Study
Innovation & DesignHigh ImpactStrong effect

Resistance to change significantly hinders big data analytics adoption in healthcare.

Employee resistance to change acts as a critical barrier, negatively moderating the link between the intention to use and the actual implementation of big data analytics in healthcare settings.

Journal Of Big Data · 2019

01

Key Findings

  • 01The technology acceptance model and task-technology fit significantly contribute to the intention to use big data analytics in healthcare.
  • 02Trust in and security of information systems positively influence the intention to use big data analytics.
  • 03Employee resistance to change negatively moderates the relationship between the intention to use and the actual use of big data analytics.
02

Application

Design takeaway

When designing and implementing new data-driven systems in healthcare, prioritize strategies that actively manage and reduce employee resistance to change.

How to apply

Before deploying a new big data analytics system, conduct a thorough assessment of employee attitudes towards change and develop targeted interventions to mitigate potential resistance.

Project actions

  • 01When researching new technology adoption, consider the human element and potential for resistance.
  • 02If your design project involves a new system, think about how you will help users overcome their reluctance to adopt it.
03

Method & Evidence

AimTo investigate the factors influencing the adoption of big data analytics in healthcare organizations, with a specific focus on the moderating role of employee resistance to change.
MethodQuantitative survey research.
ProcedureA survey questionnaire was administered to healthcare professionals to gather data on their behavioral intentions, perceived task-technology fit, trust, security, and resistance to change concerning big data analytics. Statistical analysis was performed using AMOS v21 to test the hypothesized relationships.
Sample224 participants
ContextHealthcare organizations

Variables

IVPerceived usefulness, perceived ease of use, task-technology fit, trust in information systems, security of information systems.
DVBehavioral intention to use big data analytics, actual use of big data analytics.
CVOrganizational context, type of healthcare organization, employee role.
04

Strengths & Limitations

Strengths

  • +Employs established theoretical frameworks (TAM, TTF).
  • +Investigates a critical real-world problem in healthcare innovation.

Limitations

Self-reported data can be biased, and the specific context of healthcare might not apply to all industries.

Reliability & validity

The study uses established scales for its constructs, which can contribute to reliability. Validity is addressed through the testing of hypotheses derived from theoretical models.

Think critically

How can designers proactively design for resistance to change, rather than just reacting to it after implementation issues arise?

05

Design Principles

"Technological innovation adoption is contingent not only on perceived benefits but also on the organization's capacity to manage human factors, particularly resistance to change."

Understanding and mitigating employee resistance is crucial for the successful integration of innovative technologies like big data analytics in healthcare. Design projects aiming to implement such systems must incorporate strategies that address user concerns and foster acceptance to realize the full potential of the technology.

06

What This Means for Your Design

People in healthcare want to use big data tools, but if they are resistant to new things, they won't actually use them, even if they intend to.

How to use in your project

  • 1.Use this research to justify the importance of user adoption strategies in your design process, especially when introducing novel technologies.
  • 2.Cite this study when discussing potential barriers to the implementation of your proposed design solution.
07

Add to My Project

08

Quick Cite

(2019). Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. Journal Of Big Data. https://doi.org/10.1186/s40537-019-0170-y Retrieved from https://designdex.org/study/17dcd9ae-3e1f-49f6-9394-c270cf8bc1f8/resistance-to-change-significantly-hinders-big-data-analytics-adoption-in-healthcare

Paragraph starter

The successful adoption of innovative technologies like big data analytics in healthcare is significantly influenced by employee resistance to change, as demonstrated by Shahbaz et al. (2019). This research indicates that while users may intend to use new systems, actual implementation is often hindered by this resistance, underscoring the need for design interventions that proactively address user concerns and facilitate a smoother transition.

09

Source

Journal Of Big Data

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

journal · 2019

View source

Questions about this research

What does the research say about resistance to change significantly hinders big data analytics adoption in healthcare?
When designing and implementing new data-driven systems in healthcare, prioritize strategies that actively manage and reduce employee resistance to change. Evidence: Journal Of Big Data (2019).
Why does "Resistance to change significantly hinders big data analytics adoption in healthcare." matter for design?
Understanding and mitigating employee resistance is crucial for the successful integration of innovative technologies like big data analytics in healthcare. Design projects aiming to implement such systems must incorporate strategies that address user concerns and foster acceptance to realize the full potential of the technology.
How can designers apply this research?
When designing and implementing new data-driven systems in healthcare, prioritize strategies that actively manage and reduce employee resistance to change.
What were the main findings?
The technology acceptance model and task-technology fit significantly contribute to the intention to use big data analytics in healthcare.. Trust in and security of information systems positively influence the intention to use big data analytics.. Employee resistance to change negatively moderates the relationship between the intention to use and the actual use of big data analytics.
What research method was used?
Quantitative survey research. with 224 participants.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2019 journal from Journal Of Big Data.
What should I do differently in my next project?
Before deploying a new big data analytics system, conduct a thorough assessment of employee attitudes towards change and develop targeted interventions to mitigate potential resistance.
What are the limitations?
The study relies on self-reported data, and the findings may be specific to the cultural and organizational context of the surveyed healthcare organizations.
Is there evidence that big data affects design outcomes?
While factors like perceived usefulness and ease of use encourage healthcare professionals to intend to use big data analytics, their actual adoption is significantly hampered if they resist the change. Understanding and mitigating employee resistance is crucial for the successful integration of innovative technologies Source: Journal Of Big Data (2019).
Where does this data analytics research apply?
Healthcare organizations It sits within innovation & design research on designdex.org.

Related research topics

big data design research · evidence on big data · does big data improve design outcomes · data analytics studies for designers · big data and data analytics findings · innovation & design research evidence