dataesr/scientific-paragraphs-categorization huggingface dataset:
Corporate image was operationalised using six items taken from  Cretu and Brodie (2007) , while seven items adapted from  Parasuraman (2000)  were used to measure technology adoption. Three items measuring repurchase intention have been taken from  Park and Ha (2016) , while affective customer commitment was measured using three items adapted from  Shukla et al. (2016). Consumer demographics, such as education, employment status, age and gender may influence online consumers' repurchase intention; hence, we have considered them as control variables for our study. Accordingly, the data pertaining to these variables have also been collected through the survey. All the constructs used a seven-point Likert's type measurement scale (i. e. 1 = strongly disagree; 7 = strongly agree). Before undertaking the actual survey administration, the survey instrument was pre-tested survey instrument with the responses from 20 online shoppers.
ANALySIS To test the proposed hypotheses, we used partial least square (PLS), a variance based structural equation modelling (SEM) method. The PLS technique is considered to be suitable due to following reasons: (1) given the types of relationships in the hypotheses (mediation, moderation, and direct), our research model is quite complex; (2) our study contains both reflective and formative constructs; (3) these constructs produce consistent parameter estimates  (Chin et al. , 2003). Accordingly, we have used Smart PLS 3. 0 software for the analysis  (Ringle et al. , 2014). We ran the PLS model in two stages: structural model (inner model) and measurement model (outer model).
A total of 260 articles remained after this first selection and were read in full. Only the ones where adaptation was explicitly evaluated were included in the present study; in other words, adaptations were explicitly projected under future climatic conditions (data-based or not) and their impacts were either quantified or qualified regarding their feasibility evaluation. Our final dataset included 111 references (Figure  1 ; complete list in Supplementary Table  1 ). First, we used the information from the Web of Science database to characterize the set of literature on adaptation for: authors, title, publication year, journal, web of science category, first author localization. We then described each of the 111 studies with categorical variables (Table  1 ). Analyses were performed with the R software version 3.
The second set of variables concerns the evaluation methods. We excluded the 18 review papers from our analysis (18 out of the 111 papers), as we did not consider reviewing as a way to evaluate an adaptation. Four types of information were collected: the scientific approach, the climate data source, the study scale, and the evaluation criteria. First, the scientific approach is characterized according to the three categories described in  Carter (1996) : experimentation, impact projections (i. e. , modeling) and expert judgement. Second, the performance of an adaptation under future climatic conditions depends largely on the data used to define those conditions. Climate data sources are classified by  Carter (1996)  into three categories: synthetic scenarios that consist of current meteorological data adjusted systematically (e.
Overview of the Final Selection of Articles In our database, the first journal article to focus on adaptation of grapevine to climate change was published in 2006  (Belliveau et al. , 2006) , 10 years after the first impact study of climate change in viticulture  (Bindi et al. , 1996). The number of papers has increased steeply since 2016 (Figure  2A ). Over the whole set of articles, authors from the Mediterranean area, i. e. , Spain, Portugal, Italy, and France, accounted for 58% of the articles, followed by other major viticulture regions in the world, mainly Australia, USA, Canada, Germany, and Chile (Figure  2B ). Most of the studies concern one region (87 papers) while a few others are comparative studies between countries [Australia and France  (Lereboullet et al. , 2013) , Germany and Argentina  (Uliarte et al.
ADAPTING VITICULTURE TO FUTURE WATER SCARCITY In the reviewed scientific literature, short-term (ST) and longterm (LT) adaptations, implemented, respectively, during the grapevine growing season and at vineyard plantation, were evaluated. Long-term adaptations concern: Site selection (LT1), consisting in the relocation of vineyards; Plant material (LT2), consisting in the implementation or creation of adapted grapevine cultivars and rootstock; Vineyard design (LT3), which implies changes in density, row orientation, training system; and Farm strategy (LT4), which includes wine-market orientation and diversification. Short-term adaptations concern: Irrigation Figure  3  represents the occurrence of each adaptation in the studied dataset. One study could be counted several times as it examined more than one adaptation.
First, future climatic conditions are described as a systematic adjustment of present meteorological data (e. g. , a temperature increase of 2 • C, a 50% reduction of rainfall, etc. ) in 35 studies in the article pool (Table  3 ). Climatic conditions can be directly measured under controlled conditions. Controlled experiments evaluate the combined effects of different climatic changes, as for example the effect of water stress induced by deficit irrigation under elevated temperatures created with open top chambers  (Torres et al. , 2017; Bonada et al. , 2018). Experiments that reproduce elevated CO 2 conditions remain rare and limited to climate change impact studies without the introduction of an adaptation  (Bindi et al. , 1996; Wohlfahrt et al. , 2018). Second, climate models (16 studies, Table  3 ) provide long and complete series of daily variations of a wide range of meteorological variables (CO 2 , temperature, rainfall, etc.
, 2017; Bardsley et al. , 2018)  or collective  (Lereboullet et al. , 2013; Galbreath, 2014). They have the advantage of being locally adapted and of representing extreme events with their consequences. However, climate change may tend to be underestimated as the disruptive climatic conditions and new combinations of stresses, which may go far beyond local experiences, are hard to explore. In a quarter of the selected studies, the climate evolution was not clearly specified (Table  3 ). The use of climate projection datasets is the only credible tool available for simulating the physical processes that determine climate change  (Carter, 1996). However, it does not necessarily represent all the events proposed by stakeholders, notably those highlighted by  Bardsley et al. (2018) : extreme events (heat waves and heavy storms) and changes in natural resources (rainfall during the growing season and volumes of groundwater recharge).
Data sources for future climatic conditions are poorly hybridized, despite the complementarity they offer. In our dataset, a single study coupled past evolution of meteorological data with winegrowers' perceptions  (Lereboullet et al. , 2013). Similarly, only two studies (Table  3 ) combined climate modeling at local scale with stakeholders' perceptions  (Sacchelli et al. , 2017; Tissot et al. , 2017).
Suitability mapping has been used in four studies of our dataset, mainly to evaluate site selection and irrigation adaptations  (Hannah et al. , 2013; Fuhrer et al. , 2014; Teixeira et al. , 2014; Resco et al. , 2016). Empirical models have been used in one study to evaluate the effect of various cases of irrigation management under future climate change  (Teixeira et al. , 2014; Olen et al. , 2016). However, empirical models show their limits when evaluating an adaptation under alternative management conditions and future climatic conditions on which experiments have not yet been run. Process-based models have also been used in 8 studies to evaluate adaptations dealing with irrigation  (Grantham et al. , 2010; Pieri et al. , 2012; Fraga et al. , 2018; Phogat et al. , 2018) , plant material  (Pieri et al.
Common Method Bias Concern Since this study employed a single data collection method, the presence of common method variance was tested to exclude the chance of common method bias (CMB). The issue of CMB was controlled in the study by using conventional scales  (Podsakoff et al. , 2003). The total explained variance of the three factors (using a minimum eigenvalue of one) was 56. 47%. The first factor accounted for 38. 27% of the variance, the second factor 9. 67%, and the third factor 8. 54%. Because multiple factors were included, and the first factor did not dominate the variance, this was an indication that CMB was not an issue here  (Teo and Noyes, 2010). To further validate CMB, Harman's single-factor test was also performed using SPSS statistical software. This test allows all variables to be loaded on a single factor.
Usually, this test should indicate a poor fit of the data as compared to the actual model selected for the study. The model with a single factor explained only 38. 27% of the variance, which was below the threshold level of 50%. To further validate the results of Harman's single-factor test, a similar test was performed in the SPSS AMOS software package, using the common latent variable test. The results of this test were: chi-square χ2 = 1287. 40; degree of freedom df = 148, p <. 001; root mean square error of approximation RMSEA = 0. 09; CFI = 0. 87; normed fit index: NFI = 0. 86, as compared to the three-factor model with χ2 = 455. 97; df = 131; p <. 001; RMSEA = 0. 05; comparative fit index CFI = 0. 96; NFI = 0. 95. This shows that a single-factor solution is not an appropriate model in comparison to the three-factor solution proposed by the study.
Data In 2005, Kenya joined the WHO Framework Convention on Tobacco Control and adopted several policies to control tobacco use such as smoke free places, prohibiting all forms of advertising and promotion of tobacco products, tobacco packaging warning messages. The tobacco control act was finally adopted in 2007 and later challenged by the tobacco manufacturer British American Tobacco Limited. Since then, there is resurgence of studies on tobacco use in Kenya. All data used in this study emanate from the 2014 Global Adult Tobacco Survey (GATS). The GATS is a survey conducted in several countries. It aims at monitoring adult tobacco use (smoking and smokeless) and tracking key tobacco control indicators. The 2014 GATS for Kenya is the first of its kinds on tobacco survey asking rich questions on background characteristics, tobacco use, cessation, economic indicators, knowledge, attitudes and perceptions towards tobacco use to a nationally representative household survey of adults aged 15 years and older  (KNBS, 2014).
Very few of these variables are found in the dataset. Thus, we use the mass media and knowledge about the dangers of tobacco use. All these three levels are interwoven with each other  (Flay et al. , 1999). It is noteworthy to emphasize that the wealth/income index was constructed using the polychoric principal component analysis (pPCA) on 13 questions on household assets. Applying PCA on binary variable seems inappropriate because the assumption of normality with discrete variable is conceptually unappealing. To circumvent this issue,  Kolenikov and Angeles (2009)  suggested that there are normal latent distributed continuous variables underlying observed binary variables. The correlation matrix of these latent variable is estimated and a standard PCA is then applied to this matrix. We follow this procedure known as the pPCA and use the first component as the measure of wealth index or socioeconomic status.
Consistently,  Dolnicar and Zare (2020)  have recently proposed that COVID-19 will disrupt the paid peer-to-peer accommodation type, and recent data show that paid peer-to-peer services have witnessed a dramatic drop in business volumes during the lockdown  (Glusac, 2020). Moreover, given the economic crisis and the continued social/physical distancing applied in many countries around the world, these business models will likely continue to suffer for many months. Brian Chesky, Airbnb's chief executive, declared: 'Airbnb's business has been hit hard, with revenue this year forecasted to be less than half of what we earned in 2019'. Nonetheless, Airbnb will now be listed in the stock market this year, which shows that its future is far from over  (Driebusch, Farrell, & Lombardo, 2020). In this paper, we propose and hypothesize that the paid peer-to-peer accommodation type is not dead but that there will be a change in bookings in favour of full flats rather than shared flats.
statista. com). Specifically, the price for the shared flat was £ 53 whereas the price for the hotel and the full flat was £ 117. We used comparable offers in terms of number of guests, services and location, as shown in Figure  2. We told participants they were going to visit the location (London) alone. To avoid any confounding effect, the listings did not contain any other additional information. [Figure  2  near here] The independent variable consisted of two different levels (0=non-pandemic; 1=pandemic). Using a between-subject design, participants were asked to think of the pre (vs. during) pandemic times. Afterwards, all participants were shown 3 accommodation choices and they were asked to pick one. Since all the data were collected in October, we asked participants to think of the times before (vs.
now during) the pandemic and then they were led to think about their accommodation choice, based on three different offerings: shared flat on Airbnb (-1), hotel on Booking. com (0), and full flat on Airbnb (1). Enhanced behavioural realism  (Morales et al. , 2017; Viglia & Dolnicar, 2020)  was used in the dependent variable by giving 10 Booking/Airbnb vouchers of the value of €50 based on their Booking/Airbnb preference. Additionally, we collected some demographic data (age and gender) and the nationality of participants (60% of British participants) to control for potential cultural effects. Finally, we included an attention check, i. e. , "will the travel take place during the pandemic? ", to see whether participants remembered the situation they were assigned to (pandemic or not).
Results A total of 134 people participated in the experiment. Of these, 59% were males. In terms of age, 34. 3% of the participants were 18-24 years old, 48. 5% were 25-34, 11. 9% were 35-44, and 5. 2% were in 45-54 age group. The attention check worked as designed, i. e. , participants correctly identified the condition they were assigned to (pandemic or not). We performed a chi-square test of independence in SPSS 27 to examine the relation between the pandemic (or not) scenario and the type of accommodation (shared flat vs. hotel vs. full flat). The relation between these variables is significant, X 2 (1, n = 134) = 37,04, p =. 001. Specifically, at their respective average prices (£53 for the shared flat and £117 for the hotel and the full flat), the majority of the respondents preferred shared flat (48,6%) to hotel (33,8%) and full flat (17,6%) in a non-pandemic situation.
g. connection with hosts in digital form only). The paper presents some limitations that open up for a rich future agenda. While we have included behavioural realism in the dependent variable by giving a specific incentive (Booking/Airbnb voucher) based on participants' choices, the studies were undertaken during the COVID-19 pandemic, and therefore it was impossible to organize a field study. Future studies might want to look at actual behaviour by collaborating directly with Booking. com and Airbnb. Trying to collaborate with operators directly has been proved difficult for their data policy sharing issues. This might change in the near future due to the pressure to openly share data. At the moment of writing, a promising signal in this sense is the European Commission's announcement  (European Commission, 2020 -March)  to have reached a landmark data-sharing agreement with vacation and collaborative economy platforms (i.
In total, 334 winegrowers answered the survey, representing around 3% of the current winegrowing population in Languedoc-Roussillon and representing slightly more than 10% of the winegrowers contacted. The questionnaire and the answers are available online in an opensource dataset  (Fernandez-Mena, 2020). The questionnaire was composed of 101 questions with semi-open and multiple-choice answers divided into four sections: (i) the vineyard characteristics (size, labels, irrigation, soil features) (ii) soil management practices (tillage, mowing, herbicide use, compost) (iii) the typical (or most frequent) service plant management strategy at the vineyard scale, defined by the spatial design and its duration, plus the use of sown crops or spontaneous vegetation (iv) their objectives, such as target yield, target services and constraints to the adoption of service plants Although some vineyards have differentiated management according to plots, most of the questions relating to service plants concern the main management service plant strategy, i.
Statistical analysis was performed using 'R software' (R Core Team, 2014) and using 'car' R package  (Fox and Weisberg, 2019). Multiple correspondence analysis and hierarchical clustering on principal components were performed using 'FactomineR' R package  (Lê et al. , 2008). Plots were done using 'dplyr' R package  (Wickham et al. , 2018) , 'explor' R package  (Barnier, 2020) , and 'ggplot2' R package  (Wickham, 2016).
Introduction The compound Poisson demand process is one of the most general and widely studied demand processes for inventory control. It has a natural business interpretation with customers arriving in continuous time, and their demand sizes following any general compounding distribution, and is therefore a standard choice in textbooks (e. g. [1] [2] [3]  ) and commercial software (e. g. Forecast Pro, Oracle NetSuite, SAP APO, SAS, and Slimstock), especially for intermittent demand patterns. After Galliher et al. [4]  studied the special case of Poisson-geometric demand, Feeney and Sherbrooke  [5]  and Chen et al. [6]  derived order-up-to policies under general compound Poisson demand. Archibald and Silver  [7]  extended this work to general (s, S) policies. Order-up-to policies can be deter-mined efficiently under compound Poisson demand, as shown by Sherbrooke  [8]  and Graves  [9]  , who provided approximate procedures, and Axsäter  [10]  , who presented an exact method.
They may worry about losses of IP with imitators or about their reputation on similar markets if they do not protect themselves defensively  [50, 66]. This converges with the fact that, while most patenting SMEs show a greater proportion of patents leading to licenses and a smaller proportion of unused patents compared to large firms, this is in view of convincing investors or generating new revenue  [67]. However, in doing so they give information to competitors through patent databases, which can be detrimental to the profitability of their intellectual assets  [68]. Consequently, they resort essentially to informal time-and cost-saving practices and methods embedded in everyday routines, such as secrecy and lead time  [19, 21, 62]. Complex design is also cited by some studies as an informal method to protect IP  [55, 69].
The typology was informed by means of unstructured, conversational interviews with over thirty key-informants conducted between May and August 2019 by the first author (Fig. 2 , step 2). Once the relevant access barriers (e. g. , roads, fences) were identified and typified, we proceeded to visually classify the previously digitized line segments according to barrier type using visual interpretation in combination with a vector dataset of provincial and national roads from the Federal Ministry of Agriculture, Livestock, and Fisheries of Argentina (Fig. 2 , step 3). We then created a weighting Fig. 2. Schematic workflow for calculating the Access Condition Index (ACI). scheme whereby each barrier type was assigned a relative permeability for the production of charcoal and for cattle, goat, and pig rearing, respectively (Fig.
Generating livelihood-specific access indices Based on the maps of weighted access barriers, we developed a livelihood-specific Access Condition Index (ACI) representing the distribution of access conditions throughout the study area for different livelihood strategies in 2000, 2010, and 2018. We implemented the ACI using a custom spatial analysis workflow developed in the Python programming language with the ESRI Arcpy library (provided with ArcMap 10. 6). To calculate the ACI, we first converted the permeabilityweighted barrier layers from vector to raster format for each livelihood and year. Second, for each 30 × 30 m raster cell, we computed a value representing the cost of crossing existing barriers to access resources in surrounding cells. To do this, we applied a cost-distance function to determine a fictive 'cumulative barrier friction' value between each cell and all other cells within a livelihood-specific buffer around it for use as an intermediary step in the index calculation (Fig.
Coding of access barriers Because the insufficient resolution of the satellite imagery and a lack of available field validation data across the department did not allow us to distinguish between fences and demarcations, we classified any barrier located within the forest matrix (as opposed to roads or the edges of farmland, which are systematically fenced) as a single category representing either a fence or a demarcation. We hereafter refer to these barriers as "internal barriers". Roads were coded according to whether they were provincial, national, or municipal in order to capture differences in the volume of transit (and thus risk to livestock), as well as in the probability of theft. Farmland was identified visually from Landsat images for each timestamp in Google Earth. The municipal road dataset was outdated so we used it minimally for coding, and classified any internal barrier that was not a straight line as a municipal road.
Acknowledgments We thank  Dr. Tim Elrik  and the  Geographic Information Center at McGill University  for granting us access to the necessary computational power to complete the analysis, and  Dr. Nestor Ignacio Gasparri  for his review and feedback. We'd also like to thank:  Feliz Francisco Córdoba ,  Mariano Hernandez , and the members of the  land committee of the department of Pellegrini  for their support in the coordination of the data collection; the  Institute of Regional Ecology (IER)  of the  University of Tucuman  for providing accommodation and a work space; Isabel and  Maximo Colombres  for providing accommodation; as well as to all of the people who participated in focus groups and key information interviews.
We conducted a systematic search  (Tranfield, Denyer, & Smart, 2003)  using keywords related to family firms and internationalization on five major electronic databases, including both disciplinespecific (ABI/Inform database) and interdisciplinary databases (EBSCO database, Google Scholar, ISI Web of Knowledge, and Scopus). 4 The initial search yielded 297 articles. Each identified paper was screened separately by at least two of the authors and an academic external to the authors' team to ensure that it met our criterion for relevance. We eliminated 77 articles that casually mentioned family business or internationalization or did not specifically explore family governance and/or dimensions of IB as dependent or independent variables. The resulting 220 articles are reported in Appendix 1.