Three empirical models were used to fit the formation of acrylamide in crisps of three different cold-sweetened potato genotypes, fried under the same experimental conditions. Statistical methods were used to compare the performance of the models, with the "Logistic-Exponential" model performing the best. The obtained model parameters for the formation of acrylamide showed improvement in precision compared to an earlier study, the precision of the parameter estimates for the degradation of acrylamide was still problematic. Nevertheless, the predictive capacity of the "Logistic-Exponential" model was tested, as this model showed a strong correlation between parameter a and the reducing sugar content of the raw potato. The predictions from this model for the formation of acrylamide in potato crisps were close to earlier reported experimental values. Therefore, the use of the "Logistic-Exponential" model as a tool to predict acrylamide in potato crisps seems promising and should be developed further.
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This article reports on a literature review on empirical research investigating learning for vocations in the context of vocational education. We included 36 studies in which learning for vocations is empirically studied. Learning for vocations is characterised based upon prevalent research traditions in the field and framed from the perspective of vocational education and organised learning practices. This framing and characterisation directed the search terms for the review. Results show empirical data on vocational learning and illustrate how learning processes for the functions of vocational education - vocational identity development, development of a vocational repertoire of actions, and vocational knowledge development - actually take place. The review further shows that, empirical illustrations of learning processes that occur in the context of vocational education and organised learning practices are relatively scarce. The findings can be typified in relation to our theoretical framework in terms of three learning processes, that is learning as a process of (a) belonging, becoming, and being, (b) recontextualization, and (c) negotiation of meaning and sense-making. We argue that more empirical research should be carried out, using the functions of vocational education and the three learning processes to better understand vocational learning.
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The formation of acrylamide in potato crisps was fitted by empirical mathematical models. Potato slices were fried under the same experimental conditions for different times. Besides the content of precursors in the raw potato slices, acrylamide and water content in the potato crisps were quantified after predetermined times (2-6 min). The temperature developments in the surrounding oil and outer cell layer of the potato slices were monitored, giving more insight in the frying process and making future comparisons between studies possible. The pattern found for the formation of acrylamide, which was similar to earlier studies, was fitted to three empirical models. Statistical methods were used to compare the performance of the models, with the "Logistic-Exponential" and "Empirical" model performing equally well. The obtained model parameters were in the range of earlier reported studies, although this comparison is not unequivocal as the experimental conditions differed between studies. The precision of parameter estimates was problematic; this should be improved by better experimental design. Nevertheless, the approach of this study will make it possible to truly compare acrylamide formation patterns and model parameters in the future, with the ability to develop a tool to predict acrylamide formation in potato crisps.
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from the article: "In the Netherlands, housing corporations are increasingly adopting self-service technologies (SSTs) to support affairs their tenants need to arrange. The purpose of the study is to examine the customers’ motivations of using SSTs in the context of the Dutch public housing sector. An empirical investigation is presented based on a sample of 1,209 tenants. Using partial least squares (PLS), the acceptance model of Blut, Wang, and Schoefer is adopted and tested. The results show that especially the need for interaction negatively influence the adoption of SSTs by tenants. Positively, subjective norm and self-efficacy influence the adoption. Furthermore, playfulness negatively influences this adoption. Developers of SSTs should focus on its ulitalitarian function, rather then invest in its playfulness. Moreover, adoption is propelled by the encouragement of others. This can be enhanced by positive word-of mouth and should therefore stimulated."
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This paper aims to offer a critical reflection on the way Talent Management (TM) is investigated in practice, by addressing the key issues regarding the quality (in terms of rigor and relevance) of academic empirical TM research and therefore the critical scrutiny of TM scholars’ work. We will argue that despite the growth in the quantity, the quality of many empirical TM papers is lagging behind and hindering the progress of the academic field of TM.
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Abstract The emergence of new technologies such as mp3 and music streaming, and the accompanying digital transformation of the music industry, have led to the shift and change of the entire music industry’s value chain. While music is increasingly being consumed through digital channels, the number of empirical studies, particularly in the field of music copyright in the digital music industry, is limited. Every year, rightsholders of musical works, valued 2.5 billion dollars, remain unknown. The objectives of this study are twofold: First to understand and describe the structure and process of the Dutch music copyright system including the most relevant actors within the system and their relations. Second to apply evolutionary economics approach and Values Sensitive Design method within the context of music copyright through positive-empirical perspective. For studies of technological change in existing markets, the evolutionary economics literature provides a coherent and evidence-based foundation. The actors are generally perceived as being different, for example with regard to their access to information, their ability to handle information, their capital and knowledge base (asymmetric information). Also their norms, values and roles can differ. Based on an analysis of documents and held expert interviews, we find that the collection and distribution of the music copyright money is still based on obsolete laws, neoclassical paradigm and legacy IT-system. Finally, we conclude that the rightsholders are heterogenous and have asymmetrical information and negotiating power. The outcomes of this study contribute to create a better understanding of impact of digitization of music copyright industry and empower the stakeholders to proceed from a more informed perspective on redesigning and applying the future music copyright system and pre-digital norms and values amongst actors.
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Digitalization is the core component of future development in the 4.0 industrial era. It represents a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide. Diverse triggering effects shape future digitalization trends. Thus, the main research goal in this study is to use sustainable competitiveness pillars (such as social, economic, environmental and energy) to evaluate international digitalization development. The proposed empirical model generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33 European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s binominal distribution to derive training and testing samples and the entire analysis has been adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear regression and ANN model summary report valuable results with a high degree of coefficient of determination (R2>0.9 for all models). Research findings state that the digitalization process is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating other relevant factors that emerge in the environment. Indicators report the consumption of electrical energy in industry and households and GDP per capita to achieve the strongest effect.
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This research contributes to understanding and shaping systems for OFMSW separation at urban Small and Medium Enterprises (SMEs, such as offices, shops and service providers). Separating SMEs’ organic fraction of municipal solid waste (OFMSW) is both an opportunity and a serious challenge for the transition towards circular cities. It is an opportunity because OFMSW represents approximately 40% of the total waste mass generated by these companies. It is challenging because post-collection separation is not feasible for OFMSW. Therefore, SMEs disposing of waste should separate their solid waste so that processing the organic fraction for reuse and recycling is practical and attainable. However, these companies do not experience direct advantages from the extra efforts in separating waste, and much of the OFMSW ends up in landfills, often resulting in unnecessary GHG emissions. Therefore, governments and waste processors are looking for ways to improve the OFMSW separation degree by urban companies disposing of waste through policies for behaviour change.There are multiple types of personnel at companies disposing of waste. These co-workers act according to their values, beliefs and norms. They adapt their behaviour continuously, influenced by the physical environment, events over time and self-evaluation of their actions. Therefore, waste separation at companies can be regarded as a Socio-Technical Complex Adaptive System (STCAS). Agent-based modelling and simulation are powerful methods to help understand STCAS. Consequently, we have created an agent-based model representing the evolution of behaviour regarding waste separation at companies in the urban environment. The model aims to show public and private stakeholders involved in solid waste collection, transport and processing to what extent behaviour change policies can shape the system towards desired waste separation degrees.We have co-created the model with participants utilising literature and empirical data from a case study on the transition of the waste collection system of a business park located at a former harbour area in Amsterdam, The Netherlands. First, a conceptual model of the system and the environment was set up through participatory workshops, surveys and interviews with stakeholders, domain experts and relevant actors. Together with our case participants, five policies that affect waste separation behaviour were included in the model. To model the behaviour of each company worker’s values, beliefs and norms during the separation and disposal of OFMSW, we have used the Value-Belief-Norm (VBN) Theory by Stern et al. (1999). We have collected data on waste collection behaviour and separation rates through interviews, workshops and a literature study to operationalise and validate the model.Simulation results show how combinations of behaviour profiles affect waste separation rates. Furthermore, findings show that single waste separation policies are often limitedly capable of changing the behaviour in the system. Rather, a combination of information and communication policies is needed to improve the separation of OFMSW, i.e., dissemination of a newsletter, providing personal feedback to the co-workers disposing of waste, and sharing information on the (improvement of) recycling rates.This study contributes to a better understanding of how policies can support co-workers’ pro-environmental behaviour for organic waste separation rates at SMEs. Thus, it shows policymakers how to stimulate the circular transition by actively engaging co-workers’ waste separation behaviour at SMEs. Future work will extend the model’s purpose by including households and policies supporting separating multiple waste types aimed at various R-strategies proposed by Potting et al. (2016).
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We give a refinement of the well known business model canvas by Osterwalder and Pigneur by splitting the basic blocks into further subblocks to reduce confusion and increase its expressive power. The splitting is used in an online tool which in addition comes with a set of questions to further structure the business modelling process and help doing thought experiments.
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Algorithmic curation is a helpful solution for the massive amount of content on the web. The term is used to describe how platforms automate the recommendation of content to users. News outlets, social networks and search engines widely use recommendation systems. Such automation has led to worries about selective exposure and its side effects. Echo chambers occur when we are over-exposed to the news we like or agree with, distorting our perception of reality (1). Filter bubbles arise where the information we dislike or disagree with is automatically filtered out – narrowing what we know (2). While the idea of Filter Bubbles makes logical sense, the magnitude of the "filter bubble effect", reducing diversity, has been questioned [3]. Most empirical studies indicate that algorithmic recommendations have not locked large audience segments into bubbles [4]. However, little attention has been paid to the interplay between technological, social, and cognitive filters. We proposed an Agent-based Modelling to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. The model aims to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect. Agent-based models simulate the behaviour of multiple individual agents forming a larger society. The behaviour of the individual agents can be elementary, yet the population's behaviour can be much more than the sum of its parts. We have designed different scenarios to analyse the contributing factors to the emergence of filter bubbles. It includes different recommendation algorithms and social network dynamics. Cognitive filters are based on the Triple Filter Bubble model [5].References1.Richard Fletcher, 20202.Eli Pariser, 20123.Chitra & Musco, 20204. Möller et al., 20185. Daniel Geschke et al, 2018
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