Mario Nuno Agostinho, Alvaro Dias and Leandro F. Pereira
This study aims to provide a new perspective on the factors determining a country’s tourism performance, understand the interrelationships among these factors and explore their…
Abstract
Purpose
This study aims to provide a new perspective on the factors determining a country’s tourism performance, understand the interrelationships among these factors and explore their implications for the future of tourism in high-income countries.
Design/methodology/approach
The study employs a fuzzy-set qualitative comparative analysis (fsQCA) using five variables from the World Economic Forum’s Travel and Tourism Development Index (TTDI). The focus is on identifying seven configurations of antecedents of Travel and Tourism Industry Gross Domestic Product (T&T Industry GDP).
Findings
The study identifies seven configurations of antecedents influencing T&T Industry GDP, revealing how these factors operate in different scenarios, specifically in countries with high and low T&T GDP. These configurations offer insights into potential future pathways for tourism development.
Research limitations/implications
The study implies that tourism is a complex phenomenon influenced by multiple interacting factors. It provides a framework for understanding how different combinations of factors can lead to high or low tourism performance, offering valuable insights for anticipating and shaping the future of tourism.
Originality/value
This study adds value by providing a more nuanced understanding of the tourism industry, challenging the notion of singular effects of variables and highlighting the importance of analyzing multiple, interacting factors in understanding and predicting tourism performance. It contributes to the field of futures studies by offering a tool for anticipating potential future scenarios and their impact on the tourism industry.
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Vandoir Welchen, Juliana Matte, Cintia Paese Giacomello, Franciele Dalle Molle and Maria Emilia Camargo
The purpose of this paper is to validate and measure the overall evaluation of electronic health record (EHR) and identify the factors that influence the health information…
Abstract
Purpose
The purpose of this paper is to validate and measure the overall evaluation of electronic health record (EHR) and identify the factors that influence the health information systems (HIS) assessment in Brazil.
Design/methodology/approach
From February to May 2020, this study surveyed 262 doctors and nurses who work in hospitals and use the EHR in their workplace. This study validated the National Usability-focused HIS Scale (NuHISS) to measure usability in the Brazilian context.
Findings
The results showed adequate validity and reliability, validating the NuHISS in the Brazilian context. The survey showed that 38.9% of users rated the system as high quality. Technical quality, ease of use and benefits explained 43.5% of the user’s overall system evaluation.
Research limitations/implications
This study validated the items that measure usability of health-care systems and identified that not all usability items impact the overall evaluation of the EHR.
Practical implications
NuHISS can be a valuable tool to measure HIS usability for doctors and nurses and monitor health systems’ long-term usability among health professionals. The results suggest dissatisfaction with the usability of HIS systems, specifically the EHR in hospital units. For this reason, those responsible for health systems must observe usability. This tool enables usability monitoring to highlight information system deficiencies for public managers. Furthermore, the government can create and develop actions to improve the existing tools to support health professionals.
Social implications
From the scale validation, public managers could monitor and develop actions to foster the system’s usability, especially the system’s technical qualities – the factor that impacted the overall system evaluation.
Originality/value
To the best of the authors’ knowledge, this study is the first to validate the usability scale of EHR systems in Brazil. The results showed dissatisfaction with HIS and identified the factors that most influence the system evaluation.
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Eric Pettersson Ruiz and Jannis Angelis
This study aims to explore how to deanonymize cryptocurrency money launderers with the help of machine learning (ML). Money is laundered through cryptocurrencies by distributing…
Abstract
Purpose
This study aims to explore how to deanonymize cryptocurrency money launderers with the help of machine learning (ML). Money is laundered through cryptocurrencies by distributing funds to multiple accounts and then reexchanging the crypto back. This process of exchanging currencies is done through cryptocurrency exchanges. Current preventive efforts are outdated, and ML may provide novel ways to identify illicit currency movements. Hence, this study investigates ML applicability for combatting money laundering activities using cryptocurrency.
Design/methodology/approach
Four supervised-learning algorithms were compared using the Bitcoin Elliptic Dataset. The method covered a quantitative analysis of the algorithmic performance, capturing differences in three key evaluation metrics of F1-scores, precision and recall. Two complementary qualitative interviews were performed at cryptocurrency exchanges to identify fit and applicability of the algorithms.
Findings
The study results show that the current implemented ML tools for preventing money laundering at cryptocurrency exchanges are all too slow and need to be optimized for the task. The results also show that while not one single algorithm is most suitable for detecting transactions related to money-laundering, the specific applicability of the decision tree algorithm is most suitable for adoption by cryptocurrency exchanges.
Originality/value
Given the growth of cryptocurrency use, this study explores the newly developed field of algorithmic tools to combat illicit currency movement, in particular in the growing arena of cryptocurrencies. The study results provide new insights into the applicability of ML as a tool to combat money laundering using cryptocurrency exchanges.
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John Goodwin, Eileen Savage and Aine O'Donovan
Significant advances have been made in using applied methodological approaches. These approaches facilitate critical and creative ways to generate new knowledge, encouraging…
Abstract
Purpose
Significant advances have been made in using applied methodological approaches. These approaches facilitate critical and creative ways to generate new knowledge, encouraging researchers to explore novel research questions which could not be sufficiently addressed using traditional “branded” methodologies. It is important that, in addition to design, researchers consider the most appropriate methods to collect data. The purpose of this paper is to explore the use of the draw and tell method in the context of an interpretive descriptive study.
Design/methodology/approach
Given the challenges associated with eliciting responses from adolescent populations, in addition to the use of a semi-structured interview guide, the authors encouraged adolescent participants to produce drawings as part of an interpretive descriptive study.
Findings
Despite the fact that drawings are seldom used with adolescents during research interviews, the authors found this method promoted conversation and facilitated deep exploration into adolescents' perspectives.
Originality/value
The authors argue that this creative approach to data collection should be embraced by researchers engaging in applied methodological research, particularly with participants who may be challenging to engage. Drawings, although seldom used with adolescent research participants, can stimulate engagement and facilitate conversations.
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Martin Jullum, Anders Løland, Ragnar Bang Huseby, Geir Ånonsen and Johannes Lorentzen
The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial transactions should be manually investigated for potential…
Abstract
Purpose
The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial transactions should be manually investigated for potential money laundering. The model is applied to a large data set from Norway’s largest bank, DNB.
Design/methodology/approach
A supervised machine learning model is trained by using three types of historic data: “normal” legal transactions; those flagged as suspicious by the bank’s internal alert system; and potential money laundering cases reported to the authorities. The model is trained to predict the probability that a new transaction should be reported, using information such as background information about the sender/receiver, their earlier behaviour and their transaction history.
Findings
The paper demonstrates that the common approach of not using non-reported alerts (i.e. transactions that are investigated but not reported) in the training of the model can lead to sub-optimal results. The same applies to the use of normal (un-investigated) transactions. Our developed method outperforms the bank’s current approach in terms of a fair measure of performance.
Originality/value
This research study is one of very few published anti-money laundering (AML) models for suspicious transactions that have been applied to a realistically sized data set. The paper also presents a new performance measure specifically tailored to compare the proposed method to the bank’s existing AML system.
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Outi Sarpila, Iida Kukkonen, Tero Pajunen and Erica Åberg