Purpose




This paper aims to examine the influence of interactive social media marketing communications on teenagers’ cognitive, affective and behavioral attitude components in South Africa. The study also considers the impact of a number of additional factors such as usage (access, length of usage, log-on frequency, log-on duration and profile update incidence) and demographic (gender, age and population group) variables on young consumers’ attitudes toward social media marketing communications.




Design/methodology/approach




A survey was used via three self-administered questionnaires, which were distributed to over 13,000 learners in the age range of 13-18 years (Generation Z cohort) at colleges and high schools in South Africa. A generalized linear model was used for statistical data analysis.




Findings




The study ascertained that social media marketing communications had a positive on each attitude component among adolescents, but on a declining scale, which correlates to the purchase funnel. The results also revealed that teenagers who used social media for long time periods; updated their profiles frequently and were from the Colored and Black population groups, displayed the most favorable attitudinal responses to social media marketing communications.




Research limitations/implications




Social media was collectively analyzed and did not consider the number of different social media types, which could be examined individually. This investigation only considered the Generation Z cohort, but other cohorts to attitudes toward social media marketing communications could also be assessed.




Practical implications




Companies and their brands should consider using and/or adapting their strategies based on the declining impact of social media marketing communications on the hierarchical attitude stages among young consumers and the divergent influence on usage and demographic variables when targeting the lucrative and technologically advanced, but capricious, Generation Z consumers.




Originality/value




This research established that social media marketing communications had a favorable influence on cognitive, affective and behavioral attitude components among young consumers, but on a declining scale, which is in congruence with the purchase funnel model. This investigation also makes an important contribution to attitudinal research in developing countries, where there is a lack of research in social media marketing communications.

/pdf/influence-of-social-media-marketing-communications-on-young-si9qkmkbvo.pdf

Influence of social media marketing communications on young consumers’ attitudes

Money laundering has been affecting the global economy for many years. Large sums of money are laundered every year, posing a threat to the global economy and its security. Money laundering encompasses illegal activities that are used to make illegally acquired funds appear legal and legitimate. This paper aims to provide a comprehensive survey of machine learning algorithms and methods applied to detect suspicious transactions. In particular, solutions of anti-money laundering typologies, link analysis, behavioural modelling, risk scoring, anomaly detection, and geographic capability have been identified and analysed. Key steps of data preparation, data transformation, and data analytics techniques have been discussed; existing machine learning algorithms and methods described in the literature have been categorised, summarised, and compared. Finally, what techniques were lacking or under-addressed in the existing research has been elaborated with the purpose of pinpointing future research directions.

Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review

Background:  In tourism, globally there is a growing interest in social media marketing research. However, most previous research on social media marketing has focused on large tourism enterprises such as chain hotels, leaving out small tourism businesses such as travel agencies and tour operators. Objective:  The aim of this research was to establish factors that influence attitude towards the use of social media marketing by travel agencies and tour operators in South Africa. Method:  The study adopteda quantitative approach through the use of questionnaires. Data used in the analysis were collected from 150 travel agencies and tour operators by means of a structured questionnaire. Multiple regression analysis and one-way ANOVA were used for data analysis. Results:  The results showed that managerial support and managers’ level of education are the two main internal factors which influence attitude towards the use of social media marketing. Pressure from competitors, perceived benefits and perceived ease of use were found to be the most prominent external factors which influence the use of social media marketing. The results also revealed that technical knowledge moderates the relationship between attitude towards social media marketing and the level of social media marketing usage. Conclusion:  The study concludes with these recommendations: Management of travel agencies, tour operating businesses and the South African government should support the use of social media marketing by small tourism businesses through providing training and workshops on social media marketing for the employees to acquire the required skills.

/pdf/application-of-the-technology-acceptance-model-and-the-4lv5vm4s6m.pdf

Application of the technology acceptance model and the technology–organisation–environment model to examine social media marketing use in the South African tourism industry

This paper aims to investigate the key drivers of social media adoption intention by small- and medium-sized enterprises (SMEs).,It uses a multi-perspective framework combining technological, organizational and environmental elements affecting SMEs. Data were collected from a random sample of 1,700 SMEs operating in the UAE. Partial least squares structural equation modeling was used to analyze the data.,The results showed that the technology construct had no significant effect on social media adoption, but both organization and environment constructs were significant.,This has implications for social media experts and anyone wishing to encourage social media use by SMEs.,Conceptually, it develops a suitable multi-perspective framework covering various factors that may affect social media use. It also tests the framework empirically on a sample of SMEs from the UAE.

Understanding social media adoption in SMEs: Empirical evidence from the United Arab Emirates

Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between research community and industry to benefit from domain knowledge and controlled access to data.

/pdf/deep-learning-and-explainable-artificial-intelligence-156hda183c.pdf

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Today, money laundering poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the clich&#233, of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting money laundering activities. Within the scope of a collaboration project for the purpose of developing a new solution for the anti-money laundering Units in an international investment bank, we proposed a simple and efficient data mining-based solution for anti-money laundering. In this paper, we present this solution developed as a tool and show some preliminary experiment results with real transaction datasets.

A Data Mining-Based Solution for Detecting Suspicious Money Laundering Cases in an Investment Bank

Today, money laundering poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the clichy of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting money laundering activities. Within the scope of a collaboration project for the purpose of developing a new solution for the anti-money laundering Units in an international investment bank, we proposed a simple and efficient data mining-based solution for anti-money laundering. In this paper, we present this solution developed as a tool and show some preliminary experiment results with real transaction datasets.

pdf/a-data-mining-based-solution-for-detecting-suspicious-money-4fluh38en2.pdf

A data mining-based solution for detecting suspicious money laundering cases in an investment bank

Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliche of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project for the purpose of developing a new solution for the AML Units in an international investment bank based in Ireland, we propose a new data mining-based approach for AML. In this paper, we present this approach and some preliminary results associated with this method when applied to transaction datasets.

Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank

Net Asset Value (NAV) calculation and validation is the principle task of a fund administrator. If the NAV of a fund is calculated incorrectly then there is huge impact on the fund administrator; such as monetary compensation, reputational loss, or loss of business. In general, these companies use the same methodology to calculate the NAV of a fund, however the type of fund in question dictates the set of business rules used to validate this. Today, most Fund Administrators depend heavily on human resources due to the lack of an automated standardized solutions, however due to economic climate and the need for efficiency and costs reduction many banks are now looking for an automated solution with minimal human interaction; i.e., straight through processing (STP). Within the scope of a collaboration project that focuses on building an optimal solution for NAV validation, in this paper, we will present a new approach for detecting correlated business rules. We also show how we evaluate this approach using real-world financial data.

pdf/a-tree-based-approach-for-detecting-redundant-business-rules-4rf6aonx9y.pdf

A Tree-based Approach for Detecting Redundant Business Rules in very Large Financial Datasets

Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliche of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight fraud activities. However, the AML systems are so complicated that simple query tools provided by current DBMS may produce incorrect and ambiguous results and they are also very time-consuming due to the complexity of the database system architecture. In this paper, we present a new approach for identifying customers quickly and easily as part of an AML application. This will help AML experts to identify quickly customers who are managed independently across separate databases of the organization. This approach is tested on large and real-world financial datasets. Some preliminary experimental results show that this new approach is efficient and effective.

/pdf/an-efficient-search-tool-for-an-anti-money-laundering-3z3aby6pxu.pdf

An efficient Search Tool for an Anti-Money Laundering Application of an Multi-national Bank's Dataset

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