This study uses an artificial intelligence (AI) model to identify arbitrage opportunities in the retail marketplace. Specifically, we develop an AI model to predict the optimal purchasing point based on the price movement of products in the market. Our model is trained on a large dataset collected from an online marketplace in the United States. Our model is enhanced by incorporating user-generated content (UGC), which is empirically proven to be significantly informative. Overall, the AI model attains more than 90% precision (...) rate, while the recall rate is higher than 80% in an out-of-sample test. In addition, we conduct a field experiment to verify the external validity of the AI model in a real-life setting. Our model identifies 293 arbitrage opportunities during a one-year field experiment and generates a profit of $7.06 per arbitrage opportunity. The result demonstrates that AI performs exceptionally well in identifying arbitrage opportunities in retail markets with tangible economic values. Our results also yield important implications regarding the role of AI in the society, both from the consumer and firm perspectives. (shrink)

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show (...) that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. (shrink)

Although related studies have examined the impact of different images of artificial intelligence products on consumer evaluation, exploring the impact on consumer purchase decisions from the perspective of algorithmic decision autonomy remains under-explored. Based on the self-determination theory, this research discusses the influence of the agent decision-making role played by different AI algorithmic decision autonomy on consumer purchase decisions. The results of the 3 studies indicate that algorithmic decision autonomy has an inverted U-shaped effect on consumer’s purchase decisions, consumer’s self-efficacy (...) mediates the relationship between algorithmic decision autonomy and purchase decisions, and consumer’s power distance moderates the relationship between algorithmic decision autonomy, self-efficacy, and purchase decisions. The research results can provide references for marketers, retailers, algorithm designers, and other parties to formulate algorithm marketing strategies, make AI algorithm decisions better serve consumers, and achieve value co-creation with consumers. (shrink)

The COVID-19 pandemic has accelerated the integration of algorithms in online platforms to facilitate people’s work and life. Algorithms are increasingly being utilized to tailor the selection and presentation of online content. Users’ awareness of algorithmic curation influences their ability to properly calibrate their reception of online content and interact with it accordingly. However, there has been a lack of research exploring the factors that contribute to users’ algorithmic awareness, especially in the roles of personality traits. In this study, we (...) explore the influence of Big Five personality traits on internet users’ algorithmic awareness of online content and examine the mediating effect of previous knowledge and moderating effect of breadth of internet use in in China during the pandemic era. We adapted the 13-item Algorithmic Media Content Awareness Scale to survey users’ algorithmic awareness of online content in four dimensions. Our data were collected using a survey of a random sample of internet users in China. The results of this study supported the moderated mediation model of open-mindedness, previous knowledge, breadth of internet use, and algorithmic awareness. The breadth of internet use was found to be a negative moderator between previous knowledge and algorithmic awareness. (shrink)

This article presents Epistemic Spatialization as a new framework for investigating the interconnected patterns of biases when identifying objects with convolutional neural networks. It draws upon Foucault’s notion of spatialized knowledge to guide its method of enquiry. We argue that decisions involved in the creation of algorithms, alongside the labeling, ordering, presentation, and commercial prioritization of objects, together create a distorted “nomination of the visible”: they harden the visibility of some objects, make other objects excessively visible, and consign yet others (...) to permanent or haphazard invisibility. Our approach differs from those who focus on high-stakes misidentifications, such as errors tied to structural racism. Examining the far more dominant series of low-stakes mistakes shows the scope of errors, destabilizing the goal of image content identification with considerable societal impact. We explore these issues by closely examining the demonstration video of a popular convnet. This examination reveals an interlocking series of biases undermining the content identification process. The picture we paint is crucial for a better understanding of the errors that result as these convnets become further embedded in everyday products. The framework is valuable for critical work on computer vision, AI studies, and large-scale visual analysis. (shrink)

In combating the ongoing global health threat of the COVID-19 pandemic, decision-makers have to take actions based on a multitude of relevant health data with severe potential consequences for the affected patients. Because of their presumed advantages in handling and analyzing vast amounts of data, computer systems of algorithmic decision-making are implemented and substitute humans in decision-making processes. In this study, we focus on a specific application of ADM in contrast to human decision-making, namely the allocation of COVID-19 vaccines to (...) the public. In particular, we elaborate on the role of trust and social group preference on the legitimacy of vaccine allocation. We conducted a survey with a 2 × 2 randomized factorial design among n = 1602 German respondents, in which we utilized distinct decision-making agents and prioritization of a specific social group as design factors. Our findings show that general trust in ADM systems and preference for vaccination of a specific social group influence the legitimacy of vaccine allocation. However, contrary to our expectations, trust in the agent making the decision did not moderate the link between social group preference and legitimacy. Moreover, the effect was also not moderated by the type of decision-maker. We conclude that trustworthy ADM systems must not necessarily lead to the legitimacy of ADM systems. (shrink)

The increasing use of artificial intelligence (AI) by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent (...) smart electric vehicle charge point, and investigate experts’ and citizens’ understanding of AI transparency. We find that citizens experience transparency as burdensome; experts hope transparency ensures acceptance, while citizens are mostly indifferent to AI; and with absent means of control, citizens question transparency’s relevance. The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens. (shrink)

The accession and implementation of new generation free trade agreements bring numerous opportunities as well as challenges to Viet Nam, regarding trade, labor and investment. The increasing number of workers abroad puts a pressure on Vietnamese government to support them in new working cultures and environments. The application of chatbot, which has been known to help certain vulnerable groups such as patients, women and migrants could be one of the tools to support Vietnamese migrant workers by providing immediate information, network (...) connection and consultation. Analyzing the results of the qualitative interviews with 11 migrant workers and the group discussion with two sections from three public social media groups and three active informants, the article argues that migrant workers as a vulnerable group who are supposed to be promising users of chatbot due to their great demand and needs, may have a complicated form of trust and expectation in chatbot. (shrink)

Public consumption of artificial intelligence (AI) technologies has been rarely investigated from the perspective of data surveillance and security. We show that the technology acceptance model, when properly modified with security and surveillance fears about AI, builds an insight on how individuals begin to use, accept, or evaluate AI and its automated decisions. We conducted two studies, and found positive roles of perceived ease of use (PEOU) and perceived usefulness (PU). AI security concern, however, negatively affected PEOU and PU, resulting (...) in less acceptance of AI—(1) use, (2) preference, and (3) participation. AI surveillance concern also had negative effects on the credibility of AI and its recommendations. We integrated extant literature on socio-demographic differences, providing an insight on how AI acceptance is based on one’s rationality regarding (1) technological risks (security/surveillance) and (2) benefits (PEOU/PU) as well as other contextual factors of socio-demographics. (shrink)