dataesr/scientific-paragraphs-categorization huggingface dataset:
10 , left, variable panel). In case additional variables currently not represented in HAMSTERS are necessary, the system allows to declare them by simply providing a name and scale (nominal, ordinal, interval, ratio, time, text or external data source). Currently as external data sources the integration of video is possible, the video is displayed below the time line. Furthermore, it is possible to connect HAMSTERS task models to an interactive application  [26]  and/or to systems' behavioral models  [4]. These capability and the capability provided by the Circus environment (including HAMSTERS and Petshop) enables to co-execute all of the models and to monitor user actions and tasks in conjunction with events in the system (like waiting times due to system computation, keyboard presses or mouse clicks).
Demonstration of the Extensions The tool and its extensions were for the user-centered design and development of an interactive television system. This section shows a small part of the overall project: goal is to demonstrate the usage of HAMSTERS extensions to represent data from the user evaluation. The user interface prototype consists of a page with 12 tiles (4 columns, 3 rows), see Fig. 4  (left). During the experimentation dots appear pseudo randomized on the tiles. Users have to click on the corresponding area of the remote control to select the indicated tile. Correct selections are confirmed by displaying a green checkmark, incorrect selections with a red cross displayed on the item. The user study followed an experimental procedure with two independent variables: type of remote control and visual feedback or no visual feedback.
Preparing and Performing the User Study: A Process Overview Following the PRENTAM process  [8]  a task analysis was performed to identify task performed with the interactive TV system. Tasks were modelled using HAMSTERS including typical TV related tasks like selecting and changing a channel, changing volume or browsing the electronic program guide (EPG). The process of setting up a user evaluation study based on task models, running the study and feeding back data into HAMSTERS is described in the following section in detail. The following step-by-step process demonstrates the general activities:  (1) Task Analysis identifying users task when interacting with TV (2) Task modelling of major tasks performed with an iTV system with iterative refinements to represent even motor and cognitive tasks in the task models in HAMSTERS (3) Setting up the user evaluation study: formulating the hypothesis for the study, preparing the script (guidelines) for the study on what activity each participant has to perform, developing and preparing the systems used for testing including the variations on the remote control.
Associating User Study Results into HAMSTERS Scenarios While performing the case study a variety of possible ways to enhance the integration of data was identified including automatic computation of frequencies and statistic tests or enabling users to freely add, manipulate and arrange data in data cells. But the goal of integrating user study results into HAMSTERS is not to provide functionalities of SPSS or Excel, but to support the analysis of user study results in combination with task models. The final mechanism thus simply supports manual entry and a simple import function. To integrate user study results in HAMSTERS there are three levels of data: • Low-level task elements: HAMSTERS provides only a basic data insertion form (see Fig. 5 ) that lists all currently defined variables in the system and associated task types (if applicable) for each run of a scenario.
Integrating and Representing User Study Results in HAMSTERS Runs of the user study are represented using the Scenario Presenter View in Hamsters. Figure  6  shows a representation of results for user 6 performing a series of selection tasks with visual feedback given by the system (SelectTargetsFE). It shows averages for success rate, mean selection time, minimum and maximum selection time and average for the rates user experience (attractiveness) and results from the AttrakDiff  [2]  questionnaire. Levels of data and their visualization: (a) directly associated with low-level task element: Scenario Presenter, all values, time-line and video; (b) Higher level tasks (e. g. for User 6 repeating 24 times the "selection an element task", showing mean, min/max, timeline for these summarized tasks) in the Scenario Presenter.
We proposed different ways to connect task models to interactive applications in the past such as the co-execution with annotated java code  [26]  or the connection with system models  [35]. The connection of the task model with the real system is useful for designers to identify user interface objects and interactions that have to be amended (improved) but also the ones that should be used (or kept as templates) for other/similar designs as they work well. Identifying tasks and parts of the system that show a good usability and a clear contribution to an overall better user experience can help to inform not only the current system, but might also be used for similar systems or applications.
g. Arbore & Busacca, 2011; Kuo, Chen, & Deng, 2012) , neural network-based IPA  (Mikulić et al. , 2012) , and impact-range performance and asymmetry analysis  (Back & Lee, 2015). Adding the impact dimension allows measurement and visualization of the effects of customer satisfaction help destination policy makers to optimize resource allocation  (Back & Lee, 2015). In this study, we use novel and integrated the multi-criterial decisionmaking tools, Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). The data are collected from an expert panel. Applying the approach, we examine Chinese tourist satisfaction with their visit to Britain. In recent years, the flux of Chinese tourism growth has been described as one of the most important sources of tourists for destinations around the world  (Fu, Cai, & Lehto, 2017; Lai, Li, & Harrill, 2013) , and it has remained the world's number one tourism source market since 2012  (UNWTO, 2016).
According to VisitBritain, the national tourism marketing organization in the UK, the number of Chinese tourists visiting Britain has increased by 33% and Chinese tourist spending increased by 48% in the first nine months of 2017. Chinese travelers in the UK spend an average of £1972 per visit, three times more than the market average. In this study, Chinese tourist satisfaction factors were based upon three-factor theory  (Matzler et al. , 2004) , a review of relevant literature, and a survey of a sample of 275 Chinese tourists who had recently completed their visit in Britain. Chinese tourists' perceptions of attribute performance were also obtained from the same survey. The data for determining the impact of each attribute was obtained from an expert panel, which consists 10 destination marketing managers.
Data collection (for Step 2 analysis in the IPIA Model) Data for Importance and Performance measurement were collected from a tourist survey. Questions in the questionnaire assessed the variables in the three-factor theory (Table  1 ). An 11-point scale was used to evaluate each attribute (from 0 indicating 'very poor' to 10 indicating 'excellent'). The tourist survey was facilitated by a UK-based tour operator with expertise in Chinese tourists visiting Britain. The tour agent was preferred since most Chinese tourists join a tour organized through a similar tour operator. Three hundred questionnaires were distributed, and 275 were received, a response rate of 91. 7%. High response rates may not obviate non-response bias, thus early and late responders were compared, and no significant differences were found.
Survey participants were asked to rate their perception regarding the service attributes of Britain as tourist destination. Data for Impact measurement were collected from interviews with destination marketing managers. The expert panel was recruited through the same tour operator used in the tourist survey. The panel consisted of ten managers responsible for designing and organizing trips. Participants were asked to make pair-wise comparisons of the ten attributes on a matrix table based on an 11 point rating scale  (Hu et al. , 2011; Hu et al. , 2009). Pair-wise comparisons were then analyzed using DEMATEL and ANP.
IPIA Matrix (Step 4 of the IPIA Model) After calculating the Importance-Performance-Impact relationships, data is presented in the IPIA table and depicted in the IPIA Matrix. To construct the IPIA Matrix, values were normalized within the minimum and maximum values of Performance and Importance dimensions. Resource allocation for optimal customer satisfaction can be determined based the IPIA matrix, thus completing Step 4 of the IPIA Model. [Table  2  about here].
Sample selection and justification Fifteen international retailers from a total of 152 registered in China (Database of Ministry of Commerce, P. R. China) were selected. Suitable firms were chosen using the following criteria as shown in Table  1. First, a high level of retail sector representativeness was required. Nearly all these firms were listed in top 100 retailers in China. Second, these firms were richly diversified in terms of sector of activity, entry mode, order of entry and country of origin, enabling our theory build. For each company, we collect the data through documentation and semi-structured interviews. To gain contextual and company-based information we also used a wide range of databases such as EBSCO, Euromonitor, Factiva, Xerfi 700doc, S & P Capital IQ as well as the firms' websites, existing case analysis of individual companies (available from the European Case Clearing House (ECCH)), archival data from the investigated companies, as well as other sources such as retail business portals (e.
Third, to prevent causal and temporal inferences we triangulated the data enabling a systematic examination of data from multiple sources which raised more questions, enabling verification, and therefore reducing the likelihood of data and researcher bias. For example, in addition to the information gathered from the managerial interviews, we collected more objective data from other sources such as S & P Capital IQ. Finally, as part of the verification of the data process we also carried out post-interview e-mail and telephone follow-ups to reiterate and clarify the data obtained, and to ensure that it was a true reflection of what the interviewees intended to express.
Data analysis procedure We conducted a five-stage data analysis (the data analysis procedure is described in Fig. 1 ). First, we assembled the data from the multiple sources in chronological order for each study unit. (e. g. , all data gathered from interviews, database, company published reports, external reports etc. ). The data was validated by undertaking parameter comparisons for each of the companies enabling the data-set build -up for each study unit. Second, we undertook the coding process using Nvivo9 and assigned labels to the each retailer's strategic choices from three dimensions: target clients, shopper value propositions and retail value chain (see Appendix A). With respect to establishing the retail value, we identified and summarized the key strategic activities for each study unit using  (Swoboda, Foscht, & Cliquet, 2008)  twelve categories: conception of products, production of private label products, procurement, logistic, retail concept, HR management, financing and accounting, organization/structure, information system, relationship with suppliers, relationship with customers and CSR (see Appendix A).
Our study focuses on the retail industry and analyses the data collected from 15 international retailers in China. Our findings reveal six potential routes of retail business model innovations: legitimatizing brand image in the local market; sharing resources within the group, transferring knowledge from headquarters; alliance with the local stakeholders; and imitating the local competitors; and new innovation for the local market. Apart from the new innovation for the local market, five of the six routes are exploitative business model innovations. From the organizational learning perspective, these six routes can be clustered into three patterns of resource deployment: extension of existing knowledge base, embedment with the local environment, and autonomic exploration of subsidiaries (see Fig.
Acknowledgements The authors thank  Prof. Stephan Schmid  at  ESCP Europe ,  Prof. Arno Haslberger , at  Webster University Vienna ,  Prof. Anne-Wil Harzing , at  Middlesex University , and  Prof. Ulrike Mayrhofer , at  University of Jean Moulin Lyon  for their helpful comments and support for an earlier version of the paper. We would also like to thank  Dr. Paul Walker  at  Middlesex University  for proof reading the paper.
Threecountry data were gathered in order to examine the influence of different socioeconomic and cultural status and different regulatory mechanisms to our derived results (i. e. , compare two developed with an emerging economy as regards the measurement of their sustainability levels). Fifth, the structure equation models are developed using the information of the SMEs' data. Sixth, the models are fitted to derive the performance index for specific region through suitable statistical software. Seventh, we derive various performance information of the participating SMEs in the form of individual comparative performance indices. Finally, the various advantages and shortfalls of the proposed supply chain sustainability performance framework are discussed.
The survey and data Supply chain characteristics data was gathered from SMEs located in specific regions of the UK, France and India. Each of these regions exhibits certain characteristics and enables to derive useful conclusions on the geographical and socio-economic differences of SMEs regarding their supply chain sustainability performance. Data collected using a structured questionnaire filled by the selected SMEs' managers, directors and/or owners. The questionnaire was constructed aiming to derive the basic characteristics of SMEs' supply chain. In particular, information was collected on focal company's business (i. e. , covering products, services, customers, suppliers, employees and economic), demand (covering customer relationship, customers' requirements, customers' contributions in superior product development and outbound logistics), internal operations (i.
96 for a 95% confidence level), then, by relying on the simple random sampling formula we should select approximately 96 SMEs. Following the suggested sample size, we have collected data on 40 SMEs located in the Midlands, UK, a very productive area of the UK with 11% of the country's GDP contributed by manufacturing and construction industry in the region. Also, 40 SMEs from the wider Normandy area have been sampled, with the latter region being distinguished for their high levels of manufacturing companies. Finally, in addition to the samples collected from the two developed countries, we have collected information concerning supply chain sustainability from 40 Indian SMEs located in the Eastern parts of the country. In the specific region, productivity, manufacturing and economic sustainability is poor in comparison to the rest of the country.
Thus, in total, our sample size comprises of 120 SMEs. The questionnaire that has been developed for capturing supply chain characteristics of SMEs and collected data are available upon request by the corresponding author. The survey used a Likert-type scale for measuring the various sustainability practice and performance indicators (observed items). An analytical description of the observed items from the questionnaire that were utilized for the construction of the latent factors of SMEs' practices and performance aspects is provided in Table  1  above. Table  15  in the "Appendix" illustrates descriptive statistics for the observed variables. The specific items have been measured using a likert scale taking discrete values from 1 to 5 (except for the items of ECO_PR_1, ECO_PER_1 and OPR_PER_1, measured on a 10-point scale).
Next, upon obtaining the factor scores of each one of the sample SMEs, we are able to calculate aggregate measurements of the sustainability levels of SMEs in each one of the three selected regions of interest. As previously mentioned, this study provides feedback to the policymakers to review their current policies and enable them to act locally in certain areas. Specifically, SEM modeling on the relationship between the latent construct of sustainability with practice and performance dimensions allows one to derive a unique sustainability score for each single SME in the dataset used for running the SEM analysis. Individual sustainability factor scores are calculated through the use of standardized regression weights combined with the observed collected data on sustainability practices and performance of each SME in the sample.
Measurement of sustainability at the individual SME level The next tables (Tables  10, 11 ) show the factor score weights of the general sustainability measurement construct as well as those for the environmental, economic, social and operational sustainability measures, for the practices and performances, respectively. Tables  10  and 11  present the factor score estimates derived from the fit of the structural equation models fitted to the three regional datasets, respectively. In particular, Table  10  contains the factor scores as were estimated from the practice measurement models, whereas the corresponding performance measurement estimates are shown in Table  11. These scores may assist in the generalization of the results obtained from our analysis, in the sense that SMEs owners/managers of the three selected regions can utilize these estimates in order to calculate their own sustainability indices, once they supply the corresponding levels of their SME scores on the various practice and performance items.
Third, sample SMEs are selected for the study and responses to questionnaire are gathered. Forth, responses are processed using SEM that leads to formulate regression type supply chain sustainability model for SMEs in a specific region. Any SME in that region can measure their absolute supply chain sustainability performance by fitting their data into the region's regression-type model. The outcome i. e. , supply chain sustainability performance of specific SME could be compared with the region's average in order to derive the means for improvement. The improvement projects will amend sustainability practices, which in turn affect positively sustainability performances. This method could be used by policy makers as well SMEs' owners, decision-makers and managers. Specific region's data analysis will reveal strengths and weakness of sustainability practices performances of SMEs that helps in deriving means for improvement.
Su arquitectura está integrada por 3 componentes. El primero es el "Search query converter", el cual se encarga de procesar las búsquedas de agentes y servicios web, a fin de que puedan descubrirse entre ellos. El modulo "DF to UDDI" permite a los agentes descubrir servicios web registrados en el UDDI, mientras que su contraparte (UDDI to DF) permite a un servicio web ubicar a los agentes. El siguiente componente (Service Description Converter) se compone de dos módulos, el "DFAgentDesc to WSDL" permite a los agentes de software publicar sus servicios en el directorio UDDI de la plataforma web, a fin de que puedan ser consumidos por otros servicios web, mientras que el módulo "WSDL to DFAgentDesc" permite que un servicio web publique sus servicios en el DF del MAS, de tal manera que los agentes puedan consumir dichos servicios de forma transparente.
1), y no lo encuentra debido a que no existen agentes con esas capacidades en el AMS, y delega la búsqueda al SMA (UDDI), para que verifique si existen servicios que provean consciencia contextual  (  Por otra parte, la Fig. 13  muestra la conversación entre los agentes y los servicios web, para el mismo caso de estudio, usando ahora la Propuesta 2. En este caso, el agente SPA envía un mensaje al AMA (1) para localizar un agente que provea servicios de consciencia contextual. En esta propuesta, en AMA se encuentran registrados tanto los agentes del MAS como los agentes de servicios web (WSA). Es así que AMA busca en su directorio.
Adicionalmente, el agente SDA de la segunda propuesta requerirá revisar periódicamente el UDDI del ESB, para determinar cambios y hacerlos efectivos en el MAS. De forma similar, el ESB requerirá un costo computacional adicional al momento de usar elementos de ruteo, transformación, entre otros; Por otra parte, la primera propuesta utiliza el agente SMA, cuyas funciones son administrar el UDDI y los agentes WSA (creación, búsqueda, composición, orquestación de WSA, entre otros); en cambio, en la segunda propuesta muchas de esas tareas son realizadas por el propio ESB (composición, orquestación, etc. ), y en parte por el SDA (creación, modificación, entre otros), pero la ubicación de WSA queda delegada al AMA, tal que los agentes del MAS pueden ubicar a los WSA como si de otro agente de la plataforma se tratase.
Locating of studies and study selection In the first step, the procedure started with a database search in EBSCO Business Source Premier, which includes 14,914 peer-reviewed journals (EBSCO 2016), and was performed in March 2016 and March 2018. The time frame for the database search was determined as 2000-2017 because there has been an increased interest in university cooperation over the last two decades (e. g. , Ankrah and AL-Tabbaa 1 3 2015;  Barnes et al. 2002; Perkmann et al. 2013 ) and a number of current concepts and strategies, such as the entrepreneurial university  (Clark 2001; Etzkowitz 2003) , the Triple Helix  (Etzkowitz and Leydesdorff 2000) , the mode 2 of knowledge production  (Gibbons et al. 2002) , and new public management  (Leisytë and Kizniene 2006) , stress the importance for collaboration and the need for third-party funding.
The search included only peer-reviewed papers published in English. Search terms applied for the database search were 'universit* cooperat*'; 'universit* collaborat*'; 'universit* allianc*'; 'inter-universit*'; 'universit* ventur*' and 'universit* partner*'. These terms are on the one hand broad enough to capture the most suitable articles and on the other sufficiently expressive to remove less What makes industry-university collaboration succeed? A… suitable articles. Although some researchers use the terms 'university' and 'higher education' as a synonym, we decided to differentiate clearly between these two terms and searched solely for universities, as determined by our research question. Higher education systems differ greatly between countries and over time, which provokes essential problems in terms of comparability  (OECD 2017).
For instance, the OECD, which has long-standing institutional experience regarding international educational statistics, states that comparison issues remain a quintessential characteristic of the data. To illustrate, the OECD's (2017) report on the state of higher education devotes six entire pages to the challenges resulting from data acquisition and different definitions across countries. In order to ensure comparability, we therefore refrained from using a broader and ambiguous term but acknowledge that this may result in the exclusion of potentially relevant studies. The database search identified 461 papers. After eliminating duplicates, 405 papers remained. For administration of all articles, the reference management program Citavi 5 was used. In the second step, we selected and evaluated the literature regarding our research question.