SEOct 30, 2018

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

arXiv:1810.12589v118 citations
Originality Synthesis-oriented
AI Analysis

This addresses ambiguity in value-based decision-making for software companies, offering insights for practitioners and researchers, though it is incremental as a case study.

The study investigated how key stakeholders define value when selecting features for a software-intensive product in a telecommunications company, identifying 36 value propositions across six dimensions and showing that stakeholders focus on different but complementary aspects.

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.

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