Data Driven Decision-making And Lean Six Sigma – A New Study
As the penetration of technology increases its pace within businesses and organisations around the world, organisations are increasingly turning to technology to assist with large-scale data driven decision-making. Many decisions within organisations are now dependent on both the technology and the quality and currency of the data being used.
Organisations are also pretty much tied into the metrics they use, in that organisational metrics are in a large part the sole providers of the data being used for decision-makers within organisations. A metric refers to the quantifiable measure that is used to identify, monitor and assess trends, changes or any form of characteristic of a particular organisational process . In short, much, if not all, of the data used in organisational decision-making tends to come from the matrix used within that organisation.
…organisational metrics are in a large part the sole providers of the data being used for decision-makers within organisations
Data and decision making
Data on its own has little meaning, however, and requires interpretation, analytical skills and considerable levels of critical thinking in order for it to provide valuable and reliable/valid insights which can be used to effect change, which is what decision-making is largely about.
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Managers, perceptions and reactions to decision-making technology
One of the areas of considerable organisational and research interest recently has been trying to understand managers’ perceptions and reactions to new technology and how it is used for either providing data for decision-making or being a significant part of the decision making and sense making processes.
Fairly obviously, the success and quality of data driven decision-making relies mainly on the quality of the data, its organisation and the quality of the analysis or sense making that turns that data into information and knowledge
The process of turning organisational activities into measurable metrics, converting into valid and reliable data and then transforming it into useful information and knowledge is a knowledge management issue and, therefore, based on the knowledge management and absorptive capacity/learning capabilities of the organisation . These processes together form a significant source of competitive advantage for most organisations
The technology acceptance model
Probably one of the most widely accepted models within the area of information systems, is the Technology Acceptance Model – initially based on work in 1986 . Since then the model has being continually updated and elaborated to include the following variables
• Voluntary innovation, or the amount to which engagement in innovation is perceived as being voluntary or forced.
• Relative advantage – the perceived value of an innovation and whether or not it is seen as being better than what went before.
• Compatibility – the degree to which the technology is seen to be consistent with the values, needs and experience of the technology adopters.
• Complexity, or how difficult the new technology is to use.
• Observability, or how readily the outcomes of the technology can be perceived.
• Piloting, or the degree to which an innovation technology can be experimented with before it is actually adopted.
• Image, or whether or not the use of the technology or innovation is seen to enhance the individual’s image or status within the organisational social system.
• Self=efficacy, or the belief that an individual is capable of using and mastering the technology and that they can use it to solve the problems that need to be solved.
• End-user support – the level of support available to new and existing users of the technology.
• Objective usability, which refers to a relatively objective comparison of the technology with other systems or technologies and how usable they are.
• Personal innovation behaviours, which refers to how willing an individual is to try out new technologies.
• Computer playfulness, or the degree of cognitive spontaneity an individual has whilst interacting with a technology or innovation.
• Social presence, which refers to a situation where people can see others are engaged with the technology and that there is a level of psychological presence by others.
• Social influence and objective norms, which refers to an individual’s perceptions about other people’s views and perceptions of their using a particular piece of technology or innovation.
• Visibility, or how readily visible the innovation technology is within the organisation.
• Job relevance and whether or not the technology enhances the individual’s task and job performance.
• Computer attitude, which is a subjective measure of whether individual likes or dislikes the technology.
• Accessibility., This refers to an individual’s physical and cognitive access to a particular system or technology. Cognitive access meaning that they can readily understand and interact with the technology and obtain the desired information from the technology.
• Demonstrative results, which simply refers to the degree to which the results are observable and communicable to others.
• Management support, or the degree to which managers and leaders provide time and resources with the adoption of the technology.
• Technology anxiety, or the level of apprehension an individual experiences whilst engaging with the system.
• Perceived enjoyment, or the level to which an individual finds using the technology enjoyable or not.
• System output, which refers to perceptions of how well the technology performs its tasks and helps with the achievement of job goals.
• Facilitating conditions. These refer to any factors which may constrain usage, such as time, physical or cognitive access, IT compatibility issues, financial factors, et cetera.
• Prior experience, or the level of previous interaction an individual has had with the technology, or similar technologies.
A new study
A new (2020) study looked at the interactions and correlations between nine of the main technology acceptance model variables to see which of the variables predict the likelihood that people will adopt data driven technology-based decision-making in Lean Six Sigma contexts
The study found that the following factors have a significant impact on the perceived usefulness of data during decision-making processes:
- The perceived quality and usefulness of the organisation’s existing knowledge management processes (including the organisation’s level of absorptive capacity/learning orientation).
- The perceived data quality.
- The level of technology readiness held by both individuals and the organisation as a whole (cultural technology readiness) .
- Level of perceived system or technology output – or how much the system or technology is seen to help with the achievement of work goals.
The study found that these four factors had a significant impact on the perceived usefulness of any data driven technology-based decision-making process.
Secondly, the study showed that the perceived ease of use of the data and the technology had a significant overall impact on whether or not individuals perceived that the decision-making process and the data was useful.
It was then found that the perceived ease of use of the data and the technology, together with the level of complexity of the decision being made, significantly predicted whether the data-driven decisions themselves would be adopted
The researchers discovered that, if any of these factors were weak or suspect in the users’ / decision-makers’ perceptions, they were very likely to revert to intuitive decision-making and ignore the data driven-decisions.
Rejikumar, G., Aswathy Asokan, A., & Sreedharan, V. R. (2020). Impact of data-driven decision-making in Lean Six Sigma: an empirical analysis. Total Quality Management & Business Excellence, 31(3-4), 279-296.
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