Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

B2C E-Commerce Customer Churn Management: Churn Detection using Support Vector Machine and Personalized Retention using Hybrid Recommendations

E-Commerce industry, especially the players in Business-to-Consumer (B2C) sector is witnessing immense competition for survival - by means of trying to penetrate to the customer base of their peers and at the same time not letting their existing customers to churn. Avoiding customer attrition is critical for these firms as the cost of acquiring new customers are going high with more and more players entering into the market with huge capital investments and new penetration strategies. Identifying potential parting away customers and preventing the churn with quick retention actions is the best solution in this scenario. It is also important to understand that what the customer is trying to achieve by opting for a move out so that personalized win back strategies can be applied. E-Commerce industry always possess huge amount of customer data which include information on searches performed, transactions carried out, periodicity of purchases, reviews contributed, feedback shared, etc. for every customers they possess. Data mining and machine learning can help in analyzing this huge volume of data, understanding the customer behavior and detecting possible attrition candidates. This paper proposes a framework based on support vector machine to predict E-Commerce customer churn and a hybrid recommendation strategy to suggest personalized retention actions

Quality Dimensions for B2C E-Commerce

Organizations have still not realized the full potential of e-commerce. One factor that is likely to influence the further adoption of e-commerce is the quality of the e-commerce system as system quality impacts user satisfaction and hence use of the system. However, in order to improve the quality of any systems, one first needs to identify measures to assess quality. Although other researchers have recognized the need for such measures, they have primarily focused on a single specific aspect of e-commerce systems, typically the user interface. In this paper we identify the key components of e-commerce systems and synthesize existing research related to quality of these components to arrive at a comprehensive list of quality dimensions, which in turn provide measures to assess the quality of e-commerce systems

What drives Consumers\u27 Trust in Proactive Services: A Best-Worst scaling approach

Increasing advancements in digital technologies, especially in artificial intelligence, are changing the nature of services. Services no longer rely on the consumers making the first move, but instead, service providers can anticipate consumers’ needs and address them proactively by so-called proactive services (PAS). Within this new service type, consumers may enable the service provider to decide upon the consideration, decision, and enactment of the service. In PAS, consumers assign these previously “owned” phases to the service provider and thereby, also devolve power to the provider. Thus, trust is an indisputable prerequisite for consumer acceptance. However, it is unclear how individual characteristics of PAS impact consumers’ trust. Addressing this research gap, this research-in-progress paper proposes a Best-Worst scaling survey in which potential consumers of two exemplary PAS state their trust with respect to different PAS characteristics. Thereby, this paper will extend the knowledge in understanding PAS

Designing an AI-enabled Bundling Generator in an Automotive Case Study

Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness

Acceptance of Feedbacks in Reputation Systems: The Role of Online Social Interactions

In an online environment, the aim of reputation systems is to let parties rate each other and to help consumers in deciding whether to transact with a given party. In current reputation systems for e-commerce, users have to trust unreliable information sources and anonymous people. As a result, users are not only hesitant to trust online seller but also to reputation systems. Therefore, there is a need to improve current reputation systems by allowing users to make buying decision based on reliable source of information. This paper proposes a new approach of sharing knowledge and experience in reputation systems by utilizing social interactions. This study examines the potentials of integrating social relations information in reputation systems by proposing a model of acceptance of feedbacks in reputation systems

Information provision measures for voice agent product recommendations— The effect of process explanations and process visualizations on fairness perceptions

While voice agent product recommendations (VAPR) can be convenient for users, their underlying artificial intelligence (AI) components are subject to recommendation engine opacities and audio-based constraints, which limit users’ information level when conducting purchase decisions. As a result, users might feel as if they are being treated unfairly, which can lead to negative consequences for retailers. Drawing from the information processing and stimulus-organism-response theory, we investigate through two experimental between subjects studies how process explanations and process visualizations—as additional information provision measures—affect users’ perceived fairness and behavioral responses to VAPRs. We find that process explanations have a positive effect on fairness perceptions, whereas process visualizations do not. Process explanations based on users’ profiles and their purchase behavior show the strongest effects in improving fairness perceptions. We contribute to the literature on fair and explainable AI by extending the rather algorithm-centered perspectives by considering audio-based VAPR constraints and directly linking them to users’ perceptions and responses. We inform practitioners how they can use information provision measures to avoid unjustified perceptions of unfairness and adverse behavioral responses