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The petrochemical industry has made the economic development of many local communities possible, increasing employment opportunities and generating a complex network of closely-related secondary industries. However, it is known that petrochemical industries emit air pollutants, which have been related to different negative effects on mental health. In addition, many people around the world are being exposed to highly stressful situations deriving from the COVID-19 pandemic and the lockdowns adopted by national and regional governments. The present study aims to analyse the possible differential effects on various psychological outcomes (stress, anxiety, depression and emotional regulation strategies) stemming from the COVID-19 pandemic and consequent lockdown experienced by individuals living near an important petrochemical complex and subjects living in other areas, nonexposed to the characteristic environmental pollutants emitted by these kinds of complex. The sample consisted of 1607 subjects who answered an ad hoc questionnaire on lockdown conditions, the Perceived Stress Scale (PSS), the Hospital Anxiety and Depression Scale (HADS), the Barratt Impulsivity Scale (BIS) and the Emotional Regulation Questionnaire (ERQ). The results indicate that people living closer to petrochemical complexes reported greater risk perception [K = 73.42, p < 0.001, with a medium size effect (η = 0.061)]. However, no significant relationship between psychological variables and proximity to the focus was detected when comparing people living near to or far away from a chemical/petrochemical complex. Regarding the adverse psychological effects of the first lockdown due to COVID-19 on the general population in Catalonia, we can conclude that the conditions included in this survey were mai

Several studies in different countries have reported that part of the population does not fully comply with the measures recommended to prevent COVID-19, and therefore poses a risk to public health. For this reason, several measures have been developed to assess the level of compliance, although many of them have methodological limitations or do not include a comprehensive set of items. The main goal of the current study was to develop a new instrument with suitable psychometric properties, which includes a more complete set of items and controls the impact of acquiescence bias. The participants were 1410 individuals (59.2% women) from Spain, who answered the new questionnaire and several items on sociodemographic and attitudinal issues. Exploratory and confirmatory factor analyses were carried out, and the results suggested that only one content factor was underlying the data. This solution was replicated in a different subsample, which shows the stability of the solution. Furthermore, the relationships between the scores of the new questionnaire and the sociodemographic and attitudinal variables are similar to those obtained in previous studies, which can be regarded as evidence of the validity of the new questionnaire.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS. The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories; health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the second edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

We have before us the sixth issue of INSAM Journal of Contemporary Music, Art and Technology. This is the second issue in a row dedicated to the global crisis caused by the Covid-19 pandemic. After the overwhelming response from all over the world to the call for papers and provocative inspections that ensued, here we wanted to discuss the ways in which technology shapes and enables work in the areas of music, arts, humanities, and the education process, this time inviting our collaborators to discuss the shortcomings and struggles of the working processes in these fields. The main theme, “Music, Art and Humanities in the Time of Global Crisis”, expanded from the Main Theme section into the interviews as well.

There is an extensive literature on the importance of the use of scientific evidence on teaching methods in higher education institutions. However, there is a gap in how evidence is used for decision making that affects students and staff in universities. This article is a contribution to the existing gap, making the analysis of a specific case on how Catalan universities have managed the decision making regarding their staff in the face of the pandemic situation with the COVID-19. In this article, through the interview with managers and faculty from different Catalan universities, it will be shown to what extent these university institutions have opted for decision making in the management of the situation based on scientific evidence or simply compliance with government regulations.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS. The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories; health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the fifth edition (October 2022), variable HEALTH20 was added to the dataset. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

EXCEPTIUS Corpus v1.0, containing the following data: - raw documents for 21 countries at national level - pre-processed data with spacy-udpipe v1.0 - automatically annotated documents for the identification of exceptional measures at sentence level Country list (ISO 3166-1 alpha-2): AT, BE, HR, CY, CZ, DK, FR, DE, HU, IE, IT, LV, LT, NL, NO, PL, SI, SE, CH, UK Folder structure: each country has a dedicated folder. Inside each folder you will find the following subfolders: - raw_text: the raw text data (.txt format) - processed: the output of the spacy-udpipe v1.0 - each line is a sentence, containing the following info: tokens, lemma, POS, UD dependency relations - model: the predictions of the trained model (XML pre@36 as reported in Table 4 of the paper). Each line is a sentence, separate by 9 tab - each for a exceptional measure class. 1: signals presence of a class. The Italy and Norway folder misses the predictions of the models.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS. The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories; health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the third edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

A observação simultânea das notícias e dos mercados financeiros sugere que estão interrelacionados. Nesta dissertação, exploramos esta observação ao nível empresarial, analisando as interações entre o sentimento das notícias, avaliado utilizando Machine Learning, e os retornos. A análise baseia-se nos preços das ações de todos os constituintes do S&P 500 e nas manchetes de notícias publicadas pela "Reuters Newswire" entre 1 de Março de 2019 e 30 de Junho de 2020. Estimando modelos dinâmicos de painel, concluímos que a relação causal entre o sentimento jornalístico diário de uma empresa e o retorno excessivo de uma empresa é mútua. O sentimento noticioso prevê retornos no dia seguinte, que não se invertem numa semana de negociação. Provou-se que os noticiários contêm informações fundamentais. Adicionalmente, o excesso de retorno prevê o sentimento, indicando que os noticiários também relatam eventos passados. Estes resultados alinham-se com pesquisas anteriores de Ahmad et al. (2016). Também investigamos a precisão fora da amostra dos modelos de painéis dinâmicos ajustados pela indústria, o nível de cobertura mediática e num conjunto de testes caracterizado pelo surto de Covid-19. Através destas análises, obtemos que a cobertura da indústria e dos meios de comunicação social não estão relacionadas com a precisão da previsão, confirmando os resultados de Hendershott, Livdan e Schürhoff (2015) e Tetlock (2010). Contrariamente às descobertas de Antweiler e Frank (2006), e de García (2013), que sugerem uma maior precisão na previsão dos sentimentos durante as recessões, verificamos que a precisão do modelo reduz após o surto de Covid-19. Simultaneously observing the news and the development of financial markets suggests that both are interrelated in some way. In this dissertation, we explore this casual observation on the firm level by analysing the interactions between news sentiment, which we assess by Machine Learning techniques, and returns. Thereby, we base our analysis on all S&P 500 constituents’ stock prices and news headlines published by the ‘Reuters Newswire’ between 1 st of March 2019 and 30 th of June 2020. Estimating dynamic panel models, we conclude that the causal relationship between the firm-specific daily news sentiment and a firms’ excess returns is mutual. News sentiment predicts next day returns that are not reversed within a trading week. Thus, we find evidence that newswires contain fundamental information. Further, excess returns predict sentiment, which indicates that newswires report about past events as well. These findings are in line with previous research of Ahmad et al. (2016). In addition, we investigate the out-of-sample accuracy of the fitted dynamic panel models by industry, level of media coverage and in a test set characterised by the outbreak of Covid-19. From these analyses, we obtain that industry and media coverage are not related to the prediction accuracy, which confirms the results of Hendershott, Livdan and Schürhoff (2015) and Tetlock (2010) respectively. Contrastingly to findings of Antweiler and Frank (2006) as well as García (2013), that suggest improved sentiment prediction accuracies during recessions, we find that the accuracy of our model is reduced following the outbreak of Covid-19.

Το έτος 2021 ανακηρύχθηκε από τον Οργανισμό Ηνωμένων Εθνών ως το Διεθνές Έτος Δημιουργικής Οικονομίας για την Βιώσιμη Ανάπτυξη. Το γεγονός αυτό, σε συνδυασμό με τα νέα δεδομένα που δημιούργησε η πανδημία covid-19 σε παγκόσμιο επίπεδο, υπογραμμίζουν τη σημασία των ασύρματων δικτύων επικοινωνίας για την εδραίωση μίας βραχυπρόθεσμης, μεσοπρόθεσμης και, κυρίως, μακροπρόθεσμης ευημερίας για κάθε πολιτιστικό φορέα. Σήμερα, την εποχή της τέταρτης τεχνολογικής επανάστασης, η ψηφιακή πραγματικότητα κερδίζει ολοένα και περισσότερο έδαφος επηρεάζοντας καθοριστικά τους όρους του οικονομικού γίγνεσθαι. Στόχος της παρούσας μελέτης είναι να χαρτογραφηθεί ο ορίζοντας του σύγχρονου ψηφιακού marketing των φορέων πολιτισμού, αναδεικνύοντας παράλληλα τις νέες προοπτικές και τάσεις του χώρου. Στο δεύτερο μέρος της μελέτης, θα επιχειρηθεί η εφαρμογή στην πράξη των πορισμάτων του πρώτου μέρους, μέσα από τη δημιουργία ιστοσελίδας για ένα μη κερδοσκοπικό οργανισμό με στόχο την προώθηση πολιτιστικού αποθέματος. Συγκεκριμένα, έπειτα από επικοινωνία με την Πρόεδρο του Σωματείου «Φίλοι των Μουσείων», κυρία Λίλα ντε Τσάβες, δόθηκε η δυνατότητα για πιλοτική αναδιαμόρφωση και αναβάθμιση του ιστοτόπου «Heritage & Museums», προκείμενου το εν λόγω website να συμμορφωθεί με τις σύγχρονες επιταγές marketing και να γίνει πιο φιλικό προς το χρήστη. Ακόμη, στο πλαίσιο της εργασίας θα καταρτισθεί πλάνο marketing ψηφιακής πολιτικής του φορέα σε βάθος ενός έτους, προκειμένου να αποτυπωθούν οι κατευθυντήριες γραμμές του θεωρητικού πλαισίου, όπως αυτό διαμορφώθηκε στο πρώτο μέρος. Εργαλεία για την εκπόνηση της μελέτης θα είναι η ελληνόγλωσση και ξενόγλωσση βιβλιογραφία και δικτυογραφία, καθώς και ο ηλεκτρονικός συγκριτισμός μεταξύ ιστοτόπων φορέων με ανάλογους στόχους και περιεχόμενο. Τέλος, στο πρακτικό μέρος της εργασίας θα αξιοποιηθούν τα μοντέλα ανάλυσης S.O.S.T.A.C, P.E.S.T.E.L και S.W.O.T κ.ά., όπως αυτά προσφέρονται από την επιστήμη του marketing. The United Nations has designated 2021 as the International Year of Creative Economy for Sustainable Development. This fact, when combined with the new data generated by the Covid -9 pandemic on a global scale, emphasises the importance of wireless communication networks in establishing short-term, medium-term, and, most importantly, long-term prosperity for every cultural institution. The goal of this research is to sketch out the landscape of modern digital marketing for cultural organisations while highlighting new perspectives and trends. The findings of the first part of the study will be put into practise in the second part of the study through the design of a website for a non-profit organisation promoting cultural heritage. The Greek-language and foreign-language bibliographies, as well as an electronic comparison of websites with similar goals and content, will be used to set up the study. Finally, the S.O.S.T.A.C, P.E.S.T.E.L, and S.W.O.T models will be used in the practical section of the paper.