DART Model Research Articles

Enhancing Value Co-Creation Through the Lens of DART Model, Innovation, and Digital Technology: An Integrative Supply Chain Resilient Model

The global supply chain process has been badly affected by the COVID-19 pandemic. Consequently, firms search for factors that influence supply chain resilience and improve firm operational performance. The current research develops an integrated resilient supply chain model that combines factors underpinned DART model, innovation, digital technology, and value co-creation to investigate supply chain resilience during the COVID-19 pandemic. The research framework was empirically tested with a data set of 329 responses from employees working in logistics firms. the study find that supply chain resilience is determined by value co-creation, the DART model, innovation, and digital technology. The factors underpinning the DART model, including dialogue, access, risk assessment, and transparency, have positively impacted value co-creation. The structural model shows that dialogue has a positive impact on developing value co-creation between manufacturing firms and stakeholders, Empirical investigation revealed that value co-creation in supply chain operations is estimated by dialogue, access, risk assessment, transparency, innovation, and digital technology and revealed substantial variance R^2 71.9% in measuring value co-creation. This research is significant as it develops a resilient supply chain model with the combination of the DART model, innovation, digital technology, and value co-creation and investigates supply chain resilience during the COVID-19 pandemic. Although current research contributes to theory, practice, and method, disclosing research limitations opens numerous avenues for future researchers. This study does not claim to include all factors that impact the value co-creation process in supply chain operations. For instance, other factors, such as IT infrastructure, commitment, and integrative quality, may positively influence value co-creation. Second, supply chain resilience is determined by value co-creation and network capability. Therefore, the research encourages academicians and policymakers to investigate the supply chain resilience phenomenon with other market-oriented facts. future researchers are suggested to investigate the current research model in longitudinal method

Value co-creation and innovation performance: insight knowledge from companies and customers in the Malaysian hotel industry

Based on the DART model, this research aims to assess the influence of value co-creation (VCC) processes on hotels' innovation performance (IP) in terms of new service development and delivery processes. Moreover, this research aims to use content analysis to ascertain customers' experiences in the hotel industry during service interactions. Data were collected from 105 four-and-five-star rated hotels in Malaysia and analysed via structural equation modelling using SmartPLS software. This study examined 52 tourists' experiences and feedback data staying in eight different four-and-five-star hotels for content analysis. Results reveal that dialogue influences new service development, whereas access influences both the delivery process and new service development. Besides, risk assessment and transparency influence the service delivery process. This study highlights that business transactions in the hotel industry begin and end with the 'customer'.

Co-creating value and its impact on customer satisfaction and customer loyalty: A banking sector perspective

The application of co-creation is important and traditionally proves that banks that offer products and services without taking into account the opinions and recommendations of customers have the opportunity to remain focused on the bank and not be successful (Keshavarz & Jamshidi, 2018). In many organizations, especially those dealing with services, customer satisfaction is considered a challenge. Customer satisfaction influences customer loyalty and a loyal customer is a constant source of revenue, and satisfied customers do not focus on replacement products found on the market. From this, we conclude that customer satisfaction increases loyalty and reduces opposition to the products and services that the company offers (Jaakkola, Helkkula, & Aarikkula-Stenroos, 2015). This study aims to measure the impact of co-creation value through the DART (dialogue, access, risk assessment, and transparency) model on customer satisfaction and customer loyalty. The method used to answer the research questions is the quantitative method, where data were collected through primary research; the population of this study is the customers of all commercial banks in Kosovo, where the sample was 300 customers of randomly selected banks. Based on the research results, referring to Spearman’s rho correlation we conclude that co-creation with customer satisfaction and customer loyalty are in direct proportion. According to the ordinary least squares (OLS) model, we conclude that co-creation affects customer satisfaction and customer loyalty. Through this research, banking structures have the opportunity to increase customer satisfaction and create loyalty to customers through the co-creation process, strengthening its components such as dialogue, access, risk assessment, and transparency.

The Dermal Advanced REACH Tool (dART): A Bayesian Model for Dermal Exposure Assessment.

The dermal Advanced REACH Tool (dART) is a tier 2 exposure model for estimating dermal exposure to the hands (mg min-1) for non-volatile liquid and solid-in-liquid products. The dART builds upon the existing ART framework and describes three mass transport processes (deposition (Dhands), direct emission and direct contact (Ehands), and contact transfer (Thands)) that may each contribute to dermal exposure. The mechanistic model that underpins the dART and calibration of the mechanistic model, such that the dimensionless score that results from encoding contextual information about a task into the determinants of the dART can be converted into a prediction of exposure (mg min-1), have been described in previous work. This paper completes the methodological framework of the dART model through placing the mechanistic model within a wider statistical modelling framework. A mixed-effects model, within a Bayesian framework, is presented for modelling the rate of dermal exposure per minute of activity. The central estimate of exposure for a particular task is provided by a calibrated mechanistic model (and thus based upon contextual information about a task). The model also describes between- and within-worker sources of variability in dermal exposure, with prior distributions for variance components based upon the literature. Estimates of exposure based upon informative prior distributions may be updated using measurement data associated with the task. The dART model is demonstrated using three worked examples, where estimates are initially obtained based upon the prior distributions alone, and then refined through accommodating measurement data on the tasks.

DART Modeli Bağlamında Ortak Değer Yaratımı ve Yolcu Sadakati: Algılanan Hizmet Yeniliğinin Aracı Rolü

The purpose of the study is to reveal the effect of value co-creation within the scope of the DART model, which consists of Dialogue, Access, Risk Assessment, and Transparency on perceived service newness and loyalty. In addition, it is aimed to determine the mediating role of perceived service newness between value co-creation and passenger loyalty. For this purpose, the data collected by the online and convenience sampling method from the passengers who travel airlines. The data is subjected to reliability analysis and confirmatory factor analysis in order to discover the variables that make up the dimensions in the study model. The relationship in the study model is evaluated by using partial least squares structural equation modeling (PLS-SEM) via the SmartPLS 3.0 package program. The findings show that value co-creation has an impact on the perceived service newness and loyalty. This study found also mediating effect of perceived service newness in the relation between value co-creation and loyalty. The study contributed the understanding of value co-creation from the passenger perspective to the value co-creation literature by using the DART model. The study also provides theoretical and managerial contributions to manage the value co-creation process effectively and efficiently.

Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

A PROPOSED MODEL OF VALUE CO-CREATION THROUGH MULTI-STAKEHOLDER COLLABORATION IN DOMESTIC PRODUCT DEVELOPMENT

This study illustrates the collaborative value co-creation process that involves multi-stakeholders in developing domestic products and services design in Indonesia. Although the value co-creation process performed effectively, the new product is still experiencing obstacles in term of commercialization and sustainability. The purpose of this research is to construct a model to encourage the process of value co-creation in the commercialization phase of domestic products. The qualitative method with a single case study approach is conducted in this research. Data collected with in-depth interviews, observations and archival. The informants are representatives of stakeholders that interact and participate in the IKKON program. Data analyzed with DART model (Dialogue, Access, Risk Assessment and Transparency) to examine the process of value co-creation in the IKKON program. This approach constructs an analysis that involves stakeholder’s experience during the value co-creation process as a basis for innovation. The results showed with collaboration; stakeholders could learn from each other to understand the needs. The collaboration with multi-stakeholder will increase the values of the product by combining local and public needs. The finding proposed a collaborative model to optimizing domestic product in terms of commercialization. This research enlarges a practical perspective in managing value co-creation and decision making.

Assessing impacts of canopy 3D structure on chlorophyll fluorescence radiance and radiative budget of deciduous forest stands using DART

Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (ε1D−3D and ε0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in ε1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (ε up to 17%) and their “blocking” effect (ε ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.

FROM VALUE CO-CREATION TO POSITIVE EXPERIENCES AND CUSTOMER SATISFACTION. A CUSTOMER PERSPECTIVE IN THE HOTEL INDUSTRY

This study evaluates whether the Value Co-creation (VCC) process in hotels contributes to positive guest experience and satisfaction. This paper utilizes the DART model (Dialogue, Access, Risk, and Transparency) as the main framework to explore VCC in hotels. This research is the first to both adjust the DART model to a customer’s viewpoint and evaluate it in the hotel context. The included data is derived from 484 international tourists lodged in Greek hotels and is analyzed with the structural equation modeling technique. Results suggest that the Dialogue component of DART does not affect the positive experience, while Access, Transparency, and Risk Assessment do, in fact, strongly influence tourist satisfaction. The current study enriches and consolidates VCC–DART theory in the hotel context.

Evaluation of the FluorWPS Model and Study of the Parameter Sensitivity for Simulating Solar-Induced Chlorophyll Fluorescence

Solar-induced chlorophyll fluorescence (SIF) has been used as an indicator for the photosynthetic activity of vegetation at regional and global scales. Canopy structure affects the radiative transfer process of SIF within canopy and causes the angular-dependencies of SIF. A common solution for interpreting these effects is the use of physically-based radiative transfer models. As a first step, a comprehensive evaluation of the three-dimensional (3D) radiative transfers is needed using ground truth biological and hyperspectral remote sensing measurements. Due to the complexity of forest modeling, few studies have systematically investigated the effect of canopy structural factors and sun-target-viewing geometry on SIF. In this study, we evaluated the capability of the Fluorescence model with the Weighted Photon Spread method (FluorWPS) to simulate at-sensor radiance and SIF at the top of canopy, and identified the influence of the canopy structural factors and sun-target-viewing geometry on the magnitude and directional response of SIF in deciduous forests. To evaluate the model, a 3D forest scene was first constructed from Goddard’s LiDAR Hyperspectral and Thermal (G-LiHT) LiDAR data. The reliability of the reconstructed scene was confirmed by comparing the calculated leaf area index with the measured ones from the scene, which resulted in a relative error of 3.5%. Then, the performance of FluorWPS was evaluated by comparing the simulated at-sensor radiance spectra with the spectra measured from the DUAL and FLUO spectrometer of HyPlant. The radiance spectra simulated by FluorWPS agreed well with the measured spectra by the two high-performance imaging spectrometers, with a coefficient of determination (R2) of 0.998 and 0.926, respectively. SIF simulated by the FluorWPS model agreed well with the values of the DART model. Furthermore, a sensitivity analysis was conducted to assess the effect of the canopy structural parameters and sun-target-viewing geometry on SIF. The maximum difference of the total SIF can be as large as 45% and 47% at the wavelengths of 685 nm and 740 nm for different foliage area volume densities (FAVDs), and 48% and 46% for fractional vegetation covers (FVCs), respectively. Leaf angle distribution has a markedly influence on the magnitude of SIF, with a ratio of emission part to SIF range from 0.48 to 0.72. SIF from the grass layer under the tree contributed 10%+ more to the top of canopy SIF even for a dense forest canopy (FAVD = 3.5 m−1, FVC = 76%). The red SIF at the wavelength of 685 nm had a similar shape to the far-red SIF at a wavelength of 740 nm but with higher variability in varying illumination conditions. The integration of the FluorWPS model and LiDAR modeling can greatly improve the interpretation of SIF at different scales and angular configurations.

Modelling of effective irradiation swelling for inert matrix fuels

The results of effective irradiation swelling in a wide range of burnup levels are numerically obtained for an inert matrix fuel, which are verified with DART model. The fission gas swelling of fuel particles is calculated with a mechanistic model, which depends on the external hydrostatic pressure. Additionally, irradiation and thermal creep effects are included in the inert matrix. The effects of matrix creep strains, external hydrostatic pressure and temperature on the effective irradiation swelling are investigated. The research results indicate that (1) the above effects are coupled with each other; (2) the matrix creep effects at high temperatures should be involved; and (3) ranged from 0 to 300 MPa, a remarkable dependence of external hydrostatic pressure can be found. Furthermore, an explicit multi-variable mathematic model is established for the effective irradiation swelling, as a function of particle volume fraction, temperature, external hydrostatic pressure and fuel particle fission density, which can well reproduce the finite element results. The mathematic model for the current volume fraction of fuel particles can help establish other effective performance models.

Accurate and fast simulation of remote sensing images at top of atmosphere with DART-Lux

DART model is one of the most comprehensive and accurate radiative transfer (RT) models to simulate remotely sensed signals in the Earth-atmosphere system. Its standard RT modelling mode, called DART-FT, relies on the discrete ordinates method. Its recently developed Monte Carlo mode using an unbiased bidirectional path tracing method, called DART-Lux, increases hundredfold DART efficiency to simulate images. Since DART-Lux does not simulate yet atmospheric RT, a hybrid method has been designed to accurately and fast simulate remote sensing images at top of atmosphere (TOA). It couples the atmospheric RT modelling of DART-FT with the very efficient Earth surface RT modelling of DART-Lux. For that, a new sky light modelling, an innovative BRF camera modelling and an adapted radiative coupling framework have been designed. The efficiency and accuracy of this hybrid method have been validated using DART-FT as a reference. Here, we present this validation for a realistic forest stand. Simulation time is reduced by a factor of 750 with a relative difference of solar plane reflectance factor smaller than 1%. This hybrid method opens new perspectives for the use of 3D modelling in remote sensing applications. It is already part of the DART version freely available for scientists (https://dart.omp.eu).

DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers.

Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol-1 (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.

A Mensuração do Processo de Cocriação de Valor em Empresas do Setor Bancário Brasileiro

A realidade emergente pautada no maior acesso a informações, na convergência tecnológica, e na rápida evolução da internet como meio para comunicação e, também, realização de negócios, exige uma reavaliação do sistema tradicional de criação de valor centralizado na empresa. Este artigo se direciona para a ideia da cocriação de valor, na qual o valor não é mais definido dentro da empresa e apenas entregue ao consumidor, mas sim criado conjuntamente com stakeholders em um processo focado nos indivíduos e suas experiências. Dessa forma, o estudo busca ampliar conhecimento acerca da cocriação de valor em empresas brasileiras com base em informações empíricas, para tanto, o objetivo da pesquisa se traduz em mensurar a cocriação de valor em empresas do setor bancário brasileiro e fazer uma comparação do desempenho entre elas. Utiliza-se o modelo DART (diálogo, acesso, risco e transparência), proposto por Prahalad & Ramaswamy (2004), como base de análise dos dados obtidos no Índice de Sustentabilidade Empresarial BM&F-ISE referente a 2017. Nos resultados, percebe-se o destaque de um dos bancos analisados, bem como o fato de que as empresas analisadas ainda precisam de maior foco no desenvolvimento de fatores relacionados a dimensão “acesso” do modelo utilizado.

Factors that influence patient value co-creation behaviour: an empirical study based on the DART model

BackgroundHealth-care services that are responsible for public welfare should be patient-focused rather than pursuing profits. Hospitals treat patients and save lives, and their service behaviour is closely linked to the quality of life and health of patients. Therefore, it is of great importance to introduce value co-creation theory to the medical field. Patients can now gain medical knowledge from the internet, and they are able to discuss treatment plans with doctors, instead of accepting what is prescribed for them passively. Support and cooperation of patients is necessary during treatment to improve treatment effects, and research shows that patient value co-creation behavior (eg, high self-motivation to search for medical information before treatment, or active interaction with doctors on treatment options) is influenced by various factors. In this study, we defined patient value co-creation behaviour based on the dialogue, access, risk assessment, transparency (DART) model, and a research model was established to explore its influencing factors. MethodsThis study established a research model consisting of three independent variables (self-efficacy, cognition of duty, and cognition of doctor value co-creation behaviour), and one dependent variable (patient value co-creation behaviour based on the DART model). A cross-sectional survey was conducted with patients (both inpatients and outpatients, n=675) at a tertiary-level hospital in Guangzhou, China. Participants were asked to complete an anonymous questionnaire, and the survey response rate was of 96·7% (653 of 675). After exclusion of incomplete or repeated questionnaires, 637 valid questionnaires remained. All items of the questionnaire were evaluated with a five-point Likert scale according to patients' perceptions, and patients' and doctors' value co-creation behaviours were evaluated by use of the DART dimensions. The structural equation model was performed using AMOS 21.0 software to examine our hypotheses and explore the factors influencing patient value co-creation behavior. FindingsThe structural equation model analysis showed that self-efficacy, cognition of duty, and cognition of doctor value co-creation behaviour had significant positive effects on patient value co-creation behaviour (standardised regression weights 0·143 [p=0·007], 0·181 [p=0·001], and 0·588 [p=0·001]), and cognition of doctor value co-creation behaviour had the greatest impact on patients' behaviour. In view of the results of empirical analysis, we proposed relevant strategies to stimulate patient value co-creation behaviour. InterpretationThe results show that doctors' value co-creation behaviour, as perceived by patients, had an important role in stimulating the patients' value co-creation behaviour. During treatment, doctors should perhaps pay more attention to patients and less to clinical technology, and provide patients with more humanistic care, making patients feel being respected, promoting their enthusiasm in participating with treatment and complying with medical advice, which may help to improve the outcome of disease treatment. FundingThis study was supported by the Key Laboratory of Public Health Policy Research and Evaluation (2015WSY0010) and the Guangzhou Public Health Services System Construction Research Base (2018–2020). The funders did not participate in study design, data collection, or analysis.

Contribution of value co-creation support elements to repurchase intention: A theoretical approach

Objetivo: o trabalho visa estabelecer ligações entre os elementos de sustentação da cocriação de valor e da intenção de recompra, por meio de alguns antecedentes como a confiança, comprometimento e satisfação.Metodologia/abordagem: por meio de uma reflexão teórica, o modelo de gerenciamento de cocriação de valor proposto por Payne, Storbacka Frow (2008) e o modelo DART de Prahalad Ramaswamy (2004) são revisitados, enfatizando os pontos de interação entre fornecedores e clientes e os elementos de sustentação da cocriação de valor.Principais resultados: a ligação entre os elementos de sustentação da cocriação de valor e antecedentes da intenção de recompra é explicitada em umframework conceitual, evidenciando as relações entre os temas, efetivadas por construtos como confiança, compromisso e fortes relacionamentos.Contribuições teóricas: a participação positiva dos elementos de sustentação da cocriação de valor para com os antecedentes da intenção de recompra fica evidenciada por meio da contribuição do modelo DART para a aprendizagem conjunta e interações personalizadas.Relevância/originalidade: na medida em que não foram identificados estudos anteriores unindo conceitualmente estes dois temas, comumente tratados separadamente, o presente trabalho esclarece como os mecanismos de interação típicos dos processos de cocriação de valor poderiam ajudar a explicar a intenção de recompra por meio de alguns de seus antecedentes.

Calibration of the Dermal Advanced REACH Tool (dART) Mechanistic Model.

The dermal Advanced REACH Tool (dART) is a Tier 2 exposure modelling tool currently in development for estimating dermal exposure to the hands (mg min-1) for non-volatile liquid and solids-in-liquid products. The dART builds upon the existing ART framework and describes three mass transport processes [deposition (Dhands), direct emission and direct contact (Ehands), and contact transfer (Thands)] that may each contribute to dermal exposure. The mechanistic model that underpins the dART and its applicability domain has already been described in previous work. This paper describes the process of calibrating the mechanistic model such that the dimensionless score that results from encoding contextual information about a task into the determinants of the dART can be converted into a prediction of exposure (mg min-1). Furthermore, as a consequence of calibration, the uncertainty in a dART prediction may be quantified via a confidence interval. Thirty-six experimental studies were identified that satisfied the conditions of: (i) high-quality contextual information that was sufficient to confidently code the dART mechanistic model determinants; (ii) reliable exposure measurement data sets were available. From these studies, 40 exposure scenarios were subsequently developed. A non-linear log-normal mixed-effect model was fitted to the data set of Dhands,Ehands, and Thands scores and corresponding measurement data. The dART model was shown to be consistent with activities covering a broad range of tasks [spray applications, activities involving open liquid surfaces (e.g. dipping, mixing), handling of contaminated objects, spreading of liquid products, and transfer of products (e.g. pouring of liquid)]. Exposures resulting from a particular task were each dominated by one or two of the identified mass transport processes. As a consequence of calibration, an estimate of the uncertainty associated with a mechanistic model estimate is available. A 90% multiplicative interval is approximately a factor of six. This represents poorer overall precision than the (inhalation) ART model for dusts and vapours, although better than the ART model for mists. Considering the complexity of the conceptual model compared with the ART, the wide variety of exposure scenarios considered with differing dominant routes, and the particular challenges that result from the consideration of measurement data both above and beneath a protective glove, the precision of the calibrated dART mechanistic model is reasonable for well-documented exposure scenarios coded by experts. However, as the inputs to the model are based upon user judgement, in practical use, the reliability of predictions will be dependent upon both the competence of users and the quality of contextual information available on an exposure scenario.

Dermal Advanced REACH Tool (dART)-Development of a Dermal Exposure Model for Low-Volatile Liquids.

This article describes the development of a mechanistic model for underpinning the dermal Advanced REACH Tool (dART), an extension of the existing ART model and its software platform. It was developed for hand exposure to low volatile liquids (vapour pressure ≤ 10 Pa at 20°C) including solids-in-liquid products. The model is based on an existing conceptual dermal source-receptor model that has been integrated into the ART framework. A structured taxonomy of workplace activities referred to as activity classes are adopted from ART. Three key processes involved in mass transport associated with dermal exposure are applied, i.e. deposition, direct emission and contact, and transfer. For deposition, the model adopts all the relevant modifying factors (MFs) applied in ART. In terms of direct emission and contact (e.g. splashes) and transfer (e.g. hand-surface contacts), the model defines independent principal MFs, i.e. substance-related factors, activity-related factors, localized- and dispersion control and exposed surface area of the hands. To address event-based exposures as much as possible, the model includes crucial events during an activity (e.g. hand immersions) and translates objective information on tools and equipment (manual or automated) to probable events (e.g. splashes) and worker behaviours (e.g. surface contacts). Based on an extensive review of peer-reviewed literature and unpublished field studies, multipliers were assigned to each determinant and provide an approximated (dimensionless) numerical value. In the absence of (sufficient) evidence, multipliers were assigned to determinants based on assumptions made during discussions by experts in the consortium. A worked example is presented to illustrate the calculation of hand exposure for a specific scenario. The dART model is not yet implemented in the ART software platform, and a robust validation of the model is necessary to determine its predictive ability. With advancing knowledge on dermal exposure and its determinants, this model will require periodic updates and refinements, in addition to further expansion of the applicability domain of the model.

Research on value co-creation with the participation of patients based on DART model – methodological remarks

Derived from business practices co-creation theory indicates that patient can be a source of value and actively contribute to it. DART model - most popular conceptualizing framework for value creation - was presented. Considering the multiplicity of perspectives of a large stakeholders group appearing in the healthcare system a direction of its development was also proposed. The possibility of using quantitative methods to study value creation between empowered patients and selected stakeholders was tested as well as work of the questionnaire that was adapted from value creation research based on the DART model that was conducted among entrepreneurs. Substantial number of inadequate questions in relation to the respondents’ experience was indicated on the other hand the need to include further groups of stakeholders was emphasized. The results signal the possibility of significant differences in the perception of meeting the requirements for co-creating value with patients by individual stakeholders, such as: doctors, state offices or pharmaceutical companies. Different perception of value makes difficult to find universal tools for measuring it. However, the economic and humanitarian importance of the issue in the healthcare system necessitates such tools further development. The tested tool has limited application in the healthcare system but may provide valuable information on the relationship between the patient and doctor or other stakeholder pairs as well as in selected contexts of co-creation.

Designing and Staging High-Quality Park and Recreation Experiences Using Co-Creation

While the field of park and recreation has long been focused on designing and delivering quality leisure experiences, the concept of experiences has become increasingly salient across almost all industries over the last two decades. From the time that Pine and Gilmore initially proposed the concept of the experience economy in a 1998 Harvard Business Review article, experiences have become a primary driver of the economy (Pine & Gilmore, 1999; 2011). If an organization is not providing experiences perceived by consumers as high quality and desirable, the organization most likely will not survive. A core concept associated with the design of experiences is co-creation—a collaborative process of value creation between participants and providers (Payne, Storbacka, & Frow 2008; Prahalad & Ramaswamy, 2004a). This paper examines the concept of co-creation and its relevance for the field of park and recreation administration. This is accomplished by introducing a three-phased model of co-creation that spans over the three phases of leisure (i.e., anticipation, participation, and reflection) and discussing co-creation’s applicability to park and recreation settings through a descriptive case study. We define co-creation as active participation, dialogue, and collaboration between participants, producers, and a variety of other stakeholders throughout the process of designing, participating in, and reflecting on an experience (Prahalad & Ramaswamy, 2004b; Rossman & Schlatter, 2015). Based on the work of Duerden, Ward, and Freeman (2015), we propose a three-phased model of co-creation, which includes co-design during the anticipation phase of an experience, co-actualization during the participation phase of the experience, and co-curation during the reflection phase of the experience. This paper provides specific examples of the different forms of co-creation during each experience phase. This paper also introduces the DART (dialogue, access, risk assessment, and transparency) model of co-creation (Prahalad & Ramaswamy, 2002; 2004a; 2004c) and provides examples of its applicability for park and recreation professionals. This paper concludes with a case study exemplifying how co-creation and its associated concepts can be used to design a swim program experience. Although park and recreation professionals have a long history of designing and delivering experiences, they risk seeing their offerings become obsolete if they do not find ways to stay on the forefront of the experience economy. As more organizations come to view themselves as experience providers, the competition for people’s time and resources will increase. We hope the insights in this paper will help park and recreation professionals embrace and apply the concept of co-creation in their experience offerings. Subscribe to JPRA