dataesr/scientific-paragraphs-categorization huggingface dataset:
The dependent variables are the absolute returns/yield change of 41 assets during the monetary event window (mew, left), the press conference window (pcw, center) and the press release window (prw, right). The 95% confidence interval is constructed using bootstrapping methods with 10,000 a samples. a For anonymity, the appendix is provided on a GitHub page: https://enarwen. github. io/. 1 ) coefficient β 1 is significant at the 10%, 5%, and 1% level. We consider the monetary event window (mew), the press conference window (pcw), and the press release window (prw) separately. The last line shows that the coefficient of the Twitter attention measure (AT A t-1 ) is statistically significant at 10% (or less) in 78% of the 41 regressions focusing on absolute returns/yield changes during the press conference window (prw).
We focus on the variables AT A t-1 and AGA t-1 because these two proxies for investors' attention are the ones that bring the best results on average. We group the 41 assets in our database into 6 main categories: stock market (Eurostoxx and Eurostoxx banks), foreign exchange (EURUSD, EURJPY, EURGBP), short-term OIS (maturity less than 5 years), long-term OIS (maturity greater or equal than 5 years), shortterm sovereign bonds (maturity less than 5 years), and long-term sovereign bonds (maturity greater or equal than 5 years). We find that attention plays a significant role mainly in the absolute changes in long-term OIS, long-term sovereign bonds, and the foreign exchange market. Focusing on the monetary event window (mew) and Twitter attention, its coefficients are significant at 5% for four of the six long-term OIS, three of the nine short-term sovereign bonds, and 14 of the 15 long-term sovereign bonds, for all stock market indexes and all exchange rates.
Using Twitter intraday timestamp In the previous section, we used only the messages sent on Twitter the day before an event, between 00:00 and 23:59, to construct our ATA attention indicator. However, an advantage of Twitter over the other data used in this paper is the availability of precise timestamp data (available to the second) allowing us to take into account the volume of messages on the day of the event until a few minutes before the start of the press conference. We therefore construct the new indicator AT A 1325,t by considering the messages sent from 00:00 to 13:25 on the day of the event t. The correlation between AT A t-1 and AT A 1325,t is equal to 0. 66. We consider a first model with only the variable AT A 1325,t and a second model with the variable AT A t-1 and the variable AT A 1325,t.
Tweeting in English or in the national language ? Our database of more than 6 million tweets is composed of 4,716,385 tweets in English (76. 5% of the database) and 1,448,090 tweets in other languages. In Equation  1 , we differentiate the attention measured computed from tweets written in English (AT A eng,t-1 ) and from tweets written in the relevant national language (AT A lang,t-1 ). As the majority of the messages are in English, the correlation between AT A t-1 and AT A eng,t-1 is large (close to 0. 99). The correlation with other languages ranges from 0. 60 to 0. 75. While a German (French, Italian, Spanish) ECB expert may tweet in German (French, Italian, Spanish) or English, depending on the audience he or she wishes to target (domestic or international), we believe that this method allows us to construct a proxy for domestic investor attention: the majority of users tend to send messages in their domestic languages.
Conclusion Alternative data from newspapers, social media, and search engines can be used to build new indicators to measure investor attention. However, it is difficult -ex-ante -to know which source to use to construct these indicators and the advantages and limitations of these different proxies. Each data source can capture the attention of a different audience (professional investors, individual investors, journalists, people from Main Street), and the effect of these indicators on the magnitude and direction of market reactions requires a specific empirical analysis depending on the type of assets or the analysis horizon (intraday, daily, long-term). In this paper, we propose a novel measure of investor attention by analyzing messages sent on Twitter about the European Central Bank. We show that the level of investor attention on the day before the announcement helps explain the magnitude of the market reactions to ECB announcements and that our results are robust to the inclusion of macroeconomic variables, market volatility, economic policy uncertainty indexes, and lagged returns.
We also show that attention measures constructed from Twitter data are more informational than measures constructed from the number of media articles or the number of Google searches. Our findings suggest that central bankers and policymakers could use those new indexes as a proxy of the expectation of the market prior to their announcements -in combination with other standard macroeconomic and financial variables already used to capture market stress. However, consistent with the efficient market hypothesis, we do not find that returns are predictable with investor attention or investor sentiment derived from Twitter. Twitter is a good proxy to forecast the magnitude of the price change, but not the direction of the market.
are available in the onlineAppendix. 11 We pay particular attention to three points: (1) the comparison between the attention indicators, (2) the comparison between the different windows, and (3) the comparison between the different assets. : Stocktwits (detailed in Equation1). The dependent variables are the absolute returns/yield change of 41 assets during the monetary event window (mew, first line), the press conference window (pcw, second line) and the press release window (prw, third line). The red and green dashed lines represent, respectively, a t-stat of -1. 96 and +1. 96. All points in orange correspond to regressions in which the attention variable coefficient (β Fig. 3. β 10 For anonymity, the appendix is provided on a GitHub page: https://enarwen. github. io/ 1 value and t-stats by investor attention measure and event window Notes: This Figure presents the value of the coefficient β 1 from Equation 2 and its associated t-stats on a total of 615 regressions (41 estimates per panel) with robust standard errors.
Purpose -This study envisages the impact of green project management practices (GPM) on sustainable competitive advantage (SCA). Moreover, the study also accessed the mediating role of green knowledge acquisition (GKA) as a mechanism between GPM and SCA. Design/methodology/approach -Using a quantitative field survey, data were collected from the multi-sector manufacturing enterprises (n = 265) in Pakistan. The measurement and structural model were tested through Analysis of Moment Structure (AMOS) by conducting a covariance structure analysis. Findings -The findings suggest that GPM practices are positively related to SCA. In addition to this direct effect, GKA play a significant role in explaining the associations between GPM practices and SCA. Our findings bring essential insights for the enterprises, policymakers, practitioners, and project managers to promote GPM practices for low-carbon projects to achieve SCA.
, 2019). Finally, developing and implementing KM plans This study employed a non-probability snowball sampling technique suggested by  Bryman and Bell (2011). This sampling technique ensures a faster response rate by using referrals and achieving a large sample size  (Shahzad et al. , 2020). The data were collected from manufacturing and technical project managers working in manufacturing enterprises across Pakistan. The study's purpose and objectives were shared with the respondents and indicating that the participation is voluntary. Questionnaires were distributed using self-administered field surveys following the previous studies in a similar context  (Khan et al. , 2019). This field research approach, although time-intensive and costly to maintain, is highly effective in regards to developing economies like Pakistan due to the limited availability of online survey methods and unwillingness on the part of local firms to respond to the study (Amankwah-Amoah et al.
Results We used the Analysis of Moment Structure (AMOS) version 28. 0  (Arbuckle, 2021)  to execute the CSA. According to  Byrne (2016) , AMOS is preferred because it supports a graphical user interface (GUI) and it is easy to use. We examined the results of the assessment of normality through the values of skewness and kurtosis. Given that the ML estimator assumes the residual error should be normally distributed, we find that our data fulfills this assumption with CR values < 10 for kurtosis and CR values > 3 for skewness  (Byrne, 2016; Kline, 2016). However, considering that the assessment of normality in AMOS is susceptible to effects of sample size, we added the Mardia's multivariate normality test as a complement. We did not find significant skewness or kurtosis values for the Mardia's multivariate test with p values = 0.
236 and 0. 548 > 0. 05, respectively. Thus, we conclude that the normality assumption is met. Furthermore, we check for outliers by calculating the z-score values for each case. We found z-score values < 2. 58 for all cases, indicating no outliers in our dataset  (Kline, 2016). We report descriptive statistics through the mean and standard deviation values of all the items (in Table  3 ), and calculate correlations between variables (in Table  4 ). Based on the guidelines from  Cox and Holcomb (2022) , we found that the mean and standard deviation of the variables do not exceed the maximum values and the sign of the correlation is not reversed. Even farther, we did not find a correlation between two variables greater than 0. 70 for all pairs, which indicates that there is no collinearity problem.
Hypothesis testing We use the ML estimator to test the hypotheses for both direct and indirect effects based on standardized coefficients. We look at the values of beta coefficient (β), standard error (SE) and critical ratio (CR) generated by AMOS 28. 0 program to confirm acceptance or rejection of the tested hypotheses. Based on Figure  2  and Table  6 , we supported all of our proposed hypotheses. In this regard, we examined the direct effect (without mediation), and found the relationship between GPM practices → SCA (c) significant at CR 4. 863 > 3. 09, with beta coefficient = 0. 348 and SE = 0. 080 at p-value = 0. 000 < 0. 001. Based on these preliminary results, we have support for hypothesis 1 (H1). Furthermore, we estimated the full model (both direct and indirect effects), and obtained the following results.
A multidisciplinary team of clinical representatives, the procurement manager, and a usability expert (JS) identified common inpatient EHR use scenarios and concerned endusers. We designed seven CLIPS simulating 59 EHR tasks (Appendix A), targeting frequently used or critical functionalities (Table  1 ). We also created a clinical dataset for the EHR. We sent the CLIPS and dataset to the EHR vendors two weeks before the usability sessions. We recruited nine end-users: three physicians (an emergency physician from the emergency unit, a cardiologist from the cardiology unit, and a neurologist from the geriatrics unit), three nurses (from emergency, cardiology, and geriatrics units), one pharmacist (from the central pharmacy), one medical clerk, and one admission officer (i. e. , non-clinical staff member trained to manage administrative and logistic duties).
Data Collection and Analysis Question 1: Can end-users Detect, Describe, and Prioritize Usability Problems?. Our usability experts first read problems identified by the participants and excluded (1) those unrelated to specific EHR characteristics (e. g. , opinions without descriptions), (2) those concerned with the technology platform (e. g. , connection failures), or (3) those rooted in data upload problems. They then combined multiple descriptions of the same problem (i. e. , deduplication) to reach a final list of usability problems. Next, we created a usability expert "comparison set". We combined lists of problems identified by participants and problems detected by usability experts. For each problem, we assigned a usability criterion and a severity level. Experts independently categorized problems using our a priori usability criteria and severity levels.
What Challenges Exist when Implementing a Usability Walkthrough during an EHR Procurement Process?. Two vendors withdrew their applications. One had concerns about their product's usability; the other did not disclose a reason. The remaining vendors were not prepared for the evaluation. We could not complete all CLIPS due to technical issues (e. g. , features not working) and database "locks" that prevented multiple users from opening the same test record simultaneously (Table  6 ). In some cases, the EHRs were missing patient data, whereas, in others, the data that participants were expected to enter were already in the chart.
Introduction In Software Product Lines Engineering (SPLE) the first step of product derivation consists in identifying the features of the desired product from a product line model  (Clements and Northrop, 2001). Selected features must comply with product line model constraints and stakeholders requirements  (Salinesi et al. , 2010). At the industrial scale, product line models may include hundreds of features  (Shahin et al. , 2021). However, in some cases, they could be larger than expected and may contain thousands of features such as the automotive product line model cited in the work of  Pett et al. (2019) , and the linux kernel which is one of the largest software product lines currently available, reaching 21, 000 features  (Nieke et al. , 2022). In such cases, the number of potential configurations can be large too.
(2019). This work proposes a collaborative product line configuration approach, called Colla-Config, that relies on free order configuration process, where stakeholders freely express their configuration decisions towards the desired product without being constrained by the configuration decisions made by the other ones. Colla-Config offers a new preference-based conflict resolution strategy, where stakeholders' preferences are elicited under the form of substitution rules to be considered if one or more of their configuration decisions could not be retained in case of conflict. Afterward, the minimal set of conflicting configuration choices is computed using the MCS (Minimal Correction Subset) algorithm  (Liffiton and Sakallah, 2008). To evaluate the proposed approach, we created a software tool that implements the Colla-Config approach and used it to conduct a usability test to gain insights into how Colla-Config supports collaborative configuration mainly preference-based conflict resolution.
Eleven Ph. D students took part and were asked to carry out a series of actions to configure a Web portal product line. During the experiment, we collected data to calculate the tool effectiveness and efficiency and measure the participants satisfaction. The remainder of this paper is organized as follows. Section 2 is dedicated to defining the main concepts related to collaborative configuration of product lines as well to the context of this work. Section 3 presents the running example used to illustrate the proposed approach and motivates this work through divers problematic challenging scenarios based on the same example. Section 4 presents the Colla-Config approach. Section 5 reports a usability test conducted to evaluate the proposed approach. Section 6 discusses the scalability of the approach.
Motivating example We choose to motivate the current work using the example of a Web portal product line, illustrated in Fig. 2 , and simplified from the Web portal feature model proposed in  Mendonca et al. (2008). The same example will serve as a running example to illustrate the Colla-Config process. (Mendonca et al. , 2008). As depicted in Fig. 2 , each Web portal has mandatory a Web server ("Web-Server") and may have one or more optional features among the following "DriverProg", "Security" and "Performance". Each of these features has a set of sub-features to which it is related thanks to one of the relationships: OR, XOR and optional. For example, "XML" and "Database" are related to "DriverProg" with XOR and therefore, only one of them may be selected if the parent feature ("DriverProg") is selected in a configuration.
As to "DataStorage", "Data Transfer" and "UserAuth", they are related to "Security" with an OR relationship; therefore when "Security" is selected, at least one of them must be selected. Furthermore, the Web portal feature model comes with a set of constraints of the two types: "require" (e. g. "File" requires "Ftp") and "exclude" (e. g. "Https" excludes "Ms"). When a single stakeholder configures the product line, the configuration, made up of the set of selected features, should comply with the feature model constraints presented above in order to be valid and free of inconsistent choices. For example, the configuration {WebServer, Content, Logging, DB, File, Security, Data Transfer, performance, Ms, Sec} is not valid for many causes: (i) "Static" is a mandatory feature, but it is not included in the configuration, (ii) "DB" and "File" are related with XOR relationship, therefore only one of them should be included; assume that "DB" is retained (iii) "Data Transfer" requires "Https"and "DB" requires "Database" however the latter are not selected; assume now that they are selected; (iv) "Ms" and "Sec" are related with XOR while they are both selected besides that "Https" excludes "Ms" while Ms is selected; assume is now that "Ms" is deselected; and (v) "Database" requires that "DriverProg" be selected and "Https" requires that "Protocols" is selected; assume that both are now selected.
The consistent final configuration is therefore {WebServer, Content, Static, Logging, DB, DriverProg, Database, Security, Data Transfer, Protocols, Https, Performance, Sec}. The requirements we just discussed, leading to compliance with constraints, are easily ensured thanks to "constraint propagation" within a stakeholder configuration  (Czarnecki and Kim, 2005). Assuming now that two stakeholders are collaboratively configuring the Web portal line, each of them is responsible of modules within his/her expertise. The first one has a role of security engineer; he is therefore focusing on security aspects and selects "Https" as protocol, "DB" as logging system and imposes "UserAuth" to access the Web portal. The second stakeholder is an expert in user experience (ux expert); he is, therefore, interested in performance and interface design; he selects "XML" for its capabilities, "Ms" as performance requirement and demands an "Active" content to ensure information freshness.
While each one of the configurations per se, after including mandatory features and applying the constraints propagation, is valid, the configuration resulted from the union of the two stakeholders choices has several problems: (1) "DB", selected by the security engineer, requires to include the feature "Database" in the configuration. However, the ux expert has already selected the "XML" feature which can not be selected at the same time as "Database"; hence "XML" selection is canceled and "Database" feature is added. The ux expert, noticing that "XML" is no longer included in the configuration, re-selects the feature. By doing so, the feature "Database" is canceled because of the XOR relationship. Since "DB" requires "Database", the latter is, one more time, selected. This scenario is repeated until the two stakeholders decide to modify their conflicting choices.
The scenario is repeated until the two stakeholders decide to modify their conflicting choices. Let us now consider the case where the configuration process is done according to a predefined order and take the following example. The product manager is responsible of the "WebServer" module and is the first stakeholder to start the configuration; he selects the features "DB", "FTP" and "Active". The security engineer can now start making his choices regarding the "Security" module; he selects "Data Transfer" and "UserAuth". Now comes the turn the ux expert in charge of "Performance" and "DriverProg" modules; he wants to select "Ms" and "XML". However, both features can not be considered because of: (i) the constraint propagation in the case of "Ms": "Ms" is excluded by "Https" which is included in the configuration as it is required by "Data Transfer" selected by the second stakeholder; (ii) XOR relationship and constraint propagation in the case of "XML": "XML" is related to "Database" with XOR, while "Database" is already selected by constraint propagation as it is required by "DB", which is selected by the first stakeholder.
Moreover, the Colla-Config tool makes use of a database to store users profile information along with their past configurations. As to feature models, they are stored in XML format. The main idea of the usability evaluation is to test the tool usability to support the Colla-Config approach, and to gain insight into how easy or difficult it is to follow and understand the Colla-Config approach using the tool. The   2006). The following subsections detail the evaluation process: the two first ones focus on the protocol along with its execution, and the last one is dedicated to the results.
Sampling and data gathering To determine the minimum number of samples needed to obtain reasonably accurate data, the central limit theorem was used, also applied by  [18] [19]. The following equation (1) was used for the calculations: 𝑛𝑛 = 𝑘𝑘 2 * 𝑝𝑝 * 𝑞𝑞 * 𝑁𝑁 𝜀𝜀 2 * (𝑁𝑁 -1) + 𝑘𝑘 2 * 𝑝𝑝 * 𝑞𝑞 ⁄ ( 1 ) where n is the minimum number of samples, k is a constant that depends on the level of confidence, e is the sampling error, p is the proportion of inhabitants that possess the characteristic we seek, and q is the number of inhabitants that don`t possess it (normally, 0. 5 for each one). For the collection of information, the research employed a case study approach. Almost 390 students from two local universities participated in the project for the measurement of acceptance of PaaS models. First, the project and its objectives were explained.
Acknowledgments This research has been supported by the  SCANDERE (Scaling up a circular economy business model by new design , leaner remanufacturing, and automated material recycling technologies) project granted from the  ERA-MIN3 program  under grant number  101003575  and funded by the project partner countries' national funding agencies. This initiative has been co-funded by the  French ADEME (Ecologic Transition Agency)  under contract number  2202D0103.
Data collection The empirical data for this study were derived from two sources: online ethnographic observation and a survey. First, the online ethnographic observations were carried out between March 2020 and June 2021 from sources including Twitter discussions, websites, and the Facebook pages of fashion companies in Finland. We also reviewed the national media, including articles in the Fashion Finland online journal and national newspapers. The online ethnographic data collection focused on changes in business practices, design, and production in small Finnish fashion companies, and thus we identified and mapped the emerging discussions on fashion in connection with the COVID-19 crisis. The data were collected in an unstructured manner which is a qualitative, flexible, and loose method of gathering data (e.