Nudge Me and I Change: The Effect of Digital Nudging on Software Engineers’ Motivation and Behaviour Towards Data Quality

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Header Abstract Content Author and article information REFERENCES Comments

Record: found
Abstract: found
Conference Proceedings: found

Is Open Access

Nudge Me and I Change: The Effect of Digital Nudging on Software Engineers’ Motivation and Behaviour Towards Data Quality

Published

proceedings-article

Author(s): Bushra Alkadhi , Robert Hendley , Rami Bahsoon

Publication date (Print): July 2024

Conference name: 37th International BCS Human-Computer Interaction Conference (BCS HCI 24)

Conference theme: The International BCS Human-Computer Interaction Conference in 2024 was supported by the BCS Interactions Special Interest Group and hosted by the University of Central Lancashire in Preston. The BCS HCI Conference welcomed submissions on all aspects of human-computer interaction. Topics included: user experience (UX), usability testing, interaction design (IxD), human-centred AI (HCAI), education, health, sustainability, the Internet of Things (IoT), interaction technologies, and emerging interactive applications.

Conference date: 15–17 July 2024

Keywords: Behaviour Change, Motivation, Nudge Theory, Data Quality, Software Engineering, Data-Driven Requirements Engineering, User Studies, Human-Centred Computing

Bookmark

Abstract

Content

Author and article information

Contributors

Bushra Alkadhi

Robert Hendley

Rami Bahsoon

Conference

Publication date: July 2024

Publication date (Print): July 2024

Pages: 193-204

Affiliations

[0001]School of Computer Science

University of Birmingham

Birmingham

UK

Article

DOI: 10.14236/ewic/BCSHCI2024.18

SO-VID: 36c23e88-3d6e-4c94-a424-efaf2779d0a2

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 37th International BCS Human-Computer Interaction Conference

Conference acronym: BCS HCI 24

Conference number: 37

Conference location: University of Central Lancashire (UCLan)

Conference date: 15–17 July 2024

Conference sponsor: Electronic Workshops in Computing (eWiC)

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/BCSHCI2024.18

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

BeechamS., BaddooN., HallT., RobinsonH., and SharpH. (2008). Motivation in software engineering: A systematic literature review.
BergramK., DjokovicM., BezencV.¸on, and HolzerA. (2022). The digital landscape of nudging: A systematic literature review of empirical research on digital nudges. Association for Computing Machinery.
BobadillaS., GlasseyR., BergelA., and MonperrusM. (2023). Sobo: A feedback bot to nudge code quality in programming courses.
BobrowskiM., MarrM., and YankelevichD. (1998). A software engineering view of data quality.
BosuM. F. and MacDonellS.G. (2013). Data quality in empirical software engineering: A targeted review. pp. 171–176.
BosuM. F. and MacdonellS.G. (2013). A taxonomy of data quality challenges in empirical software engineering. pp. 97–106.
BoveeM., SrivastavaR., and MakB. (2003). A conceptual framework and belief-function approach to assessing overall information quality. Int. J. Intell. Syst. 18, 51–74.
BrownC. (2019). Digital nudges for encouraging developer actions.
BrownC. and ParninC. (2021). Nudging students toward better software engineering behaviors.
CarabanA., KarapanosE., GoncD.¸alves, and CamposP. (2019). 23 ways to nudge: A review of technology-mediated nudging in human-computer interaction. Association for Computing Machinery.
CrosbyP. (1979). Quality is Free: The Art of Making Quality Certain. Mentor book. McGraw-Hill.
de Paula PortoD., deG.M. Jesus, FerrariF.C., and S. C. P. F. Fabbri (2021). Initiatives and challenges of using gamification in software engineering: A systematic mapping. Journal of Systems and Software 173.
Del BalzoG. (2015). Nudging in the UK, in the USA, in Denmark, in Italy: an international comparison of Behavioural Insights Teams. Ph. D. thesis.
DeMarcoT. and ListerT. (2013). Peopleware: Productive Projects and Teams. Addison-Wesley.
DemingW. E. (2000). Out of the Crisis, Volume 1 of MIT Press Books. The MIT Press.
DobynsL. and CrawfordC.-Mason (1991). Quality Or Else: The Revolution in World Business. IBMfunded PBS series. Houghton Mifflin.
DuJ. and ZhouL. (2012). Improving financial data quality using ontologies. Decision Support Systems 54(1), 76–86.
FrancaA. C. C. and F. Q. daB. Silva (2009). An empirical study on software engineers motivational factors. In 2009 3rd International Symposium on Empirical Software Engineering and Measurement, pp. 405–409.
FranchX. (2021). Data-driven requirements engineering: A guided tour. Volume 1375, pp. 83–105. Springer Science and Business Media Deutschland GmbH.
GhabanW. and HendleyR. (2019). Understanding the effect of gamification on learners with different personalities. pp. 392–400.
HallT., BeechamS., VernerJ., and WilsonD. (2008). The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick. pp. 30–39.
HansenP. and JespersenA. (2013). Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy. European Journal of Risk Regulation 1.
JuranJ. and GodfreyA. (1999). Juran’s Quality Handbook. McGraw-Hill International Editions. McGraw Hill.
KleinB. D. (1998). Data quality in the practice of consumer product management: Evidence from the field. Data Qual. 4.
KruskalW. (1981). Statistics in society: Problems unsolved and unformulated. Journal of the American Statistical Association 76(375), 505–515.
LembckeT.-B., EngelbrechtN., BrendelA., and KolbeL. (2019). To nudge or not to nudge: Ethical considerations of digital nudging based on its behavioral economics roots.
LiebchenG. A. (2010). Data cleaning techniques for software engineering data sets.
LiebchenG. A. and ShepperdM. (2008). Data sets and data quality in software engineering. pp. 39–44.
LimS., HenrikssonA., and ZdravkovicJ. (2021). Data-driven requirements elicitation: A systematic literature review. 2, 16.
LoshinD. (2001). Enterprise knowledge management: The data quality approach. Morgan Kaufmann.
MaalejW., NayebiM., JohannT., and RuheG. (2016). Toward data-driven requirements engineering. IEEE Software 33, 48–54.
MacDonaldA., AllenD., WilliamsL., FlowersP., and WalkerT. (2023). People, plastic, and behaviour change – a comment on drivers of plastic pollution, barriers to change and targeted behaviour change interventions. Environmental Science: Advances 2.
MaddilaC., UpadrastaS.S., BansalC., NagappanN., GousiosG., and vanA. Deursen (2023). Nudge: Accelerating overdue pull requests toward completion. ACM Trans. Softw. Eng. Methodol. 32(2).
MartinA., GinnsP., and PapworthB. (2017). Motivation and engagement: Same or different? does it matter? Learning and Individual Differences 55, 162.
MartinezA., MelinM., KoutsopoulosG., and ZdravkovicJ. (2021). An empirical study on datadriven requirements elicitation: Reflections from nordic enterprises.
MeskeC. and PotthoffT. (2017). The dinu-model – a process model for the design of nudges.
MethH., BrhelM., and MaedcheA. (2013). The state of the art in automated requirements elicitation. Information and Software Technology 55(10), 1695–1709.
MichieS., vanM.M. Stralen, and WestR. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science 6.
Murphy-HillE. and MurphyG. (2011). Peer interaction effectively, yet infrequently, enables programmers to discover new tools. pp. 405–414.
NaumannF. (2002). Quality-driven query answering for integrated information systems. Lectures Notes in Computer Sciences 2261.
Nicol ´asJ. and TovalA. (2009). On the generation of requirements specifications from software engineering models: A systematic literature review. Information and Software Technology 51(9), 1291–1307.
O’BrienH.L., CairnsP., and HallM. (2018). A practical approach to measuring user engagement with the refined user engagement scale (ues) and new ues short form. International Journal of Human Computer Studies 112, 28–39.
PaunovY., WM.¨anke, and VogelT. (2019). Ethical defaults: which transparency components can increase the effectiveness of default nudges? Social Influence 14, 1–13.
PedreiraO., GarcF.´ıa, BrisaboaN., and PiattiniM. (2015). Gamification in software engineering -a systematic mapping. Volume 57, pp. 157–168. Elsevier B.V.
PipinoL., LeeY., and WangR. (2003). Data quality assessment. Communications of the ACM 45. ProcaccinoJ. D., VernerJ.M., ShelferK.M., and GefenD. (2005). What do software practitioners really think about project success: an exploratory study. Journal of Systems and Software 78(2), 194–203.
RedmanT. C. and GodfreyA.B. (1997). Data Quality for the Information Age (1st ed.). USA: Artech House, Inc.
RuanL. and ZhangT. (2020). The challenge of data-driven requirements elicitation techniquessystematic literature review controlled experiment.
SchneiderC., WeinmannM., and J. v. Brocke (2018). Digital nudging: Guiding online user choices through interface design. Communications of the ACM 61, 67–73.
SharpH., BaddooN., BeechamS., HallT., and RobinsonH. (2009). Models of motivation in software engineering. Information and Software Technology 51, 219–233.
ShepperdM. (2011). Data Quality: Cinderella at the Software Metrics Ball? Association for Computing Machinery.
SunsteinC. (2014). The ethics of nudging. Yale journal on regulation forthcoming.
ThalerR. and SunsteinC. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.
VeauxR. D. D. and HandD.J. (2005). How to lie with bad data. Statistical Science 20, 231–238.
WandY. and WangR.Y. (1996). Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95.
WangR. Y. and StrongD.M. (1996). Beyond accuracy: What data quality means to data consumers.
WohlinC., RunesonP., HstM., OhlssonM.C., RegnellB., and WesslnA. (2012). Experimentation in Software Engineering. Springer Publishing Company, Incorporated.

Comments

Comment on this article

[1] BeechamS., BaddooN., HallT., RobinsonH., and SharpH. (2008). Motivation in software engineering: A systematic literature review.

[2] BergramK., DjokovicM., BezencV.¸on, and HolzerA. (2022). The digital landscape of nudging: A systematic literature review of empirical research on digital nudges. Association for Computing Machinery.

[3] BobadillaS., GlasseyR., BergelA., and MonperrusM. (2023). Sobo: A feedback bot to nudge code quality in programming courses.

[4] BobrowskiM., MarrM., and YankelevichD. (1998). A software engineering view of data quality.

[5] BosuM. F. and MacDonellS.G. (2013). Data quality in empirical software engineering: A targeted review. pp. 171–176.

[6] BosuM. F. and MacdonellS.G. (2013). A taxonomy of data quality challenges in empirical software engineering. pp. 97–106.

[7] BoveeM., SrivastavaR., and MakB. (2003). A conceptual framework and belief-function approach to assessing overall information quality. Int. J. Intell. Syst. 18, 51–74.

[8] BrownC. (2019). Digital nudges for encouraging developer actions.

[9] BrownC. and ParninC. (2021). Nudging students toward better software engineering behaviors.

[10] CarabanA., KarapanosE., GoncD.¸alves, and CamposP. (2019). 23 ways to nudge: A review of technology-mediated nudging in human-computer interaction. Association for Computing Machinery.

[11] CrosbyP. (1979). Quality is Free: The Art of Making Quality Certain. Mentor book. McGraw-Hill.

[12] de Paula PortoD., deG.M. Jesus, FerrariF.C., and S. C. P. F. Fabbri (2021). Initiatives and challenges of using gamification in software engineering: A systematic mapping. Journal of Systems and Software 173.

[13] Del BalzoG. (2015). Nudging in the UK, in the USA, in Denmark, in Italy: an international comparison of Behavioural Insights Teams. Ph. D. thesis.

[14] DeMarcoT. and ListerT. (2013). Peopleware: Productive Projects and Teams. Addison-Wesley.

[15] DemingW. E. (2000). Out of the Crisis, Volume 1 of MIT Press Books. The MIT Press.

[16] DobynsL. and CrawfordC.-Mason (1991). Quality Or Else: The Revolution in World Business. IBMfunded PBS series. Houghton Mifflin.

[17] DuJ. and ZhouL. (2012). Improving financial data quality using ontologies. Decision Support Systems 54(1), 76–86.

[18] FrancaA. C. C. and F. Q. daB. Silva (2009). An empirical study on software engineers motivational factors. In 2009 3rd International Symposium on Empirical Software Engineering and Measurement, pp. 405–409.

[19] FranchX. (2021). Data-driven requirements engineering: A guided tour. Volume 1375, pp. 83–105. Springer Science and Business Media Deutschland GmbH.

[20] GhabanW. and HendleyR. (2019). Understanding the effect of gamification on learners with different personalities. pp. 392–400.

[21] HallT., BeechamS., VernerJ., and WilsonD. (2008). The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick. pp. 30–39.

[22] HansenP. and JespersenA. (2013). Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy. European Journal of Risk Regulation 1.

[23] JuranJ. and GodfreyA. (1999). Juran’s Quality Handbook. McGraw-Hill International Editions. McGraw Hill.

[24] KleinB. D. (1998). Data quality in the practice of consumer product management: Evidence from the field. Data Qual. 4.

[25] KruskalW. (1981). Statistics in society: Problems unsolved and unformulated. Journal of the American Statistical Association 76(375), 505–515.

[26] LembckeT.-B., EngelbrechtN., BrendelA., and KolbeL. (2019). To nudge or not to nudge: Ethical considerations of digital nudging based on its behavioral economics roots.

[27] LiebchenG. A. (2010). Data cleaning techniques for software engineering data sets.

[28] LiebchenG. A. and ShepperdM. (2008). Data sets and data quality in software engineering. pp. 39–44.

[29] LimS., HenrikssonA., and ZdravkovicJ. (2021). Data-driven requirements elicitation: A systematic literature review. 2, 16.

[30] LoshinD. (2001). Enterprise knowledge management: The data quality approach. Morgan Kaufmann.

[31] MaalejW., NayebiM., JohannT., and RuheG. (2016). Toward data-driven requirements engineering. IEEE Software 33, 48–54.

[32] MacDonaldA., AllenD., WilliamsL., FlowersP., and WalkerT. (2023). People, plastic, and behaviour change – a comment on drivers of plastic pollution, barriers to change and targeted behaviour change interventions. Environmental Science: Advances 2.

[33] MaddilaC., UpadrastaS.S., BansalC., NagappanN., GousiosG., and vanA. Deursen (2023). Nudge: Accelerating overdue pull requests toward completion. ACM Trans. Softw. Eng. Methodol. 32(2).

[34] MartinA., GinnsP., and PapworthB. (2017). Motivation and engagement: Same or different? does it matter? Learning and Individual Differences 55, 162.

[35] MartinezA., MelinM., KoutsopoulosG., and ZdravkovicJ. (2021). An empirical study on datadriven requirements elicitation: Reflections from nordic enterprises.

[36] MeskeC. and PotthoffT. (2017). The dinu-model – a process model for the design of nudges.

[37] MethH., BrhelM., and MaedcheA. (2013). The state of the art in automated requirements elicitation. Information and Software Technology 55(10), 1695–1709.

[38] MichieS., vanM.M. Stralen, and WestR. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science 6.

[39] Murphy-HillE. and MurphyG. (2011). Peer interaction effectively, yet infrequently, enables programmers to discover new tools. pp. 405–414.

[40] NaumannF. (2002). Quality-driven query answering for integrated information systems. Lectures Notes in Computer Sciences 2261.

[41] Nicol ´asJ. and TovalA. (2009). On the generation of requirements specifications from software engineering models: A systematic literature review. Information and Software Technology 51(9), 1291–1307.

[42] O’BrienH.L., CairnsP., and HallM. (2018). A practical approach to measuring user engagement with the refined user engagement scale (ues) and new ues short form. International Journal of Human Computer Studies 112, 28–39.

[43] PaunovY., WM.¨anke, and VogelT. (2019). Ethical defaults: which transparency components can increase the effectiveness of default nudges? Social Influence 14, 1–13.

[44] PedreiraO., GarcF.´ıa, BrisaboaN., and PiattiniM. (2015). Gamification in software engineering -a systematic mapping. Volume 57, pp. 157–168. Elsevier B.V.

[45] PipinoL., LeeY., and WangR. (2003). Data quality assessment. Communications of the ACM 45. ProcaccinoJ. D., VernerJ.M., ShelferK.M., and GefenD. (2005). What do software practitioners really think about project success: an exploratory study. Journal of Systems and Software 78(2), 194–203.

[46] RedmanT. C. and GodfreyA.B. (1997). Data Quality for the Information Age (1st ed.). USA: Artech House, Inc.

[47] RuanL. and ZhangT. (2020). The challenge of data-driven requirements elicitation techniquessystematic literature review controlled experiment.

[48] SchneiderC., WeinmannM., and J. v. Brocke (2018). Digital nudging: Guiding online user choices through interface design. Communications of the ACM 61, 67–73.

[49] SharpH., BaddooN., BeechamS., HallT., and RobinsonH. (2009). Models of motivation in software engineering. Information and Software Technology 51, 219–233.

[50] ShepperdM. (2011). Data Quality: Cinderella at the Software Metrics Ball? Association for Computing Machinery.

[51] SunsteinC. (2014). The ethics of nudging. Yale journal on regulation forthcoming.

[52] ThalerR. and SunsteinC. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.

[53] VeauxR. D. D. and HandD.J. (2005). How to lie with bad data. Statistical Science 20, 231–238.

[54] WandY. and WangR.Y. (1996). Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95.

[55] WangR. Y. and StrongD.M. (1996). Beyond accuracy: What data quality means to data consumers.

[56] WohlinC., RunesonP., HstM., OhlssonM.C., RegnellB., and WesslnA. (2012). Experimentation in Software Engineering. Springer Publishing Company, Incorporated.

UK Computing Summit 2025: Navigating change (surviving and beyond) - 29-30 April @ Sheffield Hallam University - Register here.

Nudge Me and I Change: The Effect of Digital Nudging on Software Engineers’ Motivation and Behaviour Towards Data Quality

Contributors

Conference

Affiliations

Article

History

Product

Categories

Comment on this article