dataesr/scientific-paragraphs-categorization huggingface dataset:
Execution stage At this stage, the strategies of the planning phase were extended to filter the articles to obtain those of relevance to this review paper. The main methods applied in this review study were 1) identifying the search terms and words as an ever-evolving process, which began with using unique technical terms recognized in the area  (Hu and Bai, 2014). This study identified the following keywords: ("challenge" OR "issue" OR "barrier" OR "obstacle" OR "consideration") AND ("NFT" OR "blockchain"); 2) database filtering tools were used to enhance the relevance of the search results; features of temporal restriction were used, wherein the research period was restricted to the period from 2014 to 2021  (Zhang et al. , 2014) ; 3) subsequently, the results were manually checked with a focus on the title and abstract to ensure their relevance  (Pucher et al.
NFTs for decentralization and commons economic management The NFTs are a part of the Web3 movement, where the World Wide Web is based on blockchain technology. Even though Web 2. 0 brought massive innovation in digital technologies and its penetration into business and society, there is an over centralization of technology capabilities and data ownership with a few "Big Tech" companies, viz. Alphabet, Apple, Meta, Amazon, and Microsoft. While there is some research demonstrating that the Web3 movement can support NFTs by improving data security, scalability, and privacy  (Potts and Rennie, 2019)  and that markets are betting on NFTs as part of the future of the internet, there are also sceptics that consider NFTs to be just another technology buzzword. It is important to recognize that the underpinning blockchain technology for NFTs can support the decentralization of the internet  (Zarrin et al.
Research limitations The findings of this review should be evaluated considering its limitations. First, the review focused solely on articles published in peerreviewed journals accessible from the selected scholarly databases. Thus, broader forms of publications, such as conference papers and book chapters, were excluded. This can be a significant limitation when reviewing the erratic technology area of NFTs that is based on everchanging blockchain platforms and their application in turbulent markets. Many innovative practices and knowledge reside in the practice and contemporary applications of NFTs that may not be captured in this review. Second, the review specifically examined the challenges faced by NFTs and used this perspective to discuss the present state-of-the-art technology. Other perspectives through which to consider NFTs, for example, using an innovation lens, digital transformation or ownership view, knowledge management discipline, or legal dimensions, were considered but not included in this review.
Ronnie has also chaired prestigious research workstreams and research hub (SuperGen Energy Network) committees funded by the UKRI (EPSRC). He has also successfully developed and delivered projects funded and supported by ING Economics. Ronnie has further own applied innovations challenges organized by the European Commission including EU Datathon 2020 and European Innovation Sprint 2020. He was also invited to talk at the European Central Bank. Fadia Alhajj is an assistant professor of Finance at the American University of Middle-East, Kuwait. Dr. Fadia Al Hajj completed her BSc in Economic Sciences from the Lebanese university, 2009. She received her MSC in Economic Development and Growth, and also completed her Ph. D. on monetary policies and exchange rate regimes in Sub-Saharan Africa from Aix-Marseille University by 2018.
Method Given the nature of the research question, the study takes the form of exploratory, qualitative research using in-depth interviews to generate "rich" data  (Mayring, 2014; Myers, 2013). Background data was gathered from secondary sources, including quantitative student evaluations and curriculum analyses available through professional social media networks and other online sources. The analytical process comprised document analysis, analysis of in-depth interviews with coordinators and educators, content analysis and qualitative analysis of content  (Bardin, 2009; Mayring, 2014). Respondents were drawn from the reference-educators teaching on-site (presential) courses identified in three top-ranking business schools in Brazil. The first of these was created in 1980 by professors from the Management department of the University of Sao Paulo.
Step 4 (S4). Data was collected on the professional background and formation of the referenceeducators using the Lattes platform (the official digital platform for the academic profiles and research outputs of researchers working in Brazil) and LinkedIn. A content analysis was carried out to organize and systemize data into three categories, namely, academic formation, professional formation and publications in academic journals. Figure  2  illustrates the results of this, bringing together the data collected on the qualification of the potential reference-educator through the interviews with the course coordinators/programme managers and secondary data. Step 5 (S5). In-depth interviews were carried out with the 11 MRE that agreed to participate in the study. Semi-structured interviews took place between February and November 2018.
Marketing educators' teaching performance Step 6 (S6). All data, whether drawn from interviews, documentary sources or internet platforms, were analysed as a single data set using ATLAS. ti software. A qualitative content analysis method  (Mayring, 2014)  was used in which initial categories were created using an a priori approach drawing on themes from existing theory. These included, for example, academic background, including subcategories such as level of degree (whether graduate or postgraduate). Professional activity was also identified a priori, with subcategories such as marketing practitioner and educator. In practice, it was found that the a priori themes did not cover all the emerging findings, and new subcategories were created  (Mayring, 2014). Based on the analysis, a conceptual model was developed.
Dorotic et al. (2011)  studied five large noncompeting retailers of a Dutch coalition program and did not find evidence that point promotions at one partner significantly impacted sales at other stores. Dorotic et al. (2021)  measured cross-reward effects at a European coalition program and found that more prominent retailers benefit from reward spillovers at the detriment of smaller stores. This paper re-examines the same dataset, obtained via WCA. While  Dorotic et al. (2021)  model incidence only and use aggregate data, we model both incidence and spend and use individual-level data. Our model also allows cross-reward effects to be moderated by three dimensions of store affinity that characterize a portfolio of partners. Importantly, we take advantage of additional features in the WCA data not previously studied, notably changes in rewards across select partner stores at different times, as well as a one-time devaluation of the reward currency.
The coalition loyalty program Through WCA's data sharing program, we obtained transaction data from a coalition program with hundreds of partner stores selling a wide range of high-end product categories across a European country. As is common in other popular European coalitions (e. g. , Nectar), customers earn points and redeem rewards differently across partner stores. When shopping at a store with the coalition's credit card, customers are informed that they can earn either a low (0. 1), medium (0. 5) or high (1) number of points per dollar spent 1 , depending on the reward tier of the store. Periodically, points are automatically converted into vouchers that are received by mail and can be redeemed at select partner stores within two years 2. During the period studied, we did not observe any policy changes on voucher acceptance policies.
Data selection The data includes transactions from a random sample of customers across nearly seven years (from January 2006 to November 2012). We measure cross-reward effects only across select leading partner stores to both limit the challenges of data sparsity and the possibility of unobserved variables systematically affecting both the variation in rewards and how customers purchase across stores. We selected stores with on average at least one purchase per week during the first six years of the dataset. We do not use data at non-partner stores because all out-of-network transactions are identified with a single store ID and hence we do not observe the categories and locations for these purchases. Online partner stores are also excluded from our analysis because of the low frequency of transactions.
The first 77 months are used to estimate the models, and the last six months (June-November 2012) are kept as a holdout sample for model validation. We kept the transactions of the 1679 customers who joined the program on or before November 30, 2011 to ensure a minimum of six months of in-sample observations per person. We also aggregated spend for each customer at each store to the monthly level to reduce sparsity in the dataset (our dependent variable is the total monthly spend of each customer at each store). For months with non-zero spend, we use the logarithm of spend because this transformation results in more comparable distributions across the various types of stores which facilitates model estimation. Table  2  describes store-level summary statistics for the purchases observed throughout the entire observation period.
Geographic proximity We measure geographic proximity between stores as shown in Eq. 2, where geo:dist jk denotes geographic distance between two stores j and k and max:dist denotes the maximum distance, which in our dataset is 13. 4 km apart. If two stores are in approximately the same location, the proximity measure equals 1. At the other extreme, the two stores that are the furthest apart in our dataset have a geographic proximity measure equal to zero. A geo jk ¼ max:dist À geo:dist jk max:dist ð2Þ.
The dataset analyzed here cannot be used to easily 5 Without access to a coalition's database, a store manager interested in how familiar its customers are with other partners could proxy this metric through customer surveys. 6 The distributions of A pop jk across j (not shown) have comparable means ranging from 0. 13 to 0. 23, and overlap across all stores, suggesting that the distribution does not significantly vary by store. distinguish between the cross-reward effects of online vs. offline stores, nor to reliably estimate the parameters on a franchise affinity metric: note from Table  1  that none of the six stores with a common franchise in the sample changed their reward tiers during the observation period. Furthermore, as previously mentioned, online partner stores were excluded from our analysis due to a low transaction frequency during the observation period.
To further assess model fit in a Bayesian manner, Fig. 2  compares posterior predictive checks (PPCs) of each model's estimates for the mean monthly observation per store during the in-sample and holdout periods  (Rubin, 1984 , Gelman et al. , 1996 , Gelman et al. , 2003, p. 174). In each plot, the dotted vertical line represents the observed average monthly observation per store per person, and the grey bars represent the frequency distribution of the statistic across 300 replication datasets simulated from each model's predictive distribution. In-sample, Full is the only model with a predictive distribution that overlaps with the observed statistic. For the holdout data, Full's predictive distribution also best captures the observed statistic. We also observe a higher degree of overlap among the posterior distribution of Full and the benchmarks, except for BSP 7.
General Discussion This paper has utilized a dataset from a coalition loyalty program to assess how shared points across different types of retail stores influence how customers purchase across them, and the implications of a program-wide devaluation of points. Three dimensions of the affinity between partner stores are used as moderators for cross-reward effects. These are called popularity proximity, category proximity, and geographic proximity. Popularity proximity determined the valence of cross-reward effects in the coalition both before and after the devaluation of points. We found a smaller and more heterogenous impact across customers for category and geographic proximity. Using the model estimates, we created a heat map of cross-reward elasticities which can be useful for managers seeking to comprehend nuances in the tensions and synergies across partner stores, monitor their evolution, and to identify partners that may potentially benefit from cross-marketing opportunities.
Acknowledging the multidimensionality of these constructs while also accepting the idea that they might be experienced more globally suggest that work engagement and burnout might be better conceptualized as global entities reflecting commonalities among specific dimensions, which themselves may include specificity unexplained by these global entities. Supporting this possibility, research has supported a bifactor representation of these constructs, allowing researchers to estimate global work engagement  (Gillet et al. , 2019; Huyghebaert-Zouaghi et al. , 2021; Huyghebaert-Zouaghi, Caesens, et al. , 2022)  or burnout  (Gillet et al. , 2022; Sandrin et al. , 2022)  factors (G-factor) together with specific vigor, dedication, absorption, emotional exhaustion, cynicism, and reduced professional efficacy factors (S-factors) reflecting the unique nature of these facets beyond what they share with one another.
METHOD Procedure and participants Data were collected at two time points, over an eight-month period (October 2008 and June 2009), among a sample of teachers working in the Canadian province of Quebec. The sample was built from a list of 3000 teachers randomly selected by the Quebec Ministry of Education. Potential participants were contacted via email explaining the purposes of the study and inviting them to complete an online questionnaire twice, 8 months apart. It was emphasized that responses would remain anonymous, that participation was voluntary, and that participants were free to stop their participation at any time. The sample consisted of 1004 teachers working in primary schools (59. 8%), secondary schools (35. 5%), or other educational institutions (4. 7%) who completed the Time 1 (T1) measures.
Latent profile analyses All analyses relied on the maximum likelihood robust (MLR) estimator implemented in Mplus 8. 6  (Muthén & Muthén, 2021) , and on full information maximum likelihood procedures to handle missing data  (Enders, 2010). The main source of missingness was attrition (from 1004 participants at T1 to 663 at T2). For participants who completed each time of measurement, there were relatively few missing responses at the item level (T1:. 0% to 4. 9%, M = 2. 35%; T2:. 0% to 4. 8%, M = 2. 38%). Solutions including one to eight profiles, defined using the work engagement (global engagement and specific vigor, dedication, and absorption) or burnout (global burnout, and specific emotional exhaustion, cynicism, and professional efficacy) dimensions, were first estimated at both time points.
ACKNOWLEDGMENTS This paper was prepared, in part, as part of the first author's research internship in the  Substantive-Methodological Synergy Research Laboratory, Concordia University. Preparation of this paper was supported by grants from the  Fonds de Recherche du Québec-Société et Culture  ( 2019-SE1-252542 ) and the  Social Science and Humanity Research Council of Canada  ( 435-2018-0368 ).
Since the 1980s, knowledge-based engineering (KBE)  (Sriram et al. , 1989)   (Rezayat, 2000)  has been one of the scientific fields that supported this approach  (Danjou et al. , 2008)   (Verhagen et al. , 2012). The purpose of KBE is to be able to recycle existing knowledge for incremental innovation and to develop new material for radical innovation  (Cooper et al. , 1999)   (Van der Laan, 2008)   (Un, 2010). Through this, it is possible to automatically generate technical solutions from existing databases that are kept constantly updated. The new product development (NPD) process, which integrates knowledge management  (Hoegl and Schulze, 2005) , is useful for solutions that are integrated into environments where parameters can be completely delineated. NPD in CAD systems involves difficulties and uncertainties that increase along with the pressure to develop new products.
Although some research has been conducted to develop and integrate AI techniques to enhance the intelligent modeling capabilities of CAD systems (R. W. Mann and Coons, 1965; Brown and Chandrasekaran, 1983; Rao et al. , 1993; Fougères and Ostrosi, 2018). (Regassa Hunde and Debebe Woldeyohannes, 2022) proposes a process of merging CAD, called model-based reasoning (MBR), which is based on a litterature review of the both domains. In this synthetis, they identify 5 features defining a MBR: (1) the product parts should be stored in a hierarchical manner in order to know their relationship; (2) it is the designing experts who provide the knowledge-based reasoning and decision-making procedure, (3) the reasoning and the methodology define the rules for links of product parts (4); the cumulative analysis of the qualitative and quantitative simulation defines the efficiency of the product analyzed; (5) easy installation procedure in the database.
Interviewee selection The aim is to collect the point of view of each Style & Design actor, to make sure to assess completely the Reference Model, Impact Model, and CE dimensions of the company. It was not possible to interview each actor, because of a lack of time and resources The aim was to create the largest possible sample to ensure theoretical saturation when processing the data while making sure to capture all the types of points of view and keeping the interview campaign in a reasonable amount of resources and time inside the project (under 6 months for a three years project). It was decided that the interview corpus should feature at least one interview of each profession and hierarchical level, assuming that inside a profession, the points of view wouldn't differ too much. Therefore, we employed judgmental (purposeful) sampling as opposed to randomized sampling  (Coleman and Briggs, 2002).
Data analysis A second processing was made on the transcribed corpus. For the construction of the Reference and Impact Model, we evaluate the interviews in detail (coding and classification), semi-ignorant of prior studies as advised for inductive research  (Gioia, Corley and Hamilton, 2013). First, the coding of the complete transcribed corpus is done using data-based open coding on the software MaxQDA, which generates numerous codes  (Saldaña, 2013). Each idea or subject is given its own category. Then, to reduce the number of codes and reach a higher level of understanding, we aggregated the codes to form themes. Without deleting any codes, we analyzed the similarities and differences between the codes to create the themes. Finally, for this article, a selection and additional aggregation of themes have been made to generate a summarized view of the Impact Model.
2 billion per year)  (Johnson 2017). Recently, alternative voices, including those of artisanal cheese makers  (Paxson, 2010)  and powerful Californian wine producers, have emerged in the US claiming their interest in GIs  (Barham, 2003). This question is of particular interest to the members of the Napa Valley Vintners, who due to their growing reputation have faced the issue of counterfeited and falsely labeled products, thus fostering their interest in stronger protection. The adoption by As a result of the WTO disputes under the Doha mandate (article 23), the EU has established an official and open access multilateral register for GIs (the database eAmbrosia), launched in 2019. the US TTB (Alcohol and Tobacco Tax and Trade Bureau) of some references to terroir has also indicated policy changes  (Le Goffic and Zappalaglio, 2017).
The research shows that digital champions are deriving more than 50% of their revenue from digital investments. Two-thirds of companies do not have a clear digital vision and strategy to support DT and the required culture (Price Waterhouse Coopers, 2018). Chief Information Officer (CIO) Africa analysed the state of DT in South Africa and the challenges presented by legacy systems  (CIO Africa, 2019). Legacy systems present challenges to (1) innovation (2) creativity and (3) new cloud deployments to spark progress. Technological advances are seen to change by industry, and mostly by individual organisations such as banks, which are changing faster due to an increased threat from digital banks. Insurance organisations are investing resources to catch up with the digital economy. The introduction of Amazon and Microsoft data-centres in South Africa will result in increased migration to cloud deployment, which will, in turn, increase the uptake of Machine Learning and Artificial Intelligence (AI), to derive value from data and enable improved decision making.
Interview sample environment Professionals were interviewed to obtain a detailed analysis on how the use of technology implemented during the pandemic has impacted organisations, and the workforce, with a particular focus on the digital environment and maturity in these periods. The data analytics utilises MAXQDA software for the coding process. Ten interviewees in digital management were chosen to represent different industries namely: mining, financial services, energy, retail and oil and gas, as shown in Table  3. The interviews were carried out through face-to-face discussion, and video calls through platforms such as Microsoft Teams and Zoom. The themes identified will then be used to arrive at an approach for utilising DT as a tool to build resilience in the face of disruptive risk. There is an equal distribution in the sample across the different industries interviewed.
Problems with an internal locus primarily relate to the processes through which the firm conducts its activities (e. g. , error-prone or redundant processes). Problem locus determines the necessary knowledge, which senior headquarters managers are either more or less likely to possess. We hypothesize that the involvement of senior headquarters managers will be positively (negatively) related to solution effectiveness when problems have an internal (external) locus. We collected data on 120 non-routine problem-solving projects that occurred in 60 foreign subsidiaries of 40 multinational corporations. We administered multiple surveys with different informants. We find that, on average, the involvement of senior headquarters managers is negatively related to solution effectiveness, unless senior subsidiary managers are involved.
| Sample Our unit of analysis is the problem-solving project, and we collected data on 120 projects. In total, 60 subsidiaries located in 16 European countries from 40 multinational corporations participated in this study. Most of the data (74 observations) were collected from subsidiaries located in the U. K. and Ireland, and the remainder from subsidiaries located in other European countries (46 observations). All subsidiaries were foreign subsidiaries, that is, the headquarters were in another country. We sampled widely to achieve greater generalizability and the subsidiaries varied in terms of their characteristics at the corporate and subsidiary levels. At the corporate level this variance included different industries (21 ICT, 10 pharmaceutical, 9 from other industries) and sizes (mean turnover = $30.