Showing posts with label phd. Show all posts
Showing posts with label phd. Show all posts

Wednesday, 28 October 2026

Profitablitity, Liquidity, Solvency and Cash Flow

Profitability, liquidity, solvency and cash flow are four important concepts used to evaluate a company's financial health. They are closely related, but each measures a different aspect of business performance.

A company can be profitable but experience a cash shortage. It can have sufficient cash today but carry excessive long-term debt. Therefore, business leaders should evaluate all four dimensions rather than relying on profit alone.

1. Understanding the four financial concepts

1. Profitability

Can the company generate profit?

Profitability measures a company's ability to generate earnings from its revenue, assets or capital. It indicates whether the business model is generating an adequate financial return.


2. Liquidity

Can the company pay its short-term obligations?

Liquidity refers to the ability to pay short-term liabilities, such as supplier invoices, salaries, utilities and other bills, when they become due.

Example: A company has RM200,000 in cash and receivables that can be collected soon, but it must pay RM150,000 in short-term liabilities. It may have sufficient liquidity, provided its receivables are collected on time and the other assets can be converted into cash as expected.

Current ratio:


If current assets are RM300,000 and current liabilities are RM150,000, the current ratio is 2.0 times.

This means the company has RM2 in current assets for every RM1 of current liabilities. However, the ratio alone does not guarantee liquidity if inventory is difficult to sell or receivables are overdue.

Key indicators: Current ratio, quick ratio and working capital.

3. Solvency

Can the company meet its long-term financial obligations?

Solvency evaluates whether a company has a sustainable financial structure and can meet its longer-term obligations. It considers the relationship between debt, assets, equity and the company's ability to service its financing.

Example: A company may have valuable factories and equipment but also carry RM20 million in debt. If its cash-generating capacity is insufficient to service the debt and meet repayments, it may face solvency risks despite owning substantial assets.

Debt-to-equity ratio:


If total debt is RM6 million and shareholders' equity is RM4 million, the debt-to-equity ratio is 1.5 times.

This indicates RM1.50 of debt for every RM1 of equity. Whether this is acceptable depends on the industry, cash-flow stability and financing terms.

Key indicators: Debt-to-equity ratio, interest coverage and debt repayment capacity.

4. Cash flow

How much cash actually enters and leaves the business?

Cash flow refers to the movement of cash and cash equivalents into and out of a company over a specific period. It shows whether the company generates and retains sufficient cash to support operations, investment and financing commitments.

Cash flow is divided into three categories:

  • Operating activities: Cash received from customers and cash paid for operating expenses and suppliers.

  • Investing activities: Cash used to purchase machinery, buildings and other long-term assets, or received from selling assets.

  • Financing activities: Cash received from borrowing or issuing shares, and cash used for debt repayments, dividends and other financing activities.

Example: A company reports RM100,000 in net profit but collects only RM40,000 in cash from its customers during the period. If its cash payments are substantial, it may experience a cash shortage despite being profitable.

Key indicators: Operating cash flow, free cash flow, cash conversion cycle and cash-flow forecast.

2. Comparison at a glance

Concept

Main question

Primary focus

What can go wrong?

Profitability

Is the company earning profit?

Revenue and expenses

Persistent losses or declining margins

Liquidity

Can it pay bills due soon?

Current assets and liabilities

Insufficient cash to pay suppliers or salaries

Solvency

Can it sustain its long-term obligations?

Debt, equity and repayment capacity

Excessive debt or inability to service financing

Cash flow

Is cash coming in when needed?

Actual cash receipts and payments

Cash shortages despite reported profits

3. How the four concepts are connected

Consider a hypothetical palm oil processing company.

Profitability: Positive

The company sells its products at a price higher than its total recognized costs and reports a profit.

Liquidity: Under pressure

Customers take 90 days to pay, while suppliers require payment within 30 days. The company may struggle to pay suppliers on time.

Cash flow: Weak

Cash collected from customers is insufficient to cover immediate operating expenses and other payments.

Solvency: Depends on the longer-term position

If the company continues to borrow to cover operating shortfalls, its debt burden may increase and eventually threaten its long-term financial sustainability. A temporary cash shortage does not, by itself, mean the company is insolvent.

This example explains the central message of “Why Profitable Companies Can Still Fail?”: a company can report a profit but still fail to meet its financial obligations if it cannot convert that profit into cash at the right time.

4. Implications for business leaders

As a business leader, you should use these four concepts together when making decisions.

  • Profitability helps determine whether operations create economic value.

  • Liquidity helps ensure that daily operations and short-term commitments can be funded.

  • Solvency helps ensure that the company's financing structure remains sustainable over the long term.

  • Cash flow helps management understand the timing and availability of money to support operations, investment and repayment obligations.

For example, before approving a new processing machine, management should assess whether the investment is expected to improve profitability, whether sufficient cash is available to purchase and install it, whether borrowing would create excessive debt, and whether future operating cash flows can support the investment.

Conclusion: Profitability tells management whether a business earns money; liquidity tells it whether short-term bills can be paid; solvency tells it whether long-term obligations are sustainable; and cash flow shows how money actually moves through the business. Effective leadership requires balancing all four to achieve financial stability and sustainable growth.

Sunday, 25 October 2026

“Accounting: Is It Just Numbers?”

Speaker: Tan Sri Datuk Mohd Nasir Ahmad 

Moderator: Associate Professor Dr. Nahariah Jaffar 

Programme: Putra Business School DBA Podcast on Business Sense Date: 22 December 2025

The podcast discusses how accounting goes beyond recording numbers and serves as an essential tool for business decision-making, ethical leadership, financial sustainability and strategic planning.

Key discussion points

1. Accounting is more than recording numbers

Accounting involves recording transactions accurately, explaining financial performance and establishing accountability. The real value of accounting lies in interpreting and communicating financial information to support better decisions.

2. Ethics and integrity in accounting

Accounting records must reflect actual transactions truthfully. Accountants should maintain high ethical standards and must not manipulate or conceal unfavorable results. Accounting standards, regulations and independent audits help strengthen transparency and reduce the risk of fraud.

3. Business resilience and long-term sustainability

Accurate financial statements, including the balance sheet, profit and loss statement and cash flow statement, help management evaluate financial performance and assess whether a business can continue operating. Competent analysis of accounting information supports future planning, risk management and business resilience.

4. Different stakeholders use accounting information differently

  • Investors focus on current performance, future prospects, dividends and potential capital gains.

  • Regulators examine historical financial information to assess compliance with relevant standards and regulations.

  • Managers use historical and current data to evaluate performance, plan future activities, allocate resources and make business decisions.

5. Integrated reporting and corporate sustainability

Financial information can be combined with other relevant information to explain a company's performance, values and long-term objectives. Brand reputation and human capital are also important because a company's success depends not only on financial results but also on its people, capabilities and reputation.

6. Professional judgement and accountability

Accounting standards provide a framework for preparing financial reports, but professional judgement is still necessary when estimates and future uncertainties are involved. Accountants must exercise sound judgement while maintaining objectivity, integrity and professional responsibility.

7. The role of artificial intelligence and technology

Artificial intelligence and automation can improve the speed and accuracy of accounting processes. However, technology remains a tool. Competent professionals are needed to interpret information, exercise judgement and translate data into meaningful business decisions.

Conclusion

The key message from Tan Sri Datuk Mohd Nasir Ahmad is that accounting is not just about numbers; it is about accountability, interpretation and decision-making. Accurate accounting records, ethical professionals and competent financial analysis enable organisations to evaluate performance, manage risks, plan for the future and build business resilience. When combined with operational information, human capital and appropriate technology, accounting becomes a strategic tool for achieving long-term business sustainability.

Source: Putra Business School podcast, “Accounting: Is It Just Numbers?”

Monday, 19 October 2026

Journal Article Summary: Green Branding Influencing Skincare Products Purchase Intention

1. Overview of the Study

The journal article, “Green Branding Influencing Skincare Products Purchase Intention,” examines how social media influencers, brand trust, and brand image influence Malaysian consumers’ intention to purchase green skincare products.

The study focuses on the growing demand for skincare products marketed as environmentally friendly and made with natural ingredients. Although consumers are increasingly interested in sustainable products, businesses must convince them that their green claims are genuine and that their products are worth purchasing.

The main finding is that social media influencers have the strongest direct influence on purchase intention, followed by brand image. Brand trust, however, does not significantly influence purchase intention in this study.

2. Research Objectives

The study investigates five relationships:

  • H1: Social media influencers positively influence brand trust.

  • H2: Social media influencers positively influence brand image.

  • H3: Social media influencers positively influence purchase intention.

  • H4: Brand trust positively influences purchase intention.

  • H5: Brand image positively influences purchase intention.

These relationships help explain how digital marketing and consumer perceptions influence purchasing decisions.

3. Research Methodology

The researchers collected primary data through an online questionnaire distributed using social media.

Sample size

334

Respondents

Female respondents

93.41%

312 participants

Previously purchased green skincare

89.52%

299 respondents

Influenced by social media

82.04%

274 respondents

The researchers used a five-point Likert scale, ranging from strongly disagree to strongly agree, and applied structural equation modelling to examine the relationships among the four constructs.

The sample was predominantly female, and nearly all respondents already had some knowledge of green skincare brands. These characteristics are important when considering how widely the findings can be generalised.

4. Key Research Findings

The study tested five hypotheses and supported four of them.

Hypothesis

Relationship

Result

H1

Influencer → Brand trust

Supported

H2

Influencer → Brand image

Supported

H3

Influencer → Purchase intention

Supported

H4

Brand trust → Purchase intention

Not supported

H5

Brand image → Purchase intention

Supported

The reported coefficients show the relative strength of the relationships.

Influence on Purchase Intention

Standardised path coefficients reported in the study. Brand trust's relationship was not statistically significant.

Chart

Source: Article's structural model results. The brand trust coefficient is not statistically significant (p = 0.325); the other two relationships are significant (p < 0.01).

Finding 1: Social media influencers are the strongest driver

Social media influencers have a significant positive relationship with purchase intention, with a standardised coefficient of β = 0.564.

Influencers can introduce products, explain their benefits, share experiences, and communicate with potential customers through platforms such as TikTok, Instagram, YouTube, and Facebook.

The study also found that influencers positively influence brand trust (β = 0.722) and brand image (β = 0.711).

Finding 2: Brand image influences purchasing decisions

Brand image has a significant positive relationship with purchase intention (β = 0.271).

Consumers may be more willing to purchase a green skincare product when the brand is perceived as distinctive, reputable, and environmentally responsible.

Therefore, businesses should develop a consistent brand identity and communicate the product's benefits clearly.

Finding 3: Brand trust does not necessarily lead to purchase intention

Contrary to the researchers' expectation, brand trust does not significantly influence purchase intention (β = 0.039, p = 0.325).

One possible explanation discussed in the article is greenwashing—when a business's environmental claims are perceived as misleading or insufficiently supported.

Consumers may respond positively to influencer promotions or recognise a brand, yet remain uncertain about whether its products genuinely meet their environmental claims. The study raises this as a possible explanation rather than establishing it as a proven cause.

5. Implications for Business and Marketing Management

The findings suggest several practical strategies for green skincare businesses.

  • Select credible influencers. Work with influencers who demonstrate relevant knowledge, experience, reliability, and a good fit with the brand.

  • Build a strong brand image. Communicate product benefits, ingredient information, and environmental positioning consistently.

  • Substantiate green claims. Provide credible evidence to support claims about natural ingredients and environmental benefits, reducing the risk of perceived greenwashing.

  • Use digital platforms strategically. Develop social media content that educates consumers and supports informed purchasing decisions.

  • Understand customers' willingness to pay. Although the article reports that all respondents were willing to pay extra for green skincare, further research is needed to establish how much they would pay and how this varies by income.

From a management perspective, the study suggests that influencer marketing and brand positioning are important tools for attracting customers. However, marketing communication should be supported by credible product information rather than relying on promotional messages alone.

6. Limitations and Future Research

The article identifies several areas for further investigation:

  1. Authenticity of green claims: Research should examine how consumers can determine whether skincare products genuinely contain the natural ingredients claimed by their brands.

  2. Willingness to pay: Further studies should establish the price premium consumers are prepared to pay, particularly across different income groups.

  3. Qualitative research: Interviews with consumers and sellers could provide deeper insights into the development of the green skincare market.

In addition, the study's sample of 334 respondents was heavily female and largely familiar with green skincare. The findings may therefore not fully represent all Malaysian consumers.

7. Overall Conclusion

The journal demonstrates that social media influencers play a major role in shaping Malaysian consumers' intention to purchase green skincare products. Brand image also contributes positively to purchasing intention, whereas brand trust does not show a statistically significant direct relationship in this particular study.

The broader business lesson is that effective digital marketing must combine credible communication, strong brand positioning, and verifiable sustainability claims. A brand's popularity or perceived trustworthiness alone may not be sufficient to translate consumer interest into purchasing intention.

For business leaders, the study highlights the importance of understanding consumer behaviour and aligning marketing strategies with the expectations of environmentally conscious customers.

8. A Short Summary for Your DBA Coursework

The study examines the influence of social media influencers, brand trust, and brand image on Malaysian consumers' purchase intention towards green skincare products. Using questionnaire data from 334 respondents and structural equation modelling, the researchers found that social media influencers significantly influence brand trust, brand image, and purchase intention. Brand image also significantly influences purchase intention, while brand trust does not have a statistically significant direct effect. The findings highlight the importance of influencer marketing and brand positioning in the sustainable skincare market. However, concerns about greenwashing suggest that businesses must substantiate their environmental claims to strengthen consumer confidence. The study recommends further research into the authenticity of green skincare ingredients, consumers' willingness to pay, and qualitative insights from consumers and sellers.

Sustainable Choices in Fashion: Insights into Gen Z's Purchasing Behaviour

This research examines how Generation Z (Gen Z) consumers decide whether to purchase sustainable clothing, particularly in an era where social media and digital technology strongly influence consumer behavior.

1. Purpose of the study

The main objective is to understand how four factors influence Gen Z's intention to buy sustainable apparel:

  • Social media influence: Fashion trends, influencers, online reviews, and sustainability campaigns can shape consumers' purchasing decisions.

  • Brand loyalty: Consumers may prefer brands they trust and believe are committed to environmental and social responsibility.

  • Perceived risk: Concerns about price, product durability, quality, and whether sustainability claims are genuine may discourage purchases.

  • Product quality: Consumers are more likely to consider sustainable clothing when it offers good quality, durability, comfort, and value for money.

2. Research methodology and findings

The study uses a cross-sectional survey involving 187 respondents, predominantly young adults in India, to investigate the relationships between these factors and purchase intentions.

According to the abstract provided, all four factors significantly influence Gen Z's intention to purchase sustainable clothing. This suggests that sustainability alone may not be enough; consumers also consider trust, product performance, online influence, and purchasing risks.

3. Role of digital technology and information systems

An important aspect of this journal is the connection between consumer behavior and digital transformation. It highlights how fashion businesses can use:

  • Artificial intelligence (AI): To recommend sustainable products based on individual preferences.

  • Augmented reality (AR): To allow customers to virtually try on clothing before purchasing, potentially reducing returns and waste.

  • Blockchain technology: To improve transparency and traceability of materials, sourcing, and production.

  • Social media platforms: To communicate sustainability initiatives and build customer trust.

4. Business and management implications

The research suggests that fashion companies should integrate sustainability into their business strategies rather than treating it merely as a marketing campaign. Businesses should maintain high product standards, provide clear sustainability information, offer reliable return policies, and develop personalized online shopping experiences.

These measures may help strengthen customer confidence, improve brand loyalty, and increase purchase intention.

5. Connection to the Sustainable Development Goals (SDGs)

The topic supports three relevant United Nations Sustainable Development Goals:

  • SDG 12 – Responsible Consumption and Production: Encouraging durable clothing, responsible purchasing, and reduced textile waste.

  • SDG 13 – Climate Action: Reducing environmental impacts associated with fashion production and consumption.

  • SDG 8 – Decent Work and Economic Growth: Supporting responsible business practices and better working conditions throughout the fashion supply chain.

6. Overall conclusion

The key message of this journal is that sustainable fashion purchasing depends not only on environmental awareness but also on social influence, brand trust, perceived risk, product quality, and digital shopping experiences.

From a business management perspective, the research helps managers understand how consumer insights and information systems can support sustainable business models, strengthen customer relationships, and create competitive advantage.

Research limitation: The study surveyed 187 respondents, predominantly young adults in India. Its findings may not represent all Gen Z consumers globally, including those in Malaysia. Furthermore, purchase intention does not necessarily translate into actual purchasing behaviour.

Source: Yeo, S. F., Tan, C. L., Lim, K. B., and co-authors (2025), Sustainable Choices in Fashion: Insights into Gen Z's Purchasing Behaviour, DOI: 10.33168/JLISS.2025.0611.

Sunday, 18 October 2026

AI and Gen Z's Trust and Purchase Decisions

 “AI-Personalised Marketing and ESG Claims in Malaysian Online Fashion: How Do They Shape Gen Z’s Trust and Purchase Decisions?” 

The topic has three important business issues: artificial intelligence (AI), sustainability and consumer behaviour. It also addresses a practical management question:

How can online fashion businesses use AI to personalise marketing and communicate sustainability claims without losing the trust of young consumers?

The study is particularly relevant to Malaysia, where online shopping, social commerce, influencer marketing and sustainability messaging intersect. We need to establish whether AI marketing or sustainability claims influence consumers, but to understand why they work, when they work, and how businesses should manage them.

1. Break down the research title

Fashion eCommerce Trends 2026: How Brands Can Win Online

A. AI-personalised marketing

This refers to using AI and customer data to tailor marketing messages, product recommendations, advertisements, promotions and shopping experiences to individual consumers.

Example: A Malaysian Gen Z shopper browses modest fashion on an e-commerce platform. The platform learns from their browsing and purchase patterns and recommends similar outfits, personalised discounts or products that match their style.

Thread by Thread: How Sustainable Fashion is Weaving the Future of Apparel | by Vaishnavinichal | May, 2025 | Medium

B. ESG claims

ESG stands for Environmental, Social and Governance. In fashion marketing, these claims communicate a brand's environmental impact, social responsibility or ethical business practices.

Examples: “Made from recycled fibres,” “ethically sourced materials,” “fair wages for garment workers,” or “transparent supply chain.”

The important issue is whether consumers consider these claims credible, meaningful and supported by evidence.

Generation Z Lifestyle

C. Malaysian Gen Z consumers

Gen Z generally refers to people born approximately between 1997 and 2012, although definitions vary. In October 2026, this spans approximately ages 14–29.

It is sensible to focus on adult Gen Z consumers aged 18–29 who shop for fashion online in Malaysia. This makes recruitment and consent procedures more manageable while keeping the study focused.

Endeudarse para comprar ropa: qué hay detrás de las plataformas “compra ahora y paga después” | Moda | S Moda | EL PAÍS

D. Trust and purchase decisions

Trust concerns whether consumers believe a fashion brand is honest, competent and reliable. Purchase decisions concern their intention to buy, willingness to choose a brand, or actual purchasing behaviour.

Example: A shopper may like a personalised recommendation but distrust a brand's sustainability claim. Alternatively, credible environmental information may strengthen trust and increase purchase intention.

The central idea is that AI personalisation and ESG communication may influence consumers differently. Personalisation may make marketing more relevant, but it can also feel intrusive. ESG claims may make a brand more attractive, but unsupported or exaggerated claims can undermine credibility.

2. Why this topic matters to businesses

Consider a hypothetical Malaysian online fashion brand selling everyday clothing, sportswear and modest fashion.

A practical business scenario

MARKETING ACTION 1

AI personalisation

The brand recommends products based on browsing history, purchase patterns and stated preferences.

MARKETING ACTION 2

ESG communication

Product pages describe recycled materials, ethical manufacturing and reduced packaging, with varying levels of supporting evidence.

CONSUMER RESPONSE

Trust, perceived value and purchase intention

Consumers assess whether the recommendations are useful, whether the brand respects their privacy and whether its sustainability claims are believable.

The business dilemma is straightforward:

  • More personalisation may improve product discovery and conversion.

  • More data collection may increase privacy concerns.

  • Strong sustainability claims may differentiate a brand.

  • Weak evidence may expose the brand to accusations of greenwashing.

  • Consumers may express support for sustainability but still prioritise price, quality, convenience or style when purchasing.

The topic can produce actionable recommendations for marketing managers, e-commerce leaders, brand managers and sustainability professionals.

3. The central research problem

In this case, the research problem is that online fashion brands are increasingly able to personalise consumer experiences and communicate sustainability, yet it may be difficult to know whether these strategies build genuine trust or merely attract short-term attention.

There are four related problems.

Problem 1: AI personalisation has both benefits and risks

AI can improve relevance, convenience and product discovery. However, consumers may become uncomfortable when recommendations seem to reveal information they did not knowingly share.

For example:

  • A shopper receives recommendations that match their preferred style. This may improve the shopping experience.

  • A shopper sees an advertisement for a product shortly after discussing it privately or browsing similar products. They may feel monitored.

  • A shopper is repeatedly shown products based on past purchases. They may appreciate the convenience or feel that their choices are being restricted.

Recent research on AI-powered fashion retail examines this balance between the benefits of personalisation, trust, perceived risk and privacy concerns.

How can retailers maximise the commercial benefits of personalisation without creating distrust?

Problem 2: ESG claims may not always be credible

Consumers often cannot independently verify claims about the materials, factories or supply chains behind a garment.

Consider these two product descriptions:

Claim A: Vague sustainability marketing

“Our clothing is environmentally friendly and sustainable.”

Problem: The claim does not explain what makes the garment sustainable or provide supporting evidence.

Claim B: Specific, verifiable information

“This garment contains 70% recycled polyester by weight. See the material specification and supplier documentation.”

Potential advantage: The claim is more specific and gives the consumer a way to assess the evidence.

Even specific claims should be accurate, appropriately substantiated and clear about their scope. A recycled-material claim alone does not establish that the entire garment is sustainable.

When brands exaggerate or misrepresent their environmental performance, the practice is known as greenwashing. A 2026 study of Gen Z consumers in the fast-fashion sector reported a strong negative association between perceived digital greenwashing and brand trust.

How do the credibility and clarity of ESG claims influence Gen Z's trust and willingness to purchase?

Positive attitudes do not always translate into purchases

A consumer may genuinely support environmental protection but still buy a cheaper, less sustainable product.

This is commonly called the attitude–behaviour gap, or, when focusing on stated intentions versus actions, the intention–behaviour gap.

For example, a shopper may:

  • Prefer recycled materials but reject a garment that costs substantially more.

  • Support ethical labour practices but prioritise delivery speed.

  • Like sustainable fashion but prefer the style of a conventional alternative.

  • Intend to buy sustainable clothing but purchase whatever is promoted during a sale.

Research on sustainable fashion continues to examine these barriers and the difference between favourable attitudes and actual buying behaviour.

Does consumer trust translate into purchase intention, and does purchase intention translate into actual purchasing behaviour?

Problem 4: AI and ESG communication may interact

This is potentially the most distinctive part of your proposed research.

Imagine two fashion brands offering the same garment at the same price.

  • Brand A uses personalised recommendations and makes a broad claim that its clothing is sustainable.

  • Brand B uses personalised recommendations and provides specific, transparent evidence supporting its environmental claims.

Would consumers trust Brand B more? Would the answer change if the recommendation were highly personalised but the shopper was concerned about privacy?

These questions go beyond examining AI personalisation and sustainability marketing separately. They investigate how consumers evaluate the combined experience.

The research opportunity is to test whether AI personalisation and ESG claim credibility reinforce one another, operate independently, or create tensions that affect consumer trust and purchase decisions.

This combined relationship is a potential research gap to investigate through a systematic literature review. It should not be claimed as an established gap until you have examined the latest studies, including research on Malaysian consumers.

4. What theories could support the topic?

We need a theoretical foundation that explains why the relationships between variables should exist. We need to avoid combining too many theories simply to make the framework look sophisticated.

We are considerang three main theories or frameworks.

Theory 1: Stimulus–Organism–Response (S-O-R)

A useful overarching framework

This framework explains how external stimuli influence a person's internal psychological state, which then influences a response.

  • Stimulus: AI personalisation and ESG claim credibility.

  • Organism: Consumer trust, perceived authenticity and perceived privacy risk.

  • Response: Purchase intention or purchasing behaviour.

It provides a clear structure for connecting marketing practices with consumer decision-making.

Theory 2: Trust theory

Trust theory helps explain why consumers become willing—or unwilling—to rely on a brand.

Consumers may assess a brand's honesty, competence and integrity. In AI-enabled shopping, you should also distinguish trust in the technology from trust in the fashion brand.

For example, a shopper may trust an e-commerce platform's recommendation algorithm but distrust the retailer's environmental claims.

Theory 3: Signalling theory

Signalling theory explains how a business communicates information about product quality or otherwise difficult-to-observe characteristics.

In online fashion, shoppers cannot easily inspect the factory, verify the supply chain or test every environmental claim before buying. Product disclosures, credible certifications and traceability information can serve as signals.

A signal is more useful when it is credible and difficult to fake. A vague “eco-friendly” label may provide less assurance than a specific claim backed by verifiable evidence.

How should you select the theories?

My recommendation is to use S-O-R as the main organising framework, with trust theory explaining the central mechanism and signalling theory informing how consumers assess ESG claims.

However, you could instead use signalling theory as the primary lens if your main research emphasis is ESG credibility and greenwashing. Your final selection should follow the literature review and the specific relationships you intend to test.

A useful starting point is the 2025 study on AI-enabled services in online fashion stores, which examines trust, utility, product uniqueness and privacy over time.

5. Build a conceptual framework

I would initially recommend a model with a manageable number of variables. It should be sophisticated enough for doctoral research but not so complicated that data collection and interpretation become unmanageable.

Independent variables

Perceived AI personalisation quality

Perceived ESG claim credibility

Mediating variable

Consumer trust in the fashion brand

Explains how marketing perceptions may influence buying decisions

Dependent variable

Purchase intention

Potential moderator

Consumer privacy concern

May weaken or change the relationship between AI personalisation and trust.

This is a proposed framework, not an established finding. You would test the relationships using empirical data.

What does each variable mean?

Variable

Role

What you would measure

AI personalisation quality

Independent

Relevance, usefulness and perceived fit of recommendations

ESG claim credibility

Independent

Believability, clarity and perceived evidence supporting sustainability claims

Brand trust

Mediator

Belief that the brand is honest, dependable and acts with integrity

Purchase intention

Dependent

Likelihood or willingness to buy the advertised fashion product

Privacy concern

Moderator

Concern about how personal data are collected and used

An important distinction: If your title promises to study “purchase decisions,” a survey measuring only purchase intention may not fully deliver on that promise. You could measure recent self-reported purchases as well, or revise the title to say “purchase intention.”

Also, this initial framework treats ESG claims as perceived marketing information. If you want to investigate the entire ESG concept, you would need to address environmental, social and governance claims separately or justify a broader measurement construct.

6. Research objectives and research questions

The objectives should align directly with your research framework and the business problem.

Proposed research objectives

  1. To examine the relationship between perceived AI personalisation quality and brand trust among Gen Z consumers shopping for fashion online in Malaysia.

  2. To examine the relationship between perceived ESG claim credibility and brand trust.

  3. To investigate the relationship between brand trust and purchase intention.

  4. To determine whether brand trust mediates the relationships between AI personalisation quality, ESG claim credibility and purchase intention.

  5. To examine whether privacy concerns moderate the relationship between AI personalisation quality and brand trust.

  6. To develop managerial recommendations for online fashion retailers to strengthen consumer trust and purchase intention through responsible AI personalisation and credible ESG communication.

Proposed research questions

Research question

What it seeks to discover

RQ1

Does perceived AI personalisation quality influence brand trust?

RQ2

Does ESG claim credibility influence brand trust?

RQ3

Does brand trust influence purchase intention?

RQ4

Does brand trust explain how AI personalisation and ESG claim credibility relate to purchase intention?

RQ5

Do privacy concerns weaken the trust-building effect of AI personalisation?

RQ6

What practical marketing strategies can Malaysian online fashion retailers adopt based on the findings?

RQ6 is important because the study should lead to recommendations that managers can implement, not simply statistical conclusions.

7. Develop hypotheses that can be tested

If a quantitative research design, we can translate our conceptual framework into hypotheses.

H1 — AI personalisation and trust

Perceived AI personalisation quality is positively associated with brand trust.

Rationale: Relevant, useful recommendations may signal that a retailer understands consumer needs.

H2 — ESG credibility and trust

Perceived ESG claim credibility is positively associated with brand trust.

Rationale: Clear and credible sustainability information may increase confidence in the brand.

H3 — Trust and purchase intention

Brand trust is positively associated with purchase intention.

Rationale: Consumers may be more willing to purchase from brands they consider dependable.

H4 — Mediation by brand trust

Brand trust mediates the relationships of AI personalisation quality and ESG claim credibility with purchase intention.

Rationale: Marketing practices may influence purchase intention partly by shaping trust.

H5 — Moderation by privacy concern

Privacy concern weakens the positive relationship between AI personalisation quality and brand trust.

Rationale: Highly privacy-conscious consumers may respond less favourably to personalisation they perceive as intrusive.

These are proposed hypotheses, not findings. Existing research suggests that privacy and transparency matter, but their effects can depend on the context and the precise variables measured.

We should also consider whether ESG credibility and AI personalisation interact. For example, does personalisation make credible ESG information more persuasive, or does it make the message seem manipulative? That is a potentially valuable extension, but include it only if the literature and research design justify it.

8. How should you conduct the research?

My initial recommendation is a quantitative explanatory study, potentially supplemented by an experiment if wewant stronger evidence about cause and effect.

Option A: Quantitative survey

Step 1 — Define your population

Malaysian residents aged 18–29 who have purchased fashion online within a specified recent period and have encountered personalised recommendations or marketing. Establish eligibility criteria carefully, especially if participants cannot distinguish algorithmic personalisation from ordinary targeted advertising.

Step 2 — Develop a questionnaire

Use established measurement scales where appropriate, adapting them to the Malaysian online fashion context. Include questions on personalisation quality, ESG claim credibility, brand trust, privacy concern and purchase intention.

Step 3 — Conduct a pilot study

Test whether respondents understand terms such as AI personalisation, ESG and sustainability claims. Check questionnaire length, reliability, clarity and the quality of responses.

Step 4 — Collect and analyse data

Use an appropriate sampling strategy, assess measurement quality and test the hypothesised relationships. Consider structural equation modelling (SEM), including PLS-SEM if justified by your model, objectives and data characteristics.

How many respondents would you need?

Do not automatically choose 384 respondents simply because it is a commonly quoted survey sample size.

Your sample size should be determined by the statistical analysis, expected effect sizes, desired power, number of predictors, model complexity and the feasibility of recruiting eligible respondents.

For an initial planning discussion, you might explore a target of approximately 400–500 usable responses. However, this is only an illustrative planning range, not a statistically validated requirement. A formal power analysis should determine your final target, including allowances for incomplete or invalid responses.

Option B: Experimental research

An experiment may be particularly valuable because your title asks how marketing approaches shape trust and purchase decisions.

You could develop a simulated online fashion product page and manipulate two factors:

  • AI personalisation: high versus low.

  • ESG information: credible and specific versus vague or unsupported.

This produces a 2 × 2 factorial design.

Illustrative experimental design

Group 1

Low personalisation

Vague ESG claims

Group 2

High personalisation

Vague ESG claims

Group 3

Low personalisation

Credible ESG claims

Group 4

High personalisation

Credible ESG claims

Participants are randomly assigned to one condition. The simulated product, price, visual quality and other information should be held constant wherever possible.

After exposure, participants could rate:

  • Brand trust.

  • Perceived authenticity of ESG claims.

  • Perceived intrusiveness of personalisation.

  • Purchase intention.

  • Willingness to choose the product over an alternative.

The analysis would examine the main effect of each factor and whether the combination produces an interaction effect.

My advice: A survey is generally simpler for a DBA project with limited time and resources. An experiment is stronger for testing the effects of deliberately manipulated marketing cues. A carefully designed combination of an experiment and a follow-up survey could provide richer evidence, but only if the additional time and recruitment demands are manageable.

Neither design automatically proves actual purchasing behaviour. If actual decisions are central to your thesis, consider an incentivised choice task, a behavioural outcome or follow-up purchase data where feasible.

9. What makes this a doctoral-level study rather than an ordinary marketing project?

A DBA is expected to contribute to professional practice while demonstrating rigorous academic reasoning.

Your contribution should ideally have three dimensions.

Theoretical contribution

Test whether brand trust explains how AI personalisation quality and ESG claim credibility relate to purchase intention in the Malaysian online fashion context. Examine whether privacy concern changes these relationships.

The contribution is not simply applying a familiar model in a new country. It is demonstrating what the combined framework explains, what it does not explain, and whether its proposed relationships are supported.

Managerial contribution

Develop evidence-based recommendations for online fashion businesses about the design of personalised recommendations, the presentation of sustainability evidence, consumer data controls and brand-trust management.

Contextual contribution

Investigate whether these relationships hold for adult Gen Z consumers in Malaysia, a specific market and consumer group. Do not assume that results from China, Europe or other countries necessarily generalise to Malaysia.

For the managerial output, you could develop a practical framework such as a Trust-First AI and ESG Marketing Framework. It might guide retailers to assess personalisation relevance, transparency, evidence quality, privacy safeguards and consumer responses before launching campaigns.

This framework would be your proposed practical output; you would need to derive and validate it through your research rather than claim its effectiveness in advance.

10. Is there a genuine research gap in this topic?

This is a crucial question before registering your DBA proposal.

A preliminary literature search reveals that several components of your topic have already been studied:

  • AI-enabled personalisation and online fashion purchasing, including trust and privacy.

  • AI personalisation in sustainable advertising, including privacy concerns and transparency.

  • Digital greenwashing, Gen Z and brand trust in fast fashion.

  • Malaysian Gen Z and environmentally friendly purchase intention.

This means you should not claim that AI personalisation, consumer trust, or ESG marketing has never been researched before.

The more defensible research opportunity is to investigate the specific combination of:

  1. AI personalisation in online fashion.

  2. Credibility of ESG claims.

  3. Brand trust as a possible explanatory mechanism.

  4. Privacy concerns as a boundary condition.

  5. Adult Gen Z consumers in Malaysia.

You should verify whether this exact combination has already been examined in published studies, theses and conference papers. The search should include papers published in 2025 and 2026, not just older foundational research.

A possible gap statement

Although research has examined AI-enabled personalisation in online fashion retail and the influence of sustainability communication on consumer trust, these streams provide an incomplete basis for understanding how consumers evaluate both practices together. In particular, further investigation is warranted into how perceived AI personalisation quality and ESG claim credibility jointly relate to brand trust and purchase intention among adult Gen Z consumers in Malaysia, and whether privacy concerns alter these relationships. Addressing this issue could extend understanding of responsible digital marketing while providing online fashion retailers with practical guidance on balancing personalisation effectiveness, credible sustainability communication and consumer trust.

Notice the cautious wording: further investigation is warranted. That is more academically defensible than declaring a complete absence of research before conducting the review.

11. Suggested literature review and reading list

Start with the following recent studies. They are useful for mapping the field, but you should read the full articles and verify their methods, measures, limitations and theoretical contributions before using them in your thesis.


1. Understanding customers’ intentions to use AI-enabled services in online fashion stores – a longitudinal study

International Marketing Review, 2025

Useful for understanding trust, utility and privacy across the fashion shopping journey.

Read the article


2. When and how AI personalization drives sustainable purchases

Journal of Retailing and Consumer Services, 2026

Particularly relevant to AI personalisation, message relevance, privacy and transparency in eco-friendly advertising.

Read the article

Unraveling the Green Veil: Investigating the Affective Responses of U.S. Generation Z to Fast Fashion Greenwashing Through C-A-B Theory

3. Decoding Digital Greenwashing: A Quantitative Analysis of Signaling Theory, Brand Trust, and Purchase Intention among Generation Z Consumers in the Fast-Fashion Sector

Social Reports, 2026

Useful for developing your ESG credibility, greenwashing and trust constructs. Evaluate its methods and journal quality critically, as you should with all sources.

Read the article

Kebangkitan Gen Z: Pengguna Digital Di Malaysia

4. Sustainable Choices in Fashion: Insights into Gen Z’s Purchasing Behaviour

5. AI-powered personalization in online fashion stores: Exploring the impact on shoppers’ perceived risk and intention to buy and recommend

Information & Management, 2026

Useful for examining perceived risk, trust deficits, shopping experience and purchase intention.

For foundational theory, add these classic works to your reading plan:

  • Ajzen (1991), The Theory of Planned Behavior — useful if you investigate the relationship between intention and behaviour.

  • Mehrabian and Russell (1974), An Approach to Environmental Psychology — foundational work behind the stimulus–organism–response perspective.

  • Spence (1973), Job Market Signaling — the foundational signalling theory paper.

  • Morgan and Hunt (1994), The Commitment-Trust Theory of Relationship Marketing — useful for understanding trust in marketing relationships.

For the literature review, build a matrix recording each paper's country, population, theory, variables, methodology, findings, limitations and the research opportunity it leaves open.

12. Important decisions before you finalise the title

Your current title is relevant, but it contains a few terms that need to be clarified.

Issue

Recommendation

“AI-personalised marketing”

Specify whether this means AI-driven product recommendations, personalised advertisements, AI styling tools or all of these.

“ESG claims”

Decide whether you will measure environmental claims alone or environmental, social and governance claims.

“Gen Z”

Define your age range, preferably adult consumers aged 18–29 for a manageable study.

“Purchase decisions”

Decide whether the outcome is purchase intention, self-reported past purchases or observed behaviour.

“How do they shape”

If you use a cross-sectional survey, interpret results as relationships or associations rather than definitive causal effects.

13. Practical roadmap

  1. Phase 1 — Define the business problem. Identify the precise managerial challenge faced by online fashion retailers and the decisions your findings could improve.

  2. Phase 2 — Conduct a systematic literature review. Search academic databases such as Scopus, Web of Science and Google Scholar. Map existing research on AI personalisation, ESG credibility, trust and Gen Z purchasing.

  3. Phase 3 — Establish the research gap. Demonstrate what is already known, what remains unclear and why Malaysia provides a meaningful context for further investigation.

  4. Phase 4 — Develop the theoretical model. Finalise your constructs, conceptual framework, research questions and hypotheses. Avoid including variables without a clear theoretical purpose.

  5. Phase 5 — Design and validate the methodology. Choose the survey or experimental approach, conduct a power analysis, pilot the instruments and obtain the required ethics approval.

  6. Phase 6 — Analyse the evidence. Evaluate measurement quality, test the hypotheses and assess the practical significance of the findings, not just statistical significance.

  7. Phase 7 — Translate findings into a management framework. Recommend specific actions concerning personalization transparency, ESG substantiation, privacy controls and trust-building practices.

Final perspective

The most valuable outcome would not simply be proof that AI marketing or sustainability communication influences young consumers. It would be an evidence-based explanation of how online fashion retailers can use personalization and credible ESG communication together while protecting the consumer trust on which long-term business performance depends.

The next step is to decide the scope of your study, because that choice will determine the literature review, framework, questionnaire and methodology.

Friday, 25 September 2026

Assessment Rubrics Scale

 


Wednesday, 23 September 2026

Proposal: Accounting Standard for Environmental Damage Costs

If I were contracted by the Accounting Standards Board, I would propose developing an accounting standard for environmental damage costs for the following reasons:

(i) Why develop the accounting standard?
The standard is appropriate because environmental damage can create significant financial obligations for companies, such as costs for pollution cleanup, rehabilitation, restoration, and legal penalties. A specific standard would improve consistency, transparency, and comparability in how companies recognize and report these costs.

(ii) One key issue to be covered
The key issue would be when environmental damage costs should be recognized as a liability. For example, a company may need to recognize a provision when it has caused environmental damage and has a present obligation to undertake restoration or pay related costs, even if the actual payment will occur several years later.

(iii) How the Conceptual Framework can help
The Conceptual Framework can help determine whether an environmental obligation meets the definition of a liability and whether the related information is relevant and faithfully represented. It provides principles for deciding recognition, measurement, and disclosure, helping ensure that environmental costs are reflected appropriately in financial statements.

Summary

  • (i) Why develop the standard?

    • Environmental damage can create significant costs and financial obligations.

    • A standard would improve consistency, transparency, and comparability.

  • (ii) Key issue to cover:

    • Determine when environmental damage costs should be recognized as a liability.

    • This may include cleanup, restoration, and rehabilitation costs.

  • (iii) Role of the Conceptual Framework:

    • Helps determine whether an environmental obligation meets the definition of a liability.

    • Guides recognition, measurement, and disclosure of environmental costs.


Simple Summary

RequirementAnswer
(i) Why develop the standard?To improve consistency and transparency in reporting environmental damage costs.
(ii) Key issueWhen should environmental damage costs be recognized as a liability?
(iii) Conceptual FrameworkHelps determine whether an obligation qualifies as a liability and how it should be recognized, measured, and disclosed.

Current Ratio and Company Liquidity

If I were the CEO of a company, I would use the current ratio to assess whether the company has enough short-term assets to meet its short-term financial obligations.

The formula is:

Current Ratio = Current Assets ÷ Current Liabilities

For example, if my company has:

  • Current Assets = RM10 million

  • Current Liabilities = RM5 million

Then:

Current Ratio = RM10 million ÷ RM5 million = 2.0

This means the company has RM2 of current assets for every RM1 of current liabilities.

How I would interpret it as CEO

A higher current ratio generally indicates stronger short-term liquidity because the company has more current assets available to pay its debts. However, an excessively high ratio may also indicate that assets such as cash or inventory are not being used efficiently.

A lower current ratio indicates greater liquidity pressure. If the ratio falls below 1.0, current liabilities are greater than current assets, which may make it more difficult for the company to meet its short-term obligations.

As CEO, I would therefore not look at the current ratio alone. I would also examine cash flow, inventory turnover, accounts receivable collection, and industry benchmarks to understand the company's actual liquidity position.

In summary: The current ratio helps me determine whether my company has sufficient short-term resources to meet its short-term obligations and provides an early warning of potential liquidity problems.

Tuesday, 22 September 2026

EVALUATION OF THE POTENTIAL OF LOW ALTITUDE ECONOMY IN THE PALM OIL INDUSTRY

1.0 Introduction

The palm oil industry is a geographically extensive and operationally complex industry involving plantation management, harvesting, transportation, milling, processing and supply-chain activities. In many producing regions, plantations and mills are located across large areas, including remote and difficult-to-access locations. Consequently, companies face operational challenges related to field inspection, crop monitoring, transportation, infrastructure inspection, worker safety and environmental management.

The emergence of the Low Altitude Economy (LAE) provides an opportunity to address some of these challenges through the use of drones, Unmanned Aerial Vehicles (UAVs), artificial intelligence (AI), advanced sensors and digital platforms.

For the palm oil industry, LAE should not be viewed only as a method of transporting goods by drone. Its greater potential lies in combining aerial mobility, remote sensing, automation and data analytics to improve operational decision-making.

The potential relationship can be expressed as:

UAVs + Sensors + AI + Digital Platforms → Better Information → Faster Decisions → Higher Productivity and Safety

2.0 Current Operational Challenges in the Palm Oil Industry

The palm oil industry has several operational characteristics that make it suitable for LAE applications.

2.1 Large geographical areas

Oil palm plantations can cover thousands of hectares. Traditionally, field inspections require workers and management personnel to travel physically through plantation roads.

This creates several challenges:

  • significant travelling time;

  • fuel consumption;

  • vehicle maintenance;

  • difficult access during wet weather;

  • delays in identifying field problems; and

  • significant manpower requirements.

Aerial monitoring can provide a different approach by allowing large areas to be surveyed relatively quickly.

2.2 Crop monitoring

Conventional crop monitoring depends heavily on field workers and periodic inspections.

Important information includes:

  • planting density;

  • palm health;

  • nutrient deficiency;

  • pest attacks;

  • disease;

  • water stress;

  • drainage conditions; and

  • harvesting readiness.

The limitation of conventional inspection is that observations may be sample-based rather than comprehensive.

UAVs equipped with cameras and multispectral or thermal sensors can potentially collect information across much larger areas.

2.3 Harvesting and crop estimation

Fresh Fruit Bunches (FFB) are the main economic output of an oil palm plantation.

Management needs reliable information regarding:

  • crop maturity;

  • expected production;

  • harvesting intervals;

  • field productivity; and

  • crop forecasting.

LAE technologies can support aerial imaging and AI-based analysis to improve crop estimation.

This can contribute to better coordination between:

Plantation → Harvesting → Collection → Transport → Palm Oil Mill

3.0 Operational Challenges that LAE Can Solve

3.1 Plantation inspection

One of the clearest applications of LAE is aerial plantation inspection.

Traditional approach

Field worker → Plantation road → Physical inspection → Report → Management decision

LAE approach

UAV → Aerial image → AI/data analysis → Digital report → Management decision

The LAE approach can reduce the time required to obtain information about large plantation areas.

Instead of relying entirely on physical inspection, management can use aerial information to identify areas requiring further investigation.

3.2 Early identification of pest and disease problems

Pest and disease detection is important because delayed intervention can result in production losses.

UAVs equipped with appropriate sensors can potentially identify abnormal vegetation patterns.

For example:

Sensor → Image → AI analysis → Abnormal area detected → Ground verification → Treatment

This creates a targeted intervention model.

Instead of treating an entire plantation uniformly, management can potentially focus resources on identified problem areas.

This supports the concept of precision agriculture.

3.3 Fertiliser optimisation

Fertiliser represents a significant operating cost in plantation management.

Aerial data can potentially identify differences in vegetation condition across plantation blocks.

The information can then support:

  • targeted fertiliser application;

  • identification of nutrient-deficient areas;

  • monitoring of fertiliser effectiveness; and

  • reduction of unnecessary application.

This creates an opportunity to move from:

Uniform application

towards:

Data-driven application.

The economic benefit would potentially come from improved input efficiency rather than simply reducing the quantity of fertiliser used.

3.4 Drainage and water management

Water management is particularly important in oil palm plantations.

Excessive water can cause:

  • waterlogging;

  • poor root conditions;

  • reduced accessibility;

  • soil deterioration; and

  • harvesting difficulties.

UAVs can provide aerial information about:

  • drainage networks;

  • standing water;

  • erosion;

  • blocked drains; and

  • changes in plantation conditions.

This allows maintenance teams to prioritise areas requiring intervention.

3.5 Plantation road inspection

Plantation roads are essential because they connect harvesting areas to collection points and mills.

Poor road conditions can affect:

  • harvesting operations;

  • FFB evacuation;

  • vehicle productivity;

  • fuel consumption; and

  • delivery time.

UAVs can potentially identify:

  • damaged roads;

  • erosion;

  • flooded sections;

  • bridge problems; and

  • inaccessible areas.

This can support predictive and prioritised road maintenance.

4.0 LAE Applications in Palm Oil Milling

LAE is not limited to plantations. It can also support palm oil mill operations.

4.1 Mill infrastructure inspection

UAVs can potentially inspect difficult-to-access infrastructure such as:

  • mill roofs;

  • chimneys;

  • stacks;

  • storage tanks;

  • pipelines;

  • transmission lines;

  • cooling systems; and

  • other elevated structures.

This can reduce the need for workers to physically access certain hazardous locations.

The potential safety improvement is particularly important in an industrial environment where working at height may involve significant risk.

4.2 Thermal inspection

Thermal cameras mounted on UAVs can potentially identify abnormal heat patterns.

Applications may include:

  • electrical equipment;

  • motors;

  • transformers;

  • boilers;

  • steam systems;

  • bearings; and

  • other industrial equipment.

The principle is:

Thermal image → Abnormal temperature → Further inspection → Corrective maintenance

This supports condition-based and predictive maintenance.

4.3 Stockpile and inventory measurement

UAVs can potentially be used to estimate the volume of:

  • biomass;

  • empty fruit bunches;

  • fibre;

  • shell;

  • compost;

  • palm kernel;

  • other materials.

Three-dimensional aerial mapping can provide estimates of stockpile volume without requiring workers to physically measure the entire pile.

This can improve:

  • inventory management;

  • production planning;

  • material utilisation; and

  • financial reporting.

5.0 LAE for Worker Safety

Worker safety represents another important area where LAE can create value.

Palm oil operations can involve:

  • working at height;

  • remote field locations;

  • heavy equipment;

  • confined areas;

  • difficult terrain;

  • electrical infrastructure; and

  • industrial machinery.

UAVs can perform certain inspection activities before workers enter an area.

For example:

UAV inspection → Identify hazard → Risk assessment → Controlled human intervention

This follows an important safety principle:

Use technology to reduce unnecessary human exposure to hazardous environments.

Therefore, the economic value of LAE is not limited to productivity. It can also arise through risk reduction and improved occupational safety.

6.0 LAE for Environmental Management

Environmental management is increasingly important in the palm oil industry.

UAVs can support monitoring of:

  • rivers;

  • drainage systems;

  • plantation boundaries;

  • erosion;

  • water bodies;

  • conservation areas;

  • illegal land clearing; and

  • environmental incidents.

For example, aerial monitoring can provide evidence of changes in drainage or river conditions.

This can support faster investigation and corrective action.

The combination of:

UAV + GPS + imagery + GIS + AI

can create a digital environmental monitoring system.

7.0 New Business Opportunities Created by LAE

The greatest long-term potential of LAE is not only solving existing problems but also creating new business opportunities.

7.1 Drone-as-a-Service

Palm oil companies do not necessarily need to own large fleets of UAVs.

Specialised companies could provide:

Drone-as-a-Service (DaaS)

The plantation company pays for services such as:

  • aerial mapping;

  • crop monitoring;

  • inspection;

  • thermal imaging;

  • surveying; and

  • data analysis.

This changes the business model from asset ownership to service utilisation.

7.2 Precision Agriculture Services

Specialised LAE companies could provide integrated services:

UAV → Data → AI → Crop Diagnosis → Recommendation

This could become a new agricultural technology service industry.

The company would not simply sell drones. It would sell agricultural intelligence.

7.3 Predictive Maintenance Services

UAV companies could provide industrial inspection services to palm oil mills.

For example:

Drone inspection → Image analysis → Defect identification → Maintenance recommendation

This creates a new market for predictive maintenance and industrial analytics.

7.4 FFB Transportation Opportunities

In the longer term, UAVs or other low-altitude autonomous vehicles could potentially support transportation of selected materials.

However, the economic feasibility depends on:

  • payload;

  • distance;

  • battery capacity;

  • operating cost;

  • terrain;

  • regulations; and

  • comparison with conventional trucks.

Therefore, drone-based FFB transportation should currently be considered a potential niche application rather than a direct replacement for conventional FFB transport.

8.0 Integration with Artificial Intelligence

The most significant opportunity may arise when LAE is combined with AI.

A UAV by itself provides limited economic value.

The greater value comes from:

UAV → Data → AI → Information → Decision → Action

For example:

  1. UAV collects images of a plantation.

  2. AI identifies abnormal palm conditions.

  3. GIS identifies the exact location.

  4. Management receives an automated report.

  5. Field workers verify the problem.

  6. Targeted intervention is performed.

  7. Results are monitored using subsequent UAV flights.

This creates a closed-loop digital agriculture system.

9.0 Economic Impact on the Palm Oil Industry

The potential economic effects can be grouped into five areas.

AreaPotential LAE Contribution
ProductivityFaster monitoring and better resource allocation
CostReduced inspection time and potentially lower operating costs
SafetyReduced human exposure to hazardous locations
SustainabilityMore targeted use of fertiliser, chemicals and resources
Decision-makingFaster and more accurate operational information

The impact should therefore be evaluated using measurable Key Performance Indicators (KPIs), such as:

  • inspection time per hectare;

  • cost per hectare;

  • labour hours;

  • fuel consumption;

  • fertiliser utilisation;

  • crop-loss percentage;

  • road maintenance response time;

  • equipment downtime;

  • safety exposure hours; and

  • maintenance cost.

This provides a stronger basis for a business case than simply stating that drones are technologically advanced.

10.0 Challenges to LAE Adoption in the Palm Oil Industry

Despite its potential, several challenges need to be addressed.

10.1 Initial investment

Investment may be required for:

  • UAVs;

  • sensors;

  • software;

  • AI platforms;

  • communication systems;

  • trained personnel; and

  • maintenance.

10.2 Regulatory requirements

Drone operations are subject to aviation regulations, particularly for more complex or Beyond Visual Line of Sight (BVLOS) operations.

Companies therefore need appropriate approvals, operating procedures and trained personnel.

10.3 Data management

Large plantations can generate enormous quantities of aerial imagery.

Companies need systems for:

  • data storage;

  • processing;

  • cybersecurity;

  • GIS integration;

  • AI analysis; and

  • data governance.

10.4 Weather

Tropical weather can affect UAV operations.

Heavy:

  • rain;

  • wind;

  • thunderstorms; and

  • poor visibility

can reduce operational reliability.

10.5 Skills

Successful implementation requires employees who understand both:

Palm oil operations + digital technology.

The future plantation workforce may therefore require greater knowledge of:

  • GIS;

  • UAV operations;

  • AI;

  • data analytics; and

  • digital agriculture.

11.0 Strategic Evaluation

The potential of LAE in the palm oil industry can be evaluated using three levels.

Level 1 – Immediate opportunity

Applications that are already relatively practical include:

  • plantation mapping;

  • crop monitoring;

  • infrastructure inspection;

  • road inspection;

  • environmental monitoring;

  • stockpile measurement.

Level 2 – Emerging opportunity

More advanced applications include:

  • AI-based disease detection;

  • precision agriculture;

  • predictive maintenance;

  • autonomous inspection;

  • integrated plantation digital twins.

Level 3 – Long-term opportunity

Potential future applications include:

  • autonomous cargo transportation;

  • advanced eVTOL logistics;

  • autonomous plantation operations;

  • aerial passenger mobility;

  • highly automated agricultural ecosystems.

The timing of these applications will depend on technological maturity, economics, regulation and operational reliability.

12.0 Proposed LAE Framework for the Palm Oil Industry

A suitable conceptual framework for the industry is:

LOW-ALTITUDE TECHNOLOGIES

Drones + UAVs + Sensors + AI + eVTOL

↓

DATA GENERATION

Aerial Images + Thermal Data + GIS + 3D Mapping

↓

DATA ANALYTICS

AI + Machine Learning + Predictive Analytics

↓

OPERATIONAL APPLICATIONS

Crop Monitoring + Infrastructure Inspection + Environmental Monitoring + Maintenance + Logistics

↓

BUSINESS OUTCOMES

Productivity + Cost Efficiency + Safety + Sustainability + Faster Decision-Making

↓

ECONOMIC TRANSFORMATION

Smart and Data-Driven Palm Oil Industry

13.0 Conclusion

The Low Altitude Economy has significant potential to transform the palm oil industry because of the industry's extensive geographical coverage, dependence on field inspection, transportation requirements and need for effective environmental and safety management.

The most immediate value of LAE is likely to come from data collection, monitoring and inspection rather than replacing conventional transportation. UAVs can provide rapid aerial information about plantations, roads, drainage systems, infrastructure and environmental conditions. When combined with AI, this information can support precision agriculture, predictive maintenance and faster operational decision-making.

LAE can also create new business opportunities through Drone-as-a-Service, precision agriculture services, industrial inspection, predictive maintenance and data analytics. In the longer term, autonomous cargo and eVTOL applications may create additional possibilities, although their commercial viability will depend on technology, regulation and operating economics.

Therefore, the potential of LAE in the palm oil industry can be summarised as:

LAE can transform the palm oil industry from a predominantly labour-intensive and ground-based operating model toward a more data-driven, automated and digitally integrated industry.

The most important economic opportunity is not simply “using drones”, but using low-altitude technologies to convert aerial data into better operational decisions, improved productivity, safer work practices and new business models.

LOW ALTITUDE ECONOMY (LAE): ECONOMIC TRANSFORMATION AND ENABLING TECHNOLOGIES

1.0 Introduction

The rapid development of digitalisation, artificial intelligence (AI), autonomous systems, advanced batteries and unmanned aircraft has created new opportunities for economic activities in low-altitude airspace. One emerging concept associated with this development is the Low Altitude Economy (LAE). LAE refers broadly to an economic ecosystem in which low-altitude airspace is utilised for commercial, industrial and public-service activities through technologies such as drones, Unmanned Aerial Vehicles (UAVs) and electric Vertical Take-Off and Landing (eVTOL) aircraft.

Although the exact altitude boundary differs between jurisdictions and applications, the concept generally concerns aviation activities conducted relatively close to the Earth's surface. The IEEE describes LAE in the context of economic activities and services conducted below approximately 1,000 metres above ground level, while some national frameworks use a broader altitude range. Therefore, LAE should be understood primarily as an emerging economic ecosystem rather than as an aviation sector defined by one universally accepted altitude limit.

The importance of LAE extends beyond the use of drones. It represents a potential transformation of transportation, logistics, agriculture, infrastructure management, emergency services and urban mobility. By integrating aviation technologies with artificial intelligence, telecommunications, digital platforms and energy systems, LAE can create new business models and industrial value chains.

This report defines the Low Altitude Economy, examines its relevance to economic transformation, compares LAE with traditional aviation and logistics, and discusses the major technologies driving its development.

2.0 Definition of Low Altitude Economy

The Low Altitude Economy can be defined as:

An emerging economic ecosystem in which low-altitude airspace is utilised by manned and unmanned aerial systems, supported by digital infrastructure, aviation services, energy systems, data technologies and regulatory frameworks to deliver transportation, logistics and other commercial and public services.

The definition contains several important elements.

2.1 Low-altitude airspace

The first element is the utilisation of low-altitude airspace as an economic resource. Historically, economic activities have been concentrated primarily on land and maritime environments, while aviation has mainly been associated with higher-altitude passenger and cargo transportation.

LAE introduces a new economic dimension by allowing low-altitude airspace to become an operational environment for:

  • drone delivery;

  • agricultural activities;

  • infrastructure inspection;

  • aerial surveying;

  • emergency response;

  • passenger mobility;

  • tourism; and

  • other specialised services.

Therefore, LAE can be viewed as the development of a three-dimensional economic space.

2.2 Aircraft and aerial platforms

LAE involves a range of aerial technologies, including:

  • drones;

  • UAVs;

  • remotely piloted aircraft systems;

  • autonomous aerial vehicles;

  • cargo drones;

  • agricultural drones; and

  • eVTOL aircraft.

These technologies differ in size, payload, range, level of autonomy and intended application.

2.3 Digital and physical infrastructure

The development of LAE requires more than aircraft. It also requires supporting infrastructure such as:

  • vertiports;

  • drone landing and take-off stations;

  • charging facilities;

  • communication networks;

  • navigation systems;

  • unmanned traffic-management systems;

  • maintenance facilities; and

  • digital platforms.

Consequently, LAE should be considered an ecosystem rather than simply an aircraft industry.

3.0 Relevance of LAE to Economic Transformation

LAE is relevant to economic transformation because it introduces a new transportation and service layer into the economy. Traditional economic activities have generally relied on roads, railways, ports and conventional airports. LAE adds low-altitude airspace as another means of moving people, goods, information and services.

3.1 From two-dimensional to three-dimensional economic activity

Traditional transportation largely operates across geographical surfaces. For example, goods may move from a factory to a warehouse and subsequently to a customer through roads.

A drone-based system can potentially establish a more direct connection:

Distribution Centre → UAV → Customer

The ability to operate above physical transportation networks can reduce dependence on roads for selected applications.

However, LAE should not necessarily be viewed as a replacement for traditional transportation. Instead, it can complement existing transportation systems by providing an additional layer for specific applications.

3.2 Improvement in transportation efficiency

One potential economic benefit of LAE is reduced transportation time.

Ground transportation is affected by:

  • traffic congestion;

  • road conditions;

  • geographical obstacles;

  • road-network capacity; and

  • driver availability.

Aerial transportation can potentially provide more direct routes between locations. This is particularly valuable for time-sensitive products and services such as:

  • medical supplies;

  • emergency equipment;

  • spare parts;

  • high-value goods;

  • food delivery; and

  • urgent documents.

Therefore, LAE may improve supply-chain responsiveness and reduce delivery latency.

3.3 Increased productivity

LAE can contribute to productivity by automating repetitive and labour-intensive activities.

For example, conventional agricultural inspection may require workers to physically inspect large areas. A UAV can cover the same area using aerial imaging and sensors.

The process can become:

UAV → Data Collection → AI Analysis → Problem Identification → Management Decision

This transforms the role of the aircraft from merely a transportation device into a mobile data-collection and decision-support platform.

3.4 Development of new industries

LAE can stimulate several supporting industries.

The emerging value chain may include:

Battery Manufacturing → Sensors → UAV Manufacturing → Software → AI → Communication Networks → Airspace Management → Charging Infrastructure → Operations → Data Analytics

Therefore, the economic impact of LAE extends beyond aircraft manufacturers.

It can create demand for:

  • aerospace engineers;

  • software developers;

  • AI specialists;

  • drone operators;

  • maintenance technicians;

  • battery engineers;

  • data analysts;

  • airspace-management professionals; and

  • infrastructure developers.

4.0 Differences Between LAE and Traditional Aviation

Traditional aviation is primarily associated with the transportation of passengers and cargo using conventional aircraft between established airports.

LAE has a substantially broader operating model.

DimensionTraditional AviationLow Altitude Economy
Main aircraftCommercial aircraft and helicoptersDrones, UAVs and eVTOLs
Main activitiesPassenger and cargo transportationLogistics, inspection, agriculture, mobility, emergency services and other applications
InfrastructureAirports, runways and terminalsVertiports, drone stations and charging infrastructure
OperationPredominantly human-pilotedIncreasingly automated or autonomous
NetworkAirport-to-airportPotentially point-to-point and distributed
TechnologyConventional aviation systemsAI, sensors, autonomous systems and digital platforms
Business modelAirlines and airport servicesAviation, technology and service-platform ecosystems
Operating environmentEstablished aviation routes and airportsLow-altitude airspace and distributed operational locations

The key distinction is that LAE is not simply small-scale aviation. It represents the convergence of aviation with robotics, AI, telecommunications, logistics, energy and digital services.

5.0 Differences Between LAE and Traditional Logistics

Traditional logistics relies heavily on road, rail, maritime and conventional air transportation.

A typical logistics chain may be represented as:

Supplier → Truck → Warehouse → Distribution Centre → Truck → Customer

LAE introduces an additional transportation model:

Distribution Centre → Drone → Customer

Traditional logistics is primarily constrained by:

  • road capacity;

  • congestion;

  • distance;

  • fuel;

  • driver availability; and

  • physical infrastructure.

LAE introduces different constraints, including:

  • battery capacity;

  • payload;

  • weather conditions;

  • communication reliability;

  • airspace availability;

  • navigation;

  • cybersecurity; and

  • aviation regulations.

Therefore, LAE does not eliminate transportation constraints. Instead, it changes the nature of those constraints.

6.0 Key Technologies Driving LAE Growth

Several technologies are responsible for the development of the Low Altitude Economy.

6.1 Drones

Drones are currently among the most widely recognised technologies associated with LAE.

They can be used for:

  • parcel delivery;

  • food delivery;

  • agricultural spraying;

  • aerial photography;

  • surveying;

  • mapping;

  • environmental monitoring;

  • infrastructure inspection; and

  • emergency response.

Their economic value is particularly significant when they can perform tasks faster, more safely or more efficiently than conventional methods.

6.2 Unmanned Aerial Vehicles (UAVs)

UAVs are aircraft that can operate without a human pilot physically located inside the aircraft.

Modern UAVs can incorporate:

  • cameras;

  • LiDAR;

  • radar;

  • GPS/GNSS;

  • thermal sensors;

  • AI-based image recognition; and

  • autonomous navigation.

For example, UAVs can inspect power lines, pipelines, bridges and industrial facilities without requiring workers to physically access potentially hazardous locations.

The economic value therefore comes from both physical flight capability and data generation.

6.3 Electric Vertical Take-Off and Landing (eVTOL)

eVTOL aircraft represent another important technology in LAE.

An eVTOL aircraft uses electric propulsion and can take off and land vertically. This provides an operational advantage in locations where conventional runways are unavailable or impractical.

Potential applications include:

  • urban air taxis;

  • regional transportation;

  • medical transportation;

  • cargo transportation;

  • emergency services; and

  • tourism.

eVTOL technology is particularly relevant to the development of Advanced Air Mobility (AAM).

6.4 Artificial Intelligence

AI is an important enabling technology because future LAE operations are expected to become increasingly automated.

AI can support:

  • autonomous navigation;

  • obstacle detection;

  • route optimisation;

  • image analysis;

  • predictive maintenance;

  • traffic management;

  • demand forecasting; and

  • agricultural monitoring.

For example:

Drone + Camera + AI

can transform thousands of aerial images into information about crop health, infrastructure defects or environmental conditions.

Consequently, the economic value of LAE increasingly comes from the combination of:

Aircraft + Data + Artificial Intelligence

6.5 Communication Networks

Reliable communication is essential for large-scale UAV operations.

Aircraft may need to communicate with:

  • ground-control stations;

  • other aircraft;

  • airspace-management systems;

  • satellites;

  • customers; and

  • digital platforms.

Technologies such as 5G, 5G-Advanced and future 6G networks may support high-density aerial operations through low-latency communication and high-speed data transmission.

6.6 Unmanned Traffic Management

As the number of UAVs increases, managing aircraft within low-altitude airspace becomes increasingly important.

Unmanned Traffic Management (UTM) systems can support:

  • flight planning;

  • aircraft identification;

  • route management;

  • geofencing;

  • conflict detection;

  • airspace coordination; and

  • communication between operators and aviation authorities.

UTM therefore provides an important foundation for the safe scaling of LAE.

6.7 Battery and Energy Technology

Battery technology is a major factor affecting electric UAV and eVTOL performance.

Important areas include:

  • battery energy density;

  • battery-management systems;

  • charging speed;

  • battery life;

  • thermal management; and

  • charging infrastructure.

Improved energy technology can increase:

  • flight range;

  • payload capacity;

  • operational efficiency; and

  • aircraft utilisation.

Therefore, LAE is also connected to the wider energy transition and electrification of transportation.

6.8 Sensors and Navigation Systems

Advanced sensors enable UAVs to operate safely and collect high-quality information.

These include:

  • GPS/GNSS;

  • cameras;

  • LiDAR;

  • radar;

  • inertial measurement units;

  • ultrasonic sensors; and

  • thermal imaging systems.

The combination of sensors, AI and UAVs can produce accurate three-dimensional maps and support applications such as construction, mining, agriculture and infrastructure management.

7.0 Major Applications of LAE

7.1 Logistics and Delivery

Potential applications include:

  • parcel delivery;

  • food delivery;

  • medical supplies;

  • emergency equipment; and

  • industrial spare parts.

7.2 Agriculture

UAVs can support:

  • crop monitoring;

  • fertiliser application;

  • pesticide spraying;

  • plantation mapping;

  • pest detection; and

  • crop-health analysis.

7.3 Infrastructure Inspection

UAVs can inspect:

  • bridges;

  • power transmission lines;

  • pipelines;

  • telecommunications towers;

  • buildings; and

  • industrial facilities.

This can reduce the need for workers to enter difficult or hazardous environments.

7.4 Emergency and Disaster Management

LAE technologies can support:

  • search and rescue;

  • disaster assessment;

  • emergency supply delivery;

  • firefighting support; and

  • communication restoration.

7.5 Urban Mobility

eVTOL aircraft could potentially provide short-distance passenger transportation between:

  • airports;

  • business districts;

  • urban centres; and

  • regional destinations.

8.0 LAE as an Emerging Economic Ecosystem

A major characteristic of LAE is its cross-sector nature.

The ecosystem can be represented as follows:

Technology Providers

↓

UAV/eVTOL Manufacturers

↓

Digital and AI Platforms

↓

Communication Infrastructure

↓

Airspace Management

↓

Vertiports and Charging Infrastructure

↓

Operators

↓

Logistics and Mobility Services

↓

Consumers and Businesses

This demonstrates that LAE is not an isolated aviation industry. It integrates multiple sectors into one economic ecosystem.

9.0 Economic Benefits of LAE

The potential economic benefits include:

9.1 Lower transportation time

Direct aerial routes can reduce travel time for selected applications.

9.2 Improved productivity

Automation can reduce time spent on repetitive inspections and monitoring.

9.3 New business opportunities

Companies can develop new services such as:

  • Drone-as-a-Service;

  • Inspection-as-a-Service;

  • Agriculture-as-a-Service;

  • Delivery-as-a-Service; and

  • Mobility-as-a-Service.

9.4 Regional connectivity

LAE may improve connectivity for remote communities where conventional infrastructure is expensive or difficult to develop.

9.5 New employment opportunities

New occupations may emerge in:

  • UAV operations;

  • software engineering;

  • AI;

  • maintenance;

  • battery technology;

  • airspace management; and

  • data analytics.

10.0 Challenges and Limitations

Despite its potential, LAE faces several challenges.

10.1 Safety

Large-scale autonomous aircraft operations require robust safety systems to prevent:

  • collisions;

  • system failures;

  • loss of communication;

  • navigation errors; and

  • uncontrolled aircraft.

The challenge becomes more complex when thousands of aircraft operate simultaneously.

10.2 Regulation

Governments must establish rules covering:

  • aircraft certification;

  • operator licensing;

  • autonomous operations;

  • Beyond Visual Line of Sight (BVLOS) operations;

  • airspace management;

  • insurance;

  • liability;

  • cybersecurity; and

  • privacy.

10.3 Infrastructure

Large-scale LAE requires investment in:

  • vertiports;

  • charging stations;

  • communication networks;

  • UTM systems;

  • maintenance facilities; and

  • digital infrastructure.

10.4 Public Acceptance

Public concerns may include:

  • noise;

  • privacy;

  • safety;

  • visual impact;

  • surveillance; and

  • cybersecurity.

Therefore, technological feasibility alone is insufficient. Successful LAE development also requires social acceptance and appropriate governance.

11.0 Conceptual Framework

The relationship between technology and economic transformation can be summarised as follows:

Enabling Technologies

Drones + UAVs + eVTOL + AI + 5G/6G + Sensors + Batteries

↓

Supporting Infrastructure

Vertiports + Charging Systems + Digital Networks + UTM

↓

Economic Applications

Logistics + Agriculture + Inspection + Emergency Services + Tourism + Urban Mobility

↓

Economic Outcomes

Higher Productivity + Faster Transportation + New Services + New Markets + Employment + Regional Connectivity

↓

Economic Transformation

Integration of Aviation, Digital Technology, Logistics, Energy and Services into a Three-Dimensional Economic Ecosystem

This framework demonstrates that technology is the enabler, while economic transformation represents the broader outcome.

12.0 Discussion

The development of LAE demonstrates how technological innovation can create new economic spaces and business models. Traditional aviation has historically focused on transporting passengers and cargo between airports, while traditional logistics has concentrated on moving goods through established transportation networks.

LAE introduces a different paradigm by integrating low-altitude airspace into everyday economic activities.

Its significance is therefore not limited to the drone industry. Instead, LAE represents the convergence of:

Aviation + Robotics + Artificial Intelligence + Telecommunications + Logistics + Energy + Data

This convergence can generate new sources of productivity and economic value.

However, the economic transformation associated with LAE should not be viewed as automatic. The benefits depend on technological maturity, infrastructure investment, regulatory development, public acceptance, safety management and the development of viable business models.

Consequently, the future development of LAE will require cooperation among governments, aviation authorities, technology companies, logistics providers, infrastructure developers, researchers and consumers.

13.0 Conclusion

The Low Altitude Economy is an emerging economic ecosystem that uses low-altitude airspace for transportation, logistics, agriculture, infrastructure management, emergency response, tourism and other commercial and public services.

Unlike traditional aviation, LAE relies increasingly on smaller, automated and digitally connected aircraft operating in distributed environments. Unlike conventional logistics, it can utilise three-dimensional airspace to provide direct transportation routes and services.

The major technologies driving LAE include drones, UAVs, eVTOL aircraft, artificial intelligence, advanced communication networks, unmanned traffic management, batteries and advanced sensors.

The economic significance of LAE is therefore broader than the development of new aircraft. It has the potential to create new industries, business models, employment opportunities and infrastructure while improving transportation efficiency and regional connectivity.

Ultimately, LAE can be understood as a transition towards a three-dimensional digital economy, where low-altitude airspace becomes another productive economic layer alongside land, sea and conventional aviation.

The central relationship can therefore be expressed as:

Low-Altitude Airspace + Advanced Technology + Digital Infrastructure + New Business Models = Low Altitude Economy and Economic Transformation

References

Federal Aviation Administration (FAA). (2025). Advanced Air Mobility. U.S. Department of Transportation.

International Civil Aviation Organization (ICAO). (2025). Unmanned Aviation. ICAO.

IEEE Standards Association. (2025). IEEE 3776: Standard for Low-Altitude Economy Terminology. IEEE.

[Academic literature on Low Altitude Economy and eVTOL development should be added according to the citation style required by the university, such as APA 7th edition.]