Exploratory analyses are an important first step in psychological research, particularly in problem-based research where various variables are often included from multiple theoretical perspectives not studied together in combination before. Notably, exploratory analyses aim to give first insights into how items and variables included in a study relate to each other. Typically, exploratory analyses involve computing bivariate correlations between items and variables and presenting them in a table. While this is suitable for relatively small data sets, such tables can easily become overwhelming when datasets contain a broad set of variables from multiple theories. We propose the Gaussian graphical model as a novel exploratory analyses tool and present a systematic roadmap to apply this model to explore relationships between items and variables in environmental psychology research. We demonstrate the use and value of the Gaussian graphical model to study relationships between a broad set of items and variables that are expected to explain the effectiveness of community energy initiatives in promoting sustainable energy behaviors.
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This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples of algorithmic affordances in recommender systems’ GUIs. Their objective was to identify potential design patterns including features on which to base these patterns. Analyzing the group discussions revealed distinct thought processes and defining factors for design patterns that were shared by academic and industry partners. While the discussions were promising, they also demonstrated a varying degree of alignment between industry and academia when it came to labelling the identified categories. Since this workshop is part of the preparation for creating a design pattern library of algorithmic affordances, and since the library aims to be useful for both industry and research partners, further research into design patterns of algorithmic affordances, particularly in terms of labelling and description, is required in order to establish categories that resonate with all relevant parties
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We present a novel hierarchical model for human activity recognition. In contrast with approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a structured support vector machine. A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.
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Learning by creating qualitative representations is a valuable approach to learning. However, modelling is challenging for students, especially in secondary education. Support is needed to make this approach effective. To address this issue, we explore automated support provided to students while they create their qualitative representation. This support is generated form a reference model that functions as a norm. However, the construction of a reference models is still a challenge. In this paper, we present the reference model that we have created to support students in learning about the melatonin regulation in the context of the biological clock.
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In 2015, the Object Management Group published the Decision Model and Notation with the goal to structure and connect business processes, decisions and underlying business logic. Practice shows that several vendors adopted the DMN standard and (started to) integrate the standard with their tooling. However, practice also shows that there are vendors who (consciously) deviate from the DMN standard while still trying to achieve the goal DMN is set out to reach. This research aims to 1) analyze and benchmark available tooling and their accompanied languages according to the DMN-standard and 2) understand the different approaches to modeling decisions and underlying business logic of these vendor specific languages. We achieved the above by analyzing secondary data. In total, 22 decision modelling tools together with their languages were analyzed. The results of this study reveal six propositions with regards to the adoption of DMN with regards to the sample of tools. These results could be utilized to improve the tools as well as the DMN standard itself to improve adoption. Possible future research directions comprise the improvement of the generalizability of the results by including more tools available and utilizing different methods for the data collection and analysis as well as deeper analysis into the generation of DMN directly from tool-native languages.
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Randomised controlled trials are strongly advocated to evaluate the effects of intervention programmes on household energy saving behaviours. While randomised controlled trials are the ideal, in many cases, they are not feasible. Notably, many intervention studies rely on voluntary participation of households in the intervention programme, in which case random selection and random assignment are seriously challenged. Moreover, studies employing randomised controlled trials typically do not study the underlying processes causing behaviour change. Yet, the latter is highly important to improve theory and practice. We propose a systematic approach to causal inference based on graphical causal models to study effects of intervention programmes on household energy saving behaviours when randomised controlled trials are not feasible. Using a simple example, we explain why such an approach not only provides a formal tool to accurately establish effects of intervention programmes, but also enables a better understanding of the processes underlying behaviour change.
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This article seeks to contribute to the literature on circular business model innovation in fashion retail. Our research question is which ‘model’—or combination of models—would be ideal as a business case crafting multiple value creation in small fashion retail. We focus on a qualitative, single in-depth case study—pop-up store KLEER—that we operated for a duration of three months in the Autumn of 2020. The shop served as a ‘testlab’ for action research to experiment with different business models around buying, swapping, and borrowing second-hand clothing. Adopting the Business Model Template (BMT) as a conceptual lens, we undertook a sensory ethnography which led to disclose three key strategies for circular business model innovation in fashion retail: Fashion-as-a-Service (F-a-a-S) instead of Product-as-a-Service (P-a-a-S) (1), Place-based value proposition (2) and Community as co-creator (3). Drawing on these findings, we reflect on ethnography in the context of a real pop-up store as methodological approach for business model experimentation. As a practical implication, we propose a tailor-made BMT for sustainable SME fashion retailers. Poldner K, Overdiek A, Evangelista A. Fashion-as-a-Service: Circular Business Model Innovation in Retail. Sustainability. 2022; 14(20):13273. https://doi.org/10.3390/su142013273
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Over the past forty years, the use of process models in practice has grown extensively. Until twenty years ago, remarkably little was known about the factors that contribute to the human understandability of process models in practice. Since then, research has, indeed, been conducted on this important topic, by e.g. creating guidelines. Unfortunately, the suggested modelling guidelines often fail to achieve the desired effects, because they are not tied to actual experimental findings. The need arises for knowledge on what kind of visualisation of process models is perceived as understandable, in order to improve the understanding of different stakeholders. Therefore the objective of this study is to answer the question: How can process models be visually enhanced so that they facilitate a common understanding by different stakeholders? Consequently, five subresearch questions (SRQ) will be discussed, covering three studies. By combining social psychology and process models we can work towards a more human-centred and empirical-based solution to enhance the understanding of process models by the different stakeholders with visualisation.
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Learning by Design (LBD) is a project-based inquiry approach for interdisciplinary teaching that uses design contexts to learn skills and conceptual knowledge. Research around the year 2000 showed that LBD students achieved high skill performances but disappointing conceptual learning gains. A series of exploratory studies, previous to the study in this paper, indicated how to enhance concept learning. Small-scale tested modifications, based on explicit teaching and scaffolding, were promising and revealed improved conceptual learning gains. The pretest-posttest design study discussed in this paper confirms this improvement quantitatively by comparing the conceptual learning gains for students exposed to the modified approach (n = 110) and traditional approach (n = 77). Further modifications, which resulted in a remodified approach tested with 127 students, show a further improvement through reduced fragmentation of the task and addressed science. Overall, the remodified approach (FITS model: Focus - Investigation - Technological design - Synergy) enriches technology education by stimulating an empirical and conceptual way of creating design solutions.
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