dataesr/scientific-paragraphs-categorization huggingface dataset:
Scale validation To assess measurement quality, we subjected the survey data to confirmatory factor analysis. The results for the tests of construct reliability and validity in Table  4  are indicative of good psychometric properties. More specifically, no coefficient alpha values and composite reliabilities are lower than 0. 80, thus exceeding the recommended thresholds  (Bagozzi & Yi, 1988). The goodness-of-fit statistics indicate good fit (comparative fit index = 0. 99, root mean square error of approximation = 0. 037). Furthermore, item reliabilities (see Table  3 ) are greater than the recommended value of 0. 40  (Bagozzi & Baumgartner, 1994). Finally, factor loadings are highly significant, providing further support of convergent validity  (Bagozzi, Yi, & Phillips, 1991). We assessed discriminant validity in two ways.
Acknowledgments The authors would like to thank  Sundar Bharadwaj ,  Tim Heath ,  Wolfgang Ulaga , and  Marc Vanhuele  for their insightful and constructive comments. They also thank  Alireza Keshavarz  for research assistance. The authors acknowledge financial support by the  Sim Kee Boon Institute for Financial Economics at Singapore Management University ,  HEC Foundation , and the  Institute for the Study of Business Markets at Penn State.
(0. 79) b • Our firm is very committed to the relationship with supplier [CS]. (0. 72) b • We trust in supplier [B]'s integrity. (0. 42) b Moderator variables • CS industry product differentiation Obtained from archival industry profiles for the CS and OEM industries; draws on extensive primary Data-monitor • Importance of OEM-end customer and secondary research databases & modeling/forecasting tools. Coded on seven-point scale. relationships • Brand importance in OEM industry CS industry technology intensity R&D Intensity jt % ð Þ ¼ 1 K ∑ k¼1 K Revenue jk R&D Expenditures jk ORBIS industry j, competitors k in industry j, year t Control variables OEMs' value perceptions of CS Multiple informants. Survey CS sales CS operating revenue ORBIS OEM number of employees OEM Number of employees ORBIS CS and OEM industry intangibles intensity Intangibles Intensity jt % ð Þ ¼ 1 K ∑ k¼1 K Total Assets jk Intangible Assets jk ORBIS industry j, competitors k in industry j, year t CS and OEM industry concentration Normalized Herfindahl Index for industry j, year t ORBIS OEM industry technology intensity (See CS industry technology intensity above) ORBIS Menon et al.
Objectives: Community-acquired pneumonia (CAP) is one of the most important causes of morbidity and mortality worldwide. Etiological data for Cambodia is scarce. We aimed to describe the main etiological agents causing CAP, and their resistance patterns in Cambodia and the greater Mekong region. Methods: A review of bacterial etiologies of CAP and antimicrobial resistance in Cambodia and neighboring countries was conducted via: (1) a systematic review of published literature in all NCBI databases using Pubmed, Google scholar, EMBASE, the World Health Organization and the Cambodian Ministry of Health libraries; (2) a review of unpublished data from Cambodia provided by national and international stakeholders working at different tiers of the healthcare system. Results: Twenty three articles and five data sources reported etiologies for 5919 CAP patients diagnosed between May 1995 and December 2012, including 1421 (24.
Published (''White'') Literature Review In May 2012, all National Center for Biotechnology Information's (NCBI) databases were searched using Pubmed, with no restriction dates or languages and following the search strategy reported in Document S1. We also searched the Google scholar database (restricting the search on papers published since 2000), the World Health Organization and the Cambodian Ministry of Health (MOH) online libraries  [19, 20, 21]. Finally, we searched the references' list of eligible papers and directly contacted their authors. The EMBASE database was also searched in January 2014, to complete the review, using the key words 'communityacquired pneumonia'(drug resistance, epidemiology and etiology)', 'Cambodia', 'Laos', 'Vietnam', and 'Laos' looking for articles published until January 2013.
Unpublished (''Grey'') Literature Data on CAP in Cambodia were collected in 2012 through the CALIBAN group. Five CALIBAN stakeholders working at different levels of care agreed to share their ''raw'' data (Figure  1 ). The primary care level is defined as care given in the community, in health posts and health centers; the secondary care level refers to 2 nd -tier general or provincial hospitals, while the 3 rd tier includes referral hospitals offering medical care to the most serious cases. N Angkor Hospital for Children (AHC) is a referral pediatric hospital situated in Siem Reap Province. Patients aged ,16 years are treated free of charge. Patients have access to laboratory and radiological investigations and results are kept electronically in the hospital database and in each patient's individual medical record  [22].
N The National Institute of Public Health (NIPH) is supported by the US Centers for Disease Control and prevention (CDC) and prospectively collects data from severe LRI patients of all ages admitted to third-tier national hospitals: two are located in Phnom Penh, and one in the nearby province of Kandal. N Sihanouk Hospital Center of Hope (SHCH) is a 35-bed   referral NGO hospital in central Phnom Penh providing free care for the poor  [26]. It provides 120 000 patients with outpatient care and admits 1500 patients annually. In 2007, a prospective blood stream isolates' study was initiated in patients presenting with Systemic Inflammatory Response Syndrome. A proportion of these patients presented clinical signs and symptoms of LRI. Other CALIBAN stakeholders, including two partners who provide pediatric care in Cambodia were unable to contribute data.
Analysis and Assessment of Risk of Bias and Data Analysis Descriptive statistics were computed using Stata 12 software (Stata Corp. , College Station, TX, USA). Antimicrobial resistance data is reported as determined by the studies' authors and the tests and interpretative breakpoints they used. Results are presented as numeric tables presenting frequencies and narrative synthesis. Two independent reviewers (SG and AT) assessed the methods used by the various studies to document bacterial causes of CAP and possible bias, adapting an algorithm published by Gentile et al. [27] , derived from the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) checklist of essential items  [28]. The case definitions for each study are presented in Table  S1. Methods for selecting the study participants, the methods for measuring the bacterial pathogens' prevalence and methods to control confounding factors were graded from low to high risk of bias (Table  S2 ).
''White Data'' Inclusions The initial search in NCBI, Google Scholar, WHO and MOH and EMBASE databases and the hand search initially retrieved 240, 337, 153, 13 and 3 references, respectively (Figure  2 ). We removed duplicates and publications not fitting with our inclusion criteria and obtained 30 eligible papers, of which 26 could be accessed and analyzed. From these, three articles were excluded as they were focused on a pathogen-specific pneumonia and did not report antibiotic resistance  [29, 30, 31]. Ultimately, 23 articles were included. From 15 of them, we extracted information on CAP etiologies  [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]. Eight presented antibiotic resistance data  [18, 46, 47, 48, 49, 50, 51, 52]. We did not find published articles describing CAP in Laos.
''Grey Data'' Inclusions Five datasets were included (Figure  1 ). AHC reported information on 155 bacteremic children admitted with signs of LRI or presenting respiratory complications of an underlying infectious disease  [22]. IPC provided information for 1,904 acute LRIs in patients aged $5 years, including 959 (50. 4%) CAP cases. Bacterial etiologies were explored for 905 (94. 4%) of these  [23]. NAMRU2 shared laboratory results on 42 febrile patients with cultures positive for potential respiratory pathogens. The SHCH blood stream infection (BSI) surveillance reported 155 adult patients with confirmed BSI and LRI clinical signs. The NIPH surveillance contributed data from 164 severe LRI patients (Figure  3 ).
Studies' Characteristics The median duration for all studies considered was 24 months (Interquartile range: 18-36 months). The published studies were surveillance studies (n = 10), cross sectional (n = 3) and case-control studies (n = 2). The risk of bias for estimating bacterial etiologies of CAP was considered high in 5/15 published studies presenting etiologies (mostly due to lack of details in the descriptions of study participants' selection procedures or of laboratory techniques) and in 1/5 of unpublished sources of data in Cambodia (because of a broad case definition) (Table  S2 ). CAP etiologies were determined by culture, antigen and serological tests, and molecular diagnostic tools (Table  S1 ). In Cambodia, bacterial pathogens were identified using culture: on blood samples (all except NAMRU-2) and on sputum and/or pleural fluid samples (all except AHC and SHCH).
From an epidemiological and public health standpoint. The data from Cambodia was examined in light of regional data. Many of these studies were conducted in Thailand and Vietnam some years ago. It is somewhat reassuring that the current Cambodian situation appears similar to that described in these countries several years earlier. The epidemiology of CAP may have evolved somewhat since that time in the countries described. Regularly reviewing data from those countries might provide insights into the road lying ahead for Cambodia, where surveillance systems must be set up to monitor AMR in the community. Many developing countries have a trove of data which is not identified, structured and analyzed in a systematic way to inform policymakers. In the absence of well-conducted, clinically and biologically well-documented prospective studies, a systematic review of available national data complemented with published regional epidemiological information helps clinicians make informed choices.
How did we develop our framework? Our framework was grounded in a two-part literature search and review carried out by two of the authors (EM, MW). The first part identified key factors in a variety of services sectors that might prove relevant to customization of services in healthcare through a search on Web of Science, EBSCO and CAIRN databases using the following combinations of key words: "mass customization" AND "service" (the term "service" was included as it is a key attribute of the care process) and "personalized service" AND "management". We focused on all articles and books that developed comprehensive frameworks for mass customization or its implementation, regardless of sector of application. The second part identified articles and books on care customization in healthcare organization and delivery.
Managerial optimism affects both corporate actions and remuneration and may thus be an important control variable when estimating the effect of compensation schemes on these actions. In addition to the new empirical results, the paper also makes two methodological contributions. First, I show how to construct a measure of managerial optimism that follows the rationale proposed by  Malmendier and Tate (2005a,b, 2008)  and  Malmendier, Tate, and Yan (2011)  using information from insider trading filings. Second, I propose a new measure of managerial optimism, which is based on a firm's earnings forecasts. Both measures can be constructed using publicly available information from the Thomson Reuters insider filings database and the First Call Historical Database. Within the existing theoretical literature, the paper is most closely related to the work of  Gervais, Heaton, and Odean (2011).
Averaging across all years helps mitigate the influence of such noise on the measures. Second, fluctuations of a CEO's optimism over short horizons are less likely to have an effect on the structure of his compensation than the CEO's average optimism, which is more stable over time. I use three main data sources for my empirical analysis: information on the CEOs' compensation from Execucomp, information on the CEOs' option exercises from the Thomson Reuters insider filings database, and information on EPS forecasts, analyst consensus estimates, and realized 4 The example assumes that an optimistic CEO values incentive claims less ($9) than the firm's owners ($10). In other words, the CEO's optimism does not completely undo the effect of his risk-aversion. This is the maintained assumption both in the formal model and in the empirical analysis.
However, the very optimistic CEO receives more incentive claims than an unbiased CEO, while the extremely optimistic CEO receives less. A formal analysis of the case in which the agent's incentive compatibility constraint is not binding as well as an empirical investigation of a potentially non-monotonic effect of optimism on compensation are presented in Appendix B. earnings from the First Call Historical Database. All data on compensation, option exercises, and earnings forecasts are obtained for the years between 1996 and 2005. 6 Furthermore, I obtain financial information from the Center for Research in Security Prices (CRSP) and Compustat as well as data on the firms' board characteristics from RiskMetrics. 2. 2. 1. Optimism measure based on option exercise decisions ("LongHolder") The first measure for a CEO's optimism is based on the CEOs' option exercise decisions and follows the rationale proposed by  Malmendier and Tate (2005a,b, 2008)  and  Malmendier, Tate, and Yan (2011).
Information on the CEOs' option exercises is obtained from the Thomson Reuters insider filings database. The data files are designed to capture all insider activity as reported on the Securities and Exchange Commission (SEC) Forms 3, 4, 5, and 144, and include additional information concerning the accuracy of the reported data in the form of a cleanse indicator that denotes the overall level of confidence in each record. Corporate insiders-individuals who have access to nonpublic, material, insider information, including the CEO, are required to file Forms 3, 4, and 5 for transactions involving their companies' stock. For my analysis, Form 4 is the relevant source of information as it indicates changes in an insider's ownership position. This could be a purchase, sale, option grant or exercise, or any other transaction that causes a change in the ownership position.
After these steps, I am left with a "clean" list of all exercises of incentive stock options. For all exercise observations, I first calculate the time to expiration at the time of exercise as the difference between the expiration date of the options and the transaction date. During this process, I drop an additional 20 observations with a reported expiration date before the transaction date and four observations with an implied time to expiration of more than 200 years, as these are clearly data entry errors. Each observation in the sample is then matched with the annual closing price of the underlying stock at the end of the preceding calendar year, which I obtain from the CRSP database. 7 I keep only those observations for which price information is available and calculate the moneyness of the options at the end of the previous year as the difference between the closing price of the preceding calendar year and the exercise price divided by the exercise price.
Optimism measure based on EPS forecasts ("HighForecast") The second measure for a CEO's optimism is based on the comparison of the EPS forecasts released by the firm with the EPS that were eventually realized. More than 70% of all firms in the Execucomp database provide such forecasts during the sample period despite the fact that they are not legally obliged to do so. This finding is consistent with the survey results presented by  Graham, Harvey, and Rajgopal (2005) : Executives consider EPS the most important performance measure to be communicated to outsiders. The prime motive for voluntarily disclosing such information, according to the survey respondents, is promoting a reputation for transparent and accurate reporting. I begin my analysis with all company-issued EPS forecasts in the First Call Historical Database that were announced between January 1996 and December 2005.
I keep only forecasts for the common stock of each firm and drop observations if the announcement date falls on or after the end of the fiscal period for which the announcement was made, or on or after the date on which 6 Information on the CEOs' option exercises as recorded in the table pertaining to derivative transactions in the Thomson Reuters insider filings database is available only from 1996 onwards. Information on the Black-Scholes values of the CEOs' option grants is available from the Execucomp database only until 2005 due to changes in the reporting requirements for equity-based compensation (Financial Accounting Standards Board (FASB) 123). A benefit of this data restriction is that the results of the empirical analyses are not confounded by the recent financial crisis. the actual EPS were announced.
Option characteristics for optimistic and nonoptimistic CEOs Table  1  presents a first glance at the option compensation of optimistic and non-optimistic CEOs. Using information obtained from the Execucomp database, I compute the average Black-Scholes value of the option grants as well as the average number of options awarded per grant for each CEO-firm combination in the sample. Furthermore, I compute the average maturity and moneyness of the options, weighting each grant observation by the number of options that were awarded. Maturity is calculated as the difference in years between the year in which the options were granted and the year in which they expire. Moneyness is the difference between the firm's share price on the grant date and the exercise price divided by the exercise price.
To remove the effect of outliers, I winsorize Moneyness at the 1% level. In addition, using the insider filings data obtained from Thomson Reuters, I compute the average number of years between the vesting date of the options and their expiration date (Exercise window) as well as the average number of years that the CEO waited after the vesting date before exercising his options (Waited until exercised). As before, observations are weighted by the number of exercised options. The CEOs in the sample are classified based on the optimism measure LongHolder in Panel A and based on HighForecast in Panel B. As can be seen from the first two rows in both panels, the average option grant awarded to an optimist has a lower Black-Scholes value and contains fewer options than the average grant awarded to a non-optimist.
Compensation and CEO and firm characteristics Information on the CEOs' compensation as well as on their holdings of company stock and options is obtained from the Execucomp database and matched with the information on the CEOs' optimism. I keep only observations for which at least one of the optimism measures is available. In addition to the compensation data, I obtain financial and balance sheet information as well as information on the firms' board composition from the CRSP, Compustat, and RiskMetrics databases. For each firm and in each year, I calculate the firm's market capitalization, leverage, market-to-book ratio, and return on assets, as well as the firm's cash holdings and research and development (R&D) and capital expenditures (Capex) scaled by total assets. Leverage is calculated as total long-term debt divided by total assets.
EPS forecasting and sample selection A potential concern regarding the previous analyses using the optimism measure HighForecast is that this variable can only be constructed for CEO-firm combinations for which at least one EPS forecast is available. Firms, however, are not obliged to provide such forecasts and decide whether or not to do so voluntarily. If the determinants of the decision to provide EPS forecasts-and thus, whether or not to enter into my sample-are correlated with the determinants of the CEO's compensation, then the previous analyses may suffer from a sample selection bias. This section provides empirical evidence that alleviates these concerns. Table  6  provides summary statistics for all firms in the Execucomp database that do issue EPS forecasts during my sample period and those that do not.
Inst. ownership is the fraction of institutional owners among the firm's shareholders. Information on the number of analysts that follow a firm comes from the First Call Historical Database. Data on equity issuance are obtained from Thomson One, and data on debt issuance come from Compustat. Information on each firm's institutional ownership is obtained from Thomson-Reuters 13F data. Several results emerge from the comparison of firm characteristics. First, more than 70% of all firms issue EPS forecasts. Second, the firms that do issue forecasts do not do so every year: The conditional mean of the variable ForecastIssued is 0. 56, indicating that the firms issue EPS forecasts about every other year. Third, firms issuing EPS forecasts differ from firms not issuing forecasts. In particular, forecast issuers are on average larger, have lower leverage, a higher market-to-book ratio, and more volatile stock returns.
Firms issuing EPS forecasts Firms Table  8  presents the results of a Heckman selection model. The first column shows the first-stage probit regression which models the selection into the sample. CEO-firm combinations enter the sample if at least one EPS forecast is released during the CEO's tenure. Thus, whether or not a CEO-firm combination appears in the sample during a given year depends on the determinants of the decision to issue a forecast during all sample years. For that reason, I model the selection indicator InSample for each CEO-firm combination in the Execucomp database as a function of the average values of #Analysts, Equity issue, Debt issue, Earnings volatility, and Inst. ownership during the CEO's tenure. In addition, I include the CEO and firm characteristics that are used in the compensation regressions as well as dummies for the different sample years.
The fact that the variable HighForecast is not the fraction of earnings forecasts that were too high during the year under consideration, but a weighted average of the fraction of exceedingly high forecasts across all years, mitigates this concern. If the CEOs truly miss or meet their EPS forecasts with equal probability, then the value of HighForecast would tend towards 0. 5 for all CEOs. While the CEOs' compensation in each year would be negatively related to the fraction of missed forecasts in that particular year, it would be unrelated to the variable HighForecast, which measures the average fraction of forecasts that proved to be too high across all years. Nonetheless, the nature of my data allows for several additional analyses.
Beating salient earnings benchmarks One way to address the aforementioned concern that some CEOs receive less compensation because the realized earnings fall short of the firms' earnings targets is to construct control variables that indicate whether or not the firms' EPS exceed various EPS benchmarks. Two salient benchmarks are the EPS that were realized during the prior year and the analyst consensus forecast for the firm's earnings (see, e. g. ,  Graham, Harvey, and Rajgopal, 2005; Matsunaga and Park, 2001). For each firm and fiscal period, I construct the analyst consensus forecast as follows: First, I obtain all analyst forecasts from the First Call Historical Database that were issued during the sample period. As for the forecasts issued by the firms, I then keep only analyst forecasts for the common stock of each firm and drop observations if the forecast date falls on or after the end of the fiscal period for which the forecast was made or if either date is missing.