Factors and actors leading to the adoption of a JavaScript framework

  • Amantia Pano
  • Daniel Graziotin
  • Pekka Abrahamsson
Article

Abstract

The increasing popularity of JavaScript has led to a variety of JavaScript frameworks that aim to help developers to address programming tasks. However, the number of JavaScript frameworks has risen rapidly to thousands of versions. It is challenging for practitioners to identify the frameworks that best fit their needs and to develop new ones which fit such needs. Furthermore, there is a lack of knowledge regarding what drives developers toward the choice. This paper explores the factors and actors that lead to the choice of a JavaScript framework. We conducted a qualitative interpretive study of semi-structured interviews. We interviewed 18 decision makers regarding the JavaScript framework selection, up to reaching theoretical saturation. Through coding of the interview responses, we offer a model of desirable JavaScript framework adoption factors. The factors are grouped into categories that are derived via the Unified Theory of Acceptance and Use of Technology. The factors are performance expectancy (performance, size), effort expectancy (automatization, learnability, complexity, understandability), social influence (competitor analysis, collegial advice, community size, community responsiveness), facilitating conditions (suitability, updates, modularity, isolation, extensibility), and price value. A combination of four actors, which are customer, developer, team, and team leader, leads to the choice. Our model contributes to the body of knowledge related to the adoption of technology by software engineers. As a practical implication, our model is useful for decision makers when evaluating JavaScript frameworks, as well as for developers for producing desirable frameworks.

Keywords

JavaScript Programming frameworks Decision making Technology adoption Human aspects of software development Qualitative research Interpretivism 

Notes

Acknowledgments

We would like to thank all the participants of this study.

Our gratitude goes to the editor and three anonymous reviewers whose feedback improved our original efforts immeasurably.

Daniel Graziotin has been supported by the Alexander von Humboldt (AvH) Foundation.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly
  2. 2.Institute of Software TechnologyUniversity of StuttgartStuttgartGermany
  3. 3.Faculty of Information TechnologyUniversity of JyväskyläJyväskyläFinland

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