Digital Shopping and Consumers’ Perceptions in the United Arab Emirates

The purpose of this research is to analyse the perception of digital shoppers in the United Arab Emirates (UAE)., A survey was conducted, which included: students, employees, and professionals from different fields of UAE society. The findings show about 89 percent of those surveyed are familiar with online shopping and some of them are found comfortable that their personal information is kept confidential. Some participants still have concerns about the safety and the accuracy of online shopping. The findings show that about 45 percent of those surveyed think that online shopping is still risky. The results also show that about 20 percent of the surveyed population believe that delay in product delivery, lack of accuracy on websites, and information insufficiency are reasons services that do not comply with the needs of buyers and therefore still prefer traditional shopping over online shopping. The study concludes that there is a need for building safety awareness among shoppers which companies need to focus on. Furthermore, companies need to continually improve its offering of online services. This will result in providing customers with accurate information and in enhancing the products’ time delivery

Understanding the Adoption Intention of AI through the Ethics Lens

Understanding users and user behaviors in accepting new technologies such as AI has been ever more important. Meanwhile, information systems with AI inevitably engenders such ethical issues as transparency and accountability related to the consequences of recognition, decisions, and recommendations. Our work adds moral psychology variables to the Theory of Reasoned Action (TRA) in order to better explicate the adoption aspects of AI. For the research, we employed social desirability and self-consistency of moral psychology as underlying attitudes. And also, the moral norm is added to TRA to moderate the effect of the attitudes on the outcome variable. The empirical results indicate a direct and indirect role of the morality-related variables in explaining users’ AI adoption intentions. It was learned that moral psychology plays an important role in explaining user attitudes toward AI and subsequent intentions of adopting an AI system

MODEL ONLINE TRUST DALAM MEMEDIASI PENGARUH SOCIAL MEDIA MARKETING MELALUI INSTAGRAM TERHADAP ONLINE PURCHASE DECISION (Survei pada Pengikut Instagram Distro @bloodsclothofficial)

Penelitian ini bertujuan untuk mengukur seberapa besar online trust dalam memediasi pengaruh social media marketing melalui instagram terhadap online purchase decision. Jenis penelitian ini adalah deskriptif verifikatif dan metode explanatory survey dengan teknik purposive sampling, dengan jumlah sampel sebanyak 215 responden konsumen Distro Bloods sebagai pengikut instagram @bloodsclothofficial. Teknik analisis data yang digunakan adalah path analysis dengan alat bantu SPSS 25. Hasil menunjukkan bahwa online trust memediasi pengaruh social media marketing melalui instagram terhadap online purchase decision. Temuan ini menyiratkan bahwa untuk meningkatkan online purchase decision, perusahaan perlu memperhatikan indikator kualitas social media marketing melalui instagram agar konsumen lebih tertarik sehingga dapat membangun online trust yang pada akhirnya meningkatkan penjualan yang baik bagi perusahaan. Kata Kunci : Social Media Marketing, Instagram, Online Trust, Online Purchase Decision This study aims to measure how much online trust mediates the effect of social media marketing through Instagram on online purchasing decisions. This type of research is a descriptive verification and explanatory survey method with a purposive sampling technique, with a total sample of 215 consumers of Bloods Distro as followers of Instagram @bloodsclothofficial. The data analysis technique used is path analysis using SPSS 25. The results show that online trust mediates the effect of social media marketing through Instagram on online purchasing decisions. This finding implies that to improve online purchasing decisions, companies need to pay attention to quality indicators of social media marketing through Instagram so that consumers are more interested so that they can build online trust, which ultimately increases good sales for the company. Keyword : Social Media Marketing, Instagram, Online Trust, Online Purchase Decisio

Tell Me Why (I Want It That Way) – Effects of Explanations and Online Customer Reviews on Trust in Recommender Systems

Review-based recommender systems (RS) have shown great potential in helping users manage information overload and find suitable items. However, a lack of trust still impedes the widespread acceptance of RS. To increase users’ trust, research proposes various methods to generate justifications or explanations. Furthermore, online customer reviews (OCRs) are found to be a trustworthy and reliable source of information. However, it is still unclear how justifications compare to explanations in their influence on users’ trust and whether basing them on OCRs additionally adds trust. Hence, we conduct an online experiment with 531 participants and find that explanations exceed justifications in increasing users’ trust, while basing them on OCRs directly increases users’ intentions to use the system and adopt recommendations without increasing trust in the RS themselves. Unifying different research streams from review-based RS and Explainable Artificial Intelligence, we provide an overarching, holistic view on the conception of justifications and explanations

A Formal Account of AI Trustworthiness: Connecting Intrinsic and Perceived Trustworthiness

This paper proposes a formal account of AI trustworthiness, connecting both intrinsic and perceived trustworthiness in an operational schematization. We argue that trustworthiness extends beyond the inherent capabilities of an AI system to include significant influences from observers' perceptions, such as perceived transparency, agency locus, and human oversight. While the concept of perceived trustworthiness is discussed in the literature, few attempts have been made to connect it with the intrinsic trustworthiness of AI systems. Our analysis introduces a novel schematization to quantify trustworthiness by assessing the discrepancies between expected and observed behaviors and how these affect perceived uncertainty and trust. The paper provides a formalization for measuring trustworthiness, taking into account both perceived and intrinsic characteristics. By detailing the factors that influence trust, this study aims to foster more ethical and widely accepted AI technologies, ensuring they meet both functional and ethical criteria

Designing Caring and Informative Decision Aids to Increase Trust and Enhance the Interaction Atmosphere

Decision aids have enjoyed extensive use in various domains. While decision aid research and practice have largely focused on making these aids more functional and utilitarian, we propose that one should also purposefully design them as effective interaction partners, especially when one deploys them in contexts that require a “human touch”, such as finance or healthcare. In this paper, we report on the results from an experiment we conducted on the effects that designing caring and informative decision aids have on how users evaluate them and, subsequently, their satisfaction with them. Our results show that using explanations and expressive speech acts can enhance the extent to which users perceive decision aids as informative and caring. These strengthened beliefs subsequently enhance the extent to which users view decision aids as competent and as having integrity and improve the interaction atmosphere, which, in turn, increases users’ satisfaction with their overall interaction with the decision aid. We discuss the study’s contributions to theory and practice

Desenvolvimento de um instrumento de avaliação da qualidade percebida, satisfação e lealdade de utilizadores de infoprodutos

Com o crescimento exponencial do acesso à Internet e, consequentemente, das suas inúmeras possibilidades, observa-se o aparecimento das plataformas digitais. Desta nova tendência de mercado constata-se o crescimento de prestadores de serviços e vendas de produtos online, então onde surgem os empreendedores digitais. Ser empreendedor digital é vender seus produtos online e suprir uma carência de mercado, através da criação de um infoproduto ou produto de informação. O objetivo deste projeto é compreender e desenvolver novos conhecimentos para investigar a satisfação e a lealdade do utilizador na compra de infoprodutos. Sabe-se que as dimensões da qualidade do serviço eletrónico, têm impacto direto na satisfação do cliente, e por consequência, na sua confiança e lealdade, que afetam diretamente o seu comportamento. O que define as intenções e atitudes de compra no comércio social- digital, nada mais é do que o efeito do risco percebido na intenção de fazer compras em ambientes online, a confiança do consumidor e o comportamento de compra nas plataformas de retalho online. Através da literatura existente sobre a qualidade do serviço eletrónico nas compras online, foi possível obter novos conhecimentos para melhor compreender o impacto da satisfação do cliente no seu comportamento, a sua intenção de recompra, a propaganda boca a boca, mas também o impacto da confiança e lealdade do cliente. Espera-se que o resultado deste projeto sirva de base para que o empreendedor digital, consiga obter as melhores respostas dos utilizadores de infoprodutos e ainda, as diferentes relevâncias dos atributos de qualidade dos serviços eletrónicos que presta à comunidade digital

INVESTIGATING COLLABORATIVE EXPLAINABLE AI (CXAI)/SOCIAL FORUM AS AN EXPLAINABLE AI (XAI) METHOD IN AUTONOMOUS DRIVING (AD)

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system\u27s behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don\u27t encounter or provide information that users do not seek. In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers on enabling users to ask questions and share explanations based on their knowledge and experience to facilitate others\u27 understanding of AI systems. Mamun, Hoffman, et al. (2021) developed a CXAI system akin to a Social Question and Answer (SQA) platform (S. Oh, 2018a), adapting it for AI system explanations. The system successfully passed evaluation based on XAI metrics Hoffman, Mueller, et al. (2018), as implemented in a master’s thesis by Mamun (2021), which validated its effectiveness in a basic image classification domain and explored the types of explanations it generated. This Ph.D. dissertation builds upon this prior work, aiming to apply it in a novel context: users and potential users of self-driving semi-autonomous vehicles. This approach seeks to unravel communication patterns within a social QA platform (S. Oh, 2018a), the types of questions it can assist with, and the benefits it might offer users of widely adopted AI systems. Initially, the feasibility of using existing social QA platforms as explanatory tools for an existing AI system was investigated. The study found that users on these platforms collaboratively assist one another in problem-solving, with many resolutions being reached (Linja et al., 2022). An intriguing discovery was that anger directed at the AI system drove increased engagement on the platform. The subsequent phase leverages observations from social QA platforms in the autonomous driving (AD) sector to gain insights into an AI system within a vehicle. The dissertation includes two simulation studies employing these observations as training materials. The studies explore users\u27 Level 3 Situational Awareness (Endsley, 1995) when the autonomous vehicle exhibits abnormal behavior. These investigate detection rates and users\u27 comprehension of abnormal driving situations. Additionally, these studies measure the perception of personalization within the context of the training process (Zhang & Curley, 2018), cognitive workload (Hart & Staveland, 1988), trust, and reliance (Körber, 2018) concerning the training process. The findings from these studies are mixed, showing higher detection rates of abnormal driving with training but diminished trust and reliance. The final study engages current Tesla FSD users in semi-structured interviews (Crandall et al., 2006) to explore their use of social QA platforms, their knowledge sources during the training phase, and their search for answers to abnormal driving scenarios. The results reveal extensive collaboration through social forums and group discussions, shedding light on differences in trust and reliance within this domain