Challenges in Inventory Management in the New Era of E-Commerce in Malaysia

Challenges in Inventory Management in the New Era of E-Commerce in Malaysia

Nurhayati Kamarudin¹, Siti Norida Wahab², Wirda Syaheera Mohd Sulaiman^3*

¹Faculty of Technology Management and Technopreneurship, University Technical Malaysia Melaka (UTeM)

²Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, University Technology MARA (UiTM)

³Pusat Pembelajaran Bahasa, University Technical Malaysia Melaka (UTeM)

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000595

Received: 19 August 2025; Accepted: 25 August 2025; Published: 23 September 2025

ABSTRACT

The rapid growth of e-Commerce in Malaysia has significantly transformed business operations, creating both opportunities and challenges for inventory management. This study explores the key issues faced by e-Commerce businesses and examines strategies employed to address them. Using a quantitative approach, data were collected via a structured questionnaire distributed to retail businesses in Malacca. Regression analysis revealed that quality control has a significant positive effect on inventory management challenges (β = 0.458, p < 0.01), while order fulfilment shows a negative but significant relationship (β = -0.303, p = 0.018). Inventory tracking was not found to be a significant factor (p = 0.653). These results suggest that quality control and fulfilment remain central to efficient inventory management, although other unexplored factors may also play a substantial role. Future studies should broaden the sample to other Malaysian regions, include more managerial and strategic-level respondents, and apply advanced methods such as factor analysis or structural equation modeling to capture additional explanatory variables, including technological capacity, supply chain integration, and returns management. The findings provide insights for businesses to strengthen competitiveness, customer satisfaction, and adaptability to new technologies such as AI, IoT, and blockchain in inventory practices.

Keywords: e-Commerce, Inventory Management, Operational Challenges, Malaysia, Technological Integration

INTRODUCTION

A new era of business activities has been established in by the growth of e-Commerce, with online shopping developing in popularity. The trend towards e-Commerce has created additional challenges for inventory management in Malaysia, as companies need to adjust to the changing needs of the market. (Macas et al., 2021). The e-Commerce market in Malaysia is estimated to expand by US$ 4.3 billion in 2020 and reach US$ 8.1 billion by 2024. More than 25 million individuals, or 80% of Malaysia’s total population, frequently utilize the internet and shop online, making this region a key hub for the development and growth of e-Commerce. (Unicommerce, 2023).

Despite the fact the Malaysian e-Commerce market is expanding rapidly and rising to the top of the list for both domestic and foreign investors, there may still be certain challenges on the road to success. These challenges include managing orders and inventory, getting involved in B2B and B2C sales platforms, dealing with fluctuations in demand and fulfilment, and dealing with manual dependency in e-Commerce activities (Unicommerce, 2023).

This research hopes to solve a few existing problems and explore some novel prospects in the field of challenges in inventory management in the e-Commerce business. Hence, researchers attempted to find answers to the following questions through systematic investigation and analysis. In existing research, reverse logistics has been expanding in the e-Commerce sector because it is the consumer-facing aspect of the market. However, several initiatives have been made to address the sector’s issues such as accepting random online orders, but they are still experiencing problems with transportation, inventory management, and warehousing inefficiencies (“An Overview of Electronic Commerce (e-Commerce),” 2021). There is a need for detailed study and analysis to find out what are the types of inefficiencies they encounter and to address this gap.

The implementation of robots and drones in e-Commerce logistics is still in the early stages, and there are still challenges that get to overcome, including integration with current systems and governmental regulations (Fahlstrom & Zeisig, 2022). Nevertheless, more businesses are planned to adopt these technologies as they develop more extensively, completely changing the logistics of e-Commerce. This problem statement sets the stage for further exploration and analysis of the issues surrounding the adoption and implementation of these technologies in the context of e-Commerce.

Objectives

1. To identify challenges faced by businesses in managing inventory in the new era of E-Commerce.
2. To identify the inventory management strategies adopted by e-Commerce businesses to address inventory management challenges in the e-Commerce sector.

Limitations and Significance of Study

The main limitation of this research is the scope of analysis in certain areas may be limited by the accessibility and availability of data from involved e-Commerce businesses. Furthermore, there’s a chance that Malaysia’s cultural and regional differences will possibly limit how broadly the results can be applied. However, it is believed that the challenges identified through this research represent a significant portion of the inventory management landscape within the Malaysian e-Commerce sector.

This study focuses only on retail stores in Malacca, Malaysia, so the findings might not apply to e-Commerce practices in other states of Malaysia. The sample size is also limited because of practical reasons and the way participants were chosen randomly, which could affect how broadly the results can be used for all e-Commerce businesses in Malaysia. Not only that, relying on self-reported answers from a questionnaire sent by email might lead to biased or incomplete responses, potentially affecting the reliability of the data.

This research provides insightful information about how inventory management practices are evolving in the local e-Commerce sector, which helps entrepreneurs and decision-makers determine strategies to maximize operational effectiveness and competitiveness. The results of this study can also directly help Malaysian e-Commerce companies by assisting them in identifying and addressing necessary inventory management challenges, which will ultimately improve customer satisfaction and profitability. By identifying the main challenge in inventory management in the new era of E-Commerce, the research can assist other e-Commerce businesses owner to determine and plan appropriate solutions to overcome issues in inventory management, contributing to positive social impacts.

Key Concepts

Inventory management

The costliest asset in a business is its inventory. For some businesses, inventory typically makes up more than 10% to 15% of the total assets. It is believed that manufacturing companies certainly carry more inventory than service sector companies, but efficient inventory management is still vital to manufacturing and service organizations equally. Hence, the inventory management policy has an impact on how effectively a business utilizes its resources to produce goods and services. It’s a challenging task to create efficient inventory control systems for businesses to eliminate waste and minimize the issue of shortages in stocks (Rehman Khan & Yu, 2019).

e-Commerce

e-Commerce, or electronic commerce is known as the sale and purchase of products or services via the internet. It consists of a broad range of information, platforms, and instruments for online buyers and sellers, such as mobile purchasing and encrypted transactions over the internet. Not only that, the majority of companies which operate an online presence manage logistics and fulfilment, carry out e-Commerce marketing and sales, and manage their online stores or platforms (Laukaitis, 2020).

e-Commerce Challenges

The most significant challenge is having trust in digital payments. A standard paper on Baseline Laws and Rules could address the validity and accuracy of transactions conducted online. In addition, to these e-Commerce challenges, there is several developing nations’ emerging economies also encountered other difficulties such as poor framework planning regarding online marketing, lack of marketing strategies, marketing promotions, political issues of the nation, lack of coverage of internet, as well as expensive cost of services and products compared to traditional market (“An Overview of Electronic Commerce (e-Commerce),” 2021)

LITERATURE REVIEW

Inventory Management and e-Commerce

Inventory management plays a crucial role in the operational success of e-Commerce businesses, where the speed and accuracy of order fulfilment can directly influence customer satisfaction and business performance. Inventory models are broadly categorized into independent and dependent demand models, depending on the nature of the inventory involved. Dependent demand refers to the internal demand for items such as raw materials, components, or subassemblies, which are needed to produce finished goods (Khan & Yu, 2019). While these models have traditionally been applied to manufacturing contexts, the principles are increasingly relevant in e-Commerce, particularly for businesses managing complex product lines or integrating with third-party suppliers.

Inventory strategies in both traditional and digital business models are shaped by key factors such as customer demand, industry practices, forecast accuracy, and product availability (John et al., 2015). These considerations are especially critical in e-Commerce, where fluctuating demand and short delivery expectations require agile and accurate inventory control.

Effective inventory management also involves minimizing stockouts and managing costs, which is vital in the highly competitive e-Commerce environment. Khan and Yu (2019) outline four major types of inventory-related costs: direct, indirect, fixed, and variable. Direct costs include labour and materials directly used in order fulfilment, while indirect costs encompass overheads such as utilities and warehouse maintenance. Fixed costs, like equipment and building expenses, remain stable regardless of output, whereas variable costs change with order volume and are more responsive to fluctuations in demand. In e-Commerce, managing variable costs is especially important, as they can significantly affect pricing strategies and profit margins. Fixed costs, on the other hand, are often treated as sunk costs, meaning they cannot be recovered once incurred (Rehman Khan & Yu, 2019).

The rise of electronic commerce has reshaped how businesses manage logistics, inventory, and customer interactions. As noted by Chin and Fadli (2021), e-Commerce platforms allow consumers not only to purchase products online but also to browse, compare prices, and evaluate popular items across different vendors. This shift has changed how businesses think about inventory planning, supply chain responsiveness, and marketing. Furthermore, e-business models emphasize integration with suppliers and customers, reinforcing the importance of real-time inventory visibility and efficient restocking processes.

e-Commerce has thus become a transformative force, offering businesses opportunities to streamline operations and gain a competitive edge through faster and more cost-effective distribution (Taher, 2021). The adoption of strategies such as just-in-time production and delivery, aligned with the Toyota Management System, has been made more achievable through digital technologies. These advancements highlight the growing importance of inventory management not just as a backend function, but as a strategic component of e-Commerce success.

According to Chin and Fadli (2021) there are four primary classifications in e-Commerce that is divided into Business to Business (B2B), Business to Consumer (B2C), Consumer to Business (C2B) and Consumer to Consumer (C2C) :

1. Business-to-business (B2B): This is where some companies conduct business with other companies. Manufacturers sell to distributors, while wholesalers sell to retailers. The product price differs based on order quantity and could be negotiated.
2. Business to Consumer (B2C): In this case, the sale of goods and services to the general public is conducted using catalogues and shopping cart programs.
3. Consumer to Business (C2B): Consumers can sell their project or business idea through the internet with a budget. Businesses can then bid on the project based on their requirements respectively. Next, the consumer will evaluate the bids and decide on the organization that will complete or take over the project. The organization will introduce talented individuals, including developers and designers, and provide a platform for consumers to meet and collaborate.
4. Consumer to Consumer (C2C): There are a variety of online websites that provide free auctions, classifieds, and forums that allow users to sell and buy goods and services.

Challenges in e-Commerce

The challenges faced in e-Commerce environments are influenced by various factors, among which trust, security, and privacy are particularly critical. These elements collectively impact the reliability, safety, and overall success of online transactions, making them key contributors to the broader challenges experienced by e-Commerce businesses.

Trust is a foundational factor in e-Commerce and is widely recognized as one of its primary challenges. Unlike traditional retail, where goods and services can be physically inspected, online transactions require buyers to place their trust in sellers without direct interaction. Trust establishment in digital environments presents unique challenges due to the lack of physical verification and limited regulatory frameworks.. Behaviour-based and certificate-based trust frameworks have been developed for ad hoc and sensor networks, providing means for evaluating and distributing trust (Aivaloglou et al., 2006).

New users often evaluate the credibility of an e-Commerce platform based on perceived trustworthiness. Baturay and Toker (2019) mentioned trust can be developed through knowledge-based trust, which emerges through repeated interactions; calculus-based trust, which is based on a cost-benefit analysis; and identification-based trust, which arises when both parties understand and recognize each other’s needs. These models underscore the complexity of building trust in a virtual environment, where personal and face-to-face interaction is absent, making trust a recurring challenge in e-Commerce.

Another significant challenge is security. The purpose of security measures in e-Commerce is to safeguard digital assets from unauthorized access, tampering, or misuse. However, concerns around cybersecurity breaches and data theft remain persistent. Customers worry about the safety of their financial information, while businesses face potential financial and reputational risks from hacking incidents and data leaks. To foster trust in e-Commerce, a broad focus on both technical and non-technical elements is crucial, as the virtual environment lacks traditional trust-building cues like handshakes and body language (Fink, 2000). Additionally, the lack of robust system design can expose both companies and their users to fraud, illustrating how poor security infrastructure can contribute to ongoing operational challenges in e-Commerce.

Privacy concerns also significantly influence the challenges in e-Commerce. Many consumers are reluctant to engage in online transactions due to fears over how their personal data is collected, used, or shared. E-Commerce platforms are capable of tracking detailed user behaviour such as browsing patterns, purchasing history, and preferences which raises concerns about unauthorized data usage. Privacy and security are critical issues in e-Commerce and information systems. Privacy concerns arise from the misuse of personal data, including sharing or selling information without consent (Ackerman & Davis, 2003). These privacy concerns erode customer confidence and present ongoing barriers for businesses trying to build sustainable digital operations.

There are several studies that propose models linking privacy, security concerns, perceived risks and trust interconnecting these elements to shape consumers’ behaviours in e-Commerce. These elements influence consumer confidence and the overall success of businesses. Comprehensive security measures, privacy safeguards and trust building strategies are crucial to overcome challenges in e-Commerce environments (Smith, 2021)

Inventory Management in The New Era

The primary goal of inventory management is to optimize customer service levels while minimizing associated inventory costs. Achieving this balance requires the development of appropriate inventory replenishment policies that account for various dynamic factors such as demand variability, lead times, and production capabilities.

The evolution of inventory management has been significantly influenced by technological advances, particularly under the framework of the Fourth Industrial Revolution (Industry 4.0) a concept initially introduced by the German government. Industry 4.0 is characterized by the integration of advanced technologies such as cloud computing, big data analytics, RFID, the Internet of Services (IoS), and cyber-physical systems, all of which are designed to enhance operational efficiency and real-time decision-making across supply chains (Mashayekhy et al., 2022).

Historically, the industrial sector has undergone multiple transformative phases. The First Industrial Revolution began with the invention of the steam engine, which enabled the shift from manual labor to mechanized production. The Second Industrial Revolution emerged with the widespread use of electricity in factories, further advancing production capacity. The Third Industrial Revolution introduced more sophisticated electronic systems and information technology, paving the way for automation. Now, Industry 4.0 fundamentally redefines the supply chain by leveraging advanced digital technologies such as IoT, AI, big data analytics, blockchain, automation, and cloud computing to create a highly interconnected, transparent, and responsive ecosystem across manufacturing, distribution, and transportation sectors (Preindl et al., 2020).

The concept of the Internet of Things (IoT) was coined by Kevin Ashton, cofounder and executive director of the Auto-ID Center at MIT, in 1999 (Korade et al., 2019) IoT refers to a network of physical objects (such as devices, vehicles, buildings, and other items embedded with electronics, sensors, software, and network connectivity that enables these objects to autonomously communicate and exchange data without requiring human intervention (Kamdar et al., 2019). The development of smart factories, where machines interact dynamically without human intervention, minimizes the risks of errors and delays and represents a significant milestone in modern inventory and production management.

Traditionally, production schedules were primarily supplier-driven. Today, however, they are increasingly shaped by customer demand, requiring businesses to adopt a more agile, demand-responsive approach. To remain competitive, companies must utilize data analytics for accurate production planning and decision-making. Production rates should be continuously aligned with real-time demand signals, allowing machinery to synchronize and respond accordingly. Furthermore, inventory levels must be adjusted to accommodate fluctuating demand, ensuring uninterrupted production (Singh et al., 2025). As a result, inventory policies including replenishment frequency, review intervals, and order quantities must be adaptive and data-driven to support operational agility and efficiency.

Research Framework

Figure 1: Research Framework

This conceptual framework sets and clarifies the research direction of this study. The relationship between the independent and dependent variables is illustrated in the conceptual framework above. There are three independent variables in this research, namely order fulfilment, quality control, and tracking of inventory level. Meanwhile, the dependent variable in this research is challenges in E-Commerce.

Dependent & Independent Variables

Dependent variables (Challenges in customers’ trust, security and privacy)

E-Commerce platforms face multiple challenges that significantly impact customer satisfaction and overall business performance. Technical issues in identity verification processes can reduce consumer trust and disrupt transactions (Islam, 2024). Additionally, customer-driven factors such as poor delivery services, high product costs, delayed shipments, and lack of supply chain integration contribute to dissatisfaction. Startups struggle with high expenditures on advertising, technology, and digital infrastructure, making it difficult to attract and retain customers. Competition in the e-Commerce sector further complicates brand differentiation and intensifies price wars, leading to reduced profitability (Natarajan, n.d.).

Another major issue is managing return and refund policies. While lenient return policies foster customer trust, they can result in financial losses; conversely, overly strict policies may deter future purchases. Balancing these policies is essential for maintaining customer satisfaction and ensuring long-term business viability (Zeithaml et al., 2001). These interconnected challenges ultimately influence the success and sustainability of e-Commerce businesses

Independent Variables (Order fulfilment, quality control & inventory tracking)

Order Fulfilment

Efficient fulfilment is essential for meeting customer expectations regarding speed and accuracy. Businesses may choose between in-house fulfilment and third-party logistics (3PL) providers, depending on size and resources. Technology breakthroughs and changing consumer tastes are driving a constant evolution in the electronic commerce (e-Commerce) scene (Raji et al., 2024).

Quality Control

Quality control (QC) ensures that products meet established standards before they reach customers. This includes inspections of incoming materials and final products to avoid defects and ensure fitness for use. Effective QC reduces returns, minimizes waste, and supports brand trust. It is also integral to inventory management, as it influences decisions on raw material and finished goods handling. A robust QC system enhances customer confidence and long-term loyalty (Ebrahim et al., 2024).

Inventory Tracking

Inventory tracking involves monitoring the movement of goods throughout the supply chain from receiving and storage to fulfilment and returns. It ensures the right products are available at the right time, helping prevent stockouts or overstocking. Techniques range from manual tracking to sophisticated inventory management systems and JIT models like Kanban. Third-party logistics providers may assist, though they can introduce challenges related to cost, coordination, and control (What Is 3PL? Meaning, Benefits, and Challenges – Inbound Logistics, 2024; Darius, 2024). Accurate inventory tracking supports timely order fulfilment and boosts customer satisfaction.

These independent variables are critical operational factors that influence the dependent variable customer satisfaction which affects repeat purchase rates, brand loyalty, and overall e-Commerce success.

METHODOLOGY

This study adopts a descriptive research design and employs a quantitative approach to examine the relationship between independent variables (order fulfilment, quality control, and inventory tracking) and the dependent variable (inventory management challenges). Emphasis is placed on ensuring validity and reliability to guarantee that the data collected is both accurate and unbiased.

Data Collection

Data were gathered using a structured questionnaire, divided into three sections: demographic information, perceived challenges in e-Commerce, and inventory management strategies. All items were measured using a 5-point Likert scale. To ensure methodological transparency and replicability, the full questionnaire is provided in the Appendix.

The questionnaire was distributed to retail businesses in Malacca, Malaysia, covering outlets such as Watsons, Uniqlo, ProHealth Pharmacy, JD Sports, DEES, Al-Ikhsan, SASA, and FICO Melaka. Respondents included store staff, managers, material handlers, inventory executives, and IT/MIS controllers. While this diversity provided valuable insights, the sample leaned heavily toward store staff, with relatively few managerial-level participants. Future research should expand respondent profiles to include more managers and strategic decision-makers to capture a broader organizational perspective.

The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS). Analytical procedures included descriptive statistics such as mean, median, mode, standard deviation, and variance to summarize respondents’ profiles and response trends. A normality test was conducted to assess data distribution. For parametric data, Pearson’s correlation and regression analysis were used to evaluate the relationships between independent and dependent variables. A reliability test (Cronbach’s alpha) was also performed to assess the internal consistency of the questionnaire.

To enhance the depth and context of the findings, secondary data were collected through a review of existing literature from reputable academic databases including ScienceDirect, Zendy, and ResearchGate. This supported the theoretical underpinnings of the study and provided comparative insights. Future research should employ more advanced statistical models such as factor analysis or structural equation modeling (SEM) to explore additional variables such as technological capacity, supply chain integration, and returns management.

A pilot study was conducted as a preliminary test to assess the feasibility of the research instruments and procedures. This small-scale study helped identify and rectify potential issues before launching the main study, thus minimizing error and improving data quality.

Sample and Scope

The research was conducted in Malacca, Malaysia, selected for its strategic representation of retail e-Commerce activity in the region. The city hosts a diverse range of retail businesses such as Watsons, Uniqlo, ProHealth Pharmacy, JD Sports, DEES, Al-Ikhsan, SASA, and FICO Melaka all of which operate both physical outlets and online platforms. Data were collected from key personnel involved in e-Commerce operations, including store managers, material handlers, inventory system controllers (IT/MIS), and inventory management executives, ensuring a comprehensive understanding of inventory management challenges in the local e-Commerce landscape.

A total of 95 responses were collected. Respondents ranged in age from 18 to 55 years, with most being early to mid-career professionals. While the Malacca region was chosen for its representation of active retail e-Commerce operations, the findings may not generalize to other Malaysian states. Therefore, future studies should expand the geographical scope to regions such as Klang Valley, Penang, and Johor, which represent larger and more diverse e-Commerce hubs.

FINDINGS

Validity Test

The relationship between independent and dependent variables is measured by the Pearson correlation coefficient, which was used to conduct the validity test. The correlation coefficient can be used to determine the degree that independent and dependent variables are related. (Saunders et al. 2003). The Pearson’s correlation coefficients for the R values’ interpreting correlation range are displayed in Table 1.1 below.

Table 1.1 Range of Pearson’s Correlation Coefficients and the Interpretation

(Source: Saunders et. al., 2016)

Table 1.2 Correlations between Variables

Table 1.2 shows the correlations between the independent variables and the dependent variable. The independent variables in this research are order fulfilment, quality control, and tracking of inventory level, while the dependent variable is the challenges in e-Commerce. The correlation of order fulfilment, quality control, and tracking of inventory level was categorized as having a weak relationship with the challenges in e-Commerce, as the correlation values obtained were -0.055, 0.242, and 0.047, respectively.

The first independent variable, order fulfilment has a -0.055-correlation value at a significance level of p=0.599. Hence, this indicates that order fulfilment may not be viewed as a significant e-Commerce challenge, in accordance with this very weak and non-significant correlation, since there is no clear link between this variable and the challenges in e-Commerce faced by the businesses. The correlation value for Quality Control is 0.242, with a significance level of p = 0.018. This moderate, positive, and statistically significant correlation suggests that Quality Control is a relevant challenge in e-Commerce. It indicates that quality control issues may contribute to the challenges that businesses face in effectively managing inventory in e-Commerce. Next, the correlation value for ‘Tracking of Inventory Level’ is 0.047, with a significance level of p = 0.653. This shows a very weak and non-significant correlation, suggesting that tracking inventory levels may not be regarded as a significant challenge in the inventory management of e-Commerce activity of the stores involved.

In conclusion, based on table 4.15, only Quality Control demonstrates a statistically significant relationship with Challenges in E-Commerce, implying that it may be perceived as a significant challenge. In contrast, Order Fulfilment and Inventory Tracking do not show significant correlations with these challenges, suggesting that they are unlikely to be regarded as primary challenges in this context.

Reliability Test

The researchers used Cronbach’s alpha method to measure reliability, while the level of reliability was analysed by the range of values in the Cronbach’s alpha coefficient.

Table 1.3 Cronbach’s Alpha Coefficient Range and Strength of Association

(Source: Saunders et. al., 2016)

Table 1.4 Reliability Test for Independent Variables and Dependent Variable

Table 1.4 above shows the reliability test for both dependent variables and independent variables for this research. The total number of questions was 23; there were 15 questions for independent variables and 8 questions for the dependent variable. The Cronbach alpha value for these questions was 0.820. Based on the table of Cronbach’s alpha coefficient range and strength of association, these questions were good and highly reliable.

Regression Analysis (Model Summary)

Table 1.5 Regression Analysis (Model Summary)

According to Table 1.5, it shows that the result of the model summary demonstrated the relationship between the independent variables and dependent variable. The value of the correlation coefficient (R) was 0.353, which indicates that there was a moderate relationship between the variables.

Therefore, the value of the coefficient of the determinant (R square) was 0.125, which indicates that the challenge in e-Commerce is 12.5% affected by order fulfilment, quality control, and tracking of inventory level. Another 87.5% is influenced by the other variables that have not been studied in this research. These findings suggest that the selected predictors have a limited impact, and further research should consider additional variables to better explain challenges in e-Commerce.

Regression Analysis (ANOVA)

Table 1.6: Regression Analysis (ANOVA)

Based on Table 1.6 above, the result of the F-test value was 4.328 with a significant level of p=0.007. The F-test value was 4.328, which indicated that the overall regression is a good fit for the data and that we can conclude that there was a significant relationship between independent variables and dependent variable. The independent variables of order fulfilment, quality control, and tracking of inventory level are associated with the challenges in e-Commerce, meaning that the independent variables collectively contribute to explaining the variation in the dependent variable As a result, the regression model is significant, which means at least one of the predictors (IV1, IV2, or IV3) has a significant relationship with the dependent variable.

Regression Analysis (Coefficients)

Table 1.7: Regression Analysis (Coefficients)

The regression analysis results in Table 1.7 indicate that among the independent variables, Quality Control has the greatest impact on challenges in e-Commerce, with the highest standardized coefficient (Beta = 0.458, p = 0.001), highlighting its significant positive impact. This suggests that improving quality control processes can mitigate challenges in the e-Commerce sector. Order Fulfilment has a negative Beta value (-0.303, p = 0.018), indicating that efficient fulfilment processes help reduce challenges, though its impact is less pronounced than Quality Control. In contrast, Tracking of Inventory Level shows a non-significant influence (Beta = -0.058, p = 0.630), suggesting it does not bring challenges in e-Commerce.

Based on Table 1.7, the linear equation was developed as below:

Y = 1.951 – 0.376𝑋1 + 0.726𝑋2 – 0.084𝑋3

Where:

Y = Challenges in E-Commerce

𝑋1  = Order Fulfilment

𝑋2  = Quality Control

𝑋3 = Tracking of Inventory Level

Based on the linear equation above, the equation indicates that Order Fulfilment and Tracking of Inventory Level contribute to a reduction in challenges in e-Commerce, while Quality Control is positively associated with these challenges. The constant term (1.951) represents the base level of challenges when all three factors are at zero.

Summary of Hypothesis

Table 1.8 Summary of Hypotheses Testing

Hypothesis 1: Order Fulfilment

𝐻0: There is no significant relationship between order fulfilment efficiency and the challenges faced in e-Commerce inventory management.

𝐻1: There is a significant relationship between order fulfilment efficiency and the challenges faced in e-Commerce inventory management.

From Table 1.8, the result of the regression of order fulfilment against the challenges in e-Commerce is shown. The significant value of order fulfilment was p=0.018, which is less than 0.05, so it can be assumed that there is a significant relationship between order fulfilment efficiency and the challenges faced in e-Commerce inventory management. Therefore, the researchers accepted the alternative hypothesis (𝐻1) and rejected the null hypothesis (𝐻0).

Hypothesis 2: Quality Control

𝐻0: Quality control practices do not significantly impact the challenges experienced in e-Commerce inventory management.

𝐻2: Quality control practices significantly impact the challenges experienced in e-Commerce inventory management.

The result of the regression of quality control against the challenges in e-Commerce is shown. The significant value of order fulfilment was p<0.001, which is less than 0.05, so it can be assumed that there is a significant relationship between quality control practices and the challenges experienced in e-Commerce inventory management. Therefore, the alternative hypothesis (H2) were accepted and rejected the null hypothesis (𝐻0).

Hypothesis 3: Tracking of Inventory Level

𝐻0: Tracking of inventory levels does not significantly influence the challenges encountered in e-Commerce inventory management.

𝐻3: Tracking of inventory levels significantly influences the challenges encountered in e-Commerce inventory management.

Based on Table 1.8, the result of the regression of tracking of inventory level against the challenges in e-Commerce is shown. The significant value of order fulfilment was p=0.630, which is more than 0.05, so it can be assumed that tracking of inventory levels does not significantly influence the challenges encountered in e-Commerce inventory management. Therefore, the researchers rejected the alternative hypothesis (𝐻3) and accepted the null hypothesis (𝐻0).

DISCUSSION AND CONCLUSION

The total number of respondents for this research was 95. The age range of the respondents who answered the questionnaire is between 18 and 55 years old and above. Most of the respondents are between 25 and 34 years old, which is 54 respondents, or 56.8% of the total respondents. This indicates that most participants are in their early to mid-career stages, a phase where individuals often have considerable professional experience and adaptability to technological changes.

26 respondents are between 35 and 44 years old, which is 27.4% of the total number of respondents. Then, 13 respondents are between 18 and 24 years old, which makes up 13.7% of the total number of respondents, reflecting a limited participation from entry-level personnel who might still be building their expertise. Only 1 respondent are 45-54 years old and 55 years old or older, which makes up 1.1% of the total number of respondents respectively. This imbalance could be due to the technology-driven nature of e-Commerce, which might appeal more to younger and tech-savvy individuals. Overall, the age demographics suggest that the insights gathered are shaped predominantly by early to mid-career professionals who are likely familiar with e-Commerce processes and challenges.

Next, the total for males was 31 respondents (32.6%), which was lower than the number of females, which was 64 respondents (67.4%) from the total of 95 respondents. The gap may reflect women’s greater involvement in inventory-related roles within the sampled e-Commerce businesses. It could also imply that these positions, such as store staffs or material handlers, are more commonly associated with female employees in the given context. The gender imbalance is significant because it may influence perspectives on inventory management challenges and strategies, given the possibility of differing approaches and experiences between genders.

Furthermore, most respondents were from UNIQLO, representing 24 respondents or 25.3% of the total in this research. The second-highest group was from Watsons, with 15 respondents, or 15.8% of the total respondents. Following this, DEES had 13 respondents, or 13.7% of the total. SaSa had 12 respondents, making up 12.6% of the respondents. AL-IHKSAN contributed 11 respondents, representing 11.6% of the total, while JD SPORTS had 8 respondents, or 8.4% of the respondents. ProHealth Pharmacy was represented by 9 respondents, making up 9.5% of the total. Finally, FICO Melaka (Vibova) had the fewest respondents, with 3 respondents, or 3.2% of the total sample size of 95 respondents.

The data shows the distribution of respondents according to their job roles. Many respondents, 73 individuals, are Store Staff, representing 76.8% of the total. This is followed by Store Managers, with 10 respondents making up 10.5%. Other roles include Executive Inventory Management, with 3 respondents (3.2%), and staff with 4 respondents (4.2%). Lesser-represented roles include Material Handler with 2 respondents (2.1%), as well as Commercial team, Marketing, and Staff each with 1 respondent (1.1%). This emphasis on store staff perspectives is significant, as these individuals are likely to have firsthand experience with inventory issues such as inventory tracking, order fulfilment and quality control.

Next, the distribution of respondents based on their duration of working experience. Most respondents, which his 46 individuals, have 1-3 years of experience, accounting for 48.4% of the total, indicates that most respondents have moderate levels of experience, which may provide a balance of familiarity with industry challenges and willingness to implement new strategies. Following this, 38 respondents have 4-6 years of experience, representing 40.0%. A smaller portion, 6 respondents, have more than 6 years of experience, which constitutes 6.3% of the total sample obtained, suggesting a limited input from highly experienced professionals who may have deeper insights into long-term trends in e-Commerce inventory management.

Lastly, only 5 respondents have less than 1 year of experience, making up 5.3%. This distribution indicates that most respondents have a moderate level of experience (1-6 years), with fewer individuals having either less than a year or more than six years in their roles.

The most common category among respondents is clothing and accessories, with 38.9% (37 respondents) involved in selling these items. Health and beauty products representing 31.6% or 30 respondents while sportswear and equipment are also popular, sold by 20% of respondents (19 respondents). Pharmaceutical products account for a smaller segment, with 6.3% (6 respondents) selling them, while kitchenware is the least common, sold by just 3.2% of respondents (3 respondents). This distribution highlights a greater emphasis on clothing, beauty, and sports products among those surveyed.

Research Objective 1: To identify challenges faced by businesses in managing inventory in the new era of E-Commerce.

For this first research objective, the result of the explanatory factor was proven by the researchers using regression analysis using SPSS software. Three factors influence the challenges in e-Commerce, which are order fulfilment, quality control, and tracking of inventory level. Through hypothesis testing, two out of three of the factors that are significant to the challenges in e-Commerce are accepted.

Table 1.9 Summary of Hypothesis Testing

Based on Table 1.9, the significant values of order fulfillment, quality control, and tracking of inventory level were .018 <.05, .001 < .05, and .630 >.05, respectively. From the result of the regression analysis, it can be assumed that order fulfilment and quality control had a significant relationship with the challenges in e-Commerce.

Independent Variable 1: Order Fulfilment

Hypothesis 1: There is a significant relationship between order fulfilment efficiency and the challenges faced in e-Commerce inventory management.

Order fulfilment plays a central role in e-Commerce operations, as it encompasses the end-to-end process of receiving, processing, and delivering customer orders. Efficient order fulfilment ensures that customers receive their products accurately and on time, a critical factor in maintaining satisfaction and loyalty. However, this study found a weak and non-significant relationship between order fulfilment and inventory management challenges (correlation coefficient: -0.055, p-value: 0.599).

This weak relationship (correlation coefficient: -0.055) suggests that the surveyed e-Commerce businesses do not consider order fulfilment to be a major challenge. One possible explanation is the wide adoption of advanced technologies like warehouse management systems (WMS), robotic picking systems, and automated packing and shipping processes, which have streamlined the sector’s fulfilment operations. A Warehouse Management System (WMS) plays a crucial role in reducing errors by verifying key processes at each stage.

By requiring operators to scan the designated pick location, the SKU, and the target pallet or box, the system minimizes the chances of mistakes, making the risk of errors nearly negligible (Shanmugamami & Mohamad, 2023). Furthermore, the use of third-party logistics providers (3PLs) has enabled businesses to outsource complex fulfilment tasks to specialized service providers. These collaborations alleviate the burden on internal teams and contribute to efficiency, particularly during peak demand periods.

However, the lack of a significant correlation implies that improvements in order fulfilment efficiency may slightly reduce inventory management challenges. Streamlined fulfilment processes can lead to faster turnover rates, lower inventory holding costs, and improved stock visibility. For example, automated fulfilment systems can process orders more quickly and with fewer errors, reducing delays and the risk of customer dissatisfaction. Nevertheless, the low correlation in this study suggests that many businesses have already attained a high level of competence in this area, making it a less forcing challenge in comparison to other factors.

To stay competitive, e-Commerce businesses must continue to innovate in their fulfilment processes. Technologies like artificial intelligence (AI) for route optimization, automated storage and retrieval systems (AS/RS), and drone or autonomous vehicle delivery could further enhance efficiency. While order fulfilment may not be a significant challenge now, maintaining this operational strength is essential in a market that is evolving rapidly.

Independent Variable 2: Quality Control

Hypothesis 2: Quality control practices significantly impact the challenges experienced in e-Commerce inventory management.

Quality control was found to have a statistically significant, moderately positive correlation with inventory management challenges (correlation coefficient: 0.242, p-value=0.018). This finding emphasizes the importance of quality control for determining the challenges that e-Commerce businesses face when managing their inventory. Unlike order fulfilment, quality control is now regarded as a major source of concern, reflecting its complexities and importance in ensuring customer satisfaction.

Quality control refers to processes that ensure products meet predefined standards before being delivered to customers. In the context of e-Commerce, where customers do not physically interact with products before purchasing, maintaining high quality is critical to avoiding returns, complaints, and reputational damage. According to the study’s findings, as quality control issues increase, so do the overall inventory management challenges that businesses face. This is especially problematic in industries where products have strict regulations, such as pharmaceuticals or food and beverages, where failing to meet quality standards can result in serious consequences.

One major challenge in quality control is the inconsistency in product specifications. Variations in packaging, labelling, or product performance can cause customer dissatisfaction and increased returns. Hence, these problems frequently occur upstream in the supply chain, where poor supplier quality management or insufficient quality checks during manufacturing contribute to defects. For example, an e-Commerce company that sells electronics may face difficulties if suppliers fail to meet agreed-upon technical specifications, resulting in high rejection rates or product returns.

To address these issues, e-Commerce businesses may employ a variety of strategies. For example, implementing automated quality assurance systems, such as AI-driven defect detection and IoT-enabled monitoring, can significantly improve quality control processes’ accuracy and efficiency. Furthermore, collaborating closely with suppliers to establish clear quality standards, as well as conducting regular audits, can help to mitigate upstream quality issues. Businesses can also invest in end-to-end quality monitoring systems that track product quality throughout the manufacturing and delivery processes.

The findings of this study highlight the need for e-Commerce businesses to prioritise quality control as a key area of improvement. While the challenges are significant, addressing them effectively can lead to better customer experiences, lower operational costs, and improved overall efficiency in inventory management.

Independent 3: Tracking of Inventory Level

Hypothesis 3: Tracking of inventory levels significantly influences the challenges encountered in e-Commerce inventory management.

Inventory tracking is the continuous monitoring of stock levels for the prevention of shortages or overstocking, ensuring that businesses have sufficient inventory to meet customer demand. Inventory tracking is crucial in e-Commerce because demand fluctuations can occur quickly, which often influenced by seasonal trends, promotional events, or viral marketing. However, the study’s results indicated a weak and non-significant correlation between inventory tracking and e-Commerce challenges (correlation coefficient: 0.047, p=0.653). This result indicates no significant relationship between tracking inventory levels and the challenges businesses face in inventory management.

The lack of significance here could be explained by the employment of automated and effective inventory tracking systems, such as real-time inventory management software, which assist businesses in efficiently monitoring stock levels and anticipating demand fluctuations. For instance, tools such as barcode scanners, enterprise resource planning (ERP) systems, and other automated solutions are likely standard practices among the respondents. Consequently, tracking inventory levels may no longer be a significant pain point for these businesses, which could explain the lack of a strong relationship between this variable and inventory management challenges.

Radio frequency Identification (RAIN), is the fastest-growing RFID market segment. This passive and battery-free wireless technology connects billions of everyday items to the internet. It allows retailers to identify, locate, authenticate, and interact with tagged items, offering rich, real-time data insights and enhancing operational efficiency (Zaczkiewicz, 2024). For stores like UNIQLO and Watsons, they have integrated the RAIN RFID technology into their inventory management systems, ensuring seamless tracking of inventory levels across their operations. This advanced technology provides real-time, item-level visibility, allowing them to optimize stock availability in stores, online platforms, and for services like self-pickup. By tagging items with RAIN RFID, these businesses can efficiently monitor inventory from the supply chain through distribution centers, backrooms, and sales floors, ensuring accurate fulfilment and reducing stock discrepancies (Zaczkiewicz, 2024).

Furthermore, other stores that sell goods online studied in this research use advanced tools that provide accurate inventory status data, allowing them to better control stock levels and reduce the risk of under- or over-stocking. Most of the other store utilizes Enterprise Resource Planning (ERP) system to facilitate efficient inventory tracking by providing accurate data for decision-making and inventory management. This system enables custom reports that offer real-time insights into inventory levels, such as excess stock, shortages, and turnover rates.

This allows businesses to monitor inventory movements, plan replenishments, and track product transfers in the warehouse. The system ensures accurate counts by continuously updating inventory records, thus improving stock visibility and preventing discrepancies. Ultimately, ERP systems streamline inventory tracking, enhancing operational efficiency across all stages of the supply chain of the stores (Ince et al., 2013).

Another reason tracking may seem less challenging in e-Commerce is its digital nature. Unlike traditional retail, which requires physical inventory checks, e-Commerce businesses typically rely on centralized or third-party warehouses where digital inventory systems handle tracking tasks. As a result, the weak correlation (0.047) between tracking of inventory level and e-Commerce challenges can be attributed to the widespread adoption of advanced inventory management technologies, explaining the lack of a strong relationship with e-Commerce challenges.

Research Objective 2: To identify the inventory management strategies adopted by e-Commerce businesses to address inventory management challenges in the e-Commerce sector.

The descriptive statistics presented in the table, with a mean of 3.7776 and a standard deviation of 0.57345, indicate that inventory management strategies are generally perceived as effective and widely adopted. On a Likert scale (likely 1 to 5), a mean near 4 indicates that respondents agree moderately to strongly with statements or questions about the implementation of these strategies. This demonstrates that businesses in the e-Commerce sector understand the importance of effective inventory management and have taken measures to address challenges such as stock shortages, excess inventory, and order processing delays.

The standard deviation of 0.57345 indicates that participants had consistent opinions about inventory management strategies. This consistency indicates that these strategies are not only widely used, but also recognized as effective across the sample population. Such consistency could result from the industry’s adoption of standardized practices, such as the use of enterprise resource planning (ERP) systems or third-party logistics (3PL) providers. These systems and providers provide robust inventory management solutions, allowing businesses of all sizes to operate at maximum productivity levels.

The relatively high mean indicates that respondents believe these strategies are effective in addressing inventory challenges. However, the mean does not reach the scale’s upper limit, indicating room for improvement. This suggests that, while many businesses are on the right track, they can improve their inventory management practices. For example, integrating modern technologies like artificial intelligence (AI) or machine learning for advanced demand forecasting could help to optimize inventory levels significantly. Not only that, blockchain technology might be useful also to increase transparency in supply chain operations, could also be used to improve inventory accuracy and foster trust of customers.

The findings have broader implications for the e-Commerce industry. The positive perception of inventory management strategies reflects businesses’ increasing recognition of the importance of aligning their inventory practices with the changing demands in the e-Commerce market. As a result, real-time inventory tracking and omnichannel management are two strategies that help businesses meet customer expectations for faster delivery and more seamless experiences. Furthermore, the consistency from respondents demonstrates the value of implementing standardized practices across the sector, ensuring that even small businesses can remain competitive by applying accessible inventory management tools.

In conclusion, the descriptive statistics strongly support the study’s second research objective. The high mean indicates that e-Commerce businesses actively implement inventory management strategies, whereas the low standard deviation indicates that these practices are recognized uniformly and consistently.

Implications of the Study

The findings of this study have important implications for retail stores that operate e-Commerce websites, particularly in terms of effectively managing inventory to meet the changing demands of the online marketplace. Retailers who utilize e-Commerce platforms face unique challenges, such as fluctuating customer demand, high expectations for fast delivery, and maintaining a seamless integration of physical store and online inventory. These implications provide practical recommendations for addressing these challenges and improving inventory management performance.

A significant finding from the study is the importance of implementing strong inventory management systems that are tailored to the hybrid nature of stores with e-Commerce operations. The findings suggest that retailers should always invest in real-time inventory tracking systems to ensure accurate stock levels across both physical and online channels. Another implication is the importance of demand forecasting in retail stores with e-Commerce websites. The findings show how predictive analytics and advanced demand planning tools can help retail businesses to forecast sales trends during peak seasons, promotional campaigns, or unexpected rises in demand.

Quality control practices have also emerged as an important focus area for retail e-Commerce businesses. The study emphasizes the importance of maintaining product quality in addressing customer complaints and return rates, which are higher in online sales than in physical stores. Retailers who use e-Commerce platforms can improve customer satisfaction and loyalty by conducting strict quality assurance procedures before shipping the products, ensuring that online customers receive items in perfect condition. This is particularly crucial for maintaining a competitive advantage, as dissatisfied online customers can easily switch to competitors due to the high number of options available in the e-Commerce market.

Furthermore, the study emphasizes the importance of omnichannel inventory management strategies. Retailers with e-Commerce websites must coordinate their inventory practices to serve both in-store and online customers seamlessly. The findings also highlight the importance of technology adoption in retail e-Commerce inventory management. Retailers must embrace new technologies such as artificial intelligence (AI) for inventory optimization, blockchain for supply chain transparency, and Internet of Things (IoT) sensors for real-time inventory tracking These technologies not only improve accuracy and efficiency, but also provide retailers with valuable data insights for further enhancing their inventory management strategies. Retailers can also investigate automation in their warehouses and fulfillment centers to speed up the picking and packing processes, ensure order fulfilment processes are well done, and the ability to meet customers’ expectations. Finally, these implications provide actionable advice for retail stores that are looking for growth in the new era of e-Commerce.

Limitations of the Study

Several limitations were encountered during this study, which may have affected the findings. Firstly, gathering responses from specific companies was difficult and time-consuming, as some required higher management approval for participation, and response times often got delayed. This lengthy process limited the sample size because not all invited respondents responded in the timeframe required. Hence, time constraints were significant that restrict the number of responses obtained.

Furthermore, some companies refused to participate due to strict internal data confidentiality policies, causing the reduction on the pool of potential respondents and limiting the diversity of perspectives represented in the study. Finally, the study’s geographical scope was limited, with data collected solely from a single region, which is the state of Melaka This restriction may limit the findings’ applicability to stores that has e-Commerce sites in other regions, where differences in technology adoption and market conditions may influence inventory management practices.

Recommendations for Future Study

Based on the rejected hypotheses in this study, several recommendations can be made to enhance future research on inventory management challenges and strategies in the e-Commerce sector. The rejection of certain hypotheses suggests that the relationships between variables may be more complex than initially believed, highlighting the need for further investigation to gain deeper insights.

The rejection of Hypothesis 3, which relates to inventory level tracking, suggests that while tracking systems are essential, their effectiveness may be influenced by factors such as the technology utilized, staff training, and data accuracy. Future research could investigate the various types of inventory tracking systems employed by e-Commerce businesses, including barcode scanning, RFID, and IoT-enabled sensors, and how each contributes to addressing inventory challenges. Additionally, researchers may examine the impact of integrating inventory tracking systems with other technologies, such as artificial intelligence (AI) for predictive analytics or blockchain for enhanced transparency, to determine how these integrations can improve inventory management practices.

For future research, it would be beneficial to investigate the role of emerging inventory tracking technologies, such as Radio Frequency Identification (RFID), Internet of Things (IoT) devices, and artificial intelligence (AI) tools. These systems offer real-time monitoring, predictive analytics, and enhanced transparency, potentially optimizing inventory management. Analysing the adoption of these technologies and their ability to address inventory challenges could provide valuable insights into the evolving landscape of inventory tracking.

Future research should explore the extended applications of blockchain technology in inventory tracking to address existing challenges. Blockchain offers a reliable method for monitoring and managing stock levels. Each item can be assigned a unique identifier, allowing businesses to record and track it from the manufacturer to the end user. This real-time tracking ensures accurate inventory counts and minimizes errors commonly associated with manual inventory tracking. Additionally, blockchain simplifies the process of conducting automatic audits, saving time and effort in verifying stock levels and ensuring record accuracy (Anis, 2023). Blockchain technology also provides a revolutionary approach to inventory management by ensuring that no item can exist in the same place twice. When a product changes ownership or status such as from work in progress to finished goods, this information is instantly updated and accessible to all members of the blockchain. With its decentralised and unchangeable ledger, blockchain technology offers a potential remedy for the problems associated with maintaining efficiency and transparency in sustainable supply chains. (Difrancesco et al., 2022).

With this technology, supply chain partners can automate the execution of payments and orders. Once recorded, transactions cannot be deleted; they can only be updated by authorized parties who submit valid transactions, as verified by the system. This ensures that all supply chain members maintain an accurate and transparent end-to-end view of product information, including location, quantity, quality, and ownership. Consequently, businesses can improve their forecasting and traceability of inventory, enhance operational efficiency, create new opportunities for just-in-time optimization, optimize inventory levels, elevate service levels, reduce warehousing costs, enhance quality, and minimize errors (Zhou et al., 2022).

Advanced artificial intelligence (AI) models for demand forecasting represent another promising area for research. Integrating AI-driven demand forecasting models into current inventory management systems can significantly improve efficiency. By incorporating these models into current inventory management systems, replenishment procedures are automated and stock levels are precisely matched with expected demand (Amosu et al., 2024). The potential of reinforcement learning techniques in demand forecasting, where models enhance their accuracy by learning from feedback in dynamic market conditions. Additionally, investigating hybrid approaches that combine AI with traditional statistical methods may reveal new opportunities for optimizing demand planning. Such research can significantly improve supply chain resilience and responsiveness, particularly in industries characterized by high variability and uncertainty in consumer demand. Future research could also investigate the potential of reinforcement learning techniques in demand forecasting, allowing models to improve their accuracy by learning from feedback in dynamic market conditions. Additionally, exploring hybrid approaches that combine AI with traditional statistical methods may uncover new opportunities for optimizing demand planning. This research has the potential to significantly enhance supply chain resilience and responsiveness, particularly in industries marked by high variability and uncertainty in consumer demand.

Future studies could compare the experiences of businesses of varying sizes and operational complexities. For instance, small and medium-sized enterprises (SMEs) may encounter greater challenges in adopting advanced tracking systems due to limited resources, while larger corporations may already have established systems. A comparative analysis could reveal differences in inventory management challenges and the effectiveness of tracking systems in addressing these issues across different business scales.

Furthermore, it is essential to investigate the effectiveness of inventory tracking systems within an omnichannel context, where businesses must synchronize inventory data across multiple sales channels, including physical stores, e-Commerce platforms, and marketplaces. Future research could also explore whether tracking systems can sustain stock visibility and mitigate issues such as overselling or stock discrepancies, particularly in multi-channel environments.

Lastly, expanding the scope of future studies to include businesses in industries or regions with less developed inventory management systems could yield different results. The findings from this study may reflect the maturity of the systems utilized by the sampled businesses; however, examining businesses in less technologically advanced environments might uncover more significant challenges related to inventory tracking. This expanded focus could offer a broader and more diverse perspective on the role of inventory tracking in addressing e-Commerce inventory challenges.

Availability of Data

The datasets analysed during the current study are available from the corresponding author on request.

Competing interests      

The authors declare no competing interests.

FUNDING AND ACKNOWLEDGEMENT

The authors would like to express their sincere gratitude to Universiti Teknikal Malaysia Melaka (UTeM), Malaysia, for the support provided through the publication incentive under Grant FRGS-EC (Project No. FRGS/3/2024/SSos/UTEM/02/10).

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APPENDIX

Questionnaire questions:

SECTION A: DEMOGRAPHIC PROFILES

INSTRUCTIONS:

This section is intended to obtain general information from respondents. Please answer the question to your best accordance. Your answers will remain confidential. Kindly mark (/) in the designated box when applicable.

1. Age
   • Under 18
   • 18-24
   • 25-34
   • 35-44
   • 45-54
   • 55 and above

2. Gender

• Male
• Female

3. Store’s Brand

• ProHealth Pharmacy
• Watsons
• Sa Sa
• UNIQLO
• DEES
• JD Sports
• AL-IKHSAN
• FICO Melaka (Vibova)

4. What is your role in the selected store?
   • Store Manager
   • Material Handler
   • Inventory System Controller (IT / MIS)
   • Executive Inventory Management
   • Store Staff
   • Others (please specify) _________

5. How long have you been working in your current role?
   • Less than 1 year
   • 1-3 years
   • 4-6 years
   • More than 6 years

6. Type of Product Sold

• Sports Wear and Equipment
• Clothing and Accessories
• Health and Beauty
• Personal Care Items
• Pharmaceutical Products
• Kitchenware Products

SECTION B: INDEPENDENT VARIABLES

INSTRUCTIONS:

For SECTION B until SECTION D, the answers must be based on the technique/policies applied at your store. There is no right or wrong answer.

Please read each question below and provide your answer by choosing the appropriate number on the 5-point Likert scale provided below.

Kindly mark (/) in the designated box when applicable.

[1 = Strongly Disagree (SD); 2 = Disagree (D); 3 = Neutral (N); 4 = Agree (A); 5 = Strongly Agree (SA)]

SECTION C: DEPENDENT VARIABLE

SECTION D: Inventory Management Strategies utilized in Online Store