dataesr/scientific-paragraphs-categorization huggingface dataset:
We received usable data from 51 subjects who used the AR. The instructors guessed and coded the participants' age (≤ 30 years: 41. 9%, 31-50 years: 32. 5%, > 50 years: 25. 6%) and gender (male: 25. 6%, female: 39. 5%, male/ female couples: 34. 9%). We applied the app's tracking data and check-out scanner data. The AR tracked the time shoppers used the device. The subjects used the app for 86. 1 s (SD = 90. 7). Excluding the time for reading the instruction screen, the net usage time was 66. 9 s (SD = 41. 0). Having finished their shopping, we asked the shoppers to hand in their shopping receipts. We coded the rush hours and more relaxed shopping hours as in the main study. As an additional measure, we coded the crowding in the retail area from the scanner data. For this proxy, we determined the number of customers in the hypermarket who purchased within 30 min (M = 17.
The model and corresponding performance pay-off simulations allow us to make sense of the complex interrelatedness among our key concepts, and to formulate hypotheses. The interaction between customer feedback quality and sensing customer needs capabilities is expected to have a positive influence on a firm's innovation success when its offerings are standardized. The interaction is predicted to have a negative effect when firms offer customized services. In a second stage, we use field data from a firm-level survey of 292 Dutch firms to test the extent to which sensing capabilities and customer feedback are related to innovation-based turnover. The regression results support the findings from the stylized simulation model. Contrary to their less-customizing or non-customizing counterparts, customizing service providers can fail to increase innovation-based output when focusing strongly on articulated customer needs.
Survey We examine our hypotheses with a dataset based on a survey deployed in 2011. Items in the questionnaire covered a variety of topics, including general company characteristics, innovation types, innovation success, dynamic capabilities, innovation processes, innovation partnerships, innovation barriers, and market environment. The majority of the items were retrieved from existing scales. The questionnaire has been subjected to rigorous pre-testing procedures, including feedback collection from academic peers as well as respondents. In general, most items make use of a 7-point Likert scale ranging from 'strongly disagree' to 'strongly agree'. The measures for variables used in this study are discussed below.
Sample The survey sample consisted of single-business firms or business units with more than 10 full-time employees. Using databases from Bureau van Dijk, we retrieved contact information from Dutch firms located in the Northern Randstad (i. e. the Amsterdam Greater Region and the Utrecht Greater Region). Availability of demographic information about the entire population allowed us to stratify in terms of sector and firm size; we created a multi-industry sample representative of the industry composition in the Northern Randstad. The questionnaire was sent, in two consecutive waves, to 8054 firms. We addressed the questionnaire and accompanying letter to the CEOs or senior executives, in order to ensure that the respondents were knowledgeable about the key firm processes under investigation in this study.
As the significance of our main result is somewhat low, we conduct an extra test to ensure that the interaction effect is truly different from the structural effects caused by the non-linear nature of our model  (Ai and Norton 2003). Bowen (2012)  provides a procedure for assessing the 'secondary' effect caused by the actual interaction effect. Applying his Stata-code shows that the 95% confidence interval stretches from -2. 535 to 0. 056, implying a secondary model fit of p = 0. 061. Furthermore, the floodlight analysis reveals that, for customizing firms, the positive interaction starts to be significant when C. R. exceeds a value of   Sensing Customer Needs.
The Global Burden of Disease (GBD) is a compass that navigates researchers and clinicians in discovering the latest knowledge and the current status of health for populations and countries worldwide. The GBD helps to present various methods in evaluating health-related quality of life and assess the health burden, economic burden, years of life lost, and healthy life-years lost from each disease. Beforehand, the details and development status of the dataset need to be identified, and the advantages and disadvantages of each research methodology should be analyzed. The aim of this paper is to help readers comprehend the above basic knowledge of GBD above a certain level. This paper also exemplifies some recent studies to help readers who yearn to understand in detail the latest research using GBD.
Introduction The history of Global Burden of Disease (GBD) began in the early 1990s with the first landmark study: "World Development Report 1993: Investing in Health". [1]  This was able due to the commission of the World Bank. The 1993 GBD study soon became a role model that systematically measured the world's health problems, analyzing various diseases and aftereffects in representative regions and age groups. Ideas and data of GBD have inspired clinical doctors and researchers to continue expanding their research. [2]  GBD 2019, the latest edition, is the global standard and provides independent estimates of 204 countries, territories, and populations around the world free of charge. Many additional factors, including mortality, life expectancy estimates, disease lists, and risk factors, haven been continuously added and have contributed in improving the analytical methods  [2].
Diseases and territories covered Until the GBD study 1990, 107 diseases and 483 non-fatal complications were researched through a dataset which covered 8 regions and 5 age groups  [3]. Moreover, the GBD 2010 distributed a dataset of estimates for 291 diseases and injuries, 67 risk factors, 1,160 aftereffects in21 regions, 20 age groups, and 187 countries  [4]. In the GBD 2013 study, 188 countries including more than 300 illnesses and injuries, 79 risk factors, and 2,300 aftereffects were covered, with many landmark studies conducted on smoking  [5]. Researchers expanded the GBD to 315 diseases and injuries and 79 risk factors for 195 countries in 2015  [6]. The total of locations became 774 in 2016  [7] , including 333 diseases and injuries, 84 risk factors, and 23 age groups  [8]. The GBD 2016 study included research on alcohol, gun accidents, etc  [10].
In the GBD 2017, the dataset found a significant increase in temporal coverage. Estimates of mortality and life expectancy were increased compared to 1950, and a total of 359 new causes of disease and injury were added to the list of fatal and non-fatal causes  [8]. Contemporarily, GBD also added one new risk, bullying victimization, and 80 new risk-result pairs  [8]. The latest GBD, GBD 2019, contained the most extensive data  [2, 9]. Estimated mortality and life expectancy values have been even more increased to the most detailed level to a total of 990 locations, with new causes added to the list of fatal and non-fatal causes resulting in 369 illnesses and injuries  [2, 9].
GBD. The most representative of those advancements is the development of the Disease Burden Unit (DBU)  [4]. WHO created the DBU which was able to generate GBD estimates in 2000, 2001, and 2002, and published estimates in the annual World Health Report  [11]. The annual update to the entire series of GBD estimates began since GBD 2015  [6]. These more frequent updates, containing the most useful and timely picture of population health, were provided to policymakers, donors, and other decision-makers. The 2015 update also introduced the Socio-demographic Index (SDI), a summary measure that extended the methodologies, datasets, and tools used in previous GBDs and identified where countries or other regions are in the development spectrum  [6]. SDI, expressed through a scale from 0 to 1, is the composite mean of per capita income, average education level, and fertility ranking in all areas of the GBD study  [6].
GBD 2019 schema The fundamental aspect of the GBD data schema is the age, sex, country, and year of the diseases. Most important estimates can be derived from these core variables. In addition, GBD 2019 demographic data include population; the analogized rate of population change (2010 to 2019), total facility rate and live births  (1950, 1980, and 2019) , and net productive rate  [2, 9]. More detailed data are provided on deaths. They include under-five mortality rate, change in under-five mortality rate (2010 to 2019), probability of death between ages 15 and 60 years and life expectancy at birth (by sex), healthy life expectancy, the total number of deaths, the total number of deaths among children under 5 years, and observed and expected life expectancy  [14]. In addition, life habits and important causes of disease like alcohol consumption and daily smoking are included  [10].
Mortality-to-incidence ratio is calculated by dividing the mortality rate for the year by the incidence rate. This is an indicator used to identify inequality in cancer prognosis. Mortality-toincidence ratio is a higher-level comparative measure of a crude survival estimate than relative survival. Because of its simplicity, high-quality incidence and mortality data are available in most countries, so survival can be compared internationally  [17]. Spatiotemporal Gaussian Process Regression (ST-GPR) is a time series model primarily used to estimate risk factor exposure. This smooths the value for each risk factor. Through this, interpolating non-linear trends are possible and only meaningful signals are extracted from noise. [2, 9]  The Healthcare Access and Quality (HAQ) Index measures the access and quality of medical benefits by the use of amenable mortality  [18].
Examples of GBD 2019 Study One of our team's latest examples using GBD 2019 was carried out using the sudden infant death syndrome (SIDS) variable  [2]. This article analyzed the global disease burden of SIDS and its trends from 1990 to 2019. The result was compared to the burden of SIDS according to the SDI. In this article, we collected epidemiological data from 204 countries from 1990 to 2019 using GBD, including vital statistics and civil registrations. Through this, estimates of SIDS were modeled in terms of disease burden and mortality. Mortality rates per 100,000 population, crude mortality, and DALYs were also calculated. The results showed significant reduction in the SIDS burden in regions from 1990 to 2019, and SDI levels were important factors the reduction. Another critical study is a landmark study on smoking  [21] , measuring tobacco use from Alcohol has also been addressed by GBD researchers  [10].
This article used individual and population-level alcohol consumption. Estimates were calculated in terms of current drinking, abstention, alcohol-attributable deaths, and DALYs. The research standardized daily alcohol consumption as 10g of pure ethyl alcohol. Alcohol sales estimates were adjusted using tourist and unrecorded consumption. This research found the important conclusion that the risk of cancers and all-cause mortality has no threshold from alcohol. In other words, risks immediately increase according to consumption, and the only safe dose of alcohol is zero, which contrasts with the conventional view that small amounts of alcohol do not have negative effects on people's health. The final research example is global mortality from firearms  [22]. Researchers used deidentified aggregated data to generate estimates.
These include location-years of vital registration data and rates of death. Revised proxy measures were used to evaluate the firearm injury deaths from firearms accessibility (or availability). The proxy used isa combination of per capita gun ownership and the proportion of suicides by firearm. In this article, crucial understandings of global variation in firearm mortality rates were derived, which will support future intervention and prevention policies. Also, the researchers were able to estimate the specific value of firearm injury deaths that happened worldwide in 2016, approximately 0. 2 million.
The built-in feature of the smartphone allows consumers to store debit cards, credit cards, or reward card information to facilitate the payment transaction. Typically, the installed software is set up by inputting the user's card payment information, which is connected to a personal identity format such as a number, quick response (QR) code, or the owner's image, and stored in the wallet. Mobile wallets allow quick, convenient, and timesaving in-store payments as opposed to paying with cash or carrying physical credit cards  (Mombeuil, 2020; Mew and Millan, 2021). The mobile wallet program employs a technology known as near-field communication (NFC)  (Leong et al. , 2013 (Leong et al. , , 2020)) , which enables communication between devices through radio waves. The NFC communicates payment data with the dealer's point-of-service (POS) terminal using the personal identity format developed for the user  (Leong et al.
g. Facebook, WhatsApp) to encourage them to participate in the study. An incentive worth V 1. 52 (or CFA 1000) was offered to those who completed the survey as qualified participants. The reward was instantly sent to each participant's m-wallet account after the survey completion based on the information supplied. As a result, we checked for duplicate cases and eliminated the associated data from the dataset. To ensure data representativeness, participants were drawn from different age groups, genders, experience levels, locations, and educational levels. Initially, 741 active m-wallet users volunteered to take part in the survey. After a thorough review of the responses, 621 questionnaires were correctly completed and found to be suitable for further investigation, resulting in an 83. 81% response rate.
Analysis and results The sample comprised 621 respondents. As shown in Table  1 , males accounted for 65. 5% of qualifying responders, while females accounted for 34. 5%. Most participants (61. 7%) were between the ages of 18 and 25. The sample was dominated by undergraduates (51. 7%). Partial least squares structural equation modeling (PLS-SEM) from the SmartPLS 3. 3. 5 software  (Ringle et al. , 2015)  was used to examine the theoretical framework. The PLS-SEM technique is a causal modeling approach that focuses on maximizing the variance of the dependent latent constructs explained by the independent variables. The rationale behind the employment of PLS-SEM (variance-based SEM) in this research stems from two distinct reasons. First, since this study explores the factors affecting mobile wallet adoption rather than its confirmation, PLS-SEM is considered a more attractive and appropriate technique in such situations  (Hair et al.
Conclusions This study aimed to investigate and determine the factors that influence the adoption of mobile wallets in low-income and cash-based economies, using Cameroon, a Central African country as the case study. From previous research, eight hypotheses were developed. A research model was developed using DIT, TAM, and ISSM frameworks constructs. A survey was used to collect empirical data from 621 mobile wallet users in Cameroon and test the model. The findings of this study indicate that five factors, namely relative advantage, perceived usefulness, information quality, system quality, and service quality, positively impacted mobile wallet adoption. In contrast, the image indicated a negative impact. Compatibility and perceived ease of use were found not to significantly affect mobile wallet adoption in Cameroon.
Data analysis While reconsidering the relationship between human and non-human beings would suggest not deciding in advance what is part of the natural or of the social  (Latour, 1993) , our will to navigate through imaginaries and moral grammar associated with biodynamics leads us to capturing human beings' perspectives. We focussed our analysis on two types of data in which biodynamists reported their visions and modes of interaction with non-humans: bibliographical data to capture the principles of biodynamics; primary data to identify how actors experiment with those principles through daily practices and the tensions they face in practical situations. We were then able to decipher the internal coherence of the discourses that support biodynamic imaginary. The data analysis followed an abductive approach  (Ketokivi and Mantere, 2010)  alternating between data collection, data content analysis with NVivo 11 software and the refining of theoretical dimensions.
To address our research question, we seek evidence from association tests based on variations in the independence of analyst coverage as well as from a quasi-natural experiment. Although one might argue that evidence based on quasi-natural experiments is superior to association tests in terms of internal validity, quasi-natural experiments tend to restrict empirical analyses to smaller datasets than association tests, casting doubt on external validity. Therefore, we use both association tests and a quasi-natural experiment to examine our research question  (Armstrong et al. , 2022). We first test whether the ratio of independent analysts to brokerage analysts is associated with the level of discretionary accruals, controlling for total analyst coverage and other factors affecting earnings management.
Data Our ten observations in a given year,  15  (3) no analyst coverage and (4) missing values for the calculation of our variables. We obtained 26,475 observations in the final sample, of which 5,439, 13,819 and 7,217 belong to the pre-Global Settlement, Global Settlement and post-Global Settlement periods, respectively. For analyses with INDEPRATIO as one of the independent variables, we lose 346 observations with no brokerage analyst coverage as the variable is undefined, resulting in a sample of 26,129 observations. We discuss the sample relating to the estimation of equation (3) in Section 5. 14 See footnote 4 for more information about various effective dates of the Global Settlement components. DA is the absolute value of discretionary accruals estimated using a modified  Jones (1991)  model  (Dechow et al.
We rely on  Kelly and Ljungqvist (2012)  for a list of 17 cases of closures and mergers of sell-side research providers during the five-year Global Settlement period. The list includes 12 brokerage houses and five independent research institutions. 21 Following  Irani and Oesch (2013)  and  Chen et al. (2015) , we consider a one-year window prior to and a one-year window subsequent to each closure or merger and eliminate observations with missing data for the dependent and control variables. We further require that both windows must fall completely in the five-year Global Settlement period; that is, the one-year window prior to the closure or merger should begin on or after August 1, 2004, and the one-year window after the closure or merger should end on or before July 31, 2009. This screening leaves a sample of 1,016 firm-year observations relating to brokerage house closures or mergers and 630 relating to independent research institution closures or mergers.
Following  Chen et al. (2015) , we define an indicator variable, CLOSURE, which equals 1 if an observation falls in the period after a closure or merger (of either a brokerage house or an independent research institution), and 0 otherwise. In column (1) of Table  5 , we first replicate  Chen et al. (2015)  using data from the Global Settlement period. Consistent with  Chen et al. (2015) , the coefficient on CLOSURE is positive and significant, indicating an increase in earnings management following an exogenous decrease in analyst coverage due to closures and mergers. In column (2), we introduce a dummy variable, INDEP, which equals 1 to indicate observations relating to independent research institution closures or mergers, and 0 otherwise, and the interaction between CLOSURE and INDEP. The coefficient on CLOSURE remains positive and significant.
ACKNOWLEDGMENTS We thank  Paul André ,  François Aubert ,  Francois Degeorge ,  Hervé Stolowy  and participants at  ESSEC Business School , the 2014  EAA Annual Congress , the 2014 AAA Annual Meeting, the 2015 SFM Conference and the 2016 AFC Annual Meeting for comments and suggestions. Open access publishing facilitated by  RMIT University , as part of the  Wiley -RMIT University agreement via the Council of Australian University Librarians.
We extended  van Dick et al. 's (2018)  data set with new data from six additional countries: Brazil, Egypt, Iran, Portugal, Spain, and the UK. This left us with data from 23 different countries. Further, we incorporated the scores for culture clusters for each country based on available data from the GLOBE project  (GLOBE, 2020). Table  1  provides an overview of culture cluster membership, sample size, and demographic data. Our final dataset, scripts, and outputs are available online (at https://bit. ly/3u4JthV). Our final sample included data from 7,225 participants (53. 5% female). In this, 42 work sectors were heterogeneously represented. The three sectors that were most frequently represented were (a) banking, insurance, and financial services (16. 2%); (b) education, training, and science (10.
17, p <. 001, 95% CI = [. 14,. 21]). Finally, H1d, which predicted a direct and positive relationship between identity leadership and FIB, after controlling for the other three forms of leadership, was also supported by our data (β =. 05, p <. 01, 95% CI = [. 02,. 09]). In regard to RQ1, which inquired about which set of positive leadership behaviors would have the higher relative weight on FIB, we found that for our Model 1a, perceived LMX quality had the strongest relative weight (68. 69), followed by authentic leadership (13. 61), identity leadership (12. 03), and finally transformational leadership (5. 67). An overview of findings for all culture clusters can be found in Appendix S2.
Indirect effects of leadership and relative importance of social and personal identity for FIB (Model 2a) Model 2a also showed an excellent fit to the data (χ 2 = 4603. 75 [p <. 001], df = 412, RMSEA =. 04, CFI =. 97, and SRMR =. 02). The introduction of our identity-based mediators did not substantively affect the findings of Model 1a, except for identity leadership, which no longer showed a significant main effect. Table  4  shows that our data provided support for H2a, which predicted a significant unique and positive effect of social identification on FIB (β =. 45, p <. 001, 95% CI = [. 41,. 49]), after controlling for the effects of our four positive forms of leadership and followers' personal identification with the leader. Results indicated that the main effect of personal identification with the leader on FIB was significant but negative in sign (β = À.
10, p <. 001, 95% CI = [. 08,. 12]), as well as between identity leadership and FIB (β =. 08, p <. 001, 95% CI = [. 05,. 10]). All other proposed mediations were either insignificant or even significantly negative. Thus, our data support H5a and H6a, but did not support H3a, H4a, H4b, H5b, and H6b. Detailed results of all predicted paths can be found in Table  4. Main effects of leadership on FIB for the clustered data set (Model 1b) Model 1b also had an excellent fit to the data (χ 2 = 7,394. 38 [p <. 001], df = 3,642, and RMSEA =. 04, CFI =. 96, SRMR =. 06). An overview of the paths of Model 1b can be found in Table  4. The unique effect of transformational leadership on FIB was not significant when controlling for other positive leadership forms in most clusters. We detected a negative effect of transformational leadership on FIB in the Middle East cluster (β = À.