A wealth of studies has discussed the impact of different regulatory regimes on firms, but have ignored the differences in citizens' attitudes toward firms in different regulatory regimes. Exploring these attitude...
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A wealth of studies has discussed the impact of different regulatory regimes on firms, but have ignored the differences in citizens' attitudes toward firms in different regulatory regimes. Exploring these attitudes is crucial to understanding the micro-effects of regulatory regimes and market developments. This study aims to investigates the impact of regulatory regimes on citizens' trust in regulated sectors and uncovers the underlying impact mechanisms. Using a survey experiment within the context of algorithm regulation (N = 1224), we reveal that the coerciveness of regulatory regimes positively influences citizens' trust in regulated firms. Furthermore, we identify problem-solving and problem exposure perceptions as key mediators in this relationship. The findings contribute to the ongoing debate between regulation and trust, shedding light on their interplay in contemporary society and providing practical implications for policymakers and businesses navigating complex regulatory landscapes.
While research has explored trust in algorithmic decision-making, the factors shaping civil servants' trust perceptions remain underexamined. Using public value theory and technology adoption frameworks, this stud...
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While research has explored trust in algorithmic decision-making, the factors shaping civil servants' trust perceptions remain underexamined. Using public value theory and technology adoption frameworks, this study employs a survey experiment to analyze the effects of human-machine matching and algorithm regulation on civil servants' trust and adoption inclination. The findings indicate that both factors independently influence adoption inclination, with trust perceptions mediating this relationship, but no interaction effect is observed. Addressing gaps in technology acceptance and ethical frameworks, this study highlights the importance of algorithm regulation and human-machine matching in advancing algorithmic governance and achieving public value through procedural and performance dimensions, offering practical implications for policy and governance.
Trading in modern equity markets has come to be dominated by machines and algorithms. However, there is significant concern over the impact of algorithmic trading on market quality and a number of jurisdictions have m...
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Trading in modern equity markets has come to be dominated by machines and algorithms. However, there is significant concern over the impact of algorithmic trading on market quality and a number of jurisdictions have moved to address the risks associated with this new type of trading. The European Union has been no exception to this trend. This article argues that while the European Union algorithmic trading regime is often perceived as a tough response to the challenges inherent in machine trading, it has one crucial shortcoming: it does not regulate the simpler, basic execution algorithms used in automated order routers. Yet the same risk generally associated with algorithmic trading activity also arises, in particular, from the use of these basic execution algorithms as was made evident by the trading glitch that led to the fall of United States securities trader Knight Capital in 2012. Indeed, such risk could even be amplified by the lack of sophistication of these simpler execution algorithms. It is thus proposed that the European Union should amend the objective scope of its algorithmic trading regime by expanding the definition of algorithmic trading under the Markets in Financial Instruments Directive (MiFID II) to include all execution algorithms, regardless of their complexity.
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