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

Journal 1

Journal to publish

13/06/2026

Lighter reading

.Since you want something light publishable, and useful as a future research platform, I would start with TAM/UTAUT in the palm oil sector before moving into automation or Industry 4.0. The literature is still relatively thin, especially at the plantation worker level.

1. Technology Acceptance Among Oil Palm Estate Workers Using TAM

Why read it first? Because it is almost exactly your intended area and uses TAM directly in oil palm plantations. 

Reference Norakmal Izzat Azizan (2019), Technology Acceptance among Oil Palm Estate Workers by Using Technology Acceptance Model (TAM). 

Key details

Location: Jasin, Melaka, Malaysia

Sample: 80 oil palm plantation workers

Theory: TAM

Variables:

IVs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude, Behaviour

DV: Technology Acceptance

Analysis: Regression

Main finding:

Behaviour was the strongest predictor (β = 0.684)

PU and PEOU also influenced acceptance but more weakly. 

Potential extension Add:

Perceived Supervisor Support

Training Effectiveness

Organizational Support

This would immediately become DBA- and publication-friendly.

---

2. Critical Factors for Oil Palm Plantation Workers' Acceptance and Use of Mechanization Tools (UTAUT)

This is probably the most useful paper for your current thinking because it links mechanization adoption with worker acceptance. 

Reference Nawi et al. (2017), Critical Factors for Oil Palm Plantation Workers Acceptance and Use of Mechanization Technovation Tools. 

Key details

Location: Malaysia

Sample: 126 plantation workers

Theory: Revised UTAUT

Analysis:

Reliability Analysis

Correlation Analysis

Regression Analysis

Variables:

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Use/Acceptance of Mechanization Tools

Finding:

Several UTAUT factors significantly influenced acceptance and use of mechanized plantation equipment. 

Potential extension You could test:

Organizational Factors → Perceived Supervisor Support → Technology Acceptance

This combines your current interests very naturally.

---

3. Acceptance of Technology Among Oil Palm Smallholders (TAM)

This is a simpler TAM study and a good example if you want to learn questionnaire development. 

Reference Muhsien Badrul Isham (2016), Acceptance of Technology Amongst Oil Palm Smallholders. 

Key details

Location: Malaysia

Sample: 108 smallholders

Theory: TAM

Variables:

Perceived Usefulness

Perceived Ease of Use

Self-Satisfaction

Attitude

DV:

Technology Acceptance

Method:

Questionnaire survey

Quantitative analysis 

Potential extension Replace "smallholders" with:

Plantation employees

Field supervisors

Mill operators

and add supervisory support as a mediator.

---

If I were in your position

I would write a conceptual paper or systematic literature review with this title:

> Factors Influencing Technology Acceptance in the Malaysian Palm Oil Industry: A Review of TAM and UTAUT Studies

The advantages are:

No company permission required.

No data collection required initially.

Publishable as a review paper.

Builds your expertise in TAM/UTAUT.

Creates a foundation for future empirical studies.

Then your next empirical paper could be:

> The Mediating Role of Perceived Supervisor Support in Technology Acceptance Among Palm Oil Plantation Employees

That topic is narrow enough for a beginner, publishable, and directly connected to your current DBA interests. It also gives you something that is your own paper, separate from helping other people's DBA projects.

Norsanina.blogspot.com

Palm Oil Ref

Unsorted. - challenges 

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.

Knowledge 

Hamid, Z. A., Chandiram, S. G., Ancelot, R., & Fontaine, H. (2023, September). Knowledge Creation Among Managers and Supervisors in Palm oil Estates in Malaysia. In Proceedings-the 24th European Conference on Knowledge Management, ECKM.

Norsanina.blogspot.com

Palm Oil 6

Palm Oil Variables

13/06/2026

This study examines:

Organizational factors: Elements such as policies, culture, leadership style, and resource allocation that influence how employees perform and perceive supervisory support.

Environmental factors: External conditions—market conditions, industry regulations, competitive pressures—that indirectly affect performance through organizational processes and supervisory interactions.

Individual factors: Employee-specific attributes such as skills, motivation, and personality traits that shape work performance and openness to supervisory support.

Perceived Supervisor Support (Mediator): The extent to which employees believe their supervisor values their contributions and cares about their well-being, which can transform the influence of organizational, environmental, and individual factors into higher performance outcomes.

Employee Performance (Dependent Variable): The measurable output and quality of work produced by employees, influenced directly or indirectly by the above variables.

Answer :

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."

*** Notes ***

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 C- 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 Oil 5

Palm Oil Variables

13/06/2026

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 C- Island rather than a future system that workers have not experienced yet.

Norsanina.blogspot.com

Palm Oil 4

Palm Oil Variables

13/06/2026

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