Modification and re-validation of the ethyl acetate-based multi-residue method for pesticides in produce

The ethyl acetate-based multi-residue method for determination of pesticide residues in produce has been modified for gas chromatographic (GC) analysis by implementation of dispersive solid-phase extraction (using primary–secondary amine and graphitized carbon black) and large-volume (20 μL) injection. The same extract, before clean-up and after a change of solvent, was also analyzed by liquid chromatography with tandem mass spectrometry (LC–MS–MS). All aspects related to sample preparation were re-assessed with regard to ease and speed of the analysis. The principle of the extraction procedure (solvent, salt) was not changed, to avoid the possibility invalidating data acquired over past decades. The modifications were made with techniques currently commonly applied in routine laboratories, GC–MS and LC–MS–MS, in mind. The modified method enables processing (from homogenization until final extracts for both GC and LC) of 30 samples per eight hours per person. Limits of quantification (LOQs) of 0.01 mg kg−1 were achieved with both GC–MS (full-scan acquisition, 10 mg matrix equivalent injected) and LC–MS–MS (2 mg injected) for most of the pesticides. Validation data for 341 pesticides and degradation products are presented. A compilation of analytical quality-control data for pesticides routinely analyzed by GC–MS (135 compounds) and LC–MS–MS (136 compounds) in over 100 different matrices, obtained over a period of 15 months, are also presented and discussed. At the 0.05 mg kg−1 level acceptable recoveries were obtained for 93% (GC–MS) and 92% (LC–MS–MS) of pesticide–matrix combinations

Collaborating for Innovation: the socialised management of knowledge

Although the importance of diverse knowledge is widely recognised for open innovation, there may be a gap in our understanding of the social processes that shape how collaborators engage in knowledge exchange. This social gap may be significant because of the powerful, but largely unexplained, role attributed to trust as a social artefact. Moreover, we see trust as a process and that different types of trust are involved in the collaborative process. Thus, this paper uses a qualitative methodology to capture the experiences of innovation collaborators. As explanation of the dynamic interplays of knowledge and trust, we offer a description of phases in the process. Our analysis finds that the relationship moves from transactional to social. The early phases are characterised by technical knowledge, but the later and mature phases are identified with knowledge of the person and by personal trust. The success of innovation is a result of relationships with augmented trust. We found that a fabric of trust is woven from the weft of professional knowledge and the warp of personal knowledge to support innovation. We propose that this developing of relationships might be conceived as becoming more open in the sense of sharing with one another. If so, we seem to have described and offered a social dimension of open innovation

Using machine learning to create and capture value in the business models of small and medium-sized enterprises

Start-ups have revolutionised many economic ecosystems, becoming innovation pioneers around the world. Most are based on data-driven business models, particularly relying on machine learning technologies. However, not all start-ups that use machine learning technologies manage to create and capture value. The existing literature on the use value enabled by information technologies does not take into account the unique capabilities of machine learning. The theory of data network effects offers a promising explanation of how to create value using machine learning. However, it does not explicitly describe how to capture value using machine learning. In contrast, business model theory explains how companies use technologies to create and capture value, but not specifically through the use of machine learning technology. Therefore, this study aims to improve the theoretical understanding of the key drivers of value creation and capture in start-ups with business models driven by this kind of technology. Statistical techniques are used in a sample of 122 start-ups to explore the theoretical relationships between these two theories. The analysis reveals the link between specific value creation and capture factors of the two theories, such as efficiency, novelty, and performance expectancy. The study also provides evidence of the need to adopt a co-evolutionary perspective of value creation and capture through the use of machine learning

Value creation and appropriation from the use of machine learning : a study of start-ups using fuzzy-set qualitative comparative analysis

This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture