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Article
Publication date: 20 September 2011

Nandy Millan and Adrian Bromage

The paper comprises an extended discussion of the possibilities that Web 2.0 applications offer to doctoral researchers, and where such applications fit in the early…

619

Abstract

Purpose

The paper comprises an extended discussion of the possibilities that Web 2.0 applications offer to doctoral researchers, and where such applications fit in the early twenty‐first century in the research environment. It explores the main issues associated with their use by doctoral researchers, and how these factors have influenced the design of a series of four information and communication technologies (ICT) development courses.

Design/methodology/approach

Over 29 Web 2.0 applications were reviewed and grouped into 13 subject categories in terms of how they might support the activities of doctoral researchers. The resulting categories were then themed into four different groups to facilitate the delivery in order to address where and how Web 2.0 applications can enhance doctoral researchers' activities.

Findings

Four groups of applications emerged: social networking, online project collaboration, online virtual desks and reusable multi‐media. The four groups were developed into four courses that together comprise a new ICT skills development module intended for doctoral researchers.

Social implications

In terms of portability, the 13 categories of web‐based applications identified could, when taken together, comprise the infrastructure for a complete research environment that can be accessed anywhere in the world on an internet‐connected PC or laptop. The aim of the module is to enhance the research experience of doctoral researchers by raising awareness of the potential and possibilities associated with using Web 2.0 applications in the research environment.

Originality/value

The paper offers both ICT developers and doctoral researchers insights into the possibilities and problems of using Web 2.0 applications in the process of academic research.

Details

Interactive Technology and Smart Education, vol. 8 no. 3
Type: Research Article
ISSN: 1741-5659

Keywords

Book part
Publication date: 18 July 2022

Yakub Kayode Saheed, Usman Ahmad Baba and Mustafa Ayobami Raji

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).Need for the study: With the advance of technology, the world is…

Abstract

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

Article
Publication date: 3 November 2020

Laura García-García, Macarena Gonzalo Alonso-Buenaposada, M. Elena Romero-Merino and Marcos Santamaria-Mariscal

The purpose of this paper is to analyze the relationship between the ownership structure and the investment in research and development (R&D) for a sample of listed…

Abstract

Purpose

The purpose of this paper is to analyze the relationship between the ownership structure and the investment in research and development (R&D) for a sample of listed Spanish companies.

Design/methodology/approach

Following the agency theory and the socioemotional wealth (SEW) perspective, the authors propose that R&D investment is affected by ownership structure, specifically by the identity of the controlling owner (family firms and firms with an institutional investor) and the level of contestability by other shareholders. In order to test these hypotheses, the authors build an original database identifying, at a 10% threshold, the ultimate shareholders of a sample of 96 Spanish firms listed during 2008–2018 (1,002 obs).

Findings

The results show that there is no significant relationship between the ownership concentration and the R&D investment. Only when the authors consider the nature of the main shareholder, the authors find that in family firms there is an inverted U relationship between ownership and R&D, so that at low levels of ownership, the R&D increases, while at high levels of ownership (that we compute around 54%) the R&D decreases. Also, when the main shareholder is an institutional investor, the greater its ownership, the higher the R&D investment. Finally, the authors test that, contrary to what mainstream suggests, contestability in family firms is higher when ownership in the hands of other family shareholders increases.

Originality/value

The work uses an original database to test a nonlinear relationship between ownership and R&D investment in family firms. Also, the study addresses a topic hardly ever discussed in the literature about R&D as it is the role of the contestability by other controlling shareholders.

Objetivo

El objetivo del presente trabajo es analizar la relación existente entre la estructura de propiedad y la innovación corporativa para una muestra de empresas cotizadas españolas.

Diseño/metodología/enfoque

Utilizando los planteamientos de la teoría de la agencia y de la perspectiva de la riqueza socioemocional proponemos que la I+D empresarial está relacionada con la estructura de propiedad, específicamente con la naturaleza del accionista de control (empresas familiares y empresas con un inversor institucional como principal accionista) y con el grado de contestabilidad por parte de otros accionistas significativos. A fin de testar nuestras hipótesis, construimos ad hoc una base de datos de propiedad original en la que identificamos, al umbral del 10% de propiedad, a los accionistas últimos de una muestra de 96 empresas cotizadas españolas para el periodo 2008–2018 (1.002 obs).

Resultados

Nuestros resultados muestran que no existe relación significativa entre la concentración de propiedad y la inversión en I + D. Solo cuando consideramos la naturaleza del principal accionista encontramos que en las empresas familiares la relación entre la propiedad de la familia y la innovación corporativa adopta una forma de U invertida, tal que a bajos niveles de propiedad la I + D crece, mientras que a altos niveles de propiedad (que computamos en torno al 54% de propiedad) la inversión en I + D decrece. Asimismo, en las empresas con un inversor institucional como principal accionista, cuanto mayor es la propiedad de este inversor institucional, mayor es la I + D de la empresa. Finalmente testamos que, en contra de la corriente dominante, en las empresas familiares la propiedad en manos de otras familias incrementa el grado de contestabilidad a la familia controladora respecto a su inversión en I + D.

Originalidad

El trabajo utiliza una base de datos de propiedad original para testar una relación no lineal entre concentración de propiedad e innovación corporativa en las empresas familiares. Asimismo, el estudio aborda un tema apenas analizado en la literatura de I + D como es el papel de la contestabilidad al accionista de control.

Article
Publication date: 18 February 2021

Eric Breitbarth, Wendelin Groβ and Alexander Zienau

This paper studies a concept for protecting vulnerable population groups during pandemics using direct home deliveries of essential supplies, from a distribution logistics…

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Abstract

Purpose

This paper studies a concept for protecting vulnerable population groups during pandemics using direct home deliveries of essential supplies, from a distribution logistics perspective. The purpose of this paper is to evaluate feasible and resource-efficient home delivery strategies, including collaboration between retailers and logistics service providers based on a practical application.

Design/methodology/approach

A food home delivery concept in urban areas during pandemics is mathematically modeled. All seniors living in a district of Berlin, Germany, represent the vulnerable population supplied by a grocery distribution center. A capacitated vehicle routing problem (CVRP) is developed in combination with a k-means clustering algorithm. To manage this large-scale problem efficiently, mixed-integer programming (MIP) is used. The impact of collaboration and additional delivery scenarios is examined with a sensitivity analysis.

Findings

Roughly 45 medically vulnerable persons can be served by one delivery vehicle in the baseline scenario. Operational measures allow a drastic decrease in required resources by reducing service quality. In this way, home delivery for the vulnerable population of Berlin can be achieved. This requires collaboration between grocery and parcel services and public authorities as well as overcoming accompanying challenges.

Originality/value

Developing a home delivery concept for providing essential goods to urban vulnerable groups during pandemics creates a special value. Setting a large-scale CVRP with variable fleet size in combination with a clustering algorithm contributes to the originality.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 11 no. 2
Type: Research Article
ISSN: 2042-6747

Keywords

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