dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, this generation straddles the two worlds, having been born still in the analogue world  (Ameen et al. , 2022) ; (ii) according to  Lantos (2014) , most of the popular influencers are Millennials (e. g. Chiara Ferragni in Italy, Mrs Hinch in the United Kingdom, Cara Delevigne in the United States). Thus, they perform a crucial role in social media and in the online world; (iii) according to  Eger et al. (2021) , Millennials prefer shopping online rather than in traditional stores, being confident with different platforms. They were selected through an initial screening for those whose smartphone software had embedded AR (higher than the 7. 0 version for Android and higher than the 11. 0 version for iOS). The online survey yielded 300 useable respondents. The sample included 151 male and 149 female respondents, maintaining a balance between gender.
Measurement model Path analysis  (Wright, 1960 ) is a multiple-regression statistical analysis that is useful for examining relationships between a dependent variable and more independent variables and was employed to examine the relationships of the measurement model  (Bryman and Cramer, 1992). This quantitative analysis was developed by  Wright (1960)  to validate hypothesised relationships and show causal mechanisms through variables. By using SPSS 28. 0 and AMOS 28. 0, we could explore the underlying theoretical structure of the proposed model  (Jöreskog and Wold, 1982). We verified the reliability and validity of the model, and Cronbach's α value was 0. 935 (>0. 70), meaning excellent reliability and replicability over time. The Bartlett sphericity test (χ 2 = 2145. 265, p = 0. 000) and the Kaiser Meyer Olkin (KMO) measure (0.
The hierarchical construct in a structural model The path model was tested using AMOS 28. 0. Fig. 3  shows the results of the model with the significant path as a linear line and non-significant paths as dotted lines. Standardised path coefficients are recorded between constructs. In contrast to our hypothesis (H1b), product involvement did not have a significant effect on PI (β = 0. 02, p = 0. 506, M = 3. 89, SD = 1. 83). This effect was mediated by the AR flow experience (H1a confirmed; β = 0. 33, p = 0. 000, M = 5. 04, SD = 1. 47). The effect of interest in the shopping app was significant (β = 0. 41, p = 0. 000; M = 4. 75, SD = 1. 50; H2). The data offers interesting information on consumer responses. Specifically, the immersion using the AR application increased consumers' PI (β = 0. 73, p = 0.
The adoption of immersive technologies and product involvement perception referring to other goods in fashion could be perceived differently by consumers. More specifically, the numerous differences between high-body-involvement products in the fast fashion and luxury sectors may also be investigated. Last but not least, our study collected data in an AR context, which is limited in terms of interactivity when compared to the new fast-growing technologythe metaversethat has attracted many retailers in the past year. Thus, further studies could expand the AR model to this new world, evaluating the level of consumer flow state in the trying on of high-body-involvement products. Francesca Serravalle, Assistant Professor in Business Management-University of Turin (Italy). She held a PhD with Honour in Business and Management at the University of Turin in cooperation with the Institut d'Administration des Entreprise (IAE business school) at the University of Lyon-Magellan laboratory (France).
AbstractSmart technologies have dramatically improved environmental risk perception and altered the way organizations share knowledge and communicate. As a result of the increasing amount of data, there is a need for using business intelligence and data mining (DM) approaches to supply chain risk management. This paper proposes a novel environmental supply chain risk management (ESCRM) framework for Industry 4. 0, supported by data mining (DM), to identify, assess, and mitigate environmental risks. Through a systematic literature review, this paper conceptualizes Industry 4. 0 ESCRM using a DM framework by providing taxonomies for environmental risks, levels, consequences, and strategies to address them. This study proposes a comprehensive guide to systematically identify, gather, monitor, and assess environmental risk data from various sources.
0 concept has emerged to characterize the spread of information and communication technologies and the adoption of several technologies such as the Internet of Things (IoT), cloud computing (CC), and big data analytics (BDA). Cyber physical systems (CPSs), on which the adoption of Industry 4. 0 technologies depends, generate an increasing volume of data  [14] [15] [16] [17]. This extensive volume of data has prompted firms to implement business intelligence (BI) and data mining (DM) techniques to enhance real-time decision making  [18, 19]. DM has proven its efficiency in tackling supply chain risk factors in general  [20] [21] [22]  and in particular environmental risks  [19]. However, despite the growing research interest in Industry 4. 0 and ESCRM, a specific framework to address.
Environmental Supply Chain Risk Management A review of studies investigating ESCRM was conducted using the Scopus database. This enabled us to delineate the main environmental supply chain risks and the strategies to mitigate them. Accordingly, two main categories of environmental risks exist: (i) Endogenous risks which result from company supply chain operations including pollution and harmful emissions, as well as accidents caused by the firm's staff, operations, and machines  [57]. Endogenous risks include inefficient resource consumption, waste, and scrap generation  [58]. Further, other scholars consider non-compliance with regulations related to personnel safety, ecology, and social responsibility as an endogenous risk  [6] ; (ii) Exogenous risks which emerge from the interaction of firms with their external environment  [6, 59].
The Selection and Retrieval Phase The research questions provide the foundation of the SLR. To avoid bias in data gathering, we adopted the following criteria in assessing and selecting publications. (1) A search was conducted in several databases (SCOPUS, EMERALD, Taylor & Francis, Springer, Elsevier, and Google Scholar) in order to generate a comprehensive set of papers  [76] ; (2) The review was limited to peer-reviewed publications to guarantee quality  [77]. Articles published in peer-reviewed journals are subject to a rigorous process of evaluation prior to publication  [76]. Consequently, chapters in books, conference proceedings, and trade journals were excluded from the search; (3) Conceptual and empirical research was considered in gathering as many publications as possible. The articles identified are from 2004 to 2021; (4) Only publications in English were considered, to facilitate data analysis; (5) Subject terms related to ESCRM and Industry 4.
0 were used in screening the papers' titles, abstracts, and keywords to assess their relevance. A total of 16 keywords were defined by the authors using a brainstorming method of commonly used keywords to define the Industry 4. 0 conceptualization in managing environmental supply chain risks. The keywords were combined using basic operators and Boolean logic (AND/OR) to form a sequence of strings that could be used in database searches. The initial search yielded a total of 420 papers. The search results were saved in CSV format, which included for each paper, the title, the authors' names and affiliations, the abstract, and keywords. There were 340 papers left after the duplicates were removed. Next, we read the abstracts to see if they were relevant to the research questions, which resulted in a total of 148 articles being considered for this literature review.
Numerous organizations have started to use automated risk management frameworks to compete in today's knowledge-driven business environment  [110, 111]. DM techniques constitute a powerful tool to extract useful metrics from data on risks and ESCRM. DM tools can provide an efficient approach to detect, identify, and assess risk sources which can be further analyzed by categories to predict, mitigate, and react to real time events  [108, 112, 113]. Such features make DM a relevant approach to deal with ESCRM in the Industry 4. 0 context. The recent developments in information and communication technologies (ICT) offer wide possibilities of data gathering, storage, and assessment of different types of risks from various sources  [114, 115]. The combination of such tools helps create "Risk Intelligence (RI)"  [116].
Data Mining Approach for Managing Industry 4. 0 Environmental Risks The development of a DM-based ESCRM framework for Industry 4. 0 necessitates an integration of numerous processes related to risk identification, data gathering, and storage, all of which would help in the conversion of risk management issues into a DM problem. Data processing by DM algorithms and analysis of the results will define appropriate risk mitigation measures. We suggest adopting a DM based framework that integrates ESCRM and Industry 4. 0, based on the DM key components (RDW, DM modules) and risk management practices (Figure  7 ). The suggested DM approach to ESCRM for Industry 4. 0 consists of the following three main steps: (i) Identifying environmental risks indicators. Using various metrics, measurements and indicators, organizations can identify the risk exposure of their activities  [4].
Risk indicators can be obtained from internal data sources such as the organization's internal databases  [128]  DM is an efficient tool to predict probable risk events, identify relationships between risks and their triggering factors, synthesize risk data, and visualize the main risks  [113, 139]. Visual DMs are particularly helpful in interpreting the findings of risk analysis  [115]. DM results can be archived for future use after identifying appropriate measures to tackle the critical risks and their triggering factors based on different scenarios related to costs, feasibility, and benefits  [19]. After testing its implementation, the proposed DM framework should be developed with a dynamic approach that seeks to update and refine the results obtained with the help of DM experts and ESCRM decision makers.
The findings of the DM framework can be assessed based on their accuracy, robustness, explanatory results, efficiency, agility, and ease of use  [19]. In retrospect, the assessment of DM findings can be utilized to improve data analysis and risk management policies. lem database-is obtained through synthesizing, processing, and assessing data of the RDW according to the requirements of the DM application. Firms can assign several tasks to DM such as prediction, association, clustering, categorization, and aggregation of risk  [121, 127]. In such a manner, decision makers might use DM for different aims such as identifying risk triggers, predicting the impact of risks, detecting their root causes, prioritizing risks by degree, and modeling future trends  [115, 124]. DM is an efficient tool to predict probable risk events, identify relationships between risks and their triggering factors, synthesize risk data, and visualize the main risks.
Research Limitations Conducting a literature review and adopting a conceptual approach in research is not limiting per se. However, some of the concepts provided in our research are based mostly on reviewing extant literature from Scopus and other databases, which offer comprehensive coverage of the academic literature but may not include all the publications. Further knowledge could be also found in the grey literature (reports, theses, memos, etc. ). Notwithstanding, if the articles used in this study are not exhaustive, they are comprehensive enough to provide a reliable conceptualization of Industry 4. 0 ESCRM. Finally, although the proposed DM framework steps were elaborated based on prior literature and following a rigorous approach, other environmental risk management mechanisms might be suggested and could provide opportunities for further additional research.
RDW are built using data extracted from internal and external sources-also called supply chain database-to make data accessible[103]. Initially, a conceptual risk data model has to be elaborated to structure the data generated from the various entities/sources and their interaction. Data processing is needed to develop the appropriate format for a data warehouse [136]. A process of data cleaning, reduction, and integration is necessary [120] through smoothing, normalization, discretization, aggregation, and generalization [128]. As a result, risk metrics and indicators can be obtained by type and activity [69]. After that, risk data can be subjected to an extract transform and load (ETL) process which generates RDW by source, type, attributes, and relationships. Converted data by ETL is transformed into an analytical structure containing the risk metadata [136].
The metadata layer of the DM-based ESCRM model consists of risk indicators, metrics, sources, factors, and location in terms of activity (e. g. , manufacturing, shipping) or SC network (supplier, vendor, etc. ) and data collection methods [45,137]. RDW is the key to access risk data in the DM framework. Firms can specify what kind of technology can be employed according to the data and available resources. Issues related to the type of database server, software employed, ETL server, storage needs, user interface, and network type have to be discussed and clarified in detail [138]. Once a decision is made regarding such elements, the RDW can be operationalized by connecting its components (internal and external databases); (iii) Elaborating a DM module for assessing environmental risk management.
The DM module seeks to provide useful information for intelligent decision making in ES-CRM. Without assessment, risk data are merely worthless information. The DM module creates risk data mart (RDM) from the RDW. RDM-also called risk problem database-is obtained through synthesizing, processing, and assessing data of the RDW according to the requirements of the DM application. Firms can assign several tasks to DM such as prediction, association, clustering, categorization, and aggregation of risk [121,127]. In such a manner, decision makers might use DM for different aims such as identifying risk triggers, predicting the impact of risks, detecting their root causes, prioritizing risks by degree, and modeling future trends [115,124].
Multi-user research In a UCD project, the first step is to conduct user research. It consists in collecting information about the future users needs, habits, context, constraints and demands. This step is composed of two types of methods, primary research and secondary research. Ironically, secondary research is usually carried out first, since it is the consultation of documents bringing information about the future users who are the targets of the project. Documents can be for instance scientific research papers, marketing studies, opinion polls, technical reports, but also forums used by the target users. It is called secondary research because data is collected by other people. For now, it may be difficult to find some documents which would provide useful information concerning the multi-user aspects of a given future product.
The magnitude of the market impact depends on the surprise of the policy decision  (Bohl et al. , 2008)  and on the tonality of words used by central bankers during press conferences  (Picault and Renault, 2017). But, to the best of our knowledge, the impact of the level of attention prior to central bank announcements on the magnitude of the market reaction has never been tested. 1 1 In a recent working paper,  Masciandaro et al. (2021)  computed the similarity between tweets and central bank announcements as a proxy for monetary policy surprise before analyzing bond and stock market reactions, conditional on the level of similarity. The authors of this paper, however, do not consider the impact of investor attention in their study. Testing this hypothesis requires : (1) a good proxy of investor attention, (2) a precise identification of the release of the decision and the timing of the press conference, and (3) a high-frequency database of price/yield changes around the announcement for a wide range of assets.
This setting allows us to measure attention before the first announcement, but also between the announcement and the press conference to capture the attention directly related to the decision. Last, we take advantage of the EA-MPD database released by  Altavilla et al. (2019)  to analyze the impact of ECB announcements on a wide range of assets and to decompose the market reaction into three periods: the policy decision window (from 1:35 pm to 2 pm), the press conference window (from 2:25 pm to 3:40 pm) and the monetary event window (from 1:35 pm to 3:40 pm). Our paper's contributions are threefold. First, we find that the level of attention prior to announcements has an impact on the magnitude of the market reaction. A higher level of attention is associated with a higher absolute market reaction on ECB announcement days.
, 2022)  by showing that Twitter is not just noisy but reflects the perceptions of the general public and investors about the central bank's actions. 3 Last, while investor attention has predictive power for the magnitude of the price (yield) changes, we do not find any forecasting power on (non-absolute) market returns, even when we combine our investor attention proxy with a measure of investor sentiment before the news. This finding is consistent with the efficient market hypothesis in the sense of  Jensen (1978) : market participants cannot generate economic profits by trading on the sentiment or the level of attention prior to ECB announcements. Our paper is organized as follows. Section 2 details the data and describes how we created our investor attention proxy from Twitter. Section 3 reports our methodology and empirical findings.
Data Investor attention is not directly observable. Thus, we construct various proxies from online data to estimate the level of attention prior to each ECB announcement 4. Our main proxy is based on the use of Twitter as it allows us to construct intraday investor attention. We also use as a benchmark the relative number of search queries on Google, the number of press articles about the ECB from Factiva and Bloomberg, and the number of messages on 3 Recently,  Angelico et al. (2022)  showed that tweets can be used to build real-time measures of inflation expectations in Italy. While not directly related to our research question, their findings confirm the added value of Twitter data for economic forecasting. the social media plateform StockTwits about the ECB.
Twitter attention We took advantage of the recent opening of the Twitter API to academic researchers in 2020 to collect all tweets sent on Twitter over a 10-year period related to the European Central Bank. 5  We use the keywords "European Central Bank", "ECB," "Draghi," and "Lagarde" to construct our sample. We also use the translation of those keywords in French, German, Spanish, and Italian to capture domestic investor attention. As in  Ehrmann and Wabitsch (2022) , we remove irrelevant tweets such as tweets related to the England Cricket Board whose acronym is the same as the European Central Bank. We ended up with a database of 6,164,007 tweets sent between June 1, 2011 and January 1, 2021. The day with the maximum number of tweets is January 22, 2015 (with a total of 54,671 tweets) during which the ECB announced its expanded asset purchase program (quantitative easing).
On average, the number of tweets during a press conference day is 10 times higher than the number of tweets on no-announcement days. Figure  1  illustrates the relationship between ECB announcements and Twitter activity at the intraday level by showing the evolution of the number of tweets per minute around all press conferences in our sample. While we can see a surge in the volume of tweets at the exact time of the monetary policy decision (13:45) and during the press conference (14:30), we can also observe a significant activity prior to the announcements and between the monetary policy decision and the beginning of the press conference. The availability of data with precise timestamps allows the construction of intraday attention indicators, which a priori gives Twitter data an advantage over media or Google data as other attention indicators are only available daily.
This allows us to use the information published in the morning, before the release of the monetary policy decision (i. e. in our model between 0:00 and 13:25 on day t). We use 13:25 instead of 13:45 to match our indicator with the beginning of the "policy decision window" on the database of  Altavilla et al. (2019). We also consider using the information up to 14:15 when analyzing the impact of investor attention on the "press conference window" -using the same timestamp as in  Altavilla et al. (2019).
Other measures of attention We construct four other indicators of investor attention from (1) Google queries, (2) the number of articles on Factiva, (3) the number of news items on Bloomberg, and (4) the volume of messages on StockTwits. First, we used Google Trends to extract the evolution of the volume of requests on the subject "European Central Bank" in all countries. 6 We perform an extraction for each press conference to obtain the volume of requests on the day of the press conference and the 60 days preceding the conference. This methodology allows us to obtain daily data via Google Trends and ensures that our indicator is not biased by the search volume after the conference. Second, we use Factiva to extract the volume of articles in English on the topic "European Central Bank. " 7  We identify a total of 523,391 articles over our study period.
The day with the highest number of articles is 22 January 2015: the same day as the day with the highest number of tweets (ECB quantitative easing announcement). Third, we gather the number of articles and news items related to the ECB published by Bloomberg by utilizing the Bloomberg story count function. Since Bloomberg data are only available for the last ten years, our sample starts from 2012. We use this measure as a way to identify professional/institutional investor attention, as Bloomberg is not included in the Factiva database. A total of 363,544 Bloomberg news items about the ECB were identified over the last 10 years. Finally, we use StockTwits data to measure the attention of investors (mainly individual investors) on a social network dedicated to finance. StockTwits is predominantly 6 See an example of the keywords associated with this subject at this link: https://trends.
Market Data and Control Variables We use the Euro Area Monetary Policy Event-Study Database (EA-MPD) from  Altavilla et al. (2019)  to obtain intraday sovereign bonds yield changes (for Germany, France, Spain, and Italy), intraday stock returns (Eurostoxx 50 and the Eurostoxx Banks labeled STOXX50 and STOXXBANKS), intraday overnight indexed swaps (OIS) change with maturity from one week to 10 years and, intraday variation of the euro (EURUSD, EURGBP, EURJPY) around ECB Governing Council announcements. 9  The authors measure asset price changes for (1) the press release window: the change in the median quote from the window 13:25-13:35 before the press release to the median quote in the window 14:00-14:10 after it, labeled prw (2) the press conference window: change in the median quote from the window 14:15-14:25 before the press conference to the median quote in the window 15:40-15:50 after it, labeled 8 Data will be available after publication.