dataesr/scientific-paragraphs-categorization huggingface dataset:
For those reasons, the authors recommend (1) tying individual salespeople's rewards to the team's performance and (2) creating a team structure that has both high task and outcome interdependence. Next, organizational support appears as a third antecedent factor to CKM. Information systems literature insists on the implementation of business intelligence infrastructures such as CRM and other software and hardware systems to facilitate customer data collection, processing, and dissemination within the organization  (Piccoli & Yves, 2005). On the other hand, marketing literature insists on the ability to recognize and measure the value of customer knowledge  (Yli-Renko et al. 2001). The last driver of CKM raised by the review is inter-functional cooperation  (Menguc et al. , 2013; Mithas et al.
In this article, we include such a comparison of prices across different firms by elaborating on  Ayouba et al. (2019)  who recently introduced the concept of price advantage based on a comparison of prices among peers. This price effect is calculated indirectly by the difference between two different inefficiency scores: the first based on data in quantity and the second on data in value. Consequently,  Ayouba et al. (2019)  interpreted the difference as a price advantage if a company's evaluation is better with values rather than quantities; in this case, the company benefits from a better price environment for inputs and outputs compared to its peers. The authors have clearly shown that this overall price advantage is different from the concept of allocative efficiency, which considers only the evaluated business unit's prices.
(Chambers et al. , 1996; Chambers et al. , 1996a,b)  and  Chambers (2002)  was constructed to measure total factor productivity, using quantity data for estimations. We extend this indicator to value-base data in order to estimate an overall performance indicator. Our indicator is well fit to respond to business unit managers and decision makers' quest for improved performance in the short-and mid-run and thus keeps the input/output mix almost unchanged. Note that analyses based on profit maximisation approach not only do not use peers' price information in the evaluation process but furthermore apply to long run decisions where the input/output mix is more flexible. Juo et al. (2015)  is one of the few analyses that used a Luenberger framework to disentangle quantities and prices effects from a profitability indicator change.
First, as shown above, because our directional distance function has a direct economic interpretation and, secondly, by imposing strictly positive scores, we ensure that the input/output mix and the DMU size stay relatively unchanged. However, both models share a common feature related to the estimation of individual inputs and outputs scores: the possibility to recover specific Luenberger indicators for each of the inputs and outputs. The directional distance function in equation (  6 ) is obtained using quantity-base data. However, a similar measure can be defined based on value data (revenues and costs). In line with  Sahoo et al. (2014)  and  Zhao et al. (2019) , we assume that the value-based technology satisfies the same standard assumptions as the quantity-base technology. Indeed, it is acceptable to consider that: (i) strictly positive revenues cannot be obtained from zero costs (no free lunch), (ii) infinite revenues are not allowed with a finite cost vector, in other words, there is always an upper bound for each output price (boundedness), (iii) the value-based technology is closed, (iv) less output revenues can always be obtained with more costs and inversely (free disposability), (v) if two cost vectors can be used to obtain two output revenue vectors, then any linear combination of these cost vectors can also be used to obtain some linear combination of these value vectors (convexity).
Given the equation of the Luenberger value-efficiency change (LVEC) indicator in equation (  14 ), we observe that this measure can be rewritten as:  Ayouba et al. (2019)  introduced the static price advantage measure as the increase in a DMU's profit margin rate resulting from a favourable input or output price environment. In the same spirit, we define it as the gap between technical inefficiency scores measured with value data and physical quantity data: LVEC = [ D t V ( V t ; G V ; α V ) -D t Q ( Q t ; G Q ; α V ) ] + D t Q ( Q t ; G Q ; α V ) - [ D t+1 V ( V t+1 ; G V ; α V ) -D t+1 Q ( Q t+1 ; G Q ; α V ) ] + D t+1 Q ( Q t+1 ; G Q ; α V ) (17) PA t = D t V ( V t ; G V ; α V ) -D t Q ( Q t ; G Q ; α V ) (18) A positive value for the price advantage measure is interpreted here as the sign that the observed DMU suffered from a disadvantageous price 3 Another possibility is that the cost has decreased more rapidly than the decrease in the output revenue or that the output revenue has increased more rapidly than the cost.
This highlights again that Industry 4. 0 is not only a technological-adoption process. Moreover,  Cugno, Castagnoli, and Büchi (2021)  verify that firms adopting increasing number of 4. 0 technologies, perceive increased performance, perceive increased barriers and adopt increasing number of incentives. However, firms perceiving greater economicfinancial barriers do not adopt more incentives. This highlights a mismatch between real firms' constraints and incentives design. The above mentioned relationships between Industry 4. 0 adoption, performance perception, barriers perception, and incentives adoption are verified by  Cugno, Castagnoli, and Büchi (2021)  through a quantitative analysis on secondary data from Congiuntura Industriale in Piemonte dataset collected by Unioncamere Piemonte in 2019.
0 phenomenon) to 2022. Then, an online search using Web of Science database is carried out. From Web of Science, 12 articles are identified: 5 for the engineering manufacturing field, 3 for engineering industrial field, 2 for engineering mechanical, material science multidisciplinary fields and 1 for automation control systems, computer science theory methods, construction building technology, engineering civil, engineering multidisciplinary and engineering electrical electronic fields. 5 out of 12 articles are proceedings while the others are published papers on scientific journals, and in particular 3 articles are published by Springer Editor. From these articles, 4 articles are eliminated because not strictly related to the specific topic of the research. At this stage, the list is composed by the following articles: Rauch, Dallasega, and Matt, (2018);  Sanderson, Chaplin and Ratchev, (2019) ;  Hwang et al.
Data collection and focus group analysis The first stage of our research involves a focus group approach. In order to ensure highly interactive sessions and in-depth insights, we engaged with experts sampled from different domains and affiliations across the supply chain and academia. During the selection process we first verified the sample variety required to understand a variety of different perspectives and backgrounds. The sample included many researchers and practitioners from African institutions and companies. In addition, we included several tech-focused experts from countries such as Germany and the US, who are fully involved in assisting African companies and governments in designing and implementing roadmaps for AM adoption in Africa. Second, we validated the experience of each participant to confirm their adequacy to take part in the study; each member has at least 10 years of experience in the corresponding field.
Data collection and analysis from multiple case studies When selecting the case study companies, we assessed three conditions. First, we ensured that the selected firms belong to multiple industries and geographical locations within African supply chains, which enhances the exploratory aspect of this topic. Second, we verified the company's adoption of 3D printing for a minimum period of 5 years. Finally, we assessed the interviewees' positions and experience, along with their willingness and availability to participate. Table  4  lists the 11 participants interviewed from the five case companies and provides the mnemonics utilized hereafter to refer to the data for the 11 interviewees. Data collection took place between October 2020 and January 2021, through semi-structured interviews. This technique is an efficient data collection approach that contributes to theoretical development and provides rich empirical data  (Eisenhardt and Graebner, 2007).
Furthermore, significant collaboration between African companies has been emphasized as vital to support efficient supply chains through AM learning capabilities, especially during disruptive events such as COVID-19. Other participants highlighted the scalability and governance of AMbased dataset architecture for archival purposes. Some participants stressed the role of sharing information between applications using an application programmatic interface (API), visual process design environment, and command line interface. In addition, they highlighted the need for interoperability with common mining and visualization tools derived from CAD models to feed the predictive analysis of the AMbased value chain. Finally, participants stressed the need for a security model that protects sensitive nodes in the AM-based value chain and enables supply chain partners to safely access the data needed.
Insights of discussion Round 1 • AM technology generates a huge amount of structured and unstructured data throughout the supply chain and lifecycle stages. • AM systems support the data and lifecycle retention policies of production operations and the collaborative network or supply chain. • Supply chain efficiency is enhanced through the processing and the sensing of vast datasets. • The predictive analysis AM-based value chain performance is a sensing capability that can support supply chain exploitation activities. Round 2 • Integrating new approaches to process and analyze the in-process sensor data will enable the sensing of potential threats, challenges and opportunities. • Data analytics and predictive modeling approaches will leverage the in-process sensor data. • In-process sensor data acquisition and sharing across the supply chain will enhance supply chain collaboration, flexibility and responsiveness.
Round 3 • Clear policy is needed to enhance digital infrastructure, collect, store and process AM-based data. • AM-based datasets with scalable architecture that extract variable volumes of data with various governing approaches is of upmost importance for archival objectives. • Flexible interface is important to foster manual and automated processes (via API connection or command line interface) for data extraction from materials testing machines, CAD design software, AM build machines, post-processing operations, and part inspection processes. • The ability to examine collected data to mine the value chain performance relationships and export to data discovery and mining tools, simulation tools, and common applications is needed. • Graphics tools are needed to visualize data and representations from CAD models, in-process imaging and simulation outputs, and ultimately establish predicted performance against actual results.
Ambidextrous sensing capabilities of AM in building resilience and efficiency The companies studied have other similarities, besides the adoption of AM technology. AM-based sensor integration is another common practice between these five companies. Company EG is clearly the exemplar in terms of the coupling of sensors with AM. Following the launch of the 3D metal printer AMW300X for the manufacturing of heat exchangers, the company had to ensure improved temperature management. For this reason, EG integrated a highly integrated multi-vision sensor that can monitor the thermal processes of AM. The data extracted is then analyzed, highlighting both thermal and dimensional metrics to finely adjust the process parameters of melting and solidification. This real time measurement has enabled the company to optimize the process parameters, identify overheating and melt pool instabilities and maintain a traceable record on every component of the AM process.
For areas identified within the survey as under-reported, additional input was sought from a range of key selected experts. The EU project database Cordis was utilized to verify that these areas were under-researched. Results: Between May and July 2019, 318 respondents from 38 countries reported 624 research gaps. The main areas for attention identified were: urban environments; chemicals; and climate change, (combined n = 313 gaps). Biodiversity loss and health; transport, mobility, sustainable solutions and health; energy transition and health; waste and the circular economy and health; ethics and philosophy and health were areas that were acknowledged as under-researched (combined n = 27 gaps). These under-researched areas were identified as having certain commonalities, they: i) mostly fell in the category "problem or sector based approaches"; ii) they are essential for developing and implementing solutions; and iii) require trans-disciplinary and cross-sectoral collaboration.
Ongoing EU environmental health research To avoid replication of ongoing research, an assessment of recent and ongoing EU projects in the environment, climate and health nexus was completed. Using summaries of the 21 research areas, already outlined, we generated keywords to describe the research gaps. During this process it became clear that "exposome research" was not captured on the list, and this was subsequently added as a 22nd topic into the first category of "classical environment and health paradigm", together with "Covid-19" as a 23rd topic. The keywords were used to search for completed and ongoing research projects available on the EU project database Cordis (https://www. cordis. europa. eu). This was achieved by accessing and evaluating the research framework programs FP7, Horizon 2020, and Horizon Europe actively operating between 1st January 2015 and 15th June 2021, based on the available fact sheets, the results in brief, and other reporting, if available.
Acknowledgements The  HERA  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement N• 825417. MK acknowledges support from the  Spanish State Research Agency and Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX2018-000806-S ), and the  Generalitat de Catalunya  through the  CERCA Program. We'd like to extend specially thank those experts who have contributed to fill the gaps in the gaps in under-researched areas:  Kim Gruetzmacher ,  Maike Voss ,  Ariana Zeka ,  Gerardo Sanchez Martinez ,  Andrew Farlow ,  Sabine Gabrysch ,  Martin Herrmann ,  Clare Palmer ,  Nicole de Palmer ,  James Woodcock ,  Sonja Kahlmeier ,  Nick Dale ,  Alistair Hunt ,  Desislava Vankova ,  Ekaterina Valtcheva.
Research setting Data collection took place in five MNCs, 7 which are known to be the main producers and consumers of ethical tools  (Bondy et al. , 2004 (Bondy et al. , , 2008)). Data were gathered in France between 2012 and 2015 in these MNCs' head offices, which is where the ethics departments are located and ethical tools are developed. The MNCs were selected for the study on the basis of their ethics programme's stage of development (see Table  1 ). MNC 1 and MNC 3 had addressed questions of ethics for several years (around 10 years), MNC 4 and MNC 5 were only just beginning to consider them when data collection took place, and MNC 2 had had an ethics programme for 3 years. Thanks to these different levels of development, we were able to identify similarities that are specific to the ethical tool development process, independently of the maturity of the MNCs' consideration of ethics.
However, the ethics-related characteristics and roles of the management and executive committees at MNC 4 and MNC 5 were comparable to those of the other MNCs' ethics committees. At the time of our data collection, these committees principally consisted of the heads of ethics, representatives of the legal, control and/or internal audit, and human resources departments, and sometimes managers of operational branches or independent directors. The number of members in the ethics committee is stated in Table  1. The composition of the ethics committee is chosen by the executive committee (at MNC 2 and MNC 3) or the board of directors (MNC 1 and MNC 4). Three of the five MNCs studied set up an ethics department after they were involved in a scandal. These departments are very small relatively to the total workforce of the MNC (see Table  1 ).
The ethics departments answer directly to the ethics committees. The role of ethics department staff involves reflecting on ethical values for their group, and developing specific tools for implementation and oversight of business ethics according to those values. As our aim was to understand the ethics operationalization process, the data collection focused on the ethics department. But as explained below, our first few interviews and periods of observation indicated that it was important to extend the collection to cover part of the legal department which also takes part in designing ethical tools. In general, we use the term ''designers" to refer to members of the ethics department and members of the legal department who are involved in the ethical tools' design. 7 MNC 4 is a holding company.
Data collection and presentation The analysis is based on data from four sources (interviews, observations, internal documents, archives) documenting the design processes for twenty-eight ethical tools in the five MNCs studied. 8 Table  2  shows all the necessary details of the data collected. They provided information about the organizational environments and the operation of ethics programmes, and were sufficient for data triangulation as recommended by  Patton (2014). Twenty-six semi-structured interviews lasting between 25 and 180 min were conducted in four of the five MNCs studied. The interviewees were recruited by email or through personal contact at a business ethics-related event. A snowball effect  (Biernacki & Waldorf, 1981)  occurred as some of our contacts introduced us to others.
This observation mainly covered meetings, formal and informal discussions, brainstorming sessions and presentations (cf. Table  2 ). During the observation periods, we were able to chat informally with the main actors when interviews were impossible (principally due to schedule incompatibilities). We kept one journal for each MNC, using it to record verbatim statements and details of the discussions and meetings, notably listing the people present. We also included extracts from secondary data such as meeting agendas and minutes to illustrate or complement the notes made. This journal comprised around a hundred pages in total (typed and handwritten), some fifty of which related solely to MNC 5 where the observation period was longest. The rest were distributed as follows: 14 pages on MNC 2; 16 pages on MNC 3; 22 pages on MNC 4.
Data analysis The data were analysed using a method inspired by  Corbin and Strauss (2008). First, open coding was conducted to identify the first-order codes based on the recurrent ideas expressed by the interviewees  (Van Maanen, 1979). NVivo software was used for this coding. Our objective in this phase was to understand and track the design process for ethical tools while remaining close to the terms and meaning of our interviewees and the events observed. We therefore grouped these codes when they repeated each other or expressed the same idea. Next, for the purposes of our analysis we selected the codes that related to our research object. This initial phase of analysis brought out patterns and close similarities between the MNCs -although each one had a few specificities of its own -and this led us to opt for a * As the ethics committee has not yet been set up, the executive committee was in charge of its main functions at the time of our data collection.
The underlying idea is to make sure that actors will have no hesitation in ethically dubious situations. The more they discuss them with their peers or their superior, the more the decision-making process will reflect collective consideration of the corporate values. The moralist designers believe that each ethical dilemma is unique and requires some sharing of ideas. To achieve this, the moralist designers at MNC 2 made several attempts to set up spaces for anonymous discussions. One of these consisted of allowing anonymous reporting of ethical concerns, so that whistleblowing could cover a wider spectrum of issues than corruption and financial fraud. The default position of the French data protection agency CNIL 9 is that whistleblowing systems in France should not be anonymous. 10 Anonymity for whistleblowers is possible, however, subject to approval by the CNIL, when the issues reported encompass a broader range of matters such as workplace harassment.
The dominant legalists obstruct diffusion of the moralists' tools Our analysis reveals that the legalist designers very often disrupt the development process for an ethics control system initiated by the moralist designers, thus illustrating that the organizational structure of ethics favours legalistic approaches. In this article we analyse two significant situations drawn from our observations in MNCs: the first shows how the legalists successfully impose a rival training course to the one already introduced by the moralists; the second shows how the legalists manage to appropriate an ethical tool originally proposed by the moralists. Situation 1: Imposing a rival ethical tool (MNC 1) At the start of our data collection, the moralist designers at MNC 1 had designed and run a 2-day training course in business ethics for managers.
Method and sample We collected data in cooperation with the leader in the European DIY retail market through a field study. The DIY market is a high-revenue (more than €600 billion worldwide in 2019) industry that makes extensive use of omnichannel strategies, both in Europe and worldwide  (Statista, 2020). We ran a field study using a real-life setting with actual customers of the DIY retailer, which had recently implemented a 3D VR application to help its customers design a new kitchen. The application is considered an ORT because it is available via different channels and touchpoints. In addition, customers could use the app completely autonomously on their private devices (e. g. , tablets, smartphones) whenever and wherever they chose (omnichannel use condition), or they could use it inside the store on a digital terminal owned by the retailer (in-store/non-omnichannel condition use).
Data analysis and results We tested our research model using partial least squares structural equation modeling (PLS-SEM;  Fornell and Cha, 1994 ) so that we could simultaneously check the psychometric properties of the measurement model (reliability and validity) and estimate the parameters of the structural model (strength of the relationships among model variables). We consider PLS-SEM an appropriate method because our main aim is to predict retail patronage and our sample size is not large, though it meets the requirement of being 10 times the largest number of structural paths directed at a particular construct in the model  (Hair et al. , 2017). We used the SmartPLS 3 program  (Ringle et al. , 2015).