Key Stakeholders' Value Propositions for Feature Selection in Software-intensive Products: An Industrial Case Study

TL;DR

This case study explores how key stakeholders in a telecommunications company understand and prioritize different value propositions when selecting features for software-intensive products, emphasizing the importance of involving all stakeholder groups.

Contribution

It identifies and classifies 36 stakeholder value propositions into six dimensions, highlighting the diversity and importance of multiple perspectives in feature decision-making.

Findings

Customer value propositions are most frequently mentioned.

Different stakeholder groups focus on different value dimensions.

Involving all key stakeholders enhances decision quality.

Abstract

Numerous software companies are adopting value-based decision making. However, what does value mean for key stakeholders making decisions? How do different stakeholder groups understand value? Without an explicit understanding of what value means, decisions are subject to ambiguity and vagueness, which are likely to bias them. This case study provides an in-depth analysis of key stakeholders' value propositions when selecting features for a large telecommunications company's software-intensive product. Stakeholders' value propositions were elicited via interviews, which were analyzed using Grounded Theory coding techniques (open and selective coding). Thirty-six value propositions were identified and classified into six dimensions: customer value, market competitiveness, economic value/profitability, cost efficiency, technology & architecture, and company strategy. Our results show that although propositions in the customer value dimension were those mentioned the most, the concept of value for feature selection encompasses a wide range of value propositions. Moreover, stakeholder groups focused on different and complementary value dimensions, calling to the importance of involving all key stakeholders in the decision making process. Although our results are particularly relevant to companies similar to the one described herein, they aim to generate a learning process on value-based feature selection for practitioners and researchers in general.