The Value of It-Enabled Retailer Learning: Personalized Product Recommendations and Customer Store Loyalty in Electronic Markets

Abstract

In general, most items on this list match my preferences very well. PPR2: In general, most items on this list fit my tastes very well. PPR3: In general, most items on this list are interesting to me. Website Knowledge WBKN1: I always know where I can find the products/information I am looking for at Amazon.com's website. WBKN2: I visit Amazon.com very often. WBKN3: I have been to Amazon.com many times. Product Evaluation Cost (reverse coded) PEC1: It was very easy for me to make this purchase decision. PEC2: I had no difficulty deciding which item would be best for me. PEC3: Making this purchase decision was an easy task for me.

Explaining Continuance Intention of a Cloud-Based Virtual Learning Environment with Psychological Ownership and the Technology Acceptance Model

“…In testing H1 to H3, the three key experiences had significant effects on PO. Self-investment explained the largest variance in PO (β = 0.40, p < .001), which is consistent with prior research [11]. Teachers invest themselves into a cVLE when they create virtual spaces and learning contents, facilitated by seamless access to resources.…”

“…Scholars had suggested that individuals can be psychologically tied to a particular technology, where the self becomes intertwined with a psychologically owned object [8]- [9]. Research had also demonstrated the potential of PO as a pertinent factor in affecting users' behavior, with different technologies as potential targets of ownership [10]- [11].…”

Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn

“…Whereas previous research has investigated the human role in interactive learning settings (Amershi et al 2014), little is known about users' reactions toward implicit learning systems. Zhang et al (2011) show that retailer learning conceptualized as the quality of personalized product recommendations on an e-commerce website reduces customers' product screening and evaluation costs while enhancing decision-making quality. Besides this study, we are not aware of research that has investigated whether humans perceive the ability to learn of AI-based systems and, if so, which consequences these perceptions have.…”

A new model for customer purchase intention in e-commerce recommendation agents

“…Purchasing in an e-commerce environment exemplifies the process of choosing appropriate items. Overall, decision technologies intend to conquer the confined rationality limit of users in order to assist them in more convincing decision making through a trifling attempt (Zhang et al, 2011). Users particularly take advantage of recommender systems to handle a huge body of information and make superior decisions under both time and knowledge restrictions.…”