Limitations of Omni Channel Application – Multi-Channel Sales in Business

the website itself, you click on it and the shopping cart still has the items you have chosen. You do not have to choose again from the beginning. This can be considered a good experience, encouraging customers to buy in a comfortable, satisfied state.

According to statistics from Harvard Business Review, customers of businesses using omni-channel spend 10% more per purchase than businesses that do not use it.

1.1.2.3. Limitations of applying Omni Channel - Multi-channel sales in business

business

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• Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values ​​agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values ​​for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of ​​solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d
• Practical Application, Problems, Limitations and Causes
• E-commerce in small and medium enterprises: current situation and solutions - 11
• E-commerce in small and medium enterprises: current situation and solutions - 1

Besides the obvious benefits that the Omni Channel model brings to businesses in sales, marketing, and product promotion, this model also has some limitations as follows:

 Because of accessing many sales channels, if sales capacity is stagnant, it will cause loss and risk of resources and finance mobilized before.

 The concept of multi-channel sales is still quite new to some businesses or small stores, and they themselves are wondering whether they need to use this multi-channel sales system with such a scale. However, today, this technology is increasingly upgraded and perfected to serve all subjects and circumstances of many types of businesses.

 It is difficult for businesses to build or spend a lot of budget and resources to create a system that can link channels together. This requires businesses to find a third party that specializes in providing this service to be able to support the best management of the entire system.

1.1.3. Overview of online sales activities

1.1.3.1. Concept of online sales

Sales are considered an indispensable activity of the market economy. In a broad sense, sales is a process that includes many activities such as consumer research, business market research, building distribution channels, standards and policies, implementing promotional strategies,

advertising and finally carrying out sales activities at the point of sale. In a narrow sense, sales is the activity of providing goods according to customers' needs and then receiving money from customers.

1.1.3.2. Popular online sales models in Vietnam

Sales models are simply understood as the synthesis of all the elements that make up a salesperson to help them earn profits. Currently in Vietnam, popular sales models include the following 5 types:

 Traditional online sales model: This is a sales model that has been developed since e-commerce and online retailing have gradually become familiar. This model mainly uses online sales channels as another outlet for traditional business as usual. Sellers still have to reserve a certain source of goods to avoid problems such as shortage of goods, loss of goods and damaged goods.

 Sales model on e-commerce platforms: With this model, sellers can easily create an online store based on e-commerce platforms. With the strength of having more than 50 million available customers, the processes of operation, construction, transportation, and costs are all supported, plus many preferential benefits from e-commerce platforms, this is a model that is highly appreciated in the Vietnamese market.

 Retail collaborator model: To minimize the level of inventory, many people have turned to a new way of selling by reselling products taken from large importers. In this model, collaborators must consider the percentage of commission before deciding to sell to wholesalers to get the most optimal cost.

 Advertising business model: With this model, business people can build their sales channels on social network accounts. At the same time, always update interesting information to attract customers to follow, thereby receiving advertising costs. This is also the model that most sellers in Vietnam are applying.

 Affiliate model: Sellers will update sales content on various online sales channels to attract and optimize traffic, creating conditions for users to perform tasks on the website. Sellers will receive commission costs after the tasks are completed by users.

1.1.3.3. Advantages of online sales

The biggest advantage of online sales is that it allows users to conduct instant business activities on a global scale, from advertising, marketing products, services, negotiating and ordering to the payment stage, keeping in touch with customers. In addition, online sales also have the following advantages:

 Most effective marketing of products and services to the world.

 Create a large-scale, fast-paced sales channel that directly interacts with customers.

faster and less costly than traditional sales channels.

 Carrying out administrative procedures also becomes simpler, helping to achieve high efficiency in commercial transactions.

 Ability to operate continuously 24/24 hours, continuously every day of the week with relatively low costs. No need for supervisory staff, no need to spend money on renting a sales space, no need for a system to check, calculate money, advertise products and services. All activities are performed automatically, quickly and with absolute accuracy by the Website.

 At the same time, the website can serve many customers everywhere with different requirements on product quality, product information, price, type, and design.

 Product information is easily updated according to market changes.

1.1.3.4. Limitations of online sales

Although there are many advantages, online sales also have the following disadvantages:

 Internet security in our country is not really safe, the network system is easily hacked and data is stolen, which is detrimental to online shopping activities.

 Customers often have a reserved mentality and lack confidence in product quality.

 Sellers still do not really understand the online payment system because they do not have enough qualifications and knowledge.

1.2. Practical basis

1.2.1. Current status of e-commerce development in the world

As consumers lose interest in traditional shopping, the e-commerce market around the world has seized the opportunity and is growing rapidly and vigorously. The worldwide expansion of the internet has contributed significantly to the transformation of both commercial transactions and stores.

According to the Consumer Consumption Scoreboard 2018, the rate of online shopping in the European Union has increased from 29.7% to 55% within 10 years. According to data from the German E-commerce and Mail Order Association, in the second quarter of 2017, e-commerce revenue reached a record 13.97 billion euros, up nearly 12% over the same period in 2016. Meanwhile, according to the latest data from the Berlin-based E-commerce and Mail Order Association (BEVH), e-commerce transactions in Germany reached a record high in the second quarter of 2017. BEVH's report said that in the period from April to June, online sales revenue in Germany reached 13.97 billion euros (about 15.93 billion USD), up about 12% over the same period in 2016. In the US, in 2017, e-commerce was a bright spot in the US retail industry with the highest growth rate since 2013. In the Asia-Pacific region Duong, e-commerce revenue contributed to 40% of total global e-commerce revenue in the first quarter of 2017 thanks to booming shopping activities in China, Japan, Australia, Korea, and India. Google expert Marc Woo has forecasted that the Southeast Asia region

will become the next booming e-commerce market thanks to the rise of the middle class and the popularity of the Internet.

We can see the comprehensive change of traditional business market to e-commerce in the world and e-commerce market is dominating the global market with very fast growth rate.

1.2.2. Current status of e-commerce development in Vietnam

According to the report of the Vietnam E-commerce Association, in 2019, the growth of e-commerce in Vietnam in the past 4 years was really outstanding. This is a potential land for businesses wanting to exploit this market, specifically:

 Regarding growth rate: Along with the steady development of the economy with a GDP growth rate of over 7%, 2018 continued to witness strong growth of e-commerce. Based on survey information, the Vietnam E-commerce Association estimated that the growth rate of e-commerce in 2018 compared to 2017 reached over 30%.

 In terms of scale: In 2018, Vietnam's e-commerce continued to develop comprehensively with a growth rate of over 30%. Although it only started at approximately 4 billion USD in 2015, thanks to the high average growth rate in 3 consecutive years, the e-commerce market size in 2018 reached about 7.8 billion USD. If the growth rate in 2019 and 2020 continues at 30%, by 2020 the market size will reach 13 billion USD. This size will be higher than the target stated in the Master Plan for e-commerce development for the period 2016 - 2020, according to this target, the scale of retail e-commerce (B2C) will reach 10 billion USD by 2020.

According to the E-Conomy SEA 2018 Report by Google and Temasek, the size of Vietnam's e-commerce market in 2018 was 9 billion USD. The report also forecasts an average annual growth rate of 25% for the 2015-2018 period and a market size of 33 billion USD by 2025. If this scenario occurs, the size of Vietnam's e-commerce market in 2025 will rank third in Southeast Asia, after Indonesia (100 billion USD) and Thailand (43 billion USD).

According to the report of the Vietnam E-commerce Association, in 2019, the rate of businesses in the B2C model building websites in recent years has not changed much (in 2018, 44% were 1% higher than in 2017 and 1% lower than in 2016), but most of these businesses have paid more attention to taking care of and updating information on their website system. Specifically, 47% of businesses said they regularly updated information daily, 23% of businesses updated information weekly. This proves that businesses have paid more attention to taking care of their image and brand, ready to increase more forms of online business. In 2018, among the surveyed businesses, up to 36% of businesses said they sold on social networks, an increase of 4% compared to 2017; 12% of enterprises have business through e-commerce platforms - an increase of 1% compared to 2017; 17% of enterprises have business on mobile platforms. In B2C e-commerce transactions, the survey on the issue of receiving orders and placing orders through online tools shows: 84% of enterprises said they receive orders and place orders via email; 49% receive orders via social networks; 45% for ordering via websites - including 36% for receiving orders, 44% for placing orders; through e-commerce platforms is 13% for receiving orders, 19% for placing orders. Thus, Vietnamese enterprises have paid more attention to online business strategies. However, implementation is still at a low level, not commensurate with the scale and potential of e-commerce, many small and medium enterprises are not ready for this change.

Vietnamese consumers’ shopping habits have also changed positively. From being used to traditional business transactions, face-to-face, being able to hold, look at and possibly try the product, they have now gradually approached and loved online shopping.

According to the “Vietnam E-commerce White Book 2019”, up to 70% of Internet users participate in online shopping at least once a year, 61% of users use the Internet for the purpose of searching for information to buy products, with the rate of users accessing the Internet from 3-5 hours a day reaching 30%. The items favored by online consumers are clothes, shoes, cosmetics (61%), followed by books, stationery, gifts, household appliances (46%), and toys (46%).

technology and electronics (43%),... The online shopping value of consumers over 5 million VND accounts for the highest percentage - 35%, from 3 million VND to 5 million VND accounts for 22%, from 1 million VND to 3 million VND accounts for 26%. These results show that more and more consumers are willing to participate in and love online shopping. This is also a positive sign for the development of e-commerce in Vietnam.

Besides the remarkable achievements of Vietnam's e-commerce, the 2019 report of the Vietnam E-commerce Association also pointed out that there are still many obstacles to breakthroughs in the coming period.

For example, logistics services - last mile delivery - order fulfillment still have many limitations. Although more than 70% of online shoppers use the payment method of cash on delivery (COD), the rate of buyers returning products ordered online is still high. It is estimated that the average ratio of the total value of returned products compared to the total value of the order is up to 13%, with some businesses having to bear this ratio at 26%. This causes great difficulties for most businesses today.

In addition, consumer confidence in online shopping is still low. The report results show that the rate of online shoppers choosing to pay cash on delivery (COD) is still very high - up to 88%. This is also a very big problem with e-commerce in Vietnam. The report also shows that only 48% of respondents are satisfied with the online shopping method, which means that there is still a large proportion of potential customers that e-commerce service providers have to conquer. The biggest reason affecting consumer psychology is still the quality of goods. This is also clearly shown in the survey report on the reasons why consumers have not chosen to shop online, of which: 46% because it is difficult to verify the quality of goods, 33% because they do not trust the seller. Along with that, the report of the Department of E-commerce and Digital Economy - Ministry of Industry and Trade said that up to 83% of people surveyed were concerned about poor quality products compared to advertisements. And there are many other reasons, such as: prices are not cheaper when buying at the store while there are promotions; personal information is leaked; buying at the store is easier and faster; consumers do not have a bank card to pay; the way to buy online is still complicated for many people (Figure 1).

Figure 1.1. Obstacles to online shopping

(Source: Vietnam E-Commerce White Book 2019)

Lack of uniformity in legal policies is also an important cause of these obstacles. For example, the protection of personal information is of particular importance to e-commerce. Currently, our country has a number of legal documents (Civil Code, Penal Code, Law on Consumer Protection, Law on Information Technology, Law on Network Information Security, Law on Cyber ​​Security, etc.) and many other relevant sub-law documents that refer to the aspect of personal data protection and provisions that e-commerce businesses must comply with. However, in reality, the enforcement of laws to protect consumers in the e-commerce environment still faces many problems, sometimes the responsibilities are not clearly defined, and the sanctions are not clear and not strong enough to handle violations. It can be seen that the risk of illegal collection, use, dissemination and trading of personal information is very high, typically 34% of 568 complaints sent to the Department of Competition and Consumer Protection mainly focused on businesses illegally collecting consumer information. This is also one of the reasons for the decline in consumer confidence in e-commerce.