Open Access

Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.

Purpose – The COVID-19 pandemic has had a significant impact on the food tourism industry, leading to business closures and a drop in demand. In response to this challenge, virtual food tourism experiences such as VR have emerged as an alternative to traditional in-person experiences. Aim of this paper is to model consumer adoption of virtual food tourism by integrating the Diffusion of Innovation Theory and the Self-determination Theory. Methodology/Design/Approach – The Diffusion of Innovation Theory explains the process of innovation adoption, while the Self-determination Theory focuses on consumer motivation. This article proposes that intrinsic (autonomy, relatedness, and competence) and extrinsic (relative advantage, complexity, compatibility, trialability, and observability) motivating factors influence virtual food tourism adoption. Findings – The study suggests that extrinsic motivators can act as mediators between intrinsic motivation and adoption intention. Integrating these two theories provides a comprehensive understanding of the motivations and mechanisms driving virtual food tourism adoption. It also paves the way for the exploration of intrinsic and extrinsic motivations and specific mechanisms underlying adoption behaviours. Originality of the research – Destinations, businesses, and policy makers can better navigate the changing landscape of food tourism and leverage the potential of virtual food tourism to create engaging, accessible, and culturally enriching experiences.

The data presented here contain information on cheating behavior from experiments and general self-reported attitudes related to honesty-related social norms and trust, together with individual-level demographic variables. Our sample included 493 university students in five countries, namely, Germany, Vietnam, Taiwan, China, and Japan. The experiment was monetarily incentivized based on the performance on a matrix task. The participants also answered a survey questionnaire. The dataset is valuable for academic researchers in sociology, psychology, and economics who are interested in honesty, norms, and cultural differences.