dataesr/scientific-paragraphs-categorization huggingface dataset:
The articles for the integrative were searched on Web of Science (WoS) and Science Direct (SD) databases in a single search, looking for articles published since the very first year available in the databases -1945 to 2018. For the search, the Boolean operator 'AND' was used. The following terms were selected in the title, abstract, and keywords of the articles: critical_success_factor* and country. The first stage of this search resulted in 352 articles (133 from WoS and 219 from SD). These were collected based on the filters: language (English), document type (articles, proceedings papers, and review), categories (management, business, economics, econometrics, planning development, public administration, transportation, urban studies, transport science technology, environmental studies, environmental sciences, multidisciplinary sciences, social sciences interdisciplinary, and political science).
The innovation radar for the insertion of AVs as PSS From secondary data obtained through a literature review, four key dimensions were defined that will assist as analysis categories and guidelines: 1 infrastructure and patterns 2 social and political environment 3 consumer and market 4 technology and innovation. From the questionnaire, the CSFs were filtered as a way to organise and reach a final list that was strictly aligned with the scope of the AVs: was obtained four dimensions and 12 CSFs. In order to refine and to align the CSFs with the national context, a systematic and integrative review was carried out. From the analysis of the 36 selected articles, all the indicators originated from the search 1 that could be related to our scope were identified and distributed into the key dimensions and their corresponding CSFs.
Social and political environment Regarding the relationship between AVs and the social and political environment surrounding it, there are some important questions to discuss, such as "How disruptive will these vehicles actually be in transforming the city of today?" And "how can city governments take advantage of autonomous technology to achieve broader goals with regard to urban mobility and livability?" And also, "How much control and regulatory influence does the city take in shaping mobility flows and shifting the modal mix?" "Which datasets are needed to make the right investment decisions?" [WEF, (2018), p. 26]. AVs have the potential to catalyse the most significant transformation in urban mobility since the creation of the automobile. However, their social benefits can only be unlocked if governments understand and implement the appropriate policies and governance structures.
To correctly replicate the case study company problem, real data (jobs processing times and setup times) were collected on the shop-floor. The following statistical distributions fitting the collected data were used to generate the instances: (1) Jobs' processing times: Exponential with an average of 10. 75 h. (2) Due dates: Uniform with a minimum of 24 and a maximum of 312 h. (3) Setup times: Time required to dismount a mold: Uniform with a minimum of 15 and a maximum of 45 min Time required to mount a mold: Uniform with a minimum of 20 and a maximum of 60 min. The big M value of our mathematical model (i. e. , variable G) has been chosen as equal to 10. 000. Let us note that this value has no impact on the model's solving times, since there is no loop or sum that depends on it.
Data description and performance measure The data for our study come from the Lipper fund research service. The sample contains equity mutual funds from 12 European countries from 2002 to 2014, providing access to fund characteristics such as fund investment focus, name, launch date, and management company name. Lipper also provides information on the names of management companies and whether the companies belong to the same groups as the funds. With information on the monthly returns of the funds, we can measure fund performance according to the Fama-French three-factor model, as follows: R t À R f ;t ¼ a þ b R m;t À R f ;t À Á þ sSMB t þ hHML t þ e t ; ð1Þ where R t is the return of a considered fund in month t;R f ;t is the risk-free rate in month t; R m;t is the return of the corresponding index market in month t; SMB t and HML t denote returns on portfolios that proxy for common risk factors associated with size and book-to-market ratio, respectively; a is a constant; b,s andh are the coefficients associated with the factors; and e t is the error term with a mean of 0.
Furthermore, RDs are "a means of strengthening the credibility of the company's financial information and facilitating relations between the company and its national and international stakeholders. The COB [the French Securities and Exchange Commission] allows companies to file their annual report as their registration document, and accordingly lays down presentation and content rules for the document that go beyond those governing the content of annual reports"  (Michaïlesco, 1999, p. 90). A total of 675 reports (424 RDs and 251 SRs) were collected. Most of them were extracted from the websites of the selected companies. Certain RDs were also gathered from the AMF database. The panel data comprises 433 firm-years observations (unbalanced panel with 35 firm-years missing observations). To construct the two dependent variables (i.
e. , decision to disclose LCA information and quantity of LCA information disclosed), first a content analysis was performed of the 675 RDs and SRs included in the corpus. The first step comprised an in-depth reading of the RDs and SRs available for 2014 in order to identify the terms used to describe LCA 3  disclosures. The second step was to use ATLAS-ti software and the terms identified in the first step to search for and manually code the LCA information disclosed in the 675 reports of the corpus. The sustainable development sections in the RDs were also fully read in order to refine the research. For each type of report studied and each firm-year, all words, sentences, diagrams, figures, and tables concerning LCA were also identified and recorded in a specific database. The third step involved summarizing the information in this newly-created database in order to measure the two dependent variables for each firm-year and each type of report.
Data collection and manipulation check We collected data from a convenience sample of N = 204 (59. 8% female, 40. 2% male) undergraduate students from a French business school who received class credit for participation. All study participants were fluent in English, were enrolled in an international program following classes in English, including classes on CSR. Therefore, the questionnaire was written in English. Moreover, students are typical buyers of the considered product categories. We measured the constructs of interest using established scales that we adapted to the study context (see Appendix B). We used six items from  Wagner et al. (2009)  to measure perceived corporate hypocrisy (e. g. , "This company acts hypocritically"), five items from  Berens, van Riel, and van Bruggen (2005)  to measure CA associations (e.
Hence, participants in Study 2 saw the same  apparel brand website as in Study 1, with the difference that the "About us" page contained both the valence and the message specificity manipulation. We collected data from a convenience sample using Amazon's Mechanical Turk (MTurk) (N = 211; all in the US, fluent in English). After deleting individuals who failed the attention checks, the final sample was N = 201 (66. 2% male; 33. 8% female; 89% were 25 years or older). We measured attitudes toward the firm and participants' intentions to purchase products from the focal brand using the same scales as in Study 1.
What do we know? We are not the first to have studied the effects of demonetization. The table below offers an exhaustive list of the studies carried out to date, specifying the methodology, the period, the type of data, the location, and the main results (Table  1 ). Most of these studies draw on secondary data such as RBI data, consumer data, agricultural markets data, and so on. Some papers use quantitative and/or qualitative primary data, and others offer theoretical reflections. Our corpus mostly consists of papers on the demonetization per se, but also includes further analyses of the digitalization of finance. By and large, the available evidence highlights various sorts of negative consequences. On the macroeconomic level there is lowered growth. This translates into a decline in employment, income, remittances, and an increase in prices.
Guérin et al. (2017) The rural economy was adversely affected in terms of employment, daily financial practices, and social networks for over three months. Demonetization has further marginalized those without support networks. Ground-breaking and first-hand quantitative and qualitative survey data collected just before and after demonetization shock. Pre-and post-demonetization. Rural Tamil Nadu. Primary data: Pre-demonetization sample of 1,967 people; Post demonetization sample of 683 people. Joshi, Gupta & Rangaswamy (2019) Financial inclusion constitutes a lived process in a blend of habits, practices, and challenges in order to expand socioeconomic capabilities. Business contexts influence the adoption of PayTM use. Ethnographic investigation on street vendors in urban India, to capture the daily practices of digital money.
Analysis and reflection on digital finance and financialization in relation to demonetization Athique (2019) Transferring the monetary system's risks and costs onto citizens is an unprecedented shift in the contract between citizens and the state. Narrative of the demonetization and the international cashless agenda. Pre-and post-demonetization. Indian level. Secondary data: international media commentary; impact assessments by Indian scholars; declaration of the Government of India. Chandrasekhar & Ghosh (2017) Digital transactions can be a mean for finance to extract rentier incomes out of relatively poor populations. Narrative of the Indian experience on reducing cash usage and the phenomenon of financialization of finance. Pre-and post-demonetization period. Indian level. Secondary data: RBI data on digital payments.
Methodology We draw on secondary data from May 2014 to March 2020. Data on payments systems are from RBI's monthly reports on bank wise ATM, POS and card statistics (RBI, various date-a) and bank wise volumes in mobile transactions (RBI, various date-b) 4. Data on financial inclusion come from two sources, the RBI's annual report on Basic statistical returns of scheduled commercial banks in India (RBI, 2017) and from the Global Findex data  (Demirgüç-Kunt et al. , 2018). Data on telephone-density indicators come from the Telecom Regulatory Authority of India (TRAI, 2020). These data have allowed us to trace the evolution of electronic transactions via ATMs, and some of the digital transactions via POSs and the mobile phone tools. To date, there is no standard definition of digital finance.
, 2018). RBI data from March 2010 to September 2016 shows that the number of deposit accounts rose from 810. 1 to 1,911. 3 million (RBI, 2017) 7. But the fact of people owning accounts says nothing about their use  (Fouillet & Morvant-Roux 2018). Here, several studies show that a very large number of accounts are either dormant, or used only to channel social benefits  (Goedecke et al. , 2018; Jos et al. , 2011; Lyer, 2015). This may explain why people do not need ATMs, but the reverse causality is equally valid: it is likely that the absence of ATMs discourages the use of accounts. In early November 2016, not only were there insufficient ATMs to support the influx of users who had to withdraw the new denominations, but many ATMs quickly broke down. ATMs had to be upgraded to distribute the new denominations.
, 2018). Several lucrative incentives, such as referral and transactional rewards, encouraged customers to start using mobile payments  (Vashistha, Anderson & Mare, 2019). RBI data show the evolution of transactions carried out via mobile bank applications and UPI applications (wallet-based mobile payments are not considered in these data). From October 2016 to January 2020, mobile phone transactions exponentially increased (from 72. 6 million transactions to 1,440. 3 million) (Figure  4 ). While the number of mobile banking transactions has risen almost twenty-fold over the past three years alone, the average mobile banking transaction amount was divided by four (from 14,355 rupees in October 2016 to 3,620 in January 2020). From July 2017, RBI changed its methodology to account for mobile transactions.
For each cited organization, respondents provided a qualitative description of their link with it. Based on the "science, practice and social movement" dimensions of agroecology  (Wezel et al. 2009) , we estimated the relative importance of activities related to research, advocacy (including policy influence and awareness rising) and practice (including production and marketing) for each organization. Detailed calculations and analysis are provided in Online Resource 2. We also classified the modes of actions for each organization along a five-level scale from "incremental" to "transformational" following  Gliessman (2016). We used MaxQDA (VERBI Software 2019), to code listed organizations and types of ties mentioned in the network assessments. We defined the network boundary to include the assessed organizations ("zero-order") and their firstorder listed organizations, plus some second-order ones where the respondents specifically mentioned them.
Acknowledgements We thank the staff of the organizations promoting agroecology in Senegal who kindly answered our questions and performed the network assessments. We thank  Carla Inguaggiato of the Centre for Development and Environment in Bern  for her initial insights on social network analysis methods. We also thank two anonymous reviewers for their very insightful comments on the first version of the manuscript. This research was funded by the  Swiss National Science Foundation (SNSF) , Grant Number  176736  "AgroWork".
Data Collection and Analysis The authors collected data at Ral and Fed from 2015 to 2018. Observing Ral and Fed in routine, emergency and critical situations helped triangulate data, including interviewees' claims about their emotions as compared with their actual experience on the field. For instance, an interviewee once claimed 'craving for opening one's heart to the team' when meeting one of the authors for the first time. The observation of the interviewee's actual practices and interactions with their subordinates (including the team) helped refine the meaning of such a claim. Finally, archives have played an important role in us better understanding the cultural legacy, events (particularly past critical situations) and past interactions that shaped the teleo-affective structure of Ral and Fed.
Biomass feedstock supply Biomass-based biofuel production particularly bioethanol from sugar byproducts -molasses and bagasse, and biodiesel from Castor and Jatropha are the key strategic directions outlined in Ethiopia's biofuel strategy. Moreover, utilizing crop residues, like corn stover for bioethanol development has raised hopes due to their huge abundance and cellulosic composition. Therefore, the feedstocks examined in this case study are sugar cane molasses, bagasse, corn stover, jatropha, and castor. Since sugar industries are the only place where molasses and bagasse are generated, their annual production is estimated based on sugar production. The Ethiopian Sugar Corporation website ( Sugar Corporation, 2019 ) was used to collect sugar production data of the existing operational and under construction sugar factories.
The under-construction factories are considered since they are expected to start operation soon based on the government plan. The recent operational sugar factories' experience was used to estimate the amount of molasses and bagasse generated from the envisaged sugar production of the new factories. As the sugar factories usually undergo maintenance in July and August, the generation of molasses and bagasse was assumed to be zero over these two months. The amount of corn stover generated was estimated based on the corn production in each zone and residue-to-product ratios -RPR (t. t -1 ). The latest corn production data of 2019/20 was taken from the Ethiopia Central Statistical Agency website ( CSA, 2020 ). Here, only the top corn producer zones of the country were taken as a candidate supply locations of stover.
The amount of jatropha and castor seed produced was estimated based on the land area (ha) dedicated to their cultivation and the yield in terms of ton per ha (t. ha -1 ). Some stud- ies have revealed data on the amount of land availed for jatropha and castor plantations in some zones of Ethiopia for the local and international investors  ( Berhanu et al. , 2017 ; Lakew and Shiferaw, 2008 ; Salehu et al. , 2015 ). Therefore, the land area data from the studies were used to estimate the feedstocks production amount. The present case study assumed the average yield of 5 t. ha -1 ( MoMPNG, 2018 ) to estimate the amount of jatropha produced. On the other hand, the castor seed yield was assumed to be 1. 61 t. ha -1 based on the FAO data  ( FAO, 2018b ). The amount of jatropha and castor was assumed to be double after 10 years based on the forecast from experts in the Ethiopian Ministry of Agriculture.
Biorefinery technologies, capacities, and costs In this study, biochemical conversion with acid pre-treatment and thermochemical conversion through gasification were the two biorefinery technologies considered for bioethanol production. Whereas for the biodiesel production, the conventional homogeneous base-catalyzed transesterification using KOH and the emerging heterogeneous base-catalyzed transesterification using  CaO ( Marinkovi ć et al. , 2016 )  were considered as the biorefinery technologies. These four technologies were chosen based on the biomass feedstocks, economic and environment conditions  ( Berhanu et al. , 2017 ) , and the existence of sufficient technoeconomic data for analysis. The upper production capacity of the biorefinery was determined by considering the highest anticipated biofuel demand within the planning period.
These capacities were chosen based on the processing capacities of the biorefineries and the demands in the respective zones. The investment cost was estimated based on the data found from one of the oil distributors in Ethiopia and Chilton's law equation was used to determine the investment cost for the six capacity levels. Table  14  of Supplementary material-1 presented the estimated investment cost for the six capacities. Based on the case study area features, only road transportation mode was assumed for transporting all materials within the BBSC. The bioethanol transportation cost data from the Ministry of Transport was taken and used to estimate the biodiesel and glycerol transportation costs. The transportation costs of the biomasses, preprocessed biomasses, biofuels, and co-products are provided in Table  15  of the Supplementary material-1.
These visits had durations between 1 and 4 h and were mainly guided by the interviewed persons, sometimes with support of technical experts. The data from these field visits, mostly in form of photos or notes, were discussed after the visits by the authors and provided additional insights especially regarding the amounts of waste valorised, the innovativeness and technology-readiness-levels (from pilot-scale to full implementation). The primary data were triangulated with secondary data from desk research, that is, academic articles if available, online articles, videos and internet sites of the companies, and internal documents such as reports or presentations directly received from the companies. Interview and secondary data were transcribed and analysed according to the content method, which relies on an analytical interpretation of data and is used to make 'replicable and valid inferences from texts'  (Krippendorff, 2004, p.
18; Berg, 2009). A hand-coding was done for each case (within-case analysis). This was done by the leading author and then verified and discussed with the other author. In a first step, general features assigned to the businesses were studied. This was done to provide first insights and illustrate major issues for each case regarding its main actor(s), resources and transformation processes, value proposition, key partners, customers and strategic approach (Table  2 ). Next, data were analysed regarding the more specific innovation elements as main focus of this work. For processing this coding, the theoretical framework (Figure  1 ) was used as basis. T A B L E 1 Here, we first identified the main trends and drivers at a macro-level and stakeholders involved. Second, the contents regarding the origin, evolution and key milestones of the businesses over time, and in particular their main internal drivers to start or further develop their business, were identified.
Data collection Our data collection accumulated varied and rich sources of evidence  (Yin, 2017). It generated primary data through internal group discussions, individual formal interviews both within the firm and with external partners, as well as informal dialogues with the firm's managers. It also included secondary data, including several confidential documents (e. g. , strategy documents, internal memos, examples of contracts, customer satisfaction surveys, project proposals, pricing methods, job definitions, press releases). This material was collected, analysed and compiled in various forms, illustrating the different methods of its production, use and transformation over time by the parties: minutes of meetings, grids filled in by practitioners to review customers' behaviours, descriptions of market segments, practical tools developed by the firm, and academic publications.
The general contractor is responsible for a large part of construction activity in France and housing projects are recurrent and represent the leading activity at the European level. Multiple cases studies could provide a more in-depth understanding of the phenomena than a single case. However, CLC-kitting is still in its infancy and the studied project is one of the first experimentations of the general contractor. Therefore, it is not yet possible to have more feedback from additional projects. Data were collected from January 2019 until August 2019 when the project was finally delivered to the client. Technical and architectural trades were carried out during this timeframe and this study focuses only on materials subject to CLC-kitting-based approach. Multiple data sources and collection methods were used to enhance the validity and reliability of the paper.
For each part of the case study, data collection and analysis were conducted as follows: Starting with CLC-kitting organization, the main sources were the following: review of site access plans, on-site visits, participatory observations, and semi-structured interviews with construction managers and logistics planners. CLC was visited three times while a weekly point was made with a referent on the site to discuss the CLC-kitting progress. Semi-structured interviews were conducted with four construction managers on-site and two logistics planners on CLC. Interviews focused on identifying both the benefits and disadvantages of CLC-kitting supply system. Data analysis, therefore, allowed recommendations to be provided, at both strategic and operational levels, which can be leveraged in similar projects for better implementation.