Electronic Markets最新文献

Abstract

Data is ubiquitous in today’s digitized society. However, access to and literacy in handling data plays a pivotal role in determining who can benefit from it and who can use—or potentially misuse—it. To combat inequalities and address issues such as misinformation, it is essential to enable citizens to effectively access and understand data within their local ecosystems. To address this challenge, we focus on the case of citizen science and propose using a conversational agent to support data exploration and lower barriers to citizen engagement in research projects. Using a design science research approach, we derive design principles and develop a prototypical artifact. Moreover, we conduct an experimental evaluation, demonstrating strong interest among citizens to participate in scientific data analysis and that conversational agents hold great potential in increasing data literacy.

Online labor platforms have been criticized for fueling precarious working conditions. Due to their platform-bound reputation systems, switching costs are prohibitively high and workers are locked-in to the platforms. One widely discussed approach to addressing this issue and improving workers’ position is the portability of reputational data. In this study, we conduct an online experiment with 239 participants to test the effect of introducing reputation portability and to study the demand effect of imported ratings. We find that the volume of imported ratings stimulates demand, although to a lower degree than onsite ratings. Specifically, the effect of imported ratings corresponds to about 35% of the effect of onsite ratings. The results imply the possibility of unintended cross-market demand concentration effects that especially favor workers with high rating volumes (“superstars”).

In the data economy, data sovereignty is often conceptualized as data providers’ ability to control their shared data. While control is essential, the current literature overlooks how this facet interrelates with other sovereignty facets and contextual conditions. Drawing from social contract theory and insights from 31 expert interviews, we propose a data sovereignty conceptual framework encompassing protection, participation, and provision facets. The protection facets establish data sharing foundations by emphasizing baseline rights, such as data ownership. Building on this foundation, the participation facet, through responsibility divisions, steers the provision facets. Provision comprises facets such as control, security, and compliance mechanisms, thus ensuring that foundational rights are preserved during and after data sharing. Contextual conditions (data type, organizational size, and business data sharing setting) determine the level of difficulty in realizing sovereignty facets. For instance, if personal data is shared, privacy becomes a relevant protection facet, leading to challenges of ownership between data providers and data subjects, compliance demands, and control enforcement. Our novel conceptualization paves the way for coherent and comprehensive theory development concerning data sovereignty as a complex, multi-faceted construct.

To enter the offline channel, the community-based group buying (CGB) platform usually recruits group leaders to perform corresponding tasks (i.e., creating new customers, information disseminating, marketing, and goods delivering). A major type of group leader is the traditional offline retailer, who runs a convenience store in the community and is considered as the core bond among three parties, namely, platforms, merchants, and consumers. Drawing upon the PPM theory and TAM model, this study aims to investigate the switching intention of traditional offline retailers to embrace platform’s recruitment and undertake group leader roles towards the CGB program. With primary data collected from 365 respondents, we establish a structural equation model and conduct the empirical analysis. Results suggest that both push and pull factors exert positive effects on convenience storekeepers’ switching intention, while the perceived risk (i.e., one of the mooring factors) hinders the switching intention. However, switching cost, as another mooring factor, does not significantly predict the switching intention. These additional constructs in the push–pull-mooring (PPM) model are considerably helpful for improving the understanding of traditional offline retailer’s switching intention towards community-based group buying and could offer several managerial implications for group buying platforms.

Digital ecosystems are a highly relevant phenomenon in contemporary practice, offering unprecedented value creation opportunities for both companies and consumers. However, the success of these ecosystems hinges on their ability to establish the appropriate incentive systems that attract and engage diverse actors. Following the notion that setting “the right” incentives is essential for forming and growing digital ecosystems, this article presents an integrated framework that supports scholars and practitioners in identifying and orchestrating incentives into powerful incentive systems that encourage active participation and engagement. This framework emphasizes the importance of understanding how individuals and groups are motivated to engage in the ecosystem to incentivize them effectively. To demonstrate its applicability and value, we show its application in the context of an emergent digital ecosystem within the Smart Living domain.

Data has become a strategic asset for societal prosperity and economic competitiveness. There has long been an academic consensus that the value of data unfolds during its use. Consequently, many stakeholders have called for expanding the use and reuse of data, including the public and open variety, as well as that from private data providers. However, citizens and organizations want self-determination over their data use, that is, data sovereignty. This fundamentals paper applies a literature review to conceptualize the term in Information Systems (IS) research by summarizing current findings and definitions to add further structure to the field. It contributes to the current research streams by introducing a core conceptual model consisting of seven interacting core aspects, involving trust between data providers and consumers for data assets, supported by data infrastructure and contractual agreements on all data lifecycle stages. We evaluate and discuss this conceptual model through recent field examples and provide an overview of future research opportunities.

Human behaviour is affected by architecture, including how online user interfaces are designed. The purpose of this article is to provide insights into the regulation of behaviour modification by the design of choice architecture in light of the European Union data protection law (GDPR) and marketing law (UCPD). It has become popular to use the term ‘dark pattern’ (also ‘deceptive practices’) to describe such practices in online environments. The term provides a framework for identifying and discussing ‘problematic’ design practices, but the definitions and descriptions are not sufficient in themselves to draw the fine line between legitimate (lawful) persuasion and unlawful manipulation, which requires an inquiry into agency, self-determination, regulation and legal interpretation. The main contribution of this article is to place manipulative design, including ‘dark patterns’, within the framework of persuasion (marketing), technology (persuasive technology) and law (privacy and marketing).

Industries and energy markets around the world are facing mounting pressure to decarbonize, prompting them to transform processes and supply chains towards sustainability. However, a lack of credible sustainability data proves to be a considerable barrier for emerging markets for sustainable products: Against the background of complex and globalized supply chains, it is necessary to verify the sustainability claim of products in order to demand price premiums for sustainable products in the long run. To enable this, it is necessary that stakeholders in globalized supply chains are willing to share relevant data along the entire supply chain for increasing traceability and reducing information asymmetries. Using the example of international hydrogen supply chains, we study how data can be shared between different stakeholders using Digital Product Passports while addressing stakeholders’ concerns about data privacy and disclosure. In our work, we develop design principles that provide insight into how a Digital Product Passport should be designed to verify the hydrogen’s carbon footprint in a reliable way and to ensure the willingness of stakeholders to share their data. We follow a multi-step approach with a structured literature review followed by expert interviews and qualitative content analysis for a synthesis of design principles. Our research illustrates that a Digital Product Passport must collect data comprehensively and automatically, process it in a decentralized and tamper-proof manner, protect privacy and sovereignty of stakeholders, and ensure interoperability.

Background: Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data.

Methods: This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections.

Based on a large real-world dataset comprising Instagram posts of popular influencers, this study empirically analyzes the impact of disclosed and undisclosed advertising on consumers’ engagement with (a) the advertisement and (b) future non-advertising posts of the same author. As extant research reports inconsistent effects of ad disclosures based on inferred motives, persuasion knowledge, and source credibility, this study develops a conceptual framework incorporating these theoretical concepts. To identify undisclosed advertisements, we use data from regions with proper disclosure culture to train a model to predict if posts are advertising. Based on the predictions for > 65,000 posts of 239 macro-influencers, we find that advertising posts gather less engagement than non-advertising posts. Regarding immediate ad engagement, we find that disclosed ads gather less engagement than undisclosed ads. Contrastingly, when analyzing future engagement, we identify positive persistent effects of disclosed advertising and negative persistent effects of undisclosed advertising on consumers’ engagement with future posts of the same author. We conclude that source credibility explains the effect of disclosures on future posts, while the Persuasion Knowledge Model can explain the effect of disclosures on the current advertisement. Thus, consumers’ coping strategies triggered by activated persuasion knowledge are mostly limited to the advertisement. Our findings can explain the opposing results of extant research. From a managerial perspective, we find that by not disclosing advertising posts, influencers and marketers increase an ad’s engagement levels at the expense of persistently lowered attitudes. Conversely, in the long run, they may benefit from transparent disclosures.