key: cord-0870940-grkzr0us
authors: Bawack, Ransome Epie; Wamba, Samuel Fosso; Carillo, Kevin Daniel André; Akter, Shahriar
title: Artificial intelligence in E-Commerce: a bibliometric study and literature review
date: 2022-03-18
journal: Electron Markets
DOI: 10.1007/s12525-022-00537-z
sha: ad622a5ce3dcfeaaf4a3a4356af5057b5a6be107
doc_id: 870940
cord_uid: grkzr0us

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.

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 Dwivedi et al., , 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.

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 Lowry et al., , 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; . 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 highimpact 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. 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 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.

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.

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.

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.

Analysing the most globally cited documents 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 . The central concept of interest is personalisation, specifically leveraging recommender systems to offer more personalised product/service 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.

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.

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 .

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. 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 .

Most articles on AI in e-commerce focus on technological issues (107 articles, 47%), followed by applications (87 (Linden et al., 2003) Amazon. Com recommendations: item-to-item collaborative filtering (Sarwar et al., 2001) Item-based collaborative filtering recommendation algorithms (Herlocker et al., 2004) Evaluating collaborative filtering recommender systems (Goldberg et al., 1992) Using collaborative filtering to weave an information tapestry (Balabanović & Shoham, 1997) Fab: content-based, collaborative recommendation (Konstan et al., 1997) GroupLens: applying collaborative filtering to USENET news (Resnick et al., 1994) GroupLens: an open architecture for collaborative filtering of netnews (Su & Khoshgoftaar, 2009) A survey of collaborative filtering techniques Latent Dirichlet allocation (Blei et al., 2002) Latent Dirichlet allocation Matrix factoring techniques (Koren et al., 2009) Matrix factorization techniques for recommender systems Recommender systems Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (Resnick & Varian, 1997) Recommender systems (Burke, 2002) Hybrid recommender systems: survey and experiments scop Recommender systems survey (Ricci et al., 2011) Introduction to recommender systems handbook Social information filtering (Shardanand & Maes, 1995) Social information filtering: algorithms for automating "word of mouth" (Agrawal et al., 1993) Mining association rules between sets of items in large databases 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.

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 ).

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) .

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 ).

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). Twentynine 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 ).

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. 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.

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; 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.

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 . 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) Wang, Jhou, 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 Greenstein-Messica & Rokach, 2018) . It also gives firms the ability to predict consumer behaviours like customer churn , 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 Greenstein-Messica & Rokach, 2020) . Even further, it gives firms the ability to predict the success or failure of these strategies .

Researchers have shown that using AI to build trustbased recommender systems can help e-commerce firms increase user acceptance of the recommendations made by e-commerce platforms . This trust is created by accurately measuring the level of trust customers have in the recommendations made by the firm's e-commerce platforms or by making recommendations based on the recommendations of people the customers' trust in their social sphere .

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 Qi et al., 2016) and predict product sales based on customer sentiments Li, Wu, 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, interorganisational 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.

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 . 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.

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.

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 sentimentbased 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.

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.

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 . 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, contextspecific, 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.

AI has emerged as a technology that can differentiate between two competing f ir ms 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 technologyrelated 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. Incorporating contextual information in recommender systems using a multidimensional approach ACM Transactions on Information Systems (Kim, Song, et al., 2005; Kim, Yum, et al., 2005) Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites Expert systems with applications (O'Donovan & Smyth, 2005) Trust in recommender systems IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces (Ziegler et al., 2005) Improving recommendation lists through topic diversification WWW '05: Proceedings of the 14th international conference on World Wide Web (Xue et al., 2005) Scalable collaborative filtering using cluster-based smoothing Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (Liu et al., 2005) Opinion observer: analyzing and comparing opinions on the Web WWW '05: Proceedings of the 14th international conference on World Wide Web (Shani et al., 2005) An MDP-Based Recommender System

Journal of Machine Learning Research 2007 (Brusilovski et al., 2007) The adaptive web: methods and strategies of web personalization Book (Bo et al., 2007) E-commerce product recommendation agents: use, characteristics, and impact MIS Quarterly (Cao & Li, 2007) An intelligent fuzzy-based recommendation system for consumer electronic products Expert systems with applications (Jøsang et al., 2007) A survey of trust and reputation systems for online service provision Decision support systems Deep learning Nature Recommender system application developments: A survey Decision support systems Collaborative deep learning for recommender systems KDD '15: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Szegedy et al., 2015) Going deeper with convolutions 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Schmidhuber, 2015) Deep learning in neural networks: An overview Neural Networks Recommender systems based on user reviews: the state of the art User Modeling and User-Adapted Interaction (Isinkaye et al., 2015) Recommendation systems: Principles, methods, and evaluation Egyptian Informatics Journal Electronic payment systems Zhang, Han, et al., 2019) Financial services Das & Chen, 2007; Hill & Ready-Campbell, 2011; Ma et al., 2018; Maqsood et al., 2020; Pengnate & Riggins, 2020; Sul et al., 2017; Sun et al., 2020; Ye et al., 2018; Weiguo Zhang, Wang, et al., 2020; Zhang, Liu, et al., 2020) Clothing and fashion Liu et al., 2017) Retailing (Chang & Jang, 2009; Greenstein-Messica & Rokach, 2020; D. Lee et al., 2020; Luo et al., 2019; Park & Park, 2003) Online publishing (Cardoso & Gomide, 2007; Castillo et al., 2017; Liu et al., 2018; Ma et al., 2011) Auctions Intra organizational e-commerce Guo, Wei, et al., 2017; Guo, Zhang, et al., 2017; Stoeckli et al., 2020) Education and training (Aher & Lobo, 2013; Núñez-Valdez et al., 2018) Marketing and advertising (Al-Natour & Turetken, 2020; Bassano et al., 2017; Beladev et al., 2016; Chang, 2011; Chu et al., 2007; Cui et al., 2006; Ghiassi et al., 2016; Gong et al., 2018; Ketter et al., 2012; Kim et al., 2001; Kühl et al., 2020; Kuo et al., 2004; Lessmann et al., 2019; Li et al., , 2019 Li, Wang, et al., 2019; Li, Wang, et al., 2019; Li, Wu, et al., 2019; Li, Wu, et al., 2019; Miralles-Pechuán et al., 2018; Nikolay et al., 2011; Padmanabhan & Tuzhilin, 2003; Qi et al., 2016; Rao et al., 2016; Takeuchi et al., 2009; Wang, 2008; Wenxuan Ding et al., 2015; Yan et al., 2020) Other applications Bai et al., 2020; Brazier et al., 2002; Bukhari & Kim, 2012; Cao & Schniederjans, 2006; Jeong et al., 2003; Kiekintveld et al., 2009; Leloup, 2003; Liebman et al., 2019; Mo et al., 2018; Motiwalla & Nunamaker, 1992; Nilashi et al., 2015; Pfeiffer et al., 2020; Wei et al., 2002; Technological components Keegan et al., 2008) Network technology / infrastructure Support systems Barzegar Nozari & Koohi, 2020; Bobadilla et al., 2013; Chow et al., 2007; Chung, 2014; Da'u et al., 2020; Gupta & Kant, 2020; Ito et al., 2002; Julià et al., 2009; Kaiser et al., 2011; Khare & Chougule, 2012; Lau, 2007; Pontelli & Son, 2003; Saleh et al., 2015; Tan & Thoen, 2000; Villegas et al., 2018; Wang, Jhou, et al., 2018; Wang, Lu, et al., 2018; Watson & Rasmussen, 2008; Xiaofeng Zhang, Wang, et al., 2020; Zhang, Liu, et al., 2020; Zheng et al., 2013) Algorithm / methodology Bag et al., 2019; Carbó et al., 2007; Esmeli et al., 2021; Fang et al., 2013; Feng et al., 2019; Geng et al., 2020; Greenstein-Messica & Rokach, 2018; Gu et al., 2017; S.-U. Guan et al., 2005; Guo, Zhang, et al., 2017; Guo, Wei, et al., 2017; Herce-Zelaya et al., 2020; Hu, 2014; Iwański et al., 2018; Ji & Shen, 2015; Jiang et al., 2014; Jiang et al., 2005; Kim et al., 2017; Kim et al., 2002; Kumar et al., 2018; Kumar, Venugopal, et al., 2019; Kumar, Rajan, et al., 2019; Kuo et al., 2005; Liu et al., 2019; Liu & Shen, 2020; Liu et al., 2013; Mao et al., 2019; Martinez-Cruz et al., 2015; Nishimura et al., 2018; Oliver, 1996; Ortega et al., 2013 Ortega et al., , 2016 Ou et al., 2018; Pang et al., 2019; Park Kim, & Yu, 2019; Park et al., 2020; Patra et al., 2015; Pendharkar, 2006; Pourgholamali et al., 2020; Pröllochs et al., 2020; Qiu et al., 2018; Ranjbar Kermany & Alizadeh, 2017; Saumya et al., 2018; Si et al., 2017; Singh & Tucker, 2017; Tang et al., 2020; Tian et al., 2016; Varshney et al., 2017; Wang, Jhou, et al., 2018; Wang, Lu, et al., 2018; Wang Jhou, & Tsai, 2018; Wang, Lu, et al., 2018; Wu, Ye, et al., 2019; Xia et al., 2021; Xie et al., 2014; Yan et al., 2015; Wen Zhang, Yang, et al., 2018; Zhang, Du, et al., 2018; Zhang, Du, et al., 2019; Zhang, Han, et al., 2019; Zheng & Padmanabhan, 2006; Zoghbi et al., 2016) Other technical issues Yang Li, Zhang, et al., 2019; Li, Wu, et al., 2019; Li, Wang, et al., 2019 ) Support and implementation Privacy Viejo et al., 2012; Zhao et al., 2019 ) Fraud (Chang & Chang, 2014; Lin et al., 2016; Dongsong Zhang et al., 2014 ) Trust Guo et al., 2014; Guo, Wei, et al., 2017; Guo, Zhang, et al., 2017; Li et al., 2017; Parvin et al., 2019; Thiebes et al., 2021) Other support and implementation Hopkins et al., 2019; Kauffman et al., 2017; Li et al., 2006; Xu et al., 2019) Others Alt et al., 2019; Bondielli & Marcelloni, 2019; Buettner, 2017; Ferrara et al., 2014; Galitsky, 2006; Kwon et al., 2006; Mokryn et al., 2019; Moussawi et al., 2020; O'Neil et al., 2016; Wang, Jhou, et al., 2018; Wang, Lu, et al., 2018; Wu, Ye, et al., 2019) Aher & Lobo, 2013; Azadjalal et al., 2017; Bag et al., 2019; Bandyopadhyay et al., 2008; Beladev et al., 2016; Bukhari & Kim, 2012; Cardoso & Gomide, 2007; Castillo et al., 2017; Chang & Chang, 2014; Chang & Jang, 2009; Chang, 2011; Chu et al., 2007; Cui et al., 2006; Das & Chen, 2007; Dastani et al., 2005; Esmeli et al., 2021; Fang et al., 2013; Feng et al., 2019; Fiore et al., 2019; Geng et al., 2020; Ghiassi et al., 2016; Gong et al., 2018; Greenstein-Messica & Rokach, 2020; Gu et al., 2017; Guo et al., 2014; Guo, Zhang, et al., 2017; Guo, Wei, et al., 2017; Guo et al., 2017; Guo, Zhang, et al., 2017; Guo, Wei, et al., 2017; Guo et al., 2017; Guo et al., 2017; Guo, Wei, et al., 2017; Herce-Zelaya et al., 2020; Hill & Ready-Campbell, 2011; Hu, 2014; Iwański et al., 2018; Jeong et al., 2003; Ji & Shen, 2015; Jiang et al., 2014; Julià et al., 2009; Kiekintveld et al., 2009; Kim et al., 2017; Kim et al., 2001; Kim et al., 2002; Kühl et al., 2020; Kumar et al., 2018; Kumar, Venugopal, et al., 2019; Kumar, Rajan, et al., 2019; Kuo et al., 2004 Kuo et al., , 2005 Laorden et al., 2012; Leloup, 2003; Lessmann et al., 2019; Li et al., 2017; Li, Wu, et al., 2019; Li, Wang, et al., 2019; Li et al., 2019; Li, Wu, et al., 2019; Li, Wang, et al., 2019; Yang Li et al., 2019; Li Wu, & Mai, 2019; Li, Wang, et al., 2019; Liebman et al., 2019; Lin et al., 2003; Lin et al., 2016; Liu et al., 2018; Liu et al., 2019; Liu et al., 2017; Liu & Shen, 2020; Liu et al., 2013; Ma et al., 2018; Ma et al., 2011; Mao et al., 2019; Maqsood et al., 2020; Martinez-Cruz et al., 2015; Miralles-Pechuán et al., 2018; Mo et al., 2018; Mokryn et al., 2019; Nilashi et al., 2015; Nishimura et al., 2018; O'Neil et al., 2016; Oliver, 1996; Ortega et al., 2013 Ortega et al., , 2016 Ou et al., 2018; Pang et al., 2019; C. Park Kim, & Yu, 2019; Park et al., 2020; Park & Park, 2003; Parvin et al., 2019; Patra et al., 2015; Pendharkar, 2006; Pfeiffer et al., 2020; Pourgholamali et al., 2020; Preibusch et al., 2016; Pröllochs et al., 2020; Qi et al., 2016; Qiu et al., 2018; Ranjbar Kermany & Alizadeh, 2017; Rao et al., 2016; Saumya et al., 2018; Si et al., 2017; Singh & Tucker, 2017; Takeuchi et al., 2009; Tang et al., 2020; Tian et al., 2016; Varshney et al., 2017; Viejo et al., 2012; Wang, 2008; Wang, Jhou, et al., 2018; Wang, Lu, et al., 2018; Wang Jhou, & Tsai, 2018; Wang, Lu, et al., 2018; Wang Jhou, & Tsai, 2018; Wang, Lu, et al., 2018; Wei et al., 2002; Wenxuan Ding et al., 2015; Wu, Ye, et al., 2019; Xie et al., 2014; Xu et al., 2019; Yan et al., 2015; Yan et al., 2020; Ye et al., 2018; Dongsong Zhang et al., 2014; Weiguo Zhang, Wang, et al., 2020; Zhang, Liu, et al., 2020; Wen Zhang, Yang, et al., 2018; Zhang, Du, et al., 2018; Zhang, Du, et al., 2019; Zhang, Han, et al., 2019; Xiaofeng Zhang et al., 2020; Zhang, Liu, et al., 2020; Zhang, Han, et al., 2019; Zhao et al., 2019; Zheng & Padmanabhan, 2006; Zoghbi et al., 2016 ) System Barzegar Nozari & Koohi, 2020; Brazier et al., 2002; Carbó et al., 2007; Chow et al., 2007; Da'u et al., 2020; Dong et al., 2020; Greenstein-Messica & Rokach, 2018; Gupta & Kant, 2020; Ito et al., 2002; Kaiser et al., 2011; Keegan et al., 2008; Ketter et al., 2012; Khare & Chougule, 2012; Kwon et al., 2006; Lau, 2007; D. Lee et al., 2020; Motiwalla & Nunamaker, 1992; Núñez-Valdez et al., 2018; Saleh et al., 2015; Stoeckli et al., 2020; Tan & Thoen, 2000; Wang, Jhou, et al., 2018; Wang, Lu, et al., 2018; Watson & Rasmussen, 2008; Xia et al., 2021; Zheng et al., 2013) Other Al-Natour & Turetken, 2020; Al-Natour et al., 2006 Alt et al., 2019; Ariyaluran Habeeb et al., 2019; Bai et al., 2020; Bassano et al., 2017; Bobadilla et al., 2013; Bondielli & Marcelloni, 2019; Buettner, 2017; Cao & Schniederjans, 2006; Chung, 2014; Ferrara et al., 2014; Galitsky, 2006; Hopkins et al., 2019; Jiang et al., 2005; Kauffman et al., 2017; Li et al., 2006; Luo et al., 2019; Moussawi et al., 2020; Padmanabhan & Tuzhilin, 2003; Pontelli & Son, 2003; Sul et al., 2017; Sun et al., 2020; Thiebes et al., 2021; Villegas et al., 2018; Wu, Ye, et al., 2019) Positivist Al-Natour et al., 2006 Jiang et al., 2005; Li, Wang, et al., 2019; Li, Wu, et al., 2019; Luo et al., 2019; Moussawi et al., 2020; Sul et al., 2017; Sun et al., 2020; Wu, Ye, et al., 2019) Interpretivist Design science Aguwa et al., 2017; Aher & Lobo, 2013; Al-Natour & Turetken, 2020; Azadjalal et al., 2017; Bag et al., 2019; Bandyopadhyay et al., 2008; Barzegar Nozari & Koohi, 2020; Beladev et al., 2016; Brazier et al., 2002; Buettner, 2017; Bukhari & Kim, 2012; Conceptual Alt et al., 2019; Bassano et al., 2017; Li et al., 2006; Lin et al., 2003; Motiwalla & Nunamaker, 1992; Padmanabhan & Tuzhilin, 2003; Tan & Thoen, 2000; Thiebes et al., 2021; Zhao et al., 2019) Review Bobadilla et al., 2013; Bondielli & Marcelloni, 2019; Ferrara et al., 2014; Villegas et al., 2018 ) Data analysis Fang et al., 2013; Hill & Ready-Campbell, 2011; Kumar et al., 2018; Laorden et al., 2012; Li, Wu, et al., 2019; Li, Wang, et al., 2019; Liu et al., 2013; Ma et al., 2018; Ma et al., 2011; Mo et al., 2018; Mokryn et al., 2019; Nikolay et al., 2011; Park & Park, 2003; Singh & Tucker, 2017; Stoeckli et al., 2020; Sul et al., 2017; Sun et al., 2020; Wang, Jhou, et al., 2018; Wang, Lu, et al., 2018; Wang Jhou, & Tsai, 2018; Wang, Lu, et al., 2018; Dongsong Zhang et al., 2014; Weiguo Zhang, Wang, et al., 2020; Zhang, Liu, et al., 2020 ) Survey (Cao & Schniederjans, 2006; Moussawi et al., 2020; Wu, Ye, et al., 2019 ) Experiment Adomavicius et al., 2013; Al-Natour et al., 2006 Al-Natour & Turetken, 2020; ; Other Fiore et al., 2019; Kumar, Venugopal, et al., 2019; Kumar, Rajan, et al., 2019; Yang Li, Zhang, et al., 2019; Li, Wu, et al., 2019; Li, Wang, et al., 2019; Dongsong Zhang et al., 2014 ) AI concepts and related technologies Bondielli & Marcelloni, 2019; Dastani et al., 2005; Gupta & Kant, 2020; Villegas et al., 2018) Decision support (online reputation, dynamic pricing, promotions, product/service management, customer segmentation, loan & credit risk evaluations

Leloup

Case study (Chou & Seng

Optimisation Recommendation accuracy (Aher & Lobo

Prediction accuracy

AI design, tools and techniques (modelling, ranking, mining, forecasting, anomaly detection

Customer behaviour (satisfaction, adoption, attitudes, preferences

2020) AI concepts (knowledge sharing, data/information extraction, automation

Detecting fake websites: The contribution of statistical learning theory

Machine learning in information systems -a bibliographic review and open research issues

Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects

Incorporating contextual information in recommender systems using a multidimensional approach

Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

Artificial intelligence as digital agency

A comparison between two main academic literature collections: Web of science and scopus databases

Mining Association Rules Between Sets of Items in Large Databases

Modeling of fuzzybased voice of customer for business decision analytics. Knowledge-Based Systems

Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data. Knowledge-Based Systems

Big data analytics in E-commerce: A systematic review and agenda for future research

How to Build an AI Climate-Driven Service Analytics Capability for Innovation and Performance in Industrial Markets? Industrial Marketing Management

The adoption of online shopping assistants: Perceived similarity as an antecedent to evaluative beliefs

The role of design characteristics in shaping perceptions of similarity: The case of online shopping assistants

A comparative assessment of sentiment analysis and star ratings for consumer reviews

User profiling approaches for demographic recommender systems. Knowledge-Based Systems

Towards customer-induced service orchestration -requirements for the next step of customer orientation

bibliometrix: An R-tool for comprehensive science mapping analysis

Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research

Real-time big data processing for anomaly detection: A Survey

A critical analysis of decision support systems research revisited: The rise of design science

Campaign participation prediction with deep learning. Electronic Commerce Research and Applications

A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowledge-Based Systems

An efficient recommendation generation using relevant Jaccard similarity

A note on the impact of daily deals on local retailers' online reputation: Mediation effects of the consumer experience

Content-Based, Collaborative Recommendation

Reverse auctions with multiple reinforcement learning agents

Digital work and organisational transformation: Emergent Digital/Human work configurations in modern organisations

A novel group recommender system based on members' influence and leader impact

Learning the Models of Customer Behavior: From Television Advertising to Online Marketing

Optimal pricing in e-commerce based on sparse and noisy data

A framework for understanding artificial intelligence research: insights from practice

Empowering recommender systems using trust and argumentation

Recommender systems for product bundling. Knowledge-Based Systems

Special issue editorial: Artificial intelligence in organizations: Implications for information systems research

Latent dirichlet allocation

AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a highcontact service industry

Fast unfolding of communities in large networks

E-commerce product recommendation agents: Use, characteristics, and impact

Recommender systems survey. Knowledge-Based Systems

A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems

Statistical fraud detection: A review

A survey on fake news and rumour detection techniques

The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions

Hybrid models using unsupervised clustering for prediction of customer churn

A multi-agent system performing one-to-many negotiation for load balancing of electricity use

Random forests

The Adaptive Web Methods and Strategies of Web Personalization

The economics of IT and digitization: Eight questions for research

Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality-based product recommender framework. Electronic Markets

Hybrid Recommender Systems: Survey and Experiments

Selection of the strategic alliance partner in logistics value chain

Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems

Detecting shilling attacks in recommender systems based on analysis of user rating behavior. Knowledge-Based Systems

From data to action: How marketers can leverage AI

Agent-mediated architecture for reputation-based electronic tourism systems: A neural network approach

An intelligent fuzzy-based recommendation system for consumer electronic products

Avoiding malicious agents in E-commerce using fuzzy recommendations

Newspaper demand prediction and replacement model based on fuzzy clustering and rules. Information Sciences

Applying computational intelligence methods for predicting the sales of newly published books in a real editorial business management environment. Knowledge-Based Systems

LIBSVM: A Library for support vector machines

Analysis of fraudulent behavior strategies in online auctions for detecting latent fraudsters

Understanding the paradigm shift to computational social science in the presence of big data

Assessing customer satisfaction in a V-commerce environment

An effective early fraud detection method for online auctions

iValue: A knowledge-based system for estimating customer prospect value. Knowledge-Based Systems

On the platform but will they buy? Predicting customers' purchase behavior using deep learning. Decision Support Systems, 149, 113622

Predicting the success of group buying auctions via classification. Knowledge-Based Systems

An influence framework on product word-of-mouth (WoM) measurement. Information and Management

Recommender systems based on user reviews: The state of the art

Preference-based clustering reviews for augmenting e-commerce recommendation. Knowledge-Based Systems

A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering

Pinpoint: Problem determination in large, dynamic internet services

Mining user requirements to facilitate mobile app quality upgrades with big data

Secondhand seller reputation in online markets: A text analytics framework

Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems

A new approach to the group ranking problem: Finding consensus ordered segments from users' preference data

Predicting the influence of users' posted information for eWOM advertising in social networks

Mining customer product ratings for personalized marketing

Theories in Flux: Reimagining Theory Building in the Age of Machine Learning

A personalized recommender system based on web usage mining and decision tree induction

A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption

Predicting m-commerce adoption determinants: A neural network approach

An intelligent multi-agent e-services method-An international telecommunication example. Information and Management

A dynamic logistics process knowledge-based system -An RFID multi-agent approach. Knowledge-Based Systems

Toward a hybrid data mining model for customer retention. Knowledge-Based Systems

BizPro: Extracting and categorizing business intelligence factors from textual news articles

(Re)considering the concept of literature review reproducibility

Machine learning for direct marketing response models: Bayesian networks with evolutionary programming

Recommendation system exploiting aspect-based opinion mining with deep learning method

Yahoo! for amazon: Sentiment extraction from small talk on the Web

Modelling user preferences and mediating agents in electronic commerce. Knowledge-Based Systems

Distributed Data Mining in Peer-to-Peer Networks

How artificial intelligence will change the future of marketing

Intelligent personal assistants: A systematic literature review

Autonomous Shopping Systems: Identifying and Overcoming Barriers to Consumer Adoption

Recognizing users feedback from non-verbal communicative acts in conversational recommender systems

Session stitching using sequence fingerprinting for web page visits

Estimating aggregate consumer preferences from online product reviews

Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research

An interactive knowledge-based recommender system for fashion product design in the big data environment

Artificial intelligence for decision making in the era of Big Data -evolution, challenges and research agenda

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Setting the future of digital and social media marketing research: Perspectives and research propositions

Big data and social media: A scientometrics analysis

A generalized stereotype learning approach and its instantiation in trust modeling. Electronic Commerce Research and Applications

Predicting adoption probabilities in social networks

Learning approaches for developing successful seller strategies in dynamic supply chain management

Detecting the latent associations hidden in multi-source information for better group recommendation. Knowledge-Based Systems

Web data extraction, applications and techniques: A survey. Knowledge-Based Systems

Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences

Humanitarian supply chain: a bibliometric analysis and future research directions

Greedy function approximation: A gradient boosting machine

Reasoning about attitudes of complaining customers. Knowledge-Based Systems

Cross-domain ontology construction and alignment from online customer product reviews

An adaptive fuzzy recommender system based on learning automata

Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks

Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics

Branding in the era of digital (dis)intermediation

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

Using collaborative filtering to Weave an Information tapestry

Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval

Examining the impact of keyword ambiguity on search advertising performance: A topic model approach

Personal price aware multi-seller recommender system: Evidence from eBay. Knowledge-Based Systems

Machine learning and operation research based method for promotion optimization of products with no price elasticity history. Electronic Commerce Research and Applications

Intelligent support for sophisticated e-commerce services: An agent-based auction framework modeled after the New York stock exchange specialist system. E-Service

Augmenting the algorithm: Emerging human-in-the-loop work configurations

Pirasa: Strategic protocol selection for e-commerce agents

Recommendation using neighborhood methods with preference-relation-based similarity

A strategic framework for artificial intelligence in marketing

Artificial Intelligence in Service

Engaged to a robot? The role of AI in service

A comparison of collaborative-filtering algorithms for ecommerce

Information systems research on artificial intelligence and work: A commentary on

Conversational Recommender Systems and natural language: A study through the ConveRSE framework

Recommendation systems: Principles, methods and evaluation

A cooperative exchanging mechanism among seller agents for group-based sales

Application of the Information Bottleneck method to discover user profiles in a Web store

A Survey on Conversational Recommender Systems

Intelligent Cyber Logistics Using Reverse Auction in Electronic Commerce

Evolution of information systems research: Insights from topic modeling

Addressing cold-start: Scalable recommendation with tags and keywords. Knowledge-Based Systems

A burst-based unsupervised method for detecting review spammer groups

Evolution of knowledge sharing behavior in social commerce: An agent-based computational approach

Lying on the web: Implications for expert systems redesign

A survey of trust and reputation systems for online service provision. Decision Support Systems

Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach

Predicting Purchase Behavior of Website Audiences

Warning system for online market research -Identifying critical situations in online opinion formation. Knowledge-Based Systems

Electronic commerce: a manager's guide

A prescriptive analytics framework for efficient E-commerce order delivery. Decision Support Systems

Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence

Combining machine-based and econometrics methods for policy analytics insights. Electronic Commerce Research and Applications

AdROSA-Adaptive personalization of web advertising

Easishop: Ambient intelligence assists everyday shopping

Real-Time tactical and strategic sales management for intelligent agents guided by economic regimes

Decision support for improved service effectiveness using domain aware text mining. Knowledge-Based Systems

Predicting long-term product ratings based on few early ratings and user base analysis. Electronic Commerce Research and Applications

Forecasting market prices in a supply chain game. Electronic Commerce Research and Applications

Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey

A multidimensional trust formation model in B-to-C e-commerce: A conceptual framework and content analyses of academia/practitioner perspectives

Deep hybrid recommender systems via exploiting document context and statistics of items

Application of decision-tree induction techniques to personalized advertisements on internet storefronts

A recommender system using GA K-means clustering in an online shopping market

Learning for automatic personalization in a semantic taxonomy-based meta-search agent

Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites

Toward an Understanding of Consumer Attitudes on Online Review Usage

Distance-based outliers: Algorithms and applications

Controlled experiments on the web: Survey and practical guide

Applying Collaborative Filtering to Usenet News

Matrix Factorization Techniques for Recommender Systems

Supporting customer-oriented marketing with artificial intelligence: Automatically quantifying customer needs from social media

Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning

Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models

Understanding the role of artificial intelligence in personalized engagement marketing

Integration of Self-Organizing Feature Maps and Genetic-Algorithm-Based Clustering Method for Market Segmentation

Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce

ubiES: Applying ubiquitous computing technologies to an expert system for context-aware proactive services

Word sense disambiguation for spam filtering

Towards a web services and intelligent agentsbased negotiation system for B2B eCommerce

Information technology applications in hospitality and tourism: A review of publications from

Personalization of supermarket product recommendations

Deep learning

How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Science

Different but equal? a field experiment on the impact of recommendation systems on mobile and personal computer channels in retail

Learning to rank products based on online product reviews using a hierarchical deep neural network. Electronic Commerce Research and Applications

Visualization and analysis of clickstream data of online stores for understanding web merchandising

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Sentiment labeling for extending initial labeled data to improve semi-supervised sentiment classification. Electronic Commerce Research and Applications

A costsensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce

Pricing with local interactions on agent-based electronic marketplaces

Targeting customers for profit: An ensemble learning framework to support marketing decision-making

On automated e-business negotiations: Goal, policy, strategy, and plans of decision and action

Towards Context-aware Social Recommendation via Individual Trust. Knowledge-Based Systems

Predicting the influence of viral message for VM campaign on Weibo. Electronic Commerce Research and Applications

The effect of online reviews on product sales: A joint sentiment-topic analysis

A social recommender mechanism for location-based group commerce

A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship

Learning binary codes with neural collaborative filtering for efficient recommendation systems. Knowledge-Based Systems

A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

A Linguistic Intuitionistic Cloud Decision Support Model with Sentiment Analysis for Product Selection in E-commerce

The right music at the right time: Adaptive personalized playlists based on sequence modeling

Mining inter-organizational retailing knowledge for an alliance formed by competitive firms

Efficient Adaptive-Support Association Rule Mining for Recommender Systems

Face recognition using support vector model classifier for user authentication

Amazon.com recommendations: item-to-item collaborative filtering

Opinion observer

Online recommendations based on dynamic adjustment of recommendation lists. Knowledge-Based Systems

User preference modeling based on meta paths and diversity regularization in heterogeneous information networks

Fit evaluation of virtual garment try-on by learning from digital pressure data. Knowledge-Based Systems

Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems

Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

Trust beyond reputation: A computational trust model based on stereotypes

Evaluating journal quality and the association for information systems senior scholars' journal basket via bibliometric measures: Do expert journal assessments add value?

Global Journal Prestige and Supporting Disciplines: A Scientometric Study of Information Systems Journals

Recommender system application developments: A survey

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

Metahuman systems = humans + machines that learn

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

Mining competitor relationships from online news: A network-based approach

The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms

Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming

Man vs machine: Examining the three themes of service robotics in tourism and hospitality

An intelligent product-information presentation in E-commerce

Multiobjective e-commerce recommendations based on hypergraph ranking

A local and global event sentiment based efficient stock exchange forecasting using deep learning

The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges

Explaining data-driven document classifications

A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences

Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS)

Imagebased recommendations on styles and substitutes

Fintechs: A literature review and research agenda. Electronic Commerce Research and Applications

A novel methodology for optimizing display advertising campaigns using genetic algorithms

Know when to run: Recommendations in crowdsourcing contests

Will this session end with a purchase? Inferring current purchase intent of anonymous visitors. Electronic Commerce Research and Applications

Mail-man: A knowledgebased mail assistant for managers

How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electronic Markets

The impact of artificial intelligence on event experiences: A scenario technique approach

A literature review and classification of electronic commerce research

An intelligent knowledge-based chatbot for customer service

Deriving the pricing power of product features by mining consumer reviews

A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS. Electronic Commerce Research and Applications

A latent-class model for estimating product-choice probabilities from clickstream data

A recommender system based on implicit feedback for selective dissemination of ebooks. Information Sciences

Trust in recommender systems

Newsvendor Problems with Demand Shocks and Unknown Demand Distributions. Decision Sciences

A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce

Recommending items to group of users using Matrix Factorization based Collaborative Filtering

Improving collaborative filtering-based recommender systems results using Pareto dominance

Training attractive attribute classifiers based on opinion features extracted from review data. Electronic Commerce Research and Applications

On the use of optimization for data mining: Theoretical interactions and eCRM opportunities

ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines. Knowledge-Based Systems

Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis

Synthesizing information systems knowledge: A typology of literature reviews

Clickaware purchase prediction with push at the top

An encoder-decoder switch network for purchase prediction. Knowledge-Based Systems, 185, 104932

Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories. Knowledge-Based Systems

Agent-based merchandise management in business-to-business electronic commerce

A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method. Knowledge-Based Systems

An overview of anomaly detection techniques: Existing solutions and latest technological trends

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Systems

Scikit-learn: Machine learning in Python

Inductive Regression Tree and Genetic Programming Techniques for Learning User Web Search Preferences

The role of emotion in P2P microfinance funding: A sentiment analysis approach

Eye-tracking-based classification of information search behavior using machine learning: Evidence from experiments in physical shops and virtual reality shopping environments

Designing intelligent agents to support universal accessibility of E-commerce services

A neural graph embedding approach for selecting review sentences. Electronic Commerce Research and Applications

The impact of social media in business growth and performance: A scientometrics analysis

Efficient Parcel Delivery by Predicting Customers' Locations*. Decision Sciences

Are the most popular users always trustworthy? The case of Yelp

Shopping for privacy: Purchase details leaked to PayPal. Electronic Commerce Research and Applications

Negation scope detection for sentiment analysis: A reinforcement learning framework for replicating human interpretations

Trust-inspiring explanation interfaces for recommender systems

Modeling Side Information in Preference Relation based Restricted Boltzmann Machine for recommender systems

Mining customer requirements from online reviews: A product improvement perspective

Leveraging sentiment analysis at the aspects level to predict ratings of reviews

A survey of approaches to automatic schema matching

A hybrid multicriteria recommender system using ontology and neuro-fuzzy techniques

Social emotion classification of short text via topic-level maximum entropy model. Information and Management

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems

GroupLens: An open architecture for collaborative filtering of netnews

Recommender Systems. Communications of the ACM

Reference Publication Year Spectroscopy (RPYS) with publications in the area of academic efficiency studies: What are the historical roots of this research topic?

Introduction to recommender systems handbook

Feature subset selection for predicting the success of crowdfunding project campaigns

Review on computational trust and reputation models

Restricted Boltzmann machines for collaborative filtering

Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowledge-Based Systems

Item-based collaborative filtering recommendation algorithms

Ranking online consumer reviews. Electronic Commerce Research and Applications

E-Commerce Recommendation Applications

Deep Learning in neural networks: An overview

An MDP-based recommender system

Social information filtering

Analytics for Cross-Border E-Commerce: Inventory Risk Management of an Online Fashion Retailer. Decision Sciences

CTF-ARA: An adaptive method for POI recommendation based on check-in and temporal features

A machine learning approach to product review disambiguation based on function, form and behavior classification

Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products

Trusted P2P transactions with fuzzy reputation aggregation

Recommendation systems and convergence of online reviews: The type of product network matters! Decision Support Systems, 142, 113475

How affordances of chatbots cross the chasm between social and traditional enterprise systems

A Survey of Collaborative Filtering Techniques

Identifying legitimate Web users and bots with different traffic profiles -an Information Bottleneck approach. Knowledge-Based Systems

Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns

How mood affects the stock market: Empirical evidence from microblogs

Consumer engagement via interactive artificial intelligence and mixed reality

Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Getting insights from the voices of customers: Conversation mining at a contact center

Realising platform operational agility through information technology-enabled capabilities: A resource-interdependence perspective

Business-to-business adoption of eCommerce in China

Discovery of Web Robot Sessions Based on their Navigational Patterns. Data Mining and Knowledge Discovery

INCAS: A legal expert system for contract terms in electronic commerce. Decision Support Systems

Anomaly detection in electronic invoice systems based on machine learning

A framework for guiding and evaluating literature reviews

Trustworthy artificial intelligence

A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Price prediction of e-commerce products through Internet sentiment analysis

An artificial intelligence system for predicting customer default in e-commerce

Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems

Preventing automatic user profiling in Web 2.0 applications. Knowledge-Based Systems

Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems

What is natural?: Challenges and opportunities for conversational recommender systems

Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems

Using SVD and demographic data for the enhancement of generalized Collaborative Filtering

On discovery of soft associations with "most" fuzzy quantifier for item promotion applications

Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems

Adapting topic map and social influence to the personalized hybrid recommender system

Argument form and spokesperson type: The recommendation strategy of virtual salespersons

Collaborative deep learning for recommender systems

Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis

Topic analysis of online reviews for two competitive products using latent Dirichlet allocation

Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines

Critical themes in electronic commerce research: A meta-analysis

An integrated environment for the development of parallel applications

Analyzing the Past to Prepare for the Future: Writing a Literature Review

Managing document categories in e-commerce environments: An evolution-based approach

Collaborative filtering and deep learning based recommendation system for cold start items

Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation

Robo-Apocalypse cancelled? Reframing the automation and future of work debate

Robo-Apocalypse? Response and outlook on the post-COVID-19 future of work

Data Mining: Practical Machine Learning Tools and Techniques

Visual appearance or functional complementarity: Which aspect affects your decision making? Information Sciences

Operationalizing regulatory focus in the digital age: Evidence from an e-commerce context

Customer segmentation of multiple category data in e-commerce using a soft-clustering approach

Design of electronic-commerce recommendation systems based on outlier mining

Grey Forecast model for accurate recommendation in presence of data sparsity and correlation. Knowledge-Based Systems

DNCP: An attentionbased deep learning approach enhanced with attractiveness and timeliness of News for online news click prediction. Information and Management

Slanderous user detection with modified recurrent neural networks in recommender system

Scalable collaborative filtering using cluster-based smoothing

A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce

Data mining of customer choice behavior in internet of things within relationship network

Estimating user behavior toward detecting anomalous ratings in rating systems. Knowledge-Based Systems

Re-scale AdaBoost for attack detection in collaborative filtering recommender systems. Knowledge-Based Systems

Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score. Electronic Commerce Research and Applications

Is a picture worth a thousand views? Measuring the effects of travel photos on user engagement using deep learning algorithms

Building a recommender agent for e-learning systems

A domain-feature enhanced classification model for the detection of Chinese phishing e-Business websites

Chinese comments sentiment classification based on word2vec and SVMperf

A survey on deep learning for big data

Credit risk evaluation model with textual features from loan descriptions for P2P lending

DeRec: A datadriven approach to accurate recommendation with deep learning and weighted loss function. Electronic Commerce Research and Applications

DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function

A novel hybrid deep recommendation system to differentiate user's preference and item's attractiveness

HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture

Detecting and predicting the topic change of knowledge-based systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems

Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach

Personalized location recommendation by fusing sentimental and spatial context. Knowledge-Based Systems

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

Machine learning based privacy-preserving fair data trading in big data market

Capturing the essence of wordof-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach

Selectively acquiring customer information: A new data acquisition problem and an active learning-based solution

Improving recommendation lists through topic diversification

Latent Dirichlet allocation for linking user-generated content and e-commerce data

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