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By drawing on various theoretical approaches and a gender perspective, this paper aims to examine business model (BM) experimentation as a step towards BM experimentation capabilities as an outcome and, as such, a key antecedent to firm performance.

In this paper, using a unique data set of 444 European small and medium-sized enterprises (SMEs), the authors draw on various theoretical perspectives to devise a structural equation model that examines BM experimentation as a step towards business model innovation (BMI) as an outcome and, as such, a key antecedent to firm performance. Potential differences are examined between female-owned and non-female-owned businesses with regard to hypothesized relations.

Multi-group analysis results reveal that drivers of BM experimentation and the paths linking BM experimentation to overall firm performance are different for female owners in comparison to male owners.

Theoretical and practical implications are various. For SME entrepreneurs, experimenting with their BMs does lead to improved performance.

Theoretical and practical implications are various. For SME entrepreneurs, experimenting with their BMs does lead to improved performance.

Despite the increasing number of papers focussing on the relationship between BM and firm performance, the focus on female entrepreneurship, gender differences and BMI, more specifically the process of BMI as BM experimentation, is relatively rare.

The purpose of this paper is to discuss how experimentation with open Internet of Things data can be institutionalised in an inclusive manner at scale.

The approach is conceptual, addressing key challenges discussed in the literature on experimental cities. This exposition of the problem of scaling experimentation is anchored in findings from two projects (Dampbusters and OrganiCity), which seek to implement experimentation as a practice of sustainable digital urban development.

One central finding is that local interventions need transferable frameworks and mechanisms to achieve scaling effects of experimentation as a practice. In addition, experimentation must embed common engagement principles, structures of data and interfaces, and governance principles across use cases to be scaled.

The authors outline how and why experimentation can be a useful approach to address challenges of implementing urban informatics into concrete uses and procedures for co-creation. Based on reports from two projects, the authors develop recommendations for experimentation at scale that reflect the need for engagement principles, the need for common data structures and interfaces, as well as governance principles.

To meet their ambitious targets for a circular economy, multinationals need to speed up their innovation efforts. This requires experimentation capability. But it is not clear what this capability entails, and how companies can build it. The purpose of this paper is to give companies guidance on how they might develop experimentation capability for the circular economy.

We conduct in-depth interviews with innovators in frontrunner multinationals, H&M, IKEA and Philips. We use the Gioia method to analyse our data.

This article identifies novel institutional, strategic, and operational actions that build experimentation capability for a circular economy.

The identified actions help innovation managers experiment and speed up their innovation efforts for a circular economy.

To reverse environmental degradation, multinationals need to transform their dominant linear take-make-waste business models. This research provides actions that help them organize this transformation.

This article is based on extensive research with leading multinationals and reveals novel insights on how to innovate for a circular economy.

In this interview with Harvard innovation expert Stefan H.Thomke about his latest book, Experimentation Works: The Surprising Power of Business Experiments, he pays tribute to the scientific method and “the engine that has powered” it over the centuries, the “humble experiment.”

Professor Thomke anticipates a burgeoning role for business experimentation, one that it is already playing across the value chain, particularly in leading online companies.

Digital experimentation tools have the potential to revolutionize a company’s R&D, but they can also transform entire industries by shifting experimentation–and thus product innovation–to users and customers.

The ability to access large customer samples, to automatically collect huge amounts of data about user interactions on websites and apps, and to run concurrent experiments gives companies an unprecedented opportunity to evaluate many ideas quickly, with great precision, and at a negligible cost per additional experiment.

Product development is being transformed by rapid experimentation: all aspects of software–including user interfaces, security applications and back-end changes–can now be subjected to A/B tests.

Some organizations are responding to the opportunity of innovation, while others are not. Harvard innovation expert Stefan H. Thomke says a key reason for the slow pace of innovation is that most organizations lack "a culture of experimentation." In this article the author explains the impediments to establishing such a culture. 10;

In his latest book, Experimentation Works: The Surprising Power of Business Experiments, 10;Thomke says, to create a culture of experimentation, firms need “a new model of leadership,” which cultivates curiosity, emphasizes data-informed decisions, experiments ethically, sets grand challenges and establishes systematic training and support for rapid experimentation. Those are all positive steps in the direction of creating a culture of experimentation.

But they won’t be enough because the underlying management model of most big organizations is explicitly aimed at preventing the very kind of experimentation that Thomke is recommending.

In a digital world, technology has made it radically simpler and quicker and cheaper to carry out and evaluate multiple experiments. The whole organization needs to become an organic living network of high-performance teams. In such firms competence resides throughout the organization and that innovation can and must come from anywhere. 10;

An important discussion of how top down managements squelch experimentation and thus throttle innovation.

The purpose of this paper is to address the following questions: in what sense does experimentation as improvisation lead to methodological innovation? What are the implications of artistic experimentation as improvisation for education and learning?

The paper tracks the known concept within research of “experimentation” with a view to revealing how practice-led research in art works distinctively with experimentation. It proposes experimentation as improvisation drawing on a research project Sounding Drawing 2012 as an example. The paper situates art experimentation as improvisation in art (Cage, 1995) anthropology (Hallam and Ingold, 2007; Bateson, 1989) and the theoretical work of Arnheim (1986) on forms of cognition.

Arts research as improvisation is participatory, relational and performative retaining the research subject in its life context. The artist as researcher starts with open-ended critical questions for which there are no known methods or immediate answer. By setting up boundary conditions from the outset and understanding the situatedness and contingencies of those conditions, the artist as improviser seeks ways of not only avoiding chaos and the arbitrary but also being trapped by what is already known.

This approach is important within and beyond the arts because it consciously draws together different forms of cognition – intuition and relational knowledge and also sequential knowledge. It is also significant because it offers a different epistemology in which new knowledge emerges in the relationship between participants in the research taking form in co-creation. These qualities all position improvisation as a research paradigm and a counterpoint to positivism.

Many theories have been proposed to understand and improve the process of leadership development. One useful way to structure the literature is around three complementary perspectives, briefly summarized as the “knowing, doing and being” dimensions of leadership. While the complementarities between these perspectives have been discussed, the mechanisms by which they are linked are less clear. The purpose of this paper is to examine the role of experimentation as one such mechanism.

Building on interviews and prior literature, the authors argue that experimentation consists of two processes: task-prototyping focused on the work overseen by the leader and self-prototyping focused on how the leader relates to others. This study proposes a theoretical framework linking experimentation to action-taking (e.g. being entrepreneurial and taking on challenging assignments), which in turn links to leader effectiveness. The authors test the hypotheses on two groups of leaders (481 business school alumni and 310 financial services leaders).

The authors find evidence that both forms of experimentation provide significant explanatory power in understanding why some individuals engage in higher levels of action-taking than others. Additionally, their study confirms the central role of action-taking in leadership development.

Conceptually, this study distinguishes two dimensions of experimentation and their connection to action-taking, knowledge development and identity development. Empirically, the authors show that these two experimentation activities were significant predictors of action-taking, even after controlling for all other factors, and that action-taking (along with self-prototyping) was an important predictor of leader effectiveness. The results offer a practical framework for leadership and development professionals to use in designing and evaluating leadership development activities.

In the context of libraries adopting artificial intelligence (AI) technology, this paper aims to demonstrate the synergy between two different yet complimentary frameworks, need-based experimentation (NBE) and curiosity-based experimentation (CBE). It looks at how these frameworks interact and operate together to promote technological innovation and innovation in libraries.

The authors’ extensive professional experience in the AI adoption and innovation of libraries is drew upon in this paper. The methodology encompasses empirical observations of various libraries engaging in digital innovations through experimentations with AI technology adoption practices. Using the frameworks of NBE and CB), these observations are examined to find patterns, relationships and mutual reinforcement between the two methods. The analysis of this study is built on the authors’ observations and real-world case studies.

The research reveals that NBE and CBE work together to provide libraries with all-encompassing adoption methods for AI technology. This study indicates a dynamic interaction between NBE and CBE that boosts libraries’ methods for adopting AI technology. NBE acts as a catalyst for CBE by raising awareness of specific library needs, prompting librarians to explore AI technologies aligned with those needs. This synergy empowers librarians to creatively experiment with technology solutions that directly address pressing library challenges. Conversely, CBE fuels NBE by promoting group learning among diverse team members and fostering individual motivation to tackle library needs collaboratively. As they explore AI technology out of personal curiosity, librarians make important contributions that enhance NBE.

The novel aspect of this study is the recognition of the complementarity between NBE and CBE frameworks, which suggests that libraries should view them as intertwined rather than two separate approaches. Focusing on both methodologies increases the culture of experimentation and improves the problem-solving abilities of librarians. Innovation is fueled by controlled experimentation and innate curiosity in an atmosphere that is fostered by the mutual influence of NBE and CBE. This synthesis offers libraries a comprehensive strategy for adopting AI technology, empowering them to manage the shifting environment and realize the revolutionary promise of AI technologies.

The primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?

The review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.

This study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.

It is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

Project management practices have been losing its central place in innovation processes within the companies due to its excessive control-oriented phased approach. Hence, the purpose of this paper is to propose and examine the project management practitioners' experiences on the systematic use of an experimentation framework to manage innovation projects.

Three case studies were carried out in three different Brazilian companies for a ten month period. The authors have chosen these companies for the following reasons: they were all in an innovation context; they have never carried out a project based on experimentation and they had previous experience with project management traditional tools.

The findings have shown that our framework can contribute to the project management available toolkit; once the rigid experimentation process, the authors’ proposed made it easier for project management practitioners to adapt to more flexible approaches. Nonetheless, stakeholders' involvement has shown to be a key success factor on the deployment of the framework.

Managers still need to add expertise in flexible methods into their managerial skills, so they are able to deal with innovation just as they deal with traditional processes they were trained to. This suggestion can also be extended to the business schools.

The case studies have shown that traditional project management practices can also be applied in innovation projects. So despite of generally being the opposite of an experimentation process, those practices are relevant in project management discipline, which means that formal project management training is still a good ally to project management practitioners.