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The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

This study tests whether environmental awareness affects firm creation by using Google Trends data and a novel region-level data set from Italy.

Forward-looking entrepreneurs drive firm creation. The authors hypothesize that more environmentally conscious entrepreneurs will emerge as environmental awareness rises, increasing the number of green and energy firms. The authors test the prediction using Google Trends data and a novel region-level data set from Italy.

The authors find that not only the number of green and energy-innovative firms but also that of all innovative start-ups increases with rising environmental consciousness. The results imply some “innovation spillover” effects from green sectors to other industries with rising environmental awareness.

The paper hypothesizes that as environmental awareness rises, more environmental-conscious entrepreneurs will emerge, which would increase the number of green and energy firms. Robustness and falsification tests are also offered.

This paper aims to investigate the determinants of global interest in central bank digital currency (CBDC). It assessed whether global interest in sustainable development and cryptocurrency are determinants of global interest in CBDC.

Google Trends data were analyzed using two-stage least square regression estimation.

There is a significant positive relationship between global interest in sustainable development and global interest in CBDC. There is a significant positive relationship between global interest in cryptocurrency and global interest in the Nigeria eNaira CBDC. There is a significant negative relationship between global interest in CBDC and global interest in the eNaira CBDC. There is a significant positive relationship between global interest in CBDC and global interest in the China eCNY. There is a significant negative relationship between global interest in cryptocurrency and global interest in the Sand Dollar and DCash.

The literature has not empirically examined whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC. This study fills a gap in the literature by investigating whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC.

The potential growth in cryptocurrencies has raised serious ethical and religious issues leading to a new investment rethinking. This paper aims to identify the influence of religiosity on cryptocurrency acceptance through an extended technology acceptance model (TAM) model.

In the first phase, this research develops a conceptual model that extends the theory of the TAM by integrating the religiosity component. In the second phase, the proposed model is tested using search volume queries in daily frequencies from 01/01/2018 to 31/12/2022 and structural equation modeling (SEM).

The empirical results demonstrate a significant positive effect of religiosity on the intention to use cryptocurrency, the users' perceived usefulness (PU) and ease of use (PEOU). Besides, the authors note that PEOU positively influences the intention. Furthermore, religiosity indirectly affects the intention through the PEOU and positively impacts the intention through the PU. In the same way, PEOU has a considerable indirect effect on the intention through PU.

This study has practical and theoretical contributions by providing insights into the cryptocurrency acceptance factors. In other words, it contributes to the literature by extending TAM models. Practically, it helps managers determine factors affecting the intention to use cryptocurrencies. Therefore, they can adjust their industry according to the suitable characteristics for creating successful projects.

Identifying the effect of religiosity on cryptocurrency users' choices and decisions has a social added value as it provides an understanding of the evolution of psychological variants.

The findings emphasize the importance of integrating big data to analyze users' attitudes. Besides, most studies on cryptocurrency acceptance are investigated based on one kind of religion, such as Christianity or Islam. Nevertheless, this paper integrates the effect of five types of faith on the users' intentions.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

The purpose of this study is to determine the characteristics of the studies in terms of country, participant profile and methodology, as well as to determine what the Internet of Things (IoT) is currently contributing to higher education.

The study was developed following the methodology supported by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and the PICOS strategy, retrieving scientific literature from Web of Science, Scopus, ERIC and Google Scholar. Of the 237 studies that the search yielded, 11 were included.

The results showed that among the opportunities offered by IoT is that it not only brings the introduction of information and communication technology into the classroom, but also enhances student interest, thus, improving the quality of teaching in higher education. On the other hand, one of the challenges it faces is the attitude of teachers towards its adoption, as well as the level of digital competence of teachers.

This study presents how higher education institutions are including the IoT in their educational activities. The IoT refers to a network of digital interconnectivity between devices, people and the internet itself that enables the exchange of data between them, allowing key information about the use and performance of devices and objects to be captured to detect patterns, make recommendations, improve efficiency and create better user experiences.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.