dataesr/scientific-paragraphs-categorization huggingface dataset:
Although incorporating the three dimensions of sustainable performance is complicated  (Bom et al. , 2019) , existing research does not necessarily account for the trade-offs between economic, social and environmental performance  (Aktin and Gergin, 2016; van Loon et al. , 2021). Studies have focused on a single performance measure  (Lee and Raschke, 2020; Wagner, 2015; van Loon et al. , 2021)  rather than holistically considering the social, economic, and environmental performance, which we have addressed in our study. RQ3 was answered using focus groups among the SMEs' participants. First, various issues and challenges are identified in Table  7  and subsequently improvement measures are derived in Table  8. The issues and challenges that are derived from the focus groups exactly match with the findings from the survey data analysis.
Conclusion Although from prior research, there is evidence of SMEs achieving superior environmental performance by adopting CE, economic and social performances are not assured  (Türkeli et al. , 2018; Katz-Gerro and Sintas, 2019). This motivated us to undertake empirical research to reveal the means for achieving higher sustainability performance (economic, environmental, and social) through CE adoption. Data was gathered from around 100 SMEs from each of the four selected countries -Greece, France, Spain and the UK using a survey to study the current state of CE adoption, and subsequently, focus groups were organized which involved SMEs owners and managers, policymakers, SMEs' customers and suppliers, in each country to derive means for improving the impact of CE on sustainability performance.
Acknowledgments This research has received funding from: (1)  European Regional Development Fund  (reference  OC12R16P0367 ) for the project Support the  Shift Towards a Low Carbon Economy in Greater Birmingham and Solihull Local area Enterprise Partnership ; ( 2) MARIE SKŁODOWSKA-CURIE ACTIONS, EU Horizon 2020  (Agreement No. 788692 ) for the project  Enhancing Sustainability Performance of Small and Medium sized Enterprises' Supply chain.
The development of an aircraft industrial system faces the challenge of integrative requirements validation with de-correlated modelling languages and distributed proprietary formats. This paper specifies an ontology-centric industrial requirements validation based on a cognitive digital twin approach, aiming at addressing the potentials of utilizing MBSE ontology integration for different models and leveraging a top level ontology BFO as a semantic core to integrate cross-disciplinary requirement validation, heterogonous models and simulations towards an optimized digital consistency, interoperability and reusability, with an example of such requirements validation presented.
Requirement validation Requirement validation starts from requirement engineering including formalization, classification, etc. Concerning crossdisciplinary collaboration, a methodology of requirement validation cross different stakeholders is employed, which enhances communication and collaboration between client and engineer  (Moketar et al. 2016). Besides, a systematic modelbased approach for effective requirement validation with several stages from behavior specification to simulation and testing are introduced, which but didn't achieve an efficient integration  (Korssen et al. 2018). Later with MBSE formalism, System Modeling Language (SysML) has an Activity Diagram validating requirements for early phase system development. However, it lacks a formal semantics for direct validation  (Andrade et al.
2019), while for a complex cross-disciplinary case with heterogeneous formalisms, e. g. heterogeneous data structures and language syntaxes of modelling languages, an MBSE ontology integration approach leverages meta-models, like GOPPRRE ontology -Graph, Object, Point, Property, Role, and Relationship with extensions  (Lu et al. 2020a ). For technical integration, it refers to the ability of an ontology to generalize technical domain semantically. An instance of technical integration is the ontology of manufacturing domain. Some endeavors are made towards a common semantic description in manufacturing domain, e. g. , a preliminary upper ontology MASON for manufacturing  (Lemaignan et al. 2006) and GRACE Ontology (Foehr et al. 2013)  addressing quality optimization of discrete manufacturing.
GOPPRRE ontology As shown in Figure  4 , MetaGraph 2. 0 ontology  (Lu et al. 2020a)  enables the development of meta-models and builds models based on KARMA language  (Lu et al. 2020b ). In Figure  4  As depicted in Figure  5 , when the architecture models are developed using KARMA, all the architecture models are generated into OWL based ontology which is defined based on the GOPPRRE ontology  (Lu et al. 2020a ). The GOPPRRE ontology is defined under the descriptive information entity of BFO and IOF ontology as an application ontology. After the.
Architecture, roles and data flow The data flow begins with the requirements engineer creating/importing requirement items in the Requirement Management System (RMS). Based on the requirement ontology, corresponding requirement individuals are generated in an OWL file. Then, the OWL including industrial requirements items are integrated into BFO with the application ontology containing domain knowledge of orbital join process, and with GOPPRRE ontology including formal representation of process architecture models and simulation models. After translating individuals from OWL into corresponding dataset via a middleware, simulations can be driven, which takes BFO ontology as single source of truth of data. Simulation results are stored in repository, which can be further utilized via an analyzer for visualization, as well be validated in comparison with requirements defined in RMS.
in this paper is funded by the EU H2020 project QU4LITY (825030) -Digital Reality in Zero Defect Manufacturing. REFERENCES Andrade, E. , Maciel, P. , Callou, G. and Nogueira, B. (2009). A methodology for mapping SysML activity diagram to time Petri net for requirement validation of embedded real-time systems with energy constrains. 2009 Third International Conference on Digital Society, 266-271 Arista, R. , Mas, F. , Oliva, M. and Morales-Palma, D. (2019).
VG's effort is a radical innovation that marks a significant departure from the single-use propulsion technologies produced in the past by government agencies such as the National Aeronautics and Space Administration (NASA) to carry career astronauts to space. To build our case, we assembled an archival data set from diverse sources where the firm and its stakeholders discussed the crash, including firm and client tweets, blog posts written by company executives, corporate website and video content, press releases, media articles, and government agency briefs. We find that, in the aftermath of catastrophic innovation failure, industry participants interpret the event in starkly different ways, in terms of what happened, why, and who owns the failure-yet all try to sustain the legitimacy of the nascent industry.
Research Design and Setting We used inductive qualitative research methods focusing on a single in-depth case: the crash of VG's Space-ShipTwo vehicle during a test flight on October 31, 2014. These methods are well suited to answering our research question for several reasons. Inductive methods facilitate exploration by allowing the researcher to dive deeply into the phenomenon. They are especially useful in areas where categories and processes are not yet well understood and where the researcher aims to build and elaborate, rather than test, theory  (Edmondson and McManus 2007). A case-based approach enables the researcher to be embedded in rich empirical data and to understand the phenomenon from the perspective of its protagonists  (Lincoln and Guba 1985). In particular, single cases often prove to be "unusually revelatory"  (Eisenhardt and Graebner 2007, p.
Data Collection Firm-Level Data. We took the 2014 crash as a focal event and collected data before and after its occurrence. We placed emphasis on the month-long period following the crash, from October 31 to November 30, 2014, as it contained the most intense engagement by VG and its stakeholders surrounding the failure. 2 This period includes several key dates. On November 12, 2014, the National Transportation Safety Board (NTSB), the federal authority in charge of investigating the crash, concluded its on-scene portion of the investigation and shed considerable light on the apparent cause of the crash. On November 21, 2014, VG published a new website that evidenced a shift in the firm's organizational identity. We assembled a rich archival data set from publicly available sources, including stakeholder and company tweets, executive blog entries, corporate website content, press releases, media articles, government publications, and multimedia.
Our data provide a relatively comprehensive array of evidence of what the firm and its stakeholders said publicly leading up to and in the aftermath of the failure. Because our data contain realtime accounts of the event, they allowed us to minimize the risk of retrospective bias. Our data (described in Table  1 ) reflects the views of eight categories of stakeholders and thus reveals the diversity of individual and organizational industry participants: VG executives, partners, investors, clients, space organizations, space experts (including former astronauts, commercial test pilots, and engineers), members of the press, and federal authorities. Social Media Sources. We performed a preliminary review to assess which social media platforms VG used most frequently to connect with stakeholders and found that Twitter was the platform of choice.
We gathered all tweets published during our data collection period on seven accounts: VG's corporate account (@virgingalactic) (73 tweets), founder Sir Richard Branson's account (@richardbranson) (200 tweets), and the accounts of five clients who frequently tweeted about VG (37 tweets). They include Sir Trevor Beattie (@trevorbmbagency), Yanil Silver (@yaniksilver), Vasily Klyukin (@VKlyukin), Namira Salim (@namirasalim), and P. J. King (@pjkng). Blog Posts. Branson kept a blog on VG's parent company website  (www. virgin. com) where he discussed company business at length. We gathered the full text of the two VG-related posts he published in our data collection period. Company Website. The content and structure of a firm's website provide evidence of its activities and priorities. Following a catastrophic innovation failure, website content is likely to convey the firm's preferred interpretation of the event, as well as legitimacy-sustaining statements.
Using Archive. org's Wayback Machine, a tool that provides historical archives of a website on a regular basis, we gathered content (including text, photographs, videos, and site structure) on VG's website (www. virgingalactic. com) during our data collection period. On days when more than one archival version of the website was available, we collected the last version. Company Press Releases. Company press releases are official statements meant to be picked up by media outlets. In the aftermath of a catastrophic innovation failure, press releases can be expected to convey the firm's preferred interpretation of the event, as well as legitimacysustaining statements. We gathered the five press releases published by VG during our data collection period. Traditional Media Sources. We gathered online news articles and newscast transcripts on VG published during our data collection period.
We restricted our data collection to content published by renowned news organizations in countries such as the United Kingdom and the United States (e. g. , The Washington Post, BBC News), prestigious media agencies (e. g. , the Associated Press, Reuters), popular science and space publications (e. g. , WIRED, space. com), and renowned news shows and channels (e. g. , CNN, CBS). Of the 534 articles and transcripts, 467 were sourced via Lex-isNexis, whereas the rest were collected manually from sources not included in that database. We prioritized pieces in which VG managers or stakeholders offered direct quotes related to the failure event. NTSB Announcements. On November 1, 2014, the NTSB announced that it would send a team to the crash site to investigate the event. We collected all publicly available data related to their investigation, including four media briefing videos from YouTube, all 35 NTSB tweets (@NTSB), and the final official report and press conference media related to the agency's findings.
Industry-Level Data. In parallel, we collected industrylevel data published in 2014. We focused particularly on articles that spoke of the potential of commercial space and that discussed industry dynamics. We also collected self-descriptive data for two major players who, although not competing in the space tourism segment at the time, expressed an interest in providing this service in the future: SpaceX and Blue Origin. We gathered all content from their websites (www. spacex. com; www. blueorigin. com) and all available press releases and preflight press kits.
Data Analysis Our goal was to understand how VG's 2014 catastrophic test flight crash affected the firm's and the commercial space industry's legitimacy. We aimed to build process theory by identifying patterns of behavior that allowed VG and its stakeholders to engage with one another's interpretations of the event and reassess the legitimacy of the firm and its industry. Industry Legitimacy. We started by reading extensively about space exploration, first as an endeavor funded by governments and public agencies and more recently as an opportunity tapped by private enterprises. We surveyed the array of segments that are taking shape in the commercial space industry, including payload delivery, space tourism, and space mining, and mapped the main players. We then explored the data for evidence of an industry identity.
Acknowledgments The authors thank the editor,  Mary Tripsas , for valuable guidance during the review process and the three anonymous reviewers for insightful comments and suggestions; and  Anca Metiu ,  Martin Kilduff ,  Davide Ravasi ,  Laura Claus ,  Elizabeth Hansen , attendees of the  UCL School of Management seminar , and the  Paris Management Research Workshop , the  SKEMA Knowledge, Technology and Organization seminar , the  ESSEC YesWeQual seminars series , and conference participants at  Strategic Management Society and Academy of Management  for feedback.
Interviews lasting between 50 and 105 min were conducted (see Table  8  for a list of anonymised participants). The interviews used a protocol that included an initial question asking informants to describe the evolution of the network over time, with subsequent questions asking respondents to discuss the problems they faced, how the problems were solved, and the benefits derived from their participation in the network. All interviews were recorded and transcribed verbatim. In addition, interview data were triangulated using different data sources, such as interviews with R&D managers and academics directly involved in the activities of the network, documentation, archival resources, newspaper articles, presentations, and online resources. Data were analyzed using open coding  (Strauss & Corbin, 1998) , specifically searching for information about the range of barriers to and enablers of UIC collaborations at the different evolutionary points in time.
Performance: Firm's innovative output We measure pharmaceutical firms' innovative output for the APIs in our sample by observing their respective U. S. patent portfolios. We rely on the Thomson Reuters Newport database, which is recognized in the pharmaceutical industry as the reference database for patents and other intellectualproperty information  (Grimaldi et al. , 2015). Patents are frequently used in the operations management (e. g. ,  Chan et al. , 2018)  and organizational learning (e. g. , Jain, 2013) literatures as a valid indicator of a firm's innovative output  (Griliches, 1990). For any given API, since the end product is essentially identical across firms  (Babar, 2019) , all patents contained in our sample introduce innovative manufacturing methods, and are thus referred to as "process patents"  (Lieberman, 1987).
We first asked an expert patent attorney, who has extensive chemical and biotechnological backgrounds, to evaluate individual patents' levels of novelty and scope by, respectively, responding to the questions "How would you evaluate the overall novelty of the process patent with respect to the state of the art at the time of the patent filing?" and "How broad are the claims of the process patent with respect to the state of the art at the time of the patent filing?" In both cases, evaluations were collected on an ordinal scale ranging from 1 (very low) to 5 (very high). Two additional experts 6 evaluated different subsamples of the total set of patents. We used their ratings to check interrater reliability. Such an approach is common in studies, which rely on primary data collected through expert evaluations (e.
Predictors: Firms' manufacturing experience Our experience measures are based on quarterly sales data for the APIs in our sample from the Intercontinental Medical Statistics (IMS) Health's MIDAS pharmaceutical sales proprietary database. This database tracks quarterly sales for almost every pharmaceutical product sold by every pharmaceutical firm worldwide and is considered to be the most reliable source of sales information in the pharmaceutical industry  (Kanavos, 2014). Given that several final dosage forms may exist for the same API, we measure sales for a focal API as the total number of kilograms of the focal API sold by a firm in a quarter  (Caves et al. , 1991). In our 10-year study horizon, we observe sales from 24 distinct corporate groups 7 -hereafter referred to as "firms. " In Table  A.
Initial familiarity To control for the familiarity that firm j has with API i at the start of our observation time window  (Lawrence, 2018) , we introduce InitFam (i,j) , which reflects the number of quarters from the date of the first market authorization that firm j receives for producing API i until the first quarter of 2005 (i. e. , the start of our timeframe). We construct this measure using the Thomson Reuters Newport database.
A C K N O W L E D G M E N T S The authors would like to thank Prof. Glen Schmidt, the senior editor, and three referees for their very helpful and insightful comments from which the paper greatly benefitted. The first two authors gratefully acknowledge the financial support from the HEC Paris Foundation and Labex Ecodec: Investissements d'Avenir (ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047).
6 Details about all three patent experts are available from the authors upon request. 7 In the Newport database, a corporate group is the parent company to which the marketer (i. e. , the company responsible for sales of a drug product in a specific market) and the patent holder (i. e. , the company that holds the rights granted by a patent) belong. 8 A quarterly retention rate of 0. 975 implies an annual retention rate of 0. 975 4 = 0. 9 or, equivalently, an annual depreciation rate of 0. 1. 9 While m could arguably vary at the API-firm level, no data are available that would enable us to derive such granular measurement. 10 All results, which are discussed but not presented in the paper, are available in Supporting Information.
, which is supported by the high correlation between firm size and R&D expenses in our data set ( = 0. 90, p < 0. 01). Hence, including both variables as controls would create collinearity issues. We thus substitute R&D with SIZE in our analyses. We obtain SIZE by log-transforming firms' assets, reported in the Standard & Poor's Compustat database. Again, the main results remain consistent (cf. TableC. 3 in Supporting Information C).
'Experience in IoT for retail customer-facing applications' was the primary participant selection criterion. Each participant was interviewed twice (for approximately 30 minutes per interview), firstly at the exploratory interview stage and secondly at the follow-up confirmatory stage (after the themes were generated). For the purpose of data collection, we conducted semi-structured interviews. We came up with an interview protocol on the basis of our theoretical framework, which we shared with our respondents prior to the interview as a quality measure to facilitate full responses  (Patton, 2002). This approach was chosen as opposed to an 'informal conversation' or 'standardised' method because it provides a better opportunity to obtain detailed answers showing the respondents' specific knowledge.
Theme development was done via a hybrid inductive/deductive approach, which facilitated finding general conclusions from the full dataset (inductive) while ensuring that the more specific research objectives would also be explored (deductive)  (Boyatzis, 1998; Braun & Clarke, 2006). Specifically, we analysed the interview data to identify the major themes emerging therefrom. Through this, we identified business considerations, devices and infrastructure, project management, privacy and data and keeping humans in the loop as the major themes encountered when implementing IoT in-store. In the second phase, we examined the themes in greater detail by iteratively reading and comparing them. We also rechecked the consistency of our coding with the aim of classifying and organising our subthemes across the interviews.
Limitations and Future Research Direction As with all empirical studies, the current study had inherent limitations. Our study was an exploratory field study, and we collected empirical data through semi-structured interviews. While our findings shed light on the implementation of IoT in the service industry, they cannot be generalised without caution. Primarily, we propose their validation within a similar context, and from there, their extension to theory, as is often the case with qualitative studies, which can in turn be validated and extended to different contexts  (Davison & Martinsons, 2016, p. 247). In addition, we would welcome further research into the concept and effects of homogenising the device infrastructure both in terms of risk and quantifying benefits, which we think is essential but is outside the scope of our study.
Blockchain technologies have severely disrupted many industries  (Grover et al. , 2019) , enabling new digital business models with enormous potential but high uncertainty regarding future success  (Ying et al. , 2018). Finally, the data indicate that blockchain and the surrounding start-up environment of this technology are indeed massively maledominated with a minimal female presence not only in real businesses but also in the media and public debate  (Elsbach & Stigliani, 2019). We used three data sources to build our dataset. First, we used Crunchbase to identify start-ups built around blockchain technologies. Crunchbase, an open-source database maintained by TechCrunch, contains information about start-ups, investors, founders, trends, milestones, and other related information, including the business or revenue model of these ventures.
Introduction The 2030 agenda for sustainable development from the UN underscores the importance of achieving sustainable use of natural resources to avoid depletion  (UN, 2015). Additionally, sustainability has been identified as a potential generator of competitive advantage  (Mwangi et al. , 2021). Although large multinational organizations have started introducing sustainability management  (Lii and Kuo, 2016) , SMEs tend to be less engaged  (Johnson and Schaltegger, 2016). The Carbon Majors Report 2017, compiled from a database of publicly available emissions figures, estimates that 25 corporate and state-owned producing companies are responsible for 51% of the global industrial GHG emissions  (Carbons_Majors_Database, 2017). Unfortunately, it is difficult to find accurate percentage figures for SMEs in SMEs.