Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies

[EN] Social media are fundamental in creating new opportunities for firms and they represent a relevant tool for the communication and the engagement with customers. The purpose of this paper is to analyse the communication of Corporate Social Responsibility (CSR) activities on Twitter. We consider the listed companies included in the Dow Jones Industrial Average Index and we implement a topic model analysis on their timelines. In order to identify the topic discussed, their correlation, and their evolution over time and sectors, we apply the Structural Topic Model algorithm, which allows estimating the model including document-level metadata. This model proves to be a powerful tool for topic detection and for estimating the effects of document-level metadata. Indeed, we find that the topics are overall well identified, and the model allows catching signals from the data. Finally, we discuss issues related to the validity of the analysis, including data quality problems.Salvatore, C.; Bianchi, A.; Biffignandi, S. (2020). Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies. Editorial Universitat Politècnica de València. 269-277. https://doi.org/10.4995/CARMA2020.2020.11646OCS26927

Estimation and Testing in M-quantile Regression with Applications to Small Area Estimation

In recent years,M-quantile regression has been applied to small area estimation to obtain reliable and outlier robust estimators without recourse to strong parametric assumptions. In this paper, after a review of M-quantile regression and its application to small area estimation, we cover several topics related to model specification and selection for M-quantile regression that received little attention so far. Specifically, a pseudo-R2 goodness-of-fit measure is proposed, along with likelihood ratio and Wald type tests for model specification. A test to assess the presence of actual area heterogeneity in the data is also proposed. Finally, we introduce a new estimator of the scale of the regression residuals, motivated by a representation of the M-quantile regression estimation as a regression model with Generalised Asymmetric Least Informative distributed error terms. The Generalised Asymmetric Least Informative distribution, introduced in this paper, generalises the asymmetric Laplace distribution often associated to quantile regression. As the testing procedures discussed in the paper are motivated asymptotically, their finite sample properties are empirically assessed in Monte Carlo simulations. Although the proposed methods apply generally to Mquantile regression, in this paper, their use ar illustrated by means of an application to Small Area Estimation using a well known real dataset

Patient safety competencies in undergraduate nursing students: a rapid evidence assessment

Aims To identify patient safety competencies, and determine the clinical learning environments that facilitate the development of patient safety competencies in nursing students. Background Patient safety in nursing education is of key importance for health professional environments, settings, and care systems. To be effective, safe nursing practice requires a good integration between increasing knowledge and the different clinical practice settings. Nurse educators have the responsibility to develop effective learning processes and ensure patient safety. Design Rapid Evidence Assessment. Data Sources MEDLINE, CINAHL, SCOPUS, and ERIC were searched, yielding 500 citations published between 1 January 2004 - 30 September 2014. Review Methods Following the Rapid Evidence Assessment process, 17 studies were included in this review. Hawker's (2002) quality assessment tool was used to assess the quality of the selected studies. Results Undergraduate nursing students need to develop competencies to ensure patient safety. The quality of the pedagogical atmosphere in the clinical setting has an important impact on the students’ overall level of competence. Active student engagement in clinical processes stimulates their critical reasoning, improves interpersonal communication, and facilitates adequate supervision and feedback. Conclusion Few studies describe the nursing students’ patient safety competencies and exactly what they need to learn. In addition, studies describe only briefly which clinical learning environments facilitate the development of patient safety competencies in nursing students. Further research is needed to identify additional pedagogical strategies and the specific characteristics of the clinical learning environments that encourage the development of nursing students’ patient safety competencies

Modeling some entrepreneurship factors

Entrepreneurship is increasingly recognized as a major factor of economic growth, productivity and competitive economy. Many countries are making efforts to support entrepreneurship and are interested in knowing how government policies and other factors can influence the amount and type of entrepreneurship. For this purpose they need to understand the determinants of and obstacles to entrepreneurship. In spite of the large interest in entrepreneurship, due to the lack of internationally comparable data, the understanding of this phenomenon and its determinants remains still an open problem. In 2006 OECD launched the Entrepreneurship Indicators Programme (EIP), which was joined by Eurostat in 2007. In Measuring Entrepreneurship: A Digest of Indicators (2008) a common set of concepts and definitions is presented. Furthermore, consistent data across different countries are published: even if these data do not represent the whole set of indicators which is needed for studying the entrepreneurship process, they represent a preliminary database of internationally comparable statistics. Using this database (Structural and Demographic Business Statistics (SDBS)) and others (R&D database, Market Regulation database and Education at a Glance) we perform initial analysis of entrepreneurship across countries. Our interest is in understanding its determinants and in particular those related to education. Preliminary conclusions about the role of different educational level on entrepreneurship are obtained as a reference theoretical frame for more detailed analyses based on single country data

Collaborate for what: a structural topic model analysis on CDP data

[EN] The aim of this paper is to understand why firms engage with their suppliers to collaborate for sustainability. To this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two most prevalent reasons firms engage with their suppliers relate to several aspects of the management of the supply chain, and the services and goods mobility efficiency. It is further noted how first-tier suppliers do not dispose of established capabilities and, therefore, are still in the course of improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys.Salvatore, C.; Madonna, A.; Bianchi, A.; Boffelli, A.; Kalchschmidt, M. (2022). Collaborate for what: a structural topic model analysis on CDP data. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 139-146. https://doi.org/10.4995/CARMA2022.2022.1507413914

Corporate Social Responsibility Activities Through Twitter: From Topic Model Analysis to Indexes Measuring Communication Characteristics

The communication of corporate social responsibility (CSR) highlights the behavior of the business toward CSR and their framework of sustainable development (SD), thus helping policymakers understand the role businesses play with respect to the 2030 Agenda. Despite its importance, this is still a relatively underexamined and emerging topic. In our paper, we focus on what businesses communicate about CSR through social media and how this relates to the Sustainable Development Goals (SDGs). We identified the topics discussed on Twitter, their evolution over time, and the differences across sectors. We applied the structural topic model (STM) algorithm, which allowed us to estimate the model, including document-level metadata (time and sector). This model proved to be a powerful tool for topic detection and the estimation of the effects of time and sector on the discussion proportion of the topics. Indeed, we found that the topics were well identified overall, and the model allowed catching signals from the data. We derived CSR communication indexes directly from the topic model (TM) results and propose the use of dissimilarity and homogeneity indexes to describe the communication mix and highlight differences and identify clusters

The Impact of Day of Mailing on Web Survey Response Rate and Response Speed

The day of the week on which sample members are invited to participate in a web survey might influence propensity to respond, or to respond promptly (within two days from the invitation). This effect could differ between sample members with different characteristics. We explore such effects using a large-scale experiment implemented on the Understanding Society Innovation Panel, in which some people received an invitation on a Monday and some on a Friday. Specifically, we test whether any effect of the invitation day is moderated by economic activity status (which may result in a different organisation of time by day of the week), previous participation in the panel, or whether the invitation was sent only by post or by post and email simultaneously. Overall, we do not find any effect of day of invitation in survey participation or in prompt participation. However, sample members who provided an email address, and, thus, were contacted by email in addition to postal letter, are less likely to participate if invited on Friday (email reminders: Sunday and Tuesday) as opposed to Monday (email reminders: Wednesday and Friday). Given that no difference between the two protocols is found for prompt response, the effect seems to be due to the day of mailing of reminders. With respect to sample member’s economic activity status, those not having a job and the retired are less likely to participate when invited on a Friday; this result holds also for prompt participation, but only for retired respondents. Also, sample members who work long hours are less likely to participate when invited on a Friday; however, no effect is found for prompt response

The Point of View of Undergraduate Health Students on Interprofessional Collaboration: A Thematic Analysis

Interprofessional education (IPE) is essential to prepare future professionals for interprofessional collaboration (IPC). Learning together is essential for students because it is a way to understand the roles of other colleagues, improve their skills, knowledge, competencies, and attitudes to collaborate with the interprofessional teams. To explore how undergraduate students who attend IPE courses define IPC, a qualitative study using semistructured interviews followed by a thematic analysis was performed. Four main themes were identifed: IPC as a resource, requirements for IPC, emotions linked to IPC, and tutor\u2019s role to facilitate students\u2019 perception of IPC. Students considered IPE important to build IPC, where clinical placement tutors play a key role. The most important findings of the present study include the students\u2019 considerations about the importance of IPE when building their IPC definition and the key role played by the tutor during the placement in building IPC in clinical practic