Artificial intelligence (AI) advances and the rapid adoption of generative AI tools, like ChatGPT, present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systems approach that captures a holistic view of the structure and dynamics of the AI transformation of higher education institutions (HEIs). To fill this gap, this article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. We identify important variables and their relationships and map multiple reinforcing and balancing feedback loops accounting for the forces that drive the AI transformation and its impact on value creation in a typical HEI. The model shows how, motivated by AI technology advances, the HEI can invest in AI to improve student learning, research, and administration while dealing with academic integrity problems and adapting to job market changes by emphasizing AI-complementary student skills. We explore model insights, scenarios, and policy interventions and recommend that HEI leaders become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding policy traps that may lead to decline. We also discuss the notion of HEIs influencing the direction of AI and directions for future research on AI transformation and the sustainability of HEIs.

The platform business model has attracted significant attention in business research and practice. However, much of the existing literature studies commercial platforms that seek to maximize profit. In contrast, we focus on a platform for volunteers that aims to maximize social impact. This business model is called a platform for the common good. The article proposes a Causal Loop Diagram (CLD) model that explains how a platform for the common good creates value. Our model maps the key strategic feedback loops that constitute the core structure of the platform and explains its growth and performance through time. We show that multiple types of network effects create interlocking, reinforcing feedback loops. Overall, the article contributes towards a dynamic theory of the platforms for the common good. Moreover, the article provides insights for social entrepreneurs who seek to build, understand, and optimize platforms that maximize social value and managers of companies that seek to participate in such platforms. Social entrepreneurs should seek to leverage the critical feedback loops of their platform.

The use of artificial intelligence (AI) in the form of pricing algorithms to increase profits is becoming ubiquitous. However, the literature has focused on specific markets and algorithms so far, but it is unclear what happens across algorithms and markets. To analyze the business and economic impact of pricing algorithms, we build a computational model that considers two sophisticated AI algorithms (Q-learning and Particle Swarm Optimization) competing in prices in three different market structures (Logit, Hotelling, and linear demand models). From a social perspective, we find that PSO outperforms Q-learning, which tends to set supracompetitive prices. However, small changes in the algorithm designs may drive them to set more competitive prices, implying that a proper analysis of algorithmic competition requires considering the details of the algorithms and the market structure. When firms compete on algorithms, algorithms may generate price dispersion. Additionally, when facing a traditional competitor that uses a best-response function, algorithms tend to set supracompetitive prices, and both firms earn extra profits, but the traditional competitor benefits the most. Overall, the article contributes to understanding algorithmic competition, discusses implications for managers and policymakers, and identifies opportunities for future research.

This article explores several aspects of digital transformation, including definition, enabling technologies, and strategies. It argues that firms seeking to maximize the impact of their digital transformation strategy should aim to build a Sustainable Business Model (SBM). In addition, it introduces the seven articles of the Special Issue. Overall, the article takes a systems approach that appreciates the dynamic complexity of digital transformation and suggests some directions for future research.

The COVID-19 pandemic has had a significant impact on higher education. Steering academic institutions through the pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term effects of the pandemic on the financial health of a college using scenario analysis and stress testing with a system dynamics model of a representative tuition-dependent college. We find that different combinations of the pandemic mitigation protocols have varying effects on the financial sustainability of an academic institution. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the system, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. Scenario analysis shows that some pandemic mitigation choices may destabilize even financially healthy institutions. The article concludes that higher education needs new sustainable business models.