thesis on e commerce

Theses on e-commerce topics

As a professor at a University of Applied Sciences, Prof. Geibel regularly supervises theses on e-commerce topics in the Business Administration , Media Management , Logistics and Entrepreneurship degree programs. If you would like some non-binding advice on the topics suggested here, on how to find a topic and on how to write a thesis, just use the comment function at the end of this article or write an email. An overview of the supervision procedure can also be found at the end of the post.

Theses can also be written in cooperation with companies. The companies can propose their own topic and provide a company representative as second supervisor of the thesis. Prof. Geibel has had very good experience with this configuration and is happy to put companies and students in touch with each other.An overview of topics already worked on and suggested topics can be found in this article. Please send requests for theses to: geibel (at) ecommercinstitut.de .

For students but also for companies who would like to have a topic worked on as part of a thesis, the following list of topics for future theses could be inspiring:

Elaborated topics of final theses:

Online food retailing in Germany – a comparative market and competition analysis for Rewe Digital and Amazon Fresh.
The Digitization of Stationary Food Retailing – Potential Analysis for the Use of Digital Technologies
State of Online Marketing in the Italian Tourism Industry: Empirical Analysis and Case Study
An Analysis of Amazon’s Business Model and Customer Loyalty Strategies
Benchmarking Study on the Optimization Level of Mobile Shopping Sites of Zalando and About You
Chances and Challenges in the Area of E-Health due to Digital Transformation
Transformation from car manufacturer to mobility provider – opportunities and challenges in the development of mobility platforms
Online marketing organization in transition – how do dynamic advertising technologies influence the marketing culture in the company?
The importance of brand communities for (online) brand management. The benefits of Facebook Fan Pages using selected examples.
Potential analysis of social media channels in relation to the success of customer retention.
A current analysis of online furniture retailing in Germany
External effects in e-commerce
The digital future of stationary trade – potentials and advantage strategies in competition with online trade using the example of the textile industry
Social Media as a Marketing Platform – Opportunities and Strategies for Self-Marketing on Instagram Using the Example of Fashion Bloggers
Adblockers: An analysis of the increasing popularity of ad blockers and their impact on traditional online marketing
Market segmentation in e-commerce: An A/B impact study of different newsletter contents
Gender Commerce – Gender-specific differences in online purchasing
Analysis of the online trade with drugstore articles in Germany with special consideration of logistical aspects
Target group segmentation in e-commerce
Social media and e-commerce
Sales financing in e-commerce
Competitive strategies in e-commerce
Rise and decline of social networks
Acceptance problems in mobile payment
Key figure systems in e-commerce
Comparison & requirement-oriented evaluation of modern online store systems
Design of financial transactions as a success factor in e-commerce
The battle for the mobile Internet – an industrial economic analysis of the competitive strategies of Google, Microsoft and Apple
Success potentials of mobile marketing
Success factors of social media for non-governmental organizations
Success potentials of mobile payment systems in the German market
Storytelling in brand communication – strategies and recommendations for action depending on customer involvement
Augmented reality as a success factor in mobile commerce? Opportunities, limits and risks
Targeted customer relationships in social media touchpoints – opportunities and limits of attribution modeling
Creation of a concept for the implementation of location-based services in the marketing strategy in the hospitality industry
Content marketing in long-tail e-commerce – creation of a marketing and communication concept for an online store

Published theses:

Learn more about these successful theses from our alumni:

The importance of AI in online marketing – a critical examination of the opportunities and challenges for promotion
Blockchain and alternative technologies in autonomous driving passenger cars
Influence of digitalization in the real estate industry – challenges and opportunities for improving the UX
The Impact of Digital Transformation on Change Management – A Critical Analysis of Current Concepts in the Consulting Industry
Evaluating intrapreneurship in corporate innovations – an analysis between scale-ups and large corporations
Future Mobility Concepts – A Critical Analysis of the Impact of Digitalization on the Automotive Industry
Sustainability in online retail using the example of Amazon
Critical examination of a digital sales platform – opportunities and risks for CRM

Procedure of supervision for final theses

Based on the experience of many successful theses, the following procedure has been established for the supervision of theses:

Initial contact and consultation to narrow down the topic
Selection of the topic
Overview of literature or empirical content
Linkage with academic and professional goals
Selection of first and second supervisors
Registration of the thesis by the student
Preparation and discussion of the outline
Independent preparation of the thesis by the students
Submission of the thesis by the students
Evaluation of the thesis by the examiners
Possible publication of results of the thesis

Beyond the topics suggested here, both students and companies can make their own suggestions, for which Prof. Geibel will be happy to consider realization. Suggestions are best sent to info (at) ecommerceinstitut.de .

Privacy Overview

DSpace@MIT Home
MIT Libraries
Graduate Theses

Improving e-Commerce sales using machine learning

Alternative title

Other contributors, terms of use, description, date issued, collections.

Show Statistical Information

How it works

Useful Links

How much will your dissertation cost?

Have an expert academic write your dissertation paper!

Dissertation Services

Get unlimited topic ideas and a dissertation plan for just £45.00

Order topics and plan

Get 1 free topic in your area of study with aim and justification

Yes I want the free topic

E-commerce Dissertation Topics

Published by Carmen Troy at January 10th, 2023 , Revised On August 18, 2023

Introduction

Studying e-commerce helps in understanding how online businesses work. As a student of e-commerce, you will be required to learn about the various electronic mediums that online businesses rely upon and how online operations and transactions are carried out.

Essentially, e-commerce is studying the different technologies involved in setting up an online business. These include automated data collection systems, artificial intelligence, user design, inventory management systems, electronic data interchange systems (EDI), online transactions processing, industry-specific user tools, internet marketing, supply chain management, news breaking, and electronic funds transfer, and mobile commerce.

To help you get started with brainstorming for e-commerce topic ideas, we have developed a list of the latest topics that can be used for writing your e-commerce dissertation.

PhD qualified writers of our team have developed these topics, so you can trust to use these topics for drafting your dissertation.

You may also want to start your dissertation by requesting a brief research proposal from our writers on any of these topics, which includes an introduction to the topic, research question , aim and objectives , literature review along with the proposed methodology of research to be conducted. Let us know if you need any help in getting started.

Check our dissertation examples to get an idea of how to structure your dissertation .

Review the full list of dissertation topics for 2022 here.

2022 E-commerce Dissertation Topics

Topic 1: an assessment of the geographical constraints impacting the business flow of e-commerce- a case study of amazon..

Research Aim: This study aims to find the impact of geographical constraints on the flow of e-commerce business focusing on Amazon. In this research, we will analyse how these geographical restrictions affect the flow of business. In many areas, e-commerce may drive the decline of small stores and may also affect the local producers and the global economy.

Topic 2: An investigation of the security controls and issues of e-commerce websites in the UK online environment.

Research Aim: With the development of the global economy and an increasing number of customers running their business largely through the internet or mobile has made e-commerce grow. Creating an effective strategy is the most integral part of a modern organization; however, a company must take care of the new security concerns and problems while maintaining their quality and high standard. This study will primarily focus on the security control and issues of e-commerce websites in the UK and how they cope with them to keep the data secured.

Topic 3: Google ads vs. Search ads in e-commerce- A comparative study

Research Aim: An organization may find it challenging to decide what kind of advertisement they should use for their online campaigns. This study will provide a comparative analysis of google ads and search ads and give us an understanding of both ads, focusing on which is better according to the budget of the organisation, which will help them gain a significant audience and grow the business.

Topic 4: An examination of consumer decision-making processes- A comparative study between e-commerce and m-commerce in the United Kingdom.

Research Aim: The growth of eCommerce is increasing day by day. This research aims to find the factors affecting the online purchase decision. This study will provide a comparative analysis of eCommerce and m-commerce in the UK, focusing on different factors and characteristics of both. This study will differentiate the features of eCommerce and m-commerce and identify the main factors which influence the long run of online marketing to provide better services for consumers and make them know about new business opportunities.

Topic 5: Gender Inequality in the Ecommerce Industry.

Research Aim: Women in industries are still facing substantial issues in their daily lives as well as in their professional lives; for example, salary gaps, discrimination, maternity leave are just simple examples that influence the everyday lives of thousands of individuals around the world. This study will focus on the gender inequality and biased behaviours individuals, especially women, face in the eCommerce industry.

Covid-19 E-commerce Research Topics

Topic 1: impacts of coronavirus on e-commerce.

Research Aim: The study will focus on identifying the effects of coronavirus on E-commerce.

Topic 2: Frequent E-commerce shopping during Coronavirus pandemic

Research Aim: Coronavirus has affected almost every business, including E-commerce. This study will investigate the reasons behind increasing online shopping, challenges faced by E-commerce industries, and measures taken to improve the business.

Topic 3: Contribution of E-commerce during COVID-19

Research Aim: This study will identify E-commerce industries’ contribution during the coronavirus pandemic. What safety measures have they taken to provide safe deliveries of the products? What kind of challenges they faced?

Topic 4: Offline and online shopping after COVID-19

Research Aim: This study will focus on reviewing the current positive and negative impacts on online shopping and shopping in stores and predict the future of shopping after COVID-19, listing the differences, challenges, benefits, and risks of both online and offline shopping.

E-commerce Dissertation Topics for 2021

Topic 1: impact of digital business on the economic growth of the country: a case study of xyz country.

Research Aim: This research will focus on the significance of digital business during the pandemic and its impacts, not the country’s economic growth. It is a detailed view of the future that needs to be digitalised.

Topic 2: Brand marketing through social media

Research Aim: This research aims to focus on the importance of Brand Marketing through social media by addressing various current strategies used in brand marketing.

Topic 3: Impacts of social media on customer behaviour

Research Aim: This research aims to measure social media’s impacts on customer behaviour and address various effective strategies to attract customers through social media.

Topic 4: What factors influence the consumer's buying decisions?

Research Aim: This research aims to identify factors that influence the consumer’s buying decisions

Topic 5: Black Friday sale strategy to drive sales

Research Aim: This research aims to identify how the Black Friday sale strategy is effective in driving sales. How can huge discounts benefit sellers?

Topic 6: The role of influencer marketing in increasing sale

Research Aim: Influencer Marketers impact the customer’s perception. This research aims to the role of influencer marketing in increasing sales.

Topic 7: Impact of E-marketing on consumer purchase decision: the case of the luxury industry in the UK

Research Aim: This research aims to measure E-marketing’s impact on consumer purchase decisions in the U.K luxury industry.

Topic 8: Analysis of the customer-centric marketing strategies in attaining competitive advantage for the firm and sustaining business success

Research Aim: This research focuses on attaining customer-centric marketing strategies in a competitive advantage for the firm and sustaining business success.

Topic 9: Traditional vs. digital marketing: a comparative study of the last ten years

Research Aim: This research aims to conduct a comparative study of traditional vs. digital marketing in the last ten years.

Topic 10: The impact of relationship marketing on customer loyalty: an analysis of the Honda motor

Research Aim: This research aims to assess the impact of relationship marketing on customer loyalty. An analysis of the Honda motor will be conducted as the basis of the research.

Topic 11: The importance of search engines in e-commerce

Research Aim: This research aims to identify the importance of search engines in e-commerce.

Topic 12: E-commerce company's advertising strategy-critical analysis

Research Aim: This research aims to identify the importance of an E-commerce company’s advertising strategy.

Topic 13: Importance of customer retention in E-commerce

Research Aim: This research aims to measure the importance of customer retention in E-commerce.

Topic 14: Importance of brand loyalty in internet marketing

Research Aim: This research aims to identify the importance of brand loyalty in internet marketing.

E-commerce Dissertation Topics for 2020

Topic 1: analysing the impact of e-commerce strategies on building better relationships with customers: a case study of the uk fashion industry.

Research Aim: the UK fashion industry is a fragmented zone where a large number of famous brands have been competing to gain a competitive edge through better customer relationships. For the same purpose, effective e-commerce strategies can help in building better customer relationships. Thus, the main purpose of this research will be to analyse the impact of e-commerce strategies on building better relationships with customers of the UK Fashion industry.

Topic 2: Assessing the impact of unique website attributes on consumer buying pattern: A case study of Amazon and eBay

Research Aim: The rise of information technology has led famous brands to develop unique attributes for their websites to encourage their audience to buy. One of the most notable issues of the e-commerce industry is the ever-increasing competition amongst online retailers that offer user-friendly and unique website design and UX features to achieve favourable results. The purpose of this study will be to assess the impact of website attributes on consumer buying patterns with a focus on Amazon and e-bay.

Topic 3: How does e-commerce facilitate adding value to a business: A case study of service industry in China.

Research Aim: In today’s world with tech-savvy consumers, online purchases are much higher as compared to traditional, in-store purchases. This persuades the pioneers of the service industry to add value to their business by providing e-commerce facilities to consumers. Therefore, the main purpose of this research will be to analyse how e-commerce facilitates and adds value to a business with a focus on China service industry.

Topic 4: Critical analysis of security policies and vulnerabilities of an online banking website: Identifying the challenges and remedies to improvise risk management

Research Aim: The number of internet users around the world is increasing with each passing year, however, this has also posited various security problems for online banking websites. The main purpose of this study will be to critically analyse security policies and the vulnerabilities of online banking websites, along with the identification of challenges and remedies for improvising risk management.

Topic 5: Can e-commerce help organisations build a competitive advantage over their competitors?

Research Aim: This research will talk about the role of e-commerce in helping organisations build a competitive advantage against their competitors. This research will understand how e-commerce, through advanced technology, helps businesses attain their business objective and how it helps them facilitate their customers.

Topic 6: Exploring the regulations and guidelines set out for e-commerce companies

Research Aim: This research will understand the rules and regulations set out by the government and regulating authorities to implement safe and secure e-commerce. When offering online payment services to their customers, businesses have to make sure that they comply with the laws set out by the government so that customers’ payments are safe and secure.

Topic 7: Analyzing the best security mechanisms that should be implemented by e-commerce businesses

Research Aim: When implementing e-commerce models, it is necessary for businesses to make sure that the best security mechanisms are put in place. Customers trust companies which is why they make online payments. Thus the development of secure payment gateways is vitally important to keep the trust of customers. This research will analyse the various security mechanisms available for companies and how businesses should implement them.

Topic 8: Exploring the data privacy issues in e-commerce

Research Aim: This research will be focused on trust issues surrounding e-commerce. Customers make credit card payments trusting the company and the technology in use. However, there have been instances where companies failed to protect customer data, and their privacy is compromised. Such incidents can cause the company to lose its reputation and find itself having to deal with legal issues. This study explores the various privacy issues that customers come across when buying online.

Topic 9: E-Commerce and customer retention – What role does e-commerce play?

Research Aim: This research will explore an important aspect of businesses i.e. customer retention. The study will analyse whether or not e-commerce helps businesses in retaining customers. What are the various causes and reasons people trust e-commerce and stay loyal to a brand if it does? This research will investigate all the possibilities to conclude whether e-commerce plays a role in retaining customers or not.

Topic 10: Security limitations and challenges of implementing e-commerce.

Research Aim: This research will explore the challenges and security limitations businesses have to deal with when building an e-commerce business. The study will include the various security elements that companies have to consider when implementing e-commerce models, the challenges they encounter, and the steps that they take in order to ensure the security of customers’ data as well as their own systems.

E-commerce Marketing Dissertation Topics

Their marketing and advertising strategies largely influence e-businesses. Without having a well-rounded and educated marketing strategy, an e-business in today’s cut-throat online environment will surely struggle to succeed.

Web admins and online marketing experts employ various marketing strategies to engage potential customers on social networks, banner advertisements, and paid advertisements.

The internet has played a vital role in making data available to everyone, making it possible to target customers based on their demographics and social media profiles. Thus, from these intriguing and up-to-date e-commerce marketing topics, you can choose the most suitable one for your own dissertation project.

Topic 1: E-commerce and the importance of search engine rankings for businesses

Research Aim: This research will identify the importance of search engine ranking for e-commerce businesses.

Topic 2: Investigating internet marketing strategies employed by traditional retailers

Research Aim: This research will explore and evaluate the internet marketing strategies undertaken by businesses.

Topic 3: Retaining customers by employing e-commerce: A case study of the UK fashion industry

Research Aim: This research will explore the ways through which e-commerce businesses can retain their customers. A specific focus of this study will be the UK Fashion industry.

Topic 4: Wholly online or one foot in both worlds – The advantages and disadvantages of the two commonly employed marketing strategies - online and conventional models

Research Aim: This research will aim to understand the pros and cons of two main marketing strategies employed by companies. Also, this research will study businesses that run both online and traditional businesses.

Topic 5: To investigate internet marketing strategies employed by e-commerce retailers

Research Aim: This research will understand the internet marketing strategies undertaken by e-commerce retailers.

Topic 6: Understanding the effect of customer behaviour on internet marketing strategies

Research Aim: This research will understand how internet marketing strategies change consumer behaviour.

Topic 7: Measuring the success of marketing strategy employed by new e-businesses – A case study of the UK retail industry

Research Aim: This research will aim to measure the success of marketing strategies adopted and employed by companies in the UK retail sector.

Topic 8: Measuring the success of internet marketing strategy employed by traditional businesses – A case study of the airline industry

Research Aim: This research will explore the success of internet marketing strategies employed by traditional businesses in the airline industry of the UK.

Topic 9: The role of original and plagiarism free content in today’s e-marketing strategies

Research Aim: This research will explore an important concept of internet marketing i.e. content marketing. The importance of content quality and authenticity will be evaluated in this study.

How Can ResearchProspect Help?

ResearchProspect writers can send several custom topic ideas to your email address. Once you have chosen a topic that suits your needs and interests, you can order for our dissertation outline service which will include a brief introduction to the topic, research questions , literature review , methodology , expected results , and conclusion . The dissertation outline will enable you to review the quality of our work before placing the order for our full dissertation writing service !

E-commerce Strategy Dissertation Topics

The importance and the role of an effective e-commerce strategy should never be overlooked especially when promoting a product or service. In today’s highly “technology-oriented” world, having an internet presence is considered a requirement.

A well-rounded design can help e-businesses become leaders in their respective industries. Some related topics are listed below:

Topic 1: Evaluating internet marketing strategies employed by existing e-businesses

Research Aim: This research will aim to explore the various internet marketing strategies employed by various existing businesses. The dissertation will identify the most successful online marketing strategies for the last five years in the UK e-commerce industry.

Topic 2: The challenges and opportunities for organisations migrating to the internet

Research Aim: This research will explore the challenges and opportunities that companies come across when transitioning from a traditional to an e-commerce model.

Topic 3: Accident or Design – Do we really have an internet marketing mix model/strategy that is sure to work?

Research Aim: This research will aim to explore whether we really have an internet marketing mix model/strategy that is sure to work or do online marketing strategies become successful by accident.

Topic 4: Investigating the use of customer service in e-commerce to gain a competitive advantage

Research Aim: This research will investigate the role of friendly and efficient customer services in the success of e-commerce strategy

Topic 5: Exploring the most effective aspects of e-commerce strategy in today’s world

Research Aim: This research will outline the most important qualities of an e-commerce strategy to be successful in today’s fast-moving world.

Topic 6: Exploring the various internet business value creation strategies employed by e-businesses

Research Aim: This research will explore and investigate the different internet value creation strategies adopted by e-commerce businesses.

Topic 7: Measuring performance of an e-business marketing strategy

Research Aim: This research will aim to measure the success of an e-commerce strategy implemented by an e-commerce business. This topic can be customised to focus on a specific company or a specific strategy.

Topic 8: Investigating e-business strategies employed by educational institutes in the UK

Research Aim: The educational sector has adopted e-commerce to attract students from around the world. This study will aim to investigate the e-commerce strategies implemented by educational institutes to evaluate their success.

Topic 9: Reviewing the e-business strategies employed by the UK SME’s

Research Aim: This research will compare and analyse the best e-commerce strategy implemented by SMEs in the UK.

Topic 10: How effective e-commerce strategies can help companies in building their brand reputation

Research Aim: This research will understand the various strategies implemented by companies and will conclude whether implementing them helps companies build their brand reputation.

E-commerce Security and Trust Dissertation Topics

The importance of trust and security in e-commerce has greatly increased in recent times, thanks to the growing number of threats that exist on the internet. When companies decide to implement e-commerce models, they entrust their customers that their data and privacy will be protected.

On the other hand, customers also make e-commerce payments trusting the company with their information. Thus, exploring these two essentials of e-commerce will help understand how successful companies have been in assuring customers about their security systems. Here are some commerce trust and security topics for you to choose from.

1. E-Commerce Trust Dissertation Topics

Topic 1: trust in e-commerce – reality or myth.

Research Aim: This research will explore the trust aspect of e-commerce as to whether it really exists or is just a myth.

Topic 2: Investigating data privacy issues in e-commerce and how they affect businesses

Research Aim: This research will explore the data privacy issues in the e-commerce industry and how they affect businesses.

Topic 3: Data Protection Act: Does it help in building trust in e-commerce

Research Aim: This research will understand the data protection act. It will also analyze whether or not it helps businesses to build trust. The research will explore whether changes to this act are required or not.

Topic 4: How has anti-virus technology helped the e-commerce industry?

Research Aim: This research will explore the effectiveness of anti-virus software and whether it has helped protect the e-commerce industry.

Topic 5: Investigating strategies used by retailers to build up trust among potential and existing customers

Research Aim: This research will analyse the strategies utilised by retailers in the UK to build trust among customers.

2. E-commerce Security Dissertation Topics

Topic 1: to identifying the security limitations that led to third party attacks in the past.

Research Aim: This research will analyse the past third-party attacks and will explore the reasons as to why those happened.

Topic 2: An empirical study of e-commerce security, challenges, and solutions

Research Aim: This research will discuss the basics of e-commerce security, the challenges faced by the industry and its solutions.

Topic 3: Investigating strategies employed by e-commerce businesses to enhance the security of e-commerce transactions

Research Aim: This research will aim to understand the various strategies that are employed by e-commerce businesses to enhance the security of e-commerce transactions.

Topic 4: Exploring the effectiveness of encryption in the e-commerce industry

Research Aim: This research will investigate the effectiveness of encryption and the reason why the e-commerce industry adopted it.

Topic 5: Online reputation management: Exploring how e-commerce companies in the UK fashion industry practice it

Research Aim: This research will discuss a relatively new concept, online reputation, and will explore how the UK fashion industry practices it.

E-commerce Usability Dissertation Topics

Not many e-businesses pay enough attention to the usability of their e-commerce website. It should be noted that the complex ordering and navigation system leads to higher bounce rates, leaving companies with very little or no revenue.

Companies should build a user-friendly user interface, or else visitors will prematurely give up and abandon their shopping cart. To explore this aspect of e-commerce, here are some latest research topics:

Topic 1: A comparative analysis of the usability of the world’s leading travel websites

Research Aim: This research will compare the website user interface of leading airlines such as Emirates, Qatar Airways, Turkish Airlines, etc.

Topic 2: Evaluating the website design and structure of the leading UK retail stores

Research Aim: This research will evaluate the website design and structure of the leading retail stores in the UK.

Topic 3: Assessing the website usability and design interface of government websites in the U.A.E.

Research Aim: This research will analyse the websites user interface of government-run websites in the United Arab Emirates.

Topic 4: Reviewing user-friendly design options for an e-commerce website for an online clothing store

Research Aim: This research will explore the website UX design options that must be taken into consideration by companies to ensure user-friendliness and smooth flow.

Topic 5: An analysis of the usability of m-commerce applications

Research Aim: This research will discuss and analyse the m-commerce application of the web systems as to how they should be built, keeping user-friendliness in mind.

Topic 6: Customer preferences and behaviour: Should these be considered when building a website?

Research Aim: This research will consider two important aspects of website development – customer preferences and behaviour. The study will discuss the importance of being mindful of consumer behaviour and consumer preferences when building a brand new website.

Topic 7: The impact of poorly designed websites on a company’s revenues

Research Aim: This research will assess the effects of poorly designed websites on a company’s revenues.

Topic 8: Customer satisfaction and usability: Are they both related to the e-commerce?

Research Aim: This research will explore the most important factor related to business, i.e. customer satisfaction and how it relates to website UX designing.

Topic 9: Critically analysing the UX design technologies employed by e-commerce businesses.

Research Aim: This research will critically analyse the current technologies employed by e-commerce businesses.

Order a Proposal

Worried about your dissertation proposal? Not sure where to start?

Choose any deadline
Plagiarism free
Unlimited free amendments
Free anti-plagiarism report
Completed to match exact requirements

E-commerce Law Dissertation Topics

Governments across the globe have reacted slowly to technological advancements being made over the last fifty years. The lobby groups behind large profit-making organisations play a huge part in the e-commerce laws being made.

The Digital Millennium Copyright Act and the Copy Right Act are two classic examples of such laws created to protect the interests of those in power. Consequently, the benefits of the legislation are not being experienced by the general public. Thus, it is worth exploring this aspect of e-commerce. Some suggestions are provided below if you wish to base your e-commerce dissertation on law:

Topic 1: To compare the e-commerce regulations in the United States of America and the European Union for new and existing e-commerce businesses

Research Aim: This research will compare and analyse the e-commerce laws and regulations for new and existing businesses in the United States of America and the European Union,

Topic 2: The role of consumer protection laws in the development of e-commerce – The case of the UAE

Research Aim: This research will explore customer protection laws and their role in the development of e-commerce in the UAE.

Topic 3: Computer Misuse Act 1990: Is it relevant today in the e-commerce industry?

Research Aim: This research will explore the computer misuse act 1990 and whether or not it is relevant today in the e-commerce industry.

Topic 4: Exploring how Brexit will impact the e-commerce laws for companies in the UK

Research Aim: This research will explore the impact of Brexit on the e-commerce industry in the UK and whether or not there will be new laws.

Topic 5: The impact of the American Copyright Act extension on e-commerce

Research Aim: This research will explore the impact of the American Copyright Act extension on e-commerce.

Topic 6: Analysing the impact of international legislation on the e-commerce industry

Research Aim: This research will analyse the impact of international legislation on the e-commerce industry.

Topic 7: Investigating UK’s legislation concerning e-businesses and how it affects businesses

Research Aim: This research will understand how UK legislation has set out e-commerce rules and how it impacts businesses.

Topic 8: Exploring the effectiveness of e-commerce laws and legislation as a deterrent to cyber attacks.

Research Aim: This research will understand whether or not e-commerce laws and legislation act as a deterrent to computer attacks and how effective they have been.

Topic 9: The implications of the Data Protection Act 1988 for e-businesses

Research Aim: This research will aim to understand the implications of the Data Protection Act 1988 for e-commerce businesses of today.

Topic 10: An analysis of the lawfulness of the e-commerce industry

Research Aim: This research will explore in-depth the laws and legislation related to e-commerce and how well they are adopted and implemented by e-commerce businesses.

Mobile E-commerce Dissertation Topics

Studies performed on e-commerce by various researchers reveal that mobile e-commerce will be the next “big thing” in the e-commerce industry.

With smartphones being the emerging and driving force in technology, the use of the internet in today’s world is not limited only to desktops and laptops. All smartphones using android and IOS applications allow users to browse the internet.

Consequently, more and more retailers are upgrading their websites to make them responsive and friendlier to mobile visitors. In this regard, some savvy e-commerce retailers are developing delivery mechanisms that satisfy the new platform’s needs.

Thus, it will be interesting to explore this aspect of e-commerce as it will give an insight into the current e-commerce industry. Here are some interesting mobile e-commerce dissertation topics that you can choose from.:

Topic 1: Wireless security and its effectiveness in the e-commerce Industry

Research Aim: This research will explore the concept of wireless security and how it helps the e-commerce industry.

Topic 2: Analysing the use of m-commerce by customers today – Understanding their adoption pattern

Research Aim: This research will aim to understand the e-commerce adoption rate and what compels customers to move towards m-commerce.

Topic 3: Investigating m-commerce strategies employed airline sector in the UAE

Research Aim: This research will investigate the m-commerce strategies that are employed by the airline sector in the UAE.

Topic 4: Analysing m-commerce innovation in the travel sector of the UK

Research Aim: This research will analyse the quick adoption rate of m-commerce rate in the travel sector in the UK.

Topic 5: Combining the benefits of m-commerce with the benefits of traditional commerce and e-commerce – A study of any multi-national retailer

Research Aim: This research will present a comparative analysis of traditional commerce and e-commerce and how multinationals benefit from it. This topic can be customised to a country or company of your choosing.

Topic 6: The effects of m-commerce on economic development in Europe

Research Aim: There is no doubt that e-commerce and m-commerce have played a huge role in developing economies. This research will investigate the impact of m-commerce on Europe’s economic development.

Topic 7: Trust and security issues in m-commerce: How companies can overcome them

Research Aim: This research will present some major trust and security issues associated with m-commerce and explore how companies can overcome these challenges.

Topic 8: The impact of m-commerce user interface on companies’ revenues

Research Aim: This research will first understand the importance of user interface in m-commerce and will then assess its impact on the company’s profitability.

Topic 9: Understanding the role and importance of data security in m-commerce – How it can be ensured

Research Aim: Just like e-commerce, m-commerce also has its own data security issues. This research will understand the role of the importance of data security and discuss how it can be ensured.

Topic 10: Generating revenue through m-commerce – Challenges and opportunities

Research Aim: This research will understand the challenges and opportunities associated with revenue generation through m-commerce.

Hire an Expert Writer

Orders completed by our expert writers are

Formally drafted in academic style
Free Amendments and 100% Plagiarism Free – or your money back!
100% Confidential and Timely Delivery!
Appreciated by thousands of clients. Check client reviews

Important Notes:

As a student of e-commerce looking to get good grades, it is essential to develop new ideas and experiment with existing e-commerce theories – i.e., to add value and interest in your research topic.

The field of e-commerce is vast and interrelated to so many other academic disciplines like business , marketing , management , and even project management . That is why it is imperative to create an e-commerce dissertation topic that is particular, sound, and actually solves a practical problem that may be rampant in the field.

We can’t stress how important it is to develop a logical research topic; it is the basis of your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, and there is a possibility that the study is not viable.

This impacts your time and efforts in writing your dissertation as you may end up in the cycle of rejection at the very initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

While developing a research topic, keeping our advice in mind will allow you to pick one of the best e-commerce dissertation topics that fulfil your requirement of writing a research paper and add to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and can also be practically implemented. Take a look at some of our sample e-commerce dissertation topics to get an idea for your own dissertation.

How to Structure your E-commerce Dissertation

A well-structured dissertation can help students to achieve a high overall academic grade.

A Title Page
Acknowledgements
Declaration
Abstract: A summary of the research completed
Table of Contents
Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems. An outline of the structure of a dissertation can also be added to this chapter.
Literature Review : This chapter presents relevant theories and frameworks by analyzing published and unpublished literature on the chosen research topic to address research questions . The purpose is to highlight and discuss the selected research area’s relative weaknesses and strengths while identifying any research gaps. Break down the topic and key terms that can positively impact your dissertation and your tutor.
Methodology : The data collection and analysis methods and techniques employed by the researcher are presented in the Methodology chapter, which usually includes research design , research philosophy, research limitations, code of conduct, ethical consideration, data collection methods, and data analysis strategy .
Findings and Analysis : Findings of the research are analyzed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include graphs, charts, and tables in this chapter to identify meaningful trends and relationships.
Discussion and Conclusion : The researcher presents his interpretation of the results in this chapter and states whether the research hypothesis has been verified or not. An essential aspect of this section is establishing the link between the results and evidence from the literature. Recommendations with regards to the implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
References : Make sure to complete this following your University’s requirements
Bibliography
Appendices : Any additional information, diagrams, and graphs used to complete the dissertation but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

About ResearchProspect Ltd

ResearchProspect is a UK based academic writing service that provides help with Dissertation Proposal Writing , Ph.D. Proposal Writing , Dissertation Writing , Dissertation Editing, and Improvement .

Our team of writers is highly qualified. They are experts in their respective fields. They have been working for us for a long time. Thus, they are well aware of the issues and the trends of the subject they specialize in.

Need more Topics.?

Review Our Best Dissertation Topics 2022 complete list.

Free Dissertation Topic

Phone Number

Academic Level Select Academic Level Undergraduate Graduate PHD

Academic Subject

Area of Research

Frequently Asked Questions

How to find dissertation topics about e-commerce.

For E-commerce dissertation topics:

Examine emerging trends in online business.
Investigate consumer behavior and preferences.
Analyze impacts of technology on E-commerce.
Explore security and privacy concerns.
Study E-commerce strategies and marketing.
Choose a specific area aligning with your expertise and curiosity.

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

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

Cite this article

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

65k Accesses

74 Citations

9 Altmetric

Explore all metrics

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

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

E-commerce and Business Analytics: A Literature Review

Survey of Social Commerce Research

Explore related subjects.

Artificial Intelligence

Avoid common mistakes on your manuscript.

Introduction

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

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

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

Research method

Bibliometric analysis.

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

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

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

Summary of the data collection phase

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

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

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

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

Literature review

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

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

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

Results of the bibliometric analysis

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

Number of publications on AI in e-commerce per year

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

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

Functional perspective

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

Interpretive perspective

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

Normative perspective

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

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

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

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

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

Results of the literature review study

Classification by topic area.

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

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

Classification by research style

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

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

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

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

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

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

Classification by research theme

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

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

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

Need for more research from other countries

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

Focus on recommender systems

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

Limited research themes

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

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

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

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

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

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

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

More experiment than theory-driven research

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

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

Implications for research

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

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

Implications for practice

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

Limitations

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

Future research

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

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

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

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

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

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

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

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

Conclusions

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

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

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

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

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

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

Article Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Download references

Author information

Authors and affiliations.

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

Ransome Epie Bawack

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

Samuel Fosso Wamba & Kevin Daniel André Carillo

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

Shahriar Akter

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ransome Epie Bawack .

Additional information

Responsible Editor: Jianwei Hou

Publisher's note

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

Rights and permissions

Reprints and permissions

About this article

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

Download citation

Received : 11 September 2021

Accepted : 22 February 2022

Published : 18 March 2022

Issue Date : March 2022

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

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Artificial intelligence
Bibliometrics

JEL classification

Find a journal
Publish with us
Track your research

My Shodhganga
Receive email updates
Edit Profile

Shodhganga : a reservoir of Indian theses @ INFLIBNET

Shodhganga@INFLIBNET
University of Mumbai
Commerce, K. P. B. Hinduja College Of Commerce

Title:	Study on impact of e commerce business on customers buying behaviour with special reference to Palghar district
Researcher:	Raut, Dhirajkumar Manik
Guide(s):
Keywords:	Business Economics and Business Electronic commerce Social Sciences
University:	University of Mumbai
Completed Date:	2021
Abstract:	File attached newline
Pagination:	xvii, 262p
URI:
Appears in Departments:

Description	Size	Format
Attached File	70.47 kB	Adobe PDF
	125.13 kB	Adobe PDF
	124.95 kB	Adobe PDF
	126.64 kB	Adobe PDF
	153.96 kB	Adobe PDF
	162.86 kB	Adobe PDF
	134.08 kB	Adobe PDF
	139.33 kB	Adobe PDF
	147.33 kB	Adobe PDF
	1.31 MB	Adobe PDF
	262.21 kB	Adobe PDF
	540.37 kB	Adobe PDF
	708.47 kB	Adobe PDF
	215.57 kB	Adobe PDF
	232.53 kB	Adobe PDF
	203.64 kB	Adobe PDF
	200.46 kB	Adobe PDF
	839.53 kB	Adobe PDF
	1.02 MB	Adobe PDF
	276.6 kB	Adobe PDF

Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

A DESCRIPTIVE STUDY ABOUT THE EFFECTIVENESS OF E -COMMERCE IN THE PHILIPPINES A Thesis Presented to Professor

Related Papers

International Journal of Management

IAEME Publication

The advancement in the sector of Information technology along with other emerging technical aspects has taken to the emergence and expansion of global e-commerce worldwide which permits mankind to meet their requirements of some goods and obtains monetary means on the other hand for their livelihood by running such ventures. The research paper aims at the study of online buying habits and growing opportunities for students & professionals in the field of e-commerce, particularly for Management, Commerce, and other courses. Furthermore, general consumers' perceptions were also recorded. The financial and communal earnings from e-commerce which advance modernization, industry upgrading and financial growth are also broadly touched upon. In current years, e-commerce has grown exponentially that led to both developed and developing countries as an economic dynamic force. Owing to the rapid progress of the internet and the revolution of the smart phones, numerous "young" organizations have become nationwide market leaders and shifted into international trades. In terms of public legality, the organization may develop its niche in the market and then expand it to the conventional market. The exceptional growth in the organization's use of the internet for trade transactions has developed the emergence of business to business e-commerce, wherein, lots of buying and selling organizations access a specified website to manage their firms. Own-monitored online assessment was done and different Effects of E-Commerce in the Current Scenario of Marketing http://www.iaeme.com/IJM/index.asp 771 [email protected] responses were accumulated. Results depict that respondents buy online frequently clothes, shoes, cosmetics, electronics and pay generally by debit or credit cards; the major reasons for this kind of shopping are lower price and unavailability of the goods, lack of quality assurance and lack of varieties at the traditional markets. But, seeing the present conditions of online-marketing during covid-19, it has somehow raised an alarming situation for the online ventures in terms of delivery with safety and hygiene.

A DESCRIPTIVE STUDY ABOUT THE EFFECTIVENESS OF E -COMMERCE IN THE PHILIPPINES

Jana Abeabe

Name: Abeabe, Patricia Jana J. Maro, Mariel Margaret G. Ramos, Rochel Crs&Sec: BTM13 Subject: Mga Babasahin Hinggil sa Kasaysayan ng Pilipinas (G-SOSC001) Professor: Sir Jumel G. Estrañero University: De La Salle University Dasmariñas

Transstellar Journals

TJPRC Publication

The novel Corona Virus spreads primarily through contact with an infected person or when a person comes in contact with a surface that has the virus on it, the best way to protect one against it is to stay at home. This has hiked the global usage of online shopping. The outbreak has infected masses and is known to have changed people's shopping habits as they are afraid to shop outdoors. E-commerce is the concept of doing business online with the help of the internet and is a term inclusive and broader than 'online shopping'. E-commerce has experienced rapid growth since the humble, despite the sudden beginning of the lockdown imposed due to the pandemic. The study aims espiteon the e-commerce industry boost during COVID-19 pandemic. The main concern of the government is to reduce the impact of COVID-19 by imposing restrictions on the movement of people and hence to break the covid infection chain. This led to a manifold increase in the usage of internet facilities, be it in the field of teaching, grocery shopping, and entertainment. The people who did not prefer shopping online were left with no choice but to avail themselves e-commerce facilities. This was one of the main reasons the E-commerce industry witnessed rapid growth. The rising number of COVID-19 cases led to panic amongst the people to go the hospitals and therefore they preferred taking consultations from the doctors virtually, which was a new concept and a contributor to the growth of the e-commerce industry. The online food industry was another notable sphere, since, this sector comes under the category of essentials, it worked 24x7 and helped people to get food even during the peak of the COVID-19 crisis. Many NGOs joined hands with online service providers to cater to the masses. One of the many advantages of this industry's growth is that it has led to the quality of lifestyle, as people can avail themselves services at their doorstep.

siddharth garaniya

"Over last few years the popularity of e-commerce has enormously increased due to its quick and easy way of exchanging goods and global services. India will be booming ground for e-commerce business models. The present study is conceptual and descriptive nature. It attempts to explain the concept of ecommerce, business models for e-commerce, merits and limitations of e-commerce. It concludes that e-commerce offers several benefits to the various stakeholders. However, at present time it has several limitations, legal and technical barriers in the development of e-commerce in India which could fade away in years to come. Hence we should equip ourselves to give worm welcome to e-commerce which is an obvious outcome of globalization and technological revolution around the globe. Ms. Ayushi Y. Vadwala | Ms. Maitri S. Vadwala""E-Commerce: Merits and Demerits A Review Paper"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd106.pdf Article URL: http://www.ijtsrd.com/engineering/computer-engineering/106/e-commerce-merits-and-demerits-a-review-paper/ms-ayushi-y-vadwala"

Management & Marketing. Challenges for the Knowledge Society

DANIELA- IOANA MANEA

As COVID-19 had been posing unprecedented challenges, the economic agents as well as the individuals had to adapt to a new lifestyle. The preventing measures against the spread of the virus determined an increase in e-commerce activity in general, and in mobile commerce in particular. In this regard, sellers redefined their strategies revolving around smartphone applications for online shopping, which will gradually become the global shopping standard. Therefore, the aim of this paper is to evaluate the current situation of e-commerce, focusing on the main transformations since the beginning of the pandemic. The methods used in the paper consist in both qualitative research of the literature regarding e-commerce and a quantitative analysis that captures two aspects: on one hand it highlights the change determined by the pandemic in online commerce, and on the other hand it shows an X-ray of the EU states positioning in terms of e-commerce evolution. The latter consists in a comparat...

JOURNAL OF ENGINEERING MANAGEMENT AND COMPETITIVENESS (JEMC) Volume 12, Number 1, 2022, ISSN 2217-8147 (Online)

Iva Bubanja

The COVID-19 pandemic has widespread and long-lasting implications for the global economy causing general economic stagnation and crisis. Different industries and sectors strive to maintain the functioning of their business and stimulate companies to increasingly rely on the potential and power of digital technologies and network communications. In the trade sector, there was raising awareness about the importance of digitization and leveraging digital commerce as a strategic investment and competitive advantage. More and more trade companies started looking at digital channels not as an addition to their physical location but as a strategic source of long-term, sustained growth, which proved to be a significant boost for e-commerce all over the world. In this paper, B2B and B2C e-commerce segments are analyzed including current trends and dynamics, management of new business models, and expected implications of the latest digital technology innovations. Investments in new digital technologies tools and solutions in e-commerce had been driven by the need for frictionless shopping, personalization, and improved operational efficiency required by new-age customers. Experiences of the Serbian economy in e-commerce transactions are analyzed as well. Key words: E-commerce; Digitalization; Innovation; Competitiveness.

International Journal of Scientific Research in Science and Technology

International Journal of Scientific Research in Science and Technology IJSRST

In this research article the researcher emphasized that significant role and impact of E-commerce in Business Industries. The researcher focused on analysis of significant factors of E-commerce, positive and genitive impact of E-commerce, and challenges of E-Commerce in Business Industries. The researcher defines the dimension of E-commerce with respect to business to business (B2B) and business to consumer (B2C) towards the changing way of doing business on the web in Business Industries. The main objectives of this research article is to focus on the primary route by which e-commerce will affect the economy at large is through its impact on productivity and inflation. Businesses and consumers that use E-commerce benefit from a reduction in costs in terms of the time and effort required to search for goods and services and to complete transactions of business activities which are carried out electronically on the Internet rather than at a physical location.

International Journal of Retail & Distribution Management

Paul Lapoule

Indonesian Journal of Electrical Engineering and Computer Science

Technolgoical innovation has covered all aspects of business all over the world. With growing pace of digitalisation all over the globe, e-commerce has become one of the popular platforms for initiating business transactions. COVID-19 has showed the dependence of the society on this medium for the convenient allocation of goods and services and meeting up of consumer demands. Saudi Arabia has also witnessed a high demand in the usage of this platform to meet the daily usage necessity. In order to get insight into the topic, qualitative research has been undertaken. The study aims to investigate the increased use of e-commerce during COVID-19. The data required for the analysis has been collected from secondary sources such as web journals and research work. The finding of the qualitative analysis exhibited that the e-commerce has increased rapidly during COVID-19 time. The outcomes of the present study are helpful for the vendors to facilitate the consumers by increasing the opportu...

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

The Covid-19 pandemic is the greatest global humanitarian challenge the world has faced. The coronavirus profoundly transformed the global trends. These variations causative to the high adventitious and secondary funds that reflected for this virus. The purpose of this conceptual paper is to discuss the e-commerce trends predicament as well as how imminent progress in this have affected the consumer behaviour post pandemic.E-commerce has grown exponentially putting a strain on traditional brick and mortar stores. In today's era it's time to amplify our digital presence. This article will outline the impact of Covid-19 on e-commerce; how it has changed the key takeaway's and trends to keep in mind when trying to form a digital business strategies that will appeal to post Covid consumers. Although the pandemic densely impacted economic activities and let to complete shutdown of some sectors it definitively helped in the surge of e-commerce and increased the use of digital transactions. This paper is made with an attempt to highlight, analyse and understand the Covid impact on the e-commerce as a whole .

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

Scholars' Bank

Amazon, e-commerce, and the new brand world, description:.

Show full item record

Files in this item

View / Open Final Thesis-DAgostino.pdf (format: PDF)

This item appears in the following Collection(s)

Clark Honors College Theses [1498]

Search Scholars' Bank

All of scholars' bank.

By Issue Date

This Collection

Most Popular Items
Statistics by Country
Most Popular Authors

IMAGES

(PDF) Case Based Reasoning in E-Commerce: A Ph.D. Thesis
Site E Commerce Phd Thesis
Its A Thesis Paper Based On e Commerce
Thesis Statement
Project on E commerce
PHD Thesis Final

VIDEO

𝐂𝐨𝐮𝐫𝐬𝐞: 𝐇𝐨𝐰 𝐭𝐨 𝐖𝐫𝐢𝐭𝐞 𝐚𝐧𝐝 𝐏𝐮𝐛𝐥𝐢𝐬𝐡 𝐚 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐩𝐚𝐩𝐞𝐫 (𝐀𝐫𝐭𝐢𝐜𝐥𝐞)
Writing Effective Ph D Thesis
Thesis Writing: Outlining Part I
Industriepromotion (Wissenschaft kompakt #011)
Form for Application for Thesis Submission/Final Submission (Research Program) (Form Format)
Thesis Writing: Outlining Part III

COMMENTS

PDF AMAZON, E-COMMERCE, AND THE NEW BRAND WORLD
2016, global e-commerce was estimated to be $1.9 trillion and is expected to grow to $4. trillion by 2020.46 In the United States, the e-commerce industry is expected to grow at. years, reaching revenues of $704.1 billionby 2022.47 There are several reason. wh.
PDF The impact of e-commerce on international business
Author: Arun Tripathi. Title of thesis: The impact of e-commerce on international business: A comparative study of traditional and online business model. Supervisor: Harrison Okuogume. Year: 2023 Number of pages: 64. Number of appendices: 2. This thesis discusses about the transformative impact of e-commerce on international business.
PDF THE DESIGN AND DEVELOPMENT OF AN E-COMMERCE WEB APPLICATION
The aim of this thesis was to develop a responsive, user friendly, scalable e-commerce website with a modern, high performance JavaScript framework called React. React, HTML5, CSS3, Bootstrap 4, Node.js, and JSON are technologies used to built this project. After the developing process, the project was deployed on the Internet.
(PDF) A Review Paper on E-Commerce
E-commerce is a boom in the modern business. E-commerce means electronic commerce. E-. commerce (Electronic commerce) involves buying and selling of g oods and services, or the. transmitting of ...
PDF OPPORTUNITIES AND CHALLENGES OF E-COMMERCE TO CUSTOMERS
E-commerce has brought benefits for the customers, which was lack with the traditional way of shopping goods and services. Moreover, the findings help to understand different drawbacks people are experiencing while buying digitally. E-commerce has made it easier for people to shop for the items they need and has some dark sides.
The Impact of E-commerce on Traditional Retail: a Comprehensive
Policy commentators warn that e-commerce erodes tax revenue - true enough for some governments - but, more accurately, changing transaction costs can generate entirely new commercial and ...
Theses on e-commerce topics
Suggested topics for theses: The digitization of the stationary store. Competitive strategies in times of growing Amazon dominance. Algorithms in e-commerce logistics. Electronic commerce and the supply of the population. Use of voice control in electronic commerce. Success factors in mobile commerce.
PDF TOWARDS EXCELLENT CUSTOMER EXPERIENCE IN E- COMMERCE
16.8 percent growth in 2021 (eMarketer, 2021). 73% of internet users in the EU and 78% of Finnish internet users shopped online in 2020, (Eurostat, 2020). As e-commerce is becoming a more integral part of daily life for a high percentage of consumers, expectations toward e-retail are also increasing and evolving.
Improving e-Commerce sales using machine learning
The thesis aims to understand and illustrate the applications of Machine Learning to digital sales and marketing ecosystem for the e- Commerce industry. It first understands the e-Commerce touchpoints using which customers interact with the brands and delves deeper into the underlying technologies powering these touchpoints.
Project Management for E-Commerce Businesses
Apart from project management, the project manager has to manage short releases so that the team can cater to change requests and software patches for the e-commerce application. Such requests can come from clients or even from stakeholders. So, the project manager has to be capable enough to prioritize all such requests and motivate the team ...
PDF A Study of Customer Satisfaction and E-commerce Online Purchasing
digital world, the rise of e-commerce business in the global scope has not only brought great changes to people's purchasing habits, but also brought great changes to the whole retail and logistics industry (Leung et al., 2017). In the context of "Internet plus", cross-border e-commerce and mobile e-commerce will push e-commerce to another climax.
E-commerce Dissertation Topics and Titles
E-commerce Dissertation Topics for 2020. Topic 1: Analysing the impact of e-commerce strategies on building better relationships with customers: A case study of the UK fashion industry. Topic 2: Assessing the impact of unique website attributes on consumer buying pattern: A case study of Amazon and eBay.
A systematic literature review on e-commerce logistics: towards an e
First, the red 'early e-commerce logistics' cluster, containing articles mainly from 2006 or earlier - Griffis et al. (Citation 2012) is the exception, established the initial literature base for e-commerce logistics. Second, the green 'retail transformation' cluster with articles published between 2010 and 2015 describes how e ...
PDF The Design and Implementation of An E-commerce Site for Online Book
THE DESIGN AND IMPLEMENTATION OF AN E-COMMERCE SITE FOR ONLINE BOOK SALES. By. Swapna Kodali. Project Report Submitted to the faculty of the University Graduate School. in partial fulfillment of the requirements for the degree Master of Science in the Department of Computer and Information Sciences. Indiana University South Bend May 2007.
PDF Artificial Intelligence in E-commerce
The thesis aims to widen our understanding of artificial intelligence and explore how it has revolutionized the e-commerce landscape in improving customer experience problems. Ultimately, the study endeavours to establish that AI represents a signifi-cant opportunity for the future of our world.
Artificial intelligence in E-Commerce: a bibliometric study and
Artificial intelligence in E-Commerce: a bibliometric study ...
An Overview of Electronic Commerce (e-Commerce)
(PDF) An Overview of Electronic Commerce (e-Commerce)
Shodhganga@INFLIBNET: Study on impact of e commerce business on
The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. ... Study on impact of e commerce business on customers buying behaviour with special reference to Palghar district: Researcher: Raut, Dhirajkumar Manik: Guide(s): ...
A Descriptive Study About the Effectiveness of E -commerce in The
COMMERCE IN THE PHILIPPINES A Thesis Presented to ...
Amazon, E-Commerce, and the New Brand World
Abstract: This thesis evaluates the impact of e-commerce on brands by analyzing Amazon, the largest e-commerce company in the world. Amazon's success is dependent on the existence of the brands it carries, yet its business model does not support its longevity. This research covers the history of retail and a description of e-commerce in order ...
PDF THE IMPACT OF E-COMMERCE ON CONSUMER BEHAVIOR IN VIETNAM
obust influence on consumer decision-making (Hoa, 2022). To gain a more profound comprehension of consumers' changing shopping patterns, the writer has selected "The impact of E-commerce on consumer behavior. n Vietnam" as the primary focus of this research thesis.E-commerce has shed its novelty appea.
PDF DSpace
DSpace - University of Kansas
PDF ARTIFICIAL INTELLIGENCE IN E-COMMERCE
business, especially in the e-commerce industry. The point of this thesis is to clarify if artificial intelligence has had an essential and gradual effect on e-retailers with an iconic e-retailer company, Amazon. This thesis also introduces which kind of artificial intelligence techniques are utilized today in e-commerce with high-

Theses on e-commerce topics

Suggested topics for theses:

Elaborated topics of final theses:

Published theses:

Procedure of supervision for final theses

Leave a Reply Cancel reply

Privacy Overview

Improving e-Commerce sales using machine learning

Alternative title

Useful Links

E-commerce Dissertation Topics

Introduction

2022 E-commerce Dissertation Topics

Topic 2: An investigation of the security controls and issues of e-commerce websites in the UK online environment.

Topic 3: Google ads vs. Search ads in e-commerce- A comparative study

Topic 4: An examination of consumer decision-making processes- A comparative study between e-commerce and m-commerce in the United Kingdom.

Topic 5: Gender Inequality in the Ecommerce Industry.

Covid-19 E-commerce Research Topics

Topic 2: Frequent E-commerce shopping during Coronavirus pandemic

Topic 3: Contribution of E-commerce during COVID-19

Topic 4: Offline and online shopping after COVID-19

E-commerce Dissertation Topics for 2021

Topic 2: Brand marketing through social media

Topic 3: Impacts of social media on customer behaviour

Topic 4: What factors influence the consumer's buying decisions?

Topic 5: Black Friday sale strategy to drive sales

Topic 6: The role of influencer marketing in increasing sale

Topic 7: Impact of E-marketing on consumer purchase decision: the case of the luxury industry in the UK

Topic 8: Analysis of the customer-centric marketing strategies in attaining competitive advantage for the firm and sustaining business success

Topic 9: Traditional vs. digital marketing: a comparative study of the last ten years

Topic 10: The impact of relationship marketing on customer loyalty: an analysis of the Honda motor

Topic 11: The importance of search engines in e-commerce

Topic 12: E-commerce company's advertising strategy-critical analysis

Topic 13: Importance of customer retention in E-commerce

Topic 14: Importance of brand loyalty in internet marketing

E-commerce Dissertation Topics for 2020

Topic 2: Assessing the impact of unique website attributes on consumer buying pattern: A case study of Amazon and eBay

Topic 3: How does e-commerce facilitate adding value to a business: A case study of service industry in China.

Topic 4: Critical analysis of security policies and vulnerabilities of an online banking website: Identifying the challenges and remedies to improvise risk management

Topic 5: Can e-commerce help organisations build a competitive advantage over their competitors?

Topic 6: Exploring the regulations and guidelines set out for e-commerce companies

Topic 7: Analyzing the best security mechanisms that should be implemented by e-commerce businesses

Topic 8: Exploring the data privacy issues in e-commerce

Topic 9: E-Commerce and customer retention – What role does e-commerce play?

Topic 10: Security limitations and challenges of implementing e-commerce.

E-commerce Marketing Dissertation Topics

Topic 1: E-commerce and the importance of search engine rankings for businesses

Topic 2: Investigating internet marketing strategies employed by traditional retailers

Topic 3: Retaining customers by employing e-commerce: A case study of the UK fashion industry

Topic 4: Wholly online or one foot in both worlds – The advantages and disadvantages of the two commonly employed marketing strategies - online and conventional models

Topic 5: To investigate internet marketing strategies employed by e-commerce retailers

Topic 6: Understanding the effect of customer behaviour on internet marketing strategies

Topic 7: Measuring the success of marketing strategy employed by new e-businesses – A case study of the UK retail industry

Topic 8: Measuring the success of internet marketing strategy employed by traditional businesses – A case study of the airline industry

Topic 9: The role of original and plagiarism free content in today’s e-marketing strategies

How Can ResearchProspect Help?

E-commerce Strategy Dissertation Topics

Topic 1: Evaluating internet marketing strategies employed by existing e-businesses

Topic 2: The challenges and opportunities for organisations migrating to the internet

Topic 3: Accident or Design – Do we really have an internet marketing mix model/strategy that is sure to work?

Topic 4: Investigating the use of customer service in e-commerce to gain a competitive advantage

Topic 5: Exploring the most effective aspects of e-commerce strategy in today’s world

Topic 6: Exploring the various internet business value creation strategies employed by e-businesses

Topic 7: Measuring performance of an e-business marketing strategy

Topic 8: Investigating e-business strategies employed by educational institutes in the UK

Topic 9: Reviewing the e-business strategies employed by the UK SME’s

Topic 10: How effective e-commerce strategies can help companies in building their brand reputation

E-commerce Security and Trust Dissertation Topics

1. E-Commerce Trust Dissertation Topics

Topic 2: Investigating data privacy issues in e-commerce and how they affect businesses

Topic 3: Data Protection Act: Does it help in building trust in e-commerce

Topic 4: How has anti-virus technology helped the e-commerce industry?

Topic 5: Investigating strategies used by retailers to build up trust among potential and existing customers

2. E-commerce Security Dissertation Topics

Topic 2: An empirical study of e-commerce security, challenges, and solutions

Topic 3: Investigating strategies employed by e-commerce businesses to enhance the security of e-commerce transactions

Topic 4: Exploring the effectiveness of encryption in the e-commerce industry

Topic 5: Online reputation management: Exploring how e-commerce companies in the UK fashion industry practice it

E-commerce Usability Dissertation Topics

Topic 1: A comparative analysis of the usability of the world’s leading travel websites