Engagement in proactive recommendations

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Concern for information privacy in South Africa: An empirical study using the OIPCI

Please follow the DOI link at the top of the record to access the published version of this article online on the website of the journal.The information privacy concern of consumers concerning the processing of their personal information by online organizations (websites) is investigated in this study by means of a quantitative approach. An overview of existing concerns about information privacy instruments are presented based on a literature review. The Online Information Privacy Concern Instrument (OIPCI) is used to study consumers’ expectations and experience regarding information privacy principles in order to identify their concerns about information privacy. The study was conducted in South Africa with a demographical representative sample of 1000 participants. Gaps were identified where consumers experienced that online organizations were not meeting their privacy expectations. This indicated that the regulatory requirements (in this case, the Protection of Personal Information Act (POPI) are perceived as not being met. The results indicate that while consumers in South Africa have a high expectation for privacy, it is not met in practice. Corrective action and interventions are required from a government and online organization perspective.Women in Research Grant of UNISA.School of Computin

A study on information privacy concerns and expectations of demographic groups in South Africa

Globally, there is growing concern over transparency and fairness when processing personal information and upholding the privacy of individuals. South Africa faces specific challenges in defining and implementing privacy policies and guidelines while meeting individuals’ expectations as to how their personal information is handled. There is limited data available about individual concerns and expectations for privacy in South Africa across demographic groups. Such data can aid in informing privacy policies and guidelines and addressing differences and sensitivity among demographical groups concerning information privacy. This paper explores the information privacy concerns and expectations of individuals in South Africa. Data were collected through a cross-sectional survey using the Information Privacy Concern Instrument (IPCI) that was developed in previous studies in line with the Protection of Personal Information Act (POPIA) No. 4 of 2013 of South Africa. Privacy concern was found to be high in South Africa, while confidence in organisations meeting data privacy principles was low. Statistically significant differences showed that older participants, females and white participants had higher privacy expectations than Generation Y participants, males and black participants, who were more confident that organisations were meeting privacy principles. A visual index for information privacy concerns and expectations is proposed to comprehend it across demographic groups and to monitor change going forward. The recommendations provided can serve as input for further development of privacy guidelines by stakeholders such as the South African Information Protection Regulator and responsible parties handling personal information while considering differences among demographical groups in South Africa concerning information privacy.National Research Foundation (NRF)School of Computin

Factors and Business Impacts in Human-Computer Negotiations

Negotiation commonly takes place where there are competing interests. Negotiations require a substantial amount of cognitive effort and time commitment. Artificial Intelligence (AI) has recently been experiencing a dramatic rise. AI and computer agents may significantly affect how negotiations are conducted. Agents can exhibit human-like behavior and follow the preferences of the principals and predefined strategies, goals, and constraints. For example, some companies already used computer sales assistant to help customers and even negotiate the price and other features online. The purpose of this thesis is to contribute to the transformation of the negotiation process from human vs human to human vs computer, in the context of e-commerce. By investigating various factors that influence human-computer negotiations and the impact of these factors on negotiation outcomes, the current thesis can shed light on the cognitive process underneath human-computer negotiation in the context of online purchasing. The work of this thesis is organized into three major components. As its first component, this thesis conducted a thorough search of state-of-the-art literature on human-computer negotiation and proposed a framework for future studies. Based on prior research, a list of various kinds of computer agent attributes that may influence negotiation results and the relationships between these factors and negotiation outcomes were proposed. In addition to computer agents’ attributes, this essay included past literature that studied human participants’ individual differences and the influence of such differences. Based on the Technology Acceptance Model (TAM), this essay investigated the development of human-computer negotiation and human participants’ acceptance and perception of a computer agent. At the end of the first essay, an overall research framework is presented. Based on the framework of essay 1, an experiment was conducted in essay 2 to investigate how various agent strategies, tactics and configurations influence the outcomes of negotiations. Specifically, essay 2 investigated the effects of negotiation tactics (concession pattern/curve), synchronous vs. asynchronous modes, and solution-search mechanisms (search between multiple issues or dive into one issue at a time) on the subjective and objective outcomes of human-computer negotiations. A 3×2×2 experiment was conducted where the subjects could negotiate the purchase of a mobile plan with computer agents acting as sellers. In this experiment, three time-based negotiation concession patterns and two solution-search mechanisms were employed in synchronous vs. asynchronous mode. On the other hand, the negotiation results were evaluated from multiple levels. Specifically, not only the overall result at group level but also the result at individual level were included in this research. On the individual level, in addition to objective measurements, subjective measures of negotiation results, such as usefulness and intention to use, were also adopted. A model was generated and tested based on TAM and a so-called TIMES framework (Task, Individuals, Mechanism, Environment, and System). Essay 3 investigates a construct named “implicit power” and the influence of implicit power in the context of online purchasing where humans negotiate with computer agents. Implicit power refers to perceived power gained indirectly through hints in the exchange of offers. In most of the prior research, when researchers talked about power, they meant the kind of power that can be gained directly through communication during negotiation. But there is another kind of power that is implicitly perceived by the other party through ways other than communication and influences negotiations as well. After introducing implicit power, a model was built to test the influence of implicit power of both negotiation parties: humans and computers. Specifically, a 2×4×3 experiment was conducted. Several aspects of implicit power were studied, including anchoring, agent avatar image power, and the power of human subjects’ personality. In the experiment, the subjects negotiated the purchase of a laptop with computer agents acting as sellers. Two anchoring conditions and four different avatar images were used to test the influence of computer agents’ implicit power. As the source of human’s intrinsic power, the participant’s personality (Social Value Orientation) was also tested in three different types: prosocial, individualistic, and competitive. This research proposed the concept of implicit power and studied the influence of several kinds of implicit power. The model built in this research shows a good ability to explain the variance in the dependent variable (R-square: 0.44)

Efficiency modelling in collaborative filtering-based recommendation systems

In the past decade, Machine Learning (ML) models have become a critical part of large scale analytics frameworks to solve different problems, such as identify trends and patterns in the data, manipulate images, classify text, and produce recommendations. For the latter (i.e., produce recommendations), ML frameworks have been extended to incorporate both specific recommendation algorithms (e.g., SlopeOne [1]), but also more generalised models (e.g., K-Nearest Neighbours (KNN) [2]) that can be applied not only to recommendation tasks, such as rating prediction or item ranking, but also other classes of ML problems. This thesis examines an important and popular area of the Recommendation Systems (RS) design space, focusing on algorithms that are both specifically designed for producing recommendations, as well as other types of algorithms that are also found in the wider ML field. However, the latter will be only showcased in RS-based use-cases to allow comparison with specific RS models. Throughout the past years, there have been increased interest in RS from both academia and industry, which led to the development of numerous recommendation algorithms [3]. While there are different families of recommendation models (e.g., Matrix Factorisation (MF)-based, K-Nearest Neighbours (KNN)-based), they can be grouped in three classes as follows: Collaborative Filtering (CF), Content-based Filtering (CBF), and Hybrid Approaches (HA). This thesis investigates the most popular class of RS, namely Collaborative Filtering-based (CF) recommendation algorithms, which recommend items to a user based on similar users’ preferences. One of the current challenges in building CF engines is the selection of the algorithms to be used for producing recommendations. It is often the case that a one-CFmodel-fits-all solution becomes unfeasible due to the dynamic relationship between users and items, and the rate at which new algorithms are proposed in the literature. This challenge is exacerbated by the constant growth of the input data, which in turn impacts the efficiency of these models, as more computational resources are required to train the algorithms on large collections to attain a predefined/desired quality of recommendations. In CF, these challenges have also impacted the way providers deliver content to the users, as they need to strike a balance between revenue maximisation (i.e., how many resources are spent for training the CF models) and the users’ satisfaction (i.e., produce relevant recommendations for the users). In addition, CF models need to be periodically retrained to capture the latest user preferences and interactions with the items, and hence, content providers have to decide whether and when to retrain their CF algorithms, such that the high training times and resource utilisation costs are kept within the operational and monetary budget. Therefore, the problem of estimating resource consumption for CF becomes of critical importance. In this thesis, we address the pressing challenge of predicting the efficiency (i.e., computational resources spent during training) of traditional and neural CF for a number of popular representatives, including algorithms based on Matrix Factorisation (MF), KNearest Neighbours (KNN), Co-clustering, Slope One schemes, as well as well-known types of Deep Learning (DL) architectures, such as Variational Autoencoder (VAE), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). To this end, we first study the computational complexity of the training phase of said CF models and derive time and space complexity equations. Then, using characteristics of the input and the aforementioned equations, we contribute a methodology for predicting the processing time, memory overhead, and GPU utilisation of the CF’s training phase. Our contributions further include an adaptive sampling strategy, to address the trade-off between the computational cost of sampling the dataset and training the CF models on the said samples and the accuracy of the estimated resource consumption of the CF trained on a full collection. Furthermore, we provide a framework which quantifies both the training efficiency (i.e., resource consumption) of CF, as well as the quality of the recommendations produced by the said CF once it has been trained. Finally, systematic experimental evaluations demonstrate that our methodology outperforms state-of-the-art regression schemes (i.e., BB/GBM) by a considerable margin (e.g., for predicting the processing time of CF, the accuracy of WB/LR is 160% higher than the one of BB/GBM), with an overhead that is a small fraction (e.g., 3-4 times smaller) of the overall requirements of CF training

Engagement in proactive recommendations: The role of recommendation accuracy, information privacy concerns and personality traits

The present research explored to what extent user engagement in proactive recommendation scenarios is influenced by the accuracy of recommendations, concerns with information privacy, and trait personality. We hypothesized that people’s self-reported information privacy concerns would matter more when they received accurate (vs. inaccurate) proactive recommendations, because these pieces of advice would seem fair to them. We further hypothesized that this would particularly be the case for people high on the social personality trait Extraversion, who are by inclination prone to behaving in a more socially engaging manner. We put this to the test in a controlled experiment, in which users received manipulated proactive recommendations of high or low accuracy on their smartphone. Results indicated that information privacy concerns positively influenced a user’s engagement with proactive recommendations. Recommendation accuracy influenced user engagement in interaction with information privacy concerns and personality traits. Implications for the design of human-computer interaction for recommender systems are addressed.Economics of Technology and Innovatio