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  • Published: 20 December 2022

Towards improving e-commerce customer review analysis for sentiment detection

  • Upendra Singh 1   na1 ,
  • Anant Saraswat 1   na1 ,
  • Hiteshwar Kumar Azad 2   na1 ,
  • Kumar Abhishek 1   na1 &
  • S Shitharth 3   na1  

Scientific Reports volume  12 , Article number:  21983 ( 2022 ) Cite this article

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  • Computer science
  • Information technology
  • Scientific data

According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews to make decisions and examine business models. However, users can examine and automate this process with Natural Language Processing (NLP). NLP is a well-known technique for evaluating and extracting information from written or audible texts. NLP research investigates the social architecture of societies. This article analyses the Amazon dataset using various combinations of voice components and deep learning. The suggested module focuses on identifying sentences as ‘Positive‘, ‘Neutral‘, ‘Negative‘, or ‘Indifferent‘. It analyses the data and labels the ‘better’ and ‘worse’ assumptions as positive and negative, respectively. With the expansion of the internet and e-commerce websites over the past decade, consumers now have a vast selection of products within the same domain, and NLP plays a vital part in classifying products based on evaluations. It is possible to predict sponsored and unpaid reviews using NLP with Machine Learning. This article examined various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The automation achieves a maximum validation accuracy of 79.83% when using Fast Text as word embedding and the Multi-channel Convolution Neural Network.

Introduction

Access to e-commerce portals and online purchasing has become the new marketplaces for society as a result of rapid urbanization around the world and increasing internet penetration with the use of smart computation devices. Consumers evaluate products or services based on different evaluations. Evaluation can be specifications, ads or reviews. Reviews are one of the most influential factors affecting the sales of products and services. Reviews help alleviate the fear of being cheated and raise the confidence between consumers and businesses in the e-Commerce industry. Using Natural Language Processing (NLP), users can predict the type of review and what is the experience of the product. Due to the prevalence of fraudulent or two-word reviews on e-commerce websites, it is crucial to conduct a thorough study and analysis. The second application of NLP is that customers can determine the quality of a service or product without reading all the reviews. If there are many similar products and each has reviews, the analysis of these reviews by humans can be a long process, and the decision is utterly critical regarding selecting the product which would bring the resolution.

NLP has gained plenty of attention in analyzing text written in many languages. Machine Learning (ML), Deep Learning, and computer vision have a lot to offer in the field of NLP. Machine Learning is changing the way of thinking of humankind, and Machine Learning and Deep Learning are parts of Artificial Learning (AI). Also, NLP is an integral part of Artificial Intelligence, and some algorithms or models coincide with Machine Learning and Deep Learning. NLP is not just useful in text analysis, but this technique also analyzes audio and videos. There are a variety of challenges that can be solved using NLP’s ability to analyze feelings in text and voice. NLP opens a plethora of new possibilities and capabilities. A few analyses which have been affected by NLP are:

Improve Customer Satisfaction: NLP data analysis can be used to anticipate customer satisfaction.

Better Market Analysis: NLP is a powerful tool for gaining a better understanding of the industry and its requirements.

Employee’s satisfaction: NLP can assist in resolving the customer’s issue and the employee’s overall productivity.

In order to achieve the common aim of automation within the research community, adequate scientific literature understanding is essential. It has been calculated that 8–9% of the total research volume generated each year is increasing. An overabundance of knowledge leads to the ‘reinventing the wheel’ syndrome, which has an impact on the literature review process. Thus, scientific progress is hampered at the frontier of knowledge, where NLP can solve many problems. Analysis of customer feedback can be challenging due to the high level of qualitative nuance contained within the material and the vast volume of data obtained by businesses. Because qualitative comments, reviews, and free text are more difficult to quantify than quantitative feedback 1 , evaluating them may be more difficult. Natural Language Processing and Machine Learning will one day be able to process large amounts of text without the need for human intervention.

Text Clustering and Topic Modelling are the two methods utilized most frequently to recognize topics included within a text corpus 2 . Text pre-processing is essential to natural language processing because it takes the text and converts it into a form that is easier to understand and works with different AI techniques, allowing machine learning algorithms to function more effectively.

As previously stated, understanding and analysing reviews is critical for making purchasing decisions. Both negative and positive evaluations are equally important. A research report 3 indicated that 82 % of customers who purchase things intentionally seek negative reviews. With a 13 trillion economy in the online marketplace and the peer effect, reviews play a significant role in deciding what to buy and what not to buy. With the help of NLP, users can automate the process of analyzing the reviews. This paper examines various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The main contributions of this work are:

Collection of raw dataset reviews that are publicly available. It contains Amazon product reviews as well as metadata.

Data pre-processing and review analysis to provide insights into the various word vector representations.

Examined various Machine Learning and Deep Learning models with different Word Embedding approaches, such as BERT, Glove, Elmo, and Fast Text, to predict the sentiment of e-commerce website reviews.

The remainder of the paper is structured as follows. Section “ Related work ” discusses the background, section “ Methodology ” discusses related works methodology, and section “ Experimental analysis and Results ” discusses the result, followed by the conclusion and future work.

We have studied machine learning models using various word embedding approaches and combined our findings with natural language processing. During the analysis phase, the priority is predominantly on providing more detail about the operations performed on the dataset by BERT, Glove, Elmo, and Fast Text. An investigated was performed on wide range of combinations of NLP and deep learning strategies, as well as methodologies considered to be cutting-edge. In order to build the best possible mixture, it is necessary to integrate several different strategies. It is necessary to integrate several different strategies in order to create the best possible mixture. All models cannot integrate with deep learning techniques at their initial level because all of the procedures need to be revised. We need to redesign the techniques mentioned to achieve better results.

Related work

The qualitative quality of the data and the enormous feedback volume are two obstacles in conducting customer feedback analysis. The analysis of textual comments, reviews, and unstructured text is far more complicated than the analysis of quantitative ratings, which can be done because ratings are quantitative. Nowadays, with the help of Natural Language Processing and Machine Learning, it is possible to process enormous amounts of text effectively without the assistance of humans. In this regards, Kongthon et al. 4 implemented the online tax system using natural language processing and artificial intelligence. They have used NLP to secure future scenarios. The majority of high-level natural language processing applications concern factors emulating thoughtful behavior.

To use a very large target vocabulary without increasing training complexity, Jean et al. 5 propose a system based on consequence sampling that allows us to operate a large-scale vocabulary without increasing training complexity of the Neural Machine Translation (NMT) model. However, Refining, producing, or approaching a practical method of NLP can be difficult. As a result, several researchers 6 have used Convolution Neural Network (CNN) for NLP, which outperforms Machine Learning. However, the majority of current research focuses on learning dependency information from contextual words to aspect words based on the sentence’s dependency tree, which does not take advantage of contextual affective knowledge with regard to the specific aspect. Liang et al. 7 propose a SenticNet-based graph convolutional network to leverage the affective dependencies of the sentence based on the specific aspect. Specifically, the authors build graph neural networks by integrating SenticNet’s affective knowledge to improve sentence dependency graphs.

Emma Strubell et al. 8 , in their research work, when authors have used large amounts of unlabeled data. It has been observed that NLP in combination with a neural network model yielded good accuracy results, and the cost of computational resources determines the accuracy improvement. Based on extensive research, the author has also made some cost-cutting recommendations.

Similarly, the data from accounting, auditing, and finance domains are being analyzed using NLP to gain insight and inference for knowledge creation. Fisher et al. 9 have presented work that used NLP in the accounting domain and provided future paths. Apart from these, Vinyals et al. 10 have developed a new strategy for solving the problem of variable-size output dictionaries.

NLP-based techniques have been used in standardized dialog-based systems such as Chat boxes 11 . Also, Text Analytics is the most commonly used area where NLP is frequently used 12 . Machine learning algorithms with NLP can be used for further objectives like translating, summarizing, and extracting data, but with high computational costs.

Deep learning 13 has been seen playing an important role in predicting diseases like COVID-19 and other diseases 14 , 15 in the current pandemic. A detailed theoretical aspect is presented in the textbook 16 ‘Deep Learning for NLP and Speech Recognition’. It explains Deep Learning Architecture with applications to various NLP Tasks, maps deep learning techniques to NLP and speech, and gives tips on how to use the tools and libraries in real-world applications.

In the era of Big Data Analytics, new text mining models open up lots of new service opportunities. Bidirectional Encoder Representations from Transformers (BERT) 17 is one of these models that employs a transformer, an attention mechanism that understands the meaning of ambiguous language in text by using surrounding text (words (or sub-words) to establish context. The Stanford Question Answering Dataset (SQUAD), a dataset constructed expressly for this job, is one of BERT’s fine-tuned tasks in the original BERT paper. The SQUAD is made up of a variety of English-language literature. Questions about the data set’s documents are answered by extracts from those documents. Many engineers adapted the BERT model’s original architecture after its first release to create their unique versions.

GloVe 18 is a learning algorithm that does not require supervision and produces vector representations for words. The training is done on aggregated global word-word co-occurrence information taken from a corpus, and the representations produced as a result highlight intriguing linear substructures of the word vector space.

ELMo 19 is an example of a deeply contextualized word representation that represents the intricate properties of word use (such as syntax and semantics) and the ways in which these uses vary across different language contexts (i.e., to model polysemy). These word vectors are learned functions generated from the internal states of a deep bidirectional language model (biLM), which has been pre-trained using a substantial text corpus. They may be integrated into existing models and considerably advance the state-of-the-art in a wide variety of complex natural language processing tasks, such as question answering, textual entailment, and sentiment analysis.

The polarity determination of text in sentiment analysis is one of the significant tasks of NLP-based techniques. To determine polarity, researchers employed unsupervised and repeatable sub-symbolic approaches such as auto-regressive language models and turned spoken language into a type of protolanguage 20 . Polarity is a compelling idea for comprehending the grey region of sentiments. To further improve sentiment analysis, Trueman et al. 21 proposed a convolution-stacked bidirectional long-term memory with a multiplicative attention method for detecting aspect categories and sentiment polarity. Affective Computing and Sentimental analysis comprising human-computer interaction, machine learning, and multi-model signal processing has been proposed 22 for capturing the meaning of people’s sentiments from social media platforms. The sentiments collected sometimes suffer from imbalanced data and insufficient data. The problem of insufficient and imbalanced data is addressed by the meta-based self-training method with a meta-weighter (MSM) 23 . The MSM model is based on neuro-symbolic learning systems. An analysis was also performed to check the bias of the pre-trained learning model for sentimental analysis and emotion detection 24 .

Table  1 summarises several relevant articles and research papers on review analysis.

Methodology

The block diagram of the overall methodology used for sentiment detection in reviews is shown in Figure  1 . Three major steps are taken in order to detect sentiment in reviews: 1. Data pre-processing, 2. Word embedding, and 3. Models employed.

figure 1

Steps involved in sentiment analysis of reviews.

Pre-processing of data

Data mining is essential in NLP, and data pre-processing is crucial in model construction. Pre-processing data removes ambiguity and redundancy. To implement machine learning and deep learning algorithms, NLP requires specific text input pre-processing. Various methods are used to convert textual data into a format suitable for modeling. Data pre-processing techniques are critical in designing an NLP model that focuses only on the important parts of the text. The following are the fundamental pre-processing techniques:

Punctuation removal

Commas and other punctuation may not be necessary for understanding the sentence’s meaning, so they are removed.

Stop words removal

Stops Words (Words that connect other words and don’t provide a wider context) can be ignored and screened from the text as they are more standard and contain less useful knowledge. For example, conjunctions like ‘and’, ‘or’ and ‘but’, prepositions like ‘in’, ‘of’, ‘to’, ‘from’, and many others like the articles like ‘a’, ‘an’, and ‘the’.

Lemmatization

The process of grouping related word forms that are from the exact words is known as Lemmatization, and with Lemmatization, we analyze those words as a single word.

Word embedding

The pre-processed data is now used for creating bag of word vectors by using different word embedding techniques namely, (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Embedding from Language Model (ELMo), (iii) Global Vectors for Word Representations (GloVe) and (iv) FASTTEXT.

Bidirectional encoder representations from transformers (BERT)

BERT is an innovative model which applies bidirectional training of transformers. BERT uses Transformers, and it learns the relation between a word to another word (or sub-words) in the given text of contextual nature. In its initial form, BERT contains two particular tools, an encoder for reading the text input and a decoder for the prediction. Since BERT aims to forge a language model, the encoder phase is only necessary. Figure  2 is an illustration of BERT representation.

figure 2

Bidirectional encoder representations from rransformers (BERT) representation.

Embedding from Language model (ELMo)

ELMo 31 is an abbreviation for ‘Embedding from Language Model’, a method for representing a sequence of words as vectors. The shortcomings of Gloves and other static pre-trained embedding models give rise to the concept of ELMo. When compared to the Glove, ELMo is a different analogical embedding. ELMo vectors are used to improve the accuracy or classification of any NLP task. ELMo can fairly classify the meaning of the same word in different sentences, mentioning different contexts. ELMo architecture is a fairly broad architecture consisting of LSTM layers. As a result, language model training is accomplished effectively using the ELMo architecture. It can be represented as follows:

Contextual: Each word represented in a sentence depends on the whole context in which it is used.

Deep: To represent a word ELMo combines all the layers of a pre-trained Neural Network.

Character-based: ELMo allows the network to use the semantic clue to form a robust representation.

Global vectors for word representations (GloVe)

GloVe 32 is a distributed word representation model derived from Global Vectors. The GloVe model is an excellent tool for discovering associations between cities, countries, synonyms, and complementary products. SpaCy creates feature vectors using the cosine similarity and euclidean distance approaches to match related and distant words. It can also be used as a framework for word representation to detect psychological stress in online or offline interviews. GloVe is an unsupervised learning example for acquiring vector representations of words. It collects and aggregates global word-to-word co-occurrences from the corpus for training, and it returns a linear substructure of all word vectors in a given space.

FastText representation

FastText 33 is a widely used library for learning text representation and classifying text. It is lightweight, free, and open-source. It can work on different devices. We can further reduce it for mobile and thin clients. Facebook’s AI Research (FAIR) lab has created FastText, and basically, it learns word embeddings and text classification. The vector representations of words can be obtained by developing supervised or unsupervised learning algorithms. Pre-trained models of 294 languages are available for use. Word embedding in FastText uses neural networks for execution.

Models used

After completion of word embedding, the sentiment detection was carried out using deep learning models, namely (a) Convolutional Neural Network (CNN), (b) Bidirectional long-short term memory (BLSTM), (c) Multi-channel convolutional neural network (CNN), and (d) Random Multi-model Deep Learning (RMDL).

Convolutional neural network (CNN)

The CNN model used is a five-layer sequential model. The architecture consists of an input layer of size equal to length. The second layer is the embedding layer, which is applied to the primary layer and contains 100 neurons. The subsequent layers consist of a 1D convolutional layer on top of the embedding layer having a filter size of 32, a kernel size of 4 with the ‘ReLU’ activation function. After the 1D convolutional layer, the global max pool 1D layer is used for pooling. After getting the output from the pooling layer, two dense layers are used, with the penultimate layer having 24 neurons and a ‘ReLU’ activation function and a final output layer with one neuron and a ‘sigmoid’ activation function. Finally, the above model is compiled using the ‘binary_crossentropy’ loss function, Adam optimizer, and accuracy metrics.

Bidirectional LSTM (BiLSTM)

The LSTM model used is a four-layer sequential model. The architecture consists of an input layer with size equal to length. The input layer is routed through the second layer, the embedding layer, which has 100 neurons and a vocabulary size of 100. The output of the second layer is routed through a 100-neuron bidirectional LSTM layer. The output from the bidirectional layer is passed into two dense layers, with the first layer having 24 neurons and a ‘ReLU’ activation function and a final output layer with one neuron and a ‘sigmoid’ activation function. Finally, the above model is compiled using the ‘binary_crossentropy’ loss function, adam optimizer, and accuracy metrics. After that, Multi-channel CNN was used, which is quite similar to the previous model. Figure  3 is an illustration of BiLSTM.

figure 3

BiLSTM representation.

Multi-channel CNN

The model used in the paper consists of three channels. All three channels represent the same architecture, with channel one architecture consisting of input1 with shape equal to length, the second layer being an embedding layer applied to the first layer with vocab size and 100 neurons, followed by a Conv1D layer with filter size of 32, kernel size of 4, and activation function ‘ReLU’. Dropout layer is added to the top of the Conv1D layer with the dropout value of 0.5; after that, max-pooling layer is added with the pooling size of 2; after that result is flattened and stored in the flat one layer. Similarly, channels 2 & 3 have the same sequence of layers applied with the same attribute values used in channel 1. The results of channel 2 & channel 3 are flattened and stored into flat 2 & flat three layers consecutively. The output stored in flat 1, flat 2 & flat three is finally concatenated and stored in the merged layer. After getting the output from the merged layer, two dense layers have been used. The 1st dense layer contains ten neurons with activation function as ‘ReLU’ & it is again followed by another dense layer with one node & the activation function used is ‘Sigmoid’. Finally, a model is formed using input1, input2 & input3 & outputs given by the last dense layer. The model is compiled using the loss function as binary cross-entropy, ADAM optimizer & accuracy matrices. The architecture is shown in Figure  4 .

figure 4

Architecture of multi-channel CNN.

Random multi-model deep learning (RMDL)

RMDL is a new deep learning technique for classification that can accept text, video, images, and symbols as input. RMDL includes Random models as shown in Fig.  5 , which having three components:

One Deep neural network (DNN) classifier on the left,

One Deep CNN classifier in the middle, and

One Deep RNN classifier on the right (each unit could be LSTM or GRU).

figure 5

RDML architecture for classification.

The RMDL model used is sequential with five layers. The architecture consists of an input layer with size is the length. After the input layer, the second layer is the embedding layer with vocab size and 100 neurons. The third layer consists of a 1D convolutional layer on top of the embedding layer with a filter size of 128, kernel size of 5 with the ‘ReLU’ activation function. The fourth layer used is bidirectional LSTM with 32 neurons. The output from the bidirectional layer is passed into two dense layers, with the first layer having 24 neurons and ‘ReLU’ activation function and a final output layer with one neuron and ‘sigmoid’ activation function. Finally, the above model is compiled using the ‘binary_crossentropy’ loss function, adam optimizer and accuracy metrics.

Experimental analysis and results

This section describes and analyses the dataset description, experimental setup, and experiment results.

Dataset description

The dataset used in this work is an Amazon product review dataset obtained from Kaggle. The dataset contains following entities as columns.

Id: Unique id of the product (34,660)

Name: Name of the product

Brands: Brand of product e.g., Amazon

Categories: Category of product e.g., Electronics etc

Reviews Text: Reviews given by customers about product

Rating: Customers feedback on the product (Range from 1 to 5)

There are 34,660 samples in this dataset. First, useful features are extracted, and features with high null values are removed from the table because they have no role in prediction. The final dataset only has two columns: review text and rating. The ratings are labelled as either Negative (0) or Positive (1). Ratings greater than or equal to 3 are considered positive, while ratings less than 3 are considered negative.

Experimental setup

Table  2 gives the details of experimental set up for performing simulation for the proposed work.

Results and discussion

The preprocessed data is split into 75% training set and 25% testing data set. The divided dataset was trained and tested on sixteen different combinations of word embedding and model Fig  6 a shows the plot of accuracy between training samples & validation samples for the BERT plus CNN model. The blue line represents training accuracy & the orange line represents validation accuracy. Fig  6 b shows the confusion matrix formed by the BERT plus CNN model. The total positively predicted samples, which are already positive out of 20,795, are 13,446 & negative predicted samples are 31. Similarly, accurate negative samples are 7251 & false negative samples are 98.

figure 6

BERT Plus CNN ( a ) BERT plus CNN accuracy curve; ( b ) Confusion matrix BERT plus CNN.

Figure  7 a shows the confusion matrix formed by the BERT plus LSTM model. The total positively predicted samples which are already positive out of 20,795, are 13,081 & the negative predicted samples are 2,754. Similarly, true negative samples are 4,528 & false negative samples are 432. Figure  7 b shows the plot of Loss between training samples & validation samples. The X-axis in the figure represents the number of epochs & Y-axis represents the loss value. Furthermore, the blue line represents training loss & the orange line represents validation loss.

figure 7

BERT Plus LSTM ( a ) Confusion matrix BERT plus LSTM; ( b ) BERT plus LSTM accuracy curve.

To find the training accuracy, trainX was used as training sample input, and train labels as predictive labels (Positive, Negative) & verbose was kept as 0. The training accuracy of 98.83% was achieved. To find the testing accuracy, testX was used as testing sample input and validation labels as predictive labels (Positive, Negative) & verbose was kept as 0; the testing accuracy of 72.46 % was achieved. Figure  8 a shows the confusion matrix formed by the BERT plus RMDL model. The total positively predicted samples, which are already positive out of 20,795, are 13,356 & negative predicted samples are 383. Similarly, true negative samples are 6,899 & false negative samples are 157. Figure  8 b shows the plot of Loss between training samples & validation samples. The X-axis in the figure represents the number of epochs & Y-axis represents the loss value. Furthermore, the blue line represents training loss & the orange line represents validation loss.

figure 8

BERT Plus RMDL ( a ) Confusion matrix BERT plus RMDL ( b ) BERT plus RMDL accuracy curve.

To find the training accuracy, trainX was used as training sample input, and train labels as predictive labels (Positive, Negative) & verbose was kept as 0. The training accuracy of 98.83% was achieved. To find the testing accuracy, testX was used as testing sample input, and validation labels as predictive labels (Positive, Negative) & verbose was kept as 0; Fig.  9 a shows the confusion matrix formed by the ELMo plus CNN model. The total positively predicted samples, which are already positive out of 20,795, are 13,431 & negative predicted samples are 70. Similarly, true negative samples are 7,212 & false negative samples are 82. The precision value is 0.99409, the recall value is 0.99066 & F1-Score, which is the harmonic mean of precision & recall is 0.99402. Figure  9 b shows the confusion matrix formed by the ELMo plus LSTM model. The total positively predicted samples which are already positive out of 20,795, are 11,704 & the negative predicted samples are 2757. Similarly, true negative samples are 4525 & false negative samples are 1809. The precision value is 0.86612, the recall value is 0.80934 & F1-Score, which is the harmonic mean of precision & recall is 0.83677. To find the training accuracy, trainX was used as training sample input, and train labels as predictive labels (Positive, Negative) & value of verbose was kept as 0. The training accuracy of 97.26% was achieved. To find the testing accuracy, testX as testing sample input was used, and validation labels as predictive labels (Positive, Negative) & value of verbose was kept as 0 ; the testing accuracy of 72.87%. Figure  9 c shows the confusion matrix formed by the ELMo plus RMDL model. The total positively predicted samples, which are already positive out of 20,795, are 12,637 & the negative predicted samples are 1779. Similarly, true negative samples are 5503 & false negative samples are 876. The precision value is 0.86612, the recall value is 0.80934 & F1-Score, which is the harmonic mean of precision & recall is 0.83677.

figure 9

Elmo with CNN, LSTM and RMDL ( a ) Confusion matrix ELMo plus CNNL; ( b ) Confusion matrix ELMo plus LSTM; ( c ) Confusion matrix ELMo plus RMDL.

In GloVe plus CNN, the total positively predicted samples, which are already positive out of 27,727, are 17,639 & the negative predicted samples are 379. Similarly, true negative samples are 8,261 & false negative samples are 1448 Fig.  10 a represents the graph of model accuracy when the Glove plus LSTM model is applied. In the figure, the blue line represents training accuracy & the orange line represents validation accuracy. Figure  10 b represents the graph of model loss when the Glove plus LSTM model is applied. The blue line represents training loss & the orange line represents validation loss. Figure  10 (c) shows the confusion matrix formed by the Glove plus LSTM model. The total positively predicted samples, which are already positive out of 27,727, are 17,940 & negative predicted samples are 3075. Similarly, true negative samples are 5582 & false negative samples are 1130.

figure 10

Glove plus LSTM ( a ) Model accuracy GloVe LSTM ( b ) Model loss GloVe LSTM ( c ) Confusion matrix Glove plus LSTM.

Figure  11 a shows the confusion matrix formed by the Glove plus Multi-channel CNN model. The total positively predicted samples, which are already positive out of 6932, are 4619 & negative predicted samples are 1731. Similarly, true negative samples are 459 & false negative samples are 123. Figure  11 b shows the confusion matrix formed by the Glove plus RMDL model. The total positively predicted samples, which are already positive out of 27,727, are 17,768 & the negative predicted samples are 1594. Similarly, true negative samples are 7143 & false negative samples are 1222.

figure 11

Glove plus Multi-channel CNN and RMDL ( a ) Confusion matrix Glove plus Multi-channel CNN; ( b ) Confusion matrix Glove plus RMDL.

In FastText plus CNN model, the total positively predicted samples which are already positive out of 27,727, are 18,379 & negative predicted samples are 2264. Similarly, true negative samples are 6393 & false negative samples are 691.

Figure  12 a represents the graph of model accuracy when FastText plus LSTM model is applied. In the figure, the blue line represents training accuracy & the red line represents validation accuracy. Figure  12 b represents the graph of model loss when FastText plus LSTM model is applied. In the figure, the blue line represents training loss & red line represents validation loss. The total positively predicted samples, which are already positive out of 27,727, are 18,097 & negative predicted samples are 5172. Similarly, true negative samples are 3485 & false negative samples are 973. Figure  12 c shows the confusion matrix formed by the FastText plus Multi-channel CNN model. The total positively predicted samples, which are already positive out of 11,438, are 7043 & negative predicted samples are 1393. False-negative samples are 2273.

figure 12

FastText plus LSTM and Multi-channel CNN ( a ) Model accuracy FastText plus LSTM ( b ) Model loss FastText plus LSTM ( c ) Confusion matrix FastText Multi-channel CNN.

Table  3 shows the classification report against y_test and predictions. The target names are classified as 0 & 1. From the figure, it can see that F1-Score, which is the harmonic mean of precision & recall, has a value of 74 %.

Figure  13 a represents the graph of model accuracy when the FastText plus RMDL model is applied. In the figure, the blue line represents training accuracy, and the red line represents validation accuracy. Figure  13 b represents the graph of model loss when the FastText plus RMDL model is applied. In the figure, the blue line represents training loss & the red line represents validation loss. The total positively predicted samples, which are already positive out of 27,727, are 17,883 & negative predicted samples are 3037. Similarly, true negative samples are 5620 & false negative samples are 1187.

figure 13

FastText plus RMDL ( a ) Model accuracy FastText plus RMDL ( b ) Model loss FastText plus RMDL.

As it is well known, a sentence is made up of various parts of speech (POS), and each combination yields a different accuracy rate. The validation accuracy of various models is shown in Table  4 for various text classifiers. Among all Multi-channel CNN (Fast text) models with FastText, the classifier gives around 80% validation accuracy rate, followed by LSTM (BERT), RMDL (BERT), and RMDL (ELMo) models giving 78% validation accuracy rate. Table  4 shows the overall result of all the models that has been used, including accuracy, loss, validation accuracy, and validation loss.

Neutrality in classification

Neutrality is addressed in various ways depending on the approach employed. In lexicon-based approaches 34 , the word neutrality score is used to either identify neutral thoughts or filter them out so that algorithms can focus mainly on positive and negative sentiments. However, when statistical methods are used, the way neutrals are treated changes dramatically.

Although, some researchers 35 filter out the more numerous objective (neutral) phrases in the text and only evaluate and prioritise subjective assertions for better binary categorization. There is a widespread belief that neutral texts provide less guidance than those that make overtly positive or negative statements. As a result, in academic articles of sentiment analysis that employ statistical methodologies, researchers generally prefer to ignore the neutral category because they assume neutral texts are around the boundary of the binary classifier. In this article, we did not consider neutrality.

This article explored customer review analysis using the Amazon dataset and tested four well-known supervised classifiers. Critical grammatical sections have also been evaluated and investigated. It has been established that, of all the potential combinations of the various parts of speech, the most effective combination consists of a verb, an adverb, and an adjective. Evaluating the quality of online items relies on the positive or negative classification of remarks. As it is generally known that a sentence consists of a variety of distinct elements of speech, the many types provide a spectrum of differing degrees of accuracy. Table 1 illustrates the efficiency of various models, which compares many text classifiers, and presents the validation accuracy of various models. Among all of the models, the Multi-channel CNN (Fast text) model with fast text classifier offers about an 80% validation accuracy rate, followed by the LSTM (BERT), RMDL (BERT), and RMDL (ELMo) models, providing a 78% validation accuracy rate. This article is working on developing a fair and effective technique that will also integrate the neutrality of the reviews to enhance the analysis.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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e commerce research paper 2022

20 years of Electronic Commerce Research

  • Published: 29 March 2021
  • Volume 21 , pages 1–40, ( 2021 )

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  • Satish Kumar   ORCID: orcid.org/0000-0001-5200-1476 1 ,
  • Weng Marc Lim 2 , 3 ,
  • Nitesh Pandey 1 &
  • J. Christopher Westland 4  

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2021 marks the 20th anniversary of the founding of Electronic Commerce Research ( ECR ). The journal has changed substantially over its life, reflecting the wider changes in the tools and commercial focus of electronic commerce. ECR ’s early focus was telecommunications and electronic commerce. After reorganization and new editorship in 2014, that focus expanded to embrace emerging tools, business models, and applications in electronic commerce, with an emphasis on the innovations and the vibrant growth of electronic commerce in Asia. Over this time, ECR ’s impact and volume of publications have grown rapidly, and ECR is considered one of the premier journals in its discipline. This invited research summarizes the evolution of ECR ’s research focus over its history.

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1 Introduction

The year 2021 marks the 20th anniversary of the founding of Electronic Commerce Research ( ECR ). The journal has changed substantially over its life, reflecting the wider changes in the tools and commercial focus of electronic commerce. ECR ’s early focus was on telecommunications and electronic commerce. After reorganization and new editorship in 2014, that focus expanded to embrace emerging tools, business models, and applications in electronic commerce, with an emphasis on emerging technologies and the vibrant growth of electronic commerce in Asia. Over these years, ECR has steadily improved its stature and impact, as evidenced through various quantitative (e.g., citations, impact factors) and qualitative (e.g., peer-informed journal ranks) measures. According to Clarivate Analytics, ECR ’s impact factor in 2019 was 2.507, Footnote 1 which means that articles published in ECR between 2017 and 2018 received an average of 2.507 citations from journals indexed in Web of Science in 2019. The five-year impact factor of ECR was 2.643, 1 which indicates that articles published in ECR between 2014 and 2018 received an average of 2.643 citations from Web of Science-indexed journals in 2019. According to Scopus, ECR ’s CiteScore was 4.3, Footnote 2 which implies that articles published in ECR between 2016 and 2019 received an average of 4.3 citations from journals indexed in Scopus in 2019. The source normalized impact per paper (SNIP) of ECR was 1.962, which suggests that the average citations received by articles in the journal is 1.962 times the average citations received by articles in the same subject area of Scopus-indexed journals in 2019. Apart from these quantitative measures, ECR has also been rated highly by peers in the field, as seen through journal quality lists. For example, ECR has been consistently ranked as an “A” journal by the Excellence in Research for Australia (ERA 2010) and the Australian Business Deans Council (ABDC 2013, 2016, 2019) journal ranking lists.

This research presents a 20-year retrospective bibliometric analysis of the evolution of context and focus of ECR ’s articles [ 1 , 2 , 3 , 4 , 5 ]. To curate a rich bibliometric overview of ECR ’s scientific achievements, this study explores seven research questions (RQ) which are commonly asked by both authors and our Editorial Board members:

RQ1. What is the trend of publication and citation in ECR ?

RQ2. Who are the most prolific contributors (authors, institutions, and countries) in ECR ?

RQ3. What are the most influential publications in ECR ?

RQ4. Where have ECR publications been cited the most?

RQ5. What is the trend of collaboration in ECR ?

RQ6. Who are the most important constituents of the collaboration network in ECR ?

RQ7. What are the major research themes in ECR ?

A bibliometric analysis can offer a broad, systematic overview of the literature to delineate the evolution of electronic commerce technologies, and point the direction to trending topics and methodologies [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Our research is organized as follows. Section  2 outlines our bibliometric methodology. Section  3 goes on to performance analysis to uncover contributor and journal performance trends (RQ1–RQ4), the co-authorship analysis performed to unpack collaboration and constituent characteristics (RQ5–RQ6), and the bibliometric coupling and keyword analyses used to reveal the major themes and trends within the ECR corpus (RQ7). Section  4 applies graph theoretic analysis. Section  5 applies cluster analysis. Section 6 applies thematic analysis. Finally, we conclude the study with key takeaways from this retrospective.

2 Methodology

Bibliometric methodologies apply graph theoretic and statistical tools for analysis of bibliographic data [ 15 ] and include performance analysis and science mapping [ 16 ]. To answer research question 1 to research question 4, this study uses performance analysis to measure the output of authors’ productivity and impact, with productivity measured using publications per year, and impact measured using citations per year. We begin by measuring the productivity and impact of ECR , and then the productivity and impact of authors, institutions, and countries using both publications and citations per year metrics on top of ancillary measures such as citations per publication and h -index. Finally, we measure the impact of ECR articles using citations and shed light on prominent publication outlets citing ECR articles.

To answer research question 5 to research question 7, this study uses co-authorship, bibliographic coupling, and keyword analyses. We begin by conducting a co-authorship analysis, which is a network-based analysis that scrutinizes the relationships among journal contributors [ 17 ]. Next, we perform bibliographic coupling to obtain the major themes within the ECR corpus. The assumption of bibliographic coupling connotes that two documents would be similar in content if they share similar references [ 18 , 19 ]. Using article references, a network was created, wherein shared references were assigned with edge weights and documents were denoted with nodes. The documents were divided into thematic clusters using the Newman and Girvan [ 20 ] algorithm. Finally, we track the development of themes throughout different time periods using a temporal keyword analysis. The assumption of this analysis suggest that keywords are representative of the author’s intent [ 21 ] and thus important for understanding the prominence of themes pursued by authors across different time periods. Indeed, we found that these bibliometric methods complement each other relatively well, as bibliographic coupling was useful to locate general themes while keywords were useful to understand specific topics.

To acquire bibliographic data of ECR articles for the bibliometric analyses mentioned above, this study uses the Scopus database, which is one of the largest academic database that is almost 60% larger than the Web of Science [ 21 ]. Past research has also indicated that the citations presented within the Scopus database correlate more with expert judgement as compared to Google Scholar and Web of Science [ 22 ]. We begin by conducting a source search for “ Electronic Commerce Research ,” which resulted in 927 articles, and after filtering out non- ECR articles, we obtain a list of 516 ECR articles (see Fig.  1 ). However, ECR only gained Scopus indexation in 2005, and thus, only 443 ECR articles (2005–2020) contained full bibliometric data, whereas the remaining 73 ECR articles (2001–2004) contained only partial bibliometric data (e.g., no affiliation, abstract, and keyword entry). All 516 ECR articles were fetched and included in the performance analysis as partial bibliometric data was sufficient, but only 443 ECR articles were included in science mapping (e.g., co-authorship, bibliographic coupling, and keyword analyses using VOSviewer [ 23 ] and Gephi [ 24 ]) as full bibliometric data was required. This collection of articles met the minimum sample size of 200 articles for bibliometric analysis recommended by Rogers, Szomszor, and Adams [ 25 ].

figure 1

Research design. Note Bibliometric analysis was conducted for only 443 (primary) documents as 73 (secondary) documents lack full data (affiliation, abstract and keywords)

3 Performance analysis: productivity and impact

The publication and citation trends of ECR between 2001 and 2020 are presented in Fig.  2 (RQ1). In terms of publication, the number of articles published in ECR has grown from 20 articles per year in 2001 to 81 articles per year in 2020, with an average annual growth rate of 7.64%. In terms of citations, the number of citations that ECR articles received has grown from three citations in 2001 to 1219 citations in 2020, with an average annual growth rate of 37.19%. These statistics suggest that ECR ’s publications and citations have seen exponential growth since its inception, and that the journal’s citations have grown at a much faster rate than its publication, which is very positive.

figure 2

Annual publication and citation structure of ECR

3.2 Authors

The most prolific authors in ECR between 2001 and 2020 are presented in Table 1 (RQ2). The most prolific author is Jian Mou, who has published six articles in ECR , which have garnered a total of 95 citations. This is followed by Yan-Ping Liu and Liyi Zhang, who have published three articles each in ECR , which have received a total of 46 and 42 citations, respectively. Among the top 20 contributors, the author with the highest citation average per publication is Katina Michael (TC/TP and TC/TCP = 59 citations), who is followed closely by Yue Guo (TC/TP and TC/TCP = 51 citations); they are the only two authors who have an average citation greater than 50 for their ECR articles.

3.3 Institutions

The most prolific institutions for ECR between 2001 and 2020 are presented in Table 2 (RQ2). IBM, with 14 articles and 371 citations, emerges as the highest contributing institution to ECR . It is surprising yet encouraging to see a high number of contributions coming from practice, which reflects the ECR ’s receptiveness to publish industry-relevant research. Nonetheless, it is worth mentioning that this contribution is derived from the collective effort of IBM’s research labs around the world (e.g., Delhi, Haifa, and New York)—a unique advantage that most higher education institutions do not enjoy unless they have full-fledged research-active international branch campuses around the world. The second and third most contributing institutions are Nanjing University and Xi’an Jiaotong University, with 11 and 10 articles that have been cited 116 and 29 times, respectively. This is yet another interesting observation, as the contributions by Chinese institutions suggest that ECR is a truly international journal despite its origins and operations stemming in the United States. Finally, the University of California (TC/TP and TC/TCP = 34.86 citations) emerges as the institution that averages the most citations per publication, followed by IBM (TC/TP and TC/TCP = 26.50 citations) and Texas Tech University (TC/TP and TC/TCP = 26.20 citations).

3.4 Countries

The most prolific countries in ECR between 2001 and 2020 are presented in Table 3 (RQ2). China emerges as the most prolific contributor, with 152 articles and 1066 citations. This is followed by the United States, which has contributed 143 articles and 2813 citations. No country other than China and the United States has contributed more than 50 articles to ECR . Nevertheless, it is important to note that ECR also receives contributions from many countries around the world, as the remaining ± 50% of contributions in the top 20 list comes from 18 different countries across Asia, Europe, and Oceania.

3.5 Articles

The most cited articles in ECR between 2001 and 2020 are presented in Table 4 (RQ3). The most cited article published in ECR during this period is Füller et al.’s [ 26 ] article on the role of virtual communities in new product development (TC = 270). This is followed by Sotiriadis and van Zyl’s [ 27 ] article on electronic word of mouth and its effects on the tourism industry (TC = 188), Nonnecke et al.’s [ 28 ] article on the phenomena of ‘lurking’ in online communities (TC = 185), Lehdonvirta’s [ 29 ] article on the factors that drive virtual product purchases (TC = 170), and Bae and Lee’s [ 30 ] article on the effect of gender on consumer perception of online reviews (TC = 125). The diversity of topics in the most cited articles indicate that electronic commerce is indeed a multi-faceted subject, which we will explore in detail in the later sections.

3.6 Publication outlets

The publication outlets that have cited ECR articles the most between 2001 and 2020 are presented in Table 5 (RQ4). The list includes many prestigious journals such as International Journal of Information Management (ABDC = A*, IF = 8.210), Information and Management (ABDC = A*, IF = 5.155), and Decision Support Systems (ABDC = A*, IF = 4.721), among others. The presence of such reputed journals reflects ECR ’s own reputation of high standing among its peers. Apart from ECR , the publication outlets that have highly cited ECR include Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (TC = 218), Computers in Human Behavior (TC = 95), and ACM International Conference Proceeding Series (TC = 88), which reflect the diversity in publication outlets that ECR is making an impact (e.g., book, conference, journal).

4 Co-authorship analysis: scientific network

4.1 co-authorship.

The co-authorships in ECR between 2005 and 2020 are presented in Table 6 (RQ5). On the one hand, the co-authorship analysis shows that the share of articles written by a single author has gone down over the years from 10.94% (2005–2008) to 8.61% (2017–2020). The small and decreasing share of single-authored articles do not come as a surprise given the importance and proliferation of collaboration to address increasing thematic and methodological complexity in research [ 31 ]. On the other hand, the co-authorship analysis shows that multi-authored articles have increased their share in ECR , especially articles with three authors or more. In particular, the share of articles with three and five or more authors have increased from 31.25% and 4.69% between 2005 and 2008 to 34.45% and 14.35% between 2017 and 2020, respectively. These statistics suggests that collaboration is growing in prominence, which is consistent with recent observations reported by other premier journals in business [ 32 , 33 , 34 ], and that ECR is a good home for collaborative research.

4.2 Network centrality

The most important authors, institutions, and countries across different measures of centrality are presented in Table 7 (RQ6). In this study, we employ four measures of centrality: degree of centrality, betweenness centrality, closeness centrality, and eigen centrality.

In essence, degree of centrality refers to the number of relational ties a node has in a network. In contrast, betweenness centrality refers to a node’s ability to connect otherwise unconnected groups of nodes, wherein nodes act as a gateway for the flow of information. Whereas, closeness centrality refers to a node’s closeness to every other node in the network, whereby nodes that reflect a greater number of shortest paths than others in a network indicates the ability of those nodes to transmit information and knowledge across the network with relative ease. Finally, eigen centrality refers to a node’s relative importance in a network, whereby nodes that are connected to other highly connected nodes are crucial to information transfer.

In terms of authors, Jian Mou emerged as the most important author for degree of centrality and betweenness centrality, whereas Xin Luo and Jian-xin Wang were flagged as the most important authors for closeness centrality and eigen centrality, respectively. In terms of institutions, Renmin University emerged as the most important institution for degree centrality and betweenness centrality, whereas the University of Ottawa was rated as the most important institution for closeness centrality and eigen centrality. In terms of countries, China emerged as the most important country for betweenness centrality, whereas the United States emerged as the most important country for the other three measures of centrality. Collectively, these findings indicate the most important constituents for degree of centrality, betweenness centrality, closeness centrality, and eigen centrality in terms of authors, institutions, and countries.

4.3 Collaboration network

The author collaboration network in Fig.  3 indicates that authors groups in ECR are fairly separated from each other, especially among highly connected authors (more than five links in the network). This suggests that most authors in ECR chose to work in a single team rather than across multiple teams. The institution collaboration network in Fig.  4 reaffirms our earlier finding that Renmin University is indeed the most important constituent of the network, especially among highly connected institutions (more than five links in the network). The institution collaboration network also appears to be more complex than the author collaboration network, wherein institutions appear to be far more connected to each other, indicating a good degree of collaboration across institutional lines. The country network in Fig.  5 presents a similar network scenario, where countries appear to be fairly well connected, with the United States being at the center of the country-level collaboration network. These findings suggest that ECR authors collaborate more actively across institutions and countries than teams.

figure 3

Author co-authorship network. Note Threshold for inclusion is five or more links in the network

figure 4

Institution co-authorship network. Note Threshold for inclusion is five or more links in the network

figure 5

Country co-authorship network. Note Threshold for inclusion is five or more links in the network

5 Bibliographic coupling: thematic clusters

Bibliographic coupling is applied to unpack the major clusters (themes) within the ECR corpus. The method is predicated on the assumption that documents that share the same references are similar in content [ 18 , 35 ]. The application of bibliographic coupling on 443 ECR articles resulted in the formation of 30 clusters, wherein 11 major clusters were identified. The 11 major clusters, which contained 401 (or 90.5%) ECR articles, were ordered based on number of publications and average publication years, with more recent clusters ordered before older clusters in the case of clusters sharing the same number of publications. The summary of the 11 major clusters, which take center stage in this study, is presented in Table 8 .

5.1 Cluster #1: online privacy and security

Cluster #1 contains 74 articles that have been cited 963 times with an average publication year of 2013.09. The most cited article in this cluster is Zarmpou et al.’s [ 36 ] article on the adoption of mobile services. This is followed by Chaudhry et al.’s [ 37 ] article on user encryption schemes for e-payment systems, and Antoniou and Batten’s [ 38 ] article on purchaser’s privacy and trust in online transactions. Other articles in this cluster have considered topics such as e-commerce trust models [ 39 ], consumer privacy [ 40 ], cybercrime and cybersecurity issues [ 41 ], gender differences [ 42 ], and the development and implementation of various authentication systems [ 43 , 44 ]. Thus, ECR articles in this cluster appear to be centered on online privacy and security issues , including equivalent solutions for improved authentication and encryption to improve trust in electronic commerce.

5.2 Cluster #2: online channels and optimization

Cluster #2 contains 49 articles that have been cited 415 times with an average publication year of 2016.67. The most cited article in this cluster is Jeffrey and Hodge’s [ 45 ] article on impulse purchases in online shopping. This is followed by Biller et al.’s [ 46 ] article on dynamic pricing for online retailing in the automotive industry, and Yan’s [ 47 ] article on profit sharing and firm performance in manufacturer-retailer dual-channel supply chains. Other articles in this cluster have examined online channels such as peer-to-peer networks and social commerce [ 48 , 49 ] and optimal supply chain configuration [ 50 , 51 ]. Thus, ECR articles in this cluster appear to be concentrated on online channels and optimization , particularly in terms of the channel characteristics and price and supply chain optimization in electronic commerce.

5.3 Cluster #3: online engagement and preferences

Cluster #3 contains 49 articles that have been cited 982 times with an average publication year of 2013.98. The most cited article in this cluster is Nonnecke et al.’s [ 28 ] article on online community participation. This is followed by Sila’s [ 52 ] article on business-to-business electronic commerce technologies, and Ozok and Wei’s [ 53 ] article on consumer preferences of using mobile and stationary devices. Other articles in this cluster have explored topics such as online community participation and social impact across countries [ 54 ], online opinions across regions and its impact on consumer preferences [ 55 , 56 ], content and context factors [ 57 ], data mining techniques [ 58 ], and recommender systems and their application in online environments [ 59 , 60 ]. Thus, ECR articles in this cluster appear to be focused on online engagement and preferences , including the adoption and usage of technology (e.g., data mining, recommender systems) to curate engagement and shape preferences among target customers in electronic commerce.

5.4 Cluster #4: online market sentiments and analyses

Cluster #4 contains 41 articles that have been cited 198 times. This cluster has the highest average publication year among the 11 major clusters (2018.56), which indicates that most articles in this cluster are fairly recent. The most cited article in this cluster is Zhou’s [ 61 ] article on multi-layer affective modeling of emotions in the online environment. This is followed by Suki’s [ 62 ] article on online consumer shopping insights, and Chen et al.’s [ 63 ] article on information markets. Other articles in this cluster have investigated topics such as Internet queries and marketplace prediction [ 64 ], cross-border electronic commerce using the information systems success model [ 65 ], and electronic [ 66 ] and social [ 67 ] commerce using big data. Thus, ECR articles in this cluster appear to be centered on online market sentiments and analyses , with the use of advanced modeling techniques to unpack fresh insights on electronic commerce being relatively prominent.

5.5 Cluster #5: online reviews and ratings

Cluster #5 contains 40 articles that have been cited 611 times with an average publication year of 2017.28. The most cited article in this cluster is Bae and Lee’s [ 30 ] article on online consumer reviews across gender. This is followed by Flanagin et al.’s [ 68 ] article on user-generated online ratings, and Fairlie’s [ 69 ] on the digital divide in online access, which speaks to the technological infrastructure required to post and respond to online reviews and ratings. Other articles in this cluster have examined quantitative and qualitative feedback in online environments [ 70 ], electronic word of mouth platforms and persuasiveness [ 71 ], online reviews and product innovation [ 72 ] , recommender systems and product ranking [ 73 ], and online rating determinants [ 74 ]. Thus, ECR articles in this cluster appear to be concentrated on online reviews and ratings , including its potential differences among consumers coming from different demographic backgrounds.

5.6 Cluster #6: online exchanges and transactions

Cluster #6 contains 34 articles that have been cited 320 times with an average publication year of 2011.29. The most cited article in this cluster is Narayanasamy et al.’s [ 75 ] article on the adoption and concerns of e-finance. This is followed by Dumas et al.’s [ 76 ] article on bidding agents in e-auction, and Marinč’s [ 77 ] article on the impact of information technology on the banking industry. Other articles in this cluster have explored topics such as game theoretic aspects of search auctions [ 78 ], auction mechanism for ad space among advertisers [ 79 ], trust analysis in online procurement [ 80 ], efficiency of reverse auctions [ 81 ], and effect of hedonic and utilitarian behaviors on the e-auction behavior [ 82 ]. Thus, ECR articles in this cluster appear to be focused on online exchanges and transactions , particularly in terms of auction mechanisms and banking-related services.

5.7 Cluster #7: online media and platforms

Cluster #7 contains 30 articles that have been cited 668 times with an average publication year of 2016.23. The most cited article in this cluster is Sotiriadis and van Zyl’s [ 27 ] article on social media in the form of Twitter. This is followed by Huang and Liao’s [ 83 ] article on augmented reality interactive technology, and Hsieh et al.’s [ 84 ] article on online video persuasion in electronic commerce. Other articles in this cluster have investigated topics such as the role of social media in disseminating product information [ 85 ], the effect of video formats on person-to-person streaming [ 86 ], interpersonal relationship building using social media [ 87 ], and microblog usage [ 88 ]. Thus, ECR articles in this cluster appear to be centered on online media and platforms , particularly in terms of its variation, use, and impact in shaping consumer behavior in electronic commerce.

5.8 Cluster #8: online technology acceptance and continuance

Cluster #8 contains 26 articles that have been cited 244 times with an average publication year of 2016.37. The most cited article in this cluster is Zhou’s [ 89 ] article on the adoption of location-based services. This is followed by Chen et al.’s [ 90 ] article on the adoption of electronic customer relationship management, and Royo and Yetano’s [ 91 ] article on crowdsourcing usage in local governments. Other articles in this cluster have examined topics such as gender discrimination in online peer-to-peer lending [ 92 ], continued usage of e-auction services [ 93 ], and investor trust in peer-to-peer lending platforms [ 94 ]. Thus, ECR articles in this cluster appear to be concentrated on online technology acceptance and continuance , including determinants and discriminants that explain online technology-mediated behavior across different forms of electronic commerce such as e-auction, e-lending, e-government, and e-customer relationship management.

5.9 Cluster #9: online communities and commercialization in the virtual world

Cluster #9 contains 22 articles that have been cited 771 times with an average publication year of 2012.23. The most cited article in this cluster is Füller et al.’s [ 26 ] article on the role of virtual communities in new product development. This is followed by Lehdonvirta’s [ 29 ] article on the revenue model of virtual products, and Guo and Barnes’s [ 95 ] article on the purchase behavior of virtual products. Other articles in this cluster have investigated topics such as metaverse retailing [ 96 ], issues faced by developers of virtual worlds [ 97 ], the impact of virtual world on e-business models [ 98 ], e-commerce transactions in virtual environments [ 99 ], and customer value co-creation in virtual environments [ 26 ]. Thus, ECR articles in this cluster appear to be focused on the online communities and commercialization in the virtual world , particularly in virtual environments such as online gaming.

5.10 Cluster #10: online customer expectations, satisfaction, and loyalty

Cluster #10 contains 18 articles that have been cited 291 times with an average publication year of 2016.11. The most cited article in this cluster is Hanafizadeh and Khedmatgozar’s [ 100 ] article on consumer expectations of risk in online banking. This is followed by Valvi and Fragkos’s [ 101 ] article on purchase-centered e-loyalty, and Aloudat and Michael’s [ 102 ] article on regulatory expectations of ubiquitous mobile government. Other articles in this cluster have examined topics such as continued usage of e-services [ 103 ], determinants of e-loyalty [ 104 ] , risk expectations of e-services [ 105 ], and e-service quality implications for customer satisfaction and loyalty [ 106 ]. Thus, ECR articles in this cluster appear to be centered on online customer expectations, satisfaction, and loyalty , particularly in e-service settings such as online banking.

5.11 Cluster #11: online purchase intention

Cluster #11 contains 18 articles that have been cited 671 times with an average publication year of 2014.00. The most cited article in this cluster is Kim’s [ 107 ] article on online purchase intention using trust theory and technology acceptance model. This is followed by Gregg and Walczak’s [ 108 ] article on the effects of website quality on online purchase intention, and Taylor et al.’s [ 109 ] article on the effects of privacy concerns on online purchase intention. Other articles in this cluster have explored topics that either reaffirm the findings of the highly cited articles in this cluster, such as privacy concerns and personalization [ 109 , 110 ], or that extend the breadth of cluster coverage, such as store image [ 111 ], risk, and trust [ 112 ] as determinants of online purchase intention. Thus, ECR articles in this cluster appear to be concentrated on online purchase intentions , particularly in terms of its multi-faceted determinants that avail or transpire in electronic commerce.

6 Temporal keyword analysis: thematic evolution

Building on the thematic clusters uncovered using bibliographic coupling (see Fig.  6 ), this study performs a temporal keyword analysis to unpack the development of themes and its evolutionary trajectory in ECR over time.

figure 6

Period wise publication trend in major clusters. Note Cluster #1 = online privacy and security. Cluster #2 = online channels and optimization. Cluster #3 = online engagement and preferences. Cluster #4 = online market sentiments and analyses. Cluster #5 = online reviews and ratings. Cluster #6 = online exchanges and transactions. Cluster #7 = online media and platforms. Cluster #8 = online technology acceptance and continuance. Cluster #9 = online communities and commercialization in the virtual world. Cluster #10 = online customer expectations, satisfaction, and loyalty. Cluster #11 = online purchase intention

6.1 Thematic development from 2005 to 2008

Most ECR articles between 2005 and 2008 appear in Clusters #1, #3, and #6 (see Fig.  6 ), which indicate research concentration on online privacy and security, online engagement and preferences, and online exchanges and transactions. The keyword network in Fig.  7 confirms this observation. Apart from general keywords such as “e-commerce,” keywords such as “cryptography,” “privacy,” and “security” relate directly to the theme of Cluster #1, which is about online privacy and security. The prominence of the word “cryptography” indicates the popularity and importance of the topic during this period. Other keywords such as “auctions,” “online auctions,” and “bidding strategies” relate to the theme of Cluster #6, which is about online exchanges and transactions, with particular focus on online auction and banking. Other keywords such as “collaborative filtering,” “online communities,” and “mobile commerce” relate to the theme of Cluster #3, which is about online engagement and preferences. The bigger and bolder keywords observed in Clusters #1 and #3 suggest that the direct benefits and costs of electronic commerce were most pertinent in the early stages of ECR , with the augmented aspects of electronic commerce in Cluster #6 emerging closely behind the two leading clusters in this period.

figure 7

Keyword network between 2005 and 2008. Note Threshold for inclusion is a minimum of two occurrences

6.2 Thematic development from 2009 to 2012

Most ECR articles between 2009 and 2012 are located in Cluster #1 (see Fig.  6 ), which reveal the continued pertinence of research concentrating on online privacy and security during this period. Nonetheless, ECR experienced a substantial growth in research focusing on online media and platforms, online communities and commercialization in the virtual world, online customer expectations, satisfaction, and loyalty, and online purchase intention, as seen through ECR articles in Clusters #7, #9, #10, and #11 during this period. The keyword network in Fig.  8 adds to this observation. In particular, keywords such as “security,” “payment protocol,” and “trust management” relate to the theme of Cluster #1 on online privacy and security, whereas keywords such as “metaverses,” “second life,” “virtual reality,” and “virtual world” speak to the emergence of online communities and commercialization in the virtual world characterizing Cluster #9. Similarly, keywords such as “reputation” and “trust” are important to online customer expectations, satisfaction, and loyalty (Cluster #10) and their online purchase intention (Cluster #11). Interestingly, though Cluster #7 emerged during this period, we did not observe any unique or specific keywords relating to this cluster, which may be attributed to online media and platform research early focus on its “adoption,” a keyword that we felt resonates more with Cluster #8.

figure 8

Keyword network between 2009 and 2012. Note Threshold for inclusion is a minimum of two occurrences

6.3 Thematic development from 2013 to 2016

Most ECR articles between 2013 and 2016 continue to be situated in Cluster #1 (see Fig.  6 ), which suggest the continued pertinence of research concentrating on online privacy and security during this period. Nonetheless, there are a number of clusters that saw noteworthy growth, such as Clusters #2, #5, #7, #8, and #10, which indicate that research attention has also been invested in topics related to online channels and optimization, online reviews and ratings, online media and platforms, online technology acceptance and continuance, and online customer expectations, satisfaction, and loyalty. The keyword network in Fig.  9 supports this observation. More specifically, keywords such as “personal information” and “privacy” indicate continued research in Cluster #1, though it appears that the focus has shifted from authentication and security mechanisms to privacy matters, which may be attributed to the rise of personalized and targeted online marketing activities (e.g., tracking of user activity for personalized advertisements). Whereas, keywords such as “B2C e-commerce” and “e-government” denote emerging interest in online channels and optimization (Cluster #2), “electronic word of mouth” indicates growing interest in online reviews and ratings (Cluster #5), “cloud computing,” “IPTV,” and “social media” reveal increasing interest in online media and platforms (Cluster #7), “information technology,” “technology adoption,” and “technology acceptance model” speak to research on online technology acceptance and continuance (Cluster #8), and “product type,” “quality of service,” and “user satisfaction” resonate with research on online customer expectations, satisfaction, and loyalty (Cluster #10).

figure 9

Keyword network between 2013 and 2016. Note Threshold for inclusion is a minimum of two occurrences

6.4 Thematic development from 2017 to 2020

Most ECR articles between 2017 and 2020 are located in Cluster #4 (see Fig.  6 ), which reflect the noteworthy emergence and shift of research concentration from online privacy and security to online market sentiments and analyses. Other thematic clusters such as Clusters #2, #3, and #5 have also witnessed a massive increase in publications during this period. This implies that ECR has become relatively diverse in the research that it publishes, which also explains the rise in the number of papers that the journal publishes during this period. The keyword network in Fig.  10 sheds further light on this observation. In particular, many keywords in the network illustrate a strong research concentration on online market sentiments and analyses, such as “big data,” “data mining,” machine learning,” “sentiment analysis,” and “social network analysis” (Cluster #4). Similarly, keywords such as “dual channel supply chain,” “supply chain coordination,” and “social commerce” indicate the type of research focusing on online channels and optimization (Cluster #2), “social influence,” “social media,” and “social media marketing” reflect research in the area of online engagement and preferences (Cluster #3), and “consumer reviews,” “online reviews,” “reputation,” and “word of mouth” speak to research on online reviews and ratings (Cluster #5).

figure 10

Keyword network between 2017 and 2020. Note Threshold for inclusion is a minimum of two occurrences

7 Conclusion

This study presents a 20-year retrospective of ECR since its inception in 2001. Several research questions were proposed and pursued using a bibliometric methodology consisting of performance analysis and science mapping (e.g., co-authorship analysis, bibliographic coupling, and temporal keyword analysis).

Our first four research questions—i.e., research question 1 to research question 4—concentrated on the publication and citation trends of ECR . Through performance analysis, we found that ECR has grown exponentially in terms of its publications and citations. Most contributors of ECR come from China and the United States, which reflect (1) China’s standing as the world’s largest e-commerce market with 50 percent of the world’s online transactions occurring in this country, and (2) the United States’ standing as the world’s pioneer of e-commerce (e.g., Amazon) and her expectation for e-commerce to reach 50% of total retail sales in the country in 10 years [ 113 ]. Interestingly, IBM, a non-academic institution, emerged as the highest contributing institution to the journal, which is unsurprising given that IBM is the largest industrial research organization in the world with 12 research labs across six continents [ 114 ]. More importantly, ECR was found to be well received among its peers, with many of its citations coming from prestigious journals in the field of information systems and management. Nevertheless, we observed that ECR receives very little contribution from Africa and several parts of Asia, particularly South Asia and South East Asia. Though electronic commerce may not have been very prominent in these regions in the past, we believe that the coronavirus pandemic that has taken the world by storm in 2020 has accelerated the proliferation and adoption of electronic commerce in these regions, and thus, we would encourage authors from these regions to submit their best papers to ECR in the near future. Thus, we raise two future research questions (FRQs) for exploration:

FRQ1: What are the e-commerce innovations that avail in underexplored regions (e.g., Africa, South Asia, and South East Asia) and how do such innovations fare in terms of similarities and differences in manifestations and impact against their more richly explored counterparts (e.g., China, United States)?

FRQ2: How can global pandemics such as COVID-19 change or impact e-commerce around the world (e.g., can the pandemic accelerate e-commerce adoption across all layers of society; can the pandemic lead to new innovations; can e-commerce contribute to positive and/or negative economic and social impact during the pandemic—and if yes, what and how, and if no, why)?

Our next two research questions—i.e., research question 5 and research question 6—focused on the collaboration trends in and the important constituents of ECR in the co-authorship network. Using co-authorship analysis, we found that the collaboration culture in ECR has grown with the passage of time, as evidenced through the decreasing share of single-authored articles and the increasing share of multi-authored publications, especially in the five or more authors category. We also observed that the share of multi-authored articles has always been dominant in the journal, with such publications forming nearly 90% of the corpus at any given point in time. Indeed, these observations reflect the increasing emphasis that universities place on multi-author and inter-/multi-/trans-disciplinary collaborations in promotion and tenure practices and policies [ 115 ]. In terms of important constituents in the co-authorship network, Jian Mou emerged as the most important author across two measures of centrality, whereas Renmin University and University of Ottawa emerged as the most important institutions at the institution level, and the United States emerged as the most important constituent at the country level. Nonetheless, we noted that authors who collaborate in ECR do not work much across diverse teams, but they do, however, work a lot across institutions and countries. Future scholars could rely on the centrality networks that we have curated herein this study for potential collaboration with authors from varying institutions and countries who have a good publication record and a research interest to publish with ECR .

Our final research question—i.e., research question 7—was dedicated to unpacking the major themes in ECR . Through bibliographic coupling, our study found 11 major clusters that reflected the major themes underpinning research published in ECR : (1) online privacy and security, (2) online channels and optimization, (3) online engagement and preferences, (4) online market sentiments and analyses, (5) online reviews and ratings, (6) online exchanges and transactions, (7) online media and platforms, (8) online technology acceptance and continuance, (9) online communities and commercialization in the virtual world, (10) online customer expectations, satisfaction, and loyalty, and (11) online purchase intention. Through temporal keyword analysis, our study observed that the topics published in ECR has become more diverse over time, with a noteworthy shift from an early concentration on online privacy and security to a contemporary focus on newer, industry-informed topics, such as online market sentiments and analyses, which we reckon coincides with the emergence of the unique peculiarities of the fourth industrial revolution (IR 4.0), such as big data and machine learning, in recent years [ 116 , 117 ]. Thus, to extend the line of research that concentrates on unpacking the contemporary realities of e-commerce, we propose another two future research questions (FRQs) for exploration:

FRQ3: How can emergent technologies (e.g., artificial intelligence, big data analytics, blockchain, machine learning) be applied to improve forecasting (e.g., cybercrime, social network), optimize functions (e.g., advertising, sales), and protect stakeholders (e.g., privacy, security) in e-commerce?

FRQ4: How can e-commerce operators leverage on emergent technologies to acquire competitive advantages (e.g., how to build trust and good relationships with customers [e.g., digital natives, digital migrants], and how to respond to changes in customer demands and marketplace trends with agility), and whether these competitive advantages that they acquired are sustainable or transient (and if transient, then what can they do to curate, maintain, or replenish their competitive advantages in the long run)?

Though thorough in its approach, this study does suffer from certain limitations. First, this study relies on the Scopus for bibliometric data. Though the database has its merits, as laid out in the methodology section, the bibliographic data is not created for the purpose of bibliometric analysis. This may lead to errors in the data source. Through data cleaning, we have attempted to minimize errors, but any remaining error in the source data, which we might have missed, could have an impact on the final analysis, though we believe that the margin for such errors would be relatively small, if not, negligible. Second, ECR has been around for 20 years, but the dataset available on Scopus, which we used, is only complete for 16 years (2005–2020). Due to this limitation, the science mapping part of the study—i.e., co-authorship, bibliographic coupling, and temporal keyword analysis—had to be restricted to this period only. We do not discount the possibility that the complete set of earlier data (2001–2004) may become available on Scopus in the future, and thus, we would encourage future research aiming to conduct a bibliometric review for ECR , perhaps in the next milestone (e.g., 30, 40, or 50 years), to check on such data availability, and if available, to take advantage and conduct a full-fledged science mapping for the journal. Finally, the scientific insights that could be uncovered through a bibliometric methodology, though rich, remain limited. In particular, bibliometric reviews such as ours do not delve into expert information, such as the theories, contexts, and methods employed to create new knowledge on electronic commerce in the ECR corpus. This, in turn, makes it difficult for bibliometric reviews to put forth a comprehensive set of data-informed proposals for future research. Nonetheless, we opine that bibliometric reviews do provide a good starting point of data-informed insights that future research can rely on to understand the trajectory of the extant discussion of electronic commerce in the journal. In particular, we believe that such insights would be useful, not only for future empirical research (e.g., potential collaboration networks, research themes of interest), but also for future reviews on thematic domains in ECR (e.g., systematic reviews on online market sentiments), which can be done in a number of ways, such a critical review [ 118 , 119 , 120 ], a thematic review [ 121 , 122 ], a theory-driven review [ 123 ], a method-driven review [ 124 , 125 ], or a framework-based review [ 126 ].

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Satish Kumar & Nitesh Pandey

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Kumar, S., Lim, W.M., Pandey, N. et al. 20 years of Electronic Commerce Research . Electron Commer Res 21 , 1–40 (2021). https://doi.org/10.1007/s10660-021-09464-1

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ORIGINAL RESEARCH article

Research on e-commerce data standard system in the era of digital economy from the perspective of organizational psychology.

\r\nHongqiang Yue*\r\n

  • Henan University School of Law/Intellectual Property School, Institute of Civil and Commercial Law of Henan University, Kaifeng, China

With the rapid development of technology and the economy, the expansion of the network has had a huge impact on the rapid expansion of the industrial agglomeration e-commerce industry, as well as ensuring the shopping experience of consumers. The rapid expansion of industrial cluster e-commerce has avoided precisely the limitations of logistical bottlenecks. Current networks and modern information technologies can provide good support and maintain a huge growth potential. In addition, digital technologies such as multimedia are becoming increasingly important in industry cluster marketing, and the concept of industry cluster e-commerce models is gaining more and more attention from companies. However, virtual e-commerce systems under industrial clusters have not been well researched in the existing studies. In this paper, through extensive research, literature reading and website browsing statistics, the virtual e-commerce models of different industrial agglomerations are studied. Firstly, the concept of big data and the processing of big data are given. Secondly, the concept of industrial agglomeration and the relationship between industrial agglomeration and e-commerce are analyzed. The basic number of domestic Internet users in the last 10 years is also counted, proving that the expansion of the Internet has led to a substantial growth of Internet users in the country and that e-commerce plays a significant role in the future of business activities. Finally the study concludes that different e-commerce models have different performance and roles in industrial agglomeration e-commerce and cannot be generalized. Instead, it is not good and can only develop different industrial agglomeration e-commerce models according to different environments.

Introduction

In the long history of mankind, when people explore and discover the law of unknowns, they rely mainly on reasoning methods such as experience, theory, and assumptions, which are largely influenced by personal prejudice. Later, people invented mathematical tools such as statistics, sampling, and probability. Through careful design and extraction methods, a small number of data samples were obtained to infer the whole picture of things. Therefore, there are often deviations and distortions in understanding things. According to Victor Meyer, thanks to advances in technology, people can access all the data of a research object and understand things from different angles. Analyze the different dimensions of all data from an incomprehensible perspective. With the rapid expansion of electronic signal technology, e-commerce ( Anam et al., 2017 ; Irene, 2018 ) has changed an inevitable outcome of the expansion of the times and is also a form of transaction that adapts to market demand. The expansion of e-commerce is very gratifying. After more than 10 years of expansion, B2C ( Gui et al., 2019 ) and C2C ( Navarro-Méndez et al., 2017 ) have become the main mode of e-commerce in China. The model has the vitality of information transparency, flexible trading, high efficiency and price advantage. With the rapid propagate of the Net, by the end of 2018, the number of Internet users in China reached 1.08 billion. A great deal of Internet users has established a good customer base for the expansion of e-commerce. In addition, the continuous improvement of relevant laws and regulations and the maturity of information technology have laid the foundation for the expansion of e-commerce. By combining big data with e-commerce, e-commerce based on big data will become the main research direction of the future society ( Nik et al., 2017 ).

Mega data (big data) ( Wang et al., 2017 ; Zhou et al., 2017 ) is what we often call big data, also known as massive data. Giant data is actually a data repository. In this era, it can be used as an asset. After professional analysis, the efficiency is higher, the amount of data is larger, the data is diverse, and the sources are different, most of which are instantaneous. The communication information generated during the sales process is also generated instantaneously. For example, customer basic data, website clicks, network data, etc., are all counted in big data, some are part of customer information, and some are not counted. In the 1980s, some scholars predicted big data and believed that big data will surely ignite the new wave of the third technological revolution. Since 2009, “Big Data” has made great progress with the rapid expansion of e-commerce and cloud computing ( Liu et al., 2018 ) and is gradually becoming well known to the public. As can be seen from the latest data, the growth of data on the Internet and mobile Internet has gradually approached Moore’s Law, and global data and information have been created “over doubling every 18 months” over the years. The application of big data in industrial agglomeration ( Xuan, 2017 ; Nádudvari et al., 2018 ) e-commerce is also getting more and more wide-ranging.

Industrial clusters ( Cao et al., 2017 ; Wang and Yu, 2017 ) have a long history as well-functioning organizations. At the end of the 19th century, Marshall creatively defined the concept of “industry zone,” that is, industrial clusters. He defines “industrial zone” as the agglomeration of certain industrial zones, which is determined by two factors: history and natural resources. There are many companies of different sizes in the area. There is a close relationship between cooperation and competition, which gradually affects the integration of industrial clusters and society. According to Marshall, the reason for the emergence of “industry zones” in the region is a combination of inside and outside factors. Later, Weber believed that the phenomenon of industrial clusters was the result of regional and geographic influences. Companies with regional and geographic advantages have established close partnerships through partnerships with other related companies. Establish complex and close internal network relationships, achieve the aggregation of enterprises in a specific region, and then develop into industrial clusters. In recent decades, academia and industry have been highly involved in the expansion of synergies between industrial clusters and supply chains. They actively used industrial clusters and supply chains in corporate management ( Heiner and Marc, 2018 ) and achieved remarkable results. Clusters and supply chains can provide a competitive advantage for businesses. However, with the rapid expansion of e-commerce, industrial clusters are faced with the dilemma of optimizing transformation and upgrading. The traditional approach to supply chain management is far from meeting the needs of users. Therefore, it is a major problem to study how e-commerce uses the first-mover advantage to promote synergy between industrial clusters and supply chains.

For the core enterprises in the industrial agglomeration, because of their own advantages in terms of capital and technology, as well as a number of strong manufacturers and suppliers, so that the online market established by the enterprise has a large number of members and good prospects for development, and attracts some new members to join, once the establishment of close cooperation in this online market, its members want to move to other online market will be very expensive, so that the core enterprises in the online market to consolidate their existing position ( Yang et al., 2022 ; Han et al., 2021 ; Setiawan et al., 2022 ; Suska, 2022 ; Yu et al., 2022 ). Therefore, e-commerce has developed into a new opportunity to enhance the synergy of China’s supply chain and enhance its competitive advantage. In the end, this paper starts from the business reality of big data-based industry agglomeration e-commerce, fully considers the dependence of industrial agglomeration area on e-commerce in the era of big data, and studies the relationship between the concept of industrial agglomeration and the relationship between e-commerce and industrial agglomeration. Therefore, with the support of big data, this paper analyses the number of netizens, the level of economic expansion, etc., and compares the impact of e-commerce yield and industrial agglomeration e-commerce investment and big data and e-commerce on industrial agglomeration. The merits and demerits of e-commerce in the type of industrial agglomeration, and the expectation is to provide a summary and reference for the industry to gather e-commerce enterprises to obtain competitive advantages in the market competition.

Big Data and E-Commerce Related Definitions

Big data overview.

With the popularity of the Internet and the rapid expansion of information technology, the signal age is making a subtle transition to the big data era. The network has turned into an integral part of people’s production and life. While enjoying the convenience brought by the information network, people also continuously feedback and input information to the network. Some information involves individual privacy, and network information security has become one of the hot topics of research. At present, the social network information security problem is becoming more and more obvious, the conventional information security software has been unable to deal with the endless information security problem, the network society urgently needs a new information technology to protect the increasingly huge information assets, and the big data technology has stronger insight, more scientific decision-making power and more accurate process optimization ability compared with the conventional software. Must be able to play a positive effect.

Professor Victor is known as the “Big Data Prophet.” Big data also called huge amount of data, refers to the amount of data involved is so large that it cannot be captured, managed, processed and collated in a reasonable time through the human brain or even mainstream software tools to help enterprises to make more positive decisions. By analyzing big data, we can draw conclusions that cannot be obtained in the case of small data. The big data we usually talk about is more about getting valuable information in a short time by quickly analyzing a large amount of data.

Big Data Analysis Process and Features

In general, there are many methods for analyzing big data, and in theory it is still in the exploration stage, but no matter what kind of big data analysis method follows the basic process, the flow chart is shown in Figure 1 .

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Figure 1. Big data processing flow.

The first process of big data analysis is acquisition. Big data sampling ( Bivand and Krivoruchko, 2018 ; Cohen et al., 2018 ) means collect information collection platforms to collect users or other data. In the process of big data collection, the main problem is that the amount of data is huge, the amount of collection is large, and the data collection point is large. A large amount of data needs to be collected at the same time. Therefore, in the process of collecting big data, it is necessary to establish a larger database and how to further design the reasonable use and distribution of the database.

The second step is import and beneficiate. This mainly means that invalid information, redundant information and low-value information are excluded after the first information collection is completed, so it is necessary to execute the data before processing. Effective screening and brief analysis, and then import the resulting preliminary filtering information into another large database, this step is mainly to pre-process the big data.

The third step in big data processing is to perform statistics and analysis. This process is a process of further refinement of big data, analyzing and screening valid data, and performing statistical processing to obtain effective information.

The fourth step in big data is to deal with the mining ( Rezaei-Hachesu et al., 2017 ; Fan et al., 2018 ; Svefors et al., 2019 ) process. Unlike the above process, there is no clear path or statistical analysis method for big data information mining. It is mainly used for databases that collect large amounts of data and use various algorithms for calculations, so it is complex data. Try to get predictions or get other valid conclusions. The statistical analysis and mining process of big data is considered to be a key process for transforming data from data into value space and value sources in the process of big data information processing.

The final step is using information obtained from big data. In particular, it can be used for business decision behavior predictions, while sales companies can provide accuracy. Marketing, achieving service conversion, etc. The application prospect of big data is very broad, and it has a good application prospect in transportation, sales management, economic research and forecasting.

At present, there is no authoritative unified standard. At present, the “4V” function of big data has been widely recognized.

First, the data size is huge. In 2012, the world produced about 2.7 billion GB of data per day, the amount of data per day equals the sum of all stored data in the world before 2000. Baidu must process more than 70,000 GB of search data per minute, and Alipay generates an average of 73,000 transactions per minute. Traffic flow monitoring systems and video capture systems can generate large amounts of video data at any time. Temperature sensors in greenhouses and various detectors in the factory are also big data manufacturers. It can be said that the amount of data we generate per minute is unimaginable. Now, the scale of data that big data needs to process continues to grow, reaching orders of magnitude unimaginable in small data.

Second, there is a wide variety of data (Variety). In big data, in addition to the ever-increasing data size, the types of data that people need to deal with are beginning to emerge. The various data types are very numerous and very strange, and only a few can be handled using traditional techniques. Some are unstructured data that traditional technologies cannot handle, and this trend will be long-term, with unstructured data accounting for 90% of all data over the next decade. For example, Tudou’s video library, photos on social networking sites, records, etc., even include RFID status, mobile operator call history, video surveillance video, Weibo and status posted on WeChat. The size, format, and type of data from various sources may vary. Existing data processing techniques are useless and can cause significant difficulties when performing large amounts of processing.

Third, value is difficult to mine. The first two features show that the amount of data and data types in big data are amazing. Faced with a large amount of data, in order to mine hidden “treasures,” the analysis and processing of powerful cloud computing systems is only one aspect, not even the main one. How to analyze big data from the perspective of innovation according to needs, what to use big data ideas to examine big data to explore unimaginable economic and social values. In other words, only the combination of technology and innovation can unlock the value of big data. Otherwise, no amount of data will be useful.

Fourth, the processing speed is high (Velocity). This is the most significant feature of the big data era, unlike the era of small data and the era of probability and statistics. In traditional economic censuses, censuses and other areas, data can be tolerated for days or even a year, as the data obtained at this time still makes sense. Moreover, due to technical limitations, the collected data has been lagging behind, and the structure of statistical analysis is lagging behind, but it must be accepted. Data generation and collection is very fast, and the amount of data is growing all the time. With advanced technology, people can collect data in real time. But in most cases, if you don’t process the data in time, the advanced collection and sorting methods will be meaningless and you won’t need big data. For example, IBM proposed the concept of “big data-level stream computing,” which is designed for real-time analysis of data and results to increase practical value. Therefore, timely and fast processing of data and results is the most important feature of big data.

This is the most significant feature of the big data, unlike the era of small data and the era of probability and statistics. Due to technical limitations, the collected data is backward, and the structure of statistical analysis is also backward, but it must be accepted. Data generation and collection is very fast, and the amount of data has been growing. With advanced technology, people can collect data in real time. But in most cases, if you don’t process the data in time, the advanced collection and sorting methods will be meaningless. For example, IBM proposed the concept of “big data-level stream computing,” which aims to analyze data and results in real time to increase practical value. Therefore, timely and fast processing of data and results is the most significant feature of big data.

E-Commerce Concept

E-commerce generally refers to Internet technology, based on browser/server applications, through the Internet platform, buyers and sellers through various trade activities to achieve consumer online shopping, online payment and new business activities of various business activities and other models. The expansion history of e-commerce has a close relationship with the progress of computer network technology. E-commerce includes many models, such as B2B ( Ning et al., 2018 ) (Business to Business), B2C (Business to Consumer), C2C (Consumer to Consumer), and O2O (Online to Offline). The main centralized e-commerce model is shown in Figure 2 .

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Figure 2. Main e-commerce model.

This article focuses on C2C ( Sukrat and Papasratorn, 2018 ) e-commerce. C2C e-commerce refers to a network service provider that uses computer and network technology to provide e-commerce platforms and transaction processes to users in a paid or non-paid manner. Allow both parties to conduct online transactions on their platform. The two sides of the transaction are mainly individual users, and the trading method is based on bidding and bargaining. Like B2B and B2C, C2C is also a basic e-commerce transaction model. In real life, it is similar to the “small commodity wholesale market.” There are many self-employed people in a website, and the website’s role in e-commerce is equivalent to the “market manager” in actual market transactions. At the same time, in order to promote smooth transactions between buyers and sellers, C2C e-commerce provides a series of support services for both parties. For example, in cooperation with market information collection, credit evaluation systems and various payment methods have been established. Due to the rapid expansion of e-commerce, industrial agglomeration has become more impressive. The most prominent performance of industrial agglomeration is the industrial concentration of “Internet + traditional industries” such as “Taobao Village.” C2C is the mainstream of this e-commerce business model. C2C e-commerce “Taobao Village” is a product based on urban and rural expansion in China. It has Chinese characteristics and is a “Chinese product.” This is both a theoretical issue and a very real social phenomenon. The Chinese government has put forward the “Internet+” proposal. With the expansion of China’s strategic emerging industries, “Internet + traditional industries” will become a shortcut for China’s backward regions to seek expansion, which can shorten the time required for expansion, making C2C e-commerce a “hometown of Taobao.” Therefore, in order for the industry to complete transactions, an e-commerce platform and online and offline resources and services are needed. It can be said that the C2C model is an e-commerce model that is very suitable for industrial agglomeration. The biggest advantage of the C2C e-commerce model is that it can produce and deliver enterprise products or services on demand, so that enterprises can quickly develop into large enterprises, and the C2C e-commerce model provides consumers with cheap and affordable purchases. Product and service platforms enable businesses and consumers to achieve a win-win situation.

In traditional market transactions, the delivery of goods from producers to stores requires warehouse storage, vehicle transportation, etc., which increases inventory costs and transportation costs, resulting in increased transaction costs. Unlike real-world trading, since e-commerce joins the virtual network, both buyers and sellers trade through the e-commerce platform, so there is no need for face-to-face communication. This form saves the seller’s transaction costs, including physical store and merchandise inventory and transportation costs. At the same time, buyers can also shop without going out, and can quickly compare products of different merchants through the network, which allows buyers to get more information, more efficient and lower cost. C2C e-commerce uses Internet communication channels based on open standards. Compared with traditional communication methods (such as mail, fax, newspaper, radio, and television), communication costs are greatly reduced.

Industry Agglomeration Virtual E-Commerce

Industrial cluster concept.

Industrial clusters attract the attention of many scholars by attracting resources, economies of scale, knowledge learning and innovation, saving transaction costs, and improving cooperation efficiency. Many mathematicians have studied the composition, characteristic mechanism, and identification criteria of industrial clusters through theoretical derivation, model construction, structural equations, and case studies, and elaborated and summarized the concept of industrial clusters. The definition of industrial agglomeration is that in a relatively limited space of a certain area, geographically adjacent or different geographical entities closely related to relevant institutions and government agencies spontaneously gather together, called industrial clusters. The division of labor between entities and continuous cooperation and innovation have formed a complex cluster network, providing environmental and technical support. The difference is that industrial clusters can adapt to economic expansion, and further transformation and upgrading will form a new industrial cluster model. At the same time, mutual trust, mutual decision-making, and close cooperation have created the greatest value and benefits for the industry. Finally, for the measurement and acquisition of industrial clusters, combined with the practical significance of empirical research, the measurement of industrial clusters is unified by the concentration of specific industries, that is, specific industries. A measure of the spontaneous aggregation of related entities or institutions in a particular industry in the region. If the total quantity or total capacity reaches the previous unified level, it indicates that there is an industrial cluster in the area.

The Relationship Between E-Commerce and Industrial Clusters

With the rise and prosperity of e-commerce, the new business organization system breaks the regional and spatial barriers, promotes the use of e-commerce and partners, establishes synergy and sharing mechanisms, and continuously meets the needs of users. Proactively improve user experience and satisfaction. In addition, e-commerce platforms and logistics platforms are increasingly used in new business models. Although these platforms are very different, the role of the company cannot be underestimated. The platform typically includes several key functional modules such as trading markets, logistics platforms, enterprise services, cluster information, and corporate communities. Cluster companies can conduct informal technology and information exchange on the platform. Through the construction of an e-commerce platform, industrial cluster enterprises can share market conditions, the latest industry technologies, and related industry information in real-time and quickly, creating greater economic benefits for enterprises. This close partnership helps industry clusters increase trust and mutual benefit. In short, e-commerce applications can help industrial clusters effectively integrate regional resources, meet market demands promptly, expand clusters, and increase the level of collaboration and competitiveness of enterprises within the cluster. Currently, the introduction of e-commerce applications has further promoted the expansion of supply chain coordination. As an effective spatial organization model, industrial clusters play an increasingly important role in improving the overall economic level of the region and optimizing the allocation of industrial resources. The rapid expansion of industrial clusters provides natural conditions for the expansion of enterprises, between enterprises and between supply chain members. Similar companies continue to gather, and upstream and downstream companies in the supply chain are also gathered to promote the use of e-commerce. A deeper impact on the synergy of the supply chain. Therefore, for the sake of strengthening the application of e-commerce. Based on continuous research by many scholars, it is further proved that the rapid expansion of industrial clusters promotes the coordinated management of supply chains.

Since 1980, the economy and the world have continued to develop. The Internet and information technology are constantly innovating. In addition to constantly affecting people’s daily lives in various aspects, it also leads the transformation of modern new production organizations. Figure 3 shows the statistics of Chinese netizens in the past decade. As can be seen from the above data, since the popularity of smartphones in 2013, mobile network users have occupied almost the entire network in the past 7 years. In the future expansion, mobile network users will develop more rapidly, making the popularity of mobile Internet and smart phones break the expansion of PC networks. At anytime, anywhere, and on the Internet, the online concept of the PC era has been broken, and immediacy has become a unique personality in the age of network information. A large amount of information, rapid response and scale effect are the main features of the e-commerce. The rapid spread of mobile phone business applications shows that the use of mobile phone networks by netizens has changed from basic communication entertainment to life entertainment. Since 2013, thanks to the expansion of domestic smart phone technology, the Internet access method based on mobile Internet has opened a new period of e-commerce and access to the Internet anytime and anywhere, so that more buyers and sellers can conduct transactions through the network, and Each transaction is based on online trading of various trading tools, and the trading platform and trading model have been rapidly developed. E-commerce has become a grassland, which has an impact on the production value chain, profit model and marketing methods of traditional industries. It can be seen that the growth of the network has boosted the expansion of e-commerce. The growth of e-commerce has promoted industrial agglomeration, and industrial agglomeration has formed economic globalization.

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Figure 3. Number and proportion of mobile phone users.

Industrial Agglomeration Virtual E-Commerce Analysis

China’s e-commerce transaction scale.

In the e-commerce environment, China’s e-commerce has undergone earth-shaking changes, especially in the past 30 years, the rapid growth of signal technology and technological innovation have made all aspects related to e-commerce stand out. The cost of online transactions has been greatly reduced, network communication is extremely convenient, and e-commerce is everywhere. On the basis of China’s national conditions, the application and expansion of e-commerce in China is different from that of other countries, but its expansion is in full swing. The expansion of China’s e-commerce is a signification part of accelerating the informationization of the national economy. At the same time, the application of e-commerce has also changed the production organization of enterprises to a large extent. Enterprises and users can interact directly with e-commerce related R&D, technology expansion, production, procurement, marketing and product operations. Other services and links can fully introduce user engagement and control market demand trends in real time. Table 1 shows the scope of China’s e-commerce transactions collected from the China E-Commerce Research Center.

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Table 1. Scope of China’s e-commerce transactions.

Figure 4 shows the scope of China’s e-commerce market transactions from 2013 to 2017. It can be seen that as of 2018, China’s e-commerce still maintains a rapid growth trend. With the continuous encouragement and support of the government, all relevant systems are in a stage of continuous improvement. Under the impetus of e-commerce, enterprises and users, constantly proposing new consumer demand will help the rapid expansion of the upstream and downstream industry chains of traditional enterprises and provide new impetus for China’s economic expansion.

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Figure 4. Trends in the scale of China’s e-commerce transactions.

It can be seen from Figure 5 that from 2001 to 2008, industrial agglomeration e-commerce investment and fixed asset investment are all levels of sustained growth, which proves that e-commerce expansion is relatively rapid during this period. In the future, industrial agglomeration investment profits can be Add a lot. From 2008 to 2015, the level of China’s economy was in a period of slow growth, and the investment level during this period was almost stable. After 2015, due to the saturation of the economy, the investment level remained at a certain level and the economic expansion region was stable.

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Figure 5. Changes in national fixed asset investment and industrial agglomeration e-commerce investment from 2001 to 2018.

The Impact of the Level of Big Data Expansion on E-Commerce in Industrial Agglomeration

With the increasing popularity and expansion of the Internet, e-commerce has become an important aspect of Internet applications. In addition to the old e-commerce companies, traditional stores also opened their own online shopping malls. Consumers are also increasingly enjoying this convenient and fast way to shop. According to CNNIC’s statistical report, as of last year, the number of Internet users in China has reached 1.008 billion, and the proportion of online shopping among netizens has increased to 55.7%. In addition to online shopping, many service industries or national administrative departments have also increased the construction of online platforms, such as online car rental, travel route booking, room service, online transaction management fees, etc., further expanding the application field. E-commerce has created more business growth points. The expansion of business types and the explosive growth of business volume have brought a lot of data information. The old e-commerce companies Amazon, Alibaba and so on are all beneficiaries of big data. It can be said that without the support of big data technology, there is no e-commerce enterprise today.

The expansion of big data makes practitioners more competitive in e-commerce. From the perspective of the number of competitors, China’s e-commerce industry is currently in a highly concentrated stage. Although a large number of e-commerce companies have emerged, in the field of online retail, Taobao, Tmall, Jingdong, No. 1 store, Amazon and many others occupy most of the market. The emergence of big data has further increased barriers to entry, so the number of competitors in the online retail industry will change less. From the perspective of foreign competitors, it will undoubtedly increase the intensity of market competition. For example, the way Amazon enters the Chinese market is to acquire Joyo. From the perspective of switching costs, the e-commerce industry has typical low-cost conversion characteristics for consumers. On-site e-commerce companies often use large subsidies, promotions and free shipping to retain old users and win new users, which makes the market competitive. The pressure is constantly increasing. Pursuit of economies of scale. Most industries have significant economies of scale. E-commerce operators are pursuing economies of scale and blindly expanding, resulting in overcapacity, which ultimately led to fierce competition in the industry.

Figure 6 shows the expansion index for big data and e-commerce. As can be seen from the above data, in the future expansion process, big data is indispensable as a tool to support e-commerce and industrial agglomeration, and e-commerce is expanding very rapidly. The expansion of industrial agglomeration plays a very significant role. In the future, the expansion of e-commerce in all walks of life cannot be ignored. The future world is the electronic world and the data world. As an effective management mode of enterprise manufacturing and industrial organization, industrial cluster and supply chain management have become the inevitable requirements and strategic measures for enterprises to survive and develop in various fields. The coupled industrial cluster supply chain provides a new expansion trend for resource coordination and industrial upgrading, enabling cluster enterprises to improve traditional production methods, respond quickly to user needs, and consciously work closely together to grasp rapid changes more quickly and accurately. In order to deal with this problem, it is necessary to improve the operational efficiency of the enterprise through the information charge platform and modern management tools. Through the information platform, this work-use management becomes more complex, professional and standardized, thus freeing up enough energy to respond to industry changes.

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Figure 6. Impact of big data and e-commerce on industrial agglomeration expansion.

In industrial agglomeration, the pioneering role of core enterprises should not be overlooked. If the pioneering enterprises can be cultivated effectively, through it to other enterprises and supporting enterprises to enter the industry to play a direct demonstration and produce cohesion, so that the formation of industrial agglomeration has a driving effect. At present, some of the core enterprises in the industry have already established a relatively complete e-commerce system. If we can combine the needs of SMEs in the industry, open up some of the functions of the system to a certain extent, and realize the sharing of information and knowledge with enterprises in the industry, this is very beneficial to enhancing the enthusiasm of SMEs to participate in industrial division of labor and cooperation, and at the same time lowering the this is very beneficial to increase the enthusiasm of SMEs to participate in industrial division of labor and cooperation, and at the same time reduces the threshold for SMEs to participate in e-commerce. A well-developed social network based on marketization or externalization is the basis for the formation and development of industrial clusters. To this end, the construction of information service organizations and networks within industrial clusters should be supported and encouraged to provide a variety of information services to enterprises, reducing the wasted costs and incomplete information caused by enterprises collecting information alone. At the same time, the construction of public institutions and means of communication that facilitate interaction between producers and the market should be strengthened, cooperation between enterprises and universities or research institutes should be encouraged, and the establishment of local public institutions that provide technical training, technical support and market information to producers should be supported. In addition, the construction of information advisory services should be accelerated and a multi-level public information platform should be established. In this regard, government departments or professional information service providers can intervene to provide a full range of information service approaches and dovetail with government public data platforms to achieve low-cost information services and knowledge provision within the industry.

Analysis of Advantages and Disadvantages of Different Business Models in Industrial Agglomeration

As shown in Figure 7 , for the industrial agglomeration of the B2C e-commerce model, all goods and services of the enterprise are carried out through the network, including online shopping, online payment, logistics and after-sales. They are all done over the Internet and won’t be traded face to face. This model puts forward higher requirements for industrial agglomeration enterprises. Compared with the C2C and O2O models, the selection of the B2C e-commerce model requires that the industrial agglomeration area has a good organizational management level and complete information construction, because all activities are carried out online. Among the three e-commerce models, the B2C model has the highest information security requirements and requires more financial support and sufficient strength to ensure smooth transactions. For the C2C model, the needs of enterprises are much lower than those of B2C. For industries with insufficient funds, low level of enterprise informatization and low management level, C2C e-commerce model can be selected. The industrial cluster area builds an e-commerce trading platform through website construction. Consumers can find the trading objects and negotiate the transaction through the platform. Industrial agglomeration enterprises only need to optimize platform management, maintain transaction order, formulate transaction specifications, and improve trust mechanisms. Therefore, the C2C e-commerce model has lower requirements for the company’s capital, information and management level than the B2C model. For the O2O model, the network becomes the platform for offline transactions. For industrial clusters, the function of the C2C e-commerce model is to undertake the browsing work of consumers, let consumers understand the information through the platform, and then conduct transactions online. Therefore, it is necessary to reduce the investment cost of the C2C e-commerce model, and its management level and informatization level are lower than the B2C and O2O e-commerce models. Most industrial clusters can conduct business activities through the C2C platform.

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Figure 7. Advantages and disadvantages of different e-commerce in industrial agglomeration.

As the rising of Internet industry and other technologies, on the basis of the rapid expansion of e-commerce, the coordination problem of e-commerce has gradually emerged, affecting the organizational environment. At present, the research related to e-commerce and supply chain collaboration is getting more and more attention. As a new impetus for economic expansion, e-commerce has brought new impetus to the supply chain. In the process of supply chain coordination, e-commerce means making the required information more convenient and accurate, thus further enhancing the trust of the supply chain enterprises and the internal and external trust, and bringing economic benefits, the company has further expanded. In this paper, through the different applications of virtual e-commerce in industrial agglomeration, different e-commerce types highlight different characteristics in big data. Therefore, this paper analyses industrial agglomeration and electronics through literature comparison and data survey. The relationship between business, and through the investigation, we can see that the industrial agglomeration investment has been continuously expanded with the expansion of e-commerce and big data, which also proves that the future expansion of e-commerce is promising. Finally, the application of three different e-commerce models in industrial agglomeration is compared. The results show that different e-commerce models are determined by their own different, so we must choose the correct e-commerce model to adapt to the expansion of society through the actual situation.

Industrial agglomeration is an important way to enhance regional economic development, while e-commerce promotes the integration of enterprises into the world market. The author intends to analyse the problems of enterprise e-commerce in this context from the perspective of industrial agglomeration, and propose how to better realize the interaction between e-commerce and industrial agglomeration, so as to achieve the improvement of the competitiveness of enterprises in the industry.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

HY was responsible for designing the framework of the entire manuscript from topic selection to solution to experimental verification.

Research on the Path and Countermeasures of Cultivating and Expanding Rural Collective Economy in Kaifeng City.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : virtual e-commerce, industrial agglomeration expansion, big data, e-commerce model, standard system

Citation: Yue H (2022) Research on E-Commerce Data Standard System in the Era of Digital Economy From the Perspective of Organizational Psychology. Front. Psychol. 13:900698. doi: 10.3389/fpsyg.2022.900698

Received: 21 March 2022; Accepted: 14 April 2022; Published: 04 May 2022.

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A study of cross-border E-commerce research trends: Based on knowledge mapping and literature analysis

Yongfeng chen.

1 College of Science and Technology, Ningbo University, Cixi, Zhejiang, China

Jiajie Song

Yiding jiang.

2 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

Guan Lin Chen

3 Department of Distribution Management, Shu-Te University, Kaohsiung, Taiwan

Associated Data

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

As a result of the trend toward economic globalization, the vigorous development of cross-border e-commerce has attracted many scholars to study this field, involving many related fields, such as consumer behavior, advertising, information systems, and supply chain management. Throughout the existing literature, it can be found that most of the research focuses on certain influencing factors of the development of cross-border e-commerce, and there is no systematic and macro- overview of the development trend of research in this field in recent years, nor the integration and analysis of keywords. Given that the research in the field of cross-border e-commerce is fragmented to a large extent, to effectively explore the research trend in this field, we must understand the current situation of cross-border e-commerce. Systematic bibliometric analysis can solve this problem by providing publishing trends and information on various topics. Therefore, based on the scientific database web, this study collected 198 references related to cross-border e-commerce from 2016 to 2021, briefly introduced the current situation and development trend of cross-border e-commerce, visually analyzed and refined the journals, authors, research institutions, countries, publication years, keywords, citations of academic publications in this field, and other key information, and summarized the development trend and path of CEBC in the existing research. It is helpful for researchers to solve the current research gap, understand the future research direction in this field, and help academia establish a strict knowledge system.

Introduction

With the continuous development of Internet technologies in recent years, trade between countries has grown closer, and economic development has gradually moved toward globalization and integration. Cross-border e-commerce has great potential for development in countries or regions with similar geographical and cultural characteristics. In other words, it has the potential to develop new revenue models or methods both domestically and internationally ( Cho and Lee, 2017 ). The infinite possibilities of cross-border e-commerce have prompted it to enter a new stage of rapid development. Cross-border e-commerce has not only successfully broken traditional trade barriers between countries but also promoted world trade gradually by encouraging merchants and consumers to participate in inter-enterprise trade (global B2B) and transactions between consumers and enterprises (global B2C). The move toward borderless trade will also bring a series of major changes affecting economies and trade patterns worldwide. According to research by Cho and Lee (2017) , one-third of e-commerce represents cross-border e-commerce. Taking the European Union (EU) as an example, 15% of overseas sellers offered products to consumers in the EU through e-commerce channels in 2014 ( Kawa and Zdrenka, 2016 ), a 25% increase from previous years ( Kawa and Zdrenka, 2016 ). The development history of cross-border e-commerce shows that it will provide huge space and opportunities for global economic growth in the near future ( Wei et al., 2019 ). In recent years, research in the area of cross-border e-commerce has received more attention as a result of the industry’s rapid growth. Deng and Wang (2016) found that the pioneers of third-party platforms relying on cross-border e-commerce have advantages over the latter in terms of learning effects and conversion costs through online search and mining methods, and they can better solve the cost, technology, and market inconsistencies. Liu and Lai (2016) further summarizes and discusses the current status of the cross-border third-party logistics market, transportation business models, logistics service applications, and the impact of integration with my country’s cross-border e-commerce on the development of cross-border e-commerce. Valarezo et al. (2018) explore the determinants of an individual’s decision to implement cross-border e-commerce. Zhang et al. (2019) analyzed the characteristics and influencing factors of talent demand and concluded that there is a large gap in the demand for cross-border e-commerce talents, while Cheng et al. (2019) created a design based on the four capabilities of market knowledge, technical skills, analytical ability, and business practice ability. A cross-border e-commerce talent training model was developed to address this gap, and the effectiveness of the model was evaluated. Gao (2021) studied the influence mechanism of the application of blockchain technology in cross-border e-commerce on consumers’ purchase intention, explained the application status of blockchain technology in various fields of cross-border e-commerce, and based on this, divides the quality of the cross-border e-commerce blockchain system into three dimensions: commodity information quality, logistics service quality, and payment security.

A review of previous research reveals that the majority of articles on cross-border e-commerce concentrate on a particular aspect that has influenced the industry’s growth rather than providing a comprehensive description of the industry’s development trend in recent years. The lack of integrated analysis of keywords in the cross-border e-commerce field and the lack of statistical analysis on specific time points. With the development of globalization, there are many research branches in the field of cross-border e-commerce, and there are many factors affecting its development. Therefore, systematically summarizing the development trend of cross-border e-commerce has important reference significance for promoting its development and for researchers entering the field of cross-border e-commerce research. This research mainly uses Citespace as a tool for bibliometric analysis and selects SSCI and SCI journals included in WOS from 2016 to 2021 for analysis. This study analyzes key information such as journals, authors, research institutions and countries, publication years, keywords, and citations of publications to establish a knowledge map of cross-border e-commerce research in order to understand the basic characteristics and dynamic changes of cross-border e-commerce. The analysis provides certain theoretical guidance for follow-up research on the development of cross-border e-commerce.

This research will adopt the methods of bibliometrics and content analysis and use CiteSpace software to analyze the research process and future development trends in this field, hoping to solve the following problems: (1) Understand the fundamental characteristics of the literature in the field of cross-border e-commerce from 2016 to 2021. (2) Using the number of papers published as an indicator, create a collaboration network from three perspectives: authors, institutions, and countries to investigate the research status of cross-border e-commerce. (3) Create a keyword co-occurrence map and analyze keyword clusters to understand the overall changing trend of the cross-border e-commerce field. (4) Analyze the current situation of cross-border e-commerce development through content analysis.

Literature review

Cross-border e-commerce.

Cross-border e-commerce (CBEC) refers to cross-border logistics transactions between multiple parties from different customs regions through e-commerce platforms ( Ai et al., 2016 ). Typical participants are the two main players (buyers and sellers), e-commerce platforms (cross-border online platforms), and other third-party service companies (cross-border logistics providers and payment providers). International buyers order products through online e-commerce platforms, and cross-border transactions are handled by third parties (such as logistics companies or payment companies; Mou et al., 2019a ). With the development of electronic information technology and the deepening of economic globalization, great changes have taken place in the consumption patterns and demands of consumers. Supported by growing demand and favorable policies, cross-border e-commerce is developing vigorously in the global environment and has become an important channel for promoting international trade ( Li and Chan, 2016 ; Kim et al., 2017 ). Cross-border e-commerce, the process of selling goods directly to foreign consumers through digital intermediaries, has received increasing attention over the past few decades ( Sinkovics et al., 2007 ; Giuffrida et al., 2020 ). By 2022, business-to-consumer (B2C) cross-border online sales are expected to account for 22% of total global e-commerce ( Forrester Research, 2019 ). Based on its economic surplus and rapid growth, there is a broad consensus that CBEC has become one of the most important pillars of international trade ( UNCTAD, 2016 ).

However, cross-border e-commerce products have long transportation times, high-quality return services are difficult to achieve, and transportation costs are too high. Ding et al. (2017) pointed out that the development of cross-border e-commerce will continue to face obstacles such as cultural differences between countries, consumer behavior, laws and regulations, product and marketing issues, payment conditions, and logistics restrictions. Strzelecki (2019) studied the characteristics required to accurately identify customer needs when e-tailers provide services to customers. The willingness to repurchase can reflect the subjective probability of consumers buying from the same store repeatedly ( Wu et al., 2014 ). ( Mou et al. 2019b ) studied the relationship between customer repurchase intention and actual purchase behavior in the future. Strong repurchase intentions can attract more buyers for the company and increase the market share of the store or enterprise. Although electronic services are gaining popularity around the world, there are still strong uncertainties. Therefore, trust in a certain enterprise or commodity occupies an important position in the minds of customers and is an important determinant of their acceptance of electronic services ( Mou et al., 2016 ).

In the past, there have been many discussions on the positive impact of cross-border e-commerce on the economy and its potential growth and future development; challenges and opportunities for both the supply and demand sides of the market; intensified price competition; improved retail efficiency; positive impact on production in other industries; impact on individuals; and a study of household consumer benefits, labor productivity, and GDP growth. In addition, research on cross-border e-commerce suppliers and consumers has gradually increased. For example, by analyzing the online shopping situation that determines consumers’ purchase intentions, four kinds of clues that promote this kind of consumer behavior can be identified, namely online promotion clues; content marketing clues, personalized recommendation clues, and social comment clues. Additionally, brand familiarity is introduced into the analysis of the influence of cross-border online shopping on consumers’ purchase intention. It is concluded that these four contextual cues for cross-border online shopping have a significant positive impact on consumers’ purchase intentions ( Xiao et al., 2019 ). There is no doubt that the choice of partnership between enterprises is an important factor affecting cross-border e-commerce. On the basis of a literature review, Huang et al. (2021) concluded that the good reputation of enterprises, trust between enterprises, and information sharing are all conducive to the realization of cooperation, thus constructing a theoretical model of partner selection for cross-border e-commerce enterprises. An in-depth discussion on the choice of partners for cross-border e-commerce enterprises under the B2B model. According to relevant reviews, previous studies have not explored the relevant literature process of cross-border e-commerce. Therefore, this study summarizes the context of cross-border e-commerce research through bibliometric methods.

Research methodology

The Web of Science (WOS) is an online multidisciplinary literature database in which current research is reviewed based on high-quality journal articles in order to obtain valuable information. This study conducted bibliometric data analysis since 2016 mainly in that bibliometric methods mostly adopt a time period as the scope of search. For example, a time period of more than 5 years can be regarded as a scope of search. In order to ensure the rationality of data collection and reduce the deviations and errors in time selection ( Xu et al., 2022 ); the time period from 2016 to 2021 was considered as the scope of search for this study. The reasons why the WOS database was used include: First, this database contains the main literature from SCI and SSCI journals. Second, it is viewed as a source of literature from core journals by most countries and researchers. Lastly, other databases, such as SCOPUS and Science Direct, were not employed in this study because most of them collect papers from seminars or non-English papers, which can lead to deviations and errors in data analysis ( Su et al., 2020 ; Jia et al., 2022 ). Based on the above reasons, the WOS database was adopted in this study. In terms of data retrieval, the main research is mainly searched through the keyword “Cross Border e-commerce” or “Cross Border Electronic Commerce” or “Cross-border e-commerce” in the WOS database, and conducted according to the time point from 2016 to 2021. In the end, a total of 223 articles were obtained. After selecting the “article” type to search SCI and SSCI journal articles, 23 articles were deleted, and two articles were deleted according to the subject and abstract. The exclusion factors included irrelevant cross-border e-commerce, literature and trend analysis, non-empirical research, and non-English studies ( Su et al., 2020 ; Lin et al., 2022 ; Zhu et al., 2022 ). Finally, the contents of 198 articles were retained, and a bibliometric mapping analysis was obtained.

Furthermore, in order to effectively carry out data analysis, this study referred to Nagariya et al. (2021) and Lin et al. (2022) in terms of clustering analysis. Content categories were supplemented objectively through multiple methods to reduce the deviations, errors, and shortcomings of graphic results of conventional bibliometric methods. With respect to content analytical methods, 198 papers were systematically processed. Specifically, two university professors and three researchers read the papers and then double-checked them to ensure content correction.

The procedure framework of this research is as follows: Figure 1 .

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Procedure framework.

Publication trends

The main retrieval time of this study was selected between 2016 and 2021. The article type is limited to papers, and finally, there are a total of 198 papers that fit the theme. The number of publications per year is shown in Figure 2 . The number of articles published in a journal can reflect the research level and development level of the subject area, and the change in the popularity of a certain topic can be derived from it. Through the publication status from 2016 to 2021, it can be seen that there are more than seven papers published on this topic every year, and it can be seen that this field is at the beginning and continuous exploration stage. Through the summary of the number of published articles, it is found that the increase in the number of published articles after 2020 has increased rapidly, which may be closely related to the global new crown pneumonia epidemic, including special issues in 2021 such as “Sustainable Cross-Border Business Models,” which is aimed at cross-border businesses during the epidemic. Overall, since 2016, the research on cross border e-commerce has shown steady growth. In particular, by the end of 2021, the cumulative number of articles had reached 76. It can be seen that cross-border e-commerce has gradually become a hot topic and research frontier.

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Annual publication 2016–2021.

According to the 198 articles on cross-border e-commerce obtained from WOS data (see Table 1 ), the statistical description and analysis of the number of journal publications can help one observe the development of the disciplinary knowledge structure in the field of cross-border e-commerce and provide insights into subsequent research. The authors provide guidance for the submission of relevant papers. Among them, the Electronic Commerce Research journal has made the greatest contribution to cross-border e-commerce research. A total of 16 papers have been published, with a total of 99 citations and an average citation of 6.19. Among them, most of the literature uses the establishment of models to study the factors that affect consumers’ purchasing decisions and the relevant policies of customs on cross-border goods ( Li and Li, 2019 ). Secondly, Sustainability is the second most published journal, with a cumulative citation rate of 90, but the average citation rate is higher than the second-ranked journal at 6.43. The third-ranked journal is the European Journal of Marketing, with eight publications and 15 cumulative citations, and an average citation rate of 1.88. The citation rate is lower than in the previous two journals. It is worth discussing the Journal of International Economic Law. This journal itself only includes five articles, but the cumulative citations amounted to 24, and the average citation rate reached 4.8, which is higher than the third-ranked journals. It mainly discusses the policy challenges of a data-driven economy and the impact of international tax and trade regimes on cross-border e-commerce ( Mitchell and Mishra, 2019 ).

Statistical table of journal publication.

TC, total citations; D|TC, average number of citations per article.

Author’s cooperation network

There are a total of 138 nodes and 91 connections in the author’s co-occurrence graph, and the network density is 0.0096. It can be seen that the cooperation network density between authors is low, the author’s cooperation relationship is not close enough, and the research authors are in a relatively scattered state. The five core authors in this study published 20 papers, accounting for 10.10% of all 198 papers, which also showed that the authors did not cooperate enough. The results of Figure 3 show that only three groups of authors form cooperation networks, namely, Fu Jia and Ying Wang, Lin Xiao and Xiaheng Zhang, and Shuzhong Ma and Hongsheng Zhang, which implies that though most CBEC topics attract the attention of a majority of researchers, few researchers conduct deep research on CBEC (at most five papers so far), and the cooperation among researchers is loose without a specific focus. Moreover, we can infer that, in terms of CBEC research, most researchers work individually or in small groups without established research authorities or centers. Nevertheless, despite the lack of specific cooperation networks, researchers follow some common research directions, such as how to improve service capabilities of supply chains, relevant strategic analysis of cross-border e-commerce, and analysis of customer purchase intentions.

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Author cooperation networks with more than three articles.

The numbers of published papers per author are shown in Table 2 . It can be seen that there are five authors who have published more than three papers. Among them, the highest number of publications was five times, accounting for 3.62% of the total number of scholars. The number of authors with a publishing frequency of 2 accounted for 4.35%, and the number of authors with a publishing frequency of 1 accounted for almost as high as 92.03%. Most of the researchers are dabbling in the field of “cross-border e-commerce” for the first time, and several of them have only published one study, which shows that there are fewer high-yield core authors who are deeply involved in the field of “cross-border e-commerce” and have more outstanding achievements. Although there are no scholars with a high publication output in this field, in terms of citation frequency, the article published by Li et al. (2018) has been cited 144 times, and it mainly studies how small and medium-sized enterprises enter into the cross-border e-commerce field on the Alibaba platform, so as to improve their market competitiveness. It can be seen that researchers pay more attention to this.

Authors with more than three papers and their affiliated institutions.

When studying the citation rate of authors through the analysis of the WOS database, it is concluded that there are six articles with a high citation rate (more than 40) and two articles with a high citation rate, one of which is a study by Li et al. (2018) . The study mainly investigates how entrepreneurs of under-capacity small and medium-sized enterprises (SMEs) can drive the digital transformation of their companies in the context of scarce resources and aims to expand the understanding of digital entrepreneurship and digital transformation through an inductive process model. Another highly cited article is that by Liu and Li (2020) , which proposes a blockchain-based framework based on the cross-border e-commerce environment, integrates a series of blockchain-based models, and develops a corresponding set of techniques and methods to address product traceability issues. These techniques and methods contribute significant research value to integrating decentralized management systems in supply chains. Kim et al. (2017) probed into the influence of distance on CBEC and concluded that a shorter distance can raise the loyalty of buyers. Courier procedures were simplified to reduce buyers’ hesitation in purchase due to distance. Additionally, Valarezo et al. (2018) analyzed the CBEC data from Instituto Nacional de Estadística (INE) to identify factors influencing individuals’ purchases and deemed that males show a relatively higher acceptance of CBEC commodities. In addition, there are four other highly cited studies, and the main research topics are mostly regarding express logistics services and obstacles, advantages and disadvantages, and the driving factors of cross-border e-commerce (refer to Table 3 ).

Most frequently cited authors.

Countries and institutions

An analysis of the issuing institutions can reflect the high-yield research institutions and cooperation in this field. We draw the co-occurrence map of publishing institutions using CiteSpace, and obtain statistical information on the publishing situation of research institutions (See Table 4 ). The top research institutions in terms of published papers are Xidian University (five papers), Zhejiang Gongshang University (five papers), Zhejiang University (five papers), and other universities, indicating that these institutions conduct more in-depth cross-border e-commerce research and have high authority. From the perspective of the type of research institutions, the number of universities that have published more than three papers among 125 universities or research institutions has reached 13. It can be seen that the research on cross-border e-commerce is not carried out by a few universities, and it has gradually spread. From a geographical point of view, research institutions are mainly concentrated in China, the United Kingdom, the United States, and other countries, which are closely related to the current development of the country’s cross-border e-commerce field. The co-occurrence graph of the issuing agency has 124 nodes and 81 connections, and the network density is 0.0106 (See Figure 4 ). Most nodes are distributed in a sporadic state, and the connections between nodes are few and thin, indicating that the research institutions are scattered, the cooperative research results are few, and an academic research team that integrates and develops has not yet been formed. The existing cooperation between research institutions is mainly based on the close cooperation of several universities in China, and there is occasional cooperation between international institutions, such as the Hong Kong Polytechnic University, the South China University of Technology, Minjiang University, York University, and other universities that have mutual cooperation relationships.

Organizations with more than three papers.

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The organization’s cooperation network with more than three articles.

This study analyzes data recorded from 2016 to 2021 (See Table 5 ). A total of 24 countries have conducted relevant research in the field of “cross-border e-commerce,” and a total of five countries have published at least five articles. China is the country with the highest productivity in “cross-border e-commerce,” accounting for 63.96% (i.e., 126 publications are from Chinese authors). The United States is the country with the second highest output in the cross-border e-commerce sector, accounting for 6.09% (i.e., 12 publications are from authors in the United States). The United Kingdom is the third country in terms of output in the field of “cross-border e-commerce,” accounting for 5.08% (i.e., 10 publications are from United Kingdom authors) followed by South Korea (eight articles), and Taiwan (seven articles). Overall, China’s publication volume in the field of “cross-border e-commerce” exceeds that of the other four countries combined, which may be closely related to the development of China’s domestic cross-border e-commerce industry, which proves that China’s cross-border e-commerce industry leads the position in e-commerce.

Countries with more than five publications.

Keyword analysis

The timeline graph provides a holistic view of the cluster time span and how those clusters are connected, with the results shown below. The keyword co-occurrence map can intuitively reflect the frequency of keywords in the research field, and the topic of the article can be clearly understood through keyword analysis. This study mainly formed six clusters. Nodes in each row represent keywords in each cluster, and links represent relationships between different keywords. Furthermore, the results between clusters show that the keyword correlations in each cluster are high. Cluster 0 is the largest cluster because it contains the most articles. Consecutive large nodes and extensive links in this cluster demonstrate its liveness, with the label of cluster 0 representing the most noteworthy topic among them. Clusters 0–1 also have larger nodes and involve more keywords, suggesting that they are relatively prominent topics in cross-border e-commerce. In the CiteSpace interface, we selected keyword as the node type, set the time slice to 1, used g -index for the selection criteria, and set k  = 25. After running the software, the keyword co-occurrence map was obtained ( Figure 5 ). There are 198 nodes and 620 connections in the graph, and the network density is 0.0562. Keywords with a frequency greater than or equal to 1 and a centrality greater than or equal to 0.1 are listed, as shown in the figure.

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Timeline of the “Cross-border e-commerce” cited network.

Cluster 0 is marked as “purchase intention,” which means that the study of this cluster can be generalized as the study of purchase intention. However, looking at the keywords of this category, words with strong relationships include trust, internet, impact, online, information, electronic commerce, b2c e-commerce, price, behavior, economic growth, eBay, channel, acceptance, word of mouth, sourcing strategy, social commerce, reputation, customer satisfaction, and competition. These words reflect that this topic mainly investigates the influence of various factors on customer purchase intention in cross-border e-commerce. For example, Guo et al. (2018) examined the impact of sellers’ trust on buyers and their perceived risk of chargeback fraud on sellers’ transaction buyer intentions in the context of cross-border e-commerce and developed a conceptual model that identified a set of institutional mechanisms to enhance seller trust and reduce their perceived risk. In addition, Chen J. and Yang L. (2021) explored the mediating effect of network structure embedded in customer experience and consumer purchase intention in the context of cross-border e-commerce. Tikhomirova et al. (2021) analyzed the interaction between consumers’ national culture, trust tendency, credibility perception, and other personal traits in different e-commerce environments and their impact on purchase intention.

Cluster 1 is labeled “web service,” which means that the study of this cluster can be summarized as the study of web services. However, looking at the keywords of this category, words with strong relationships include management, determinant, integration, strategy, design, firm, firm performance, orientation, decline, capability, organization, and competitive advantage. These words reflect that the issue is mainly in studying the operation and management of network services and supply chains and their important role in cross-border e-commerce. For example, Wang et al. (2020) elaborated on how cross-border e-commerce generates supply chain service capabilities, thereby improving the quality of supply chain relationships between e-commerce and other platform users. For another example, Fang (2021) starts with the concept and application principle of the green supply chain, analyzes how to integrate the green supply chain into each link of the logistics industry chain, and the problems existing in the operation of green logistics, and explores from the perspective of the green supply chain. The path of innovation and development of e-commerce enterprises and the method of introducing the concept of green environmental protection into the management decision-making of the logistics industry are proposed. In addition, Yu et al. (2021) , based on the resource-based view and organizational capability theory, examined the impact of information technology (IT) on enterprise performance through supply chain integration (SCI) from the upstream and downstream perspectives of the supply chain.

Cluster 2 is labeled “performance,” which means that the study of this cluster can be generalized as the study of performance. However, looking at the keywords of this category, the words with strong relationships include technology, future, demand, algorithm, air, delivery, choice, machine, and optimization. These words reflect that the topic mainly explores various related performance capabilities and the impact of cross-border e-commerce development. For example, Ai et al. (2016) studied the impact of cross-border logistics performance on the development of the manufacturing industry. Through the comparison of various logistics performance factors, it was found that solving cross-border payment and regulatory legal issues can promote the development of cross-border e-commerce. In addition, Ma and Liang (2021) studied the factors affecting the export performance of cross-border e-commerce companies and found that, compared with industry competition, high-quality business services, diverse product choices, and low-priced products can promote the export performance of cross-border e-commerce enterprises. Furthermore, Xia and Liu (2021) adopted IoT tracking technology and multi-objective decision-making to propose an optimal management and coordination method to improve cross-border e-commerce supply chain performance. Through the sorting of the above cluster 2, the categories of performance mainly involve logistics performance, enterprise export performance, supply chain performance, and electronic trade performance.

Cluster 3 is labeled “cross-border e-commerce,” which means that the study of this cluster can be generalized as the study of cross-border e-commerce. Words with strong relationships include framework, business, innovation, network, indicator, evolution, China belt, and business electronic commerce. Comparing the citation frequencies of these words, logistics has the highest citation frequency, followed by intention, framework, and business. These words reflect the issue of exploring cross-border e-commerce. In the application of this clustering, consumers’ purchase intention is a hot topic of various researchers, which runs through the papers from 2016 to 2021. It can be seen that with the continuous change in the social environment, consumers’ purchase intentions will also change. Exploring the influencing factors of cross-border e-commerce consumers’ purchase intention can provide decision support for the management and operation of cross-border e-commerce, so as to better promote the development of cross-border e-commerce ( Lu et al., 2021 ).

Cluster 4 is labeled “perceived risk,” which means that the study of this cluster can be generalized as the study of perceived risk. Looking at the keywords of this category, the words with strong relationships include customer loyalty, ambiguity, service, moderating role in the online marketplace, consumer trust, satisfaction, perspective, and drive. These keywords highlight how to influence customer satisfaction and purchase intention in the cross-border e-commerce environment. For example, Luo and Ye (2019) combined the characteristics of consumers on the IOO platform, with the structure of social capital. Starting from the dimensions of relationship and cognition, it draws the conclusion that consumers’ social capital of different dimensions can affect customer loyalty through different values. Others, such as Chen N. and Yang Y. P. (2021) , believe that the cross-border e-commerce platform can be regarded as a social network, and the roles of platform companies, service providers, sellers, and consumers, among other, on the platform will be embedded in the network because of their mutual connection. Sellers can achieve the purpose of improving consumers’ purchase intentions by combining different strategies of network structure characteristics (network density, network centrality) and customer experience. Yang et al. (2021) combined perceived risk, transfer cost, and user loyalty VSL framework theory, constructed a new cross-border e-commerce mobile market user transfer intention framework, and analyzed user loyalty in the cross-border e-commerce mobile market, transfer costs, and transfer intentions.

Cluster 5 is labeled “cross-border logistics,” which means that the study of this cluster can be summarized as the study of cross-border logistics. Looking at the keywords of this category, the core words mainly include chain network competition, risk supply chain, decision, efficiency, energy, quality, system, and big data. In the application of clustering, researchers mainly focus on how to improve the efficiency of the supply chain and improve its ability to deal with risks ( Liu and Li, 2020 ; Zhu and Zhou, 2020 ; Fang and Wang, 2021 ; Niu et al., 2021 ; Xia and Liu, 2021 ). In addition, following the trend of environmental protection, the research on green supply chains has gradually formed a trend. Fang (2021) starts with the concept and application principle of the green supply chain and analyzes how it can be integrated. The integration of supply chains into all aspects of the logistics industry chain, and the problems existing in the operation of green logistics, explore the path of innovation and development of e-commerce enterprises from the perspective of the green supply chain and put forward the method of introducing the concept of green environmental protection into the management and decision-making of the logistics industry ( Zhang and Liu, 2021 ). Based on the action mechanism and model of the cross-border e-commerce green supply chain centered on customer behavior, and according to the green level evaluation requirements of the green supply chain, the analytic hierarchy process is used to evaluate the green supply chain.

Discussion and implications

Research discussion.

This study mainly uses WOS for data analysis and finally selects 198 papers published between 2016 and 2021. The results of the data analysis draw the following conclusions:

  • Cross-border e-commerce research began to show an upward trend in 2019. In particular, in 2021, due to the impact of the new crown pneumonia epidemic, related cross-border e-commerce issues have attracted more attention from researchers.
  • On the issue of cross-border e-commerce, among the four major e-commerce journals in recent years, Electronic Commerce Research has shown steady growth in its number of publications. The main factor is that the journal was published in 2019 The Special Issue: Cross-border e-Commerce Initiatives under China’s Belt and Road Initiative and the Special Issue: Electronic Commerce in the Asia-Pacific region issued during 2020 all have related themes for submissions, so a large number of researchers have submitted papers to this paper. Contrarily, what is more interesting is that the open-source journal Sustainability ranks second in the number of publications on cross-border e-commerce issues.
  • With regard to highly cited literature, there are currently two highly cited articles in the WOS database, one of which is the only one with a cross-border e-commerce citation rate exceeding 100 ( Li et al., 2018 ). Another study is an article published in the International Journal of Information Management in 2020, applying blockchain technology in the supply chain of cross-border e-commerce ( Liu and Li, 2020 ), which was cited in a short 2-year period. The citation rate also broke 60, which also means that the application of artificial intelligence technology in cross-border e-commerce has become a hot spot in recent years.
  • For the part of the cooperation network between the country and the author, according to the findings of the data, the current research on the main hot spots of cross-border e-commerce is mainly in China, and there are cooperation networks in a few regions. However, it is still mainly based on cooperation with Chinese universities, such as Hong Kong Polytechnic University, South China University of Technology, Minjiang University, and York University, among others. This part also means that there is less international cooperation in cross-border e-commerce research.
  • The part of keyword network analysis mainly includes six clusters, namely purchase intention, web service, performance, cross-border e-commerce, perceived risk, and cross-border logistics. These six categories include the development of cross-border e-commerce website technology, the discussion of performance evaluation indicators for cross-border enterprises, cross-border transportation and logistics, and research on cross-border e-commerce consumer behavior. The topic of purchase intention has always been a research topic that cross-border e-commerce purchase intentions are keen on, but the topic of combining cross-border e-commerce supply chain and artificial intelligence is a topic that can be continuously paid attention to in the future.

Implications for academic research

Based on the above research results, it can be inferred that research on cross-border e-commerce is still relatively nascent, especially because the related research is mainly based in Asia. Therefore, future research can discuss the differences in the current situation of cross-border e-commerce promotion in different countries or regions so as to further understand how to promote operations in different environments and regions. Further, most of the previous studies used questionnaires to analyze cross-border e-commerce consumer behavior. In the follow-up research, we should consider adopting the method of integrating neuroscience to analyze cross-border e-commerce purchase behavior. From the perspective of the topic, follow-up research can begin from artificial intelligence combined with cross-border e-commerce and cross-border e-commerce green supply chains, as outlined in previous studies by Zhu and Zhou (2020) and Fang and Wang (2021) . Together with Liu and Li (2020) and other researchers, they discuss green supply chains. This direction is also in line with current mainstream issues such as carbon neutrality and carbon emissions. Artificial intelligence combined with cross-border e-commerce is also a direction that can be further discussed in the future ( Xia and Liu, 2021 ).

Implications for practical

In short, we have found that CBEC research presents different evolution and development paths. The research perspectives in recent years have evolved from consumers’ purchase behaviors, to CBEC development and policies. During the later period, researchers began studying the opportunities and challenges of CBEC. Also, the COVID-19 pandemic has accelerated the development of global CBEC topics. CBEC is ushering in a new round of opportunities and challenges. In conclusion, CBEC research has evolved from consumers’ purchase behaviors to the influences of new post-pandemic technologies and improvements in the global supply chain on CBEC.

Research limitations

This study mainly uses the method of bibliometrics to analyze the literature, and there are certain limitations to this method. Regarding the method of data collection, this research mainly uses the WOS database to analyze the current situation of the literature on cross-border e-commerce published in SSCI/SCI from 2016 to 2021. Because the data analysis only covers articles included in SSCI/SCI, papers from conferences such as SCOUP or core workshops in the field of e-commerce (e.g., WHICEB, ICE-B) are not considered. Therefore, it is suggested that follow-up research should deepen the discussion on articles that address cross-border e-commerce topics included in core international conferences or databases.

Data availability statement

Author contributions.

YC and GLC designed the research and provided guidance throughout the entire research process. YC, GLC, and JS collected the references, did the literature analysis, and wrote the manuscript. ML, XM, and SW helped translating and offered modification suggestions. SW participated in the collecting, analyzing, and organizing of the literature. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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