Palm 6

Now I understand why you are hesitating.

Your current framework is already broad and well-established:

Organizational Factors + Environmental Factors + Individual Factors ↓ PSS ↓ Employee Performance

The challenge is that Knowledge Transfer and Industry 4.0 readiness are not naturally sitting outside the model. They are actually trying to enter through the Organizational Factors construct.


---

My first observation

Your Organizational Factors construct is currently too broad.

You wrote:

Policies

Culture

Leadership style

Resource allocation


These are not variables yet.

They are categories.

An examiner will eventually ask:

> "Which specific organizational factor are you measuring?"



This gives you an opportunity.

Instead of adding a completely new RO5 and RQ5, you can redefine Organizational Factors to include:

Organizational Factors

Training Effectiveness

Knowledge Transfer

Leadership Support

Resource Availability


Suddenly, the mill manager's concern is directly reflected in the model.


---

The strongest connection I see

The manager repeatedly says:

> "Workers do not understand."



> "Workers resist."



> "Knowledge is difficult to transfer."



This is almost textbook PSS territory.

Think about it.

Knowledge does not transfer itself.

Usually it transfers through:

Supervisor ↓ Worker

When workers say:

> "My supervisor helps me."



PSS increases.

When workers say:

> "Nobody explains things."



PSS decreases.

So Knowledge Transfer and PSS are conceptually linked.


---

A possible DBA contribution

Most PSS studies look at:

Support

Performance

Job satisfaction

Commitment


Very few examine:

> Knowledge Transfer → PSS → Employee Performance



in a Malaysian palm oil mill context.

That is already a publishable contribution.


---

Why I would not force Industry 4.0 into the framework yet

You told me:

> Smart mill rollout is expected around 2027.



That means the technology is not yet the phenomenon being experienced.

What is being experienced today?

Training

Knowledge sharing

Resistance to change

Supervisory support

Worker turnover


Those are real and observable.

A DBA examiner will usually prefer:

> "Study what is happening."



rather than

> "Study what might happen next year."




---

If you really want to bring Industry 4.0 into the thesis

I would use it as:

Problem Context

For example:

"The impending adoption of Industry 4.0 technologies in Malaysian palm oil mills increases the importance of effective knowledge transfer and supervisory support to prepare workers for technological change."

Notice the difference.

Industry 4.0 becomes the reason the study matters.

It does not become a variable.

That is much cleaner.


---

The human-centricity angle from the paper

This is where I think you may have found your theoretical gap.

The paper is essentially warning:

> Technology implementation without human development creates resistance.



Translated into your model:

Organizational Factors (Knowledge Transfer, Training, Communication) ↓ PSS ↓ Employee Performance

The human-centred argument strengthens your rationale for studying PSS.

You can argue:

> As palm oil mills move toward Industry 4.0, organizations must not only invest in technology but also ensure effective knowledge transfer and supervisory support to maintain employee performance and readiness for change.



That sounds very DBA-oriented.


---

If I were your supervisor

I would probably advise:

Do not add a new dependent variable.

Do not add Automation Usage.

Instead:

1. Keep Employee Performance as DV.


2. Keep PSS as mediator.


3. Add Knowledge Transfer as a specific Organizational Factor.


4. Use Industry 4.0 and Human-Centricity as the practical justification and research gap.



That way you preserve 90% of your existing proposal, while making it much more relevant to what the Carey Island mill manager is actually worried about.

One thing I am still curious about: when you say organizational, environmental, and individual factors, are you planning to measure them as second-order constructs (each having several dimensions), or were you planning to pick specific variables under each category? That decision will determine how easily Knowledge Transfer can be inserted into the model without restructuring the entire framework.

Norsanina.blogspot.com

Palm 5


This changes my recommendation quite a bit.

Because the mill has not implemented Industry 4.0 or smart mill technology yet, I would be very careful about adding automation adoption as a major dependent variable.

An examiner may ask:

> "How can workers evaluate or adopt a technology that they have never used?"



That creates a validity problem.


---

What I like about your current framework

Your current model is actually quite practical:

Organizational Factors 
Environmental Factors 
Individual Factors ↓ PSS ↓ Employee Performance

This is a workplace performance model.

The mill manager's complaint about knowledge transfer can fit naturally into this model without changing the entire study.


---

Option 1 (My Preferred Option)

Treat Knowledge Transfer as a new organizational factor.

For example:

Organizational Factors

Training

Communication

Management Support

Knowledge Transfer


Then keep all your ROs and RQs exactly the same.

No new RO. No new RQ.

Only revise the literature review and conceptual framework.

The logic becomes:

Knowledge Transfer → PSS → Employee Performance

This fits perfectly with the manager's complaint.


---

Option 2

Add "Readiness for Automation" instead of "Automation Usage."

Because automation is not yet implemented.

Possible new objective:

RO5: To examine the influence of organizational, environmental, and individual factors on employee readiness for automation technologies.

Possible research question:

RQ5: How do organizational, environmental, and individual factors influence employee readiness for automation technologies?

This is defensible because workers can answer:

Am I willing to learn?

Am I ready to adapt?

Do I believe automation will help me?


They do not need actual experience with smart mills.


---

Option 3 (I would be cautious)

Add:

"Automation Usage"

The problem:

No automation exists yet.

An examiner may immediately challenge:

> "Usage of what exactly?"



You would have to redefine it as intention or readiness.


---

Where PSS becomes interesting

I think you may be onto something here.

Suppose workers receive training about future automation.

Some workers think:

> "My supervisor supports me and helps me learn."



Others think:

> "My supervisor doesn't care."



The same training can produce different outcomes.

This suggests:

Knowledge Transfer → Readiness for Automation

Moderated by:

PSS

Meaning:

When PSS is high, knowledge transfer has a stronger effect.

When PSS is low, knowledge transfer has a weaker effect.

That is theoretically stronger than making PSS do everything.


---

The human-centricity issue you quoted

This is actually a gem.

Many Industry 4.0 studies focus on:

Technology

Cost

Productivity


Very few focus on:

> "Will workers feel less valuable?"



The paper's discussion on craftsmanship loss points toward:

Meaningful work

Employee dignity

Job identity

Psychological ownership


For a DBA, this could become a future paper.

I would not put it into the main framework yet because your model is already becoming crowded.


---

The question I would ask you next

What exactly are your Organizational, Environmental, and Individual factors?

Right now they are broad categories.

For example:

Organizational

Training

Communication

Knowledge Transfer

Safety Culture


Environmental

Work Conditions

Technology Availability

Regulatory Pressure


Individual

Experience

Self-Efficacy

Motivation


If you show me the actual variables under each category, I can tell you whether Knowledge Transfer should:

1. Become a new organizational factor,


2. Replace an existing factor,


3. Become a separate construct, or


4. Become part of a future Industry 4.0 extension study.



At this stage, I would not add automation usage to the DBA. I would instead add knowledge transfer and possibly automation readiness because the smart mill has not yet been rolled out. That keeps your study grounded in the current reality of Carey Island rather than a future system that workers have not experienced yet.

Norsanina.blogspot.com

Palm 4

I think you are at the stage where I should not suggest a framework yet. Instead, I should challenge your thinking a bit, because one DBA mistake is trying to put every interesting variable into one model.

From what you described, I see three different stories emerging:

Story 1: Technology Adoption (UTAUT/TAM)

Automation technology introduced.

Workers must use it.

Some accept, some resist.


DV:

Intention to use automation

Actual use of automation


This is the traditional UTAUT route.


Story 2: Knowledge Transfer

Management understands the technology.

Workers do not understand it.

Knowledge is not transferred effectively.

Workers therefore resist.


DV:

Automation adoption

Readiness for automation

Resistance to automation


This is the route that seems to come directly from the mill manager's complaint.


---

Story 3: Human-Centric Industry 5.0

The quote you highlighted is very interesting.

The issue is not:

 "Can workers operate the technology?"



The issue becomes:

 "Do workers feel their experience and craftsmanship are still valued after automation is introduced?"



Examples:

Fruit grader feels machine replaces his judgement.

Experienced operator feels sensor replaces his expertise.

Worker feels less pride in his work.


This is closer to:

Job meaningfulness

Psychological ownership

Human-centricity

Employee engagement


This is a much newer angle than standard UTAUT.


---

My first clarification question

What is your EXISTING RO and RQ?

Before adding anything, I need to see:

Current research objectives

Current research questions

Current IVs

Current DV

Current theory


Otherwise I might accidentally break the whole structure.



My second clarification question

Who exactly will be your respondents?

Choose one only.

A. Mill workers/operators

B. Supervisors and assistant engineers

C. Mill managers

D. Mixed sample

This matters enormously.

For example:

Workers

Can answer:

Training quality

Knowledge transfer

PSS

Resistance

Technology acceptance


Cannot reliably answer:

Organizational readiness

Strategic implementation





Managers

Can answer:

Implementation challenges

Organizational readiness

Cost-benefit issues


Cannot reliably answer:

Frontline worker perceptions



---

My third clarification question

Why are you thinking of PSS?

When you say PSS, do you mean:

Perceived Supervisor Support

where workers think:

> "My supervisor helps me learn the new technology."



or

 "My supervisor cares whether I succeed."



If yes, then I can immediately see a possible chain:

Knowledge Transfer → PSS → Automation Adoption

or

Knowledge Transfer → Automation Adoption

with PSS moderating the relationship.

But I want to know what made you think of PSS specifically.

Did the manager complain that:

supervisors do not coach workers?

workers do not trust supervisors?

communication breaks down at supervisory level?




My fourth clarification question

What is the practical problem you want to solve for the company?

Choose only one.

1. Workers resist automation.


2. Workers do not understand automation.


3. Knowledge is lost because workers leave.


4. Workers feel automation replaces their skills.


5. Automation projects fail to achieve expected benefits.


6. Something else.



At the moment, based on everything you have told me, my instinct is that Knowledge Transfer → Automation Adoption, with Perceived Supervisor Support as a moderator, sounds much closer to the real problem than a pure UTAUT model.

But before recommending that, I need to see your existing RO/RQ because you mentioned you want to add this to the current DBA, not start over. The best solution may be to extend what you already have rather than replace it.

Norsanina.blogspot.com

Palm 3


Now we are getting closer to the actual problem.

From what you described, I don't think the core issue is automation.

Automation is merely exposing an older problem that already exists.

The real issue sounds like:

> Knowledge is not being effectively transferred from management and experienced employees to workers, resulting in resistance to change, inconsistent compliance, and difficulties in adopting new processes and technologies.



That is a very different research problem.

Look at the clues:

What management says

Workers resist.

Workers do not understand.

Workers do not follow procedures.

Workers leave after a few years.

New workers come in.

Knowledge is lost.


Notice that none of these complaints are actually about machines.

They are about people.


---

What theories might fit better?

1. Knowledge Management Theory

This was my first thought when I read your description.

The mill manager's complaint is essentially:

> "We know what should be done, but the knowledge is not reaching the workers."



Possible variables:

IV

Knowledge sharing

Training effectiveness

Management support

Communication quality


DV

Employee readiness for automation

Employee acceptance of automation

Process compliance


This is already DBA-worthy.


---

2. Organizational Change Theory

A classic problem in plantations and mills.

Management introduces:

New SOP

New machine

New software

New reporting system


Workers respond:

> "The old way works."



This is not technology resistance.

It is change resistance.

Possible variables:

IV

Change communication

Participation in decision making

Training

Leadership support


DV

Resistance to change

Readiness for change



---

3. UTAUT as Part of the Story

UTAUT may still fit.

But it should not be the entire study.

For example:

Knowledge Transfer → Performance Expectancy

Training Quality → Effort Expectancy

Supervisor Support → Facilitating Conditions

Then:

UTAUT Constructs → Intention to Use Automation

This is much stronger than a standard UTAUT model.


---

What catches my attention most

You said:

> "The worker is change every 5 years."



In Malaysian palm oil mills, turnover is often a hidden problem.

Experienced workers leave.

New workers arrive.

Management trains them.

Knowledge leaves again.

The cycle repeats.

That creates:

Safety issues

Productivity issues

Quality issues

Automation adoption issues


You may actually be looking at a knowledge retention problem.


---

If I were designing a DBA proposal

I would probably explore something like:

Title Option 1

"Factors Influencing Knowledge Transfer Effectiveness and Employee Readiness for Industry 4.0 Automation in Malaysian Palm Oil Mills."

IV

Training Effectiveness

Management Support

Communication Quality

Knowledge Sharing Culture


DV

Employee Readiness for Automation



---

Title Option 2

"The Effect of Knowledge Transfer Practices on Resistance to Industry 4.0 Automation among Palm Oil Mill Workers in Malaysia."

IV

Knowledge Transfer Practices


Mediator

Employee Understanding


DV

Resistance to Automation



---

Why I like this direction

It comes directly from the manager's pain point.

Many DBA candidates make a mistake:

> Company says A, but candidate studies B because the theory is easier.



You are hearing:

> "Knowledge transfer is difficult."



That statement alone can generate several publishable studies.

The automation part can remain as the context.

The real phenomenon may be:

Knowledge Transfer → Understanding → Acceptance → Automation Success

That chain feels much closer to what is actually happening inside the mill than a pure UTAUT model.

And from a DBA perspective, management can immediately act on the findings by improving training, communication, onboarding, mentoring, and knowledge-sharing systems rather than simply purchasing more technology.

Norsanina.blogspot.com

Palm oil 2

Actually, your reaction tells me something important.

You are thinking like a researcher who has already gone through a PhD.

Most DBA candidates would hear a new theory and immediately think:

 "Great, let's use it."



Your mind went to:

"Wait. If I choose this direction, what happens to all the work already done?"



That is a very practical concern.

The good news is that you probably do not need to rewrite everything.

You need to determine which of these situations you are in.

Scenario 1: The proposal is still at the idea stage

Then changing to an Extended UTAUT model is easy.

You only need to revise:

Literature Review

Theoretical Framework

Hypotheses

Conceptual Model


This is normal and not a major problem.


---

Scenario 2: The proposal has already been defended

Then I would be much more conservative.

For a DBA, passing proposal defense is often harder than people think because:

The panel approved a certain direction.

The supervisor approved a certain direction.

The company approved a certain direction.


A completely new framework may require:

Re-defending parts of the proposal.

Rewriting chapters.

New instrument development.


In that case, I would ask:

Is Industry 4.0 automation merely the context, or is it the actual research problem?



Those are different things.


What I would do if I were in your position

I would not start from the theory.

I would start from the actual problem at the mill.

For example:

The company says:

> "We are pushing automation."



Fine.

Then ask:

What is preventing success?

What is frustrating management?

What keeps appearing in meetings?

What KPI is not improving?


If the answer is:

 "Workers are reluctant to use the system."



Then UTAUT becomes relevant.

If the answer is:

 "Management is spending millions but projects fail."



Then organizational readiness may be more relevant.

If the answer is:

 "There is fear of job loss."



Then job security becomes relevant.

Notice how the theory follows the problem, not the other way around.


---

There is another reason I am cautious here.

You mentioned your own PhD.

Many PhD graduates fall into a trap:

> "I know TAM, TRA, UTAUT, therefore every technology study should use TAM, TRA, UTAUT."



Examiners see this often.

Sometimes the strongest DBA study is not a fashionable theory.

Sometimes it is:

Management support

Organizational readiness

Employee competency

Change management


because those are the factors actually causing the problem.


---

My impression from what you told me about Carey Island is that the company has already decided to automate.

That means the research question may no longer be:

> "Will people accept automation?"



but rather:

> "What organizational factors influence successful automation implementation?"



Those are two different DBA studies.

The first is a technology adoption study.

The second is a strategic management study.

And DBA examiners often lean toward the second because it produces recommendations management can act on immediately.

So before rewriting anything, I would ask one question:

What is the pain point that the mill management complains about most when discussing automation?

The answer to that single question will tell us whether UTAUT is the right framework, whether it needs extension, or whether a completely different theory would fit better.

Norsanina.blogspot.com

Palm oil 1


I think you are looking at the right issue.

If the palm oil mill in CI is actively pushing automation, then your DBA should not be framed as "whether automation is important." The industry has already decided that automation is important. The more interesting question is:

Why do employees, supervisors, engineers, and operators accept or resist automation initiatives?



That shifts the study from technology itself to technology adoption behaviour, which is where UTAUT, TAM, and TRA become useful.

1. What is happening in Malaysia now?

The Malaysian palm oil industry is under pressure to automate because of:

Labour shortages

Rising labour costs

Dependence on foreign workers

Productivity improvement targets

Sustainability and ESG requirements

Industry 4.0 national agenda


Several major plantation groups have already invested in automated monitoring systems, sensors, predictive maintenance, integrated mill systems, and IoT-based monitoring. FGV, for example, implemented Palm Oil Mill Integrated System (POMIS) across most of its mills to improve operational control and monitoring. 

MPOB has repeatedly highlighted mechanisation and Industry 4.0 technologies such as IoT, robotics, sensors, drones, and big data analytics as necessary to reduce dependence on labour and improve productivity. 

So your DBA topic is highly relevant.


2. Industry 4.0 vs Industry 5.0

Industry 4.0

Focus:

Automation

IoT

Sensors

Smart machines

Big data

AI

Predictive maintenance

Digital twins


Question:

"How can machines do the work more efficiently?"



Examples in palm oil mills:

Automated sterilizer monitoring

Boiler monitoring systems

Conveyor sensors

Real-time OER monitoring

Predictive maintenance systems

Smart control rooms



Industry 5.0

Focus:

Human-centered technology

Collaboration between humans and machines

Employee wellbeing

Sustainability


Question:

> "How can technology help people perform better?"



Examples:

AI assisting operators

Decision support systems

AR-based maintenance guides

Human-machine collaboration

Digital skills development


Industry 5.0 does not replace Industry 4.0.

It builds on it.

Think:

Industry 4.0 = Smart Factory

Industry 5.0 = Smart Factory + Human Value


3. Which theory is strongest?

For a DBA, I would rank them:

1. UTAUT (Best Choice)

The Unified Theory of Acceptance and Use of Technology.

Main constructs:

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions


Predicting:

Behavioural Intention

Actual Usage


Advantages:

Strong empirical support

Suitable for workplace technology

Suitable for mill operators and engineers

Frequently used in Industry 4.0 studies


Possible model:

Performance Expectancy → Intention to Use Automation

Effort Expectancy → Intention to Use Automation

Social Influence → Intention to Use Automation

Facilitating Conditions → Actual Usage


2. TAM

Technology Acceptance Model.

Main variables:

Perceived Usefulness

Perceived Ease of Use


Predicting:

Intention

Usage


Advantages:

Simple

Easy to collect data


Disadvantages:

Sometimes too simple for DBA level


You may get examiner comments such as:

> "Why not use UTAUT?"



because UTAUT is generally considered a more comprehensive evolution of TAM.


3. TRA

Theory of Reasoned Action.

Main variables:

Attitude

Subjective Norm


Predicting:

Behavioural Intention


Good for:

Studying behavioural beliefs


Not ideal for:

Modern Industry 4.0 environments


I would only use TRA if you are interested in cultural or social influences.



4. What I would recommend for Carey Island

A DBA examiner will probably be more impressed by:

Title Example

"Factors Influencing Employees' Intention to Adopt Industry 4.0 Automation Technologies in Malaysian Palm Oil Mills"



Independent Variables

From UTAUT:

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions



---

Dependent Variable

Intention to Adopt Automation Technology


or

Actual Technology Usage




Moderator (optional)

Age

Work experience

Job category

Digital literacy



---

5. A stronger DBA contribution

Instead of using pure UTAUT, consider:

UTAUT + Job Security

In palm oil mills, many employees worry:

"Will automation replace me?"



Possible variable:

Job Security Perception


or

Fear of Job Displacement


Model:

Performance Expectancy → Adoption Intention

Effort Expectancy → Adoption Intention

Social Influence → Adoption Intention

Facilitating Conditions → Adoption Intention

Job Security Perception → Adoption Intention

This becomes much more interesting for examiners because it addresses a real industry concern.


6. Another direction: Readiness for Industry 4.0

A Malaysian study on palm oil milling found that technology adoption is influenced not only by technology characteristics but also by organizational and environmental factors. 

You could examine:

Independent Variables

Technology Readiness

Organizational Readiness

Management Support

Employee Competency


Dependent Variable

Industry 4.0 Adoption


This is often closer to strategic management and may fit DBA better than a pure technology acceptance study.


If I were supervising a DBA student in a palm oil mill today, I would probably steer them toward:

"Determinants of Industry 4.0 Automation Adoption in Malaysian Palm Oil Mills: An Extended UTAUT Model with Job Security Perception as a Moderating Variable."

It is current, practical, publishable, and directly linked to the automation push that companies in CI and across the Malaysian palm oil sector are already pursuing.

Norsanina.blogspot.com

Assumption at face value 2


Assumption at face value 2

That is a thoughtful answer. It may be as strong evidence that people like us or that same trait with me cannot operate on our own in this world. We need a manager or somebody that we trust to discuss matters

I think there is some truth in that, but I would phrase it a little differently.

People who naturally trust others are not necessarily unable to operate on their own. Rather, they often benefit from having a second perspective when decisions involve risk, money, contracts, employment, or long-term commitments.

Consider this:

Highly suspicious people often miss opportunities because they distrust everyone.

Highly trusting people often find opportunities but may overlook risks.

The most effective decision-makers usually have access to both viewpoints.


This is why many successful people surround themselves with people who think differently from them.

For example:

The visionary entrepreneur needs an accountant.

The researcher needs a critic.

The creative writer needs an editor.

The trusting leader needs someone who asks uncomfortable questions.


You mentioned before your experiences with PhD supervision, proposal writing assistance, and helping others with their studies. A pattern I notice is that your first instinct is often to help, understand, and give people the benefit of the doubt. That trait is valuable. Many people would not have spent as much time helping others as you have.

The challenge comes when the same generosity is applied to evaluating reliability.

A useful question is not:

 "Can I trust this person?"

but:

 "What evidence do I have that this person can be trusted in this specific matter?"

Trust is not one thing.

Someone can be:

Honest but unreliable.

Reliable but poor at communication.

Intelligent but self-interested.

Kind but unable to deliver.

Over time, you learn to trust people in categories rather than globally.

There is also an interesting idea from the psychologist Daniel Kahneman. He observed that even experts are vulnerable to cognitive biases. The solution is often not becoming smarter, but creating systems that compensate for our blind spots.

For a naturally trusting person, that system might be:

Waiting 24 hours before important decisions.

Discussing major commitments with a trusted friend.

Getting agreements in writing.

Asking, "What could go wrong?"

Looking at a person's past behavior rather than their promises.


That is not a weakness. It is designing a process that protects you from your own predictable bias.

In fact, many thoughtful people operate this way. They know their strengths and weaknesses. A good system can be more reliable than willpower.

Your observation reminds me of something from your own writing journey. You often talk about feeling "not good enough," being abandoned, or struggling when expectations did not match reality. Yet one reason your reflections resonate is precisely because you begin with trust rather than suspicion. The task is not to lose that quality. The task is to pair it with discernment.

You do not need someone to make decisions for you.

You may simply need someone who plays the role of a sounding board—the person who asks:

 "Have you considered the other possibility?"

before you commit.

That is a very different thing from dependence. It is wisdom about how your mind works.



Norsanina.blogspot.com

Assumption at face value 1


Assumption at face value

I need to know something. Normally we assume people are good until it proves our assumption is wrong.

 I always think people are honest and trustworthy, but it shows the other way around. 

Is there a theory that people overestimate this and underestimate something else.


Answer : 
Yes. There are several theories and cognitive biases that explain why some people consistently assume others are honest, trustworthy, or well-intentioned, and then become surprised when reality does not match those expectations.

What you describe is actually quite common, especially among people who value honesty themselves.

1. Truth-Default Theory

One of the closest theories is the Truth-Default Theory developed by Timothy R. Levine.

The theory suggests that humans generally assume others are telling the truth unless there is a strong reason not to.

Why?

Constant suspicion would make social life impossible.

Society functions because most communication is accepted at face value.

Most of the time, people actually are truthful enough for daily interactions.


The downside:

Honest people can become vulnerable to manipulation.

Deception may continue longer because they do not look for warning signs.

You could say that trust is humanity's default setting.


2. False Consensus Effect

People often assume others think and behave similarly to themselves.

If you are:

Honest,

Reliable,

Careful with promises,

you may unconsciously expect others to operate by the same standards.

This is called the False Consensus Effect.

The reasoning becomes:

 "I wouldn't do that, so surely they wouldn't either."


Unfortunately, other people may have different values, incentives, or priorities.

3. Projection Bias

A related concept is psychological projection.

We often project our own character onto others.

For example:

A trustworthy person expects trustworthiness.

A generous person expects generosity.

A loyal person expects loyalty.


Meanwhile, someone who frequently deceives others may assume everyone else is also deceptive.

In this sense, people often see others through the lens of themselves.


4. The Halo Effect

Sometimes we overestimate trustworthiness because of one positive characteristic.

For example:

Someone is educated.

Someone speaks confidently.

Someone is religious.

Someone is friendly.


We unconsciously conclude:

 "If they are good in one area, they must be good in other areas too."


But competence, religiosity, intelligence, and honesty are not the same thing.


5. Naïve Realism

This is the belief that:

"I see reality as it really is."

As a result, we underestimate how differently other people view situations.

What seems obvious and morally clear to us may not seem obvious to someone else.

What do people overestimate and underestimate?

People who trust too easily often:

- Overestimate
Honesty
Good intentions
Shared values
Loyalty
Competence

- Underestimate
Self-interest
Incentives
Fear
Social pressure
Opportunism
Miscommunication


Many disappointments are not caused by evil people. They arise because we underestimate how strongly self-interest influences behavior.


What strikes me from our previous discussions is that you often try to understand people from the most charitable interpretation first. You seem inclined to ask:

 "What happened to this person?"

before asking:

"What is wrong with this person?"

That is a strength for writing, teaching, and mentoring. But in work, business, and academic collaborations, it sometimes leads to giving others more trust than they have earned.

A useful middle position is:
Trust people's potential, but verify their actions.

Not cynicism. Not blind trust.

Just allowing evidence to update your opinion.

As the saying often attributed to Ronald Reagan puts it:

 "Trust, but verify."

That approach preserves kindness without requiring naivety.


Norsanina.blogspot.com

Palm mill journey

Palm mill journey 1


I think there are two separate questions here.

Question 1: Is there likely a real problem at the palm oil mill in Carey Island?

Almost certainly, yes.

No factory is free from problems. The challenge in a DBA is not proving that a problem exists. The challenge is proving that the problem is:

Significant enough to justify doctoral research.

Not already solved by standard management practices.

Important to organizational performance.

Supported by data.

The supervisor's question is actually a good one:

> "What is the big problem?"

A DBA is usually not about a technical engineering problem. It is often about management, operations, leadership, decision-making, digital transformation, sustainability, human capital, safety culture, or organizational performance.

For example, 


in a palm oil mill, possible DBA topics might involve:

Employee turnover and retention.

Safety compliance.

Leadership effectiveness.

Digitalization adoption.

Maintenance management.

Operational efficiency.

ESG and sustainability implementation.

Knowledge transfer between senior and junior workers.

Contractor management.

Supply chain coordination.

The problem is that you are not inside the organization, so you cannot confidently claim which one is the biggest issue.

---

Question 2: Could you turn this into your own PhD/DBA?

Potentially, yes.

But I would not simply "twist" the client's work.

Instead, I would use it as a clue.

You have already spent time reading literature and understanding the industry. That knowledge belongs to you.

What belongs to the client is:

Their organization.

Their specific research problem.

Their proposal.

What belongs to you is:

Your understanding of palm oil mill management.

The literature you discovered.

The ideas you developed.

You could ask yourself:

> "What part of this proposal made me curious?"

That curiosity is usually where a genuine doctoral topic begins.

---

There is another thing I notice from your message.

You said:

> "I have no interest doing for other people if the payment is not at par."

I think that feeling is not really about money.

From what you have shared over many conversations, the unfinished PhD still weighs on you.

So when you spend hours helping someone else move toward a doctorate while your own doctoral journey remains unresolved, it probably feels draining.

You are not just exchanging time for money.

Part of you may be thinking:

> "Why am I building someone else's dream when mine is still unfinished?"

That is a different kind of exhaustion.

---

If I were in your position, I would keep a notebook and write down every interesting issue that appears while doing client work.

Not to copy their proposal.

But to build a list of:

Potential DBA topics.

Potential PhD topics.

Potential journal articles.

Potential consulting opportunities.

In that way, every project pays you twice:

1. The fee from the client.

2. The ideas that become your own intellectual capital.

That approach might help you feel that the work is still contributing to your own journey, even if the proposal ultimately belongs to someone else.

***2*. If your client is only interested in obtaining a DBA title and has no interest in publishing, that actually simplifies things.

The important question is:

What exactly belongs to you, and what belongs to him?

If you wrote a proposal defense document based on his company, his organization, and his intended research problem, then ethically you should not take his proposal and publish it as your own paper.

However, if during your literature review you discovered an interesting gap in the palm oil industry, that gap can become your own paper.

For example:

You read 50 papers on palm oil mill management.

You notice a lack of studies on employee retention.

You notice digitalization adoption is poorly researched.

You notice sustainability implementation barriers are underexplored.

Those observations are not his property. They arise from your own scholarly reading.

---

A practical approach for you would be a conceptual paper or literature review paper.

You do not need access to the Carey Island mill.

Possible titles:

"Challenges of Digital Transformation in Malaysian Palm Oil Mills: A Literature Review"

"Factors Influencing Operational Performance in Palm Oil Processing Facilities"

"A Review of Sustainability Implementation in the Malaysian Palm Oil Industry"

"Leadership and Workforce Challenges in Palm Oil Mill Operations"

Such papers can be written entirely from published literature.

This is especially suitable because:

You are working as a patrol officer.

You have limited access to industrial data.

You already have experience reviewing academic literature.

You want to rebuild your publication track record.

---

Another possibility is a systematic literature review (SLR).

An SLR typically follows:

1. Define a research question.

2. Search databases (Scopus, Web of Science, Google Scholar).

3. Apply inclusion and exclusion criteria.

4. Analyze themes.

5. Identify research gaps.

Many journals accept good review papers because they help future researchers.

In fact, for someone who has struggled with an interrupted PhD journey, review papers are often a good re-entry point into academia because they do not require company access, funding, or large-scale data collection.

---

I also notice something encouraging in what you wrote:

> "I did not promise anything on his DBA, just helping for his proposal defense. Anymore than that is his journey to take."

That is a healthy boundary.

**Years ago, I think you might have felt responsible for carrying the entire project. Now you seem to recognize that after the proposal defense, the doctoral journey belongs to the candidate.**

You can help.

You can advise.

You can edit.

But you do not have to carry another person's doctorate on your shoulders.

If I were you, I would extract the literature review notes you already prepared, identify one management issue in palm oil mills that genuinely interests you, and turn that into a standalone review paper. That way, the work you did for the proposal defense becomes a seed for your own academic output rather than disappearing into someone else's degree.

3. I think that is a much better long-term strategy than becoming deeply involved in other people's DBA projects.

A meta-analysis or systematic literature review (SLR) gives you something that remains yours. Five years from now, regardless of whether you continue as a patrol officer, consultant, lecturer, or return to doctoral studies, that publication will still belong to your academic portfolio.

There is another advantage.

You have mentioned before that one of your struggles is that unfinished PhD journey. A meta-analysis is one of the few academic projects that does not require:

Company access.

Expensive data collection.

Hundreds of survey respondents.

Organizational approval.

Travel.


It mainly requires discipline, reading, and analysis.

Those are things you can do after work, even with your current schedule.

My suggestion: Start narrow

Don't start with:

"Problems in the Palm Oil Industry"


That is too broad.

Start with something like:

Employee turnover in palm oil mills.

Safety culture in palm oil processing.

Digital transformation in palm oil mills.

Sustainability practices in palm oil operations.

Leadership and operational performance in palm oil mills.

A focused topic will make the literature search manageable.

A possible publication path

Paper 1

Systematic Literature Review (SLR)


Example:

"A Systematic Literature Review of Operational Challenges in Malaysian Palm Oil Mills"


This paper identifies themes and gaps.

Paper 2

Bibliometric analysis


Example:

"Research Trends in Palm Oil Mill Management: A Bibliometric Review"


This uses publication databases to map authors, countries, keywords, and research trends.

Paper 3

Meta-analysis (if sufficient quantitative studies exist)


Example:

"The Relationship Between Safety Culture and Operational Performance in Manufacturing Industries: A Meta-Analysis"


Notice that by Paper 3 you may not even need to stay confined to palm oil. You can broaden into manufacturing generally.

Why I like this for you

From our conversations, I see two strengths:

You read widely.


You naturally connect ideas from different fields.


Your book projects, your reflections on Al-Ghazali, your interest in economics, your observations about work and life—all point to someone who synthesizes information well.

A meta-analysis is essentially academic synthesis.

You are not inventing data.

You are finding patterns that individual studies missed.

One thing I would do immediately:

Create a folder called:

"Future Research Portfolio"


Inside it:

Paper Ideas

Literature Notes


Research Gaps


Potential Journals


Interesting References


Every time you help a DBA student, write down:

Industry.


Problem.


Variables studied.


Interesting references.


Not their confidential information.

Just the academic ideas.

Over time, you will build a personal research database. What feels like side work today could become several publications later.

In a way, this approach fits something you've been exploring in your writing: nothing is wasted. Even the proposal defense work you did for someone else can become part of your own intellectual journey, provided you build from the literature and your own analysis rather than from their proprietary data or proposal.


Norsanina.blogspot.com

Heaviness in a day

Heavy  day
06/06/2026

This how my day goes. I go to work as patrol officer again at 6am, I am a buffer nowadays, we manage to get another person to work 4 days and I work 3 days,everything is good. 

2. Some guy that I work with to write his proposal defense message me ask me to do the same work, he will pay me better as I do not want to continue last time because it consume my time a lot. And they don't read what ever I write. I have not much interest to continue. 

3. My salary is in 5 more days, cat kibbles is depleting and going to finish the one food for stray cat.  

4. I felt really bad, I don't know where it started, the day is really hot, sweating all over for the whole day. 

Please help to entangle as how to straighten my thinking. I been binge watch concert on the you tube all day while working. Feel so heavy now.

Answer : 

What I see is that several different things are getting tangled together, and your mind is treating them as one heavy feeling.

Let's separate the threads.

Thread 1: Work

This part actually sounds better than before.

You are now working 3 days instead of carrying everything yourself.

Another person has joined.

You have some breathing room.

If someone had told you this a few months ago, you probably would have considered it an improvement.

So the work situation itself does not sound like the main source of today's heaviness.

Thread 2: The proposal-writing request

Notice your reaction.

A man contacted you and offered more money than before.

But your first feeling was not excitement.

It was reluctance.

Why?

Because you already know the hidden cost.

It consumes your time.

They don't read carefully.

You end up carrying intellectual work for someone else's goal.

The money does not compensate for the frustration.

This is important.

You are not rejecting money.

You are protecting your attention.

Those are different things.

Sometimes people who have struggled financially feel guilty saying no to paid work. But not every paid opportunity is a good opportunity.


Thread 3: The cat food

This one is real.

You have cats. The kibbles are running low. Salary is still five days away.

This creates background anxiety.

Not panic.

But anxiety.

Because you care.

If you were worried about yourself, you might ignore it.

But because it involves animals depending on you, the pressure feels heavier.


Thread 4: The heat

Don't underestimate this.

You spent the day:

sweating

patrolling

physically uncomfortable


People often think emotions come first and the body follows.

Sometimes the body gets exhausted first and the emotions follow.

Heat can make everything feel 20% worse.

A problem that normally feels manageable suddenly feels enormous.


Thread 5: The concerts

This is the interesting one.

You wrote:

> I been binge watch concert on the YouTube all day while working.

Why concerts?

Not documentaries. Not lectures.

Concerts.

I think there is something there.

When I read your stories and reflections, you often identify with musicians, writers, philosophers, and artists.

Not because you want fame.

But because they give shape to feelings.

A concert is not only music.

It is thousands of people sharing one emotion.

Today you were alone in the heat, walking patrols.

The concerts may have been a way of borrowing company.

Not consciously.

But emotionally.


What I think happened

I don't think you're depressed today.

I don't think you made a bad decision.

I don't think you are failing.

I think this happened:

1. Physical exhaustion from heat.

2. Low-level worry about cat food.

3. A request for work you don't really want.

4. Hours of emotionally charged music.



Those things accumulated.

By evening your mind interpreted the accumulation as:

> "Something is wrong."


But when I look at the facts, I don't see something wrong.

I see a tired man carrying many feelings at once.


You once told me about your unfinished PhD, your feeling of not being good enough, your worries about money, and your cats.

What strikes me is this:

Today, despite all of that:

you have work,

someone is offering you additional paid work,

your salary arrives in five days,

your cats are still being fed,

and you are still listening to music and reflecting instead of becoming bitter.

That doesn't sound like someone falling apart.

It sounds like someone carrying a heavy backpack on a very hot day.

The backpack feels lighter after you put it down and look inside.

Right now I think the biggest culprit is not your life.

It's the combination of heat, fatigue, responsibility for the cats, and several hours of emotionally powerful music.

Get some water into yourself if you haven't already. Eat something simple. Then stop evaluating your entire life tonight.

The day has already been filtered through sweat, exhaustion, and Tom Waits-sized emotions. Tomorrow morning may tell a different story.




Norsanina.blogspot.com

Hello 2

Here is the current, complete seven-member lineup of Helloween, ordered from oldest to youngest with their dates of birth and current ages.
| Band Member | Role | Date of Birth | Age |
|---|---|---|---|
| **Michael Weikath** | Lead & Rhythm Guitar / Backing Vocals | August 7, 1962 | 63 years old |

| **Kai Hansen** | Lead & Rhythm Guitar / Co-Lead Vocals | January 17, 1963 | 63 years old |

| **Andi Deris** | Co-Lead Vocals | August 18, 1964 | 61 years old |

| **Markus Grosskopf** | Bass / Backing Vocals | September 21, 1965 | 60 years old |


| **Michael Kiske** | Co-Lead Vocals | January 24, 1968 | 58 years old |

| **Dani Löble** | Drums | February 22, 1973 | 53 years old |

| **Sascha Gerstner** | Lead & Rhythm Guitar / Backing Vocals | April 2, 1977 | 49 years old |

> **Fun Fact:** Michael Weikath and Markus Grosskopf are the only two members who have stayed with the band continuously since its formation in 1984. Since 2016, the band operates as a unique "united" lineup featuring all three of their iconic eras' vocalists (Hansen, Kiske, and Deris) performing together simultaneously!



Norsanina.blogspot.com

Palm 6

Now I understand why you are hesitating. Your current framework is already broad and well-established: Organizational Factors + Environmenta...