Purpose
Given the important role of frugal innovation for firms in the developing and emerging countries, the purpose of this paper is to investigate the effect of transformational leadership (TL) on ...frugal innovation through the mediating roles of tacit and explicit knowledge sharing.
Design/methodology/approach
The paper used a quantitative research method and structural equation modeling to test the relationship among the latent factors based on a sample of 339 participants from 120 Vietnamese firms.
Findings
Findings reveal the significant impacts of TL on aspects of frugal innovation, namely, frugal functionality, frugal cost and frugal ecosystem. Moreover, the paper highlights the mediating roles of tacit and explicit knowledge sharing (KS) in the relationship between TL and frugal innovation in terms of frugal functionality and frugal cost.
Research limitations/implications
To bring a deeper understanding of the benefits and important role of knowledge resources, future research should investigate the potential mediating mechanisms of knowledge management processes in the relationship between specific leadership styles and frugal innovation.
Practical implications
The paper provides a valuable understanding and novel approach for managers and directors of firms in developing and emerging countries to improve their firms’ frugal innovation capability through leadership practice and knowledge resources.
Originality/value
This study contributes to bridging research gaps in the literature and advances the insights of how TL directly and indirectly fosters frugal innovation via mediating roles of tacit and explicit KS.
Spatial action-effect binding denotes the mutual attraction between the perceived position of an effector (e.g., ones own hand) and a distal object that is controlled by this effector. Such spatial ...binding can be construed as an implicit measure of object ownership, thus the belonging of a controlled object to the own body. The current study investigated how different transformations of hand movements (body-internal action component) into movements of a visual object (body-external action component) affect spatial action-effect binding, and thus implicit object ownership. In brief, participants had to bring a cursor on the computer screen into a predefined target position by moving their occluded hand on a tablet and had to estimate their final hand position. In Experiment 1, we found a significantly lower drift of the proprioceptive position of the hand towards the visual object when hand movements were transformed into laterally inverted cursor movements, rather than cursor movements in the same direction. Experiment 2 showed that this reduction reflected an elimination of spatial action-effect binding in the inverted condition. The results are discussed with respect to the prerequisites for an experience of ownership over artificial, noncorporeal objects. Our results show that predictability of an object movement alone is not a sufficient condition for ownership because, depending on the type of transformation, integration of the effector and a distal object can be fully abolished even under conditions of full controllability.
Drowning is a global health challenge, claiming an estimated 359 000 lives each year. Commensurate with causes of death of a similar magnitude, such as maternal mortality (303 000 deaths) and malaria ...(429 000 deaths) it is a neglected public health issue. The causes of this neglect are hypothesized to be many: under-reporting of deaths; lack of recognised ownership of the issue within government; poor framing of the issue by advocates. Whilst these factors all contribute to the low profile of the issue, it is clear that the lack of evidence-based interventions is a significant barrier to effective action. The drowning prevention community has tended to be made up of action-led organisations and driven by technical expertise with little engagement in formal evidence. NGOs in general have tended to be more comfortable producing ‘tacit knowledge’ – that which is intuitive and unarticulated and gained through practical experience over ‘explicit knowledge’ – that which can be codified and acquired by formal study. Explicit knowledge has tended to be the domain of universities, who as publicly funded, professional bureaucracies operate a near monopoly on its production. This binary understanding of ‘knowledge’ and the purposes of its production may be overcome by establishing closer collaboration between NGOs and universities focussed on the co-production of knowledge for mutual benefit. The Royal National Lifeboat Institution (RNLI) is a UK-based NGO that works on drowning prevention and water safety in the UK and in LMICs such as Bangladesh and Tanzania. Over the passed 3 years the RNLI has embarked upon a range of partnership models with academia. This has presented many challenges, such as competing concepts of evidence and differences in ways of working. Close collaboration has also led to mutual benefit and more rapid uptake of research by RNLI and presents future opportunities for effective collaboration between NGOs-Academia
Lakes are key components of biogeochemical and ecological processes, thus knowledge about their distribution, volume and residence time is crucial in understanding their properties and interactions ...within the Earth system. However, global information is scarce and inconsistent across spatial scales and regions. Here we develop a geo-statistical model to estimate the volume of global lakes with a surface area of at least 10 ha based on the surrounding terrain information. Our spatially resolved database shows 1.42 million individual polygons of natural lakes with a total surface area of 2.67 × 10
km
(1.8% of global land area), a total shoreline length of 7.2 × 10
km (about four times longer than the world's ocean coastline) and a total volume of 181.9 × 10
km
(0.8% of total global non-frozen terrestrial water stocks). We also compute mean and median hydraulic residence times for all lakes to be 1,834 days and 456 days, respectively.
► This study examines the effects of individual motivation factors and social capital on employees’ knowledge sharing intentions. ► This study classifies employee knowledge sharing intentions as ...either tacit or explicit. ► Organizational rewards have a negative effect on employees’ tacit knowledge sharing intentions but a positive influence on explicit ones. ► Reciprocity, enjoyment, and social capital significantly influences employees’ tacit and explicit knowledge sharing intentions.
Due to the importance of knowledge in today's competitive world, an understanding of how to enhance employee knowledge sharing has become critical. This study develops an integrated model to understand key factors of employee knowledge sharing intentions through constructs prescribed by two established knowledge management research streams, namely, those concerning individual motivations and social capital. This study classifies employee knowledge sharing intentions as either tacit or explicit and investigates whether the level of the determinants and their influences differ between the two. The research model is tested with survey data collected from 2010 employees in multiple industries. Analysis results show that the proposed model significantly explains the variance of employees’ tacit and explicit knowledge sharing intentions. This finding indicates that the model's unified perspective enhances our knowledge of how to improve employee knowledge sharing. The new findings reveal that organizational rewards have a negative effect on employees’ tacit knowledge sharing intentions but a positive influence on their explicit knowledge sharing intentions. The analysis results confirm that reciprocity, enjoyment, and social capital contribute significantly to enhancing employees’ tacit and explicit knowledge sharing intentions. Additionally, these factors have more positive effects on tacit than on explicit knowledge intentions. The implications of the new findings are discussed.
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific ...knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
Purpose
This paper aims to examine the influence of collaborative culture on employee’s knowledge sharing and how it associated with radical and incremental innovation in Chinese firms.
...Design/methodology/approach
This study used the quantitative approach and structure equation model to test hypotheses with data collected by questionnaire from 371 participants in 68 Chinese firms.
Findings
The research findings indicated that collaborative culture positively fosters the KS behaviors of employees for radical and incremental innovation. The findings confirm the mediating role of tacit and explicit knowledge sharing and reveal that collaborative culture has a significant impact on incremental innovation, whereas knowledge sharing behaviors have greater impacts on radical and incremental innovation.
Research limitations/implications
Future research should investigate the impact of collaborative culture on innovation under moderating effects of ownership form or mediating roles of behavioral variables to have better understanding on the relationship among them.
Practical implications
This study offers leaders a deeper understanding of the necessary factors and new pathways to stimulate employees’ tacit and explicit knowledge sharing for innovation.
Originality/value
The paper has significant contributed to theoretical and practical initiatives on the theory of innovation which highlighted the crucial role of collaborative culture in facilitating a positive climate for knowledge sharing and innovation.
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown ...to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting XGB) in predicting survival using the Formula: see text-index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression (Formula: see text-index Formula: see text), and in the case of XGB even better (Formula: see text-index Formula: see text). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models' predictions. We concluded that the difference in performance can be attributed to XGB's ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models' predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.
•Individual employees’ knowledge hiding behaviors have two-side effects on the firm’s innovation quality.•There is an inverted U-shape relationship between the explicit knowledge hiding and the ...firm’s innovation quality.•There is an inverted U-shape relationship between the tacit knowledge hiding and the firm’s innovation quality.•Knowledge flow within the firm positively moderate the curvilinear relationships between knowledge hiding and the firm’s innovation quality.
This study explores how two dimensions of employees’ knowledge-hiding behaviours, explicit knowledge hiding and tacit knowledge hiding, influence a firm’s innovation quality. Furthermore, knowledge flow within the firm is examined as a moderator in these relationships. We tested corresponding hypotheses based on a research sample of 791 respondents across different industries and regions of China. Empirical results reveal that both explicit and tacit knowledge-hiding behaviours have inverted U-shaped relationships with innovation quality, and knowledge flow within the firm positively moderates these curvilinear relationships. The theoretical contributions of the study are to provide a more advanced understanding of the link between knowledge-hiding behaviours and innovation quality, as well as the role of knowledge flow within the firm. It is therefore suggested that practitioners encourage effective knowledge flow that helps to reduce individual employees’ intentions of knowledge hiding and strengthen their innovation capability, which in turn promotes a firm’s innovation quality.
How many near-neighbors does a molecule have? This fundamental question in chemistry is crucial for molecular optimization problems under the similarity principle assumption. Generative models can ...sample molecules from a vast chemical space but lack explicit knowledge about molecular similarity. Therefore, these models need guidance from reinforcement learning to sample a relevant similar chemical space. However, they still miss a mechanism to measure the coverage of a specific region of the chemical space. To overcome these limitations, a source-target molecular transformer model, regularized via a similarity kernel function, is proposed. Trained on a largest dataset of ≥200 billion molecular pairs, the model enforces a direct relationship between generating a target molecule and its similarity to a source molecule. Results indicate that the regularization term significantly improves the correlation between generation probability and molecular similarity, enabling exhaustive exploration of molecule near-neighborhoods.Understanding molecular near neighbours is key for molecular optimization. Here, authors propose a transformer model that improves correlation between generation probability and molecular similarity, enhancing exploration of molecular neighbourhoods.
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