Search results

In the context of Portugal, this study examines the stereotypes of accountants held by laypeople and how they are influenced by financial crises and accounting scandals.

To better understand the social images of accountants, the authors adopt a structural approach based on the big five model (BFM) of personality. The authors test this approach on a Portuguese community sample (N = 727) using a questionnaire survey. The results are analyzed considering the socioanalytic theory.

The results suggest the existence of a stereotype dominated by features of conscientiousness, which is related to the superior performance of work tasks across job types. This feature comprises the core characteristics of the traditional accountant stereotype, which survives in a context challenged by financial scandals and crises. The findings highlight the social acceptance of accountants as an occupational group but do not suggest the possibility of accountants benefiting from the highest levels of social status when considered in relation to the traditional accountant stereotype.

By combining the BFM and the socioanalytic theory, this study provides a unique theoretical approach to better understand the social images of accountants. The findings demonstrate the suitability of using the BFM to study the social perceptions of accountants. They also indicate a paradox based on the survival of the traditional stereotype. This stereotype appears to be resistant to scandals and financial crisis, instead of being impaired, giving rise to another prototype with concerns about integrity.

The purpose of this study is to deepen understanding of the effects of using social media technologies to acquire technological knowledge and organizational learning competences, of technological knowledge competences on organizational learning and finally of organizational learning on organizational performance.

The study was performed by analyzing data from a sample of 197 technology firms located in Spain. The hypotheses were tested using a structural equations model with the program LISREL 8.80.

This study’s conceptual framework is grounded in complexity theory – along with dynamic capabilities theory, which complements the resource-based view. The study contributes to the literature by proposing a model that reflects empirically how business ecosystems that use social media technologies enable the development of interorganizational and social collaboration networks that encourage learning and development of technological knowledge competences.

It would be interesting for future studies to consider other elements to conceptualize and measure social media technologies, including (among others) significance of the various tools used and strategic integration. The model might also analyze other sectors and another combination of variables.

The results of this study have several managerial implications: developing social media technologies and interorganizational social collaboration networks not only enables the organizational learning process but also encourages technological knowledge competences. Through innovation processes, use of social media technologies also contributes to strengthening companies’ strategic positioning, which ultimately helps to improve firms’ organizational performance.

Since social media technologies drive information systems in contemporary society (because they enable interaction with numerous agents), the authors highlight the use of complexity theory to develop a conceptual framework.

The study also deepens understanding of the connections by which new experiential learning contributes to the generation of coevolutionary adaptive business ecosystems and digital strategies that enable development of interorganizational and social collaborative networks through technological knowledge competences. Only after examining the impact of social media technologies on organizational performance in prior literature, did the authors underscore that both quantity and frequency of social media technology use are positively related to improvement in knowledge processes that lead to employees’ creation and acquisition of new metaknowledge.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This paper aims to explore the effects of a surge in tourism short-term rentals (STR) on housing prices in municipalities within Portugal’s two largest Metropolitan Statistical Areas.

This study applies the difference-in-differences (DiD) methodology by using a feasible generalized least squares (FGLS) estimator in a seemingly unrelated regression (SUR) equation model.

The results show that the liberalization of STR had a significant impact on housing prices in municipalities where a higher percentage of housing was transferred to tourism. This transfer led to a leftward shift in the housing supply and a consequent increase in housing prices. These price increases are much higher than those found in previous studies on the same subject. The authors also found that municipalities with more STR had low housing elasticities, which indicates that adjustments to the transfer of real estate from housing to tourism were made by increasing house prices, and not by increasing supply quantities.

The study suggests that an unforeseen consequence of allowing property owners to transfer the use of real estate from housing to other services (namely, tourism) was extreme housing price increases due to inelastic housing supply.

This is the first time that the DiD methodology has been applied in real estate markets using FGLS in a SUR equation model and the authors show that it produces more precise estimates than the baseline OLS FE. The authors also find evidence of a supply shock provoked by STR.

Using COVID-19 pandemic as a more immediate empirical reference, this paper aims to understand the biosecurity risks arising from tourist activities and, through a more prospective analysis, to consider the relevance of public health issues in the context of tourism-sustainability nexuses.

The text assumes a hybrid format, incorporating elements resulting from empirical research and essayistic viewpoints. The collection of empirical elements was based on documental research in several sources, such as newspapers, international institutions of an intergovernmental nature and the discussion forum of the travel platform TripAdvisor.

By assuming mobility and large agglomerations of people from different origins, mass tourism has fostered multiple outbreaks of COVID-19 and the rapid global spread of contagion chains. The pandemic clearly exemplified the responsibility of tourism in the dispersion of biotic agents with severe ecological, economic, social and public health repercussions. It is, therefore, urgent to rethink the tourism growth trajectory and more effectively consider the biosecurity risks associated with mobility in discussions on tourism and sustainability. At the same time, tourism must be delineated in terms of the great aims of sustainability, and this transversal purpose to which it contributes should be considered an intrinsic condition of its own sectorial sustainability as an economic activity.

The biosecurity challenges posed by mass tourism are a very topical issue, still little considered in sustainability policies and on which there is a marked deficit in scientific research.