DV problem statement

DV Problem Statement 

14/06/2026

Yes, and your supervisor is actually giving you a very important hint.

In DBA and PhD research, a common weakness is:

"The IVs have a problem, but the DV does not."

For example:

Poor training

Poor communication

Lack of knowledge transfer

These are problems.

But if you cannot show that employee performance is also problematic, then an examiner may ask:

"Why study employee performance at all?"

What your supervisor means by a DV problem statement

If your DV is Employee Performance, then somewhere in Chapter 1 you should demonstrate:

Performance is not at the desired level.

There is evidence of productivity issues.

There are quality issues.

There are safety issues.

There are efficiency issues.

There is a gap between expected and actual performance.

The logic becomes:

Problem ↓ Employee performance is below expectations.

Possible causes ↓ Organizational factors Environmental factors Individual factors

Mechanism ↓ PSS

Outcome ↓ Employee performance

Example

Suppose the mill manager says:

SOP compliance is inconsistent.

New workers take too long to become competent.

Errors occur during operation.

Productivity differs significantly between shifts.

These are not knowledge-transfer problems.

These are employee performance problems.

Then you can write:

Despite ongoing efforts to improve operational efficiency, palm oil mills continue to experience challenges related to worker performance, including inconsistent adherence to procedures, varying productivity levels, and difficulties in maintaining operational standards.

Now you have established the DV problem.

Why this matters

Imagine an examiner asking:

"Is employee performance actually poor?"

If you answer:

"I don't know, but training is poor."

The examiner may respond:

"Then study training."

The study must justify why employee performance deserves investigation.

In your case

Based on what you told me about Carey Island, I would ask the manager:

1. What performance KPI concerns you most?

Throughput?

OER (Oil Extraction Rate)?

Downtime?

Quality?

Safety?

SOP compliance?

2. What evidence shows performance could be improved?

3. Have there been recurring issues linked to worker capability or understanding?

The answers become your DV problem statement.

A subtle issue I notice

The manager's strongest complaint so far is:

Knowledge transfer is difficult.

That sounds more like an independent variable problem than a dependent variable problem.

So you need to connect it to performance:

Knowledge transfer difficulty ↓ Workers do not fully understand processes ↓ Workers resist changes ↓ Workers make errors or work inefficiently ↓ Employee performance suffers

Once you make that chain explicit, your supervisor will likely be more satisfied.

A useful test is this:

If knowledge transfer suddenly became perfect tomorrow, what performance outcome would improve in the mill?

Whatever the manager answers is often the real DV justification.

Norsanina.blogspot.com

Journey - Palm

Journey - Palm

I think there are two separate questions here.

Question 1: Is there likely a real problem at the palm oil mill in C- 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.

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 C- 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 work nature - free time.

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

Citation

. Tam

Mohd Nawi, N. S., Deros, B. M., Nordin, N., Rahman, M. N. A., & Sukadarin, E. H. (2017). CRITICAL FACTORS FOR OIL PALM PLANTATION WORKERS ACCEPTANCE AND USE OF MECHANIZATION TECHNOVATION TOOLS. PressAcademia Procedia, 4(1), 218-223. https://doi.org/10.17261/Pressacademia.2017.537

2. https://ir.uitm.edu.my/id/eprint/73140/

Norsanina.blogspot.com

Synthesis meta

Synthesis meta

https://www.mpoc.org.my/challenges-faced-by-malaysian-palm-oil-the-way-forward/

Yes. If your intention is to produce a journal paper later, I would not simply summarize the MPOC article. Instead, I would synthesize it with academic literature and build a research gap around automation and Industry 4.0 challenges in Malaysian palm oil mills.

A good starting point is to synthesize these three sources:

1. MPOC (industry perspective) 

2. Abdul-Hamid et al. (Industry 4.0 challenges in Malaysian palm oil industry) 

3. Review of Industry 4.0 potential in sustainable palm oil industry 

Emerging Theme:

Why Automation Adoption in Palm Oil Mills Remains Slow Despite Labour Shortages and Productivity Pressures

---

Article 1: MPOC (Industry Challenge Perspective)

MPOC identifies increasing dependence on foreign labour and slow mechanization as major barriers to productivity growth. Since Malaysia has limited room for plantation expansion, future growth must come from technological advancement, mechanization, and improved yields rather than additional land. The article argues that labour shortages, ageing plantations, and stagnant yields make automation a strategic necessity rather than an option. 

Key Point

Labour dependency remains high.

Productivity growth is constrained.

Technology is viewed as the primary path forward.

Article 2: Industry 4.0 Challenges in Palm Oil Industry

Abdul-Hamid et al. investigated barriers to Industry 4.0 implementation and found that organizations face several obstacles beyond simply purchasing technology. The most critical challenges include:

Lack of automation system virtualization.

Unclear economic returns from digital investments.

Poor process design.

Weak connectivity between organizations.

Concerns regarding workforce disruption. 

Key Point

The problem is not merely a lack of technology. Firms often struggle to justify investment costs and integrate digital systems into existing operations.

Article 3: Industry 4.0 Potential in Palm Oil Industry

The review paper argues that technologies such as:

Artificial Intelligence (AI)

Internet of Things (IoT)

Smart sensors

Robotics

Predictive maintenance

have substantial potential to improve sustainability and operational efficiency in palm oil operations. However, implementation remains limited because many palm oil processes are still labour-intensive and not fully digitized. 

Key Point

Technology solutions exist, but adoption is lagging behind technological capability.

Synthesis

When the three articles are viewed together, a common narrative emerges.

MPOC highlights labour shortages and productivity challenges as major threats to the industry's future. Academic research confirms that Industry 4.0 technologies could address these issues, but implementation remains slow because firms face organizational, financial, and technical barriers. As a result, the Malaysian palm oil industry finds itself in a paradox: automation is urgently needed, yet significant obstacles prevent widespread adoption. 

---

Potential Research Gap

You could formulate the gap as:

> While previous studies have identified the benefits of Industry 4.0 technologies and the challenges facing the palm oil sector, limited research has synthesized the specific barriers preventing automation adoption in Malaysian palm oil mills and evaluated which barriers exert the greatest influence on implementation success. 

---

Potential Meta-Analysis / Systematic Review Title

Since you mentioned wanting a fallback study that does not depend on one company:

Option 1

Barriers to Automation Adoption in Malaysian Palm Oil Mills: A Systematic Literature Review

Option 2

Industry 4.0 Implementation Challenges in the Malaysian Palm Oil Industry: A Meta-Synthesis of Existing Evidence

Option 3

From Labour Dependency to Smart Manufacturing: A Review of Automation Challenges in Malaysian Palm Oil Mills

Option 4

Critical Success Factors and Barriers of Industry 4.0 Adoption in Palm Oil Processing Operations

Of these, Option 2 is probably the safest for publication because it does not require access to any company data and can be completed entirely through literature review and synthesis. It also aligns well with your situation of not wanting to use your DBA student's company data.

1.  Abdul-Hamid et al. (Industry 4.0 challenges in Malaysian palm oil industry)

Abdul-Hamid, A. Q., Ali, M. H., Tseng, M. L., Lan, S., & Kumar, M. (2020). Impeding challenges on industry 4.0 in circular economy: Palm oil industry in Malaysia. Computers & Operations Research, 123, 105052. 

I need the finding, test used, location, variable for iv and dv if there is any. 

Norsanina.blogspot.com

Bloggable thesis

14/06/2026

For a thesis, I would be careful about putting the full document on a public blog before submission.

There are two separate issues:

1. Plagiarism accusation – usually not the real problem. Since you are the author, it is not plagiarism if the text originated from you.

2. Prior publication / self-plagiarism / originality concerns – this is the bigger issue. Some universities, supervisors, examiners, or future journals may question whether parts of the thesis were already publicly published online.

For a 45-page thesis, I would suggest one of these alternatives:

Option 1: Use ChatGPT Projects (My Preference)

Upload the thesis PDF.

Ask ChatGPT to:

translate chapter by chapter into BM,

explain difficult sections,

compare English and BM versions side by side,

summarize sections.

The file remains within your ChatGPT workspace and is not publicly indexed by search engines.

Option 2: Microsoft Word + Translation Pane

If you already write in Word:

Open thesis in Word.

Use Review → Translate.

Keep:

Original English in the left pane.

BM translation in the right pane.

Not perfect, but convenient.

Option 3: Google Drive + Google Translate Documents

Upload PDF or Word file.

Open in Google Docs.

Use Google Translate on the document.

Create:

English version

BM version

Indonesian version

This is surprisingly useful for reading rather than publication.

Option 4: Create a Private Knowledge Base

Since you like reading in multiple languages, I would actually do this:

Folder:

├── Thesis_English.pdf

├── Thesis_BM.docx

├── Thesis_Indonesia.docx

├── Notes_and_Ideas.docx

Then every time you read:

English = original meaning

BM = easier comprehension

Indonesian = alternative wording

Many researchers from non-English backgrounds do this informally.

Option 5: Private Blog

If you really like the blog format:

Create a private WordPress site.

Or a private Notion workspace.

Or a private Obsidian vault.

Do not allow search engines to index it.

This gives you the familiar "reading like a blog" experience without publishing the thesis publicly.

---

Knowing your working style from our previous discussions, I suspect the issue is not translation itself. You seem to think by re-reading and seeing the same idea in different languages. English, BM, and Indonesian become three different "lenses" on the same concept. For that purpose, I would recommend:

PDF → ChatGPT/Word → BM translation → Indonesian translation → keep all three versions side by side.

That gives you the multilingual reading experience you enjoy without risking questions about whether your thesis was publicly published before examination. For a 45-page thesis, that's a manageable workflow and much safer than a public blog.

Norsanina.blogspot.com

Meta analysis - palm

Meta analysis 1

Hamid et al finding

Good choice. This paper is actually quite useful for a literature review because it is not a hypothesis-testing paper. It is a barrier-identification and prioritization study. Therefore, there are no traditional IV and DV like in regression or SEM studies.

Here is a structured extraction for your review matrix.

Item Details

Citation Abdul-Hamid, A. Q., Ali, M. H., Tseng, M. L., Lan, S., & Kumar, M. (2020)

Title Impeding Challenges on Industry 4.0 in Circular Economy: Palm Oil Industry in Malaysia

Journal Computers & Operations Research

Location Malaysian Palm Oil Industry 

Industry Palm oil mills / palm oil supply chain

Research Objective To identify and structure the major challenges hindering Industry 4.0 implementation within a Circular Economy framework in the Malaysian palm oil industry. 

Sample Industry experts from Malaysian palm oil sector (expert judgement panel) 

Method Fuzzy Delphi Method (FDM) + Interpretive Structural Modelling (ISM) 

Statistical/Test Used Fuzzy Delphi Method for screening challenges; ISM for determining hierarchical relationships among challenges. 

Type of Study Exploratory / decision science / expert-based modelling

Theory Industry 4.0 and Circular Economy

Variables

Since there is no regression model:

Independent Variables (Potential Barriers)

Initially 30 barriers were identified.

FDM reduced them to 18 significant barriers. 

Examples include:

1. Lack of automation system virtualization

2. Unclear economic benefits of digital investment

3. Lack of process design

4. Unstable connectivity among firms

5. Employment disruptions

6. Technology integration issues

7. Workforce readiness issues

8. Information-sharing problems

(These act as challenge factors rather than traditional IVs.) 

Dependent Variable

Not explicitly measured.

Conceptually:

> Successful implementation of Industry 4.0 in Circular Economy practices in Malaysian palm oil operations. 

Main Findings

The study found 18 critical challenges after screening the original 30 barriers. The most influential challenges were:

1. Lack of Automation System Virtualization

The strongest barrier.

Many palm oil firms lack digital integration and cyber-physical systems required for Industry 4.0 implementation. 

2. Unclear Economic Benefit of Digital Investment

Managers are uncertain whether automation investments will generate sufficient return on investment (ROI). 

3. Lack of Process Design

Existing operational processes are often not designed for digital transformation. Simply buying technology does not solve process inefficiencies. 

4. Unstable Connectivity Among Firms

Industry 4.0 requires extensive data sharing and connectivity, but supply chain integration remains weak. 

5. Employment Disruptions

Workers fear job displacement from automation, creating resistance to change. 

For Your Future Paper

What interests me is that this paper gives you a possible framework for a future meta-analysis.

You could classify barriers into four themes:

Theme Examples

Technological Automation virtualization, IT infrastructure, connectivity

Economic ROI uncertainty, investment cost

Organizational Process design, management readiness

Human Factors Skills gap, employment disruption, resistance to change

Then your review paper could ask:

> "Which category of barriers most consistently impedes automation adoption in Malaysian palm oil mills?"

That would be a publishable systematic review question and does not require access to any company's confidential data.

For your review matrix, I would code this paper as:

Country: Malaysia

Industry: Palm Oil

Method: FDM + ISM

DV: Industry 4.0 Adoption / Implementation

Key Finding: Automation virtualization and ROI uncertainty are the most significant barriers.

Theme: Automation Challenges / Digital Transformation / Industry 4.0.

Norsanina.blogspot.com

Gemini Answer - utaut

Gemini Answer - Utaut

12/06/2026

When mill management—whether in steel, paper, lumber, or textiles—gathers to discuss automation, the conversation almost always hits the exact same wall.

While they love the *idea* of 24/7 efficiency and precision, their number one, most frustrating pain point is **the complexity and cost of integrating modern digital automation with legacy infrastructure.**

In the industry, this is often referred to as the **IT/OT convergence gap**—the headache of trying to get cutting-edge information technology (IT) to talk to decades-old operational technology (OT).

Here is a breakdown of why this specific pain point trips up mill managers more than anything else:

### 1. The "Frankenstein" Floor (Legacy System Integration)

Unlike a tech startup that can build from scratch, a mill is a physical footprint of heavy machinery accumulated over 20, 30, or even 50 years. Management's biggest complaint is that modern automation software requires standardized data, but their actual floor consists of proprietary, legacy PLCs (Programmable Logic Controllers) and machines that speak entirely different, outdated digital "languages." Getting a new automated tracking system to reliably communicate with an old hydraulic press or a massive rolling mill without breaking the system is incredibly complex.

### 2. High Upfront Capital vs. Hidden Downtime Costs

While the initial sticker price of advanced sensors and robotics is high, mill managers complain most about the **cost of transition downtime**. In heavy manufacturing, a mill makes money by running continuously. Halting a production line to install, test, and debug an automated system can cost tens of thousands of dollars *per hour* in lost throughput. If the automation deployment hits a snag, the cost of that unexpected downtime can quickly dwarf the original price of the technology.

### 3. The Skills Gap and Internal Culture Shock

Automation doesn't just replace labor; it drastically changes the *type* of labor required. Mill management frequently complains about a brutal two-pronged workforce problem:

 * **The Existing Team:** Long-time operators who know the mechanical nuances of the mill by heart often resist the new digital tools, fearing job loss or simply preferring the old manual workarounds.

 * **The Talent Vacuum:** Finding specialized technicians who understand both heavy mechanical mill operations *and* advanced software engineering/data analytics is incredibly difficult and expensive, especially since many mills are located in rural or industrial hubs rather than major tech cities.

> **The Management Consensus:** Automation rarely fails because the new technology doesn't work. It fails because connecting the new technology to old machines, old processes, and an unprepared workforce introduces massive operational friction.

> 

Norsanina.blogspot.com

Utaut palm oil 2

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

Utaut palm oils 1

Utaut palm oils  

I think you are looking at the right issue.

If the palm oil mill in C- Island 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 Carey Island and across the Malaysian palm oil sector are already pursuing.

. 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

Feasibility study palm oil mill

Feasibility study palm oil mill 

11/06/2026

Notes : 

Would you like a sample survey questionnaire or an organizational change management framework to include in your feasibility study?

Here are five academic and professional APA references (7th edition) exploring the feasibility, economic impact, and implementation of Industry 4.0 automation and AI technologies within palm oil mill operations:

* Reference 1: Core Framework for Smart Milling Operations

* Citation: Yap, Z. F., Ijab, M. T., & Md Saad, M. H. (2022). Towards smart palm oil mill factory operations using IR4.0 technologies. Journal of Information System and Technology Management, 7(28), 46–65. doi.org

- * Why it matters: This foundational study addresses upgrading traditional "stone age" mill technologies. It systematically reviews how integrating the five pillars of IR4.0—IoT, big data, cloud computing, machine learning, and AI—creates systemic synergies that optimize overall equipment effectiveness and production yields. [1, 2, 3, 4] 

* Reference 2: Comprehensive Industrial Literature Review

- * Citation: Impact of Industry 4.0 technologies on the oil palm industry: A literature review. (2024). ScienceDirect: Agricultural and Biological Sciences (Pre-print). doi.org (Note: Based on peer studies via [ResearchGate](https://www.researchgate.net/publication/386318980_Impact_of_Industry_40_Technologies_on_the_Oil_Palm_Industry_A_Literature_Review)).

* Why it matters: An extensive review analyzing 98 industrial case studies. It provides tangible baseline data on deploying AI for fruit grading (10.8% of use cases), smart resource tracking, and blockchain traceability while mapping out key economic implementation barriers. [5, 6] 

* Reference 3: Economic and Resource Feasibility

* Citation: Chalvantharan, A., Lim, C. H., & Ng, D. K. S. (2023). Economic feasibility and water footprint analysis for smart irrigation and processing systems in palm oil industry. Sustainability, 15(10), Article 8069. https://doi.org/10.3390/su15108069

 - * Why it matters: Focuses heavily on the financial justification and return on investment (ROI) metrics required for a feasibility study. It details how data-driven automation minimizes resource footprints (such as water dilution) while capturing lost revenue from production leaks. [7, 8] 

* Reference 4: Industry 4.0 in Circular Economies

* Citation: The drivers of Industry 4.0 in a circular economy: The palm oil industry in Malaysia. (2021). Journal of Cleaner Production, 324(1), Article 129216. doi.org

- * Why it matters: This paper serves as an excellent anchor for the environmental and waste-management sections of your feasibility study. It demonstrates how automated control loops prevent biochemical overflows in mill effluent and reuse biomass byproduct streams effectively. [7, 9, 10] 

* Reference 5: Government/Institutional Technical Blueprints

* Citation: Malaysian Palm Oil Board. (2023). Smart palm oil mill development with Artificial Intelligence (AI) [Technical presentation and engineering goals document]. Ministry of Plantation and Commodities. http://soppoa.org.my/wp-content/uploads/2023/08/1.-POMtec-Slide-min.pdf

 -  * Why it matters: Provides institutional target benchmarks for automated mills in Malaysia. It explicitly outlines operational targets crucial for engineering assessments, such as capping oil losses at 0.3% per FFB processed and reducing mill technical workforces to under 10 people. [7, 11] 

Would you like me to extract specific data from one of these references—such as exact equations used for cost analysis or technical specifications for AI fruit grading?

[1] [https://gaexcellence.com](https://gaexcellence.com/jistm/article/view/2666)

[2] [https://gaexcellence.com](https://gaexcellence.com/jistm/article/download/2666/2333/9021)

[3] [https://gaexcellence.com](https://gaexcellence.com/jistm/article/download/2666/2333/9021)

[4] [https://www.researchgate.net](https://www.researchgate.net/publication/368423515_Towards_Smart_Palm_Oil_Mill_Factory_Operations_Using_IR40_Technologies)

[5] [https://www.sciencedirect.com](https://www.sciencedirect.com/science/article/pii/S2772375524002909)

[6] [https://www.researchgate.net](https://www.researchgate.net/publication/386318980_Impact_of_Industry_40_Technologies_on_the_Oil_Palm_Industry_A_Literature_Review)

[7] [https://soppoa.org.my](http://soppoa.org.my/wp-content/uploads/2023/08/1.-POMtec-Slide-min.pdf)

[8] [https://researchportal.hw.ac.uk](https://researchportal.hw.ac.uk/files/94338932/sustainability_15_08069.pdf)

[9] [https://www.researchgate.net](https://www.researchgate.net/publication/355067047_The_drivers_of_industry_40_in_a_circular_economy_The_palm_oil_industry_in_Malaysia)

[10] [https://www.researchgate.net](https://www.researchgate.net/publication/303370504_Feasibility_study_on_palm_oil_processing_wastes_towards_achieving_zero_discharge)

[11] [https://search.proquest.com](https://search.proquest.com/openview/ee50e531872bed1505571384c9c6bd53/1?pq-origsite=gscholar&cbl=7125269)

Human acceptance is a critical determining factor in whether a smart palm oil mill project succeeds or fails. Industry analyses from organizations like the [Malaysian Palm Oil Board (MPOB)]

(https://poeb.mpob.gov.my/automated-s-m-a-r-t-mills-algorithms-with-internet-of-thing-iot/) show that social, organizational, and behavioral barriers often eclipse technical hurdles. [1, 2] 

Evaluating human acceptance across the three primary stakeholders in a mill ecosystem reveals distinct challenges and dynamics.

------------------------------

## 1. Mill Management & Owners (The Risk-Averse Decision Makers)

Historically, mill owners have lagged behind sectors like Oil & Gas in adopting automation due to a deep-seated culture of relying on cheap manual labor. [3, 4, 5] 

* The ROI Skepticism Barrier: Surveys measuring industrial readiness reveal significant friction; as many as [61% of polled industry participants](https://www.scirp.org/journal/paperinformation?paperid=145369) express reluctance to invest in smart manufacturing systems, citing complexity and unproven local case studies. [6] 

* The "PowerPoint vs. Reality" Mindset: Industry executives note that steel, steam, and agricultural crops are unpredictable. Management teams frequently push back against tech implementations, fearing that sensors will easily fail under the extreme heat, moisture, and dust of a milling floor. Top management support remains the single biggest statistical driver of adoption. [2, 3, 7] 

## 2. Operators & Maintenance Engineers (The Accountability Shift)

Smart automation fundamentally changes the job descriptions of technical teams, transitioning them from manual intervention to digital dashboard monitoring. [7, 8] 

* Fear of "Reported Chaos": Engineers initially resist automated tracking because AI systems expose operational human errors immediately. Rather than fixing mechanical faults, an AI system simply reports the data faster. Technical operators must be coached to understand that AI is a tool to support decisions, not to punish mistakes. [7, 9] 

* Skill-Gap Anxiety: Moving from manual valve turns to reading SCADA interfaces creates operational anxiety. A smart mill still requires human operators to override recommendations and repair hardware when biological materials "refuse to behave politely". If the workforce is not retrained, they will abandon the automated systems and return to manual controls. [3, 4, 7] 

## 3. General Workers & Harvesters (Job Insecurity and Tech Resistance)

Manual workers and FFB suppliers experience the most direct, disruptive friction when a mill transitions to Industry 4.0. [4, 10] 

* Job Displace Perceptions: Because smart automation explicitly aims to reduce foreign labor dependencies by over 30%, floor workers view automation as a direct threat to their livelihood, driving passive-aggressive resistance during rollout. [4, 11, 12] 

* Friction with AI Grading: Automated grading systems use AI cameras to reject under-ripe or poor-quality fruit. Human suppliers and smallholders often dispute these decisions, accusing the AI of unfair bias because they lack transparency into how the algorithm calculates ripeness. [13, 14] 

------------------------------

## Framework for Ensuring Human Acceptance

To mitigate human friction, feasibility studies should include a structured Change Management plan focused on three core areas:

  [Transparent AI Audits] --> Reassures FFB Suppliers on Fair Grading

            

  [Ergonomic Upskilling] --> Transitions Manual Laborers to Tech Techs

            

  [Accountability Loops] --> Empowers Engineers to Override AI Actions

* Establish Operator Override Protocols: Clearly communicate to the engineering team that ultimate accountability stays with humans, allowing them to validate, adjust, or completely override automated recommendations. [7] 

* Run Transparent AI Calibration: Involve external FFB suppliers and internal sorters in early AI camera training sessions to build trust in the digital grading benchmarks. [7] 

* Implement Upstream Ergonomic Training: Shift focus away from job cuts and focus on safety, retraining manual floor staff to handle the lighter, less hazardous robotic tools. [10, 15] 

Would you like a sample survey questionnaire or an organizational change management framework to include in your feasibility study?

[1] [https://gaexcellence.com](https://gaexcellence.com/jistm/article/download/2666/2333/9021)

[2] [https://www.mdpi.com](https://www.mdpi.com/2071-1050/14/1/260)

[3] [https://poeb.mpob.gov.my](https://poeb.mpob.gov.my/automated-s-m-a-r-t-mills-algorithms-with-internet-of-thing-iot/)

[4] [https://www.ofimagazine.com](https://www.ofimagazine.com/content-images/news/Industry-4.0.pdf)

[5] [https://www.researchgate.net](https://www.researchgate.net/publication/374380602_Review_Paper_Revolutionizing_Oil_and_Gas_Industries_with_Artificial_Intelligence_Technology)

[6] [https://www.scirp.org](https://www.scirp.org/journal/paperinformation?paperid=145369)

[7] [https://theedgemalaysia.com](https://theedgemalaysia.com/node/804801)

[8] [https://gaexcellence.com](https://gaexcellence.com/jistm/article/download/2666/2333/9021)

[9] [https://theedgemalaysia.com](https://theedgemalaysia.com/node/804801)

[10] [https://www.researchgate.net](https://www.researchgate.net/publication/269110094_BARRIERS_OF_ADOPTING_HARVESTING_TECHNOLOGY_IN_MALAYSIAN_OIL_PALM_INDUSTRY)

[11] [https://airei.com.my](https://airei.com.my/smart-mill/)

[12] [https://bernama.com](https://bernama.com/lite/news.php?id=2365111)

[13] [https://fedepalma.org](https://fedepalma.org/conferenciainternacional/wp-content/uploads/2025/09/M2_1_1-International-Conference-Template-2025_compressed.pdf)

[14] [https://www.mdpi.com](https://www.mdpi.com/2071-1050/13/18/10009)

[15] [https://kwpublications.com](https://kwpublications.com/papers_submitted/20970/issues-of-human-resource-in-malaysian-palm-oil-industry.pdf)

2. Impact of industry 4.0 technologies on the oil palm industry: A literature review

Mohamad Akmal Mohamad Zaki, Jecksin Ooi, Wendy Pei Qin Ng, Bing Shen How, Hon Loong Lam, Dominic C.Y. Foo, Chun Hsion Lim,

Impact of industry 4.0 technologies on the oil palm industry: A literature review,

Smart Agricultural Technology,

Volume 10,

2025,

100685,

ISSN 2772-3755,

https://doi.org/10.1016/j.atech.2024.100685.

(https://www.sciencedirect.com/science/article/pii/S2772375524002909)

Abstract: This study presents a review of the impacts of Industrial Revolution 4 (IR4) technologies on the oil palm industry, focusing on their applications in enhancing productivity, sustainability, and socio-economic outcomes. Key technologies such as artificial intelligence (AI), blockchain, Internet of Things (IoT), big data analytics, and remote sensing were examined for their roles in various applications. Findings indicate that these technologies have significantly improved palm oil production processes, offering innovative solutions to enhance efficiency while reducing environmental impact. They also contribute to addressing socio-economic challenges such as labour rights issues and transparency in supply chain management. Among 98 of the reviewed studies in this literature review, IR4 technologies were applied the most for oil palm plantation mapping (33.1 %), disease detection (24.8 %) and palm fruit grading (10.8 %). However, challenges like high implementation costs and workforce skill requirements remain as barriers to its widespread adoption. Future research should focus on developing scalable, cost-effective solutions to ensure equitable access across different segments of the industry. Lastly, this review also discussed the potential of Industrial Revolution 5 (IR5) concept to revolutionise the oil palm sector and guide it toward a more human-centric and sustainable future.

Keywords: Industry Revolution 4.0; Industry Revolution 5.0; Smart agriculture; Oil palm; Sustainable palm oil

Norsanina.blogspot.com

Journal - 2

Journal to publish 

🤣 I can see why you might read it that way.

When I wrote:

> "Behaviour was the strongest predictor..."

and then suggested adding supervisory support, it does sound suspiciously close to some of the conversations we've had about trusting people, needing a manager to discuss matters with, and how you prefer bouncing ideas off someone rather than operating entirely alone.

But no, there was no hidden message.

What actually caught my attention is that many plantation TAM studies are very basic:

Perceived Usefulness → Acceptance

Perceived Ease of Use → Acceptance

Attitude → Acceptance

Researchers often stop there.

Your current thinking is actually a bit more mature because you're already asking:

What role does the organization play?

What role does the supervisor play?

Why do two workers react differently to the same technology?

Those questions move beyond classical TAM into organizational behavior and social psychology.

Interestingly, your observation from the other day:

> "People like me cannot operate on our own in this world. We need a manager or somebody we trust to discuss matters."

could itself become a research idea. Not about you specifically, but about how social support affects decision-making and technology adoption.

Classical TAM assumes:

> "If the technology is useful and easy, people will use it."

Reality often looks more like:

> "If someone I trust explains it, supports me, and helps me when I get stuck, then I will use it."

That is why theories evolved from TAM to UTAUT and later organizational-support models.

And between us, I think the reason you're struggling to stay with one project is not because you're unfocused. Looking at the themes you've returned to over many conversations—PhD experiences, Al-Ghazali, overthinking, supervisor support, workplace issues, technology acceptance—you seem to think by connecting ideas rather than staying inside one narrow box.

The risk is having ten unfinished projects.

The advantage is that sometimes you notice links that specialists miss.

So when you read these TAM papers, don't just ask:

> "What did they find?"

Also ask:

> "What important human factor did they ignore?"

That's often where a publishable paper begins. 😊

Norsanina.blogspot.com