Recent advancements in technology have created new possibilities for customizing product design without significantly changing the costs of production. By using so-called configurators, which are a form of platform-based technology, firms now have the means to customize products more efficiently. Previous research has mainly focused on the technical side of configurators, whereas the business and organizational issues involved during their introduction are much less understood. The purpose of this chapter is to explore the challenges of adopting configurators in an organization. We combine a literature review from 1980 to the present with findings from an empirical study of a newly developed sales configurator for maritime components.

This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation (RAG) in tourism applications. Through systematic empirical evaluation of three different LLM variants across multiple parameter configurations, we demonstrate the effectiveness of our testing methodology in assessing both functional correctness and extra-functional properties. Our framework implements 17 distinct metrics that encompass syntactic analysis, semantic evaluation, and behavioral evaluation through LLM judges. The study reveals significant information about how different architectural choices and parameter configurations affect system performance, particularly highlighting the impact of temperature and top-p parameters on response quality. The tests were carried out on a tourism recommendation system for the Varmland region in Sweden, utilizing standard and RAG-enhanced configurations. The results indicate that the newer LLM versions show modest improvements in performance metrics, though the differences are more pronounced in response length and complexity rather than in semantic quality. The research contributes practical insights for implementing robust testing practices in LLM-RAG systems, providing valuable guidance to organizations deploying these architectures in production environments. 

This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation (RAG) in tourism applications. Through systematic empirical evaluation of three different LLM variants across multiple parameter configurations, we demonstrate the effectiveness of our testing methodology in assessing both functional correctness and extra-functional properties. Our framework implements 17 distinct metrics that encompass syntactic analysis, semantic evaluation, and behavioral evaluation through LLM judges. The study reveals significant information about how different architectural choices and parameter configurations affect system performance, particularly highlighting the impact of temperature and top-p parameters on response quality. The tests were carried out on a tourism recommendation system for the Värmland region, utilizing standard and RAG-enhanced configurations. The results indicate that the newer LLM versions show modest improvements in performance metrics, though the differences are more pronounced in response length and complexity rather than in semantic quality. The research contributes practical insights for implementing robust testing practices in LLM-RAG systems, providing valuable guidance to organizations deploying these architectures in production environments.

The role of idea screening is to identify promising ideas and select the most qualified ones to proceed into the NPD process. Equally important is understanding which ideas should be avoided to prevent potential failures and avoid unnecessary expenditure of resources. During intuitive decision-making, experts may favor the familiar over the new and may factor personal affection into the decision. Thus, in the context of screening ideas for innovative products, relying entirely on intuition may be risky since the task might require decision-makers to identify radical innovations and look outside of their own comfort zone. Sensemaking is a process that enables people to develop plausible meanings and take actions based on that meaning making. In idea screening, sensemaking can occur when a person struggles to understand an idea or how to proceed with screening. Instead, generative screening can be used by organizations when the intent is to explore new opportunities, use raw ideas as triggers for inspiration. 

This chapter reports the findings from a longitudinal study spanning 12 years. It contributes to an improved understanding of how servitization actually happens, and, more specifically, how it affects the servicizing organization. The focal company AIR was a B2B manufacturing company in the aviation business, specializing in manufacturing jet engines. The case describes and analyzes how a unit at AIR made the successful transition from a pure make-to-print actor (product/production logic) to offering overall solutions (service logic), where we focus on the challenges that AIR encountered. It provides six major findings; the most surprising is perhaps the necessity to handle ambidexterity for successful servitization.

Organizations use idea screening to select which ideas will be developed into innovation projects. Extant research has revealed that, during idea screening, expert evaluators use intuition to make rapid holistic decisions about idea quality, analyze ideas in detail to determine their value and employ sensemaking to understand and elaborate on ideas. However, much of this takes place in the minds of the evaluators, which limits our understanding of how experts identify good ideas. This study addresses the issue by mapping the activities that expert evaluators use to screen ideas and by examining how these activities are used to identify high quality ideas. To study this, 204 idea screening cases were collected, using the think aloud method on two samples of experts. The data were analyzed using grounded theory techniques to outline the range of idea screening activities that expert evaluators use and then complemented with fuzzy set qualitative comparative analysis (fsQCA) to understand how these activities are combined together to make judgments of idea quality. The findings break down intuition, analysis, and sensemaking into seven key activities that experts use when screening ideas and detail their dimensions on the micro‐level. The fsQCA further shows that experts combine these activities into distinct patterns in order to find high quality ideas, where sensemaking plays a crucial role. This demonstrates that finding high quality ideas often requires effortful interpretation, while also generating opportunities to develop ideas further. These findings add depth to our understanding of how evaluators think when screening ideas, suggesting that expert evaluators can be used for both the screening and development of ideas during the idea screening stage. Integrating idea development into screening can potentially lead to the more effective use of resources, while saving time at the front‐end of innovation.