Category: Notes

This study examines how artificial intelligence (AI) shapes organizational innovation capability across three stages of new product development (NPD)—concept development, product development, and implementation—and investigates whether employees’ AI competence amplifies these effects. Drawing on a cross-sectional survey of 400 managers in Germany, we find that greater AI usage intensity is associated with higher innovation capability at each stage, with the strongest gains in concept development. Benefits decrease in later stages, where successful progress demands more human expertise, physical interaction, and emotional intelligence, areas in which current AI tools remain comparatively weak. These results challenge assumptions of uniform AI utility throughout NPD and argue for fit-to-task deployment. Furthermore, employees’ AI competence significantly strengthens the relationship between AI use and innovation capability, underscoring the need to pair technology investments with workforce upskilling. Thus, managers should allocate AI resources strategically to match stage-specific demands, prioritize concept development for near-term impact, and cultivate skills that unlock AI’s value in product development and implementation. This study advances the literature on AI-enabled innovation by offering a stage-contingent perspective and highlighting human–AI complementarity as key driver of innovation outcomes. Overall, the findings provide guidance for organizations seeking to maximize innovation via targeted AI strategies.

https://www.sciencedirect.com/science/article/pii/S0019850126000441

https://cpwalker.substack.com/p/context-engineering-why-hayeks-knowledge

https://itamargilad.com/product-operating-model/

https://www.computerweekly.com/news/366632943/From-Whitehall-to-WhatsApp-The-rise-of-conversational-government

Despite placing items in virtual shopping carts, online shoppers frequently abandon them —an issue that perplexes online retailers and has yet to be explained by scholars. Here, we identify key drivers to online cart abandonment and suggest cognitive and behavioral reasons for this non-buyer behavior. We show that the factors influencing consumer online search, consideration, and evaluation play a larger role in cart abandonment than factors at the purchase decision stage. In particular, many customers use online carts for entertainment or as a shopping research and organizational tool, which may induce them to buy at a later session or via another channel. Our framework extends theories of online buyer and non-buyer behavior while revealing new inhibitors to buying in the Internet era. The findings offer scholars a broad explanation of consumer motivations for cart abandonment. For retailers, the authors provide suggestions to improve purchase conversion rates and multi-channel management.

https://link.springer.com/article/10.1007/s11747-009-0141-5

Triggered emails are personalized messages that are automatically sent as a response to specific actions or states of customers. Typical examples of this type of campaign include cross-selling recommendations, cart abandonment reminders, and re-engagement emails. Despite the widespread growth of these strategies, there has been no formal evaluation of their effectiveness. This paper investigates the impact of one type of triggered email campaign by using an experimental approach. We identify customers who had recently browsed the website of a multichannel retailer but had abandoned the process before making a purchase. Approximately half of the sample was randomly selected to receive automated emails with different configurations, while the other half receive no message at all. Comparison of the sales of these two groups indicates that browse abandonment emails have increase revenues in the online channel and in the triggered category. In terms of the design of the campaign, we found that the implementation of triggered emails plays an important role in their effectiveness. In this regard our result indicates that retargeting based on longer navigation histories is associated with larger conversions and that recommendations of wider assortments are associated with larger revenues.

https://journals.sagepub.com/doi/abs/10.1016/j.intmar.2021.02.002

https://www.productmanagement.ai/p/everything-about-ai

Taxonomy

How things are classified

A taxonomy is a structured classification system, usually hierarchical.

• Focus: Grouping and labelling
• Structure: Parent–child hierarchies
• Answers: “What type of thing is this?” and “Where does it sit?”

Example

• Education → Undergraduate → Level 4 → Module
• Product → Feature → Sub‑feature

In practice, taxonomies are used for navigation, tagging, filtering, and findability, and are explicitly described in enterprise metadata and taxonomy management work.

Think: A well‑organised folder tree or controlled vocabulary.

Ontology

What things are, and how they relate

An ontology defines:

• Concepts
• Properties
• Relationships
• Rules or constraints

It goes beyond hierarchy to express meaning and semantics.

• Focus: Meaning and relationships
• Structure: Network/graph
• Answers: “What is this?”, “How does it relate to other things?”, “What can be inferred?”

Example

• A Module is part of a Qualification
• A Student enrols in a Module
• An Assessment is submitted by a Student

Ontologies are widely discussed in research paradigms and in modern data/AI systems because they support reasoning and inference, not just classification.

Think: A shared conceptual model of a domain.

Topology

How things are arranged or connected

A topology describes the shape or structure of connections, without focusing on meaning or categories.

• Focus: Arrangement and connectivity
• Structure: Patterns of links
• Answers: “How are things connected or organised in space or structure?”

Examples

• Network topologies (hub‑and‑spoke, mesh)
• Team topologies (stream‑aligned teams, enabling teams)
• System architectures

In organisational and systems contexts, topology is used to visualise flows, dependencies, and interaction patterns, as seen in team and system design materials.

Think: A map of connections, not meanings.

Side‑by‑side summary

Concept	Core question	What it defines	Typical use
Taxonomy	What category?	Labels & hierarchies	Navigation, tagging, search
Ontology	What is it & how does it relate?	Concepts & semantics	Reasoning, data integration
Topology	How is it arranged?	Structural connections	Architecture, systems, teams

Shortcut

• Taxonomy = classification
• Ontology = meaning
• Topology = structure

Or, in one sentence:

A taxonomy sorts things, an ontology explains them, and a topology shows how they’re connected.

https://searchengineland.com/ai-search-playbook-machine-readable-content-472412