dataesr/scientific-paragraphs-categorization huggingface dataset:
The aim of this study is to assess the determinants of member commitment. Because we choose to examine the links between members and their cooperative, we use unique datasets from a large multipurpose cooperative in France. This allows us to confront our results based on farmers' choices to the results found in the studies that explore attitudinal determinants  (Hakelius and Hansson, 2016; Barraud-Didier et al. , 2014; Hernandez-Espallardo et al. , 2013; Österberg and Nilsson, 2009). Furthermore, we explore and discuss how innovation can be used as a specific instrument to align the objectives between the cooperative and its members. Innovation promotion can be a key strategy for agricultural cooperatives. In our case study, the cooperative has recently implemented a new leading strategy based on innovative environmentally friendly farm practices to meet members' demand.
Data Our study is based on a sample of 3,205 members of a large French multipurpose cooperative located in Western France. A dataset involving more cooperatives could have been useful for the scope of the study but an abundance of information would have been lost in the confidentiality compromise among cooperatives because of competition issues. Consequently, we have used a database with highly detailed members' information from one of the 5 largest agricultural cooperatives in France and among the 15 largest in the European Union. The cooperative is a multi-purpose cooperative whose sales were greater than 4 billion euros in 2013. The database provides information on various socioeconomic member attributes in 2013. The cooperative differentiates itself from other agricultural cooperatives by orienting its strategy towards farm innovation.
Discussion This empirical study provides new insightful results. First, we focus on the relationships between the cooperative and its members, using original datasets from a large multipurpose cooperative. It allows us to obtain information on members' choices and to confront our results with those obtained through surveys. Second, the study provides promising results about how farm innovation can play a key role in aligning members' goals with the ones of the cooperative. These results may not only be of benefit to cooperative managers but also to policymakers. Indeed, in a context of environmental pressure, policymakers should help cooperatives to develop environmental solutions. As cooperatives have incentives to promote innovation to their membership, the effectiveness of environmental subsidies may be enhanced.
By applying a longitudinal analysis to a dataset of 1730 firm-year observations of strategic equity alliances in the software industry in 25 countries over an 11-year period, the results show that smaller firms in strategic equity alliances are less able to integrate the organizational capabilities of larger firms, because smaller firms and the organizational and structural properties that come with smaller size may hamper the capacity to absorb the partner's organizational capital. On the other hand, smaller firms benefit more from production factor resources compared to larger partner firms. Overall, the findings indicate that entrepreneurial networks provide important opportunities as well as threats for the smaller entrepreneurial firms. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.
The evolution of IMC The term "IMC" gained currency in the 1990s  (Schultz et al. , 1993a (Schultz et al. , , 1993b) )  and was driven by dramatic changes in the marketing and mass communication environments, although scholars  (Hutton, 1996; Spotts et al. , 1998)  argue that the IMC concept emerged much earlier within the context of the marketing discipline. The integrated approach to marketing communications emerged from a recognition that firms must use an array of 529 IMC antecedents communications messages and channels to manage stakeholder relations effectively. Thus, IMC can be seen as a natural evolution in marketing communications rather than a transformation in marketing thought  (Kliatchko, 2005). Regardless of its historical origins, by 2000, IMC was seen by many as a way of better managing the growing range of marketing communications choices which were becoming available to marketers, including the internet and sophisticated consumer databases  (Duncan, 2002).
Direct marketing and planned brand identity Direct marketing is a channel-agnostic form of advertising which allows businesses to communicate about their brand straight to the customer, with advertising techniques that can include cell phone text messaging, email, interactive consumer websites, online display advertisements and database marketing. Direct marketing as a key element of IMC  (Schultz et al. , 1993a (Schultz et al. , , 1993b;; Duncan, 2002)  is highly developed in US and UK advertising agencies  (Kitchen et al. , 2008). It has been stated that when an organisation achieves a high level of IMC, a strong relation between IMC and marketing communication performances can be observed  (McGrath, 2005; Porcu et al. , 2012; Vantamay, 2011). Direct marketing can create and increase familiarity and brand awareness by visual and verbal exposure of a brand to the consumer, as well as creating strong brand associations in the consumer's memory  (Rossiter and Percy, 1987; Yoo et al.
Data collection and sample Survey data were collected to empirically test the research hypotheses. The study was conducted within two London-based universities. These data were collected from internal stakeholders (existing students and employees) between November 2015 and July 2016. Further, 600 printed questionnaires were sent to stakeholders of UniversityI, from which 329 usable questionnaires were returned and analysed. From 600 questionnaires distributed to students and employees of UniversityII, and after making every possible effort to increase the response rate, a total of 337 usable completed questionnaires was processed and analysed. A majority of respondents from both universities were female (UniversityI 57. 4 per cent and UniversityII 45. 7 per cent). Most of the UniversityI and UniversityII respondents were between the ages of 24 to 30 years (50.
Results and analysis Exploratory factor analysis (EFA) was used to examine the large numbers of variables and to describe such variables in terms of their common underlying factors  (Hair et al. , 2006). EFA determines the factor structure of measures that examines internal reliability and discovers underlying structures in relatively large sets of variables  (Tabachnick and Fidell, 2007). EFA was run through SPSS 21 separately for the two sets of questionnaires. Appendix II shows the descriptive statistics, factor loadings, reliabilities, construct reliability and average extracted variance. The results show that the items loaded ranging from 0. 744 to 0. 947 (UniversityI) 0. 709 to 0. 935 (UniversityII) satisfied the minimum criteria for factor loadings  (Churchill, 1979; Hair et al. , 2006).
As part of the follow-up, participants are requested to complete the same set of questionnaires every year. Moreover, each month, participants are invited by email to fill in optional questionnaires related to dietary intakes, determinants of eating behaviors, nutritional and health status. This study is conducted in accordance with the Declaration of Helsinki, and all procedures were approved by the Institutional Review Board of the French 75 Institute for Health and Medical Research (IRB Inserm n°0 000388FWA00005831) and the Commission Nationale de l'Informatique et des Libertés (CNIL n°908,450 and n°9 09,216). All participants provided informed consent with an electronic signature. This study was registered in EudraCT on 25th February 2013 (n°2013-000929-31).
Sociodemographic, economic and lifestyle data The sociodemographic, economic and behavioral data that were the closest to the food choice motives questionnaire were used. In particular, data were collected on sex, age (years), BMI (kg/m 2 ), educational level (primary, secondary, university (≤ 3 years), university (> 3 years)), household composition (one adult, two adults, household with at least one child, household with at least one teenager, household with at least one child and one teenager), smoking status (never smoker, former smoker, current smoker) physical activity (low, medium, high) and monthly household income (<1200, 1200-1800, 1800-2700, >2700 euros per household unit). The monthly household income is calculated per household consumer unit (CU). One CU is attributed to the first adult in the household, 0.
Logistic regression analysis was performed to estimate the association between dilemma for meat, fish and dairy products and mPNNS-GS score (dependent variable). Analysis of variance and logistic regression analyses were adjusted for, sex, age, education level, income, household composition, smoking status, physical activity, BMI and energy intake. Sensitivity analyses were performed on the implicit and explicit approaches separately. All tests of significance were twosided, and a P value <0. 05 was considered significant. All statistical analyses were performed using SAS software (version 9. 3, SAS Institute Inc. ).
Characteristics of the sample From the initial 122,091 subjects who received the questionnaire measuring food choice motives regarding sustainable foods, a total of 46,958 (38. 5%) completed it. Among them, 402 subjects (0. 9%) were excluded due to missing data for BMI, educational level or household composition. An additional 18,056 participants (38. 5%) who had less than three dietary records available were excluded and 5565 participants (11. 9%) due to underreporting, leaving 22,935 subjects (48. 8% of those who completed the questionnaire) available for analysis  (17, 247  women and 5688 men). Compared with excluded individuals, included individuals were more often men, were older, had a higher income and were less likely to have children (all P < 0. 0001). They also showed a higher educational level (P < 0.
Our data showed that older subjects were more likely to perceive dilemma compared with younger subjects, for all food groups considered. Older age groups are known to be more interested in healthy eating compared with younger subjects  [20, 40, 41, 46, 48]. It is however still unclear whether environmental concerns vary with  Analyses are performed on a subsample of individuals (N = 17,685) for whom mPNNS-GS score could be assessed 4 mPNNS-GS has a range of 0-13. 5 points, with a higher score indicating a better overall diet quality 5 Since there are no dietary guidelines available for meat, guidelines for meat/fish/eggs are considered in these analyses. Adherence to dietary guidelines for meat/fish/eggs corresponds to an intake of 1-2 servings/day 6 Adherence to dietary guidelines for fish corresponds to an intake of fish ≥2 servings/week 7 Adherence to dietary guidelines for dairy products corresponds to an intake between 2.
Acknowledgments We thank all scientists, dieticians, technicians, and assistants who help carry out the  NutriNet-Santé study. We especially thank  Younes Esseddik ,  Yasmina Chelghoum ,  Paul Flanzy  and  Thi Hong Van Duong , computer scientists;  Veronique Gourlet ,  Charlie Menard ,  Fabien Szabo ,  Nathalie Arnault ,  Laurent Bourhis  and  Stephen Besseau , statisticians; and the dieticians. We are grateful to volunteers from the  NutriNet-Santé study.
Funding The  NutriNet-Santé Study  is supported by the  French Ministry of Health (DGS) , the  National Public Health Agency (Santé Publique France) , the  French National Institute for Health and Medical Research (INSERM) , the  French National Institute for Agricultural Research (INRA) , the  Medical Research Foundation (FRM) , the  National Conservatory for Arts and Crafts (CNAM)  and the  University of Paris 13. This study is part of the  OCAD  project which was supported by the  French National Research Agency (Agence Nationale de la Recherche)  in the context of the 2011  Systèmes Alimentaires Durables  ( OCAD  project,  ANR-11-ALID-002-06 ).
In order to solve this problem, we propose a technical ontology focused on format concepts and linked to technical databases filled with syntax representation and conversion formulae. Thanks to semantic and technical data description of involved messages, we generate data transformations using three main steps for each chosen service: (i) First we search for available outputs using process logic. We have to find out which previous output messages can be used to create our input target message. (ii) Then, using this available data, we try to compute the whole message transformation using semantic links between tags, format descriptions, and technical information about known transformations. (iii) If the whole message is not covered by the computed transformation, we first try to find an available transformation service using our service matchmaking mechanism described above.
0 cases in SMEs, as exposed by researchers. The Elsevier, Emerald, Springer and Taylor and Francis databases were surveyed. We considered publications, which followed the formal introduction of Industry 4. 0 (i. e. after 2011) and used the following keywords to perform our research in abstracts, keywords and titles: •' Industry 4. 0' and 'Small and medium' •' Digital production' and 'Small and medium' •' Digital manufacturing' and 'Small and medium' •' Internet of things' and 'Small and medium' •' Cyber Physical System' and 'Small and medium' •' Cyber Factory' and 'Small and medium' •' Production Planning and Control' and 'Small and medium' We have observed in our preliminary readings that many authors describe uses of the Industry 4. 0 technologies in production processes without referring specifically to 'planning and control' in their abstracts, keywords or title.
Control Control favours the interaction of employees with the system by using historical data and predetermined thresholds. MacKerron et al. (2014)  presented an e-Kanban system that monitors supply processes by using RFID technology. Changes in quantities consumed would then alert managers. Xia et al. (2016)  proposed using production data to compare the performance of different production resources and thus define expected levels of performance. When the targeted level is not reached, a new design project is launched to modify the production system. The works of  Constantinescu et al. (2014) ,  Constantinescu, Francalanza, and Matarazzo (2015)  focused on making information available for aid decision-making. Different data acquired from sensors were linked to specific tasks and transmitted operators for decision-making.
Data Collection In this study, we utilized a cross-sectional e-mail survey guided by  Dillman's (2007)  total design test method. The sample of Indian manufacturing firms was drawn from CII Naoroji Godrej Institute of Manufacturing Excellence database. We selected over 1200 respondents from the membership list. The title of the specific respondents sought was primarily the Vice President or General Manager of SCM, accounting/finance, human resource department (HRD) and corporate social responsibility (CSR). The data was collected through a two-part electronic survey (see  Dillman, 2007 ) from 3 rd January to 26 th May, 2016. The first part consisted of questions related to the respondent and their firm (i. e. name, age, gender, designation, number of employees, annual revenue) and the second part consisted of questions related to coercive pressures, normative pressures, mimetic pressures, topmanagement commitment, flexible orientation, control orientation and absorptive capacity of the firm.
Data Analyses and Results We tested the indicators for assumption of constant variance, existence of outliers, and normality (see  Eckstein et al. 2015). We used residuals plot by predicted value, rankits plot of residuals, and statistics of skewness and kurtosis. To further detect multivariate outliers, we used Mahalanobis distances of predicted variables  (Stevens, 1984). The maximum absolute values of skewness and the kurtosis of the indicators in the remaining dataset were found to be 2. 03 and 4. 14 respectively. These values are well within the limits recommended by  Kline (2011) : univariate skewness <3, kurtosis <10. For multicollinearity, the variance inflation factors (VIF) were calculated. All the VIFs were less than 3. 0, and therefore considerably lower than recommended threshold of 10.
Limitations and Further Research Directions Our study has its own limitations. Firstly, in the current study we have gathered data at one point in time. A longitudinal study would further enrich our understanding by offering information over time to provide an in depth understanding of how organizational culture affects the shaping process of PMS and how the assimilation of PMS allows the organization to gain competitive advantage. Guide and Ketokivi (2015)  noted that despite of any level of precaution, the common method bias (CMB) remains an issue with data gathered at one point in time. Hence, the longitudinal data may reduce CMB  (Podsakoff and Organ, 1986; Ketokivi and Schroeder, 2004 ) that undermines the validity of studies with data from a single source at a single point in time. Secondly, the current study focuses on the organization perception rather than actual adoption.
Introduction One key point of the organization of a manufacturing system is the planning of its production. Each enterprise has several customers, with their specificity and delivery dates. The enterprise has a given quantity of resources (human and/or material, for example machines) that it has to occupy in order to have the best possible return and to satisfy as best as possible customer demand. When an enterprise is selling only one type of product, planning may be easy. But when the number of product types, customers or resources is increased, planning becomes more complex. Finding an optimal solution may be difficult. Enterprise Resource Planning (ERP) is an informatic tool which can manage every departments in an enterprise. Information about departments are stored in an unique database, all data are shared, that avoid unnecessary re-entry of information.
Conclusion and Further Work This paper presented an application of our generic decision support tool to an application of lot sizing and scheduling in a textile industry. The results given by our method are better than the previous known results. Because our tool is generic, it is easy to use with a minimum work development. The only things to do are to build a list algorithm, to compute an objective function, and to transform the real data in usable instances. Many problems can be solved as long as a list algorithm can be used to provide a solution to the considered problem. As a first perspective, our tool could be used connected with the Information System of the company. Internet of Things may collect data on the shopfloor to better describe the system. Scheduling and assignment solution could be transformed to Fabrication Orders directly transmitted to the shop-floor via the Manufacturing Enterprise System.
Thanks to a connection to the Enterprise Resource Planning, when a customer gives a new order, our tool could simulate when this customer will be delivered, thanks to our criteria. Our tool could be improved by using other metaheuristics such as population based metaheuristics, for example particle swarm optimization. Using this, our tool could be performed using parallel computation on GPUs. Then, we could implement a database of our industrial problems we already solved, to better solve the next ones. To assess the quality of our result, we should find some benchmarck in the literature. By solving the available data, we will be able to position the quality of our tool among all other existing methods. Existing methods are dedicated, so the fact that our tool is generic is already an originality.
Data analysis The data analysis comprised three main steps. First, we wrote thick descriptions of the global context of the national science-and-technology policy, the development of N&N, the research activity and space reconfiguration of each team, and the political and scientific environment in which the teams operated. Second, we identified the extent to which teams reconfigure their physical, social and mental spaces, drawing on five codes derived from the literature  (Hernes, 2003 (Hernes, , 2004b)). Physical space was coded along two dimensions, which we refer to as: (1) the rationale for the construction of the infrastructure that hosts the team and equipment, and the pressure that enabled or constrained this construction; and (2) the formal rules of the organization. Mental space was coded along one dimension, referred to as: (3) the extent to which organizational members make sense of N&N, and the meanings and symbols that are related to it.
As Epsilon is embedded in an established discipline, the core of Epsilon's identity is surface science. This is reinforced by the nature of the research conducted, which is more monodisciplinary than multidisciplinary, as promoted by policy-makers: 'Our research is basic research, and publications are just very, very basic, fundamental research-type publications' (Epsilon, team leader). This third organizational response is of interest, as even though the physical space was not reconfigured, N&N was still found at the periphery of the mental and social spaces. Although we did not observe in our data a core integration of N&N into the social and mental spaces without integration into the physical spaces, this finding suggests that the social space is constructed in practices, and in relation to the meaning attached to the physical space.
• Ports: The expressive power of a component model depends mainly on the quality of its ports  (Bennouar, Khammaci and Henni, 2010). In HACS ports are the interaction points between HACS components, they are classified into two types; data ports and control ports. • Data ports: are composed of a set of data Parameters (in, out, in-out) to transmit data and action Parameters: to exchange the service flow between components. • Control ports: are specific ports divided into three types: (1) The exception port is to throw an exception, (2) the event port is to expose conditions required to execute a component (3) The usability port contains a float value to express the usability value of a HACS component. The usability value serves for determining the optimal value representing the quality criteria of a HACS component c by maximizing positive non-functional attributes X, such as reputation, availability and reliability and minimizing negative non-functional attributes Y, such as the execution cost and response time.
Extending outside the boundaries of the firm, for Company B, continual training plans empowered the IT organization to become more effective in supporting the customer base. Training sessions were carried in-house. Chosen team members were assigned and trained on specific technologies. They attended conferences to stay ahead of the learning curve and setup labs and databases of training materials for in-house training activities. In-house training facilitated the spread of knowledge and reduced the corresponding knowledge acquisition costs. Company B involved the employees, as their internal customers, in the deployment of hosting projects. The IT organization established biweekly knowledge sharing sessions with internal customers (employees) in order to discover the challenges and help reduce adoption issues.