I like my app but I wanna try yours: exploring user switching from a learning perspective

Purpose The purpose of this paper is to explain the “good-to-good” app switching phenomenon that has not been specifically addressed in the prior switching literature. Drawing on the consumer learning theory, this study explores how external social word of mouth (WOM) and internal satisfaction influence app users’ switching intention through social learning route and analogical learning route. This study also examines the moderating effect of app heterogeneity. Design/methodology/approach An online survey was used to collect data. Two categories of mobile apps with different levels of within-category heterogeneity were targeted in survey questions. A total of 525 valid survey responses were collected. Findings Social WOM about a competing app increases users’ switching intention through both social norm influence and social information influence, resulting in a direct effect on switching intention and an indirect effect through the perceived attractiveness of a competing app. Users’ satisfaction with an adopted app positively influences the perceived attractiveness of an unadopted competing app, offering evidence for analogical learning in user switching. Meanwhile, users’ satisfaction imposes a direct negative effect on switching intention. A higher level of within-category heterogeneity strengthens (weakens) the positive effect of social WOM (satisfaction) on users’ perceived attractiveness of a competing app. Originality/value This study complements the existing switching literature by disentangling the “good-to-good” switching phenomenon in the mobile app market from the consumer learning perspective. This study extends the understanding of cross-category user switching by considering different levels of product heterogeneity.

Human-Like Sketch Object Recognition via Analogical Learning

Deep learning systems can perform well on some image recognition tasks. However, they have serious limitations, including requiring far more training data than humans do and being fooled by adversarial examples. By contrast, analogical learning over relational representations tends to be far more data-efficient, requiring only human-like amounts of training data. This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches. Results from the MNIST dataset and a novel dataset, the Coloring Book Objects dataset, are provided. Comparison to existing approaches indicates that analogical generalization can be used to identify sketched objects from these datasets with several orders of magnitude fewer examples than deep learning systems require.

Implicit Association Test as an Analogical Learning Task

The Implicit Association Test (IAT) is a popular tool for measuring attitudes. We suggest that performing an IAT could, however, also change attitudes via analogical learning. For instance, when performing an IAT in which participants categorize (previously unknown) Chinese characters, flowers, positive words, and negative words, participants could infer that Chinese characters relate to flowers as negative words relate to positive words. This analogy would imply that Chinese characters are opposite to flowers in terms of valence and thus that they are negative. Results from three studies (N = 602) confirmed that evaluative learning can occur when completing an IAT, and suggest that this effect can be described as analogical. We discuss the implications of our findings for research on analogy and research on the IAT as a measure of attitudes.

The Implicit Association Test as an analogical learning task

The Implicit Association Test (IAT) is a popular tool for measuring attitudes. We suggest that performing an IAT could, however, also change attitudes via analogical learning. For instance, when performing an IAT in which participants categorize (previously unknown) Chinese characters, flowers, positive words, and negative words, participants could infer that Chinese characters relate to flowers as negative words relate to positive words. This analogy would imply that Chinese characters are opposite to flowers in terms of valence and thus that they are negative. Results from three studies (N = 602) confirmed that evaluative learning can occur when completing an IAT, and suggest that this effect can be described as analogical. We discuss the implications of our findings for research on analogy and research on the IAT as a measure of attitudes.

On the benefits of digital libraries of case studies of analogical design: Documentation, access, analysis, and learning

Digital libraries of case studies of analogical design have been popular since their advent in the early 1990s. We consider four benefits of digital libraries of case studies of analogical design in the context of biologically inspired design. First, a digital library affords documentation. The 83 case studies in our work come from 8 years of extended, collaborative design projects in an interdisciplinary class on biologically inspired design. Second, a digital library provides on-demand access to the case studies. We describe a web-based library of case studies of biologically inspired design called the Design Study Library (DSL). Third, a compilation of case studies supports analyses of broader patterns and trends. As an example, an analysis of DSL's case studies found that environmental sustainability was a major factor in about a third of the case studies and an explicit design goal in about a fourth. Fourth, a digital library of case studies can support analogical learning. Preliminary results from an exploratory study indicate that DSL may support novice learning about the processes of biologically inspired design.

IDA

This article presents an intelligent corporate governance analysis and rating system, called IDA System, capable of retrieving SEC required documents of public companies and performing analysis and rating in terms of recommended corporate governance practices. With the techniques of analogical learning, local knowledge bases, databases, and question-dependent semantic networks, the IDA system is able to automatically evaluate the strengths, deficiencies, and risks of a company's corporate governance practices based on the documents stored in the “SEC EDGAR database by (U.S. Securities and Exchange Commission 2013)”. A produced score reduces a complex corporate governance process and related policies into a single number which enables concerned government agencies, investors and legislators to assess the governance characteristics of individual companies.