“Data collectors believe that accurate manual data acquisition is a value-adding activity, take pride in accurate data acquisition and view it as an important skill within their peer group.”
When improving the quality of manually collected data (MCD) then the attitude of the person charged with collecting the data is an important factor in the compliance to the data collection processes. Do they have the belief that their data collection efforts are a valuable and a worthwhile activity?
However, while attitude is definitely a key factor, research has shown there is a fairly inconsistent relationship between attitude alone and behaviour (1) Our intentions are a far stronger predictor of our behaviour than our attitude alone. The Theory of Planned Behaviour (TPB) is a well-recognised model developed to identify the factors that link human intention with behaviour.
The stronger the data collector’s intention to follow the data collection process, the stronger the likelihood that the data will be collected accurately. But how do we influence intention?
The TPB presents 3 factors that need to be in place to positively influence intention. These are:
- Attitudinal beliefs
- Subjective norms
- Perceived behavioural control
This means we need to address each of these 3 factors to improve the overall intention and therefore compliance with the manual data collection process.
All humans have a set of belief systems and values that shape our attitude. The beliefs and therefore attitude towards MDC can be driven by several factors. Below are common issues to be addressed.
- The perceived value of the data to the company.
- The relative value of the data collection process when compared to other activities in their role.
- The understanding and visibility of how the data is used by the company.
- The impact on the individual and the organisation of poor data.
- The degree of ownership the individual has of data collection as part of their job role.
The subjective norm is concerned with the perception that an individual has in relation to how important other people, particularly those whos opinion and position they respect, consider the task of MDC. Besides this, consider the individual’s motivation to comply with this perceived social pressure.
It is possible to measure these by asking some simple questions such as:
“If another operator reviewed a work order you had previously completed and found key data was missing or inaccurate would they disapprove? Would they view this as reflecting badly on your competency as a professional operator?”
If the answer is no, then it may be the case that the operator collecting the data may initially have good intentions but social pressure (or the lack of it) could weaken this intention over time to where the quality deteriorated further or the MDA process was not complied with. This means we need to develop a sense of positive social pressure with the task of MDC.
Subjective norms strongly connect with culture and in particular the sub-culture of specific trade groups. Operators may value quick thinking and a strong understanding of the plant process and its limitation and capability. Maintenance may value the ability to repair equipment quickly and get the machine back on up and running quickly. This is important because these groups are both involved in MDC but if they assign no significant value to the task as a group then it is unlikely that their will motivated to carry out the task with consistency and quality over the long term.
Building a Culture of Reliability Excellence (CoRE) can help educate groups as to the important of data in the RMS and in reaching the business goals and ultimately improving the reliability of the equipment, reducing cost and probably more importantly, providing job security for the individual.
Perceived Behavioural Control
Perceived Behavioural Control is the third element of the TPB and is defined as a person’s perception of how easy or hard, e.g., level of effort, a task will be to successfully complete. The level of control a person perceives they have over a behaviour will therefore affect their intention to perform that behaviour.
Research shows that is can be further broken down into 2 factors. These are called self-efficacy and controllability.
Self-efficacy is a person’s conviction they can consistently and successfully achieve a specific outcome or goal.
Self-Efficacy is specific to a task or activity. For example, self-efficacy is not a general characteristic of an individual but is a characteristic of an individual’s perceived performance of a specific task.
In terms of MDC this could be reading a complicated gauge, assessment of a machine’s condition, entering failure mode information into a CMMS or using a vibration monitoring tool.
There are several factors that feed into the level of self-efficacy. Each of these needs to be considered when attempting to improve an individual’s self-efficacy for a particular task.
Controllability is an individuals’ own judgement of their capability to perform a behaviour or task.
In terms of MDC these tend to be external factors such as access to accurate gauges, functionality of computer systems, physical access to equipment, access to support personnel or access to special tools required, e.g., oil sampling kits, vibration monitoring machines, computer input terminals etc.
Below are some examples of aspects that can affect PBC:
The emphasis and priority that management and supervision place on MDC can have a positive or negative impact on an individual’s PBC of MDC activities. If supervision don’t understand the importance and ultimate use (know-why) of the data, then they may not assign a significant priority to the task and this could reduce the individual’s perception of the controllability aspect of PBC. “My daily tasks focus on keeping the plant running, that is what my supervisor asks about so that is what I focus on. Data collection is never discussed.”
Time pressure can reduce PBC through increased error rates. If someone is rushed there is more chance they will take shortcuts, omit information or record errors in their reporting. This will reduce their self-efficacy and to some extent controllability in being able to carry out the MDC activity, resulting in a lower PBC.
I might come as no surprise that feedback to an individual affects PBC however sometimes, feedback can actually be unhelpful or detrimental.
Research (Lee and Strong) has backed up the somewhat intuitive idea that if the MDC team understands why they are collecting data and what it is used for and also the consequences of poor data collection then they will be more likely to collect the data with accuracy and completeness. If they collect data with accuracy and completeness this will further increase their self-efficacy and therefore contribute towards improving their behaviour and intention towards MDC.
However, there are also situations where feedback will not make a difference and may be detrimental. If a manual data collector does not feel they have a high level of PBC, i.e., they have both the self-efficacy and control of MDC, then they are likely to regard feedback, particularly negative feedback, with little meaning as they will feel they are in a situation to improve the situation because they can’t act on the information provided in the feedback.
In summary, management should ensure that they address blockers to the level of PBC of manual data collectors before introducing a feedback system.
Actions for Improving Intention and Behaviour
In the section above, I’ve summarized the factors that influence an individual’s attitude toward MDC based on the latest phycological research. The three factors are:
- Attitudinal beliefs
- Subjective norms
- Perceived behavioural control
The interaction between these 3 factors is shown diagrammatically above.
In this section, I’ll cover practical actions that can be taken within an organisation to improve each of the 3 factors.
1. Attitudinal Beliefs
The organisation has to place a high value on MDC quality and set accurate data collection as a core expectation of the relevant job roles.
- Educate leaders on how data is used and the consequences and risks of poor MDC on business decisions support systems. This will increase their interest and understanding of the need for high-quality data and increase the likelihood they will foster this attitude in their teams.
- Add performance targets to an individual’s performance contract. Ensure that the individual understands the KPIs, the targets, how to measure progress towards the target and, most importantly, feel it is within their control.
Example 1: Greater than 90% of work orders they have completed meet the data quality audit criteria.
Example 2: Greater than 95% of downtime records created are linked to the work order created to remedy the defect.
- Create internal marketing materials and training related to high-quality data collection.
Example 1: Add a section to the company and also site-specific induction process.
Example 2: Create specific training videos or other material on the use of key system
Example 3: Provide awards or other recognition for individuals how to excel in their behaviours related to manual data collection.
Example 4: Create an internal communications video with one or a number of senior managers explaining why data quality is vital to the organisation and voicing their endorsement and support for improving the data collection process.
- Link competency and high performance in MDC to competency requirements for job roles.
- Improve, and preferable automate, the process of feedback, both positive or negative, to the MDCs.
Example 1: Create a weekly or monthly automated data audit report.
Example 2: Send the MDCs the results of the reports and the resultant actions they have driven., i.e., if they collect downtime information, send them the month MTBF (Mean Time Between Failure) reports.
2. Subjective norms
Improving how groups perceive the task of MDC. If individuals perceive an action as being socially recognised and important by those they respect and take notice of will be more likely to have the believe if has value and therefore greater intention to carry out the task. Of course, the individual that others respect also must believe this is a value-added activity too. This is why it is vital to address each individual’s attitude toward MDC even if it is not a significant factor in influencing actual behaviour.
- Carry out a baseline survey to understand the current level of value MDCs perceive is placed on MDC by their respected peers and leaders.
- Encourage others to voice their opinions, ideas and frustrations around MDC, e.g., through formal data review meeting, continuous improvement suggestions or integrated into team meetings.
- Educate each role involved in the data collection process on the follow-on effects and dependencies of their step in the process. This creates a view for each individual of the wholistic process involving a team with a single goal rather on group individuals focusing on their task in the data collection process. By “walking in the other person’s shoes” they understand their viewpoint appreciate their challenges and even see ways to improve their part of the process.
- Identify data collection champions. These people must be those who are recognised and respected as option leaders (not always the same as actual organisational leaders) amongst the group. Work to convince them that quality MDC is a worthwhile activity and they will convince others and help develop it into the subjective norms of the group.
3. Level of Perceived Behavioural Control (PBC)
As explained above, an individual’s PBC is split into 2 elements. These are self-efficacy and controllability. Understand which element is affecting PBC and to what extent before effective interventions can be made. For example, if it’s self-efficacy, which specific aspect of the MDC role is this in relation to?
Like-wise if it is controllability, what aspect do they feel they have reduced control of, e.g., time, feedback, support, etc.
- Provide training and guidance. An individual must be clear on how to carry out the task and what the expectations for high-quality MDC are. Formal training backed up with quick reference guides or short online video are ideal for this purpose.
- Carry out the task repeatedly: It sounds obvious by mastery of a task comes from doing the task successfully multiple times. This builds perceived self-efficacy around the future performance of the task in the individual.
- Share peer success: If a person’s peers have managed to master an MDC task, particularly if they were previously not particularly confident or skilled at the task, then they will become subconsciously recognised as role models, i.e., “John is an operator too. He never used to be confident at entering work order history data but now he is. I’m as good as John so I should have no problem learning how to do this too.”
- Moral support: A person’s self-efficacy can be increased by others showing confidence it the person’s ability to carry out the task successfully. Supervisors are well placed to provide this support during one-to-one performance review and day to day conversations.
- Timing of Tasks: If we are under stress or feel tired or fatigued while carrying out an MDC task, then our minds can link this state to the task being carried out through a process called anchoring. The feeling is “anchored” to the task because they are happening simultaneously. This can reduce self-efficacy. Avoid scheduling MDC tasks at points in the day when individuals are likely to be more stressed or mentally fatigued, i.e., the end of a long shift.
- The organisation must demonstrate through actions that the necessary system changes, training and time is provided for the individual to feel they are in control of successfully completing the task.
- Carry out an MDC process re-engineering exercise, with input from the MDCs. Implementing changes to reduce wasteful or non-value adding tasks.
- Use new technology to help make MDC faster, easier and more accurate. This could include computer system updates, automation of collection through electronic sensors, use of hand-held devices or even voice recognition software to transform spoken narrative into text. Artificial intelligence and the Internet of Things (IoT) will have may applications in MDC.
- To improve the quality of manual data collection, the organisation needs to manage the behaviours of those responsible for manual data collection.
- The end uses (customers) of maintenance and reliability data must understand the motivational factors of those collecting the data.
- An individual’s intentions and beliefs around MDC tasks will influence their action behaviours, but the intention is a far stronger predictor action.
- It stands to reason that management should put measures and an action plan in place to foster both positive intention and beliefs.
- The Theory of Planned Behaviours model suggests that a 3-pronged approach is required focusing on Attitudinal Beliefs, Subjective norms and Perceived Behavioural Control.
- Providing feedback on data quality is often the logical first step that organisations take in improving data quality. This is a necessary step but may be counter-productive and actually demotivating if the data collector’s perceived behavioural control is low. I.e., “I know the information is not of a high quality but other factors like time pressure and a poorly designed computer system mean that don’t feel I can improve it, even if I wanted to.”
Share on facebook
Share on google
Share on twitter
Share on linkedin
Share on pinterest