literature review artificial intelligence

Artificial intelligence in customer relationship management: literature review and future research directions

Journal of Business & Industrial Marketing

ISSN : 0885-8624

Article publication date: 30 March 2022

Issue publication date: 19 December 2022

Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.

Design/methodology/approach

A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus database, and 2 bibliometric techniques were used: bibliographic coupling and keywords’ co-occurrence.

Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain (Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations) and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand, managers in planning an appropriate and coherent strategy.

Originality/value

To the best of the authors’ knowledge, this study is the first to systematise and discuss the literature regarding the relationship between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent important directions in CRM management research and practices.

Bibliometric analysis
Research agenda
Artificial intelligence
Machine learning
Customer relationship management

Ledro, C. , Nosella, A. and Vinelli, A. (2022), "Artificial intelligence in customer relationship management: literature review and future research directions", Journal of Business & Industrial Marketing , Vol. 37 No. 13, pp. 48-63. https://doi.org/10.1108/JBIM-07-2021-0332

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Customer relationship management (CRM) activity involves collecting, managing and intelligently using data with the support of technology solutions to develop long-term customer relationships and exceptional customer experience (CX) ( Boulding et al. , 2005 ; Payne and Frow, 2005 ; Rababah, 2011 ). The data obtained from all customer contact points, if well managed, can support companies in generating personalised marketing responses, creating new ideas, tailoring products and services and, thus, delivering high customer value and gaining competitive advantage ( Kumar and Misra, 2021 ; Payne and Frow, 2005 ; Paquette, 2010 ). In the digital age, the increasing volume, velocity and variety of data, as well as their processing capacity, have led to new technology solutions, including the advancement of artificial intelligence (AI) techniques ( Brynjolfsson and McAfee, 2017 ). AI refers to a system’s ability to interpret a large quantity of data correctly, learn from such data and use these learnings to reach specific goals and tasks ( Kaplan and Haenlein, 2019 ).

Both companies that develop CRM systems and those that use CRM enjoy advances in AI technology solutions, which have become essential to survive in the CRM context (Pearson, 2019). In fact, new CRM features, such as personality insight services, website morphing, chatbot services, programmatic advertising and emotional, image and facial recognition technologies, require considerable data to be crunched in real time, which would be almost impossible to implement without AI’s advancements (Pearson, 2019).

Alongside the relevance of AI in the business world, academia also claims that AI is the next step towards a novel and more capable management of customer relations ( Kumar et al. , 2020 ; Lokuge et al. , 2020 ; Vignesh and Vasantha, 2019 ). As CRM “is the outcome of the continuing evolution and integration of marketing ideas and newly available data, technologies, and organisational forms” ( Boulding et al. , 2005 ), AI plays a fundamental role because AI solutions applied to CRM enable companies to better assimilate and analyse customer data ( Brynjolfsson and McAfee, 2017 ; Libai et al. , 2020 ), making them increasingly able to anticipate, plan and take advantage of upcoming opportunities ( Mishra and Mukherjee, 2019 ).

Despite AI becoming increasingly pervasive in managerial contexts, management scholars have provided little insights into AI during the past two decades ( Raisch and Krakowski, 2020 ). The AI literature has mainly evolved along two separate disciplines: computer science and operations research, whose scholars have mainly investigated operational tasks that machines can handle, and organisation and management research, where managerial tasks reserved for humans are analysed ( Raisch and Krakowski, 2020 ). Recently, the growing awareness of AI importance and the potential impact it might have on CRM have led to a large proliferation of publications, resulting in an accumulation of knowledge on the topic that is quite scattered and fragmented ( Schröder et al. , 2021 ). This is also attributed to the fact that there are several definitions of CRM, each looking at CRM from a different perspective as a strategy, a process or an information system ( Khodakarami and Chan, 2014 ; Richards and Jones, 2008 ). When dealing with AI–CRM relationship, these different perspectives drive different fields of knowledge, from business management to innovation science, causing advances in research to occur in isolated silos with few interdisciplinary exchanges ( Loureiro et al. , 2021 ). Furthermore, as CRM includes sales, marketing, service and operations activities, its interfunctionality has made the AI–CRM research even more fragmented in different business areas. On such grounds, it seems worth for both the business and academic worlds to systematise the literature on AI in CRM into a full body of structured knowledge, which can guide managers as well as inspire scholars’ future research.

Previous reviews in the field have focused on specific aspects, such as the challenges and applications of Big Data and AI on customer journey modelling ( Arco et al. , 2019 ; Chatterjee et al. , 2019 ), or the potential impacts of Big Data and AI, respectively, on the key success factors of CRM ( Zerbino et al. , 2018 ) and consumers’ decision-making ( Klaus and Zaichkowsky, 2020 ).

To the best of our knowledge, a wide-ranging review dedicated to mapping the literature concerning AI in the CRM domain is still lacking. Based on these premises, this paper aims to trace the state-of-the-art of AI in CRM and, thus, identify rising themes and promising paths for future research. For this purpose, the authors conduct a methodical, transparent and replicable review of AI in CRM, using bibliometric techniques to map the research field without subjective bias ( Zupic and Čater, 2015 ). In particular, the current study combines bibliographic coupling to analyse references and establish intellectual linkages among articles and keywords’ co-occurrence to comprehensively understand the leading keywords, allowing the construction of structural images of the research domain.

Our study contributes to advancing the domain of AI in CRM ( Donthu et al. , 2021 ), addressing new important directions for future research. Furthermore, it offers relevant insights to practitioners.

The rest of the paper is organized as follows. Section 2 describes the methodology, including the search strategy and data collection. Section 3 provides the results with data analysis and visualisation, whereas Section 4 discusses the main contributions and future research paths. Finally, Section 5 presents the conclusions, contributions and limitations of the study.

2. Methodology

To achieve the research goal, we performed a literature review combined with a bibliometric analysis. Bibliometrics is described as “the mathematical and statistical analysis of bibliographic records” ( Pritchard, 1969 ) and is used to establish intellectual linkages among articles and keywords, thus providing a big picture of rising trends and potential research opportunities ( Boyack and Klavans, 2010 ; Marchiori and Franco, 2020 ). Bibliometric techniques offer the advantage of introducing quantitative rigor compared to narrative literature reviews, which might be invalidated by the subjective bias of the researcher ( Tranfield et al. , 2003 ).

The first stage of the method concerns the search and collection of the articles to be analysed, which should truly represent the field of AI in CRM ( McCain, 1990 ), and the second stage employs several bibliometric analyses to map the field and identify the most important themes within it.

2.1 Search strategy and data collection

To identify the appropriate articles for our aim, we executed a search on the Scopus database by using keywords related to AI in CRM. Using the AND operator, we combined the search string for AI (“artificial intelligence” OR “AI” OR “machine learning” OR “deep learning” OR “Big Data”) with that for CRM (“customer relationship management” OR “CRM” OR “customer management” OR “customer experience” OR “CX” OR “customer journey”) in the title, abstract and keywords.

The definitions and motivations behind the choice of these keywords are listed as follows. Following Kumar et al. (2020) , we considered AI to be a generic term referring to a technology that can imitate humans and carry out tasks in an intelligent manner. The term Artificial Intelligence is a little bit loose, but it is essentially about using machine learning - and specifically deep learning - to enable applications ( Brynjolfsson and McAfee, 2017 ). Especially regarding AI in CRM, the prevalence of AI applications concerns machine learning (ML) and its successor technologies, particularly deep learning ( Libai et al. , 2020 ). Thus, as the terms “machine learning,” “deep learning” and “artificial intelligence” are related and often used interchangeably, we included all of them in the search string ( Borges et al. , 2020 ). In particular, we considered ML as a branch of AI that can learn from data, detect patterns and make decisions with minimal human intervention ( Kumar et al. , 2020 ) and deep learning as a technological evolution of ML that can learn from data as well as from its mistakes without human intervention ( Zaki, 2019 ). AI is often connected with the term Big Data ( Arco et al. , 2019 ), as Big Data is considered raw fuel of AI and significantly impacts AI capabilities and value creation ( Deshpande and Kumar, 2018 ; Saidulu and Sasikala, 2017 ). Thus, to exclude papers potentially related to AI, we also included “Big Data” in the search string.

Regarding the keywords used for CRM, given that CRM is linked and often interchanged with the terms “customer experience” and “customer journey”, we included the latter in the search string ( Buckley and Webster, 2016 ). CRM activities involve collecting and intelligently using data to build enduring customer relationships and a consistently superior CX by leveraging the comprehension of the customer journey ( Buckley and Webster, 2016 ; Lemon and Verhoef, 2016 ; Payne and Frow, 2005 ).

CRM and CX are often so much linked together that some companies (e.g. Oracle) see the management of CX as part of advanced CRM ( Lemon and Verhoef, 2016 ). However, CRM concerns planning, implementing, monitoring and improving customer relationships, whereas CX management mainly focusses on how to improve CX at a touchpoint level ( Holmlund et al. , 2020 ).

Similarly, customer journey literature and CRM are often interrelated, as CRM is considered by CRM providers (e.g. salesforce), as well as academics, as a source for customer journey mapping, allowing data centralisation and making them available to different touchpoints ( Payne and Frow, 2005 ). Moreover, given the speed of the current generation of servers and the sophistication of Big Data analytics tools, some have hypothesised that customer journey analytics will be the next CRM ( Fluss, 2017 ).

The search was performed in November 2020, and we obtained 1,032 articles. Filtering this initial data set out, English articles belonging to the Engineering and Business, Management and Accounting categories and articles and reviews underwent a double-blind peer-review process, and excluding duplicates, we finally retrieved 212 articles ( Figure 1 ). These articles ranged from 2001 (2 articles) to 2020 (68 articles, available in November). Only one article was published before 2001, in 1989. Figure 2 represents the temporal distribution of articles in the field of AI in CRM and shows a steep recent growth in the literature.

2.2 Bibliometric analysis: bibliographic coupling and keyword analysis

After identifying the articles focussed on the theme under investigation, we performed two bibliometric analysis – bibliographic coupling and keywords’ co-occurrence – to trace the state-of-the-art of AI in CRM contexts. VOS viewer version 1.6.15 was used to construct and display the bibliographic maps ( Van Eck and Waltman, 2010 ). The VOS viewer has already been used to review the literature on industrial marketing ( Valenzuela Fernandez et al. , 2019 ), information technology management ( Khan and Wood, 2015 ), the inter-connection between Big Data and business strategy ( Ciampi et al. , 2020 ) and Big Data and co-innovation ( Bresciani et al. , 2021 ) through bibliometric analyses. The 212-article data set and relative cited reference data were imported into VOS viewer. Before applying the bibliometric analysis, the completeness of the information within the data set was checked and missing cited reference data were added manually.

First, we conducted a bibliographic coupling of sampled articles to cluster papers based on shared references. This was achieved by counting how many times two articles cited the same references. Bibliographic coupling and co-citation analysis are the two most important science mapping techniques. A co-citation network is formed when two articles (nodes) are cited together by another document, whereas a bibliographic coupling network is formed when both articles (nodes) refer to a third document within their references, forming a link ( Van Eck and Waltman, 2020 ). While co-citation analysis focusses on older literature and bibliographic coupling focusses on more recent research, we opted for bibliographic coupling as we dealt with very recent papers ( Schröder et al. , 2021 ). In the bibliographic coupling network, each link has a strength, depicted by a positive numeric value; the higher this value, the stronger is the link ( Van Eck and Waltman, 2020 ). Thus, the more citations both articles have in common, the stronger is their bibliometric connection. Based on the bibliometric mapping performed by VOS viewer, 99 articles strongly connected within our data set were classified into clusters that likely addressed the same themes ( Waltman et al. , 2010 ; Schröder et al. , 2021 ).

Second, we analysed the keywords’ co-occurrence to discover the most important research topics and the conceptual structure underlying the field of AI in CRM at the time of conducting this study ( Callon et al. ,1983 ; Niknejad et al. , 2021 ). For this purpose, the number of sample articles in which two keywords appear together was counted. In particular, the keywords’ co-occurrence network is formed when the keywords (nodes) appear together, forming a link ( Van Eck and Waltman, 2020 ). Then, the most frequently related keywords were classified into clusters using bibliometric mapping.

To better interpret keyword mapping, we applied an overlay network visualisation, in which items appear in coloured scales with the “average publication year” and the “average normalized number of citations received by the article in which a keyword occurs” ( Van Eck and Waltman, 2020 ). These longitudinal visualisations enabled the assessment of the evolution of the conceptual structure of the research domain of AI in CRM. For the co-occurrence analysis, both the author keywords and index keywords of the sampled articles were considered. Before applying the bibliometric analysis, the extracted keywords were refined and standardised for 1,560 keywords ( Khan and Wood, 2015 ; Kim et al. , 2018 ). In particular, we removed synonyms and derivative words and standardised words with similar meanings. For example, “database”, “data-base” and “database system” were standardised into “database”. Moreover, terms separated by hyphens were standardised. For instance, “social-media” was grouped into “social media” and “omni-channel” into “omnichannel”. Abbreviations in brackets were considered as additional keywords. In addition, where appropriate, in the keywords containing “and” and “&”, these were removed and the keywords were separated.

While bibliographic coupling looks at the background, keywords’ co-occurrences look at the content of the sampled articles. Thus, by combining the results of the keywords’ co-occurrence and bibliographic coupling, we recognised the thematic structure of the clusters. To perform this step, we mapped in a spreadsheet the relevant features of the sampled articles with reference to the following: article purpose, research questions/hypotheses, methodology, context, theoretical background, gaps in the literature, key findings and type of involved technology. Mapping these aspects supported us in identifying the theme that characterises each cluster and, at the same time, paved the way for identifying the gaps and research opportunities.

3.1 Bibliographic coupling

Table 1 shows that the article with the strongest link (29 citations and 92 total link strengths) is Zerbino et al. ’s (2018) , which studies Big Data-enabled CRM. Within the same line of enquiry, the highest link strength paper is Hallikainen et al. 's (2020) , which explores the use of Big Data analytics in CRM with a focus on B2B firms. These findings indicate that Big Data can be considered the cornerstone of AI applications in CRM systems ( Borges et al. , 2020 ; Deshpande and Kumar, 2018 ; Saidulu and Sasikala, 2017 ).

The article by Chatterjee et al. (2019) , ranking seventh in terms of total link strength within the sampled articles, is among the first to discuss approaches and challenges of AI–CRM integration, which is defined as a “hybrid modern system” required by firms to better analyse customers’ data strategically, improve their overall business process and ensure accurate decision-making without human intervention.

In addition, among the 10 articles with higher total link strengths, most are very recent as well as already heavily cited ( Table 1 ). Three of these articles look at the state-of-the-art and research opportunities of using text or image mining techniques in service research ( Villarroel Ordenes and Zhang, 2019 ) and, in particular, in CX management ( McColl-Kennedy et al. , 2019 ; Holmlund et al. , 2020 ). This evidence supports the increasing scholarly attention focussed on investigating AI techniques, such as text and image mining, and shows that applying these techniques to CX can offer significant insights in that matter.

The bibliographic coupling of the articles shows that papers are clustered around three main groups, depicted by three colours ( Figure 3 ). In particular, the node size is proportional to the total link strength of each article, whereas the line thickness represents the co-occurrence frequency of pairs. In addition, the position of a node gives insights into the nodes’ connections: the closeness of two articles indicates that they have several citations in common ( Marchiori and Franco, 2020 ).

The following two articles at the centre of the graph form the pillars in this research domain: Rust and Huang (2014) , a study on how the IT and service revolution are transforming marketing science by enhancing the ability to provide more personalised services and deepen customer relationships, and Liu et al. (2017) , a study on the use of linguistic-based text analytics (text mining and sentiment analysis) to derive latent brand topics and classify brand sentiments on social media.

Turning to the three formed clusters, the red cluster is the largest (38 articles), followed by the green and blue clusters (32 and 29 articles, respectively).

In the red cluster, articles deal with the management of Big Data and their impact on CRM ( Hallikainen et al. , 2020 ; Zerbino et al. , 2018 ), with a particular focus on knowledge management and information assets for, from and about customers ( Chatterjee et al. , 2020a , 2020b ; Del Vecchio et al. , 2020 ; Talón-Ballestero et al. , 2018 ).

In contrast, in the green cluster, there are articles proposing AI-based techniques that support CRM business activities, such as customers’ life event prediction ( De Caigny et al. , 2020a , 2020b ), customer churn prediction ( De Caigny et al. , 2020a , 2020b ), high-value customer identification ( Chang et al. , 2016 ; Chiang, 2019 ) and sentiment analysis ( Liu et al. , 2017 ; Mukherjee and Bala, 2017 ).

Finally, in the blue cluster, we find articles that deal with how to integrate Big Data insights into automated processes related to key customer touchpoints to improve customer value ( Spiess et al. , 2014 ) and how to use AI to deliver coherent streams of connections through different touchpoints for effective customer engagement ( Singh et al. , 2020 ). In the centre, still in blue, some articles examine how AI might affect the core characteristics of CRM ( Libai et al. , 2020 ) and improve operational efficiency and customer service ( Prentice and Nguyen, 2020 ), in particular with the use of bots ( Trivedi, 2019 ).

To further investigate the degree of connectivity among articles, we analysed the citations among them. Table 2 lists the articles according to the number of local citations (i.e. how many times an article cites or is cited by other articles within the sample) and global citations, which refers to the total citations for the article. As presented in Table 2 , global citations are remarkable, revealing how the issue of AI in the management of customer relationships is relevant, cross-cutting and draws the attention of academics in other research areas ( Niknejad et al. , 2021 ).

3.2 Keywords’ co-occurrence

Out of the dataset of 1,560 keywords, the top 55 keywords with at least 4 occurrences defined as the “number of articles in which a keyword occurs” were selected ( Van Eck and Waltman, 2020 ) ( Figure 4 ). The keyword’s number of occurrences defines the node size. The most frequently related keywords are classified into three clusters. The lines represent the connections among the keywords, and the colours identify the clusters to which the keywords belong. In addition, keywords closer to each other have a stronger relationship than farther keywords ( Van Eck and Waltman, 2020 ). Table 3 supports the results of Figure 4 by presenting the occurrences (weight) of the keywords among the different clusters.

If we look at the results of the cluster analysis, we can distinguish among three main conceptual streams in the academic discussion regarding AI and CRM, which are coherent with the previous three clusters identified with the bibliographic coupling analysis.

In the red cluster, the most frequently occurring keywords are Big Data , information management and social media. In addition, in this cluster, there are several keywords related to networks and data processing (such as internet, social media, social networking, database, mathematical models and computer software ). In particular, the strong link between Big Data and sentiment analysis highlights a rising interest in the applications of sentiment analysis on Big Data produced through telecommunication networks and social media.

In the green cluster, the most common keywords are machine learning, sales and marketing. In general, in this cluster, there are keywords of systems/techniques/models based on ML and AI (mainly related to classification and regression) bounded to keywords of business activities and practices related to CRM (such as sales, marketing, public relations, commerce and forecasting).

In the blue cluster, the most frequently occurring keywords are artificial intelligence, customer experience and customer relationship management, which are also strongly related. In general, in this cluster, there are keywords of data science (such as Big Data analytics, business intelligence, Internet of Things, data mining and data analysis ) related to keywords of customer-centric vision (such as customer relationship management, customer experience , customer journey, customer management and customer loyalty ).

We also examined the keywords’ co-occurrence by adopting a temporal perspective to trace the development of the conceptual structure of the field over time. For this purpose, we examined the average publication year of keywords ( Van Eck and Waltman, 2020 ). Figure 5 represents the overlay visualisation of the keywords’ co-occurrence network, where a colour scale indicates the average publication year of keywords. Figure 5 shows that the keywords corresponding to the red cluster are the oldest, whereas those in the blue cluster are the most recent ones, showing a very recent academic interest.

In addition, we examined the development of the conceptual structure of the field of AI in CRM, looking at the co-occurrence of keywords by considering the importance within the academic community. For this purpose, we considered the “average normalized number of citations received by the articles in which a keyword occurs” ( Van Eck and Waltman, 2020 ) ( Figure 6 ):

[…] the normalized number of citations of an article equals the number of citations of the article divided by the average number of citations of all articles published in the same year and included in the data that is provided to VOS viewer ( Van Eck and Waltman, 2020 ).

Thus, normalisation accounts for the fact that older articles have had more time to receive citations ( Van Eck and Waltman, 2020 ). In general, keywords within the blue cluster are the most cited, although with some exceptions.

Overall, the results of these analyses make it possible to draw some interesting insights about the rising trends within the field of AI in CRM.

Within the red cluster, the keyword with the most recent average publication year is Big Data (i.e. 2018), higher than that of artificial intelligence (i.e. 2015,8). Big Data also has a higher average normalised citation index than artificial intelligence (1,14 versus 0,97). This shows that the red cluster, despite being the oldest, is still evolving; thus, new research opportunities on information management when dealing with Big Data, as well as its impact on CRM, arise.

In addition, as shown in Figures 5 and 6 , keywords such as text mining, Big Data analytics, customer journey and Internet of Things are very recent, and the articles in which they occur are widely cited, meaning that they are strong rising themes within the field of AI in CRM.

Text mining, which appears in the green cluster, is an AI-powered technique for transforming unstructured text into structured data suitable for analysis or driving ML algorithms. Furthermore, it is strongly related to sales and deep learning. The former relationship highlights the rising trend of using text mining to spot and prevent decreasing sales ( McColl-Kennedy et al. , 2019 ), while the latter emphasises the increasing interest in deeper text analytics enabled by deep learning ( Ojo and Rizun, 2019 ). However, deep learning in the CRM domain is still in its infancy. The keyword deep learning occurred only seven times in the sample, with a paper average publication year of 2019,7.

The keyword of Big Data analytics belongs to the blue cluster and is strongly related to customer relationship management, customer experience and business intelligence . The differences among Big Data analytics, ML and AI applied to CRM should be stressed. Big Data analytics develop insights from data and information within CRM systems to support decision-making. ML optimises decision-making by creating predictions, whereas AI produces actions and makes decisions independently ( Grover et al. , 2018 ; Holmlund et al. , 2020 ).

The customer journey keyword also belongs to the blue cluster and is strongly related to artificial intelligence, customer experience and privacy , but the node size and position show that it has not yet caught much scholarly attention. However, Figures 5 and 6 show that the customer journey keyword is very recent, and the articles in which it occurs are widely cited, meaning that customer journey has become an important research topic unbound from CRM. In addition, the strong nearness between privacy and customer journey keywords depicts the nascent consideration of the privacy issue that necessarily occurs when AI is deployed in the interaction between brand and users ( Puntoni et al. , 2020 ).

The Internet of Things keyword is strongly related to artificial intelligence, customer relationship management and customer experience. The Internet of Things (IoT) powered by AI is dramatically transforming CX and CRM. While IoT deals with interacting devices through the internet, AI makes devices learn from their data and experience. IoT supports organisations to innovate CRM in terms of trackage of customers’ behaviour in real time and automation of data sourcing, enhancement of situational awareness, sensor-driven decision analytics for retailing and marketing and automated monitor and replying to the customer ( Lokuge et al. , 2020 ; Ng and Wakenshaw, 2017 ).

Finally, chatbot , an AI-enabled tool frequently used in organisations to facilitate processes, especially those related to after sales and personalisation ( Przegalinska et al. , 2019 ), has not yet captured much scholarly attention ( Figure 6 ).

4. Discussion

The results illustrate a sharp fit among the three clusters resulting from bibliographic coupling, which holds articles sharing common references, and the three clusters resulting from the keywords’ co-occurrence, which groups the most frequently related keywords. On this basis, combining the findings of bibliographic coupling and keywords’ co-occurrence, we identified three subfields of research on AI in CRM and captured promising paths for future development, as summarised in Figure 7 .

The first subfield of research, labelled “Big Data and CRM as a database”, considers CRM as a database on business prospects and customers and focusses on the information management of Big Data within CRM. This subfield is the oldest to be studied, even though it is not yet mature. Within this subfield, we identified two main themes: information management and social media.

Information management is concerned with the analytical part of CRM ( Buttle, 2009 ). It involves collecting, organising and using information related to customers and supports executives in developing insights into consumer preferences and behaviour ( Thakur and Chetty, 2019 ) . Given the growing interest in value creation from incorporating Big Data into CRM decisions, companies are recognising the value of data to obtain an increasing amount of detailed information about their customers and the power of Big Data analytics to improve the decision-making process ( Bernardino and Neves, 2016 ). However, the advent of Big Data has led to even more challenges as companies struggle to develop analytical capabilities, intended as abilities that organisations use for extracting useful information from data, which can support organisations’ ability to identify, attain and retain profitable customers ( Kumar et al. , 2020 ; Mikalef et al. , 2020 ; Wang and Feng, 2012 ). Investigating this issue is not only interesting for companies’ managers but also for all other entities who are involved in information management (e.g. data managers, data architects, regulators and suppliers) ( Kumar et al. , 2020 ).

The results also maintain a rising interest in social CRM (SCRM) ( Anshari et al. , 2015 ; Chang, 2018 ; El Fazziki et al. , 2017 ) and SCRM analysis ( He et al. , 2015 ). In today’s competitive business environment, companies increasingly need to listen to, as well as understand, customers’ expectations, opinions and conversations on social media networks and analyse them within a CRM system to obtain meaningful insights regarding their business opportunities. In this view, Del Vecchio et al. (2020) demonstrated how the integration of Big Data analytics and netnography is relevant for the development of an effective CRM strategy. However, the application of AI techniques to social network analysis approaches to transform the considerable data available on social media into actionable insights for CRM is still little explored in the literature, and fresh research along this perspective is more than welcome.

In addition, as companies are increasingly investing resources in Big Data and social media without completely acknowledging the return on these investments, scholars should deepen this issue, investigating how to measure first, and then maximise the return on Big Data applied to CRM and SCRM investments. In this context, scholars might supplement the performance methods usually used in marketing with those frequently used in information system research to improve the assessment of these investments ( Maklan et al. , 2015 ).

Findings have also proved a growing interest in the application of sentiment analysis on Big Data obtained through social media networks ( El Fazziki et al. , 2017 ; He et al. , 2015 ). Sentiment analysis can be a powerful weapon to increase customers’ vision not only outside the company but also within, exploiting CRM capabilities in collecting and analysing customer data. Organisations can use sentiment analysis to analyse verbal and textual exchanges with customers throughout the customer journey, from negotiation to post purchase, to request or after sale assistance, and new research along all these perspectives is recommended. In accordance with Kietzmann and Pitt (2020) , we also encourage academics to use AI to obtain value from the text and other contents created by companies and their customers.

The second subfield of research focuses on “ AI and ML techniques applied to CRM activities ” and in accordance with Wang and Hajli (2017) , we observe that this constantly growing body of research has mostly addressed the development, analysis and comparison of different AI and ML techniques. However, to completely benefit from AI and ML techniques within CRM systems, business organisations need to approach them from a strategic viewpoint rather than from a mere technical viewpoint ( Iansiti and Lakhani, 2020 ). In particular, CRM requirements, capabilities and practices must be reviewed, and their impacts on people’s behaviour and performance must be understood, ensuring that the new technological applications fit the organisational context and CRM strategy ( Catalan-Matamoros, 2012 ).

In addition, articles within this subfield compare innovative ML-enabled techniques, developed for specific functional applications, such as high-value customer identification, customer churn prediction and customer lifetime value prediction, with long-established techniques. In particular, the two most promising techniques identified in the study are text mining and deep learning . The fruitful areas of practical development of text mining are the prevention of sales decreases and derivation of latent brand topics. Areas of rising managerial and academic interest regarding the use of deep learning in CRM are opinion analysis, entity recognition and predictive modelling.

In fact, some studies have validated a specific technique in a real-world setting ( Spiess et al. , 2014 ; Chatterjee et al. , 2020a , 2020b ; Mogaji et al. , 2020 ); however, these examples are still few and isolated, and the literature does not yet provide an overarching big picture that presents and compares these new techniques and their applications. As the selection of a technique depends on many factors (e.g. sector, marketplace, level of personalisation or customer intrusion), it would be useful to provide an overview of the different techniques along with their features, application domains, requirements and outputs, thus providing guidelines to support managers to choose the most suitable technique that optimally uses the available data within a specific context.

Researchers can also advance the theory in the field by finding common patterns between techniques with different CRM-related purposes and applications or by identifying new techniques in business applications that can be adopted to carry out unconventional CRM-related activities. For instance, a convolutional neural network (CNN) has been adopted to analyse textual information ( De Caigny et al. , 2020a , 2020b ), but it can also be applied to a different data source (e.g. time-series data) or for a different purpose (e.g. sentiment and intent analyses of customer reviews). Therefore, future research can investigate the incorporation of time-series data into customer churn prediction models based on a CNN or of time-series data in sentiment and intent analyses of customers’ reviews based on a CNN.

The third subfield of research, labelled as “ Strategic management of AI-CRM integrations ”, looks at AI–CRM integration from a broader strategic viewpoint, rather than analysing specific technological applications. This subfield considers CRM as a tool that drives strategy through actionable insights, rather than as a database, and focusses on AI applied to CRM within a customer-centric vision. This third cluster contains the most recent papers, which start to debate AI considering the challenges, benefits and advantages it can provide to CRM processes and considering the required organisational, cultural and strategic changes. The shift in perspective from technology development to strategy advancement reflects the growing interest in conducting a renewed examination of how technology interacts with the CRM strategy. AI will have a disruptive impact on the strategy development process. For instance, AI can identify future events in the market, estimate product demand ( Arco et al. , 2019 ; Campbell et al. , 2020 ; Kumar et al. , 2020 ), implement a dynamic customer strategy ( Yi, 2018 ), optimise targeting decisions, customise messaging to specific target audiences and identify bestselling characteristics to address ( Kumar et al. , 2019 ). As other tasks will be redefined, AI will lead to several possibilities for CRM strategies and process innovations ( Tekic et al. , 2019 ). However, this literature is still in its infancy, with very few case studies. For instance, researchers can deeply investigate the strategic, operational and organisational changes that AI–CRM integration entail ( Wang and Hajli, 2017 ), how these changes will affect employees as well as customers ( Kumar et al. , 2020 ) and how AI–CRM integration will influence the success of CRM projects. In examining these aspects, scholars should broaden their perspective and consider hybrid organisational systems, which include both humans and AI, exploring their interactive behaviours ( Raisch and Krakowski, 2020 ).

In addition, we identify three main themes that can be addressed in the future: customer journey, chatbots and IoT.

Customer journey mapping and customer decision journey might be impacted by the fresh knowledge made available by AI, characterised by higher accuracy, suitability and timeliness, thus leaving room for investigation ( Lemon and Verhoef, 2016 ). AI can allow a deeper dive into customer decision journeys, identifying an opportunity for an intervention or change ( Lemon and Verhoef, 2016 ) as, for instance, automatically identifying potential anomalies in customer behaviour or negative sentiments. As AI will infer customer behaviours, trends and preferences ( Marinchak et al. , 2018 ), privacy issues will become a priority. Chatterjee et al. (2020a , 2020b ) are among the first to investigate the adoption of AI-integrated CRM systems from a privacy perspective, paving the way for an interesting path for future research. To the best of our knowledge, no study has yet deeply investigated risks, regulatory strategies and customer protection policies concerning AI applications in CRM systems.

Another promising research theme concerns chatbots, which are increasingly used in customer service to tackle requests or complaints, even though customers are still perceiving some risks in their use. Recently, academics have started exploring the implementation processes, impacts, drivers and challenges of chatbot application in marketing ( Sujata et al. , 2019 ) and CRM ( Anjali Daisy, 2020 ), providing insights to reduce the customers’ perceived risk in its use ( Trivedi, 2019 ). However, no studies have yet investigated the ethical issues beyond conversational service automation platform construction (chatbots). In addition, chatbots used together with other technologies, such as smartphones, virtual assistants and augmented reality, will increase the omnichannel strategy intricacy ( Wilson-Nash et al. , 2020 ), and AI will help to predict the optimal combination of channels to reach customers ( Hopkinson and Singhal, 2018 ). The use of AI to deliver coherent streams of interaction across diverse touchpoints is another relevant research issue that calls for further research ( Singh et al. , 2020 ).

Another rising theme is IoT, which is considered a key disruptive technology for CRM in the future ( Lokuge et al. , 2020 ). Allowing the collection of customers’ real-time data, IoT makes the relationship with CRM intriguing. How firms might use IoT to design and build exceptional CX, how IoT can bring CRM to a higher level, the so-called “CRM of everything” and how real data can generate insights to take actions are some of the open questions that need to be researched ( Abu Ghazaleh and Zabadi, 2020 ; Lokuge et al. , 2020 ).

Taking an overarching perspective, the reviewed articles contribute to advancing knowledge in the field of AI in CRM by categorising phenomena, providing intellectual insights or developing frameworks to create an overall understanding. However, papers that advance theories formulating propositions or test theories remain scant. Thus, we wish future contributions to expand the theoretical reference background to better comprehend how AI is shaping CRM.

In this light, more research that elaborates strategic management theories on pivotal decisions concerning AI–CRM integration is required. For example, in terms of making or buying decisions, both the resource-based view (RBV) theory ( Barney, 1991 ) and the transaction cost economics (TCE) ( Williamson, 1979 ) theory can serve as theoretical lenses to evaluate whether AI–CRM integration should be developed in-house or outsourced. In particular, following RBV, the AI application should be implemented internally if it is considered a fundamental capability for developing knowledge in managing customer relationships and maintaining competitive advantage ( Grover et al. , 2018 ; Zerbino et al. , 2018 ). Following the TCE theory, considerations related to asset specificity, opportunism, frequency of transaction and environmental/behavioural uncertainty should be taken into account when deciding between developing AI internally or outsourcing. Turning now to the decision on the level of AI automation–augmentation, management scholars should examine the extent to which AI results influence human decision-making within CRM strategies to clarify the real impact of different levels of AI automation–augmentation on CRM performance. To date, in most cases, within the CRM domain, the final decision still remains with humans. However, the more AI applications are automated, the more they will lead the decision-making process. In this view, humans should interact with AI algorithms to drive the CRM decision-making process. For example, AI analyses consumers’ past behaviours to transfer the most promising sales opportunities to vendors. Based on this issue, the prospect theory, which maintains that human decision-making depends on choosing among options that can themselves rest on biased judgments ( Kahneman et al. , 1979 ), can be adopted as a theoretical perspective to examine the influence of AI on human decision-making. Some may fear that the automation afforded by AI technologies will replace humans, whereas others state that they will elevate employees’ roles, which will allow them to invest their time in creative tasks rather than in mere operating processes ( Campbell et al. , 2020 ). What is certain is that humans will have less and less interface with unprocessed data, and their tasks will increasingly be impacted by AI ( Rust, 2020 ). Thus, future research should also investigate what humans’ new tasks will be, what capabilities a company must have to remain competitive and how humans will interact with AI. Furthermore, humans should be able to transparently identify the logic behind a given decision and to verify the morality of the action; thus, AI must be programmed with a rule-based system of ethics. However, we observed a lack of studies on ethics applied to AI–CRM integration. Future studies can also grasp ethical issues by using the lens of different pre-existing ethical theories, such as deontological, utilitarian or virtue ethics ( Manna and Nath, 2021 ) and, thus, help managers find the right balance between ethical concern consideration and AI application effectiveness.

Finally, only a few studies have used a longitudinal perspective for analysing how AI–CRM integration projects are implemented and develop over time; thus, we recommend that future studies should adopt a process theory approach in investigating this subject.

5. Conclusion

Answering the call of Raisch and Krakowski (2020) to develop comprehensive perspectives on the AI debate in management, this study identifies and describes three subfields that shape and characterise this literature within the CRM domain: Big Data and CRM as a database, AI and ML techniques applied to CRM activities and strategic management of AI–CRM integrations.

The findings suggest that CRM is evolving from a data-driven strategy to an AI-driven strategy ( Colson, 2019 ). In addition, very recently, scholars have been approaching the topic with a broader strategic perspective to harness the power of AI to improve CRM, rather than only investigating specific technological applications to maximise the operational efficiency or CX within a single CRM activity. However, further development of the two subfields “Big Data and CRM as a database” and “AI and ML techniques applied to CRM activities” might be beneficial to support the growth of the third subfield.

Based on our findings, we developed a conceptual model ( Figure 8 ) that integrates the identified subfields and proposes a three-step strategy for AI implementation in CRM:

information management of Big Data;

technology investigation of AI and ML techniques applied to CRM activities; and

AI-driven business transformation.

The model sets out the initiatives and actions that should be deployed in each step to achieve an AI-driven CRM strategy. For this purpose, managers need to set the strategic goals of business transformation from the beginning and start by creating a unique customer data platform from which new information can be integrated into CRM decisions. Subsequently, they can investigate specific algorithms meant to solve narrow business challenges related to customer relationships; in this context, managers need to reflect upon new employees’ competencies, keeping in mind the strategic, operational and organisational changes. Finally, once specific applications have been successfully implemented, managers can move to the last step, where an overarching AI-driven CRM strategy is fully realised. The model can assist executives and managers in identifying the appropriate and consistent business strategy for the effective integration of AI into CRM systems.

This study makes several significant contributions to theory and practice. First, from an academic viewpoint, this study traces the development of research on AI in CRM, depicting the main subfields that have characterised the recent evolution of this fragmented literature. These findings are underpinned by a robust literature review and bibliometric techniques that allow mapping of the research field without subjective bias. Accordingly, this study empowers scholars to gain a one-step overview of AI in CRM and positions their intended contributions within this field (Donthu et al. , 2021). Second, this study outlines the main underdeveloped issues on this topic and addresses avenues for future novel research. The results will enable academics from various domains (e.g. Big Data, AI, customer relationship and marketing management) to work further to develop a common understanding of the relationship between AI and CRM, which will ultimately advance knowledge and benefit organisations. Third, this study develops a three-step conceptual model for AI implementation in CRM, which might support scholars in further deepening the knowledge in this field, also through empirical analysis. The model is far from encompassing all actions for an effective implementation; consequently, further research is required to enrich it and make it contingent to different business contexts.

From a managerial viewpoint, this study offers insights for organisations and managers willing to enable CRM systems to take advantage of the opportunities offered by AI, providing guidelines to capture the main directions along which AI–CRM integration is evolving and managerial practices to make this integration forceful and productive. The proposed model serves as a guideline tool for executives and managers to plan an appropriate and consistent strategy for AI implementation in CRM and improve efficiency in the information management of Big Data, technology investigation of AI and ML techniques and AI-driven business transformation. In addition, the model can provide practical knowledge for organisations to conduct a self-introspection of their strategy and assess if they are missing actions for an effective implementation of AI into CRM systems.

Despite these contributions, our study has some limitations that can also offer avenues for extending research. First, limitations are mainly related to the shortcomings of citing behaviour, because bibliographic coupling does not capture the objectives or motivations that guided the authors in citing prior articles ( Vogel and Güttel, 2013 ; Soranzo et al. , 2016 ). Furthermore, articles with more references are over-weighted as they probably present more intersections with the references of other articles ( Agostini and Nosella, 2019 ). Second, future work can operationalise and test the proposed conceptual model, identifying possible moderators, mediators and controlling factors to build a comprehensive and thorough understanding.

In conclusion, we hope that our study can be a source of inspiration for future studies to advance knowledge of AI in the CRM domain, as it provides useful knowledge to design and publish fresh research and highlights emerging themes that can offer significant theoretical and empirical contributions to the field of CRM and beyond.

literature review artificial intelligence

Search and selection of articles considered in this study

Temporal distribution of the filtered initial data set

Bibliographic coupling network, with a minimum of one citation

Keywords’ co-occurrence network, with a minimum of four occurrences

Overlay visualisation – average publication year

Source: (colour printing)

Overlay visualisation – average normalised citations

Promising paths for future research related to AI in CRM

Three-step strategy for AI implementation in CRM

More related articles in the data set (based on total link strength)

Acknowledgements

This research is linked to the fund cod. VERB_SID19_01 of the University of Padua.

Corresponding author

About the authors.

Cristina Ledro is a PhD student in Management Engineering and Real Estate Economics at the Department of Management and Engineering of the University of Padova, Italy. She holds a master’s degree in Management Engineering from the University of Padova. Her research focus is within the area of customer relationship management. Her research interests include customer experience, value creation, performance measurement and digital technologies.

Anna Nosella , PhD, is Full Professor of Business Strategy at the Department of Management and Engineering of the University of Padova. Her research interest focusses on innovation management, dynamic capabilities and strategies. Her papers have been published in Technovation , Long Range Planning , Management Decision , Journal of Engineering and Technology Management , Journal of Business Research , International Journal of Human Resource Management , Strategic Organization .

Andrea Vinelli , PhD, is a Professor of Operations and Supply Chain Management and Service Operations Management at the Department of Management and Engineering at the University of Padova, Italy. Director of the MBA Programme at the CUOA Business School, Italy. His research and consulting interests lie in the areas of operations strategies, supply networks, servitisation and digital operating models, with specific expertise in the fashion industry and sustainability.

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Research Paper
Published: 18 March 2022
Volume 32 , pages 297–338, ( 2022 )

Cite this article

Ransome Epie Bawack 1 ,
Samuel Fosso Wamba 2 ,
Kevin Daniel André Carillo 2 &
Shahriar Akter 3

54k Accesses

49 Citations

9 Altmetric

Explore all metrics

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

Artificial Intelligence in E-commerce: A Literature Review

Research on the Use, Characteristics, and Impact of e-Commerce Product Recommendation Agents: A Review and Update for 2007–2012

E-commerce and Business Analytics: A Literature Review

Avoid common mistakes on your manuscript.

Introduction

Electronic commerce (e-commerce) can be defined as activities or services related to buying and selling products or services over the internet (Holsapple & Singh, 2000 ; Kalakota & Whinston, 1997 ). Firms increasingly indulge in e-commerce because of customers' rising demand for online services and its ability to create a competitive advantage (Gielens & Steenkamp, 2019 ; Hamad et al., 2018 ; Tan et al., 2019 ). However, firms struggle with this e-business practice due to its integration with rapidly evolving, easily adopted, and highly affordable information technology (IT). This forces firms to constantly adapt their business models to changing customer needs (Gielens & Steenkamp, 2019 ; Klaus & Changchit, 2019 ; Tan et al., 2007 ). Artificial intelligence (AI) is the latest of such technologies. It is transforming e-commerce through its ability to “correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019 . p. 15). Depending on the context, AI could be a system, a tool, a technique, or an algorithm (Akter et al., 2021 ; Bawack et al., 2021 ; Benbya et al., 2021 ). It creates opportunities for firms to gain a competitive advantage by using big data to uniquely meet their customers' needs through personalised services (Deng et al., 2019 ; Kumar, Rajan, et al., 2019 ; Kumar, Venugopal, et al., 2019 ).

AI in e-commerce can be defined as using AI techniques, systems, tools, or algorithms to support activities related to buying and selling products or services over the internet. Research on AI in e-commerce has been going on for the past three decades. About 4000 academic research articles have been published on the topic across multiple disciplines, both at the consumer (de Bellis & Venkataramani Johar, 2020 ; Sohn & Kwon, 2020 ) and organisational levels (Campbell et al., 2020 ; Kietzmann et al., 2018 ; Vanneschi et al., 2018 ). However, knowledge on the topic has not been synthesised despite its rapid growth and dispersion. This lack of synthesis makes it difficult for researchers to determine how much the extant literature covers concepts of interest or addresses relevant research gaps. Synthesising research on AI in e-commerce is an essential condition for advancing knowledge by providing the background needed to describe, understand, or explain phenomena, to develop/test new theories, and to develop teaching orientations in this research area (Cram et al., 2020 ; Paré et al., 2015 ). Thus, this study aims to synthesise research on AI in e-commerce and propose directions for future research in the IS discipline. The innovative approach of combining bibliometric analysis with an extensive literature review is used to answer two specific research questions: (i) what is the current state of research on AI in e-commerce? (ii) what research should be done next on AI in e-commence in general, and within information systems (IS) research in particular?

This study's findings show that AI in e-commerce primarily focuses on recommender systems and the main research themes are sentiment analysis, optimisation, trust, and personalisation. This study makes timely contributions to ongoing debates on the connections between business strategy and the use of AI technologies (Borges et al., 2020 ; Dwivedi et al., 2019 , 2020 ). It also contributes to research on how firms can address challenges regarding the use of AI-related benefits and opportunities for new product or service developments and productivity improvements (Makridakis, 2017 ). Furthermore, no study currently synthesises AI in e-commerce research despite its rapid evolution in the last decade triggered by big data, advanced machine learning (ML) algorithms, and cloud computing. Using well-established e-commerce classification frameworks (Ngai & Wat, 2002 ; Wareham et al., 2005 ), this study classifies information systems (IS) literature on AI in e-commerce. These classifications make it easier for researchers and managers to identify relevant literature based on the topic area, research style, and research theme. A future research agenda is proposed based on the gaps revealed during the classification to guide researchers on making meaningful contributions to AI knowledge in e-commerce.

Research method

Bibliometric analysis.

Bibliometric analysis has been increasingly used in academic research in general and in IS research to evaluate the quality, impact, and influence of authors, journals, and institutions in a specific research area (Hassan & Loebbecke, 2017 ; Lowry et al., 2004 , 2013 ). It has also been used extensively to understand AI research on specific fields or topics (Hinojo-Lucena et al., 2019 ; Tran et al., 2019 ; Zhao, Dai, et al., 2020 ; Zhao, Lou, et al., 2020 ). In this study, a bibliometric analysis was conducted to understand research on AI in e-commerce using the approach Aria and Cuccurullo ( 2017 ) proposed. This methodology involves three main phases: data collection, data analysis, and data visualisation & reporting. The data collection phase involves querying, selecting, and exporting data from selected databases. This study's data sample was obtained by querying the Web of Science (WoS) core databases for publications from 1975 to 2020. This database was chosen over others like Google Scholar or Scopus because WoS provides better quality bibliometric information due to its lower rate of duplicate records (Aria et al., 2020 ) and greater coverage of high-impact journals (Aghaei Chadegani et al., 2013 ). The following search string was used to query the title, keywords, and abstracts of all documents in the WoS collection:

(‘‘Electronic Commerce’’ OR ‘‘Electronic business’’ OR ‘‘Internet Commerce’’ OR “e-business” OR “ebusiness” OR "e-commerce” OR “ecommerce” OR “online shopping” OR “online purchase” OR “internet shopping” OR “e-purchase” OR “online store” OR “electronic shopping”). AND (“Artificial intelligence” OR “Artificial neural network” OR “case-based reasoning” OR “cognitive computing” OR “cognitive science” OR “computer vision” OR “data mining” OR “data science” OR “deep learning” OR “expert system” OR “fuzzy linguistic modelling” OR “fuzzy logic” OR “genetic algorithm” OR “image recognition” OR “k-means” OR “knowledge-based system” OR “logic programming” OR “machine learning” OR “machine vision” OR “natural language processing” OR “neural network” OR “pattern recognition” OR “recommendation system” OR “recommender system” OR “semantic network” OR “speech recognition” OR “support vector machine” OR “SVM” OR “text mining”).

This search string led to 4414 documents that made up the initial dataset of this study. For quality reasons, only document types tagged as articles, reviews, and proceeding papers were selected for this study because they are most likely to have undergone a rigorous peer-review process before publication (Milian et al., 2019 ). Thus, editorial material, letters, news items, meeting abstracts, and retracted publications were removed from the dataset, leaving 4335 documents that made up the final dataset used for bibliometric analysis. Figure 1 summarises the data collection phase.

Summary of the data collection phase

Table 1 summarises the main information about the dataset regarding the timespan, document sources, document types, document contents, authors, and author collaborations. The dataset consists of documents from 2599 sources, published by 8663 authors and 84,474 references.

Bibliometrix Footnote 1 is the R package used to conduct bibliometric analysis (Aria & Cuccurullo, 2017 ). This package has been extensively used to conduct bibliometric studies published in top-tier journals. It incorporates the most renowned bibliometric tools for citation analysis (Esfahani et al., 2019 ; Fosso Wamba, 2020 ; Pourkhani et al., 2019 ). It was specifically used to analyse the sources, documents, conceptual, and intellectual structure of AI in e-commerce research. Publication sources and their source impacts were analysed based on their h-index quality factors (Hirsch, 2010 ). The most significant, impactful, prestigious, influential, and quality publication sources, affiliations, and countries regarding research on AI in e-commerce were identified. This contributed to the identification of the most relevant disciplines in this area of research. Documents were analysed using total citations to identify the most cited documents in the dataset. Through content analysis, the most relevant topics/concepts, AI technologies/techniques, research methods, and application domains were identified.

Furthermore, citation analysis and reference publication year spectroscopy (RPYS) were used to identify research contributions that form the foundations of research on AI in e-commerce (Marx et al., 2014 ; Rhaiem & Bornmann, 2018 ). These techniques were also used to identify the most significant changes in the research area. Co-word network analysis on author-provided keywords using the Louvain clustering algorithm was used to understand the research area's conceptual structure. This algorithm is a greedy optimisation method used to identify communities in large networks by comparing the density of links inside communities with links between communities (Blondel et al., 2008 ). This study used it to identify key research themes by analysing author-provided keywords. Co-citation network analysis using the Louvain clustering algorithm was also used to analyse publication sources through which journals communities were identified. It further contributed to identifying the most relevant disciplines in this research area by revealing journal clusters.

The bibliometric analysis results were reported from functionalist, normative, and interpretive perspectives (Hassan & Loebbecke, 2017 ). The functionalist perspective presents the results of the key concepts and topics investigated in this research area. The normative perspective focuses on the foundations and norms of the research area. The interpretive perspective emphasises the main themes that drive AI in e-commerce research.

Literature review

An extensive review and classification of IS literature on AI in e-commerce complemented the bibliometric analysis. It provides more details on how research in this area is conducted in the IS discipline. The review was delimited to the most impactful and influential management information systems (MIS) journals identified during the bibliometric analysis and completed by other well-established MIS journals known for their contributions to e-commerce research (Ngai & Wat, 2002 ; Wareham et al., 2005 ). Thus, 20 journals were selected for this review: Decision sciences, Decision support systems, Electronic commerce research and applications, Electronic markets, E-service journal, European journal of information systems, Information and management, Information sciences, Information systems research, International journal of electronic commerce, International journal of information management, Journal of information systems, Journal of information technology, Journal of management information systems, Journal of organisational computing and electronic commerce, Journal of strategic information systems, Journal of the association for information systems, Knowledge-based systems, Management science, MIS Quarterly .

The literature review was conducted in three stages (Templier & Paré, 2015 ; Webster & Watson, 2002 ): (i) identify and analyse all relevant articles from the targeted journals found in the bibliometric dataset (ii) use the keyword string to search for other relevant articles found on the official publication platforms of the targeted journals, and (iii) identify relevant articles from the references of the articles identified in stages one and two found within the target journals. All articles with content that did not focus on AI in e-commerce were eliminated. This process led to a final dataset of 229 research articles on AI in e-commerce. The articles were classified into three main categories: by topic area (Ngai & Wat, 2002 ), by research style (Wareham et al., 2005 ), and by research themes (from bibliometric analysis).

Classification by topic area involved classifying relevant literature into four broad categories: (i) applications, (ii) technological issues, (iii) support and implementation, and (iv) others. Applications refer to the specific domain in which the research was conducted (marketing, advertising, retailing…). Technological issues contain e-commerce research by AI technologies, systems, algorithms, or methodologies that support or enhance e-commerce applications. Support and Implementation include articles that discuss how AI supports public policy and corporate strategy. Others contain all other studies that do not fall into any of the above categories. It includes articles on foundational concepts, adoption, and usage. Classification by research style involved organizing the relevant literature by type of AI studied, the research approach, and the research method used in the studies. The research themes identified in the bibliometric analysis stage were used to classify the relevant IS literature by research theme.

Results of the bibliometric analysis

Scientific publications on AI in e-commerce began in 1991 with an annual publication growth rate of 10.45%. Figure 2 presents the number of publications per year. Observe the steady increase in the number of publications since 2013.

Number of publications on AI in e-commerce per year

Institutions in Asia, especially China, are leading this research area. The leading institution is Beijing University of Posts and Telecommunications, with 88 articles, followed by Hong Kong Polytechnic University with 84 articles. Table 2 presents the top 20 institutions publishing on AI in e-commerce.

As expected, China-based affiliations appear most frequently in publications (4261 times). They have over 2.5 times as many appearances as US-based affiliations (1481 times). Interestingly, publications with US-based affiliations attract more citations than those in China. Table 3 presents the number of times authors from a given country feature in publications and the corresponding total number of citations.

Functional perspective

Analysing the most globally cited documents Footnote 2 in the dataset (those with 100 citations) reveals that recommender systems are the main topic of interest in this research area (Appendix Table 10 ). Recommender systems are software agents that make recommendations for consumers by implicitly or explicitly evoking their interests or preferences (Bo et al., 2007 ). The topic has been investigated in many flavours, including hybrid recommender systems (Burke, 2002 ), personalised recommender systems (Cho et al., 2002 ), collaborative recommender systems (Lin et al., 2002 ) and social recommender systems (Li et al., 2013 ). The central concept of interest is personalisation, specifically leveraging recommender systems to offer more personalised product/service recommendations to customers using e-commerce platforms. Thus, designing recommender systems that surpass existing ones is the leading orientation of AI in e-commerce research. Researchers have mostly adopted experimental rather than theory-driven research designs to meet this overarching research objective. Research efforts focus more on improving the performance of recommendations using advanced AI algorithms than on understanding and modelling the interests and preferences of individual consumers. Nevertheless, the advanced AI algorithms developed are trained primarily using customer product reviews.

Interpretive perspective

Four themes characterise research on AI in e-commerce: sentiment analysis, trust & personalisation, optimisation, AI concepts, and related technologies. The keyword clusters that led to the identification of these themes are presented in Table 4 . The sentiment analysis theme represents the stream of research focused on interpreting and classifying emotions and opinions within text data in e-commerce using AI techniques like ML and natural language processing (NLP). The trust and personalisation theme represents research that focuses on establishing trust and making personalised recommendations for consumers in e-commerce using AI techniques like collaborative filtering, case-based reasoning, and clustering algorithms. The optimisation theme represents research that focuses on using AI algorithms like genetic algorithms to solve optimisation problems in e-commerce. Finally, the AI concepts and related technologies theme represent research that focuses on using different techniques and concepts used in the research area.

Normative perspective

Research on AI in e-commerce is published in two main journal subject areas: computer science & AI and business & management. This result confirms the multidisciplinary nature of this research area, which has both business and technical orientations. Table 5 presents the most active publication outlets in each subject area. The outlets listed in the table could help researchers from different disciplines to select the proper outlet for their research results. It could also help researchers identify the outlets wherein they are most likely to find relevant information for their research on AI in e-commerce.

However, some disciplines set the foundations and standards of research on AI in e-commerce through the impact of their contributions to its body of knowledge. Analysing document references shows that the most cited contributions come from journals in the IS, computer science, AI, management science, and operations research disciplines (Table 6 ). It shows the importance of these disciplines to AI's foundations and standards in e-commerce research and their major publication outlets.

The IS discipline is a significant contributor to AI in e-commerce research, given that 24 out of the 40 top publications in the area can be assimilated to IS sources. Table 7 also shows that 7 out of the top 10 most impactful publication sources are assimilated to the IS discipline. The leading paper from the IS field reviews approaches to automatic schema matching (Rahm & Bernstein, 2001 ) and it is the second most globally cited paper in the research area. Meanwhile, the leading paper from the MIS subfield reviews recommender system application developments (Lu et al., 2015 ).

Collaborative filtering, recommender systems, social information filtering, latent Dirichlet allocation, and matrix factoring techniques are the foundational topics in research on AI in e-commerce (Table 8 ). They were identified by analysing the most cited references in the dataset. These references were mostly literature reviews and documents that discussed the basic ideas and concepts behind specific technologies or techniques used in recommender systems.

Furthermore, the specific documents that set the foundations of research on AI in e-commerce and present the most significant historical contributions and turning points in the field were identified using RPYS (Appendix Table 11 ). 2001, 2005, 2007, 2011, and 2015 are the years with the highest number of documents referenced by the documents in the sample. The most cited studies published in 2001 focused on recommendation algorithms, especially item-based collaborative filtering, random forest, gradient boosting machine, and data mining. The main concept of interest was how to personalise product recommendations. In 2005, the most referenced documents focused on enhancing recommendation systems using hybrid collaborative filtering, advanced machine learning tools and techniques, and topic diversification. That year also contributed a solid foundation for research on trust in recommender systems. In 2007, significant contributions continued on enhanced collaborative filtering techniques for recommender systems. Meanwhile, Bo & Benbasat ( 2007 ) set the basis for research on recommender systems' characteristics, use, and impact, shifting from traditional studies focused on underlying algorithms towards a more consumer-centric approach. In 2011, major contributions were made to enhance recommender systems, like developing a new library for support vector machines (Chang & Lin, 2011 ) and the Scikit-learn package for machine learning in Python (Pedregosa et al., 2011 ). In 2015, the most critical contributions primarily focused on deep learning algorithms, especially with an essential contribution to using them in recommender systems (Wang et al., 2015 ).

Results of the literature review study

Classification by topic area.

Most articles on AI in e-commerce focus on technological issues (107 articles, 47%), followed by applications (87 articles, 38%), support and implementation (20 articles, 9%), then others (15 articles, 6%). Specifically, most articles focus on AI algorithms, models, and methodologies that support or improve e-commerce applications (76 articles, 33.2%) or emphasise the applications of AI in marketing, advertising, and sales-related issues (38 articles, 16.6%). Figure 3 presents the distribution of articles, while Appendix Table 12 presents the articles in each topic area.

Classification of MIS literature on AI in e-commerce by topic area

Classification by research style

Most authors discuss AI algorithms, models, computational approaches, or methodologies (168 articles, 73%). Specifically, current research focuses on how AI algorithms like ML, deep learning (DL), NLP, and related techniques could be used to model and understand phenomena in the e-commerce environment. It also focuses on studies that involve designing intelligent agent algorithms that support learning processes in e-commerce systems. Many studies also focus on AI as systems (31 articles, 14%), especially on recommender systems and expert systems that leverage AI algorithms in the back end. The “others” category harboured all articles that did not clearly refer to AI as either an algorithm or as a system (30 articles, 13%) (see Fig. 4 and Appendix Table 13 ).

Classification of MIS literature on AI in e-commerce by type of AI

Furthermore, most publications use the design science research approach (198 articles, 86%). Researchers prefer this approach because it allows them to develop their algorithms and models or improve existing ones, thereby creating a new IS artefact (see Fig. 5 and Appendix Table 14 ).

Classification of MIS literature on AI in e-commerce by research approach

Also, authors adopt experimental methods in their papers (157 articles, 69%), especially those who adopted a design science research approach. They mostly use experiments to test their algorithms or prove their concepts (see Fig. 6 and Appendix Table 15 ).

Classification of MIS literature on AI in e-commerce by research method

Classification by research theme

Based on the main research themes on AI in e-commerce identified during the bibliometric analysis, most authors published on optimisation (63 articles, 27%). They mostly focused on optimising recommender accuracy (25 articles), prediction accuracy (29 articles), and other optimisation aspects (9 articles) like storage optimisation. This trend was followed by publications on trust & personalisation (31 articles, 14%), wherein more articles were published on personalisation (17 articles) than on trust (14 articles). Twenty-nine articles focused on sentiment analysis (13%). The rest of the papers focus on AI design, tools and techniques (46 articles), decision support (30 articles), customer behaviour (13 articles), AI concepts (9 articles), and intelligent agents (8 articles) (see Fig. 7 and Appendix Table 16 ).

Classification of MIS literature on AI in e-commerce by current research themes

This study's overall objective was to synthesise research on AI in e-commerce and propose avenues for future research. Thus, it sought to answer two research questions: (i) what is the current state of research on AI in e-commerce? (ii) what research should be done next on AI in e-commerce in general and within IS research in particular? This section summarises the findings of the bibliometric analysis and literature review. It highlights some key insights from the results, starting with the leading role of China and the USA in this research area. This highlight is followed by discussions on the focus of current research on recommender systems, the extensive use of design science and experiments in this research area, and a limited focus on modelling consumer behaviour. This section also discusses the little research found on some research themes and the limited number of publications from some research areas. Implications for research and practice are discussed at the end of this section.

Need for more research from other countries

Research on AI in e-commerce has been rising steadily since 2013. Overall, these results indicate a growing interest in the applications of AI in e-commerce. China-based institutions lead this research area, although US-based affiliations attract more citations. Tables 2 and 3 indicate that China is in the leading position regarding research on AI in e-commerce. Observe that Amazon Inc. (USA), JD.com (China), Alibaba Group Holding Ltd. (China), Suning.com (China), Meituan (China), Wayfair (USA), eBay (USA), and Groupon (USA) are referenced among the largest e-commerce companies in the world (in terms of market capitalisation, revenue, and the number of employees). Footnote 3 These companies are primarily from China and the USA. These findings correlate with Table 3 , which could indicate that China and the USA are investing more in the research and development of AI applications in e-commerce (especially China, based on Table 2 ) because of the positions they occupy in the industry. This logic would imply that companies seeking to penetrate the e-commerce industry and remain competitive should also consider investing more in the research and development of AI applications in the area. The list of universities provided could become partner universities for countries with institutions that have less experience in the research area. Especially with the COVID-19 pandemic, e-commerce has become a global practice. Thus, other countries need to contribute more research on the realities of e-commerce in their respective contexts to develop more globally acceptable AI solutions in e-commerce practices. It is essential because different countries approach e-commerce differently. For example, although Amazon’s marketplace is well-developed in continents like Europe, Asia, and North America, it has difficulty penetrating Africa because the context is very different (culturally and infrastructurally). While mobile wallet payment systems are fully developed on the African continent, Amazon’s marketplace does not accommodate this payment method. Therefore, it would be impossible for many Africans to use Amazon’s Alexa to purchase products online. What does this mean for research on digital inclusion? Are there any other cross-cultural differences between countries that affect the adoption and use of AI in e-commerce? Are there any legal boundaries that affect the implementation and internationalisation of AI in e-commerce? Such questions highlight the need for more country-specific research on AI in e-commerce to ensure more inclusion.

Focus on recommender systems

AI in e-commerce research is essentially focused on recommender systems in the past years. The results indicate that in the last 20 years, AI in e-commerce research has primarily focused on using AI algorithms to enhance recommender systems. This trend is understandable because recommender systems have become an integral part of almost every e-commerce platform nowadays (Dokyun Lee & Hosanagar, 2021 ; Stöckli & Khobzi, 2021 ). As years go by, observe how novel AI algorithms have been proposed, the most recent being deep learning (Chaudhuri et al., 2021 ; Liu et al., 2020 ; Xiong et al., 2021 ; Zhang et al., 2021 ). Thus, researchers are increasingly interested in how advanced AI algorithms can enable recommender systems in e-commerce platforms to correctly interpret external data, learn from such data, and use those learnings to improve the quality of user recommendations through flexible adaptation. With the advent of AI-powered chatbots and voice assistants, firms increasingly include these technologies in their e-commerce platforms (Ngai et al., 2021 ). Thus, researchers are increasingly interested in conversational recommender systems (De Carolis et al., 2017 ; Jannach et al., 2021 ; Viswanathan et al., 2020 ). These systems can play the role of recommender systems and interact with the user through natural language (Iovine et al., 2020 ). Thus, conversational recommender systems is an up-and-coming research area for AI-powered recommender systems, especially given the ubiquitous presence of voice assistants in society today. Therefore, researchers may want to investigate how conversational recommender systems can be designed effectively and the factors that influence their adoption.

Limited research themes

The main research themes in AI in e-commerce are sentiment analysis, trust, personalisation, and optimisation. Researchers have focused on these themes to provide more personalised recommendations to recommendation system users. Personalising recommendations based on users’ sentiment and trust circle has been significantly researched. Extensive research has also been conducted on how to optimise the algorithmic performance of recommender systems. ML, DL, NLP are the leading AI algorithms and techniques currently researched in this area. The foundational topics for applying these algorithms include collaborative filtering, latent Dirichlet allocation, matrix factoring techniques, and social information filtering.

Current research shows how using AI for personalisation would enable firms to deliver high-quality customer experiences through precise personalisation based on real-time information (Huang & Rust, 2018 , 2020 ). It is highly effective in data-rich environments and can help firms to significantly improve customer satisfaction, acquisition, and retention rates, thereby ideal for service personalisation (Huang & Rust, 2018 ). AI could enable firms to personalise products based on preferences, personalise prices based on willingness to pay, personalise frontline interactions, and personalise promotional content in real-time (Huang & Rust, 2021 ).

Research also shows how AI could help firms optimise product prices by channel and customer (Huang & Rust, 2021 ; Huang & Rust, 2020 ) and develop accurate and personalised recommendations for customers. It is beneficial when the firm lacks initial data on customers that it can use to make recommendations (cold start problem) (Guan et al., 2019 ; Wang, Feng, et al., 2018 ; Wang, Jhou, et al., 2018 ; Wang, Li, et al., 2018 ; Wang, Lu, et al., 2018 ). It also gives firms the ability to automatically estimate optimal prices for their products/services and define dynamic pricing strategies that increase profits and revenue (Bauer & Jannach, 2018 ; Greenstein-Messica & Rokach, 2018 ). It also gives firms the ability to predict consumer behaviours like customer churn (Bose & Chen, 2009 ), preferences based on their personalities (Buettner, 2017 ), engagement (Ayvaz et al., 2021 ; Sung et al., 2021 ; Yim et al., 2021 ), and customer payment default (Vanneschi et al., 2018 ). AI also gives firms the ability to predict product, service, or feature demand and sales (Cardoso & Gomide, 2007 ; Castillo et al., 2017 ; Ryoba et al., 2021 ), thereby giving firms the ability to anticipate and dynamically adjust their advertising and sales strategies (Chen et al., 2014 ; Greenstein-Messica & Rokach, 2020 ). Even further, it gives firms the ability to predict the success or failure of these strategies (Chen & Chung, 2015 ).

Researchers have shown that using AI to build trust-based recommender systems can help e-commerce firms increase user acceptance of the recommendations made by e-commerce platforms (Bedi & Vashisth, 2014 ). This trust is created by accurately measuring the level of trust customers have in the recommendations made by the firm’s e-commerce platforms (Fang et al., 2018 ) or by making recommendations based on the recommendations of people the customers’ trust in their social sphere (Guo et al., 2014 ; Zhang et al., 2017 ).

Sentiment analytics using AI could give e-commerce firms the ability to provide accurate and personalised recommendations to customers by assessing their opinions expressed online such as through customer reviews (Al-Natour & Turetken, 2020 ; Qiu et al., 2018 ). It has also proven effective in helping brands better understand their customers over time and predict their behaviours (Das & Chen, 2007 ; Ghiassi et al., 2016 ; Pengnate & Riggins, 2020 ). For example, it helps firms better understand customer requirements for product improvements (Ou et al., 2018 ; Qi et al., 2016 ) and predict product sales based on customer sentiments (Li, Wang, et al., 2019 ; Li, Wu, et al., 2019 ; Li, Zhang, et al., 2019 ). Thus, firms can accurately guide their customers towards discovering desirable products (Liang & Wang, 2019 ) and predict the prices they would be willing to pay for products based on their sentiments (Tseng et al., 2018 ). Thus, firms that use AI-powered sentiment analytics would have the ability to constantly adapt their product development, sales, and pricing strategies while improving the quality of their e-commerce services and personalised recommendations for their customers.

While the current research themes are exciting and remain relevant in today’s context, it highlights the need for researchers to explore other research themes. For example, privacy, explainable, and ethical AI are trendy research themes in AI research nowadays. These themes are relevant to research on AI in e-commerce as well. Thus, developing these research themes would make significant contributions to research on AI in e-commerce. In the IS discipline, marketing & advertising is where AI applications in e-commerce have been researched the most. This finding complements Davenport et al. ( 2020 )’s argument, suggesting that marketing functions have the most to gain from AI. Most publications focus on technological issues like algorithms, support systems, and security. Very few studies investigated privacy, and none was found on topics like ethical, explainable, or sustainable AI. Therefore, future research should pay more attention to other relevant application domains like education & training, auctions, electronic payment systems, inter-organisational e-commerce, travel, hospitality, and leisure (Blöcher & Alt, 2021 ; Manthiou et al., 2021 ; Neuhofer et al., 2021 ). To this end, questions that may interest researchers include, what are the privacy challenges caused by using AI in e-commerce? How can AI improve e-commerce services in education and training? How can AI improve e-commerce services in healthcare? How can AI bring about sustainable e-commerce practices?

Furthermore, research on AI in e-commerce is published in two main journal categories: computer science & AI and business & management. Most citations come from the information systems, computer science, artificial intelligence, management science, and operations research disciplines. Thus, researchers interested in research on AI in e-commerce are most likely to find relevant information in such journals (see Tables 5 and 6 ). Researchers seeking to publish their research on AI in e-commerce can also target such journals. However, researchers are encouraged to publish their work in other equally important journal categories. For example, law and government-oriented journals would greatly benefit from research on AI in e-commerce. International laws and government policies could affect how AI is used in e-commerce. For example, due to the General Data Protection Regulation (GDPR), how firms use AI algorithms and applications to analyse user data in Europe may differ from how they would in the US. Such factors may have profound performance implications given that AI systems are as good as the volume and quality of data they can access for analysis. Thus, future research in categories other than those currently researched would benefit the research community.

More experiment than theory-driven research

Most of the research done on AI in e-commerce have adopted experimental approaches. Very few adopted theory-driven designs. This trend is also observed in IS research, where 69% of the studies used experimental research methods and 86% adopted a design science research approach instead of the positivist research approach often adopted in general e-commerce research (Wareham et al., 2005 ). However, this study's findings complement a recent review that shows that laboratory experiments and secondary data analysis were becoming increasingly popular in e-commerce research. Given that recommender systems support customer decision-making, this study also complements recent studies that show the rising use of design science research methods in decision support systems research (Arnott & Pervan, 2014 ) and in IS research in general (Jeyaraj & Zadeh, 2020 ). This finding could be explained by the fact that researchers primarily focused on enhancing the performance of AI algorithms used in recommender systems. Therefore, to test the performance of their algorithms in the real world, the researchers have to build a prototype and test it in real-life contexts. Using performance accuracy scores, the researchers would then tell the extent to which their proposed algorithm is performant. However, ML has been highlighted as a powerful tool that can help advance theory in behavioural IS research (Abdel-Karim et al., 2021 ). Therefore, key research questions on AI in e-commerce could be approached using ML as a tool for theory testing in behavioural studies. Researchers could consider going beyond using AI algorithms for optimising recommender systems to understand its users' behaviour. In Fig. 4 , observe that 73% of IS researcher papers reviewed approached AI as an algorithm or methodology to solve problems in e-commerce. Only 14% approached AI as a system. Researchers can adopt both approaches in the same study in the sense that they can leverage ML algorithms to understand human interactions with AI systems, not just for optimisation. This approach could provide users with insights by answering questions regarding the adoption and use of AI systems.

Furthermore, only 6% of the studies focus on consumer behaviour. Thus, most researchers on AI in e-commerce this far have focused more on algorithm performance than on modelling the behaviour of consumers who use AI systems. It is also clear that behavioural aspects of using recommender systems are often overlooked (Adomavicius et al., 2013 ). There is relatively limited research on the adoption, use, characteristics, and impact of AI algorithms or systems on its users. This issue was raised as a fundamental problem in this research area (Bo et al., 2007 ) and seems to remain the case today. However, understanding consumer behaviour could help improve the accuracy of AI algorithms. Thus, behavioural science researchers need to conduct more research on modelling consumer behaviours regarding consumers' acceptance, adoption, use, and post-adoption behaviours targeted by AI applications in e-commerce. As AI algorithms, systems, and use cases multiply in e-commerce, studies explaining their unique characteristics, adoption, use, and impact at different levels (individual, organisational, and societal) should also increase. It implies adopting a more theory-driven approach to research on AI in e-commerce. Therefore, behavioural science researchers should be looking into questions on the behavioural factors that affect the adoption of AI in e-commerce.

Implications for research

This study contributes to research by innovatively synthesising the literature on AI in e-commerce. Despite the recent evolution of AI and the steady rise of research on how it could affect e-commerce environments, no review has been conducted to understand this research area's state and evolution. Yet, a recent study shows that e-commerce and AI are currently key research topics and themes in the IS discipline (Jeyaraj & Zadeh, 2020 ). This paper has attempted to fill this research gap by providing researchers with a global view of AI research in e-commerce. It offers a multidimensional view of the knowledge structure and citation behaviour in this research area by presenting the study's findings from functional, normative, and interpretive perspectives. Specifically, it reveals the most relevant topics, concepts, and themes on AI in e-commerce from a multidisciplinary perspective.

This contribution could help researchers evaluate the value and contributions of their research topics in the research area with respect to other disciplines and choose the best publication outlets for their research projects. This study also reveals the importance of AI in designing recommender systems and shows the foundational literature on which this research area is built. Thus, researchers could use this study to design the content of AI or e-commerce courses in universities and higher education institutions. Its content provides future researchers and practitioners with the foundational knowledge required to build quality recommender systems. Researchers could also use this study to inform their fields on the relevance of their research topics and the specific gaps to fill therein. For example, this study reveals the extent to which the IS discipline has appropriated research on AI in e-commerce. It also shows contributions of the IS discipline to the current research themes, making it easier for IS researchers to identify research gaps as well as gaps between IS theory and practice.

Implications for practice

This study shows that AI in e-commerce primarily focuses on recommender systems. It highlights sentiment analysis, optimisation, trust, and personalisation as the core themes in the research area. Thus, managers could tap into these resources to improve the quality of their recommender systems. Specifically, it could help them understand how to develop optimised, personalised, trust-based and sentiment-based analytics supported by uniquely designed AI algorithms. This knowledge would make imitating or replicating the quality of recommendations rendered through e-commerce platforms practically impossible for competitors. Firms willing to use AI in e-commerce would need unique access and ownership of customer data, AI algorithms, and expertise in analytics (De Smedt et al., 2021 ; Kandula et al., 2021 ; Shi et al., 2020 ). The competition cannot imitate these resources because they are unique to the firm, especially if patented (Pantano & Pizzi, 2020 ). Also, this research paper classifies IS literature on AI in e-commerce by topic area, research style, and research theme. Thus, IS practitioners interested in implementing AI in e-commerce platforms would easily find the research papers that best meet their needs. It saves them the time to search for articles themselves, which may not always be relevant and reliable.

Limitations

This study has some limitations. It was challenging to select a category for each article in the sample dataset. Most of those articles could be rightfully placed in several categories of the classification frameworks. However, assigning articles to a single category in each framework simplifies the research area's conceptualisation and understanding (Wareham et al., 2005 ). Thus, categories were assigned to each article based on the most apparent orientation from the papers' titles, keywords, and abstracts. Another challenge was whether or not to include a research paper in the review. For example, although some studies on recommender systems featured in the keyword search results, the authors did not specify if the system's underlying algorithms were AI algorithms. Consequently, such articles were not classified to ensure that those included in this review certainly had an AI orientation. Despite our efforts, we humbly acknowledge that this study may have missed some publications, and others may have been published since this paper started the review process. Thus, in no way does this study claim to be exhaustive but rather extensive. Nonetheless, the findings from our rigorous literature review process strongly match the bibliometric analysis findings and those from similar studies we referenced. Therefore, we believe our contributions to IS research on AI in e-commerce remain relevant.

Future research

In addition to recommendations for future research discussed in the previous sections, the findings of this study are critically analysed through the lens of recent AI research published in leading IS journals. The aim is to identify other potential gaps for future research on AI in e-commerce that could interest the IS community.

One of the fundamental issues with AI research in IS today is understanding the AI concept (Ågerfalk, 2020 ). Our findings show that researchers have mostly considered algorithms and techniques like ML, DL, and NLP AI in their e-commerce research. Are these algorithms and techniques AI? Does the fact that an algorithm helps to analyse data and make predictions about e-commerce activities mean that the algorithm is AI? It is crucial for researchers to clearly explain what they mean by AI and differentiate between different types of AI used in their studies to avoid ambiguity. This explanation would help prevent confusion between AI and business intelligence & analytics in e-commerce. It would also help distinguish between AI as a social actor and AI as a technology with the computational capability to perform cognitive functions.

A second fundamental issue with AI research in IS is context (Ågerfalk, 2020 ). Using the same data, an AI system would/should be able to interpret the message communicated or sought by the user based on context. Context gives meaning to the data, making the AI system’s output relevant in the real world. Research on AI in e-commerce did not show much importance to context. Many authors used existing datasets to test their algorithms without connecting them to a social context. Thus, it is difficult to assess whether the performance of the proposed algorithms is relevant in every social context. Future research should consider using AI algorithms to analyse behavioural data alongside ‘hard’ data (facts) to identify patterns and draw conclusions in specific contexts. It implies answering the crucial question, what type of AI best suits which e-commerce context? Thus, researchers would need to collaborate with practitioners to better understand and delineate contexts (Ågerfalk, 2020 ) of investigation rather than make general claims on fraud detection or product prices, for example.

The IS community is also interested in understanding ethical choices and challenges organisations face when adopting AI systems and algorithms. What ethical decisions do e-commerce firms need to make when implementing AI solutions? What are the ethical challenges e-commerce firms face when implementing AI solutions? Following a sociotechnical approach, firms seeking to implement AI systems need to make ethical choices. These include transparent vs black-boxed algorithms, slow & careful vs expedited & timely designs, passive vs active implementation approach, obscure vs open system implementation, compliance vs risk-taking, and contextualised vs standardised use of AI systems (Marabelli et al., 2021 ). Thus, future research on AI in e-commerce should investigate how e-commerce firms address these ethical choices when implementing their AI solutions and the challenges they face in the process.

AI and the future of work is another primary source of controversy in the IS community (Huysman, 2020 ; Willcocks, 2020a , b ). Several researchers are investigating how AI is transforming the work configurations of organisations. Workplace technology platforms are increasingly observed to integrate office applications, social media features and AI-driven self-learning capabilities (Baptista et al., 2020 ; Grønsund & Aanestad, 2020 ; Lyytinen et al., 2020 ). Is this emergent digital/human work configuration also happening in e-commerce firms? How is this changing the future of work in the e-commerce industry?

IS researchers have increasingly called for research on how AI transforms decision making. For example, they are interested in understanding how AI could help augment mental processing, change managerial mindsets and actions, and affect the rationality of economic agents (Brynjolfsson et al., 2021 ). A recent study also makes several research propositions for IS researchers regarding conceptual and theoretical development, AI-human interaction, and AI implementation in the context of decision making (Duan et al., 2019 ). This study shows that decision-making is not a fundamental research theme as it accounts for only 13% of the research papers reviewed. Thus, future research on AI in e-commerce should contribute to developing this AI research theme in the e-commerce context. It involves proposing answers to questions like how AI affects managerial mindsets and actions in e-commerce? How is AI affecting the rationality of consumers who use e-commerce platforms?

This study shows that relatively few research papers on AI in e-commerce are theory-driven. Most adopted experimental research methods and design science research approaches wherein they use AI algorithms to explain phenomena. The IS community is increasingly interested in developing theories using AI algorithms (Abdel-Karim et al., 2021 ). Contrary to traditional theory development approaches, such theories developed based on AI algorithms like ML are called to be focused, context-specific, and as transparent as possible (Chiarini Tremblay et al., 2021 ). Thus, rather than altogether abandoning the algorithm-oriented approach used for AI in e-commerce research, researchers who master it should develop skills to use it as a basis for theorising.

Last but not least, more research is needed on the role of AI-powered voice-based AI in e-commerce. It is becoming common for consumers to use intelligent personal assistants like Google’s Google Assistant, Amazon’s Alexa, and Apple’s Siri for shopping activities since many retail organisations are making them an integral part of their e-commerce platforms (de Barcelos Silva et al., 2020 ). Given the rising adoption of smart speakers by consumers, research on voice commerce should become a priority for researchers on AI in e-commerce. Yet, this study shows that researchers are still mostly focused on web-based, social networking (social commerce), and mobile (m-commerce) platforms. Therefore, research on the factors that affect the adoption and use of voice assistants in e-commerce and the impact on consumers and e-commerce firms would make valuable contributions to e-commerce research. Table 9 summarises the main research directions recommended in this paper.

Conclusions

AI has emerged as a technology that can differentiate between two competing firms in e-commerce environments. This study presents the state of research of AI in e-commerce based on bibliometric analysis and a literature review of IS research. The bibliometric analysis highlights China and the USA as leaders in this research area. Recommender systems are the most investigated technology. The main research themes in this area of research are optimisation, trust & personalisation, sentiment analysis, and AI concepts & related technologies. Most research papers on AI in e-commerce are published in computer science, AI, business, and management outlets. Researchers in the IS discipline has focused on AI applications and technology-related issues like algorithm performance. Their focus has been more on AI algorithms and methodologies than AI systems. Also, most studies have adopted a design science research approach and experiment-style research methods. In addition to the core research themes of the area, IS researchers have also focused their research on AI design, tools and techniques, decision support, consumer behaviour, AI concepts, and intelligent agents. The paper discusses opportunities for future research revealed directly by analysing the results of this study. It also discusses future research directions based on current debates on AI research in the IS community. Thus, we hope that this paper will help inform future research on AI in e-commerce.

Download the bibliometrix R package and read more here: https://www.bibliometrix.org/index.html

Global citation refers to the total number of times the document has been cited in other documents in general and local citations refer to the total number of times a document has been cited by other documents in our dataset.

https://axiomq.com/blog/8-largest-e-commerce-companies-in-the-world/

Abbasi, A., Zhang, Z., Zimbra, D., Chen, H., & Nunamaker, J. F. (2010). Detecting fake websites: The contribution of statistical learning theory. MIS Quarterly, 34 (3), 435–461. https://doi.org/10.2307/25750686

Abdel-Karim, B. M., Pfeuffer, N., & Hinz, O. (2021). Machine learning in information systems - a bibliographic review and open research issues. Electronic Markets, 31 (3), 643–670. https://doi.org/10.1007/s12525-021-00459-2

Article Google Scholar

Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2013). Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. Information Systems Research, 24 (4), 956–975. https://doi.org/10.1057/isre.2013.0497

Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23 (1), 103–145. https://doi.org/10.1145/1055709.1055714

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17 (6), 734–749. https://doi.org/10.1109/TKDE.2005.99

Ågerfalk, P. J. (2020). Artificial intelligence as digital agency. European Journal of Information Systems, 29 (1), 1–8. https://doi.org/10.1080/0960085X.2020.1721947

Aghaei Chadegani, A., Salehi, H., Md Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ale Ebrahim, N. (2013). A comparison between two main academic literature collections: Web of science and scopus databases. Asian Social Science, 9 (5), 18–26. https://doi.org/10.5539/ass.v9n5p18

Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD Record, 22 (2), 207–216. https://doi.org/10.1145/170036.170072

Aguwa, C., Olya, M. H., & Monplaisir, L. (2017). Modeling of fuzzy-based voice of customer for business decision analytics. Knowledge-Based Systems, 125 , 136–145. https://doi.org/10.1016/j.knosys.2017.03.019

Aher, S. B., & Lobo, L. M. R. J. (2013). Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data. Knowledge-Based Systems, 51 , 1–14. https://doi.org/10.1016/j.knosys.2013.04.015

Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and agenda for future research. Electronic Markets, 26 (2), 173–194. https://doi.org/10.1007/s12525-016-0219-0

Akter, S., Wamba, S. F., Mariani, M., & Hani, U. (2021). How to Build an AI Climate-Driven Service Analytics Capability for Innovation and Performance in Industrial Markets? Industrial Marketing Management, 97 , 258–273. https://doi.org/10.1016/j.indmarman.2021.07.014

Al-Natour, S., Benbasat, I., & Cenfetelli, R. (2011). The adoption of online shopping assistants: Perceived similarity as an antecedent to evaluative beliefs. Journal of the Association for Information Systems, 12 (5), 347–374. https://doi.org/10.17705/1jais.00267

Al-Natour, S., Benbasat, I., & Cenfetelli, R. T. (2006). The role of design characteristics in shaping perceptions of similarity: The case of online shopping assistants. Journal of the Association for Information Systems, 7 (12), 821–861.

Al-Natour, S., & Turetken, O. (2020). A comparative assessment of sentiment analysis and star ratings for consumer reviews. International Journal of Information Management, 54 , 102132. https://doi.org/10.1016/j.ijinfomgt.2020.102132

Al-Shamri, M. Y. H. (2016). User profiling approaches for demographic recommender systems. Knowledge-Based Systems, 100 , 175–187. https://doi.org/10.1016/j.knosys.2016.03.006

Alt, R., Ehmke, J. F., Haux, R., Henke, T., Mattfeld, D. C., Oberweis, A., Paech, B., & Winter, A. (2019). Towards customer-induced service orchestration - requirements for the next step of customer orientation. Electronic Markets, 29 (1), 79–91. https://doi.org/10.1007/s12525-019-00340-3

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11 (4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Aria, M., Misuraca, M., & Spano, M. (2020). Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research. Social Indicators Research, 149 (3), 803–831. https://doi.org/10.1007/s11205-020-02281-3

Ariyaluran Habeeb, R. A., Nasaruddin, F., Gani, A., Targio Hashem, I. A., Ahmed, E., & Imran, M. (2019). Real-time big data processing for anomaly detection: A Survey. International Journal of Information Management, 45 , 289–307. https://doi.org/10.1016/j.ijinfomgt.2018.08.006

Arnott, D., & Pervan, G. (2014). A critical analysis of decision support systems research revisited: The rise of design science. Journal of Information Technology, 29 (4), 269–293. https://doi.org/10.1057/jit.2014.16

Ayvaz, D., Aydoğan, R., Akçura, M. T., & Şensoy, M. (2021). Campaign participation prediction with deep learning. Electronic Commerce Research and Applications, 48 , 101058. https://doi.org/10.1016/j.elerap.2021.101058

Azadjalal, M. M., Moradi, P., Abdollahpouri, A., & Jalili, M. (2017). A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowledge-Based Systems, 116 , 130–143. https://doi.org/10.1016/j.knosys.2016.10.025

Bag, S., Kumar, S. K., & Tiwari, M. K. (2019). An efficient recommendation generation using relevant Jaccard similarity. Information Sciences, 483 , 53–64. https://doi.org/10.1016/j.ins.2019.01.023

Bai, X., Marsden, J. R., Ross, W. T., & Wang, G. (2020). A note on the impact of daily deals on local retailers’ online reputation: Mediation effects of the consumer experience. Information Systems Research, 31 (4), 1132–1143. https://doi.org/10.1287/isre.2020.0935

Balabanović, M., & Shoham, Y. (1997). Content-Based, Collaborative Recommendation. Communications of the ACM, 40 (3), 66–72. https://doi.org/10.1145/245108.245124

Bandyopadhyay, S., Rees, J., & Barron, J. M. (2008). Reverse auctions with multiple reinforcement learning agents. Decision Sciences, 39 (1), 33–63. https://doi.org/10.1111/j.1540-5915.2008.00181.x

Baptista, J., Stein, M.-K., Klein, S., Watson-Manheim, M. B., & Lee, J. (2020). Digital work and organisational transformation: Emergent Digital/Human work configurations in modern organisations. The Journal of Strategic Information Systems, 29 (2), 101618. https://doi.org/10.1016/j.jsis.2020.101618

Barzegar Nozari, R., & Koohi, H. (2020). A novel group recommender system based on members’ influence and leader impact. Knowledge-Based Systems, 205 , 106296. https://doi.org/10.1016/j.knosys.2020.106296

Bassano, C., Gaeta, M., Piciocchi, P., & Spohrer, J. C. (2017). Learning the Models of Customer Behavior: From Television Advertising to Online Marketing. International Journal of Electronic Commerce, 21 (4), 572–604. https://doi.org/10.1080/10864415.2016.1355654

Bauer, J., & Jannach, D. (2018). Optimal pricing in e-commerce based on sparse and noisy data. Decision Support Systems, 106 , 53–63. https://doi.org/10.1016/j.dss.2017.12.002

Bawack, R. E., Wamba, S. F., & Carillo, K. (2021). A framework for understanding artificial intelligence research: insights from practice. Journal of Enterprise Information Management, 34 (2), 645–678. https://doi.org/10.1108/JEIM-07-2020-0284

Bedi, P., & Vashisth, P. (2014). Empowering recommender systems using trust and argumentation. Information Sciences, 279 , 569–586. https://doi.org/10.1016/j.ins.2014.04.012

Beladev, M., Rokach, L., & Shapira, B. (2016). Recommender systems for product bundling. Knowledge-Based Systems, 111 , 193–206. https://doi.org/10.1016/j.knosys.2016.08.013

Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Special issue editorial: Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22 (2), 281–303. https://doi.org/10.17705/1jais.00662

Blei, D. M., Ng, A. Y., & Jordan, M. T. (2002). Latent dirichlet allocation. Advances in Neural Information Processing Systems, 3 (Jan), 993–1022.

Google Scholar

Blöcher, K., & Alt, R. (2021). AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry. Electronic Markets, 31 (3), 529–551. https://doi.org/10.1007/s12525-020-00443-2

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008 (10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

Bo, X., Benbasat, I., Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31 (1), 137–209. https://doi.org/10.2307/25148784

Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46 , 109–132. https://doi.org/10.1016/j.knosys.2013.03.012

Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2012). A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems, 26 , 225–238. https://doi.org/10.1016/j.knosys.2011.07.021

Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17 (3), 235–255. https://doi.org/10.1214/ss/1042727940

Bondielli, A., & Marcelloni, F. (2019). A survey on fake news and rumour detection techniques. Information Sciences, 497 , 38–55. https://doi.org/10.1016/j.ins.2019.05.035

Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management , 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225

Bose, I., & Chen, X. (2009). Hybrid models using unsupervised clustering for prediction of customer churn. Journal of Organizational Computing and Electronic Commerce, 19 (2), 133–151. https://doi.org/10.1080/10919390902821291

Brazier, F. M. T., Cornelissen, F., Gustavsson, R., Jonker, C. M., Lindeberg, O., Polak, B., & Treur, J. (2002). A multi-agent system performing one-to-many negotiation for load balancing of electricity use. Electronic Commerce Research and Applications, 1 (2), 208–224. https://doi.org/10.1016/S1567-4223(02)00013-3

Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5–32. https://doi.org/10.1023/A:1010933404324

Brusilovski, P., Kobsa, A., & Nejdl, W. (2007). The Adaptive Web Methods and Strategies of Web Personalization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol 4321 LNCS . Springer Science & Business Media.

Brynjolfsson, E., Wang, C., & Zhang, X. (2021). The economics of IT and digitization: Eight questions for research. MIS Quarterly, 45 (1), 473–477.

Buettner, R. (2017). Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality-based product recommender framework. Electronic Markets, 27 (3), 247–265. https://doi.org/10.1007/s12525-016-0228-z

Bukhari, A. C., & Kim, Y.-G. (2012). Integration of a secure type-2 fuzzy ontology with a multi-agent platform: A proposal to automate the personalized flight ticket booking domain. Information Sciences, 198 , 24–47. https://doi.org/10.1016/j.ins.2012.02.036

Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12 (4), 331–370. https://doi.org/10.1023/A:1021240730564

Büyüközkan, G., Feyzioǧlu, O., & Nebol, E. (2008). Selection of the strategic alliance partner in logistics value chain. International Journal of Production Economics, 113 (1), 148–158. https://doi.org/10.1016/j.ijpe.2007.01.016

Cacheda, F., Carneiro, V., Fernández, D., & Formoso, V. (2011). Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web , 5 (1). https://doi.org/10.1145/1921591.1921593

Cai, H., & Zhang, F. (2019). Detecting shilling attacks in recommender systems based on analysis of user rating behavior. Knowledge-Based Systems, 177 , 22–43. https://doi.org/10.1016/j.knosys.2019.04.001

Campbell, C., Sands, S., Ferraro, C., Tsao (Jody), H.-Y., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63 (2), 227–243. https://doi.org/10.1016/j.bushor.2019.12.002

Cao, Q., & Schniederjans, M. J. (2006). Agent-mediated architecture for reputation-based electronic tourism systems: A neural network approach. Information and Management, 43 (5), 598–606. https://doi.org/10.1016/j.im.2006.03.001

Cao, Y., & Li, Y. (2007). An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Systems with Applications, 33 (1), 230–240. https://doi.org/10.1016/j.eswa.2006.04.012

Carbó, J., Molina, J. M., & Dávila, J. (2007). Avoiding malicious agents in E-commerce using fuzzy recommendations. Journal of Organizational Computing and Electronic Commerce, 17 (2), 101–117. https://doi.org/10.1080/10919390701293972

Cardoso, G., & Gomide, F. (2007). Newspaper demand prediction and replacement model based on fuzzy clustering and rules. Information Sciences, 177 (21), 4799–4809. https://doi.org/10.1016/j.ins.2007.05.009

Castillo, P. A., Mora, A. M., Faris, H., Merelo, J. J., García-Sánchez, P., Fernández-Ares, A. J., De las Cuevas, P., & García-Arenas, M. I. (2017). Applying computational intelligence methods for predicting the sales of newly published books in a real editorial business management environment. Knowledge-Based Systems, 115 , 133–151. https://doi.org/10.1016/j.knosys.2016.10.019

Chang, C. C., & Lin, C. J. (2011). LIBSVM: A Library for support vector machines. ACM Transactions on Intelligent Systems and Technology , 2 (3). https://doi.org/10.1145/1961189.1961199

Chang, J.-S., & Chang, W.-H. (2014). Analysis of fraudulent behavior strategies in online auctions for detecting latent fraudsters. Electronic Commerce Research and Applications, 13 (2), 79–97. https://doi.org/10.1016/j.elerap.2013.10.004

Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63 , 67–80. https://doi.org/10.1016/j.dss.2013.08.008

Chang, S. E., & Jang, Y. T. (2009). Assessing customer satisfaction in a V-commerce environment. Journal of Organizational Computing and Electronic Commerce, 19 (1), 30–49. https://doi.org/10.1080/10919390802605083

Chang, W.-H., & Chang, J.-S. (2012). An effective early fraud detection method for online auctions. Electronic Commerce Research and Applications, 11 (4), 346–360. https://doi.org/10.1016/j.elerap.2012.02.005

Chang, W.-L. (2011). iValue: A knowledge-based system for estimating customer prospect value. Knowledge-Based Systems, 24 (8), 1181–1186. https://doi.org/10.1016/j.knosys.2011.05.004

Chaudhuri, N., Gupta, G., Vamsi, V., & Bose, I. (2021). On the platform but will they buy? Predicting customers’ purchase behavior using deep learning. Decision Support Systems, 149 , 113622. https://doi.org/10.1016/j.dss.2021.113622

Chen, C. C., & Chung, M.-C. (2015). Predicting the success of group buying auctions via classification. Knowledge-Based Systems, 89 , 627–640. https://doi.org/10.1016/j.knosys.2015.09.009

Chen, K., Luo, P., & Wang, H. (2017). An influence framework on product word-of-mouth (WoM) measurement. Information and Management, 54 (2), 228–240. https://doi.org/10.1016/j.im.2016.06.010

Chen, L., Chen, G., & Wang, F. (2015). Recommender systems based on user reviews: The state of the art. User Modeling and User-Adapted Interaction, 25 (2), 99–154. https://doi.org/10.1007/s11257-015-9155-5

Chen, L., & Wang, F. (2013). Preference-based clustering reviews for augmenting e-commerce recommendation. Knowledge-Based Systems, 50 , 44–59. https://doi.org/10.1016/j.knosys.2013.05.006

Chen, M.-Y. (2013). A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Information Sciences, 220 , 180–195. https://doi.org/10.1016/j.ins.2011.09.013

Chen, M. Y., Kiciman, E., Fratkin, E., Fox, A., & Brewer, E. (2002). Pinpoint: Problem determination in large, dynamic internet services. Proceedings of the 2002 International Conference on Dependable Systems and Networks , 595–604. https://doi.org/10.1109/DSN.2002.1029005

Chen, R., Wang, Q., & Xu, W. (2019). Mining user requirements to facilitate mobile app quality upgrades with big data. Electronic Commerce Research and Applications, 38 , 100889. https://doi.org/10.1016/j.elerap.2019.100889

Chen, R., Zheng, Y., Xu, W., Liu, M., & Wang, J. (2018). Secondhand seller reputation in online markets: A text analytics framework. Decision Support Systems, 108 , 96–106. https://doi.org/10.1016/j.dss.2018.02.008

Chen, Y.-S. (2012). Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems, 26 , 259–270. https://doi.org/10.1016/j.knosys.2011.08.021

Chen, Y. L., Cheng, L. C., & Hsu, W. Y. (2013). A new approach to the group ranking problem: Finding consensus ordered segments from users’ preference data. Decision Sciences, 44 (6), 1091–1119. https://doi.org/10.1111/deci.12048

Chen, Y. L., Tang, K., Wu, C. C., & Jheng, R. Y. (2014). Predicting the influence of users’ posted information for eWOM advertising in social networks. Electronic Commerce Research and Applications, 13 (6), 431–439. https://doi.org/10.1016/j.elerap.2014.10.001

Cheung, K. W., Kwok, J. T., Law, M. H., & Tsui, K. C. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems, 35 (2), 231–243. https://doi.org/10.1016/S0167-9236(02)00108-2

Chiarini Tremblay, M., Kohli, R., & Forsgren, N. (2021). Theories in Flux: Reimagining Theory Building in the Age of Machine Learning. MIS Quarterly, 45 (1), 455–459.

Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23 (3), 329–342. https://doi.org/10.1016/S0957-4174(02)00052-0

Chong, A.Y.-L. (2013a). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40 (4), 1240–1247. https://doi.org/10.1016/j.eswa.2012.08.067

Chong, A. Y. L. (2013b). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40 (2), 523–530. https://doi.org/10.1016/j.eswa.2012.07.068

Chou, T. H., & Seng, J. L. (2009). An intelligent multi-agent e-services method-An international telecommunication example. Information and Management, 46 (6), 342–350. https://doi.org/10.1016/j.im.2009.05.006

Chow, H. K. H., Choy, K. L., & Lee, W. B. (2007). A dynamic logistics process knowledge-based system - An RFID multi-agent approach. Knowledge-Based Systems, 20 (4), 357–372. https://doi.org/10.1016/j.knosys.2006.08.004

Chu, B.-H., Tsai, M.-S., & Ho, C.-S. (2007). Toward a hybrid data mining model for customer retention. Knowledge-Based Systems, 20 (8), 703–718. https://doi.org/10.1016/j.knosys.2006.10.003

Chung, W. (2014). BizPro: Extracting and categorizing business intelligence factors from textual news articles. International Journal of Information Management, 34 (2), 272–284. https://doi.org/10.1016/j.ijinfomgt.2014.01.001

Cram, W. A., Templier, M., & Paré, G. (2020). (Re)considering the concept of literature review reproducibility. Journal of the Association for Information Systems, 21 (5), 1103–1114. https://doi.org/10.17705/1jais.00630

Cui, G., Wong, M. L., & Lui, H. K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52 (4), 597–612. https://doi.org/10.1287/mnsc.1060.0514

Da’u, A., Salim, N., Rabiu, I., & Osman, A. (2020). Recommendation system exploiting aspect-based opinion mining with deep learning method. Information Sciences, 512 , 1279–1292. https://doi.org/10.1016/j.ins.2019.10.038

Das, S. R., & Chen, M. Y. (2007). Yahoo! for amazon: Sentiment extraction from small talk on the Web. Management Science, 53 (9), 1375–1388. https://doi.org/10.1287/mnsc.1070.0704

Dastani, M., Jacobs, N., Jonker, C. M., & Treur, J. (2005). Modelling user preferences and mediating agents in electronic commerce. Knowledge-Based Systems, 18 (7), 335–352. https://doi.org/10.1016/j.knosys.2005.05.001

Datta, S., Bhaduri, K., Giannella, C., Wolff, R., & Kargupta, H. (2006). Distributed Data Mining in Peer-to-Peer Networks. IEEE Internet Computing, 10 (4), 18–26. https://doi.org/10.1109/MIC.2006.74

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48 (1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

de Barcelos Silva, A., Gomes, M. M., da Costa, C. A., da Rosa Righi, R., Barbosa, J. L. V., Pessin, G., De Doncker, G., & Federizzi, G. (2020). Intelligent personal assistants: A systematic literature review. Expert Systems with Applications, 147 , 113193. https://doi.org/10.1016/j.eswa.2020.113193

de Bellis, E., & Venkataramani Johar, G. (2020). Autonomous Shopping Systems: Identifying and Overcoming Barriers to Consumer Adoption. Journal of Retailing, 96 (1), 74–87. https://doi.org/10.1016/j.jretai.2019.12.004

De Carolis, B., de Gemmis, M., Lops, P., & Palestra, G. (2017). Recognizing users feedback from non-verbal communicative acts in conversational recommender systems. Pattern Recognition Letters, 99 , 87–95.

De Smedt, J., Lacka, E., Nita, S., Kohls, H. H., & Paton, R. (2021). Session stitching using sequence fingerprinting for web page visits. Decision Support Systems, 150 , 113579. https://doi.org/10.1016/j.dss.2021.113579

Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27 (4), 293–307. https://doi.org/10.1016/j.ijresmar.2010.09.001

Deng, S., Tan, C. W., Wang, W., & Pan, Y. (2019). Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research. Journal of Advertising, 48 (4), 356–365. https://doi.org/10.1080/00913367.2019.1652121

Dong, M., Zeng, X., Koehl, L., & Zhang, J. (2020). An interactive knowledge-based recommender system for fashion product design in the big data environment. Information Sciences, 540 , 469–488. https://doi.org/10.1016/j.ins.2020.05.094

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48 , 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L. C., Misra, S., Mogaji, E., Sharma, S. K., Bahadur Singh, J., Raghavan, V., Raman, R., P. Rana, N., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A. Walton, P., & Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management , 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., Kumar, V., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A., & Wang, Y. (2020). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management , 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168

Esfahani, H. J., Tavasoli, K., & Jabbarzadeh, A. (2019). Big data and social media: A scientometrics analysis. International Journal of Data and Network Science, 3 (3), 145–164. https://doi.org/10.5267/j.ijdns.2019.2.007

Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2021). Towards early purchase intention prediction in online session based retailing systems. Electronic Markets, 31 (3). https://doi.org/10.1007/s12525-020-00448-x

Fang, H., Zhang, J., & Şensoy, M. (2018). A generalized stereotype learning approach and its instantiation in trust modeling. Electronic Commerce Research and Applications, 30 , 149–158. https://doi.org/10.1016/j.elerap.2018.06.004

Fang, X., Hu, P. J. H., Li, Z. L., & Tsai, W. (2013). Predicting adoption probabilities in social networks. Information Systems Research, 24 (1), 128–145. https://doi.org/10.1287/isre.1120.0461

Fasli, M., & Kovalchuk, Y. (2011). Learning approaches for developing successful seller strategies in dynamic supply chain management. Information Sciences, 181 (16), 3411–3426. https://doi.org/10.1016/j.ins.2011.04.014

Feng, S., Zhang, H., Wang, L., Liu, L., & Xu, Y. (2019). Detecting the latent associations hidden in multi-source information for better group recommendation. Knowledge-Based Systems, 171 , 56–68. https://doi.org/10.1016/j.knosys.2019.02.002

Ferrara, E., De Meo, P., Fiumara, G., & Baumgartner, R. (2014). Web data extraction, applications and techniques: A survey. Knowledge-Based Systems, 70 , 301–323. https://doi.org/10.1016/j.knosys.2014.07.007

Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479 , 448–455. https://doi.org/10.1016/j.ins.2017.12.030

Fosso Wamba, S. (2020). Humanitarian supply chain: a bibliometric analysis and future research directions. Annals of Operations Research , 1–27. https://doi.org/10.1007/s10479-020-03594-9

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29 (5), 1189–1232. https://doi.org/10.1214/aos/1013203451

Galitsky, B. (2006). Reasoning about attitudes of complaining customers. Knowledge-Based Systems, 19 (7), 592–615. https://doi.org/10.1016/j.knosys.2006.03.006

Geng, Q., Deng, S., Jia, D., & Jin, J. (2020). Cross-domain ontology construction and alignment from online customer product reviews. Information Sciences, 531 , 47–67. https://doi.org/10.1016/j.ins.2020.03.058

Ghavipour, M., & Meybodi, M. R. (2016). An adaptive fuzzy recommender system based on learning automata. Electronic Commerce Research and Applications, 20 , 105–115. https://doi.org/10.1016/j.elerap.2016.10.002

Ghiassi, M., Zimbra, D., & Lee, S. (2016). Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. Journal of Management Information Systems, 33 (4), 1034–1058. https://doi.org/10.1080/07421222.2016.1267526

Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23 (10), 1498–1512. https://doi.org/10.1109/TKDE.2010.188

Gielens, K., & Steenkamp, J.-B.E.M. (2019). Branding in the era of digital (dis)intermediation. International Journal of Research in Marketing, 36 (3), 367–384. https://doi.org/10.1016/j.ijresmar.2019.01.005

Gokmen, T., & Vlasov, Y. (2016). Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations. Frontiers in Neuroscience, 10 , 333. https://doi.org/10.3389/fnins.2016.00333

Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to Weave an Information tapestry. Communications of the ACM, 35 (12), 61–70. https://doi.org/10.1145/138859.138867

Goldberg, K., Roeder, T., Gupta, D., & Perkins, C. (2001). Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval, 4 (2), 133–151. https://doi.org/10.1023/A:1011419012209

Gong, J., Abhishek, V., & Li, B. (2018). Examining the impact of keyword ambiguity on search advertising performance: A topic model approach. MIS Quarterly, 42 (3), 805–829. https://doi.org/10.25300/MISQ/2018/14042

Greenstein-Messica, A., & Rokach, L. (2018). Personal price aware multi-seller recommender system: Evidence from eBay. Knowledge-Based Systems, 150 , 14–26. https://doi.org/10.1016/j.knosys.2018.02.026

Greenstein-Messica, A., & Rokach, L. (2020). Machine learning and operation research based method for promotion optimization of products with no price elasticity history. Electronic Commerce Research and Applications, 40 , 100914. https://doi.org/10.1016/j.elerap.2019.100914

Griggs, K., & Wild, R. (2003). Intelligent support for sophisticated e-commerce services: An agent-based auction framework modeled after the New York stock exchange specialist system. E-Service Journal, 2 (2), 87–104. https://doi.org/10.2979/esj.2003.2.2.87

Grønsund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations. Journal of Strategic Information Systems , 29 (2). https://doi.org/10.1016/j.jsis.2020.101614

Gu, X., Wu, S., Peng, P., Shou, L., Chen, K., & Chen, G. (2017). CSIR4G: An effective and efficient cross-scenario image retrieval model for glasses. Information Sciences, 417 , 310–327. https://doi.org/10.1016/j.ins.2017.07.027

Guan, J., Shi, D., Zurada, J. M., & Levitan, A. S. (2014). Analyzing Massive Data Sets: An Adaptive Fuzzy Neural Approach for Prediction, with a Real Estate Illustration. Journal of Organizational Computing and Electronic Commerce, 24 (1), 94–112. https://doi.org/10.1080/10919392.2014.866505

Guan, S.-U., Chan, T. K., & Zhu, F. (2005). Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis. Electronic Commerce Research and Applications, 4 (4), 377–394. https://doi.org/10.1016/j.elerap.2005.07.002

Guan, Y., Wei, Q., & Chen, G. (2019). Deep learning based personalized recommendation with multi-view information integration. Decision Support Systems, 118 , 58–69. https://doi.org/10.1016/j.dss.2019.01.003

Gunnec, D., & Raghavan, S. (2017). Integrating Social Network Effects in the Share-Of-Choice Problem. Decision Sciences, 48 (6), 1098–1131. https://doi.org/10.1111/deci.12246

Guo, G., Qiu, H., Tan, Z., Liu, Y., Ma, J., & Wang, X. (2017a). Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowledge-Based Systems, 138 , 202–207. https://doi.org/10.1016/j.knosys.2017.10.005

Guo, G., Zhang, J., & Thalmann, D. (2014). Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowledge-Based Systems, 57 , 57–68. https://doi.org/10.1016/j.knosys.2013.12.007

Guo, G., Zhang, J., Zhu, F., & Wang, X. (2017b). Factored similarity models with social trust for top-N item recommendation. Knowledge-Based Systems, 122 , 17–25. https://doi.org/10.1016/j.knosys.2017.01.027

Guo, H., Pathak, P., & Cheng, H. K. (2015). Estimating Social Influences from Social Networking Sites-Articulated Friendships versus Communication Interactions. Decision Sciences, 46 (1), 135–163. https://doi.org/10.1111/deci.12118

Guo, X., Wei, Q., Chen, G., Zhang, J., & Qiao, D. (2017). Extracting representative information on intra-organizational blogging platforms. MIS Quarterly, 41 (4), 1105–1127. https://doi.org/10.25300/MISQ/2017/41.4.05

Gupta, S., & Kant, V. (2020). Credibility score based multi-criteria recommender system. Knowledge-Based Systems, 196 , 105756. https://doi.org/10.1016/j.knosys.2020.105756

Guttman, R. H., Moukas, A. G., & Maes, P. (1998). Agent-mediated electronic commerce: A survey. Knowledge Engineering Review, 13 (2), 147–159. https://doi.org/10.1017/S0269888998002082

Ha, S. H., & Lee, J. H. (2009). Dynamic dissemination of personalized content on the web. Journal of Organizational Computing and Electronic Commerce, 19 (2), 96–111. https://doi.org/10.1080/10919390902821218

Hamad, H., Elbeltagi, I., & El-Gohary, H. (2018). An empirical investigation of business-to-business e-commerce adoption and its impact on SMEs competitive advantage: The case of Egyptian manufacturing SMEs. Strategic Change, 27 (3), 209–229. https://doi.org/10.1002/jsc.2196

Han, J., Zheng, L., Huang, H., Xu, Y., Yu, P. S., & Zuo, W. (2019). Deep Latent Factor Model with Hierarchical Similarity Measure for recommender systems. Information Sciences, 503 , 521–532. https://doi.org/10.1016/j.ins.2019.07.024

Han, J., Kamber, M., & Pei, J. (2001). Data mining: Concepts and technologies. Data Mining Concepts Models Methods & Algorithms, 5 (4), 1–18.

Hanani, U., Shapira, B., & Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11 (3), 203–259. https://doi.org/10.1023/A:1011196000674

Hansen, J. H. L., & Hasan, T. (2015). Speaker recognition by machines and humans: A tutorial review. IEEE Signal Processing Magazine, 32 (6), 74–99. https://doi.org/10.1109/MSP.2015.2462851

Hassan, N. R., & Loebbecke, C. (2017). Engaging scientometrics in information systems. Journal of Information Technology, 32 (1), 85–109.

He, J., Fang, X., Liu, H., & Li, X. (2019). Mobile app recommendation: An involvement-enhanced approach. MIS Quarterly, 43 (3), 827–850. https://doi.org/10.25300/MISQ/2019/15049

He, W., Zhang, Z., & Akula, V. (2018). Comparing consumer-produced product reviews across multiple websites with sentiment classification. Journal of Organizational Computing and Electronic Commerce, 28 (2), 142–156. https://doi.org/10.1080/10919392.2018.1444350

Herce-Zelaya, J., Porcel, C., Bernabé-Moreno, J., Tejeda-Lorente, A., & Herrera-Viedma, E. (2020). New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Information Sciences, 536 , 156–170. https://doi.org/10.1016/j.ins.2020.05.071

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22 (1), 5–53. https://doi.org/10.1145/963770.963772

Hernando, A., Bobadilla, J., Ortega, F., & Gutiérrez, A. (2017). A probabilistic model for recommending to new cold-start non-registered users. Information Sciences, 376 , 216–232. https://doi.org/10.1016/j.ins.2016.10.009

Hill, S., & Ready-Campbell, N. (2011). Expert Stock Picker: The Wisdom of (Experts in) Crowds. International Journal of Electronic Commerce, 15 (3), 73–102. https://doi.org/10.1093/JEC1086-4415150304

Himabindu, T. V. R., Padmanabhan, V., & Pujari, A. K. (2018). Conformal matrix factorization based recommender system. Information Sciences, 467 , 685–707. https://doi.org/10.1016/j.ins.2018.04.004

Hinojo-Lucena, F. J., Aznar-Díaz, I., Cáceres-Reche, M. P., & Romero-Rodríguez, J. M. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9 (1), 51. https://doi.org/10.3390/educsci9010051

Hirsch, J. E. (2010). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85 (3), 741–754. https://doi.org/10.1007/s11192-010-0193-9

Hirt, R., Kühl, N., & Satzger, G. (2019). Cognitive computing for customer profiling: Meta classification for gender prediction. Electronic Markets, 29 (1), 93–106. https://doi.org/10.1007/s12525-019-00336-z

Hogenboom, A., Ketter, W., van Dalen, J., Kaymak, U., Collins, J., & Gupta, A. (2015). Adaptive Tactical Pricing in Multi-Agent Supply Chain Markets Using Economic Regimes. Decision Sciences, 46 (4), 791–818. https://doi.org/10.1111/deci.12146

Holsapple, C. W., & Singh, M. (2000). Electronic commerce: From a definitional taxonomy toward a knowledge-management view. Journal of Organizational Computing and Electronic Commerce, 10 (3), 149–170. https://doi.org/10.1207/S15327744JOCE1003_01

Hong, W., Thong, J. Y. L., & Tam, K. Y. (2004). The effects of information format and shopping task on consumers’ online shopping behavior: A cognitive fit perspective. Journal of Management Information Systems, 21 (3), 149–184. https://doi.org/10.1080/07421222.2004.11045812

Hopkins, J., Kafali, Ö., Alrayes, B., & Stathis, K. (2019). Pirasa: Strategic protocol selection for e-commerce agents. Electronic Markets, 29 (2), 239–252. https://doi.org/10.1007/s12525-018-0307-4

Hu, Y.-C. (2014). Recommendation using neighborhood methods with preference-relation-based similarity. Information Sciences, 284 , 18–30. https://doi.org/10.1016/j.ins.2014.06.043

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49 (1), 30–50. https://doi.org/10.3239/s11747-020-00749-9

Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21 (2), 155–172. https://doi.org/10.1177/1094670517752459

Huang, M. H., & Rust, R. T. (2020). Engaged to a robot? The role of AI in service. Journal of Service Research , 24(1), 30–41. https://doi.org/10.1177/1094670520902266

Huang, Z., Zeng, D., & Chen, H. (2007). A comparison of collaborative-filtering algorithms for ecommerce. IEEE Intelligent Systems, 22 (5), 68–78. https://doi.org/10.1109/MIS.2007.4338497

Huysman, M. (2020). Information systems research on artificial intelligence and work: A commentary on “Robo-Apocalypse cancelled? Reframing the automation and future of work debate.” Journal of Information Technology, 35 (4), 307–309. https://doi.org/10.1177/0268396220926511

Iovine, A., Narducci, F., & Semeraro, G. (2020). Conversational Recommender Systems and natural language: A study through the ConveRSE framework. Decision Support Systems, 131 , 113250. https://doi.org/10.1016/j.dss.2020.113250

Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16 (3), 261–273. https://doi.org/10.1016/j.eij.2015.06.005

Ito, T., Hattori, H., & Shintani, T. (2002). A cooperative exchanging mechanism among seller agents for group-based sales. Electronic Commerce Research and Applications, 1 (2), 138–149. https://doi.org/10.1016/S1567-4223(02)00010-8

Iwański, J., Suchacka, G., & Chodak, G. (2018). Application of the Information Bottleneck method to discover user profiles in a Web store. Journal of Organizational Computing and Electronic Commerce, 28 (2), 98–121. https://doi.org/10.1080/10919392.2018.1444340

Jannach, D., Manzoor, A., Cai, W., & Chen, L. (2021). A Survey on Conversational Recommender Systems. ACM Computing Surveys (CSUR), 54 (5), 1–36.

Jeong, W. S., Han, S. G., & Jo, G. S. (2003). Intelligent Cyber Logistics Using Reverse Auction in Electronic Commerce. Journal of Organizational Computing and Electronic Commerce, 13 (3–4), 191–209. https://doi.org/10.1207/s15327744joce133&4_03

Jeyaraj, A., & Zadeh, A. H. (2020). Evolution of information systems research: Insights from topic modeling. Information & Management, 57 (4), 103207. https://doi.org/10.1016/j.im.2019.103207

Ji, K., & Shen, H. (2015). Addressing cold-start: Scalable recommendation with tags and keywords. Knowledge-Based Systems, 83 , 42–50. https://doi.org/10.1016/j.knosys.2015.03.008

Ji, S., & juan, Zhang, Q., Li, J., Chiu, D. K. W., Xu, S., Yi, L., & Gong, M. (2020). A burst-based unsupervised method for detecting review spammer groups. Information Sciences, 536 , 454–469. https://doi.org/10.1016/j.ins.2020.05.084

Jiang, G., Ma, F., Shang, J., & Chau, P. Y. K. (2014). Evolution of knowledge sharing behavior in social commerce: An agent-based computational approach. Information Sciences, 278 , 250–266. https://doi.org/10.1016/j.ins.2014.03.051

Jiang, Z., Mookerjee, V. S., & Sarkar, S. (2005). Lying on the web: Implications for expert systems redesign. Information Systems Research, 16 (2), 131–148. https://doi.org/10.1287/isre.1050.0046

Jøsang, A., Ismail, R., & Boyd, C. (2007). A survey of trust and reputation systems for online service provision. Decision Support Systems, 43 (2), 618–644. https://doi.org/10.1016/j.dss.2005.05.019

Julià, C., Sappa, A. D., Lumbreras, F., Serrat, J., & López, A. (2009). Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach. International Journal of Electronic Commerce, 14 (2), 89–108. https://doi.org/10.1093/JEC1086-4415140203

Kagan, S., & Bekkerman, R. (2018). Predicting Purchase Behavior of Website Audiences. International Journal of Electronic Commerce, 22 (4), 510–539. https://doi.org/10.0456/10864415.2018.1485084

Kaiser, C., Schlick, S., & Bodendorf, F. (2011). Warning system for online market research - Identifying critical situations in online opinion formation. Knowledge-Based Systems, 24 (6), 824–836. https://doi.org/10.1016/j.knosys.2011.03.004

Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: a manager’s guide . Addison-Wesley Professional.

Kandula, S., Krishnamoorthy, S., & Roy, D. (2021). A prescriptive analytics framework for efficient E-commerce order delivery. Decision Support Systems, 147 , 113584. https://doi.org/10.1016/j.dss.2021.113584

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62 (1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

Kauffman, R. J., Kim, K., Lee, S.-Y.T., Hoang, A.-P., & Ren, J. (2017). Combining machine-based and econometrics methods for policy analytics insights. Electronic Commerce Research and Applications, 25 , 115–140. https://doi.org/10.1016/j.elerap.2017.04.004

Kazienko, P., & Adamski, M. (2007). AdROSA-Adaptive personalization of web advertising. Information Sciences, 177 (11), 2269–2295. https://doi.org/10.1016/j.ins.2007.01.002

Keegan, S., O’Hare, G. M. P., & O’Grady, M. J. (2008). Easishop: Ambient intelligence assists everyday shopping. Information Sciences, 178 (3), 588–611. https://doi.org/10.1016/j.ins.2007.08.027

Ketter, W., Collins, J., Gini, M., Gupta, A., & Schrater, P. (2012). Real-Time tactical and strategic sales management for intelligent agents guided by economic regimes. Information Systems Research, 23 (4), 1263–1283. https://doi.org/10.1287/isre.1110.0415

Khare, V. R., & Chougule, R. (2012). Decision support for improved service effectiveness using domain aware text mining. Knowledge-Based Systems, 33 , 29–40. https://doi.org/10.1016/j.knosys.2012.03.005

Khopkar, S. S., & Nikolaev, A. G. (2017). Predicting long-term product ratings based on few early ratings and user base analysis. Electronic Commerce Research and Applications, 21 , 38–49. https://doi.org/10.1016/j.elerap.2016.12.002

Kiekintveld, C., Miller, J., Jordan, P. R., Callender, L. F., & Wellman, M. P. (2009). Forecasting market prices in a supply chain game. Electronic Commerce Research and Applications, 8 (2), 63–77. https://doi.org/10.1016/j.elerap.2008.11.005

Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58 (3), 263–267. https://doi.org/10.2501/JAR-2018-035

Kim, D. J., Song, Y. I., Braynov, S. B., & Rao, H. R. (2005a). A multidimensional trust formation model in B-to-C e-commerce: A conceptual framework and content analyses of academia/practitioner perspectives. Decision Support Systems, 40 (2), 143–165. https://doi.org/10.1016/j.dss.2004.01.006

Kim, D., Park, C., Oh, J., & Yu, H. (2017). Deep hybrid recommender systems via exploiting document context and statistics of items. Information Sciences, 417 , 72–87. https://doi.org/10.1016/j.ins.2017.06.026

Kim, J. W., Lee, B. H., Shaw, M. J., Chang, H. L., & Nelson, M. (2001). Application of decision-tree induction techniques to personalized advertisements on internet storefronts. International Journal of Electronic Commerce, 5 (3), 45–62. https://doi.org/10.1080/10864415.2001.11044215

Kim, K., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications, 34 (2), 1200–1209. https://doi.org/10.1016/j.eswa.2006.12.025

Kim, W., Kerschberg, L., & Scime, A. (2002). Learning for automatic personalization in a semantic taxonomy-based meta-search agent. Electronic Commerce Research and Applications, 1 (2), 150–173. https://doi.org/10.1016/S1567-4223(02)00011-X

Kim, Y. S., Yum, B. J., Song, J., & Kim, S. M. (2005b). Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert Systems with Applications, 28 (2), 381–393. https://doi.org/10.1016/j.eswa.2004.10.017

Klaus, T., & Changchit, C. (2019). Toward an Understanding of Consumer Attitudes on Online Review Usage. Journal of Computer Information Systems, 59 (3), 277–286. https://doi.org/10.1080/08874417.2017.1348916

Knorr, E. M., Ng, R. T., & Tucakov, V. (2000). Distance-based outliers: Algorithms and applications. The VLDB Journal, 8 (3), 237–253. https://doi.org/10.1007/s007780050006

Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2009). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18 (1), 140–181. https://doi.org/10.1007/s10618-008-0114-1

Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). Applying Collaborative Filtering to Usenet News. Communications of the ACM, 40 (3), 77–87. https://doi.org/10.1145/245108.245126

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42 (8), 30–37. https://doi.org/10.1109/MC.2009.263

Kühl, N., Mühlthaler, M., & Goutier, M. (2020). Supporting customer-oriented marketing with artificial intelligence: Automatically quantifying customer needs from social media. Electronic Markets, 30 (2), 351–367. https://doi.org/10.1007/s12525-019-00351-0

Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2018). Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning. Journal of Management Information Systems, 35 (1), 350–380. https://doi.org/10.1080/07421222.2018.1440758

Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2019a). Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models. Journal of Management Information Systems, 36 (4), 1313–1346. https://doi.org/10.1080/07421222.2019.1661089

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019b). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61 (4), 135–155. https://doi.org/10.1177/0008125619859317

Kuo, R. J., Chang, K., & Chien, S. Y. (2004). Integration of Self-Organizing Feature Maps and Genetic-Algorithm-Based Clustering Method for Market Segmentation. Journal of Organizational Computing and Electronic Commerce, 14 (1), 43–60. https://doi.org/10.1207/s15327744joce1401_3

Kuo, R. J., Liao, J. L., & Tu, C. (2005). Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce. Decision Support Systems, 40 (2), 355–374. https://doi.org/10.1016/j.dss.2004.04.010

Kwon, O., Yoo, K., & Suh, E. (2006). ubiES: Applying ubiquitous computing technologies to an expert system for context-aware proactive services. Electronic Commerce Research and Applications, 5 (3), 209–219. https://doi.org/10.1016/j.elerap.2005.10.011

Laorden, C., Santos, I., Sanz, B., Alvarez, G., & Bringas, P. G. (2012). Word sense disambiguation for spam filtering. Electronic Commerce Research and Applications, 11 (3), 290–298. https://doi.org/10.1016/j.elerap.2011.11.004

Lau, R. Y. K. (2007). Towards a web services and intelligent agents-based negotiation system for B2B eCommerce. Electronic Commerce Research and Applications, 6 (3), 260–273. https://doi.org/10.1016/j.elerap.2006.06.007

Law, R., Leung, R., & Buhalis, D. (2009). Information technology applications in hospitality and tourism: A review of publications from 2005 to 2007. Journal of Travel and Tourism Marketing, 26 (5–6), 599–623. https://doi.org/10.1080/10548400903163160

Lawrence, R. D., Almasi, G. S., Kotlyar, V., Viveros, M. S., & Duri, S. S. (2001). Personalization of supermarket product recommendations. In Data Mining and Knowledge Discovery (Vol. 5, Issues 1–2, pp. 11–32). Springer. https://doi.org/10.1023/A:1009835726774

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521 (7553), 436–444.

Lee, D., & Hosanagar, K. (2021). How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Science, 67 (1), 524–546. https://doi.org/10.1287/mnsc.2019.3546

Lee, D., Gopal, A., & Park, S. H. (2020). Different but equal? a field experiment on the impact of recommendation systems on mobile and personal computer channels in retail. Information Systems Research, 31 (3), 892–912. https://doi.org/10.1287/ISRE.2020.0922

Lee, H.-C., Rim, H.-C., & Lee, D.-G. (2019). Learning to rank products based on online product reviews using a hierarchical deep neural network. Electronic Commerce Research and Applications, 36 , 100874. https://doi.org/10.1016/j.elerap.2019.100874

Lee, J., Podlaseck, M., Schonberg, E., & Hoch, R. (2001). Visualization and analysis of clickstream data of online stores for understanding web merchandising. Data Mining and Knowledge Discovery, 5 (1–2), 59–84. https://doi.org/10.1023/A:1009843912662

Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180 (11), 2142–2155. https://doi.org/10.1016/j.ins.2010.02.004

Lee, S., & Kim, W. (2017). Sentiment labeling for extending initial labeled data to improve semi-supervised sentiment classification. Electronic Commerce Research and Applications, 26 , 35–49. https://doi.org/10.1016/j.elerap.2017.09.006

Lee, Y. H., Hu, P. J. H., Cheng, T. H., & Hsieh, Y. F. (2012). A cost-sensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce. Decision Support Systems, 53 (1), 245–256. https://doi.org/10.1016/j.dss.2012.01.018

Leloup, B. (2003). Pricing with local interactions on agent-based electronic marketplaces. Electronic Commerce Research and Applications, 2 (2), 187–198. https://doi.org/10.1016/S1567-4223(03)00023-1

Lessmann, S., Haupt, J., Coussement, K., & De Bock, K. W. (2019). Targeting customers for profit: An ensemble learning framework to support marketing decision-making. Information Sciences . https://doi.org/10.1016/j.ins.2019.05.027

Li, H., Su, S. Y. W., & Lam, H. (2006). On automated e-business negotiations: Goal, policy, strategy, and plans of decision and action. Journal of Organizational Computing and Electronic Commerce, 16 (1), 1–29. https://doi.org/10.1080/10919390609540288

Li, J., Chen, C., Chen, H., & Tong, C. (2017). Towards Context-aware Social Recommendation via Individual Trust. Knowledge-Based Systems, 127 , 58–66. https://doi.org/10.1016/j.knosys.2017.02.032

Li, S., Zhang, Y., Yu, Z., Zhang, F., & Lu, H. (2019a). Predicting the influence of viral message for VM campaign on Weibo. Electronic Commerce Research and Applications, 36 , 100875. https://doi.org/10.1016/j.elerap.2019.100875

Li, X., Wu, C., & Mai, F. (2019b). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56 (2), 172–184. https://doi.org/10.1016/j.im.2018.04.007

Li, Y.-M., Chou, C.-L., & Lin, L.-F. (2014). A social recommender mechanism for location-based group commerce. Information Sciences, 274 , 125–142. https://doi.org/10.1016/j.ins.2014.02.079

Li, Y. M., Wu, C. T., & Lai, C. Y. (2013). A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 55 (3), 740–752. https://doi.org/10.1016/j.dss.2013.02.009

Li, Y., Wang, S., Pan, Q., Peng, H., Yang, T., & Cambria, E. (2019c). Learning binary codes with neural collaborative filtering for efficient recommendation systems. Knowledge-Based Systems, 172 , 64–75. https://doi.org/10.1016/j.knosys.2019.02.012

Li, Yu., Lu, L., & Xuefeng, L. (2005). A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Systems with Applications, 28 (1), 67–77. https://doi.org/10.1016/j.eswa.2004.08.013

Liang, R., Wang, J., & qiang. (2019). A Linguistic Intuitionistic Cloud Decision Support Model with Sentiment Analysis for Product Selection in E-commerce. International Journal of Fuzzy Systems, 21 (3), 963–977. https://doi.org/10.1007/s40815-019-00606-0

Liebman, E., Saar-Tsechansky, M., & Stone, P. (2019). The right music at the right time: Adaptive personalized playlists based on sequence modeling. MIS Quarterly, 43 (3), 765–786. https://doi.org/10.25300/MISQ/2019/14750

Lin, Q.-Y., Chen, Y.-L., Chen, J.-S., & Chen, Y.-C. (2003). Mining inter-organizational retailing knowledge for an alliance formed by competitive firms. Information & Management, 40 (5), 431–442. https://doi.org/10.1016/S0378-7206(02)00062-9

Lin, W., Alvarez, S. A., & Ruiz, C. (2002). Efficient Adaptive-Support Association Rule Mining for Recommender Systems. Data Mining and Knowledge Discovery, 6 (1), 83–105. https://doi.org/10.1023/A:1013284820704

Lin, W. H., Wang, P., & Tsai, C. F. (2016). Face recognition using support vector model classifier for user authentication. Electronic Commerce Research and Applications, 18 , 71–82. https://doi.org/10.1016/j.elerap.2016.01.005

Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7 (1), 76–80. https://doi.org/10.1109/MIC.2003.1167344

Liu, B., Hu, M., & Cheng, J. (2005). Opinion observer. Proceedings of the 14th International Conference on World Wide Web , 342. https://doi.org/10.1145/1060745.1060797

Liu, D.-R., Chen, K.-Y., Chou, Y.-C., & Lee, J.-H. (2018). Online recommendations based on dynamic adjustment of recommendation lists. Knowledge-Based Systems, 161 , 375–389. https://doi.org/10.1016/j.knosys.2018.07.038

Liu, H., Jiang, Z., Song, Y., Zhang, T., & Wu, Z. (2019). User preference modeling based on meta paths and diversity regularization in heterogeneous information networks. Knowledge-Based Systems, 181 , 104784. https://doi.org/10.1016/j.knosys.2019.05.027

Liu, K., Zeng, X., Bruniaux, P., Wang, J., Kamalha, E., & Tao, X. (2017). Fit evaluation of virtual garment try-on by learning from digital pressure data. Knowledge-Based Systems, 133 , 174–182. https://doi.org/10.1016/j.knosys.2017.07.007

Liu, N., & Shen, B. (2020). Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems, 188 , 105010. https://doi.org/10.1016/j.knosys.2019.105010

Liu, R., Mai, F., Shan, Z., & Wu, Y. (2020). Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach. Information & Management, 57 (8), 103387. https://doi.org/10.1016/j.im.2020.103387

Liu, X., Datta, A., & Rzadca, K. (2013). Trust beyond reputation: A computational trust model based on stereotypes. Electronic Commerce Research and Applications, 12 (1), 24–39. https://doi.org/10.1016/j.elerap.2012.07.001

Lowry, P. B., Moody, G. D., Gaskin, J., Galletta, D. F., Humpherys, S. L., Barlow, J. B., & Wilson, D. W. (2013). Evaluating journal quality and the association for information systems senior scholars’ journal basket via bibliometric measures: Do expert journal assessments add value? MIS Quarterly, 37 (4), 993–1012. https://doi.org/10.25300/MISQ/2013/37.4.01

Lowry, P., Romans, D., & Curtis, A. (2004). Global Journal Prestige and Supporting Disciplines: A Scientometric Study of Information Systems Journals. Journal of the Association for Information Systems, 5 (2), 29–77. https://doi.org/10.17705/1jais.00045

Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74 , 12–32. https://doi.org/10.1016/j.dss.2015.03.008

Luo, X., Lu, X., & Li, J. (2019). When and How to Leverage E-commerce Cart Targeting: The relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization. Information Systems Research, 30 (4), 1203–1227. https://doi.org/10.1287/isre.2019.0859

Lyytinen, K., Nickerson, J. V, & King, J. L. (2020). Metahuman systems = humans + machines that learn. Journal of Information Technology , 36 (4), 427–445. https://doi.org/10.1177/0268396220915917

Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q., & Niu, X. (2018). Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications, 31 , 24–39. https://doi.org/10.1016/j.elerap.2018.08.002

Ma, Z., Pant, G., & Sheng, O. R. L. (2011). Mining competitor relationships from online news: A network-based approach. Electronic Commerce Research and Applications, 10 (4), 418–427. https://doi.org/10.1016/j.elerap.2010.11.006

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90 , 46–60. https://doi.org/10.1016/j.futures.2017.03.006

Manahov, V., & Zhang, H. (2019). Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming. International Journal of Electronic Commerce, 23 (1), 12–32. https://doi.org/10.1080/10864415.2018.1512271

Manthiou, A., Klaus, P., Kuppelwieser, V. G., & Reeves, W. (2021). Man vs machine: Examining the three themes of service robotics in tourism and hospitality. Electronic Markets, 31 (3), 511–527. https://doi.org/10.1007/s12525-020-00434-3

Manvi, S. S., & Venkataram, P. (2005). An intelligent product-information presentation in E-commerce. Electronic Commerce Research and Applications, 4 (3), 220–239. https://doi.org/10.1016/j.elerap.2005.01.001

Mao, M., Lu, J., Han, J., & Zhang, G. (2019). Multiobjective e-commerce recommendations based on hypergraph ranking. Information Sciences, 471 , 269–287. https://doi.org/10.1016/j.ins.2018.07.029

Maqsood, H., Mehmood, I., Maqsood, M., Yasir, M., Afzal, S., Aadil, F., Selim, M. M., & Muhammad, K. (2020). A local and global event sentiment based efficient stock exchange forecasting using deep learning. International Journal of Information Management, 50 , 432–451. https://doi.org/10.1016/j.ijinfomgt.2019.07.011

Marabelli, M., Newell, S., & Handunge, V. (2021). The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges. The Journal of Strategic Information Systems, 30 (3), 101683. https://doi.org/10.1016/j.jsis.2021.101683

Martens, D., & Provost, F. (2014). Explaining data-driven document classifications. MIS Quarterly, 38 (1), 73–99. https://doi.org/10.25300/MISQ/2014/38.1.04

Martinez-Cruz, C., Porcel, C., Bernabé-Moreno, J., & Herrera-Viedma, E. (2015). A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311 , 102–118. https://doi.org/10.1016/j.ins.2015.03.013

Marx, W., Bornmann, L., Barth, A., & Leydesdorff, L. (2014). Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS). Journal of the Association for Information Science and Technology, 65 (4), 751–764. https://doi.org/10.1002/asi.23089

McAuley, J., Targett, C., Shi, Q., & Van Den Hengel, A. (2015). Image-based recommendations on styles and substitutes. SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval , 43–52. https://doi.org/10.1145/2766462.2767755

Milian, E. Z., de Spinola, M., & M., & Carvalho, M. M. d. (2019). Fintechs: A literature review and research agenda. Electronic Commerce Research and Applications, 34 , 100833. https://doi.org/10.1016/j.elerap.2019.100833

Miralles-Pechuán, L., Ponce, H., & Martínez-Villaseñor, L. (2018). A novel methodology for optimizing display advertising campaigns using genetic algorithms. Electronic Commerce Research and Applications, 27 , 39–51. https://doi.org/10.1016/j.elerap.2017.11.004

Mo, J., Sarkar, S., & Menon, S. (2018). Know when to run: Recommendations in crowdsourcing contests. MIS Quarterly, 42 (3), 919–943. https://doi.org/10.25300/MISQ/2018/14103

Mokryn, O., Bogina, V., & Kuflik, T. (2019). Will this session end with a purchase? Inferring current purchase intent of anonymous visitors. Electronic Commerce Research and Applications, 34 , 100836. https://doi.org/10.1016/j.elerap.2019.100836

Motiwalla, L. F., & Nunamaker, J. F. (1992). Mail-man: A knowledge-based mail assistant for managers. Journal of Organizational Computing, 2 (2), 131–154. https://doi.org/10.1080/10919399209540179

Moussawi, S., Koufaris, M., & Benbunan-Fich, R. (2020). How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electronic Markets . https://doi.org/10.1007/s12525-020-00411-w

Nassiri-Mofakham, F., Nematbakhsh, M. A., Baraani-Dastjerdi, A., & Ghasem-Aghaee, N. (2009). Electronic promotion to new customers using mkNN learning. Information Sciences, 179 (3), 248–266. https://doi.org/10.1016/j.ins.2008.09.019

Neuhofer, B., Magnus, B., & Celuch, K. (2021). The impact of artificial intelligence on event experiences: A scenario technique approach. Electronic Markets, 31 (3), 601–617. https://doi.org/10.1007/s12525-020-00433-4

Ngai, E. W. T., & Wat, F. K. T. (2002). A literature review and classification of electronic commerce research. Information and Management, 39 (5), 415–429. https://doi.org/10.1016/S0378-7206(01)00107-0

Ngai, E. W. T., Lee, M. C. M., Luo, M., Chan, P. S. L., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50 , 101098. https://doi.org/10.1016/j.elerap.2021.101098

Nikolay, A., Anindya, G., & Panagiotis, G. I. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57 (8), 1485–1509. https://doi.org/10.1287/mnsc.1110.1370

Nilashi, M., & bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 14 (6), 542–562. https://doi.org/10.1016/j.elerap.2015.08.004

Nishimura, N., Sukegawa, N., Takano, Y., & Iwanaga, J. (2018). A latent-class model for estimating product-choice probabilities from clickstream data. Information Sciences, 429 , 406–420. https://doi.org/10.1016/j.ins.2017.11.014

Núñez-Valdez, E. R., Quintana, D., González Crespo, R., Isasi, P., & Herrera-Viedma, E. (2018). A recommender system based on implicit feedback for selective dissemination of ebooks. Information Sciences, 467 , 87–98. https://doi.org/10.1016/j.ins.2018.07.068

O’Donovan, J., & Smyth, B. (2005). Trust in recommender systems. International Conference on Intelligent User Interfaces, Proceedings IUI , 167–174. https://doi.org/10.1145/1040830.1040870

O’Neil, S., Zhao, X., Sun, D., & Wei, J. C. (2016). Newsvendor Problems with Demand Shocks and Unknown Demand Distributions. Decision Sciences, 47 (1), 125–156. https://doi.org/10.1111/deci.12187

Oliver, J. R. (1996). A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce. Journal of Management Information Systems, 13 (3), 83–112. https://doi.org/10.1080/07421222.1996.11518135

Ortega, F., Hernando, A., Bobadilla, J., & Kang, J. H. (2016). Recommending items to group of users using Matrix Factorization based Collaborative Filtering. Information Sciences, 345 , 313–324. https://doi.org/10.1016/j.ins.2016.01.083

Ortega, F., Sánchez, J. L., Bobadilla, J., & Gutiérrez, A. (2013). Improving collaborative filtering-based recommender systems results using Pareto dominance. Information Sciences, 239 , 50–61. https://doi.org/10.1016/j.ins.2013.03.011

Ou, W., Huynh, V.-N., & Sriboonchitta, S. (2018). Training attractive attribute classifiers based on opinion features extracted from review data. Electronic Commerce Research and Applications, 32 , 13–22. https://doi.org/10.1016/j.elerap.2018.10.003

Padmanabhan, B., & Tuzhilin, A. (2003). On the use of optimization for data mining: Theoretical interactions and eCRM opportunities. Management Science, 49 (10), 1327–1343. https://doi.org/10.1287/mnsc.49.10.1327.17310

Pang, G., Wang, X., Hao, F., Xie, J., Wang, X., Lin, Y., & Qin, X. (2019). ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines. Knowledge-Based Systems, 181 , 104786. https://doi.org/10.1016/j.knosys.2019.05.029

Pantano, E., & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, 55 , 102096. https://doi.org/10.1016/j.jretconser.2020.102096

Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information and Management, 52 (2), 183–199. https://doi.org/10.1016/j.im.2014.08.008

Park, C., Kim, D., Yang, M. C., Lee, J. T., & Yu, H. (2020a). Click-aware purchase prediction with push at the top. Information Sciences, 521 , 350–364. https://doi.org/10.1016/j.ins.2020.02.062

Park, C., Kim, D., & Yu, H. (2019). An encoder–decoder switch network for purchase prediction. Knowledge-Based Systems, 185 , 104932. https://doi.org/10.1016/j.knosys.2019.104932

Park, H., Song, M., & Shin, K.-S. (2020b). Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories. Knowledge-Based Systems, 187 , 104825. https://doi.org/10.1016/j.knosys.2019.06.033

Park, J. H., & Park, S. C. (2003). Agent-based merchandise management in business-to-business electronic commerce. Decision Support Systems, 35 (3), 311–333. https://doi.org/10.1016/S0167-9236(02)00111-2

Parvin, H., Moradi, P., Esmaeili, S., & Qader, N. N. (2019). A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method. Knowledge-Based Systems, 166 , 92–107. https://doi.org/10.1016/j.knosys.2018.12.016

Patcha, A., & Park, J.-M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51 (12), 3448–3470. https://doi.org/10.1016/j.comnet.2007.02.001

Patra, B. K., Launonen, R., Ollikainen, V., & Nandi, S. (2015). A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Systems, 82 , 163–177. https://doi.org/10.1016/j.knosys.2015.03.001

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12 , 2825–2830.

Pendharkar, P. C. (2006). Inductive Regression Tree and Genetic Programming Techniques for Learning User Web Search Preferences. Journal of Organizational Computing and Electronic Commerce, 16 (3–4), 223–245. https://doi.org/10.1080/10919392.2006.9681201

Pengnate (Fone), S., & Riggins, F. J. (2020). The role of emotion in P2P microfinance funding: A sentiment analysis approach. International Journal of Information Management, 54 , 102138. https://doi.org/10.1016/j.ijinfomgt.2020.102138

Pfeiffer, J., Pfeiffer, T., Meißner, M., & Weiß, E. (2020). Eye-tracking-based classification of information search behavior using machine learning: Evidence from experiments in physical shops and virtual reality shopping environments. Information Systems Research, 31 (3), 675–691. https://doi.org/10.1287/ISRE.2019.0907

Pontelli, E., & Son, T. C. (2003). Designing intelligent agents to support universal accessibility of E-commerce services. Electronic Commerce Research and Applications, 2 (2), 147–161. https://doi.org/10.1016/S1567-4223(03)00012-7

Pourgholamali, F., Kahani, M., & Bagheri, E. (2020). A neural graph embedding approach for selecting review sentences. Electronic Commerce Research and Applications, 40 , 100917. https://doi.org/10.1016/j.elerap.2019.100917

Pourkhani, A., Abdipour, K., Baher, B., & Moslehpour, M. (2019). The impact of social media in business growth and performance: A scientometrics analysis. International Journal of Data and Network Science, 3 (3), 223–244. https://doi.org/10.5267/j.ijdns.2019.2.003

Praet, S., & Martens, D. (2020). Efficient Parcel Delivery by Predicting Customers’ Locations*. Decision Sciences, 51 (5), 1202–1231. https://doi.org/10.1111/deci.12376

Pranata, I., & Susilo, W. (2016). Are the most popular users always trustworthy? The case of Yelp. Electronic Commerce Research and Applications, 20 , 30–41. https://doi.org/10.1016/j.elerap.2016.09.005

Preibusch, S., Peetz, T., Acar, G., & Berendt, B. (2016). Shopping for privacy: Purchase details leaked to PayPal. Electronic Commerce Research and Applications, 15 , 52–64. https://doi.org/10.1016/j.elerap.2015.11.004

Pröllochs, N., Feuerriegel, S., Lutz, B., & Neumann, D. (2020). Negation scope detection for sentiment analysis: A reinforcement learning framework for replicating human interpretations. Information Sciences, 536 , 205–221. https://doi.org/10.1016/j.ins.2020.05.022

Pu, P., & Chen, L. (2007). Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems, 20 (6), 542–556. https://doi.org/10.1016/j.knosys.2007.04.004

Pujahari, A., & Sisodia, D. S. (2019). Modeling Side Information in Preference Relation based Restricted Boltzmann Machine for recommender systems. Information Sciences, 490 , 126–145. https://doi.org/10.1016/j.ins.2019.03.064

Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from online reviews: A product improvement perspective. Information and Management, 53 (8), 951–963. https://doi.org/10.1016/j.im.2016.06.002

Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451–452 , 295–309. https://doi.org/10.1016/j.ins.2018.04.009

Rahm, E., & Bernstein, P. A. (2001). A survey of approaches to automatic schema matching. VLDB Journal, 10 (4), 334–350. https://doi.org/10.1007/s007780100057

Ranjbar Kermany, N., & Alizadeh, S. H. (2017). A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electronic Commerce Research and Applications, 21 , 50–64. https://doi.org/10.1016/j.elerap.2016.12.005

Rao, Y., Xie, H., Li, J., Jin, F., Wang, F. L., & Li, Q. (2016). Social emotion classification of short text via topic-level maximum entropy model. Information and Management, 53 (8), 978–986. https://doi.org/10.1016/j.im.2016.04.005

Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89 , 14–46. https://doi.org/10.1016/j.knosys.2015.06.015

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994 , 175–186. https://doi.org/10.1145/192844.192905

Resnick, P., & Varian, H. R. (1997). Recommender Systems. Communications of the ACM, 40 (3), 56–58. https://doi.org/10.1145/245108.245121

Rhaiem, M., & Bornmann, L. (2018). Reference Publication Year Spectroscopy (RPYS) with publications in the area of academic efficiency studies: What are the historical roots of this research topic? Applied Economics, 50 (13), 1442–1453. https://doi.org/10.1080/00036846.2017.1363865

Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). Springer.

Ryoba, M. J., Qu, S., & Zhou, Y. (2021). Feature subset selection for predicting the success of crowdfunding project campaigns. Electronic Markets, 31 (3), 671–684. https://doi.org/10.1007/s12525-020-00398-4

Sabater, J., & Sierra, C. (2005). Review on computational trust and reputation models. Artificial Intelligence Review, 24 (1), 33–60. https://doi.org/10.1007/s10462-004-0041-5

Salakhutdinov, R., Mnih, A., & Hinton, G. (2007). Restricted Boltzmann machines for collaborative filtering. ACM International Conference Proceeding Series, 227 , 791–798. https://doi.org/10.1145/1273496.1273596

Saleh, A. I., El Desouky, A. I., & Ali, S. H. (2015). Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowledge-Based Systems, 75 , 192–223. https://doi.org/10.1016/j.knosys.2014.12.002

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, WWW 2001 , 285–295. https://doi.org/10.1145/371920.372071

Saumya, S., Singh, J. P., Baabdullah, A. M., Rana, N. P., & Dwivedi, Y. K. (2018). Ranking online consumer reviews. Electronic Commerce Research and Applications, 29 , 78–89. https://doi.org/10.1016/j.elerap.2018.03.008

Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5 (1), 115–153. https://doi.org/10.1023/A:1009804230409

Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. Neural Networks, 61 , 85–117. https://doi.org/10.1016/j.neunet.2014.09.003

Shani, G., Heckerman, D., & Brafman, R. I. (2005). An MDP-based recommender system. Journal of Machine Learning Research, 6 (Sep), 1265–1295.

Shardanand, U., & Maes, P. (1995). Social information filtering. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 210–217. https://doi.org/10.1145/223904.223931

Shi, Y., Wang, T., & Alwan, L. C. (2020). Analytics for Cross-Border E-Commerce: Inventory Risk Management of an Online Fashion Retailer. Decision Sciences, 51 (6), 1347–1376. https://doi.org/10.1111/deci.12429

Si, Y., Zhang, F., & Liu, W. (2017). CTF-ARA: An adaptive method for POI recommendation based on check-in and temporal features. Knowledge-Based Systems, 128 , 59–70. https://doi.org/10.1016/j.knosys.2017.04.013

Singh, A., & Tucker, C. S. (2017). A machine learning approach to product review disambiguation based on function, form and behavior classification. Decision Support Systems, 97 , 81–91. https://doi.org/10.1016/j.dss.2017.03.007

Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47 , 101324. https://doi.org/10.1016/j.tele.2019.101324

Song, S., Hwang, K., Zhou, R., & Kwok, Y. K. (2005). Trusted P2P transactions with fuzzy reputation aggregation. IEEE Internet Computing, 9 (6), 24–34. https://doi.org/10.1109/MIC.2005.136

Stöckli, D. R., & Khobzi, H. (2021). Recommendation systems and convergence of online reviews: The type of product network matters! Decision Support Systems, 142 , 113475. https://doi.org/10.1016/j.dss.2020.113475

Stoeckli, E., Dremel, C., Uebernickel, F., & Brenner, W. (2020). How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electronic Markets, 30 (2), 369–403. https://doi.org/10.1007/s12525-019-00359-6

Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009 , 1–19. https://doi.org/10.1155/2009/421425

Suchacka, G., & Iwański, J. (2020). Identifying legitimate Web users and bots with different traffic profiles — an Information Bottleneck approach. Knowledge-Based Systems, 197 , 105875. https://doi.org/10.1016/j.knosys.2020.105875

Sul, H. K., Dennis, A. R., & Yuan, L. I. (2017). Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns. Decision Sciences, 48 (3), 454–488. https://doi.org/10.1111/deci.12229

Sun, Y., Liu, X., Chen, G., Hao, Y., & Zhang (Justin), Z. (2020). How mood affects the stock market: Empirical evidence from microblogs. Information & Management, 57 (5), 103181. https://doi.org/10.1016/j.im.2019.103181

Sung (Christine), E., Bae, S., Han, D.-I.D., & Kwon, O. (2021). Consumer engagement via interactive artificial intelligence and mixed reality. International Journal of Information Management, 60 , 102382. https://doi.org/10.1016/j.ijinfomgt.2021.102382

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 , 1–9. https://doi.org/10.1109/CVPR.2015.7298594

Takeuchi, H., Subramaniam, L. V., Nasukawa, T., & Roy, S. (2009). Getting insights from the voices of customers: Conversation mining at a contact center. Information Sciences, 179 (11), 1584–1591. https://doi.org/10.1016/j.ins.2008.11.026

Tan, F. T. C., Pan, S. L., & Zuo, M. (2019). Realising platform operational agility through information technology–enabled capabilities: A resource-interdependence perspective. Information Systems Journal, 29 (3), 582–608. https://doi.org/10.0487/isj.12221

Tan, J., Tyler, K., & Manica, A. (2007). Business-to-business adoption of eCommerce in China. Information & Management, 44 (3), 332–351. https://doi.org/10.1016/j.im.2007.04.001

Tan, P.-N., & Kumar, V. (2002). Discovery of Web Robot Sessions Based on their Navigational Patterns. Data Mining and Knowledge Discovery, 6 (1), 9–35. https://doi.org/10.1023/A:1013228602957

Tan, Y.-H., & Thoen, W. (2000). INCAS: A legal expert system for contract terms in electronic commerce. Decision Support Systems, 29 (4), 389–411. https://doi.org/10.1016/S0167-9236(00)00085-3

Tang, P., Qiu, W., Huang, Z., Chen, S., Yan, M., Lian, H., & Li, Z. (2020). Anomaly detection in electronic invoice systems based on machine learning. Information Sciences, 535 , 172–186. https://doi.org/10.1016/j.ins.2020.03.089

Templier, M., & Paré, G. (2015). A framework for guiding and evaluating literature reviews. Communications of the Association for Information Systems, 37 (1), 112–137. https://doi.org/10.17705/1CAIS.03706

Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31 (2). https://doi.org/10.1007/s12525-020-00441-4

Tian, F., Wu, F., Chao, K. M., Zheng, Q., Shah, N., Lan, T., & Yue, J. (2016). A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews. Electronic Commerce Research and Applications, 16 , 66–76. https://doi.org/10.1016/j.elerap.2015.10.003

Tran, B., Vu, G., Ha, G., Vuong, Q.-H., Ho, M.-T., Vuong, T.-T., La, V.-P., Ho, M.-T., Nghiem, K.-C., Nguyen, H., Latkin, C., Tam, W., Cheung, N.-M., Nguyen, H.-K., Ho, C., & Ho, R. (2019). Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. Journal of Clinical Medicine, 8 (3), 360. https://doi.org/10.3390/jcm8030360

Tseng, K.-K., Lin, R.F.-Y., Zhou, H., Kurniajaya, K. J., & Li, Q. (2018). Price prediction of e-commerce products through Internet sentiment analysis. Electronic Commerce Research, 18 (1), 65–88. https://doi.org/10.1007/s10660-017-9272-9

Vanneschi, L., Horn, D. M., Castelli, M., & Popovič, A. (2018). An artificial intelligence system for predicting customer default in e-commerce. Expert Systems with Applications, 104 , 1–21. https://doi.org/10.1016/j.eswa.2018.03.025

Varshney, D., Kumar, S., & Gupta, V. (2017). Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems, 133 , 66–76. https://doi.org/10.1016/j.knosys.2017.07.003

Viejo, A., Sánchez, D., & Castellà-Roca, J. (2012). Preventing automatic user profiling in Web 2.0 applications. Knowledge-Based Systems, 36 , 191–205. https://doi.org/10.1016/j.knosys.2012.07.001

Villegas, N. M., Sánchez, C., Díaz-Cely, J., & Tamura, G. (2018). Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems, 140 , 173–200. https://doi.org/10.1016/j.knosys.2017.11.003

Viswanathan, S., Guillot, F., & Grasso, A. M. (2020). What is natural?: Challenges and opportunities for conversational recommender systems. ACM International Conference Proceeding Series , 1–4. https://doi.org/10.1145/3405755.3406174

Vizine Pereira, A. L., & Hruschka, E. R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, 82 , 11–19. https://doi.org/10.1016/j.knosys.2015.02.016

Vozalis, M. G., & Margaritis, K. G. (2007). Using SVD and demographic data for the enhancement of generalized Collaborative Filtering. Information Sciences, 177 (15), 3017–3037. https://doi.org/10.1016/j.ins.2007.02.036

Wang, F.-H. (2008). On discovery of soft associations with “most” fuzzy quantifier for item promotion applications. Information Sciences, 178 (7), 1848–1876. https://doi.org/10.1016/j.ins.2007.11.018

Wang, G., Ma, J., Huang, L., & Xu, K. (2012). Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems, 26 , 61–68. https://doi.org/10.1016/j.knosys.2011.06.020

Wang, H.-C., Jhou, H.-T., & Tsai, Y.-S. (2018a). Adapting topic map and social influence to the personalized hybrid recommender system. Information Sciences . https://doi.org/10.1016/j.ins.2018.04.015

Wang, H. C., & Doong, H. S. (2010). Argument form and spokesperson type: The recommendation strategy of virtual salespersons. International Journal of Information Management, 30 (6), 493–501. https://doi.org/10.1016/j.ijinfomgt.2010.03.006

Wang, H., Wang, N., & Yeung, D. Y. (2015). Collaborative deep learning for recommender systems. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2015 - Augus , 1235–1244. https://doi.org/10.1145/2783258.2783273

Wang, Q., Li, B., & Singh, P. V. (2018). Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis. Information Systems Research, 29 (2), 273–291. https://doi.org/10.1287/isre.2017.0735

Wang, W., Feng, Y., & Dai, W. (2018c). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29 , 142–156. https://doi.org/10.1016/j.elerap.2018.04.003

Wang, Y., Lu, X., & Tan, Y. (2018d). Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines. Electronic Commerce Research and Applications, 29 , 1–11. https://doi.org/10.1016/j.elerap.2018.03.003

Wareham, J., Zheng, J. G., & Straub, D. (2005). Critical themes in electronic commerce research: A meta-analysis. Journal of Information Technology, 20 (1), 1–19. https://doi.org/10.1057/palgrave.jit.2000034

Watson, G. R., & Rasmussen, C. E. (2008). An integrated environment for the development of parallel applications. Proceedings of the 2nd International Workshop on Parallel Tools for High Performance Computing , 11 (2), 19–34. https://doi.org/10.1007/978-3-540-68564-7

Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly , 26 (2), xiii–xxiii. 10.1.1.104.6570

Wei, C. P., Hu, P. J., & Dong, Y. X. (2002). Managing document categories in e-commerce environments: An evolution-based approach. European Journal of Information Systems, 11 (3), 208–222. https://doi.org/10.1057/palgrave.ejis.3000429

Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69 , 29–39. https://doi.org/10.1016/j.eswa.2016.09.040

Wenxuan Ding, A., Li, S., & Chatterjee, P. (2015). Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation. Information Systems Research, 26 (2), 339–359. https://doi.org/10.1057/isre.2015.0568

Willcocks, L. (2020a). Robo-Apocalypse cancelled? Reframing the automation and future of work debate. Journal of Information Technology, 35 (4), 286–302. https://doi.org/10.1177/0268396220925830

Willcocks, L. (2020b). Robo-Apocalypse? Response and outlook on the post-COVID-19 future of work. Journal of Information Technology, 36 (2), 188–194. https://doi.org/10.1177/0268396220978660

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. In Data Mining: Practical Machine Learning Tools and Techniques . Morgan Kaufmann. https://doi.org/10.1016/c2009-0-19715-5

Wu, B., Ye, Y., & Chen, Y. (2019). Visual appearance or functional complementarity: Which aspect affects your decision making? Information Sciences, 476 , 19–37. https://doi.org/10.1016/j.ins.2018.10.011

Wu, J., Huang, L., & Zhao, J. L. (2019). Operationalizing regulatory focus in the digital age: Evidence from an e-commerce context. MIS Quarterly, 43 (3), 745–764. https://doi.org/10.25300/MISQ/2019/14420

Wu, R. S., & Chou, P. H. (2011). Customer segmentation of multiple category data in e-commerce using a soft-clustering approach. Electronic Commerce Research and Applications, 10 (3), 331–341. https://doi.org/10.1016/j.elerap.2010.11.002

Xia, H., Wei, X., An, W., Zhang, Z. J., & Sun, Z. (2021). Design of electronic-commerce recommendation systems based on outlier mining. Electronic Markets, 31 (2). https://doi.org/10.1007/s12525-020-00435-2

Xie, F., Chen, Z., Shang, J., & Fox, G. C. (2014). Grey Forecast model for accurate recommendation in presence of data sparsity and correlation. Knowledge-Based Systems, 69 (1), 179–190. https://doi.org/10.1016/j.knosys.2014.04.011

Xiong, J., Yu, L., Zhang, D., & Leng, Y. (2021). DNCP: An attention-based deep learning approach enhanced with attractiveness and timeliness of News for online news click prediction. Information and Management, 58 (2), 103428. https://doi.org/10.1016/j.im.2021.103428

Xu, Y., Yang, Y., Han, J., Wang, E., Ming, J., & Xiong, H. (2019). Slanderous user detection with modified recurrent neural networks in recommender system. Information Sciences, 505 , 265–281. https://doi.org/10.1016/j.ins.2019.07.081

Xue, G. R., Lin, C., Yang, Q., Xi, W., Zeng, H. J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 114–121. https://doi.org/10.1145/1076034.1076056

Yan, S. R., Zheng, X. L., Wang, Y., Song, W. W., & Zhang, W. Y. (2015). A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce. Information Sciences, 318 , 51–72. https://doi.org/10.1016/j.ins.2014.09.036

Yan, Y., Huang, C., Wang, Q., & Hu, B. (2020). Data mining of customer choice behavior in internet of things within relationship network. International Journal of Information Management, 50 , 566–574. https://doi.org/10.1016/j.ijinfomgt.2018.11.013

Yang, Z., Cai, Z., & Guan, X. (2016a). Estimating user behavior toward detecting anomalous ratings in rating systems. Knowledge-Based Systems, 111 , 144–158. https://doi.org/10.1016/j.knosys.2016.08.011

Yang, Z., Xu, L., Cai, Z., & Xu, Z. (2016b). Re-scale AdaBoost for attack detection in collaborative filtering recommender systems. Knowledge-Based Systems, 100 , 74–88. https://doi.org/10.1016/j.knosys.2016.02.008

Ye, X., Dong, L., & Ma, D. (2018). Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score. Electronic Commerce Research and Applications, 32 , 23–36. https://doi.org/10.1016/j.elerap.2018.10.004

Yim, D., Malefyt, T., & Khuntia, J. (2021). Is a picture worth a thousand views? Measuring the effects of travel photos on user engagement using deep learning algorithms. Electronic Markets, 31 (3), 619–637. https://doi.org/10.1007/s12525-021-00472-5

Zaïane, O. R. (2002). Building a recommender agent for e-learning systems. Proceedings - International Conference on Computers in Education, ICCE, 2002 , 55–59. https://doi.org/10.1109/CIE.2002.1185862

Zhang, D., Yan, Z., Jiang, H., & Kim, T. (2014). A domain-feature enhanced classification model for the detection of Chinese phishing e-Business websites. Information and Management, 51 (7), 845–853. https://doi.org/10.1016/j.im.2014.08.003

Zhang, D., Xu, H., Su, Z., & Xu, Y. (2015). Chinese comments sentiment classification based on word2vec and SVMperf. Expert Systems with Applications, 42 (4), 1857–1863. https://doi.org/10.1016/j.eswa.2014.09.011

Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018a). A survey on deep learning for big data. Information Fusion, 42 , 146–157. https://doi.org/10.1016/j.inffus.2017.10.006

Zhang, W., Wang, C., Zhang, Y., & Wang, J. (2020a). Credit risk evaluation model with textual features from loan descriptions for P2P lending. Electronic Commerce Research and Applications, 42 , 100989. https://doi.org/10.1016/j.elerap.2020.100989

Zhang, W., Du, Y., Yang, Y., & Yoshida, T. (2018b). DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function. Electronic Commerce Research and Applications, 31 , 12–23. https://doi.org/10.1016/j.elerap.2018.08.001

Zhang, W., Du, Y., Yoshida, T., & Yang, Y. (2019a). DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function. Information Sciences, 470 , 121–140. https://doi.org/10.1016/j.ins.2018.08.039

Zhang, X., Liu, H., Chen, X., Zhong, J., & Wang, D. (2020b). A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness. Information Sciences, 519 , 306–316. https://doi.org/10.1016/j.ins.2020.01.044

Zhang, X., Han, Y., Xu, W., & Wang, Q. (2019b). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences . https://doi.org/10.1016/j.ins.2019.05.023

Zhang, Y., Chen, H., Lu, J., & Zhang, G. (2017). Detecting and predicting the topic change of knowledge-based systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems, 133 , 255–268. https://doi.org/10.1016/j.knosys.2017.07.011

Zhang, Z., Wei, X., Zheng, X., & Zeng, D. D. (2021). Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach. Information & Management, 58 (7), 103484. https://doi.org/10.1016/j.im.2021.103484

Zhao, G., Lou, P., Qian, X., & Hou, X. (2020a). Personalized location recommendation by fusing sentimental and spatial context. Knowledge-Based Systems, 196 , 105849. https://doi.org/10.1016/j.knosys.2020.105849

Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., & Yang, Y. (2020b). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection, 133 , 169–182. https://doi.org/10.1016/j.psep.2019.11.014

Zhao, Y., Yu, Y., Li, Y., Han, G., & Du, X. (2019). Machine learning based privacy-preserving fair data trading in big data market. Information Sciences, 478 , 449–460. https://doi.org/10.1016/j.ins.2018.11.028

Zheng, X., Zhu, S., & Lin, Z. (2013). Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems, 56 , 211–222. https://doi.org/10.1016/j.dss.2013.06.002

Zheng, Z., & Padmanabhan, B. (2006). Selectively acquiring customer information: A new data acquisition problem and an active learning-based solution. Management Science, 52 (5), 697–712. https://doi.org/10.1287/mnsc.1050.0488

Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving recommendation lists through topic diversification. Proceedings of the 14th International Conference on World Wide Web , 22. https://doi.org/10.1145/1060745.1060754

Zoghbi, S., Vulić, I., & Moens, M. F. (2016). Latent Dirichlet allocation for linking user-generated content and e-commerce data. Information Sciences, 367–368 , 573–599. https://doi.org/10.1016/j.ins.2016.05.047

Download references

Author information

Authors and affiliations.

ICN Business School, CEREFIGE - Université de Lorraine, 86 rue du Sergent Blandan, 54003, Nancy, France

Ransome Epie Bawack

TBS Business School, 6 Place Alfonse Jourdain, 31000, Toulouse, France

Samuel Fosso Wamba & Kevin Daniel André Carillo

School of Management and Marketing, University of Wollongong, Wollongong, NSW, 2522, Australia

Shahriar Akter

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ransome Epie Bawack .

Additional information

Responsible Editor: Jianwei Hou

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Bawack, R.E., Wamba, S.F., Carillo, K.D.A. et al. Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electron Markets 32 , 297–338 (2022). https://doi.org/10.1007/s12525-022-00537-z

Download citation

Received : 11 September 2021

Accepted : 22 February 2022

Published : 18 March 2022

Issue Date : March 2022

DOI : https://doi.org/10.1007/s12525-022-00537-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Artificial intelligence
Bibliometrics

JEL classification

Find a journal
Publish with us
Track your research

IMAGES

Artificial Intelligence
(PDF) A review of artificial intelligence
(PDF) Artificial Intelligence in Government Services: A Systematic
(PDF) Impacts of Artificial Intelligence on Public Administration: A
(PDF) The role of artificial intelligence in healthcare: a structured
Artificial Intelligence

VIDEO

A Textbook Penned Entirely by Artificial Intelligence #shorts
Best Literature Review AI Tool
What are AI Writing Detectors and ChatGPT Detections Tools
10 Days 10 AI Tools: #2 WriteSonic #shorts #writesonic #aitools
AI tool that you MUST know for your Powerful Literature review
PhD students need to be transparent about the use of AI tools for academic writing

COMMENTS

Artificial intelligence and the conduct of literature reviews
Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines.
A systematic literature review on the impact of artificial intelligence
Artificial intelligence (AI) can bring both opportunities and challenges to human resource management (HRM). While scholars have been examining the impact of AI on workplace outcomes more closely over the past two decades, the literature falls short in providing a holistic scholarly review of this body of research.
A Literature Review of Artificial Intelligence
1956, Joh n McCarthy came up with. the term to define a new computer. science field that aims to make. machines act like human beings. A Literature Review of Artificial Intelligence. Agha Wafa ...
(PDF) A literature review on artificial intelligence
International Journal of Information and Management Sciences. Volume 19, Number 4, pp. 535-570, 2008. A Literature Review on Artiﬁcial Intelligence. S. A. Oke. Universit y of Lagos. Nigeria ...
A systematic literature review of artificial intelligence in the
Applying the rules and guidelines of systematic reviews is crucial for researchers who undertake this approach (Kitchenham and Charters, 2007).Commencing the review process using a protocol to identify, select, and assess the relevant literature will make the systematic review highly efficient (Tranfield et al., 2003).The systematic process should be reproducible, objective, transparent ...
Artificial Intelligence and Business Value: a Literature Review
Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and ...
Review Article Artificial intelligence in information systems research
The literature review conducted by Hofmann et al. (2019) was primarily concerned with the effects of AI and ML in the context of the radiology value chain, ... Russel & Norvig's book Artificial Intelligence: A Modern Approach was the most frequently cited source for defining AI, though the actual edition of the book varied, ...
Explainable artificial intelligence: a comprehensive review
Artificial intelligence (AI) has been considered the most prevalent technology over the last couple of decades. According to a report by the International Data Corporation (IDC), the AI global expenditures are forecasted to reach nearly $100 billion in 2023, which is more than double the spending of $37.5 billion in 2019 (IDC 2020).In the meantime, Statista, which is a well-known online portal ...
Technology Trust 2021 literature reviews
One implication of Li et al.'s (2020) work is that AI-supported, ontology-based searches may effectively prevent some of the screening burden through better precision. Overall, the two screening steps are therefore among the most time-consuming activities of the literature review process (Carver et al., 2013).
PDF Artificial Intelligence and Business Value: a Literature Review
Artificial Intelligence and Business Value: a Literature Review Ida Merete Enholm1 & Emmanouil Papagiannidis1 & Patrick Mikalef1 & John Krogstie1 Accepted: 7 August 2021 # The Author(s) 2021 Abstract Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off
Artificial intelligence and the conduct of literature reviews
Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing ...
Artificial intelligence to automate the systematic review of scientific
Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the ...
Artificial intelligence for cybersecurity: Literature review and future
Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the security posture against various security issues and cyberattacks. ... We performed a systematic literature review (SLR) on the use ...
(PDF) Artificial Intelligence for Cybersecurity: Literature Review and
The term cybersecurity refers to a set of technologies, processes and. practices to protect and defend networks, devices, software and data. from attack, damage or unauthorized access [1 ...
Artificial intelligence in customer relationship management: literature
Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.,A total of 212 peer-reviewed articles published between 1989 and 2020 were ...
A literature review on artificial intelligence and ethics in online
In order to guide our steps, in Section 6.2 we review the existing literature on artificial intelligence and online learning, giving a special focus to conversational agents as a supporting tool that has lately been growing in online learning environments. Later, in Section 6.3 we review the existing literature on ethics and online learning to summarize the main concerns identified in this ...
Artificial Intelligence
The structure taken by the literature review is as follows: 1. Overview of historical development; 2. Definitional and compositional analysis; 3. Ethical & Social, Legal, Economic and Political impact and sector-specific solutions; 4. The regulatory way forward. This literature review is a first step in understanding the existing paradigms and ...
Artificial Intelligence in Project Management: Systematic Literature Review
Artificial intelligence (AI) is transforming various domains of human activity, including project management. This paper provides a systematic literature review of AI applications in project ...
Resistance to artificial intelligence in health care: Literature review
Resistance to AIH is ongoing, yet relevant research is still in its infancy, and knowledge accumulation in this area is fragmented. Several research streams examining resistance to AIH have emerged, including information systems (IS; [13, 14]), marketing [17, 18], health care [19], and medicine [20], but the divergent streams of the literature have not been bridged.
Home
Artificial Intelligence Review is a fully open access journal publishing state-of-the-art research in artificial intelligence and cognitive science. Publishes critical evaluations of applications, techniques, and algorithms in the field. Provides a platform for researchers and application developers. Presents refereed survey and tutorial ...
Artificial Intelligence & Machine Learning in Finance: A literature review
Intelligence & Machine Learning in Finance: A literature review. International Journal of Accounting, Finance, Auditing, Management and Economics, 3(6-1), 437-455 .
A literature review of Artificial Intelligence applications in railway
1. Introduction. Artificial Intelligence (AI) is defined as a computerized system that is able to perform physical tasks and cognitive functions, solve various problems, or make decisions without explicit human instructions ( Kaplan and Haenlein, 2019 ). Nowadays, AI has become one of the most important areas of research in almost all fields in ...
Artificial intelligence in E-Commerce: a bibliometric study and
This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS ...

Artificial intelligence in customer relationship management: literature review and future research directions

Design/methodology/approach

Originality/value

1. Introduction

2. Methodology

2.1 Search strategy and data collection

2.2 Bibliometric analysis: bibliographic coupling and keyword analysis

3.1 Bibliographic coupling

3.2 Keywords’ co-occurrence

4. Discussion

5. Conclusion

Acknowledgements

Corresponding author

Related articles

All feedback is valuable

Join us on our journey

Questions & More Information

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Cite this article

Similar content being viewed by others

Artificial Intelligence in E-commerce: A Literature Review

Research on the Use, Characteristics, and Impact of e-Commerce Product Recommendation Agents: A Review and Update for 2007–2012

E-commerce and Business Analytics: A Literature Review

Introduction

Research method

Results of the bibliometric analysis

Functional perspective

Interpretive perspective

Normative perspective

Results of the literature review study

Classification by research style

Classification by research theme

Need for more research from other countries

Focus on recommender systems

Limited research themes

More experiment than theory-driven research

Implications for research

Implications for practice

Limitations

Future research

Conclusions

Author information

Corresponding author

Additional information

Publisher's note

Rights and permissions

About this article

Share this article

JEL classification

IMAGES

VIDEO

COMMENTS