done in a separate appendix uploaded together with the main manuscript in the submission system of the journal.

Conclusion
In this editorial piece, we explored how DS methodologies can inform the development of impactful tools and provided practical guidance for preparing manuscripts to be submitted to Technovation or other top journals in field of innovation management.As such, studies informed by DS can complement the extant body of knowledge available in these journals (e.g., Dabić et al., 2021) by making it more accessible and instrumental for innovation managers, entrepreneurs, public policy makers, and other practitioners in the field of technological innovation.
. Previous reviews of studies informed by DS have provided various Abbreviations: DS, Design Science; IoT, Internet of Things.Contents lists available at ScienceDirect Technovation journal homepage: www.elsevier.com/locate/technovationhttps://doi.org/10.1016/j.technovation.2023.102692Received 20 December 2022; Received in revised form 27 December 2022; Accepted 4 January 2023 examples of how designing and testing novel tools can trigger efforts to develop new theory or adapt existing theories