dataesr/scientific-paragraphs-categorization huggingface dataset:
The study follows the classic firm solicitationcustomer reaction paradigm used extensively in prior research in direct marketing  (Bult & Wansbeek, 1995; Colombo & Jiang, 1999; Donkers, Paap, Jonker, & Franses, 2006; Malthouse, 1999; Van Diepen et al. , 2009). The data consist of the donation and solicitation histories of 6,134 donors over 9 years. These donors were acquired by the charitable organization over the first 7 years, followed by an additional year of observations so that even the most recent donors have at least 1 year of historical data on which we base our model predictions. We depict the data structure in Fig. 4. We hypothesize that the firm is interested in predicting donors' monthly activity (i. e. , will this donor be donating during the upcoming month?). Donations are highly cyclical with some months receiving more donations (e.
The logit (resp. , random forest) model provides an average lift at 5% of 5. 33 (resp. , 5. 42) over 12 independent predictive tasks. In comparison, the LSTM model, which does not rely on domain knowledge or feature engineering, and uses the raw data as predictors, achieves an average lift at 5% of 6. 48. The LSTM model, therefore, provides a + 26. 60% (resp. , + 24. 05%) improvement 6  over the logit model (resp. random forest). The logit model performs remarkably well at high lift values (i. e. , 20%), whereas the random forest model shines at lower lift values (lift at 1%). This result might suggest that the best traditional model to deploy depends on the degree of targeting the analyst seeks. Random forest models are particularly good at identifying tiny niche of super-responsive donors, and therefore are well suited for ultra-precise targeting.
Logit models generalize well. Hence, they appear more appropriate to target wider portions of the donor base. Interestingly, the LSTM model beats both benchmark model across the board and performs well at all lift levels. For completeness, we report additional metrics, such as RMSE, LogLoss, Precision, Recall, and F-measure in Table  3. The LSTM model achieves superior results on these metrics as well. On average, the logit (resp. , random forest) model suffers from an RMSE + 1. 33% (resp. , + 1. 07%) higher than the LSTM model, as well as a LogLoss + 2. 11% (resp. + 74. 88%) higher. Fig. 5. Once we obtain the predictions for 1 month, we incorporate the actual solicitations and donations observed for that month in the calibration data set, we recalibrate the model, and obtain predictions for the next month.
Empirical Application #2 Objective While an LSTM model does not depend on the analyst's ability to craft meaningful model features, traditional benchmarks do heavily rely on human expertise. Consequently, when an LSTM model shows superior results over a traditional response model-as we have shown in the previous illustration -we cannot ascertain whether it is due to the superiority of the LSTM model, or to the poor performance of the analyst who designed the benchmark model. To alleviate that concern, we asked 297 graduate students in data science and business analytics from one of the top-ranked specialized masters in the world to compete in a marketing analytics prediction contest. 7 Each author participated and submitted multiple models as well, for a total of 816 submissions. With the LSTM model competing against such a wide variety of human expertise and modeling approaches, it becomes easier to disentangle the model performance from its human component.
Average time between solicitations (1) The average time between solicitations can be used to capture changes in the frequency of solicitation over time. Dummy for missing solicitation recency (1) When the customer is newly acquired and has not received any solicitations, the recency of solicitation cannot be calculated; the average time between solicitations either. We replace these invalid values with a dummy to avoid any confounding effects. campaign. The holdout data consisted of the remaining 61,744 individuals who had been solicited, but for whom the responses were unknown to the data analysts. Participants were asked to submit a file of 61,744 individual decisions indicating whether the charity should solicit or not each contact in the holdout data. If the participant indicated a solicitation for a particular individual, the charity endured a solicitation cost of 2 €, and we added the donation made by that individual to the gross revenue of the campaign, if any.
They had also completed another assignment on the same charity database and were, therefore, familiarized with the organizational context of the exercise. Each participant was given up to three trials. They submitted their recommendations (a text file of 61,744 yes/no decisions) through an online interface, and the website reported their net financial results in real-time (gross revenue, minus solicitation costs). The website provided no information about individual donations. At the end, only the contestants' top performance was retained and compared to the financial results of their cohort. The best performing contestant received 20/20 for the assignment, which constituted a significant fraction of the course's overall grade. The contestant with the worst performance received 8/20 (a failing grade).
Database The database included the complete donation history of the 123,672 individuals originally solicited for the fundraising campaign, for a total of 1,066,376 one-off donations, and 1,364,500 automatic deductions (i. e. , automated monthly payments by credit card or bank wire). Data specified to which campaign the donations were linked to, if any, as well as the mean of payment (e. g. , check, credit card, bank wire, cash) and the channel (Web donation or not). The most recent donor was acquired 2 weeks before the launch of the campaign; the most ancient made her first donation 26 years prior. The database also contained the most recent 10 years of solicitation history, for a total of 4,365,405 solicitations (earlier solicitations were not recorded in the database). It specified whether it was an online (i.
LSTM Model For this exercise, the authors developed two separate LSTM models. The first one predicted the likelihood that each donor was going to respond favorably to the solicitation (0/1), and we calibrated it on the entire calibration data  (N = 61,928). The second LSTM model predicted the donation amount in case of donation, and we calibrated it on the individuals who donated in the calibration data (N = 6,456). Both models were then applied to the holdout data and combined to obtain expected revenue from a solicitation. For the purpose of sequence generation, we grouped the data in bimonthly increments, for a total of 24 steps per calendar year (e. g. , January 1-15 is period 1, January 16-31 is period 2). Both LSTM models used the same sequences of raw indicators as inputs, namely: 1.
Online solicitation (0/1) 2. Offline solicitation (0/1) 3. Online, one-off donation (0/1) 4. Online, automatic deduction (0/1) 5. Offline, one-off donation (0/1) 6. Offline, automatic deduction (0/1) 7. One-off donation amount (0/log(amount)) 8. Automatic deduction amount (0/log(amount)) For instance, if a contact donates 50 € by check on February 4, and is solicited by email on February 11, the sequence data for that period (3rd period of the year) indicates "online solicitation = 1," "offline, one-off donation = 1," and "one-off donation amount = log(50)," with all the other indicators equal to 0. The only differences between the two independent LSTM models are: (a) the data we use to train the models and (b) the output functions. For the response model, the output is processed through a sigmoid function to ensure a probability between 0 and 1; for the amount model, the output is exponentiated to guarantee an amount prediction in the positive domain.
Applications of LSTM Neural Networks in Marketing Though we set our studies in a direct marketing context, LSTM neural networks can provide a solution to the general class of prediction tasks that involve panel data. We foresee that, since panel data is ubiquitous in marketing, LSTM neural networks can find widespread applications in marketing academia and practice. We discuss some possible applications below. The campaign of interest was launched in late June, which is an unusual timing. Additional features included the likelihood of making a donation during the summer, the likelihood of responding to past campaigns launched in June, etc. Response rate Ratios of donations vs. solicitations, either over the entire contact history, over a recent period (e. g. , three years), or over specific solicitations (e.
Brand Choice and Market Share Forecasting Using Scanner Data Demand forecasting for products within a category is a critical task for retailers and brand managers alike. The multinomial logit model (MNL) is commonly used to predict brand choice and market share using marketing-mix and loyalty variables  (Guadagni & Little, 1983). Artificial feedforward neural networks (ANN) have also been shown to effectively predict household brand choices, as well as brand market shares  (Agrawal & Schorling, 1996). Since brand choices can be modeled as sequential choices, and data complexity increases exponentially with the number of brands (with interaction effects), LSTM neural networks offer suitable alternatives. Similar to our studies, we could encode brand choices and the decision environment as we encoded solicitations and donations: as a multidimensional vector.
Technical Considerations It would be presumptuous to claim that LSTM models offer an ideal, one-fit-all solution to panel data analytics. In particular, the analyst is invited to be mindful of the following challenges. First, hyperparameter tuning is not a trivial task. While a simple grid search may be sufficient to achieve optimal performance, Bayesian optimization may be required on occasion. Second, as in all deep learning models, overfitting is a constant concern. Many solutions have been proposed, and can even be combined together, such as early stopping, L1 regularization, L2 regularization, and dropout  (Michelucci, 2018). Unfortunately, it is not clear that one approach will systematically provide optimal results, irrespective of the data structure or network architecture. Third, while LSTM models offer a markedly improved solution to the problem of exploding gradients (over vanilla RNN models), they are not guaranteed to be shielded from it entirely.
Conclusions Ben Weber (2019) stated that "One of the biggest challenges in machine learning workflows is identifying which inputs in your data will provide the best signals [i. e. , features] for training predictive models. For image data and other unstructured formats, deep learning models are showing large improvements over prior approaches, but for data already in structured formats, the benefits are less obvious" [italics added]. In this paper, we have shown that recent neural network architectures, traditionally used in natural language processing and machine translation, could effectively do away with the complicated and time-consuming step of feature engineering, even when applied to highly structured problems such as predicting the future behaviors of a panel of customers. We apply the LSTM neural networks to predict customer responses in direct marketing and discuss its possible application in other contexts within marketing, such as market-share forecasting using scanner data, churn prediction, or predictions using clickstream data.
Martínez, Schmuck, Pereverzyev Jr. , Pirker, and Haltmeier (2020)  used 274 features to predict customer behaviors in a non-contractual setting. One of the authors, who has extensive industry experience, has built predictive models with 600 features and more. Feature engineering is not only a timeconsuming process, it is also error-prone, complex, and highly dependent on the analyst's domain knowledge (or, sometimes, lack thereof). On the other hand, LSTM neural networks rely on raw unsummarized data to predict customer behaviors and can be scaled easily to very complex settings involving multiple streams of data. Feature engineering is not an obsolete skill. When model explainability and controllability are important, a simpler model with well-crafted features may be best  (De Bruyn, Viswanathan, Beh, Brock, & von Wangenheim, 2020) , even at the expense of slightly reduced model accuracy  (Rudin, 2019).
We added questions that we felt could measure the variables of interest (VOIs). If anything, it could serve as a base to develop a full-fledged questionnaire in the future. With these constraints in mind, we had our questionnaire distributed electronically to hundreds of small-time investors who were clients of a network of financial advisers with whom we were and still are acquainted. The questionnaire appears in Appendix B; we added various questions to hide the true nature of the questionnaire so as not to bias the respondents' participation. This is a common technique employed in psychological testing  (Mesly 2015). In the section that follows, we focus only on the main results to illustrate that our proposed framework could make sense under certain conditions, keeping in mind of 21 that we take a psychological approach as opposed to large databases commonly used in econometrics.
Results We received 221 responses, of which 195 were deemed valid, accounting for 48. 5% female and 51. 5% male. We divided the results into two parts: one running linear regressions using SPSS 25 and the other exploring a structural equation model (SEM) using Amos, part of the same IBM statistical software. The key significant linear regression appears in Table  5. Nearly 46% of the variance in what we define as a spinning debt (calculated as the average of debt + exuberant + unsustainable) is explained by risky behavior (calculated as the average of speculative behaviors + poorly diversified portfolio + short-term horizon). The relationship is linear and significant at 0. 000. This presumes that each component of each VOI is independent and has equal weight. This result is in line with standard financial theory.
Methodology The present study was based on data from mystery shoppers, collected by a service provider offering mystery shopper data in Scandinavia. The total sample comprised 515 store visits by mystery shoppers at 15 distinct consumption contexts in Sweden (e. g. , building materials, home decoration, books; see the Appendix). The shoppers filled out a brief questionnaire dealing with their store visit. Specifically, they stated their impressions of the employee with whom they mainly interacted while being in the store and indicated whether the employee responsible for their primary service encounter was a man or a woman. The proportion of female employees (61. 6%) was generally larger than that of male employees (38. 4%), consistent with the notion that women are over-represented in sales and service occupations  (Brockbank and Airey, 1994; Pettinger, 2005; Pilcher, 2007; Sparks, 1992).
We used the most characteristic items for warmth and competence, as available in our dataset, and thus relied on "friendly" and "competent," respectively (cf. Söderlund and Berg, 2019). While these items were highly correlated (r = 0. 72, p <. 001), they are still theoretically distinct. Furthermore, the variance inflation factors (VIFs) were only slightly above 2. 00, well below the standard cutoff values of 5  (Hair et al. , 2006)  or 10  (Neter et al. , 1989) , indicating that multi-collinearity is unlikely a threat to the interpretation of our results. To test the roles of warmth and competence into the interplay between gender congruency and WOM ratings, we proceeded as follows. First, we conducted a simple mediation analysis with competence and warmth as parallel mediators (PROCESS Model 4).
Limitations and future research The present research is not without limitations. Our dataset did not specify the gender of the shoppers, although information from the service provider suggests that roughly two-thirds of the shoppers were women. While the lack of this demographic detail somewhat limits the confidence in our congruency conclusions, it should be noted that gender congruency effects have frequently been found to operate irrespective of consumers' own gender (e. g. ,  Beldad et al. , 2016; Krishna et al. , 2010; Neale et al. , 2016; Wilder et al. , 2018). Nevertheless, future research should optimally examine the role of shoppers' own gender and the gender composition of each customer-employee dyad to get a better understanding of different gender (in) congruency effects. Whereas similarity-attraction and homophily theories postulate more favorable consumer responses in male-male and female-female dyads  (Byrne, 1997; McPherson et al.
Greenwashing We obtain greenwashing data from Trucost. We follow Marquis et al. (  2016 ) and measure greenwashing (GW )as the difference between two ratios that Trucost developed to assess companies' environmental transparency; that is, absolute disclosure ratio minus weighted disclosure ratio. This measure seeks to assess the extent to which symbolic transparency (measured by absolute disclosure ratio) exceeds substantive transparency (measured by weighted disclosure ratio), i. e. , it proxies for the potential tendency of companies to create a misleading impression of transparency and accountability by disclosing relatively benign environmental metrics rather than those more representative of their overall environmental harm. The absolute disclosure ratio is the proportion of relevant environmental indicators for which a firm discloses quantitative figures.
Environmental costs We employ environmental cost data from Trucost to capture firms' environmental damages (DAM AGE). Trucost provides dollar values of firms' environmental damages. They use an input-output model that assesses firms' environmental impact across operations, supply chains, and investment portfolios to compute a firm's environmental costs. Trucost's model estimates the firm's total emissions and then multiplies these quantities by their respective environmental damage cost factors derived from prior environmental economics literature  (Trucost, 2014). A firm's environmental damages are based on six areas of direct and indirect emissions: greenhouse gases (GHGs), water, waste, land and water pollutants, air This table presents the sample distribution by year and industry. pollutants, and natural resource use.
Sample We merge greenwashing data from Trucost with PMC data from  Hoberg et al. (2014)  and  Hoberg and Phillips (2016)  and financial data from Datastream. The number of US firms featured in both databases is 393, resulting in an initial sample of 2,848 firm-year observations for the 2005-2015 period. The non-availability of necessary control variables for some firms reduces our sample to a final size of 324 US firms over the 2005-2015 period, yielding an unbalanced panel of 2,024 firm-year observations. Table  1  presents a detailed distribution of our sample observations across years and industries.
Columns 1 to 3 feature models excluding the interaction term with environmental damages. The direct impact of PMC on greenwashing does not appear to be significant, regardless of the PMC measure used. Columns 4 to 6 report regression results for our model including the interaction term. Again, the direct relationship between PMC and 6 Including various relevant control variables as we do reduces engogeneity concerns. However, we cannot completely rule out the possibility of ommitted variables issues. A quasi-natural experiment using shocks to competition intensity (e. g. , tariff reductions) would help alleviate this endogeneity concern. However, because of the limited number of tariff reductions after 2005 (the year in which Trucost data become available), we cannot use this approach. We acknowledge this limitation.
g. conferences, working groups), online gatherings (e. g. webinars, interactive platforms), the establishment of science-policy interfaces (e. g. via boundary organizations), member/expert databases and by enabling inclusive participation (e. g. through selection of committee members and targeted funding to less well-resourced participants). These activities require careful planning and facilitation, as well as accountability towards the interests of different parties (e. g. conference formats that are made accessible to nonscientists or non-native English speakers) [18, see also Refs. 30 ,38]  (see also Appendix B).
First, the networks' co-production-related strategies, approaches and activities were assessed. To achieve this, the core team conducted interviews with the coordinators of the networks and analysed their websites, strategy documents and other available resources, such as scientific papers. In addition, to gather the perspectives of network members themselves, the core-team and representatives of some networks organized workshops at network gatherings where various members discussed potentials and limitations of co-production at network level. Data were analysed with procedures of qualitative content analysis  [43]. Insights of these analyses were summarized and shared with the actors involved. Second, to deepen the analysis and enable learning and co-production between the different networks, several workshops were organized (four workshops in Switzerland with GLP, BioDiscovery, GMBA, PAGES and MRI; one workshop in South Africa with GLP, SAPECS, Lira 2030, ITD Alliance, T2S).
On Step 01, the data collection was firstly performed via secondary data, using academic literature and grey literature such as technical reports, news, magazines, documentaries  (Cavazza et al. , 2019a; Threlfall and ThoughtLab, 2019). Through this literature review, it was possible to define key dimensions and to list a series of general CSFs that could fit in the AVs' scope. This data served as pre-defined categories of analysis to be used on the next step. Thus, we were able to select 18 factors that were organised in a questionnaire, containing their labels and a guideline so that the respondents could rank them in order of importance from 0 (lowest importance) to 5 (highest importance). As the methodological intent of step 01 was to map and realise a general verification about CSFs closely related to the insertion of AVs as PSS in a country, it is worth to highlight that, based on the SAE levels of automation (SAE, 2016), for the questionnaires were considered the levels 4 and 5 (any kind of human intervention required during the operation).
The questionnaires were submitted to pre-tests with professors and doctoral students from the Federal University of Lavras -Brazil before conducting the primary data collection. After this, they were applied to experts in urban mobility at the 26th International Colloquium of Gerpisa from 11 June to 14 June 2018. Were obtained 37 responses, being these from eight different countries: USA (1), Norway (1), Germany (1), England (1), Colombia (1), Mexico (2), France (7) and Brazil (23). Next, all the questionnaires were tabulated in order to comprehend which CSFs are more and less critical in terms of AVs as PSS. These outputs were transformed into extra input to generate a matrix for data analysis and categorisation. On Step 02, a systematic integrative review was developed to cover all the possible CFSs (at a macro level) for countries found in the literature.