Smart Contract and Collaborative Platforms

The collaborative economy (CE) involves an intensification of peer-to-peer commerce either directly or through the presence of an intermediary. Collaborative online exchanges are supported by digital processes that involve increased use of new technologies. As an intrinsically connected economy, the EC is therefore inclined to integrate the most recent technological advances, in particular smart contracts. In a recent article, Ertz and Boily raised that this technology can have important impacts for the development of the CE the intensification of exchanges between peers. This chapter consists of a conceptual review analyzing how the CE connects to smart contract technology by observing in particular the motivations of users on digital sharing platforms. The chapter also presents the organizational and managerial implications associated with the implementation of smart contracts in terms of governance, transaction costs, and user trust on collaborative online platforms. A comparison with conventional contracts is also initiated.

Gaining User Trust in Crowdsourcing Startup Using Desirability Business Model (Case Study at PT. Gojek in Surabaya)

This study aims to explore the strategy of gaining user trust in a crowdsourcing startup based on the Desirability Business Model. This study may uncover the user trust of crowdsourcing startup which may help startup enhancing engagement and participation from crowd. The difficulties in crowdsourcing is engage user to stay with application for a long time, so this study try to help startup finding indicators to gain user trust. This paper first propose a model to depict the effect of four parameter of Desirability Business Model with User Trust, which may influence Gojek users, then using Stratified Random Sampling Technique with a total sample of 97 people which are the subject is the society in Surabaya that in a month is at least 2 times and a maximum of more than 10 times using the Go Ride application on the Gojek company. The data collection used a questionnaire distributed through google form and social media such as Line and WhatsApp, while for the tabulation stage, it will be processed using Smart PLS-SEM. The results of this study show that of the four indicators in the Desirability Business Model variable only two indicators have a positive effect on user trust firstly, Value Proposition consisting of Performance, Design, Accessibility, Convenience, Risk Reduction, Cost Reduction and Newness then secondly, Channels consisting of Awareness, Evaluation, Purchase and After Sales. To gain user trust on the crowdsourcing startup, business owners can focus on two things firstly, provide beneficial value of the product or service offered to the user and secondly, design channel which can make business communicates with its users to convey a value proposition.

NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems

Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time “NeuroVis,” real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.

A resilient micro-payment infrastructure: an approach based on blockchain technology

Resilient micro-payment infrastructures are critical assets to digital economy as they help protecting transactions and promote micro shopping. In this paper, we present a micro-payment infrastructure based on blockchain technology that is capable of decreasing the complexity of transactions’ verification, reducing losses, and protecting against various cyber attacks. This infrastructure is user trust-aware, in the sense that it builds a trust function capable of providing real time management of the user’s trust levels based on historic activity and then adapts the level of verification and risk of user’s misconduct. Moreover, three different trust models are developed to provide different estimations of the tokens’ block size to be submitted to the blockchain network for verification and management of the user waiting time. The micropayment infrastructure provides different security services such as authentication, doublespending and double-selling prevention, tokens forging prevention, transaction traceability, and resilience to cyber-attack. In addition, its reactivity is improved through the reduction of the verification delay and user waiting time.

Influences on User Trust in Healthcare Artificial Intelligence (HAI): A Systematic Review (Preprint)

BACKGROUND Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used as well as tackling rising costs and staff shortages. The realisation and success of AI depends heavily on people’s trust in its applications. Yet, the influences on trust in AI applications in healthcare so far have been underexplored OBJECTIVE The objective of this study was to identify aspects (related to users, the AI application and the wider context) influencing trust in healthcare AI (HAI). METHODS We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched 7 electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, Ovid ProQuest Dissertations & Thesis Global, Ovid PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German with no publication date restriction. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. RESULTS Overall, 3 studies, one mixed-method and 2 qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and related to wider context (AI company, media, social network of the user). The factors resulted in an updated logic model illustrating the relationship between these aspects. CONCLUSIONS Trust in healthcare AI depends on a variety of factors, both external and internal to the AI application. This study contributes to our understanding of what influences trust in HAI by highlighting key influences as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications.

Initial evidence for biased decision-making despite human-centered AI explanations

In explainable artificial intelligence (XAI) research, explainability is widely regarded as crucial for user trust in artificial intelligence (AI). However, empirical investigations of this assumption are still lacking. There are several proposals as to how explainability might be achieved and it is an ongoing debate what ramifications explanations actually have on humans. In our work-in-progress we explored two posthoc explanation approaches presented in natural language as a means for explainable AI. We examined the effects of human-centered explanations on trust behavior in a financial decision-making experiment (N = 387), captured by weight of advice (WOA). Results showed that AI explanations lead to higher trust behavior if participants were advised to decrease an initial price estimate. However, explanations had no effect if the AI recommended to increase the initial price estimate. We argue that these differences in trust behavior may be caused by cognitive biases and heuristics that people retain in their decision-making processes involving AI. So far, XAI has primarily focused on biased data and prejudice due to incorrect assumptions in the machine learning process. The implications of potential biases and heuristics that humans exhibit when being presented an explanation by AI have received little attention in the current XAI debate. Both researchers and practitioners need to be aware of such human biases and heuristics in order to develop truly human-centered AI.

Pengaruh E-Payment Trust terhadap Minat Transaksi pada E-Marketplace Menggunakan Framework Technology Acceptance Model (TAM) 3

<p class="Abstrak">Transaksi melalui <em>e-Marketplace</em> dilakukan menggunakan transaksi pembayaran secara <em>digital</em> yang disebut sebagai layanan <em>e-Payment</em>. Oleh karena itu, <em>e-Payment</em> memegang peranan penting dalam proses transaksi jual beli pada <em>e-Marketplace</em> khususnya dalam transaksi pembayaran. Seringkali pengguna memiliki kekhawatiran tersendiri dalam melakukan transaksi pembayaran menggunakan <em>e-Payment</em>, salah satu kekhawatiran paling mendasar adalah mengenai jaminan integritas keamanan data dan privasi data pelanggan. Kepercayaan pengguna dipandang menjadi suatu resiko besar yang dapat memberikan pengaruh terhadap minat pembelian pada <em>e-Marketplace</em>. Melalui penelitian ini, akan dianalisis bagaimana tingkat pengaruh kepercayaan pengguna pada <em>e-Payment</em> di Indonesia terhadap transaksi pada <em>e-Marketplace</em> memakai metode <em>Technology Acceptance Model </em>(TAM) versi 3. Hasil penelitian ini merupakan bahan evaluasi bagi <em>vendor</em> <em>e-Marketplace</em> guna melakukan analisis seberapa sering pengguna melakukan transaksi di dalam <em>e-Marketplace</em> sehingga semakin memberikan kepercayaan pengguna untuk melakukan transaksi menggunakan layanan <em>e-Payment</em>.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Transactions through e-Marketplace are carried out using digital payment transactions or commonly referred to as e-Payments. Therefore, e-Payment plays an important role in the process of buying and selling transactions on the e-Marketplace, especially in payment transactions. Often users have their own concerns in making payment transactions using e-Payment, one of the most basic concerns is about guaranteeing data security integrity and customer data privacy. User trust is seen as a big risk that can have an influence on buying interest in the e-Marketplace. Through this research, it will be analyzed how the level of influence of user trust in e-Payment in Indonesia on the impact of purchases on e-Marketplace using the Technology Acceptance Model 3 (TAM 3) framework. The results of this study can be used as evaluation material for e-Marketplace vendors to analyze how often users make transactions in the e-Marketplace so that it gives more confidence to users to make transactions using e-Payment services.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>