dataesr/scientific-paragraphs-categorization huggingface dataset:
Descriptive features of publications The analysis of the 56 papers in our database explained in the following section shows the main characteristics of the SCM articles documenting research in which the SCT was applied. Because we considered all publications under the sustainability lens, the economic focus for our research is represented by traditional SCM studies. The following information was compiled to elucidate the range of publications we analysed: number of publications per year, list of journals, industry sectors represented and research methods applied. Using these main classifications, it was possible to create a picture of the publications from the 12-year study period.
For example, trust could emerge as a factor when developing further strategies and managing operations. Other factors can be also considered, which includes cultural (e. g. local traditions) and institutional (e. g. stakeholder engagement) elements. Some limitations were identified in this research. The first refers to the overlook of leadership practices and the empowerment of supply chain members. This lack does not deny the credibility and reliability of the data gathered, but emerge as a source for new studies. In this way, future research can consider the inclusion of these elements and improve qualitative research that relates the SCT and SCS. Also as limitations of this research, we can consider the focus on only two databases, which may have resulted in missing publications. Another limitation of this paper is the optimistic view of social capital and its benefits; however, it is possible that a relationship based on strong trust can also give rise to conflict and enable opportunistic behaviours on the part of individuals.
Gender has been among the most neglected issues by transport researchers and practitioners when focusing on designing new mobility services. Therefore, adopting a gender-sensitive approach is an ongoing challenge that has been largely overlooked in the academic literature and conventional transport planning. AV deployment and SAVs, in particular, offer an opportunity to reconsider such important issues and bring them to the centre of relevant debates and policy decisions  [34]  (CIVITAS, 2020). Equivalent gender mobility issues have been observed across different countries (ITF, Di Ciommo and Hoadley, 2021  [35] ; Clayton, 2020  [36] ), since women have been found to have a lower attachment to car ownership levels and trust in new technologies on average. Studies to date  [36, 37]  have tended to examine gender differences as a single sociodemographic variable in datasets through hypothetical Willingness-To-Accept/Willingness-To-Pay (WTA/WTP)  [36]  or actual experience of SAV use  [38]  rather than gender being the primary variable in framing the study.
• The third section is focused on respondent socio-economic characteristics. A common definition of AVs was offered to all participants at the beginning of the survey to provide essential background about the survey and to avoid any misunderstandings of the notion, i. e. , "An Autonomous Vehicle (AV) is a vehicle which takes over speed and steering control completely and permanently, on all roads and in all situations. The driver-passenger cannot drive manually because the vehicle does not have a steering wheel. The driver-passenger only sets the travel destination". Respondents were asked to consider a fully automated vehicle, i. e. , an autonomous vehicle according to SAE Level 5. The survey was designed following standard research ethics processes, and all data were collected anonymously.
; ε itn is an IID extreme value type I distribution, error term. The distributed random ASC includes an error term (η n ) which is normally distributed. U itn = β k * X iktn + γ 11 * δ 1 + γ 12 * δ 1 * Woman + γ 21 * δ 2 + γ 22 * δ 2 * Woman + γ 31 * δ 3 + γ 32 * δ 3 * Woman + γ 41 * δ 4 + γ 42 * δ 4 * Woman + γ 51 * δ 5 + γ 52 * δ 5 * Woman + ε itn (1) As noted in the relevant literature [66,67], ML models cannot be calculated analytically, so we resort to simulation. For this paper, the Apollo choice modelling software  [68]  was utilised to estimate the model using 500 Halton draws.
Sample Descriptive Statistics The sample forms a unique dataset regarding geographical and cultural coverage that has not been analysed before. Figures  3 4 5 6 and Tables  2  and 3  show the socio-economic characteristics of the sample, in terms of age (Table  2 ) and gender (Figure  3 ), household size (Figure  4 ), car ownership (Table  3 ), level of education (Figure  5 ) and employment status (Figure  6 ). The data is presented alphabetically and divided into northern and southern cluster groups. Concerning age and gender, we can observe a prevalence of men, and mean age above 38 across all the countries. When compared to statistical data about drivers in the countries the study was conducted in, we can find the prevalence of men in the driving population (license holders) (for example, UK  [69] , IL [70]), however the differences are not too large.
There is an obvious negative relationship between the mean age of the sample and the percentage of women -higher the mean age, lower the number of women participants. The lowest percentage of women and the oldest age were found in Hungary while the highest percentage of women and youngest age was in Cyprus. This limits the generalisability of the findings for gender for the older respondents, although in this case, the dataset provides more information about male preferences and attitudes towards AV and SAV usage. The median age in Europe is 43, with Cyprus having the lowest median of only 37. 3 in 2018  [71] , which is, except for Hungary, similar to the results collected in the survey. In Israel, the median age was 30. 5 in 2020, which is lower than the average of the sample, given Israel is the youngest country with the largest number of children per household  [72].
This strict protocol started with 112 articles and resulted in a final data set of 85 articles identified in a stepwise procedure. Step 1database. The focus was on articles included in the Business Source Complet, Emerald Publishing, Science Direct, Scopus and Springer databases. Their multidisciplinary approaches to in economics and business topics makes them appropriate for our review. Business Source Complet indexes more than 1,320 peerreviewed journals from around the world, Emerald Publishing includes 360 journals, Science Direct 2,000, Scopus 21,500 and Springer 1,900 journals. Step 2topic choice. We ensured substantive relevance by requiring the term "sharing econom*" in the article titles, keywords or abstracts. Step 3additional topics. Based on the definitions provided in the introduction, we checked for the inclusion in the articles of the terms "logistics" or "crowd logistics".
Step 8verification. We ensured substantive and empirical relevance by reading all remaining abstracts. Step 9consolidation. To ensure substantive context and empirical relevance, we read all the remaining articles in their entirety. The results were are exported to Endnote bibliographic software, and the data were analyzed using Alceste software that proposes bibliometric analysis based on analysis of textual data (www. image-zafar. com/Logicieluk. html). The analysis uses different basic bibliometric indicators: purely quantitative measures based on counts of bibliographic elements such as publication date, subject area, journal, authors, bibliographical references and measures for links between different bibliographic elements (co-publication and word co-occurrence based on word cartography and descending hierarchical classification -DHC).
3. 1. 2 Subject areas and structure of the literature. The number of scientific publications is evidence of academic interest in a particular subject. Several scientific disciplines are contributing to work on sharing economy logistics and crowd logistics. To illustrate the variety of subject areas related to the 85 articles on sharing economy logistics and crowd logistics, we use the Scimago Journal and country rankings (www. scimagojr. com/). This international system covers all the databases we included in our research protocol. It makes it easier to identify the thematic orientation of the journals and conferences proceedings in our database and is dedicated to analysis, representation and retrieval and visualization of scientific information and is aimed at revealing its structure. It can offer a very intuitive image of the interconnection among different subject areas and subject area categories.
Linguistic analysis to identify sharing economy logistics and crowd logistics trends We used Alceste software to analyze the words in titles, keywords and abstracts for the 85 articles. "The co-occurrence of words indicator examines the frequency with which two given words ('co-words) in a particular field are used together. For each word, its cooccurrence with another word is analysed, along with its frequency"  (Okubo, 1997, p. 30). The frequency of word associations is used to construct word clouds representing the major themes in the field under study and the relationships among them. Alceste software allows a series of linguistic analyses (of titles, keywords and abstracts). First, it allows detailed analysis of the vocabulary in a corpus, to construct a dictionary of the words, their roots and their frequency.
This representation shows the links between sharing economy logistics and platformbased collaborative consumption. The sharing economy logistics business model centres on delivery services that include crowd logistics and support sustainability. Because it is not possible to represent all of the data in a unique multidimensional word cloud, Alceste software proposes a variety of methods based on multidimensional analysis techniques, including DHC. DHC involves successive fractionations of the text and representative extracts, based on appearance in text segments of the same terms. Each homogeneous thematic class is examined on the basis of a "profile". For each profile, Alceste identifies a list of the most significant words and sentences. By calculating the x 2 , we can determine strong or weak belonging of a word to a class: the x 2 highlights the most relevant terms for a given class and the most influential references per cluster (Figure  3 ).
Several shared mobility solutions have been proposed to address this problem  (Qi et al. , 2018) , including Post 4. 0 using ground drones  (Kunze, 2016) , mobile crowd-tasking involving a large pool of citizen workers to achieve last-mile delivery  (Wang et al. , 2016) , crowdsourcing of lastmile deliveries of online orders by exploiting the social networks of retail store customers or reception of deliveries by friends and neighbours. These solutions reduce emissions and ensure IJCS 5,1 speedy and reliable delivery  (Devari et al. , 2017; Akeb et al. , 2018). Peer-to-peer product sharing platforms integrated with package drop-off/pick-up services in urban districts are also reducing the impact on the environment  (Martin et al. , 2019). The second strand of work focuses on public transport-based crowdshipping for sustainable city logistics  (Sampaio et al.
Conclusion The purpose of this paper was to provide a systematic review of the rapidly evolving concepts of sharing economy logistics and crowd logistics. We proposed a structured approach to the body of work on this area. We conducted bibliometric analysis of 85 articles (academic papers and conference proceedings) included in five databases, during the time period 2007-2019, using lexical analysis tools and Alceste software. Bibliometric analysis is a scientific synthesis method. It allows extensive coverage of the literature and enables the IJCS 5,1 sorting of relevant articles based on some common features, to identify the main topics and emerging fields. The scientific synthesis method is generalizable to other areas. We used it to address the following research questions: RQ1. How is the literature on sharing economy logistics and crowd logistics structured? A 1.
Interviews and sampling Our research used original face-to-face interviews with wine producing company managers from the Appellation d'Origine Contrôlée (AOC) 2  area, and the original database from the LACCAVE project, which records wine producing companies clearly experiencing CC effects. We sent emails to schedule appointments to all 698 winemaking companies on the list for the three regional wine clusters, with 283 located in Languedoc, 226 in Bordeaux and 189 in Champagne. We received 41 responses from Languedoc, 34 from Bordeaux and 31 from Champagne. Finally, we conducted face-to-face interviews with 92 wineproducing company managers (13% of the 698 winemaking companies on the database): 34 in Languedoc, 30 in Bordeaux and 28 in Champagne. These interviews took place in 2015-2016 and we ensured that the key factor "organic vs conventional wine production" was well represented and distributed among the three regions.
Research method We adopt a case study approach given its suitability to understand the complexities and multifaceted aspects of social phenomena in complex settings  (Ahrens and Chapman, 2006; Yin, 2009). Furthermore, it is useful in examining social aspects of real-life contexts where individuals and organisational processes have strong and intricate relationships  (Yin, 2009). Data are collected on a large high-technology government hospital in Catalonia, Spain (referred to as Hospenda) with 600 beds serving almost half a million population. Hospenda underwent an external accreditation process for the second time in 2013. The unit of analysis is the collective of the management group of Hospenda involved in the adoption and implementation of accreditation, comprising individuals with responsibility and discretion for changing policies and practices.
This is the group referred to as "management" in this organisational entity for which we aim to interpret the findings on implementation of an external control system using the analytical tools of Smith and Lewis' (2011) organisational dualities of learning, performing, organising and belonging. We draw on the concepts of organisational tensions, salience and ambivalence from the literature as these have emerged as having meaning and relevance from detailed analysis of the data. It is through a synthesis of individual views that a collective view emerges on findings related to the research question. To enable a comprehensive understanding of the accreditation system, we also collect data from external assessors and regional health department advisors. Data collection consisted of semi-structured interviews, archival records, review of internal and external reports, and observational information gathered during site visits.
A semi-structured interview approach was used. Interview questions based on a critical review of the literature studies in the previous section of this paper and focusing on structures, processes and outcomes of accreditation were designed to be "open-ended, neutral, singular, and clear"  (Patton, 2002, p. 295). Data were collected in two phases over a 12-month period with 27 face-to-face interviews conducted in total, averaging 45 min each (8 in the first phase and 19 in the second) (refer Appendix 1 for list of interviewees from Hospenda, the regional Catalan health department and the independent accreditation assessors). A purposive sampling strategy  (Patton, 2002)  was adopted for identifying hospital staff and external connected partiessurveyors, assessors, advisors and regional health department staff who had responsibility for accreditation.
During the first set of interviews, the key role of the accreditation system in securing contractual agreements with the main purchaser of public healthcare services emerged. Furthermore, at that time the hospital was in the process of being re-accredited and hospital management were vocal in articulating their attitudes towards this external control system. Following this, the research question was refined, and the researchers requested to interview candidates with extensive experience of accreditation during the second phase of data collection. Individuals from a range of hospital departments were identified, including, nursing, medical, economic and finance, human resources, general services and projects, and organisation systems (see Appendix 2 for summary hospital organisational management chart).
To better elucidate the micro-level evidence of technological innovation and social change in a globalized wine setting, data was gathered from the world's top 15 FWM listed by The Drinks Business (2013) and the Dico du Vin (2017) (see Appendix 1). More than 80 webpages concerning these FWM were analyzed. Flying winemakers offer a good basis for exploring the microfoundations of nomadic dynamic capabilities. First, the wine industry is global by nature, featuring separation between production and consumption locations, and cross-border issues involving tariffs and taxes since the Roman Empire. Second, the wine industry is a traditional industry that has undergone major changes in the last 30 years at both local and global level (new players, globalization, new demand)  (Castellano and Khelladi, 2016).
Data collection We collected two types of data: secondary data (TGTG Press Kit, 2019) and primary data obtained from semi-structured interviews with the TGTG provider and restaurant managers and customers (i. e. , users of the TGTG app). Given the difficulty of identifying restaurant managers and consumers who have used the app, we opted for the snowball sampling technique, which uses interpersonal relations and connections between interviewees. Snowball sampling is often used when the population under investigation is "hidden" due to, for example, either the low number of potential participants or the sensitive nature of the subject matter  (Browne, 2005). For this research, snowball sampling was an effective method to recruit restaurant managers and customers, especially restaurant managers.
Data analysis Based on the conceptual framework (Fig. 1 ), two coding dictionaries were developed. One was employed to analyze verbatim from the TGTG provider and the TGTG press kit, which covers such topics as the functions of the app, how these functions are expected to be used, what types of value the TGTG app is expected to offer to users, how these types of value are expected to be delivered to users, and why these types of values are important for users. The other was employed to analyze verbatim from the restaurant managers and customers interviewed, which covers such topics as what functions are used, how these functions have been used, the types of value gained from using the app, and why these types of value have been important for the restaurant managers and customers. A content analysis using the QSR NVivo 12 software was performed case by case and according to the category of respondents (the TGTG provider, restaurant managers, and customers).
In addition, to ensure internal validity, following Vo-Thanh and  Kirova (2018) , two of the authors analyzed the first three interviews together according to the three categories of respondents. Afterward, each author analyzed the rest of the corpus separately, using the same developed dictionaries. The results obtained by the two authors were then compared using the QSR NVivo 12 software. To achieve this comparison, the function of the coding comparison query provided in QSR NVivo 12 was used, as indicated by Vo-Thanh and  Kirova (2018). Divergences were discussed to reach a consensus. Moreover, the two authors cross-checked the results together to examine similarities and differences regarding the app's CVP and VIU among the three categories of respondents and to draw a single set of cross-category conclusions related to perceived values.
Accordingly, this article explores, contextualizes, and extends  Brush et al. 's (2009)  model to represent the growth determinants of women-owned businesses in a developing economy. In doing so, it advances women entrepreneurship by helping address two questions: (i) Does  Brush et al. 's (2009)  framework characterize the determinants of growth within women-owned businesses in a developing country? (ii) How can the framework evolve to account for growth factors pertinent to women-owned businesses in a developing country? Guided by these questions, we inductively analyzed data from 31 women entrepreneurs and crucial documents (including press briefings and minutes of meetings) related to the garment manufacturing sector in Lagos, Nigeria. The findings presented in this article have a profound effect on entrepreneurship-policy-development initiatives and provide instructive guidance needed for developing programs that can be influential toward supporting womenowned enterprises operating in the developing world.
Sampling method The data for this study were collected using two sampling techniques: purposeful and snowball sampling. First, we purposefully selected respondents from the researchers' contacts within the Nigerian garment manufacturing sector. These women entrepreneurs referred other women, creating a "snowball" effect  (Baltar & Brunet, 2012; Eager et al. , 2019; Watson & McGowan, 2019). This process continued until the return of new data ceased, implying data saturation had been reached  (Mason, 2010).
, 2018). In total, we interviewed 31 women entrepreneurs (See Table  1 ). Each interview was audio-recorded and lasted 45 to 60 minutes. In addition to interviews, archival data (such as newspaper articles, press briefings, and minutes of meetings) available through trade associations these women entrepreneurs were affiliated with (including Lagos State Tailors and Fashion Design Association of Nigeria-LASTFADAN and the Nigerian Union of Tailors-NUT) enabled us to gain further insights into the factors the women considered influential to the growth their enterprises within the garment manufacturing industry in Lagos, Nigeria. Thus, this research benefited from data triangulation  (Crick, 2020; Mason, 2010).
Sample-size justifications While we acknowledge the possible limitations of our sample size, we adopted rigorous procedures to ensure our data's richness  (Groenland & Dana, 2019). The entrepreneurs we first contacted volunteered to provide information about other potential participants. In other words, we utilized the principles of snowball sampling. The snowball technique led us to 36 potential participants, of which 31 were interviewed. At this point we achieved data saturation  (Saunders et al. , 2018). To validate having achieved data saturation, we compared and evaluated the contents of each transcript against the others  (Crick, 2020). Specifically, we observed that similar data-driven and a priori themes reoccurred from respondents 27 to 31. Rather than continuing to carry out additional interviews with the other participants identified through snowball sampling, we stopped in order to avoid what  Groenland and Dana (2019)  described as data overload.
To supplement data obtained through interviews, we collected and analyzed artifacts, including newspaper articles, press briefings, and meeting minutes detailing stories about women entrepreneurs in the garment business. Much of this evidence was provided by the trade associations these women entrepreneurs were affiliated with. This data triangulation process enabled us to receive objective and well-rounded insight  (Decrop, 1999; Eisenhardt, 1991)  into the factors women considered influential to the growth of their enterprises.