Early Phase Of Innovation Research Articles

Assessing health technology implementation during academic research and early-stage development: support tools for awareness and guidance: a review.

For successful health technology innovation and implementation it is key to, in an early phase, understand the problem and whether a proposed innovation is the best way to solve the problem. This review performed an initial exploration of published tools that support innovators in academic research and early stage development with awareness and guidance along the end-to-end process of development, evaluation and implementation of health technology innovations. Tools were identified from scientific literature as well as in grey literature by non-systematic searches in public research databases and search engines, and based on expert referral. A total number of 14 tools were included. Tools were classified as either readiness level tool (n = 6), questionnaire/checklist tool (n = 5) or guidance tool (n = 3). A qualitative analysis of the tools identified 5 key domains, 5 innovation phases and 3 implementation principles. All tools were mapped for (partially) addressing the identified domains, phases, and principles. The present review provides awareness of available tools and of important aspects of health technology innovation and implementation (vs. non-technological or non-health related technological innovations). Considerations for tool selection include for example the purpose of use (awareness or guidance) and the type of health technology innovation. Considerations for novel tool development include the specific challenges in academic and early stage development settings, the translation of implementation to early innovation phases, and the importance of multi-disciplinary strategic decision-making. A remaining attention point for future studies is the validation and effectiveness of (self-assessment) tools, especially in the context of support preferences and available support alternatives.

How position in the network determines the fate of lexical innovations on Twitter

This study analyzes the diffusion of lexical innovations on Twitter to understand how the social network position of adopters impacts their success. Looking at both successful and failed neologisms, we categorize them into "changes" which become established and "buzzes" which decline over time. Using a corpus of 650 million French tweets, we reconstruct user networks and characterize adopters of innovations during different diffusion phases based on prestige, centrality, clustering, and external ties. In the early innovation phase, change and buzz adopters have similar peripheral profiles. During propagation, changes spread to prestigious, central individuals while buzzes do not, which predicts their eventual success or failure. By the establishment phase, changes reach highly central users with closer external ties. The results align with sociolinguistic theories about weak ties for innovation and strong ties for establishment. Additionally, logistic regression models based on early adopter profiles can predict the fate of innovations. This work sheds light on the diffusion dynamics of online lexical innovations and the crucial role of user network factors.

The Crowdless Future? Generative AI and Creative Problem-Solving

The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. The challenge attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while human crowd solutions exhibited higher novelty—both on average and for highly novel outcomes—human-AI solutions demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably, human-AI solutions cocreated through differentiated search, where human-guided prompts instructed the large language model to sequentially generate outputs distinct from previous iterations, outperformed solutions generated through independent search. By incorporating “AI in the loop” into human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations. Funding: This work was supported by Harvard Business School (Division of Research and Faculty Development) and the Laboratory for Innovation Science at Harvard (LISH) at the Digital Data and Design (D3) Institute at Harvard. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.18430 .

Approach for the monetary evaluation of process innovations in early innovation phases focusing on manufacturing and material costs

In early innovation phases, the monetary evaluation of process innovations is a challenge for companies due to a lack of data. However, an innovation evaluation is essential in an early innovation phase to ensure that process innovations deliver economic value added (EVA) in early innovation phases and to channel technology transfer expenditures in a goal-oriented manner. This paper presents an approach for a semi-quantitative procedure for the monetary evaluation of process innovations in the early innovation phase focusing on manufacturing and material costs. Exemplarily, the approach is applied to process innovations of the Collaborative Research Center 1368 on oxygen-free production. In order to ensure the net present value orientation within the innovation evaluation, the procedure developed is based on a driver tree of the EVA. To link value drivers of the EVA and innovation-driven factors influencing EVA, the EVA driver tree is further systematized with a focus on manufacturing and material costs using a literature-based impact model. Based on the last level of the impact model, a guideline for a semi-structured expert interview is developed. Using this interview guideline, data is collected in the form of innovation-driven influencing factors, which represent the input for the final monetary innovation evaluation. An adapted weighted scoring model is used to draw a semi-quantitative conclusion regarding the EVA achieved by the process innovation. The practical application of the approach developed to process innovations in oxygen-free production has shown that, in the context of three process innovations under consideration, their implementation with the aim of achieving an EVA through reduced manufacturing and material costs at the current innovation status is not effective. However, based on the impact model developed, corresponding levers can be identified to positively influence the EVA and thus also the industrialization of the process innovation. Finally, further necessary steps are identified to evolve the presented approach into a complete method for monetary innovation evaluation in early innovation phases.

Accelerating Innovation With Generative AI: AI-Augmented Digital Prototyping and Innovation Methods

Easy-to-use generative Artificial Intelligence (AI) is democratizing the use of AI in innovation management and may significantly change the way how we work and innovate. In this article, we show how large language models such as GPT can augment the early phases of innovation, in particular, exploration, ideation, and digital prototyping. Drawing on six months of experimenting with large language models in internal and client innovation projects, we share first-hand experiences and concrete examples of AI-assisted approaches. The article highlights a large variety of use cases for generative AI ranging from user journey mapping to idea generation and prototyping and foreshadows the promising role LLMs may play in future knowledge management systems. Moreover, we argue that generative AI may become a game changer in early prototyping as the delegation of tasks to an artificial agent can result in faster iterations and reduced costs. Our experiences also provide insights into how human innovation teams purposively and effectively interact with AIs and integrate them into their workflows.

A threshold for collaborative innovation: exploring the dimensions of liminality in a data economy initiative

Collaborative innovation requires space for different parties to meet, exchange information, engage in dialogue, and eventually create new solutions. Despite the increased scholarly and practical interest, understanding how such spaces are initiated and sustained remains scarce. In this study, we focus on a national data economy initiative to investigate a virtual and mental space where relevant and interested cross‐sectoral parties can join, share information and identify shared interests for research and development in the data economy. We investigate the early phases of collaborative innovation through the lens of liminality and show how individuals' sensemaking narratives manifest the different dimensions of liminality. We contribute to the research on collaborative innovation and spaces by theorising liminality in the context of our paper as a three‐dimensional concept of confusion–clarity, isolation–communal, and stagnation–movement that creates a threshold for collaborative innovation. Our study offers a more nuanced understanding of the less studied, yet critical, early phase of collaborative innovation, hence contributing to the research and practice of the emergence of collaborative innovation.

OP07 Dealing With Uncertainty In Early Health Technology Assessment: An Exploration Of Methods For Decision-Making Under Deep Uncertainty

IntroductionIn early stages, the consequences of innovations are often unknown or deeply uncertain. This complicates health economic modelling. The field of decision-making under deep uncertainty (DMDU) uses exploratory modelling (EM) to help decision-makers make sound decisions under conditions of deep uncertainty (i.e., when stakeholders do not know, or cannot agree on, the system model, the probability distributions to place over the inputs to these models, which consequences to consider, and their relative importance). The aim of this research was to evaluate the potential of EM for the early evaluation of health technologies.MethodsEM and early health economic modelling (EHEM) were applied to an early evaluation of minimally invasive surgery (MIS) for acute intracerebral hemorrhage, and were compared to derive differences, merits, and drawbacks of EM.ResultsThe approaches fundamentally differ in the way uncertainty is handled. Where in EHEM the focus is on the value of the technology, while accounting for the uncertainty, EM focuses on the uncertainty. EHEM aims to assess whether the innovative strategy is potentially cost-effective, often using assumptions. EM on the other hand focuses on finding robust strategies (i.e., strategies that give relatively good outcomes over a wide range of plausible futures). This was also reflected in our case study. For example, EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a strategy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs.ConclusionsEM seems most suited in the very early phases of innovation (i.e., when a problem is signaled). Here, it can explore the robustness of many potential strategies under uncertainty. When potential strategies are selected, EHEM seems useful to optimize these strategies. Yet, EM methods are complex and might only be fully effective when a policy window exists that facilitates flexible research and adoption strategies.

Dealing With Uncertainty in Early Health Technology Assessment: An Exploration of Methods for Decision Making Under Deep Uncertainty

ObjectivesIn early stages, the consequences of innovations are often unknown or deeply uncertain, which complicates early health economic modeling (EHEM). The field of decision making under deep uncertainty uses exploratory modeling (EM) in situations when the system model, input probabilities/distributions, and consequences are unknown or debated. Our aim was to evaluate the use of EM for early evaluation of health technologies. MethodsWe applied EM and EHEM to an early evaluation of minimally invasive endoscopy-guided surgery (MIS) for acute intracerebral hemorrhage and compared these models to derive differences, merits, and drawbacks of EM. ResultsEHEM and EM differ fundamentally in how uncertainty is handled. Where in EHEM the focus is on the value of technology, while accounting for the uncertainty, EM focuses on the uncertainty. EM aims to find robust strategies, which give relatively good outcomes over a wide range of plausible futures. This was reflected in our case study. EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a policy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs. ConclusionsEHEM and EM were found to complement each other. EM seems most suited in the very early phases of innovation to explore existing uncertainty and many potential strategies. EHEM seems most useful to optimize promising strategies, yet EM methods are complex and might only add value when stakeholders are willing to consider multiple solutions to a problem and adopt flexible research and adoption strategies.

Supporting Collaborative Innovation Processes in Smart Product Value Creation Networks

The development of smart products and systems includes a dramatically growing share of non-mechanical domains like microelectronic and software. This determines fundamentally new collaboration challenges and needs for system and product developers and their industrial organizations. Besides the rising number of relevant engineering disciplines (e.g. product-related internet-based service engineering and data platform engineering), the collaboration across several disciplines needs to become more systematic and has to be based on consistent system models. Since traditional value chains are transforming into new agile and distributed value creation networks, engineering collaboration approaches and solutions also have to stronger emphasize model-based, cross-company engineering collaboration. Also, the number of engineering stakeholders involved is increasing, while at the same time stakeholders are taking on new roles and completely new players are emerging, the collaboration share also has to grow continuously. This paper introduces a generic methodical approach for modeling collaborative innovation processes in value creation networks for smart products and supporting them with information technology by using an integrated, cross-company knowledge base. The support of engineering collaboration with a generic common reference innovation process along the value creation network with a seamless transformation into the engineering phase, are part of the focused approach goals. This generic approach has been instantiated according to the specific needs of early innovation phases in the field of automotive microelectronic integration considering the whole value creation network.

See What I Mean? Analogical Objects for Knowledge Mediation in Early Phases of Cross-Industry Innovation

The objective of this research is to explore the nature and role of analogies as objects for knowledge transfer in cross-industry collaborations. A case study of an organization seeking cross-industry innovation (CII) across two industry sectors was conducted, and the empirical data were analyzed qualitatively. We found that analogies used as knowledge mediation objects could be classified as explanatory or inventive, each expressed as linguistic or visual representations. Explanatory analogical objects help build prior knowledge of a foreign industry domain, thus easing later use of inventive analogical objects to identify how knowledge from one industry can be applied in another industry for innovation purposes. In these roles, the analogies serve as boundary objects. Both explanatory and inventive analogies can also serve as epistemic objects, motivating for further collaborative engagement. Visual representations of analogies help bridge the abstract with the concrete, thereby easing the process of creating analogies. They also enable nonverbal communication, thus helping bypass language barriers between knowledge domains. The reported research expands current research literature on knowledge mediation objects to the context of CII and provides added detailed understanding of the use of analogies in CII.

Commercialization of disruptive innovations: Literature review and proposal for a process framework

The challenges of disruptive innovations have gained significant attention from both academics and practitioners, commercialization being one of the most critical phases. At the same time, however, it is the less studied area of disruptive innovation. Therefore, this article examined scholarly papers on the commercialization of disruptive innovations through a multidisciplinary systematic literature review. It resulted in the analysis of 64 high-quality peer-reviewed academic articles. The analysis highlighted the commercialization models and main constructs that are affecting the commercialization process: market orientation, market learning, user's involvement, market configuration, adoption networks and stakeholders, and innovation transference. The study evidences how commercialization has evolved from a later stage in innovation to influence even the early phases of innovation, characterized in turn by exploration, learning and ecosystem creation activities. Additionally, the analysis led to a proposition that established an integrated commercialization model for high uncertainty innovations. The model has three phases: 1) Concept/value proposition validation, 2) Business model validation & Market creation, and 3) Creating sales in the majority market. Lastly, the article contributes to a better understanding of commercialization processes in high uncertainty innovations, bridging also the academic-practitioner divide.

User-centered requirements engineering to manage the fuzzy front-end of open innovation in e-health: A study on support systems for seniors’ physical activity

BackgroundAlthough e-health potentials for improving health systems in their safety, quality and efficiency has been acknowledged, a large gap between the postulated and empirically demonstrated benefits of e-health technologies has been ascertained. E-health development has classically been technology-driven, often resulting in the design of devices and applications that ignore the complexity of the real-world setting, thus leading to slow diffusion of innovations to care. Therefore, e-health innovation needs to consider the mentioned complexity already from the start. The early phases of innovation, fuzzy front-end (FFE) defined as “the period between when an opportunity is first considered and when an idea is judged ready for development” has been identified to have the highest impact on the innovation process and its outcome. The FFE has been recognized as the most difficult stage to manage in the innovation process as it involves a high degree of uncertainty. Such a phase becomes even more difficult when different sectors and organizations are involved. Therefore, effective methods for involving different organizations and user groups in the FFE of innovation are needed. ObjectiveThe aim of this study was to manage the FFE of a collaborative, open innovation (OI) process, to define a software system supporting seniors’ physical activity (PA) by applying a framework of methods from software requirements engineering (RE) to elicit and analyze needs and requirements of users and stakeholders, as well as the context in which the system should be used. MethodsNeeds and requirements of three future user groups were explored through individual- and focus group interviews. Requirements were categorized and analyzed in a workshop with a multidisciplinary team: a system overview was produced by conceptual modelling using elicited functional requirements; high-level non-functional requirements were negotiated and prioritized. Scenario descriptions of system’s supportive roles in different phases of a behavioral change process were developed. ResultsUser-centered RE methods were successfully used to define a system and a high-level requirements description was developed based on needs and requirements from three identified user groups. The system aimed to support seniors’ motivation for PA and contained four complementary sub-systems. The outcome of the study was a Concept of Operations (ConOps) document that specified the high-level system requirements in a way that was understandable for stakeholders. This document was used both to identify and recruit suitable industrial partners for the following open innovation development and to facilitate communication and collaboration in the innovation process. ConclusionsApplying software RE methods and involving user groups in the early phases of OI can contribute to the development of new concepts that meet complex real-world requirements. Different user groups can complement each other in conveying needs and requirements from which systems can be designed. Empirical studies applying and exploring different methods used to define new e-health solutions can contribute with valuable knowledge about handling innovation FFE.

Variability of teachers’ technology integration in the classroom: A matter of utility!

Technology integration in the classroom is seen as an important educational innovation to enhance teaching and learning processes in the 21st century. Teacher motivation has been seen as a critical prerequisite for successful technology integration in the classroom, particularly in early phases of educational innovation. However, whether teacher motivation is a stable versus variable construct in such innovation contexts and how teacher motivation is decisive for effective technology integration are open issues. Most previous studies relied on cross-sectional studies and were restricted to quantity measures which only investigated the mere usage of technology but not the quality of technology integration. Against this background, we implemented a web-based teacher diary for in-service teachers (N = 18) in the beginning of a government initiative in which classes were equipped with one-to-one technological infrastructure. By using a mixed method approach, we found considerable variability of teacher motivation, frequency, and quality of technology integration across lessons over a period of six weeks. More importantly, the variability of the quality of technology integration was explained by teachers’ technology-related utility beliefs, as a crucial facet of technology integration and specific factors within the different instructional contexts. The findings highlight the role of teacher motivation as well as the particular context in technological educational innovations.

Managing Climate Change by Managing Stakeholders? Information Processing in Urban Innovation

Urban renewal is increasing in relevance, mainly due to the need to address climate change. In pursuit of this goal, urban innovations target the development and implementation of collectively used infrastructures and resources in cities, like smart city solutions. These endeavors require the involvement of stakeholders to identify their needs and to garner their active support in urban environments. As a result, urban innovation projects can implement concrete solutions for ecological sustainability in local ecosystems that display multiple and frequent conflicting interests. Our study draws on stakeholder theory to investigate how the breadth of stakeholder involvement (the number of different stakeholder groups) and the depth of stakeholder involvement (the collaboration intensity with external stakeholders during the early innovation phase) affect the implementation orientation of urban innovation projects. Building on the perspective of information processing theory, we suggest that the efficacy of stakeholder involvement depends on the use of digital involvement and formalization tools. To explore these relationships, we analyze 106 urban innovation projects for energy efficiency improvements in urban districts. We use a text mining analysis of planning documents to assess the implementation orientation of targeted innovations and survey data to measure the independent variables. The results provide empirical evidence that (i) a broad involvement of stakeholders affects urban innovations’ implementation negatively, but a deep stakeholder involvement has the opposite effect, and that (ii) intensive use of digital involvement tools support stakeholder involvement, while (iii) a high degree of IT supported project formalization weakens the positive effect of stakeholder involvement intensity on the implementation orientation.

Hazard Assessment of Polymeric Nanobiomaterials for Drug Delivery: What Can We Learn From Literature So Far.

The physicochemical properties of nanobiomaterials, such as their small size and high surface area ratio, make them attractive, novel drug-carriers, with increased cellular interaction and increased permeation through several biological barriers. However, these same properties hinder any extrapolation of knowledge from the toxicity of their raw material. Though, as suggested by the Safe-by-Design (SbD) concept, the hazard assessment should be the starting point for the formulation development. This may enable us to select the most promising candidates of polymeric nanobiomaterials for safe drug-delivery in an early phase of innovation. Nowadays the majority of reports on polymeric nanomaterials are focused in optimizing the nanocarrier features, such as size, physical stability and drug loading efficacy, and in performing preliminary cytocompatibility testing and proving effectiveness of the drug loaded formulation, using the most diverse cell lines. Toxicological studies exploring the biological effects of the polymeric nanomaterials, particularly regarding immune system interaction are often disregarded. The objective of this review is to illustrate what is known about the biological effects of polymeric nanomaterials and to see if trends in toxicity and general links between physicochemical properties of nanobiomaterials and their effects may be derived. For that, data on chitosan, polylactic acid (PLA), polyhydroxyalkanoate (PHA), poly(lactic-co-glycolic acid) (PLGA) and policaprolactone (PCL) nanomaterials will be evaluated regarding acute and repeated dose toxicity, inflammation, oxidative stress, genotoxicity, toxicity on reproduction and hemocompatibility. We further intend to identify the analytical and biological tests described in the literature used to assess polymeric nanomaterials toxicity, to evaluate and interpret the available results and to expose the obstacles and challenges related to the nanomaterial testing. At the present time, considering all the information collected, the hazard assessment and thus also the SbD of polymeric nanomaterials is still dependent on a case-by-case evaluation. The identified obstacles prevent the identification of toxicity trends and the generation of an assertive toxicity database. In the future, in vitro and in vivo harmonized toxicity studies using unloaded polymeric nanomaterials, extensively characterized regarding their intrinsic and extrinsic properties should allow to generate such database. Such a database would enable us to apply the SbD approach more efficiently.

Meaning Frames: The Structure of Problem Frames and Solution Frames

In recent years, focus on the designer's ability to frame wicked problems has underlined the important positioning of the designer as a key player in the early phases of innovation. However, further clarification and development of the theory and terminology of framing are needed in order to understand and support the rather complex framing process that the design team engages in during the early phases of innovation. There is a need to understand how design teams move from an overall framing of the wicked problem, in literature termed the “ problem frame,” to creating a meaningful solution. Through in-depth case studies of the framing processes at five design companies, we learn how designers use the overall problem frame as a stepping-stone to constructing a set of “ solution frames” in order to move toward a meaningful solution that integrates different perspectives. Together with the problem frame these sets of additional solution frames constitute an overall framing of the meaningful product—a “ meaning frame.” This overall meaning frame clarifies and sets the boundaries, the values and goals, and the criteria for evaluation of a proposed solution. As such, the study sheds light on the otherwise hidden reasoning process of framing toward a meaningful solution, rather than framing the problem, which a majority of current literature discusses.

The Shadow of Future Value Distribution: Anticipating Value Misappropriation

Future value creation among partners can only be drafted in the early phases of multilateral collaborative innovation, raising future value distribution concerns. Innovation ecosystems offer differ...

HOW DO FIRMS OPEN UP THE FRONT-END OF SERVICE INNOVATION? A CASE STUDY OF IT-BASED SERVICE FIRMS IN THAILAND

This paper focusses on the openness in the front-end phase of service innovation and its impact on innovation success. The early stages of innovation are fuzzy and unstructured, thus often being called “fuzzy front-end” (FFE) by scholars. The FFE begins when an opportunity is considered worthy of further ideation, exploration, and assessment and ends when a firm decides to invest in — or terminate — an idea. Although openness has been identified as pivotal to innovation performance, little effort has been put into exploring its role in the early phase of innovation. By drawing on the data of a multiple case study in Thai online service firms, we are able to identify four key dimensions of FFE openness competence: prior related knowledge, top management support, the presence of workable prototype, and slack resource. Furthermore, we found three openness activities that often take place in the FFE phase of successful online service innovation, i.e., external search, inter-firm partnerships and customer experimentation. From a managerial perspective, our study provides useful insights to innovation managers aiming at enhancing front-end performance through openness.

OP97 Digital Home Services: Overcoming Critical Barriers In Early Decision Making

Introduction:There is a lack of adoption and diffusion of health innovations needed to drive the implementation of important breakthroughs in value-based health care. To stimulate organizational changes, decision-makers need to see potential benefit at an early stage. The aim of the present study was to assess the potential effects of a conceptualized intention to provide digital home-based care and compare it to the current provision of such care. The new intervention aims to strengthen the municipality's care services by offering a digital communication platform to recipients of home-based health services and their dependents. The platform is designed to be implemented nationally and is in line with home service needs identified in several white papers.Methods:An interdisciplinary team united to determine and quantify potential effects of the project. Effects of the digitalized service were distinguished in priced quantitative, unpriced quantitative and qualitative effects. A ten-year present value calculation with a calculation rate of four percent was used for the estimates. A risk analysis was also carried out.Results:The present value calculation resulted in estimated savings equal to EUR 25.8 million , with present value investments costs of EUR 5.5 million over ten years. This resulted in net present value per invested euro in the public sector equal to EUR 3.2. Overall assessment of uncertainty related to the intervention's socio-economic profitability was deemed average. Based on data quantified estimates from the conceptual phase, the project succeeded in the decision-making and funding needed to proceed into the next developmental phase of the project.Conclusions:The present approach to early assessment may provide much desired decision support in an early innovation phase when data are still missing. Our experience is that early stakeholder involvement and the early assessment and quantification of value gains are of utmost importance to overcome the critical barriers to organization health innovations.

Takin' Care of Business Models: The Impact of Business Model Evaluation on Front‐End Success

In recent years, the discussion about business models has garnered increasing attention in practice and research. It is argued that thinking about business model alternatives supports managers, entrepreneurs, or R&D groups in, for instance, developing technological ideas into successful new businesses. Due to a business model's holistic perspective on ideas, it aims to understand not only what kind of value can be created for customers with a certain project but also how this value can be captured and what the corresponding value architecture looks like. Although existing research argues that technological development and business model development should go hand in hand, a quantitative examination of the actual benefits of business model evaluation in the technology‐driven front end of innovation has yet to be performed. This study relates business model evaluation to dimensions of front‐end success and examines environmental turbulence as a moderating variable. Hypotheses are tested by applying structural equation modeling on a set of 151 R&D groups. Results indicate that the extent of business model evaluation in R&D groups' early innovation phases positively relates to front‐end effectiveness and efficiency. Further findings reveal that environmental turbulence increases the strength of these relationships, indicating the stronger relevance of business model evaluation in more turbulent environments.