dataesr/scientific-paragraphs-categorization huggingface dataset:
Tang (2021)  provides several examples showing that a development program is more likely to be sustainable if the underlying constraints and context are taken into consideration. Successful application of data analytics in humanitarian health settings is highly dependent on the link with practice. Researchers must first acquire a deep understanding of the problem, underlying constraints and the context before diving into analytical modeling or else risk focusing "purely on developing mathematical methods to solve stylized problems (puzzles) without reference to any real problem situation", creating a vicious circle "where some academics know more and more about less and less and become disconnected from the real world"  (Dyson et al. , 2021).
A challenging but necessary way forward Humanitarian health operations are complex and involve a multifaceted relationship between data, analytical techniques, collaboration and impactful research. A proper understanding of the connections and loops in the CLD (Figure  1 ) can lead to the escaping of what is often stated as a "deadlock". In this section, we show how this can be accomplished through an exemplary case (Section 4. 1), and we use the CLD (Figure  1 ) to develop recommendations to steer future research in directions where analytics are useful (Section 4. 2). 4. 1 Exemplary case: Data system and analytics design for humanitarian health supply chains to maximize end-user adoption Attempts to use software systems to digitize stock and flow data for vaccine supply chains in LMICs have met with limited success.
Among the reasons for this are the following: System design is often focused on administrative reporting and central planner needs  (Ramanujapuram and Malemarpuram, 2020). Too much too soon. Systems attempt to digitize all parts of the supply chain information flow in one go. This increases cognitive complexity and data entry effort, leading to low adoption and poor data quality. End-user-friendly design is based on perceptions of endusers in developed countries instead of including actual end-users in LMICs. The Electronic Vaccine Intelligence Network (eVIN), a software tool developed by Logistimo, captures vaccine stock, in-bound receipts, orders and temperature recordings. Following a pilot implementation in one state in India, it now operates almost nationwide (Ministry of Health and Family Welfare, Government of India, 2018).
The implementation of eVIN has led to an increase in systematic data capture and record updating, fewer stock-outs of vaccines and a reduction in doses wasted/discarded. In addition, the captured data by eVIN created numerous analytical studies on the factors that lead to stock-outs and ways to redesign the replenishment process. This case study demonstrates that systematic data collection and rigorous analytics are feasible in humanitarian health supply chains in LMICs if carefully designed and applied. Several factors contributed to the success of eVIN: Developing a bottom-up design that aligns with the topdown supply chain planning hierarchy. Starting simple and progressively advancing the complexity of what gets captured in the system. This allows the user to adapt and select the level of system complexity according to their degree of familiarity and ease with the system.
Improving accessibility and quality of data Research is needed on the reasons behind the frequent occurrence of noise and imprecise elements in field data such that improvement efforts are targeted toward effective incentives or changes. Comes et al. (2020)  indicate the need for experimental and empirical research to assess and quantify the perceived priority of information to understand information acquisition in different contexts. This kind of research would be insightful in both humanitarian development and relief settings. In addition, future research could investigate the application of statistical techniques to deal with missing or noisy data in humanitarian health supply chains, building models using sparse data and developing modelling techniques targeting insights rather than purely numerical results.
Alignment of data and reporting requirements between different donors might result in improved data quality and accessibility, while reducing the burden on local staff. Researchers could investigate the impact of donor focus on data collection efforts and investigate how a shift in donor focus might improve data quality and accessibility. Another way to improve accessibility and quality of data is through the collaboration in data collection, construction of shared databases and model platforms  (Nair, 2022). To set up a shared database that is useful for all parties involved, there is a need for coordination between different organizations and within the organization. This results in the need of a neutral body collecting and processing these data and making them freely available to all in an easy-to-use format.
Previous research has explored the design principles that need to be considered in ERP systems for humanitarian organizations in a disaster relief context  (Falagara Sigala et al. , 2020). Future research could investigate similar coordination efforts for data sharing both within and between humanitarian organizations and research institutions. Building shared databases raises many interesting research questions: who is going to collect the data, who pays for it and how is it governed? Lack of trust among supply chain partners is a major issue because collaboration requires sharing of sensitive data and information  (Dubey et al. , 2020; Nair, 2022). Tang (2021)  emphasizes the challenge to monitor and respond to conditions in LMICs when information is not available or verifiable. Blockchain technology is increasingly put forward to collect information and facilitate data sharing in a safe and transparent way in humanitarian settings  (Dubey et al.
4. 2. 4 Research on the use and usefulness of data analytics AI and big data are increasingly put forward as potential "game changers" in the humanitarian field  (Swaminathan, 2018; Rodríguez-Espíndola et al. , 2020; Tang, 2021). Most publications focus on the benefits of big data  (Sharma and Joshi, 2019). However, it is not a given that AI and big data always contribute to humanitarian health operations. Research on perceptions of AI and big data and the feasibility of applying them in resource-constrained environments offers insights on the potential hurdles of implementing these techniques in humanitarian settings. Research is needed to investigate the performance of easy-to-implement heuristics compared to optimal solutions, as there is often a gap between them. This includes research on the usefulness of AI in humanitarian health settings: Does AI significantly outperform current practice or heuristics that are easy to implement? If so, can we derive heuristics from AI solutions that are intuitive and easy to implement in practice? The importance of easy-to-implement heuristics should not be underestimated, as decision-makers are known to follow intuitive rules to make quick decisions in stressful conditions  (Comes et al.
, 2020). Researchers should be aware of potential biases when relying on big data. Trustworthiness of data sources may be questionable, particularly when big data is generated on social media platforms  (Ali et al. , 2016; Kunz, 2019). Information obtained through big data technologies and practices is partial and linked to the geographical and social context of the people who produce it  (Sharma and Joshi, 2019). An important limitation of using big data comes from the "digital divide" between those who have access to and those with limited or non-existent access to connectivity and digital technologies  (Kunz, 2019; Sharma and Joshi, 2019). This is especially relevant for humanitarian health operations, where connectivity may be limited in areas hit by a disaster and where a large share of the population in LMICs still does not have internet access.
Collaboration and stakeholder engagement Stakeholder engagement throughout an entire research project is necessary to make the research useful and impactful. To support the work of humanitarian health organizations with data analytics, it is imperative that academic research has a clear link with practice. Spending time with practitioners enables researchers to understand their reality, concerns and constraints  (Pedraza-Martinez and Van Wassenhove, 2016). This again underscores the importance of gaining insights on the ground and obtaining data first-hand through field studies  (Besiou and Van Wassenhove, 2020; Kunz et al. , 2017). It implies using both qualitative (interviews and case studies) and quantitative (optimization and simulation) methods and including multiple stakeholders  (Besiou and Van Wassenhove, 2015; Dyson, 2021; Van Wassenhove, 2019) , as well as data from governments and organizations.
Conclusion Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Researchers in this field have the potential to contribute to relevant solutions for humanitarian decision makers. However, a gap between practitioner challenges and academic literature remains. Although new techniques for data capturing and processing can potentially improve data analytics in the future, larger data sets and quantification alone will not provide a "silver bullet" for all problems in humanitarian health settings. This paper has revealed the dynamic mechanisms behind data, analytical techniques, research collaboration and impactful research in the field of humanitarian health supply chains based on a systems approach. The resulting CLD shows the interconnections and feedback loops between the system elements.
This allows to explain observed behavior and challenges from the three presented cases. Based on the CLD, we show that humanitarian operations involve complex systems and doing research in this field presents abundant challenges: Humanitarian organizations often focus on myopic solutions because of competing priorities. Obtaining reliable and relevant context-related data is often imperative but takes time and requires collaboration between researchers and practitioners. An overload of data exists that are neither in the right format nor are easily accessible. Currently applied analytical techniques fail to grasp the complexity of humanitarian health operations. Researchers do not always work on real-life problems. Nevertheless, researchers can significantly contribute to relevant solutions for humanitarian decision-makers.
Humanitarian health supply chains are, above all, a rewarding and impactful environment in which to do research. Using the developed CLD, we provide several directions for future research that we believe will increase the impact of data analytics in fostering sustainable solutions: research on improving the accessibility and quality of data; qualitative research; interdisciplinary research; research on the use and usefulness of data analytics in humanitarian health settings; and stakeholder engagement. Note that increasing the impact of data analytics in fostering sustainable solutions will not entirely overcome the gap between practitioner challenges and academic literature. Practitioners sparsely publish in academic journals for a variety of reasons, including different incentive structures to academics.
The release intention was measured by three items adapted from  [64, 65]. Prior to answering the questionnaires, all participants were provided with a written informed consent and signed. The study and questionnaire design were carried out in accordance with the approval of the Ethics Committee of the Tongmyong University. The data was collected from an online survey of 552 tourism platform users residing in China, ranging from 18 to 60 years old. For data collection, we used an online panel by Dynata, are search company. The company randomly selected users between 18 and 60 who had at least one type of experience using the tourism platform from a panel in China and invited them to participate in the study.
Descriptive analysis of demographic characteristics SPSS 25. 0 was used for the descriptive statistical analysis of the sample data. The demographics of the sample are as follows: in terms of gender, there are more female users than male users; in terms of age, users aged 35-45 years old account for the most significant number of users, 24. 1%; in terms of education, the majority of users have a bachelor's degree, accounting for 55. 4%; in terms of frequency of use of tourism platform, users who use it 7-9 times a week account for the majority, accounting for 41. 8%. These data indicate that the data collected by the questionnaire of this study covers a wide range of data, which is in line with the actual situation of tourism platform users, as shown in Table  2.
Measurement model analysis First, the reliability of the measurement model was assessed. The data results showed that the Cronbach's alpha for all variables was greater than the critical value of 0. 7 (0. 701-0. 872)  [66] , and the Composite Reliability for all variables was also greater than the critical value of 0. 7 (0. 831-0. 921)  [67]. Therefore, the measurement model has good reliability (see Table  3 ). Second, the convergent validity of the measurement model was assessed. The outer loadings (λ) of all question items were greater than the critical value of 0. 7 (0. 728-0. 874)  (Hair et al. , 2017)  and the AVE (average variance extracted) of all variables were greater than the critical value of 0. 5 (0. 595-0. 796)  (Hair et al, 2017). The measurement model had good convergent validity,as shown in Table  3.
Questionnaire Design This study used the questionnaire research method to randomly take a stratified sampling of people aged 14-33 years old in China to conduct an empirical study. The questionnaire had a total view count of 3169, with a total of 2028 valid data collected, an overall completion rate of 63%, and an average response time of 3 minutes and 34 seconds. The questionnaire focuses on three single or composite variables to help companies develop relevant marketing strategies for different conditions of consumers, namely, respondents' basic personal information, purchase choices, frequency, reasons, channels, habits and expectations for green products, and attitudes toward existing responses to some of the relevant marketing behaviors. The questionnaire first collected respondents' basic personal information through questions 1 to 6, including age, gender, occupation, highest education, monthly income after tax, and city of living.
Conclusion The descriptive analysis and chi-squared test of the data through SPSS provided can reveal a range of salient features in consumer buying behavior. These features cover not only the consumers' preferences and motivations but also present differences in purchasing decisions among different groups of people. From the distribution of monthly after-tax income to attitudes towards purchase frequency, from the motivations for green products to the choice of purchase channel, each data provides insights that help to gain a deeper understanding of the drivers behind consumers' purchasing behavior. The data shows that the vast majority of consumers' monthly after-tax incomes are concentrated at lower levels, which means that price sensitivity dominates purchasing decisions. However, at the same time, consumers are highly concerned about the health and environmental attributes of green products, suggesting that they are not only considering economic factors when making purchasing decisions but also focusing on the long-term value and impact of the products.
Data collection To test the hypotheses, we used data from a survey conducted in Shanghai, China. Although Beijing is the administrative capital of China, Shanghai ranks first in terms of MNE headquarters in China (Shanghai Municipality Commission of Commerce (  2021 )) and is considered the country's business capital and, thus, is where the subsidiaries' senior managers are located. As such, our criteria for the sample were MNEs' subsidiaries in Shanghai that had national operations coverage and CSR operations or strategies to ensure they were representative of the targeted population. The final sample included firms from several industries, namely textile, appliance manufacturing, electronics, entertainment, financial and insurance services, chemical, commerce, and pharmaceutical, among others.
The first group of key informants was also involved in developing and refining the survey. The literature (Torres de Oliveira & Figueira, 2018) explains that access to Chinese firms is quite challenging; however, this research managed to interview 155 managers from different DM MNE subsidiaries who were associated with CSR development. The questionnaire was implemented by telephone or in person; these methods were chosen in order to ensure that participants who had been involved in CSR were the ones completing the survey and to ensure that surveys were completed in full. Since some of the respondents were Chinese native speakers, this research had the support of a Chinese official translator to assist in the data collection. Contact creation was eased by the fact that one researcher lived in China at the time, between May and December of 2016.
Common method biases In CSR studies, common method and social desirability biases can lead to data issues  (Du et al. , 2010; Husted & Allen, 2006). Therefore, an effort was made to reduce possible biases during the questionnaire design phase by asking respondents to express their own activities and perceptions of pressure and not to compare themselves to the rest of the business community in Shanghai or even China. We followed  Podsakoff et al. , (2003)  recommendation to use Harman's Single-Factor Test to address this issue. Using the program SPSS 22, all items used in this study were entered into an exploratory factor analysis, and the unrotated factor solution was examined to analyse the variance of the variables. It concluded that the research data did not suffer from a common method bias issue because the variance explained by one factor was less than 50 % -more specifically, 28.
To validate the proposed model, we use real open-access data, that is, actual infection data from Norway obtained from publications by the Norwegian Institute of Public Health. We also checked newspaper articles and publications by health organizations to verify statements about diagnostic test availability and create simulated inventory gaps. We simulate a competitive policy (take what you need), a one-sided collaborative policy (give when you can), and a two-sided collaborative policy (take what you need, give when you can). We then compared the results of the simulations with a base case of an isolation policy (neither take nor give), which is what truly happened in Norway and led to surpluses of diagnostic tests when they were not needed but also to shortages when they were needed. We established that two-sided collaboration is best because it flattens the curve while offering acceptable levels of risk (not self-sacrificing) and establishing equity (donating to countries in need).
Data collection and model calibration We used actual data from Norway detailing the daily numbers of people infected, deceased, and hospitalized (both normal beds and beds in the ICU) for the model calibration and to identify values of exogenous variables for which no real information is available (e. g. , forecast adjustment time by the diagnostic test supplier, delays in opening society). The values of the COVID-19 transmission parameters came from publications issued by the Norwegian Institute of Public Health, which indicate average infectivity time, average quarantine time, percentage of infected people who develop symptoms, average ICU time, and target test percentage. We could not gain access to real data about diagnostic test inventory levels, ordering policies, or production lead times, so we compared the simulated inventory gap with statements in public reports regarding shortages and surpluses.
Psychological safety and knowledge sharing mediate the relationship between organizational innovation climate and work behavior. The study's shortcomings, practical consequences, and suggestions for further research were all examined at length. The Research Topic's fourth article looked at Australian organizations to analyze the roles of entrepreneurship and intrapreneurship in converting innovation intention into performance (Abeysekera). The Theory of Planned Behavior provided the theoretical foundation for the study. Entrepreneurship and intrapreneurship were used in the study as mediator constructs to address potential research issues. Data that contrasted performance increases between the COVID-19 financial years 2019-2020 and 2020-2021 were descriptively assessed in the study. Businesses that actively pursue innovation outperform those that do not.
Since the objective of the present review was to furnish readers with a comprehensive, multi-level overview of the overlap and differences between TL and EL, both conceptual and empirical papers were accepted, the latter including both qualita-tive and quantitative investigations of EL. All included papers had to be published in English, and had to be presented as full-length, original articles, which represented either an empirical or a conceptual contribution relevant to answering one of the three RQs. Furthermore, the included papers had to be available in one of the seven databases and meta-database search engines used (see. In addition to these criteria for inclusion, an SLR demands specific exclusion criteria. These were thus defined, with the goal of ensuring that no papers of low quality or thematic unsuitability were accepted into the sample.
Primary search In order to adhere to the subject matter while ensuring that all possible, pertinent contributions would be included, an exhaustive all-fields search was carried out across a number of major databases and search engines. These were identified via analysis of previous SLRs, both within the field of EL (e. g. Clark et al. 2019; Harrison et al. 2016 ) and within the journal targeted for publication, the Review of Managerial Science (e. g. Feser 2022;  Salmony and Kanbach 2022). This led to the selection of the databases of Taylor and Francis, Emerald, Wiley Online, and SAGE Journals, as well as the meta-database search engines Web of Science, ScienceDirect, and JSTOR. In line with previous publications (e. g. Alaassar et al. 2022 ; but see also  Deyanova et al. 2022) , Google Scholar's search engine was additionally utilized, and findings compared to the final sample to determine whether a significant number of peer-reviewed works might have been excluded due to selection of these databases.
No major contributions were found that had been excluded without warrant. Keyword strings were kept constant across all databases and search engines. The combination of "Entrepreneurial leadership" coupled with "Transformational leadership" was employed, using the Boolean operator "AND" to conjoin the two, thus excluding any articles which failed to mention both. Since the RQs focus on examining the overlap and difference between the concepts of EL and TL specifically, using these concrete terms without variation was deemed most fitting. Including research on entrepreneurial or visionary leaders, for example, would have broadened the scope beyond the core definitional and conceptual discourse the RQs sought to unravel; articles not even containing the term "Entrepreneurial leadership" were considered to represent a tangential contribution to the discourse.