Social Shopping: The Good, the Bad, and the Ugly
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ItemOiling the ‘Tireless Selling-Machine’ – Exploring Requirements for the Deployment of Social Bots in Social Commerce( 2022-01-04)Social media have become major platforms of commerce and changed the way we communicate and consume. Phenomena such as social bots add new dynamics to discussions and the spreading of information with the possible aim to influence or shape opinions and decisions. This study examines the requirements under which organizations would use social bots for commercial purposes. Interviews with 12 experts yielded a collection of requirements, including limitations, ethical considerations, and potentials for possible uses in marketing, social commerce, and customer service. It can be concluded that using social bots can be beneficial for commercial organizations, but that there is still a need for clarification of legalities.
ItemLet me Entertain You – the Influence of Augmented Reality on Purchasing Intention in E-Commerce( 2022-01-04)Augmented reality (AR) in e-commerce helps consumers to envision products in their respective surroundings, so fosters customer experience. Our online experiment with 302 probands explores the effect of AR on purchasing intention, taking into account motivational variables—perceived usefulness, entertainment and perceived ease of use (PEOU). Consumers viewing products in an AR-enabled online shop rated perceived usefulness, entertainment and PEOU significantly higher than the control group viewing the non-AR-enabled online shop. Performing mediation analysis, we found that AR significantly influences purchase intention, which is mediated by perceived entertainment. Our findings add to the understanding of the interplay of the motivational variables perceived usefulness, entertainment and PEOU as well as the impact of AR on customer decision making. From a managerial point of view, our findings suggest that in the current stage of the technology, AR is perceived as a playful add-on to online shopping, positively impacting purchase intention.
ItemData Trading Similarity Signature An Extended Data Trading Framework for Human and Non-Human Actors( 2022-01-04)Fair and secure data trading is one of the most prominent challenges of the 21st century. This paper presents a second iteration of an approach to develop a data marketplace concept by checking consumer requirements. The main problem we identified is data quality and the question: Would a dataset fulfill the consumer requirements? Starting from an approach that uses a binary response set to answer the question of whether requirements are met, we concluded that a description of consumer requirements needs to be quantitatively comparable. The novel approach presented here identifies similarities between datasets and consumer requirements. It forms a unique, fingerprint-like similarity signature for each dataset, which can be interpreted by both human and non-human actors. The approach is deducted and designed by using the Design Science Research Methodology and discussed critically in the end.