dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, a case study allows readers to test learned lessons in other cases or situations, leading to transferability, which is different from the generalization that occurs in quantitative studies  [52]. For CLC-kitting costs, data were collected through review of project documents, including CLC and suppliers invoices, prices lists, and project financial balance. An identification of constructions logistics costs shows their distribution and evolution along the supply chain from ordering to packaging waste evacuation. Finally, the findings of this paper are discussed within similar literature while identifying the next perspectives of this research project.
As the work progresses, the construction management team releases partial deliveries by indicating to suppliers, the CLC as delivery address. For consumable materials or materials not anticipated during the design phase, construction managers can directly place orders on an online platform, which is connected to the general contractor suppliers' database. CLC then receives different suppliers' deliveries and a team is responsible for the preparation of the kits before being stored. Two days before being used on site, the construction team requests kits delivery. CLC prepares the truck delivery, as shown in Figure  4. Once arrived at the site, a team is responsible for materials handling inside the building and dispatching the kits items in students rooms. During the dispatching, the team checks the delivered materials based on a sheet prepared in advance for each room.
Handling and dispatching has been made easier thanks to the early commissioning of the building elevator. At the end of the team intervention, waste related to kits packaging is sent back to the platform, allowing the truck to be used on their way back. In an early phase of the project, the general contractor sales department order the total required quantities. As the work progresses, the construction management team releases partial deliveries by indicating to suppliers, the CLC as delivery address. For consumable materials or materials not anticipated during the design phase, construction managers can directly place orders on an online platform, which is connected to the general contractor suppliers' database. CLC then receives different suppliers' deliveries and a team is responsible for the preparation of the kits before being stored.
study was approved by the Comité de Protection des Personnes du Sud-Ouest et Outre-Mer 4 N° AM-36/1/CPP15-024. The database of the Palustop prospective study was anonymized and declared to the French regulatory commission (Commission Nationale Informatique et Libertés, CNIL, n°917186). The passive follow-up database was anonymized and declared to the Commission Nationale Informatique et Libertés (CNIL) (authorization N°2135463). Samples collected by the National Reference Centre were registered by the French Ministry for Research (declaration number DC-2010-1223; collection Nu2).
1). Product innovativeness reflects the extent to which the entrant product is new relative to other existing products in the market  (Danneels & Kleinschmidt, 2001). Product line breadth is the variety of products offered by the entrant in the product category at entry time  (Bayus & Putsis, 1999). We test our hypotheses using data from firm entries into four product categories in hightechnology consumer durable industries in the U. S. We examine three important empirical questions. First, what is the complementary impact of marketing and R&D capabilities on firms' market entry timing, product innovativeness, and product line breadth? Second, to what extent does entry timing mediate the relationship between marketing and R&D capability complementarity and product line breadth/product innovativeness? Third, how do entry timing, product innovativeness, and product line breadth affect firms' survival in their product category? By addressing these questions, our study makes three primary contributions.
Sampling and data collection To test our hypotheses, we examine data of firm entries into the U. S. market for portable media players, portable computers, digital cameras, and smartphones across a 26-year window. The census period end date was set to retrospectively ensure no further new market entries were apparent in these product categories. These product categories were suitable for the study as they were introduced relatively recently so that the requisite data are still available; still, the industries are mature enough that market entries across the timing spectrum could be tracked  (Schoenecker & Cooper, 1998). In addition, the similarity among our four product categories enhances the internal validity of our study  (Srinivasan, Pauwels, Silva-Risso, & Hanssens, 2009). To classify market entrants in our sample, the pioneer for each product category was identified using news archives, company histories, financial reports, and online databases  (Wang, Chen, & Xie, 2010).
The main online news archive database used was Nexis, which provided access to leading news sources such as Business Wire, PR Newswire, and The New York Times, among others, for product announcements containing relevant search terms. When the pioneering product in a new product category was found, the entrant was classified as the pioneer and the date was recorded. The subsequent entrants were then tracked forward for an average duration of ten years following the first entry in each category. Overall, the dataset includes 142 market entries across the four product categories.
Dependent variables The performance of each entrant was determined by its survival duration, operationalized as the elapsed time (months) from the date the firm entered to the date that it exited the product category  (Yao et al. , 2020). The exit of each entrant was verified through archival records derived from the Factiva database, company websites, press releases, and financial reports. We used the date of data collection (August 2020) as the cutoff date for firms that are still active in the product categories to date. For failed firms, the exit time is derived from (1) the announcement date of withdrawal from the market or (2) the announcement date of firms being purchased or dissolved. For firms that did not make official announcements of exits, we traced product announcements and reviews each year from various sources until we were satisfied that the firm is no longer active in the category.
To control for the effects of firm resources and their inventive inclination we include the number of new patents applied by firms in the year of entry ( 0 ) derived from USPTO. Considering firms' tendency to seek new product-market opportunities and to create change in its market we controlled for fixed asset intensity in the estimation; measured as fixed assets divided by total assets derived from COMPUSTAT  (Segev, 1989). Market concentration was controlled for using the measure derived from the share of industry sales produced by the four leading firms at entry year  (Lee et al. , 2000). These data were derived from the U. S. Census Bureau report. To control for the higher likelihood of success of diversifying entrants (de alio), we included dummy variables to distinguish between them and firms that entered the industry as a start-up (de novo).
The fraction of electrical power converted to thermal energy and dissipated in the product was calculated using Eq. (  4 ), with ṁpo as the mass flowrate of the product in the ohmic heater, C pp is the specific heat of the product, T pi is the inlet temperature of the product, and T po is the outlet temperature of the product. E c = n c × m c × C pc × (T s -T c ) (2) E p = n c × m p × C pp × (T s -T p ) The energy required for final cooling prior to aseptic packaging was calculated using Eq. (  5 ), where ṁwc is the mass flowrate of cooling water, C pw is the specific heat of water, T iw is the inlet temperature water, and T ow is the outlet temperature of water in the heat exchanger: Software. EDraw Max (ver. 9. 1, 2018; Sheung Wan, Hong Kong) was used for the representation of the appertization and ohmic heating flowcharts.
The LCA analysis was performed using the professional SimaPro software (ver. 8. 1. 0 Analyst) and adapted Ecoinvent 3. 2 database. LCA methodology. LCA is a standardized and widespread methodology to study environmental consequences associated with food  42. The LCA procedure is outlined by ISO 14040 and ISO 14044  43. A comprehensive LCA comprises four coherent and iterative phases (1) goal and scope definition, (2) life cycle inventory, (3) impact assessment, and (4) interpretation of the results. Definition of the goal and scope. The objective of this study was to perform an attributional life cycle environmental assessment of CTwJ processing and packaging upon appertization and ohmic heating systems. The functional unit (FU) was considered as 1 kg of CTwJ, which is a single reference product, requiring 1.
The background's emissions coefficients, such as the emissions of electricity generation and distribution, were adapted from the Ecoinvent database. Life cycle impact assessment. In the third phase of an LCA study, impact category selection and characterization are mandatory; however, normalization and weighing are optional  46. The IMPACT 2002+ methodology was applied as the baseline impact assessment methodology, given its inclusion of various impact and damage categories. It is also a combination hybrid IMPACT 2002, Eco-Indicator 99, CML, and IPCC.
Uncertainty analysis of the impact assessment. There are some sources of uncertainties affecting the LCA results, including data quality, scenarios, and mathematical models underlying the impact assessment methods  44, 47. As shown in Table  1 , studies on the LCA of tomato processing have employed different impact assessment methodologies (see Table  1 ). In this study, an uncertainty analysis was performed to investigate the effect of impact assessment selection on the LCA results of the case study. For this purpose, six impact assessment methodologies, i. e. , EDIP 2003, CML-IA baseline, EDP 2013, ILCD 2011 Midpoint, ReCiPe midpoint, and IMPACT 2002+ were considered to be evaluated by LCA. The analyses were conducted using SimaPro V8. 0. 3. 14.
[39] ). Publicly available city dashboards seem largely concerned with visualising data but less with providing context -this is especially the case with real-time dashboards (those that focus only on providing a screen of real-time data e. g. London Dashboard 2  ). and general data dashboards (such as the Dublin Dashboard, discussed further below). Context is more than just cross-referencing data and must include information about how the visualisations are created. Decisions about data collection, data processing and design need to be communicated  [49]. Decisions taken before and while the data is visualised affect what information can be transferred. Research has identified extensive design guidelines  [41, 64]  and principles for decision-makers  [7]  which are an essential first step to begin to facilitate sense-making.
1) Diary Study For the diary study, participants were asked to engage with two specific dashboards (Dublin Dashboard 3  and Edmonton Citizens Dashboard 4  ) in their own time over the course of 10 days. Engagement times (times spent interacting with the dashboards) ranged anywhere from 30 to 120 minutes with the average being 50 minutes per participant. The Edmonton Citizen Dashboard was randomly chosen from a cluster of Performance Dashboards  [60]  whereas the Dublin Dashboard was deliberately chosen from a data portal cluster  [60]  as we considered an Irish dashboard to be of particular interest and potentially more relevant to users. We selected these dashboards in order to minimise participant bias by avoiding any potential conflict of interests which may arise when people are looking at a dashboard of their city.
Of the people contacted, 13 agreed to participate in the study. Ages ranged from mid-20s to mid-60s, and both men and women were represented. The participants were evenly split between Agriculture (3), Transport (3), Health (4), Environment (3). Participants were encouraged but not required to take part in all methods (diary, interview, focus group). However, all participants were required to familiarise themselves with the two city dashboards (Dublin Dashboard and the Edmonton Citizens Dashboard, Figure  1 ). Prior to their involvement with the study, 50% of our participants were not aware of city dashboards while one person indicated being familiar with the Dublin Dashboard. This triangulated  [46]  method of data collection resulted in very rich and varied data. A first analysis focused on identifying the types of statements participants made using Speech Act Theory  [52]  which were then assigned a polarity 5  based on the sentiment  6  expressed in the statement.
Customer-centric strategies such as mass-customisation rely heavily on customer involvement such as co-designing products and services. In this sense, companies struggle to provide a better shopping experience by enabling technologies and Industry 4. 0 practices. Online configurators are an example of these technologies that exist since decades but whose role in increasing value for the customer and the company is growing. This paper addresses the integration of sustainability preferences into configurators, with a focus on the 3D-printing domain. The basic ideas of this research work are, on one hand, that configurators can be enriched with readable and meaningful environmental indicators to increase customers environmental awareness based on the measurement of the impact of their choices, and on the other hand, such historical data about customer choices and orders available within configurators databases could support a more sustainable management of products and services portfolios.
When we talk about product configurators today, most certainly we mean digital applications that can be used on websites by a very large number of users at the same time to configure the respective "perfect" product. A product configuration is a knowledge-based system that supports the user in creating customized products by specifying modules and options. The conceptualisations of the configuration knowledge can be classified to (a) rule-based, (b) model-based, and (c) case-based approaches. Each approach relies on a different ontology that is required to represent the domain knowledge and describe the object types (classes) and the relations among object instances. In 2013, the Austrian media specialist CyLEDGE published an analysis  [8]  based on more than 800 online configurators worldwide.
Research Methodology A case study-based research is conducted in order to answer the following question: How to implement a customer-driven sustainability improvement strategy? The basic idea of this research is that configurators can be enriched with readable and meaningful environmental indicators to support two objectives: (i) to increase customers' awareness of the environmental impact of their choices, and, ultimately, (ii) to improve products and services portfolio management based on historical data about customer choices and orders available within configurators databases. The case study was developed in the 3D-printing area and specifically for the 3DBenchy model. 3D-printing has been put forth as a key driver of customisation and masscustomisation. Furthermore, it exhibits a high potential to support rapid prototyping of customised products and thus helps to speed-up production ramp-up and time-to-market.
The Economic Perspective of 3D-Printing Among the most impacting parameters on the comparative costs of 3D-printing processes is used material  [12]. In this sense, both filament coils and resins have a price that varies depending on the material chosen and the manufacturer. From flexible filament material through metal to traditional PLA, the price range is significant. The amount of raw material used depends on many printing parameters such as the percentage of filling, the thickness of the outer layer, etc. Fig. 1  shows a 3D-printing model on Ultimaker Cura 4. 0. 0 [13] software indicating material cost (e. g. , with a required quantity of 13g of PLA; the material cost of the model is 0. 26 €). However, to obtain the total cost it is also necessary to take into account the electricity consumed, the number of hours of machine usage, the manpower, the failure rate, the maintenance time of the machine, the realisation of the part in CAD, etc.
The Environmental Impact Assessment and 3D-Printing Life Cycle Assessment (LCA)  [17] , which appeared in the 1960s, makes it possible to quantify the impacts of a "product" (i. e. , goods, service, or process), from the extraction of the raw materials that make it up to its safe disposal, including its distribution and use (see Fig. 3 ). The flows of materials and energy entering and leaving at each stage of the life cycle are inventoried, and an exhaustive balance is made of the consumption of energy, natural resources, and emissions into the environment. These balances of incoming and outgoing flows are called "Life Cycle Inventories (LCI)". These consumption and emission data are used to evaluate the potential environmental impacts of the product under study. These depend in turn on the functional unit considered for the study.
The principles of LCA are defined by the international standards of the ISO 14040 series  [19]. The results of an LCA are presented in the form of indicators of potential impacts (e. g. , "kg of CO2 equivalents for the greenhouse effect", "kg of H+ equivalents for acidification", etc. ) and physical flows (e. g. , "MJ of non-renewable energy", "kg of common waste", etc. ). These potential impact indicators are calculated from the LCI data and characterisation models that allow the evaluation of these environmental impact indicatorsfrom the LCI data. For example, the LCI data relevant to the calculation of the "global warming" impact indicator are the emissions of greenhouse (CO2, CH4, N2O, etc. ) into the air. The characterisation model used for the potential impact indicator on global warming is generally that of the IPCC1, which associates a global warming potential (GWP) calculated in kg CO2 equivalent with the emissions of each gas.
Configurator Development The architecture of the configurator is described in Fig. 5. A Guided User Interface (GUI) supports the customer in configuring the 3DBenchy model. Main options implemented so far include quality level and filling rate selection. The configuration is stored in a local modelling database with several attributes reflecting customer choices. The configuration GUI feeds a model to calculate the environmental and economic indicators. These are shown in a result GUI. The calculation of environmental indicators, CO2 for instance, is supported by an extraction from an environmental database. The development relies on EXCEL VBA which is a programming language that allows the use of Visual Basic code to execute the many features of the EXCEL Application (a set of macros). To make the configurator more user-friendly and to be able to illustrate the concepts, a set of forms were developed to support the configuration process by the user.
The higher the fill rate, the greater the amount of filament used. The fill rate will also affect the weight of the part. Help is available throughout the configurator to assist the user, who does not necessarily have any knowledge of 3D-printing, in his/her choice of the various parameters. These aids are symbolised by the question mark buttons. The carbon emission assessment is based on the impact of electricity consumption and of plastic. CarbonDB data was used for assessing electricity consumption impact  [22]. CarbonDB is an open collaborative LCA database (of activities, products and services) focused on climate and energy impacts. It is also an open-source tool for consulting these data. Depending on the time of use of the 3D-printer and the electrical consumption of the printer, the carbon impact of 3Dprinting can be determined.
This witness the relevance and reliability of the results from the case study. Furthermore, the proposed model is also compatible with the technologies covered by Ultimaker Cura software. Ultimaker Cura is a professional slicing software designed to take advantage of the full potential of the printers and provide a reliable and smooth printing experience. This software, once the slicing is done, returns the printing time and the quantity of raw material used. These two parameters are the two variables used in our configurator. Thanks to this slicer, as can be seen in Fig. 8 , we were able to show that on the printer used only 15 3DBenchy could be printed at the same time. This number is, therefore, the maximum size of our batches. While the case study has shown the applicability and reliability of the economic and environmental assessment of customized solutions, several improvement avenues are still to be explored.
Sample and Data To test the hypotheses, this study gathered primary data through an online survey among Swiss entrepreneurs in summer 2019. Switzerland is an adequate country context for the study because it has a favourable entrepreneurship ecosystem (ranked second in the world according to the Global Entrepreneurship Index 2018 1 ) and fosters social entrepreneurship through different organization such as the SEIF foundation 2 or the Schwab foundation 3 , thus, assuring the presence of a broad number of different types of entrepreneurs. The authors contacted entrepreneurs of firms that were listed on large Swiss entrepreneurship platforms such as startup. ch, membership lists from the impact hubs, lists of university spin-off such as those from Eidgenössische Technische Hochschule Zurich (ETH) or the Ecole Polytechnique Fédérale de Lausanne (EPFL), award lists such as those from the SEIF foundation award, Schwab foundation award or Swiss Tech Award, and venture capitalists' investments lists such as those from Venture Kick or Credit Swiss Entrepreneur Capital AG.
Sample and Survey Administration This study was carried out in the Indian e-commerce context. Since India stands second in the world in terms of internet penetration (TRAI, July, 2017), there remains an enormous growth potential for the e-commerce sector in India. Thus, the Indian e-commerce environment looks like a perfect situation for investigating the impact of VCC on repurchase intention among e-commerce consumers. Accordingly, responses for this study were collected from the regular e-commerce consumers, i. e. online shoppers in India. To ensure that data collection was done from regular online shoppers, we asked the following qualifying questions: "Do you shop online?" and "During the last six months, how many times did you shop online?" Only respondents who answered "yes" to the first qualifying question and had shopped online more than twice during the last six months were allowed to complete the remaining part of the survey.
The frequency of purchase via a given channel indicates consumers' experience in using the channel  (Ashley and Leonard, 2009). A pilot study with online retail experts showed that consumers shopping at least twice during a six-month period through online channel are considered regular online customers. When conducting the survey, participants were suggested to respond the questions based on their recent experiences with any one online retailer  (Cretu & Brodie, 2007). More specifically, following retrospective experience in survey administration method, respondents were first asked to explain their experience with the retailer followed by the survey questions  (Bougie et al. , 2003). We used an online structured questionnaire for data collection. Amazon Mechanical Turk (Mturk) was used to administer the survey.