Marcelo Henriques de Brito, Paula Esteban do Valle Jardim

This peer-reviewed paper presents original scientific research in computer science and information systems, addressing "Group Behavioral Biases Affect Financial Decisions Unlike Individual Behavioral Biases". The authors discuss theoretical foundations, system architectures, empirical evaluations, and practical implications for modern digital ecosystems. Published in IJCSIS Vol. 15 No. 1 (2020).

Samantha Papavasiliou, Carmen Reaiche

The shift of public sector services to digital platforms has had ongoing impacts on individuals interactions with government entities and services. This research explores the effectiveness of encouraging users to adopt an eGovernment channel choice to lodge their annual income tax return in Australia. Through the application of a quasi-randomised control trial, two different user groups were tested in order to compare and contrasts the variable of an early message intervention and the potential impact on the lodgers shift to digital channels (i.e. eGovernme nt support encouraging historically non -digital lodgers group vs null support). This small study suggests that individuals are encouraged to use digital services when Government provides support, and that they are more likely to displays predictive behaviours of adoption to these digital channels. Furthermore, there are two main characteristics which predict the users choosing to shift (i.e. age and deductive benefits). Through the use of strategically placed encouragement and digital assistance, it makes an individual more likely to shift to digital channels. However, this research demonstrates predictive analytics has a stronger place in the long term adoption of eGovernment services.

Yosuke Tohata, Akiko Takahashi, Hideyuki Kobayashi, Yoshiaki Rikitake, Yoshikatsu Kubota

KOSENs not only try to increase students’ engineering abilities and literacy, which is the ability to use knowledge and skills, but also their competency, which we consider is the ability to apply knowledge and skills to any problem that might arise outside the walls of school. So t he present study analyzes the relationship between a teacher’s evaluation of students’ self-assessment by “Daifuku-cho,” which is used for teacher -and-student interaction in PE class, and their competency improvement using PROG test scores. The result confirmed that students’ daily self -reflection and teachers’ feedback on them are important, and the outcome of their daily self -reflection can quantitatively show the growth in their competency. In particular, we report that the interaction between students and their teacher through the use of “Daifuku-cho” enhanced their learning and also that by comparing what the students wrote in “Daifuku- cho,” their grades and the results of PROG test, we suggest that the better students reflected themselves using “Daifuku-cho,” the more their competency developed.

Youssouf Ismail Cherifi, Abdelhakim Dahimene, 2 1Universite de M’Hamed Bougara, Boumerdes Researcher, Algeria Researcher

Identifying the speaker has become more of an imperative thing to do in the modern age. Especially since most personal and professional appliances rely on voice commands or speech in general terms to operate. These systems need to discern the identity of the speaker rather than just the words that have been said to be both smart and safe. Especially if we consider the numerous advanced methods that have been developed to generate fake speech segments. The objective of this paper is to improve upon the existing voice-based biometrics to keep up with these synthesizers. The proposed method focuses on defining a novel and more speaker adapted features by implying artificial neural networks and transfer learning. The approach uses pre -trained networks to define a mapping from two complementary acoustic features to a speaker adapted phonetic features. The complementary acoustics features are paired to provide both information about how the speech segments are perceived (type 1 feature) and produced (type 2 feature). The approach was evaluated using both a small and large closed-speaker data set. Primary results are encouraging and confirm the usefulness of such an approach to extract speaker adapted features whether for classical machine learning algorithms or advanced neural structures such as LSTM or CNN.

Eduard Daoud, Dang Vu, Hung Nguyen, Martin Gaedke

ResearchAndMarkets wrote in their report on May 15, 2018, that up to 1.2 Trillion USD in 2017 of products are counterfeited goods. The report estimated this damage globally at 1.82 Trillion USD in 2020. This paper does not consider copyright infringement, digital piracy, counterfeiting or fraudulent documents, but rather examines the prevention of counterfeiting on a technological basis. The presence of counterfeit products on the European and US markets increase, the intervention of inspection bodies and authorities alone is obviously not sufficient, but consumers could make their contribution and improve the situation. In this paper, we research the possibility to reduce counterfeit products using machine learning-based technology. Image and text recognition, and classification based on machine learning have the potenti al to be come the key technology in the fight against counterfeiting. Image recognition and classification of product information empowers the end customer to identify counterfeits accurately and efficiently by comparing them with trained models. The goal of this paper is to create an easy, simple, and elegant application, which empowers the end-users to identify counterfeit products and as such contribute to the fight against product piracy.

Jorg H. Mayer, Markus Esswein, Reiner Quick, Sanjar Sayar

Nowadays, even apps for corporate management are respectable. Manager app portals complement such “run-a-business” apps with apps that make managers ’ business life easier. The right mix of apps make s the difference. Accommodating the user perspective, the objective of this article is to examine which apps disproportionately influence managers’ perception regarding the usefulness of information systems (IS) . Applying the Kano model and considering both “analyst-” and “consumer-type” managers, we employ findings from a manager focus group survey to discuss the strongest differentiators: (1) Offer collaboration bars and push notifications for analyst managers. Get consumer managers “online” with news tickers . (2) Make core reports more interactive with drill downs and filters for manager self-service. (3) Take “fun and enjoyment ” into account when accommodating managers ’ business life . (4) Consider that tablets create their own manager use case as a “first-stop information shop.”

Ruel Welch, Temitope Alade, Lynn Nichol

It has been observed that mobile learning (mLearning) in institutions like Museums in the United Kingdom (UK) has been underutilized. mLearning usage could potentially increase productivity by delivering just-in-time technical knowledge to the science museum group ( SMG) staff. This study uses the unified theory of acceptance and use of technology (UTAUT) model to determine factors affecting mLearning adoption at the SMG. Two research questions were formulated based on an adaptation of the UTAUT model. 1) What are the determinants of behavior intentions to use mLearning at the SMG? 2) Does gender or age have a moderating effect on the factors that determine behavior intentions to use mLearning at the SMG? 118 respondents were surveyed from the SMG. Data obtained were analyzed using Structured Equation Modelling on IBM SP SS 20 and Amos version 2 5. Results indicate that the UTAUT constructs, performance expectancy, effort expectancy, social influence and facilitating conditions are all significant determinants of behavioral intention to use mLearning. A newly proposed construct, self-directed learning was not a significant determinant of behaviour intentions. Further examination found age and gender moderate the relationship between the UTAUT constructs. These findings pr esent several useful implications for mLearning research and practice for ICT service desk at the SMG. The research contributes to mLearning technology adoption and strategy.

Alexandre V. Maschio, Nuno M. R. Correia

This article reports a case study in which a digital learning object (DLO) was developed to assist in pedagogical practice in higher education (in the audiovisual area) and presents the technical and theoretical stages of the tool development process (DLO) and its assessment. The objective of the research was mainly to evaluate the pedagogical contribution of DLO through the perception of students who were subdivided into four groups, performed two practical exercises at different times and order, during a 60-hour course. Both exercises of the same complexity were performed without and with the aid of the digital tool. Subsequently, the participants answered forms to be able to evaluate the tool, in addition to having their audiovisual prod ucts developed during the course/research been used for a blind analysis to infer qualitative gain at work due to possible time savings generated by the automation function of the tool. In the end, it was found that the DLO tool was very well evaluated con ceptually and considered relevant, differentiated, with credibility and high intention of use (among other metrics). The blind analysis showed that there was no qualitative difference due to the possible gain in time between the works developed with or without the aid of the tool.

Pedro Isaías

Editorial preface for Volume 15, Issue 1 of the IADIS International Journal on Computer Science and Information Systems (IJCSIS). This issue brings together peer-reviewed original research contributions covering the wide spectrum of Information Systems, Artificial Intelligence, Software Engineering, and Digital Transformation.