Need a perfect paper? Place your first order and save 5% with this code:   SAVE5NOW

The Impact of Artificial Intelligence on Consumer Behaviors


These findings are intended to help retailers better understand how AI is changing consumer behavior in the US retail sector. The study employed a quantitative research method. The core data will be gathered via an online survey. The convenience sampling approach will be used. This research has a 400-person sample size. The data will be analyzed using the IBM SPSS v28 for Windows statistical software package.

1. Introduction

1.1 Background

Digital transformation of retail operations is a must in order to compete in dynamic markets; these technologies have several ramifications for marketing efforts to increase their efficacy. Marketers may use AI to better understand their consumers and target them, as well as personalize their marketing efforts. Artificial intelligence (AI) increases the creation, optimization, and distribution of valuable resources for all parties involved in marketing, including the client. Global GDP is expected to rise by $15 trillion by 2030 as a result of artificial intelligence (AI). Artificial intelligence (AI) has grown in popularity in several industries, including marketing, so has its use. Customers’ interactions with companies are fundamentally altered by AI, which fundamentally transforms the nature of marketing. As a consequence, in the era of artificial intelligence, marketers must prepare for the changes that will occur; understanding the influence of AI on marketing is crucial. There is still a lack of study on the impact of AI on customer behaviors, making it challenging for marketers to employ this technology effectively. In order to foresee and alter customer behavior throughout the consumer journey, from need recognition, information search, evaluation, and purchase decision-making, marketers must understand how AI may be employed in their marketing operations and tasks.

1.1.1. Research Problem

How does Artificial Intelligence impact client buying behavior in the online retailing sector? is the focus of this study and the answer to that question is the focus of this investigation. Do consumers heed the advice of artificial intelligence (AI) systems at the stores where they shop?

1.1.2. Research Questions

  1. Do customers’ purchasing habits change as a result of Artificial Intelligence?
  2. Does the demographic information (gender, age, level of education, and yearly income) have a major impact on the purchasing habits of customers?

1.1.3. Research Objectives

  1. To look at the connection between artificial intelligence and the purchasing habits of customers
  2. Based on their demographics, it is important to study the disparities in purchasing habits of consumers (gender, age, educational level, and annual income).

2. Literature review

2.1 Artificial intelligence (AI)

Image and voice recognition, as well as decision making and language translation, are all examples of artificial intelligence (Guralnick, 2021). Contissa et al (2018) claims that AI is capable of doing the “three Ds”: detection, determination, and development. An AI’s ability to recognize and identify the most common characteristics of a subject matter is known as detection. Artificial Intelligence can tell you which features are most popular and which ones to avoid. An AI system’s ability to choose a course of action after taking into account several factors and choosing which one is most important. When it comes to artificial intelligence, the term “develop” refers to the capacity of AI to learn from new information and research, as well as how it evaluates and changes its viewpoint. Computerized systems that utilize data to do human-like tasks in a way which increases their chances of success are known as artificial intelligence (AI). The most critical component of AI is data, particularly Big Data. “Big Data” is a term used to describe the strategies and schemes used by organizations throughout the world to analyze the data they collect from their internet customers. The key characteristics of Big Data are its enormous volume, rapidity, and diversity of findings gleaned from the internet (Gao & Zhang, 2020). Structured and unstructured data are the two types of huge data that AI uses. Artificial Intelligence (AI) is able to do complex calculations on structured data, such as demographics and transaction records, and provide accurate results in real time; such data may be easily organized in spreadsheets. Many customers’ data is unstructured, yet it is more complex and must be managed in order to provide relevant insights that cannot be presented in spreadsheets. In order for computers to do tasks in a way that is similar to that of humans, a group of technologies known as artificial intelligence (AI) was developed on three levels. It’s possible to come across examples of artificial narrow intelligence (ANI) in our everyday lives (Sterne, 2017). ANI is able to do some of the same things that computers have been taught to perform. Among these tasks are photo recognition, predictive analysis, and customer segmentation. As an example, Zalando recommends new products based on the history of past purchases made by its customers. The second level is AGI, which is capable of outperforming human intellect in a wide range of domains. A.I. is capable of self-organization and self-decision. AGI covers image recognition, language processing, voice recognition, intelligent computers, and robots. Despite the fact that we still don’t comprehend human nature and the brain, artificial superintelligence (ASI) is the third level of AI that excels human intelligence in every topic through creative and scientific reasoning. The consequences of this new level’s birth were completely unexpected; it has the ability to wipe out all of mankind (Dash et al., 2019).

Kreutzer & Sirrenberg (2020) categorize AI into five areas, some of which are used in marketing, such as speech recognition, image recognition, text recognition, and decision-making, while autonomous robots and vehicles are more typically used in industry. The way consumers interact with businesses will be transformed by speech recognition, which is based on fundamental neural network software. What the consumer says is interpreted by the integrated voice recognition technology. As an example, Alexa, Amazon’s e-commerce voice AI, is included into the Echo, enabling users to purchase products by just saying “Alexa, buy it” (Verma, et al., 2021). Consumers’ shopping mall strolls are made more pleasant using Alpine AI, a kind of interactive artificial intelligence (AI). By analyzing videos or photographs posted on social media, Image Recognition helps marketers better understand their customers’ habits. Customers’ comments on the photographs and offers provided may be used by marketers to determine their consumption habits (Cannella, 2018). Decision-making campaigns may be managed by Albert AI and Harley Davidson, which analyze data acquired after posting an ad and make recommendations based on this data. Scanning shop shelves using Autonomous Robots like Schnuck may aid service staff by verifying stock levels and the order in which things are presented (Chintalapati & Pandey, 2022).

2.2. Machine Learning (ML), Artificial intelligence (AI) and Deep Learning (DL)

Artificial intelligence’s subset of machine learning is often used in marketing. Using computer software or algorithms, computational algorithms are instructed how to recognize the correct output intake and then continually improve as new data is processed. Having well-trained machine learning techniques is a huge asset for businesses of all sizes. An algorithm is a less costly and more reliable asset for the advertising department than a human employee. The most common algorithms used to teach MLS include classification algorithm, unsupervised, semi-supervised teaching, and reinforcement learning (Woschank et al., 2020). To put it another way, machine learning is essential to AI. Any automated system, such as an AI, is incapable of living in an unpredictable environment without the capacity to learn and explore the world in the same manner that individuals do (Klinger et al., 2018).

An example of this is machine learning, which is a kind of machine learning (ML). In order to understand data in a non-linear way via the use of an efficient approach of unsupervised learning, it is reliant on neural networks akin to human brains. Advances in computer technology, especially graphics processing units (GPUs), have made deep learning more feasible than before (Nguyen et al., 2019). A variety of artificial intelligence (AI) capabilities may be made possible by deep learning. These technologies use big data analytics to gather information and then connect people with brands. Deep learning, for example, uses profile and data mining findings to enhance scheduling and job assignments in retail establishments, enabling businesses to better manage their staff and better serve their customers. Indeed, AI technologies like natural language, algorithms, and machine learning will help AI dominate marketing (Mathews, 2019).

2.3. Artificial intelligence’s (AI) effect on advertising

In both B2C and B2B marketing, AI is becoming more crucial. As a result of KRC Research’s findings, AI is expected to have a higher impact on marketing than social media platforms like Facebook and Twitter. Brynjolfsson & Mcafee (2017) conducted a Forrester study on 717 marketers and found that 79 percent of them feel AI makes the workflow more strategic than it was before. Makridakis (2017) claim that marketing operations are heavily influenced by artificial intelligence (AI). The retail business, which is characterized by frequent contact with customers and creates a great amount of data on customer traits and transactions, is the most affected by AI. Customers get personalized recommendations based on AI analysis of their data in real time. Natural language processing (NLP), predictive analytics, and algorithms, all of which fall under the umbrella of artificial intelligence (AI), may be valuable for gleaning information about the environment in which a user interacts with a brand; one such application is Quill (Stone et al., 2016). As a consequence, marketers will rely on AI since it will transform retail marketing strategies and customer behavior (Lukint et al., 2016).

As a result of increased operations like automatic payments, improved search engine quality, as well as 24-hour customer care, AI has supplied clients with a boatload of advantages. AI creates a unique experience for the user by providing appropriate product recommendations, personalized customer support, and after-sales help. In addition to enhancing the customer-company relationship, AI helps consumers to evaluate the products in a virtual environment. Of course, most people think AI will enhance their lives by tackling complex difficulties, while some worry it will remove their jobs entirely (Cath et al., 2018).

2.4. Artificial Intelligence (AI) and its effect on consumer purchasing behavior.

In order to get, utilize, and discard products and services that meet their requirements and desires, consumers engage in a series of physical activities and decision-making processes. Predictions about future behavior may be aided by looking at how this process is carried out (Frank, 2021). There are five steps in the consumer buy decision-making process, which are identified as needs awareness; information search; appraisal of alternatives; purchase choice; and post-purchase behavior. It’s possible for customers to skip a step or a number of steps in the process. It all depends on what’s going in their head at the time (Kaplan, 2020).

The human mind complicates consumer buy behavior analysis, but artificial intelligence (AI) can assist in evaluating and forecasting customer purchase behavior on a digital platform. When it comes to the internet, consumers express themselves in a variety of ways such as searching, commenting on other people’s posts, like and commenting on other people’s posts, and engaging in face-to-face interactions (Deng et al., 2020). Because of this, the amount of data available to customers is increasing in volume, speed, variety, and accuracy at an accelerating rate. In order to make sense of this massive amount of data, artificial intelligence (AI) may play a role. When it comes to deciding on marketing strategies and predicting sales, marketers rely on consumer purchasing behavior analytics. Product displays and cataloging are heavily influenced by these findings (Prentice et al., 2020). As a result, it’s critical to comprehend the customer journey, and AI may assist marketers in doing so at various points along the way. Each step of the customer experience may be impacted by AI, thus it’s important to know how it influences consumer purchasing habits (Jain & Aggarwal, 2020).

2.4.1. Want and Need Recognition

Recognizing a need is the first step. Marketing strategy should be developed to fulfill client wants. Rather than brands, people’s desires drive classification. As a consequence, following someone’s wishes is tough (Flanagan, 2021).

Customers’ online requests and wishes are understood by A.I. will let marketers create real-time consumer profiles. Online activity such as social media updates, purchases, comments, and postings establishes customers’ digital footprints, which are subsequently updated by machine learning. The Microsoft AI system Azure helped the media firm Astr construct client profiles by analyzing billions of data points, calculating customers’ demands in seconds, and then tailoring online content to their tastes. For example. AI also aids marketers in identifying needs and aspirations. Pinterest, for example, uses image recognition to identify people’ style preferences based on the photographs they post to the site. Also, Adobe Audience Manager’s AI-powered personalized modeling helped target consumers with similar traits and interests (Kietzmann, Paschen & Treen, 2018).

Per the Davenport et al. (2020), online firms think AI can accurately predict customer needs and aspirations. As a consequence, several online merchants now use AI to assess client preferences and send things without a formal request from customers, allowing them to buy or return unwanted items (Agrawal et al., 2018). Amazon is a great example of anticipatory shipping; it delivers products to the nearest delivery location (Avinaash, 2018). These changes may impact marketing strategies and customer behavior. So, during the requirement identification stage, AI can understand changing customer demands and preferences and provide suitable recommendations in online buying (Schweyer, 2019).

2.4.2. Information Search

The consumer’s next step is information search. It begins with customer recognition. Then they start thinking about what they can give to meet their demands. Marketers’ job is to get their brands in front of customers’ minds. Marketers employ sponsored search, organic search, and retargeting to boost brand presence and convey critical consideration factors (Chowdhary, 2020).

AI looks to be causing a new industrial revolution, with early adopters winning. According to a Gartner study, websites that offer voice and visual search will see a 30% rise in digital commerce income by 2021 (Collins et al., 2021).

Marketers may use AI-powered search to locate, rank, and offer the perfect results to customers in real-time. For example, after deploying Rich Relevance, an AI-powered, tailored online search tool, The Works, a renowned discount retailer, had a 37% boost in e-commerce sales in 2017. Also, Google’s new platform anticipates consumer searches (Enholm et al., 2021).

Deep learning can forecast user tendencies and provide adverts via a recommendation engine. For example, ‘CHINESE GOOGLE’ makes a fortune by targeting adverts using AI. AI and machine learning increase the likelihood of a consumer clicking a product, which may help optimize product mix display, particularly when retargeting or employing demographic ad text (Chiu, Zhu & Corbett, 2021).

Trend marketers need AI because it can help them target consumers more efficiently and provide individualized communication. For example, Google Adwords gives advertisers with quality leads. Google uses AI to evaluate search query data to identify the most valuable subset of users. For example, AI helped Zendesk, a customer support software firm, acquire more high-quality leads by generating deeper consumer profiles and targeting Facebook users who fit those profiles (Rzepka, Berger & Hess, 2021).

To put it another way, businesses can’t compete if they can’t score and produce new leads. You can discover which leads are most likely to convert with the right machine learning integration. In order to get the most out of AI and a precise search tool, you need to analyze data and look for relevant leads. Using artificial intelligence (AI), sales productivity tool Cien may improve lead scoring and reduce sales cycles. Other examples include Einstein, a Salesforce tool that may help you prioritize which leads to monitor. In order to function, it syncs email and calendar data with a database. When clients are buying online, an AI can give them with the knowledge they need to make an informed decision, and then select deals that meet their requirements and desires (Buck et al., 2021).

2.4.3. Evaluation

People are curious about items they wish to acquire, and this curiosity drives them into researching them. Different brands are often compared and contrasted by customers. Relevant information is used by marketers to persuade customers that their products and services are the best option (Hernández-Orallo, 2017).

AI may aid in the personalization of content. Artificial intelligence (AI) is a technology for creating relevant content based on customer data. This information will most likely be beneficial to these prospects. In truth, any company may employ this level of ingenuity to create blog material. Another benefit of AI is website personalization. Providing more tailored and relevant material improves the utility and interest of websites, promoting user interaction. One-on-one advertising is shown. Employing Ai systems and machine learning on your website may attract return visits. Using these incentives may boost exchange rates, and also the Personal AI platform is an excellent example (Larson et al., 2021).

Product reviews, social network followers and likes, as well as website visits and itineraries, are all utilized to personalize information. Marketers may use data to suggest content development based on customer graphics, colors, and so on (Sterne, 2017). Marketers may also customise emails and Facebook posts depending on the preferences of their customers. Because AI generates smart content, it assists marketers in meeting their marketing goals. Making content for a wide range of goods and services takes time and money, but AI can provide customised material at a reasonable cost. These APIs allowed for more cost-effective and targeted marketing strategies (Brown & Samuelson, 2019).

It is simple to put together a lead scoring system. Artificial intelligence might be used to provide new content to a brand’s social media followers. AI can assist marketers in analyzing their trustworthiness and persuasiveness by picking customers with high buy intents. In addition, the AI may provide a range of information about related items and provide real-time suggestions (Hanisch, 2020).

2.4.4. Purchasing Decision Making

The fourth stage of the customer journey is the purchase decision. After evaluating the brands they’ve examined, buyers choose what and where to purchase the highest-ranked brand. This is the beginning of the buying process. Customers’ purchase decisions, on the other hand, might be influenced by environmental factors (Stone et al., 2020). Consumers will spend more money if they distrust the quality of their brands. Marketers should emphasize the brand’s value in contrast to competitors in order to drive customers from the selection phase to the purchase activity (Forrest & Hoanca, 2015). information on how and where to shop, assurances or return policies, and offering incentives to make a purchase, for example With the help of Staples’ intelligent purchasing system, clients may make orders in a number of methods, including through voice command, text message, or email. Staples. AI has increased the number of user leads for companies that have used it. To put it another way, AI might revolutionize the purchase process (Schweyer, 2019).

Sales departments, for example, may benefit from AI-analytics that may help them do their jobs more effectively and intelligently, since AI is becoming more popular in the corporate sector. One example is Nudge, a sales tool powered by artificial intelligence (AI) that lets reps personally connect with each potential customer. And they can record and transcribe on-the-fly chats using Chorus. Using an intelligent email assistant like Conversica, they’ll be notified when a lead is ready to go forward with a sale. In addition, Inside Sales helps them to raise their sales targets. With the help of Tact, an AI-driven tool, they are able to focus on completing sales rather than administrative duties. Using previous data and selecting prospects who are most likely to convert, AI does really provide salespeople with accurate predictions and Intelligent Recommendations (Marinchak et al., 2018). An AI might suggest the best product depending on a customer’s preferences. Orders may be placed via phone, text, or email. A contract may be signed faster using artificial intelligence.

2.4.5. Post-purchasing behaviors

Post-purchase behaviors refers to customer behavior after obtaining and utilizing the chosen brands or items. Companies must get user feedback on their goods. Satisfied customers retain and attract more other customers, increasing revenue, while disgruntled customers cause issues. Consumers frequently express their happiness or discontent with a brand via word of mouth. Marketers should address any issues raised by leads (Rodgers & Nguyen, 2022).

Inbound marketing requires AI. Marketers may use AI-powered “Chatbots” to follow up with consumers. The program is Autodesk. All client queries are answered by a virtual assistant in 5 minutes. Chatbots are algorithms. As a result, advertising may target new audiences via deep learning. Chatbots can assist several individuals at once. Post-purchase behaviors AI may identify customer discontent and react properly (Ninness & Ninness, 2020).

AI is essential at every stage of the consumer journey. Marketers may use AI to create more accurate customer profiles faster. During the information search step, AI can help marketers identify the best leads for improved targeting, provide consumer suggestions, and choose the best content to satisfy their needs. When used in the evaluation stage, AI may deliver convincing insights to buyers. The use of intelligent purchasing technologies that simplify the process and alert prices dynamically may aid customers. Finally, AI can delight consumers and value their purchases. Marketers may use AI to forecast and change consumer behavior.

As a result of the above debate, the following ideas are proposed:

H1: There is a correlation between the use of artificial intelligence and the purchasing habits of individuals.

H2: The purchasing habits of clients change significantly depending on their demographics (gender, age, level of education, and annual income).

The Research Conceptual Framework
Figure 1: The Research Conceptual Framework


In order to achieve the stated objectives, a descriptive study methodology will be used. The study will employ a quantitative research method. Both descriptive statistics and inferential statistics will be used to gather information. In addition to primary materials such as books and journals, secondary sources included newspapers and websites. The core data will be gathered using an online questionnaire that will be meant to capture the implications of AI on consumer behavior. There will be 10 questions on artificial intelligence and eight questions about customer behavior in the questionnaire, which will be based on several research. Likert-type scale questions ranged from 1 (strongly disapprove) to 5 (strongly support) (strongly agree). Non-probability sampling will be the sampling method. Convenience sampling will be used as a sampling strategy. The sample size for this research is 400 (Weller et al., 2018). The tests for content and construct validity will be conducted. The questionnaire includes demographic information as well. People who bought online in the United States in the one month before February 2022 will receive a questionnaire. The research will perform a variety of statistical analyses, such as Cronbach’s alpha, Correlation analysis, ANOVA, the Mann-Whitney U-test, Kruskal Wallis One-Way Analysis of Variance Test, and Structural Equation Modeling, using the Statistical Package for the Social Science (IBM SPSS v28).


Contissa, G., Lagioia, F., Lippi, M., Micklitz, H. W., Palka, P., Sartor, G., & Torroni, P. (2018). Towards consumer-empowering artificial intelligence. In International Joint Conference on Artificial Intelligence (pp. 5150-5157).

Guralnick, D. (2021). Innovations in Learning and Technology for the Workplace and Higher Education: Proceedings of” The Learning Ideas Conference” 2021 (Vol. 349). Springer Nature.

Figueiredo, F., Gonçalves, M. J. A., & Teixeira, S. (2021, December). Information Technology Adoption on Digital Marketing: A Literature Review. In Informatics (Vol. 8, No. 4, p. 74). Multidisciplinary Digital Publishing Institute.

Gao, F., & Zhang, L. (2020). Application of artificial intelligence and big data technology in digital marketing. In Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence (pp. 270-272).

Sterne, J. (2017). Artificial intelligence for marketing: practical applications. John Wiley & Sons.

Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability14(3), 43-53.

Kreutzer, R. T., & Sirrenberg, M. (2020). Fields of application of artificial intelligence—customer service, marketing and sales. In Understanding Artificial Intelligence (pp. 105-154). Springer, Cham.

Cannella, J. (2018). Artificial Intelligence in marketing.

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights1(1), 100002.

Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research64(1), 38-68.

Woschank, M., Rauch, E., & Zsifkovits, H. (2020). A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability12(9), 3760.

Klinger, J., Mateos-Garcia, J. C., & Stathoulopoulos, K. (2018). Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology. Mapping the Development of the Artificial Intelligence General Purpose Technology (August 17, 2018).

Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., Lopez Garcia, A., Heredia, I., … & Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review52(1), 77-124.

Mathews, S. M. (2019). Explainable artificial intelligence applications in NLP, biomedical, and malware classification: a literature review. In Intelligent computing-proceedings of the computing conference (pp. 1269-1292). Springer, Cham.

Brynjolfsson, E., & Mcafee, A. N. D. R. E. W. (2017). Artificial intelligence, for real. Harvard business review1, 1-31.

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures90, 46-60.

Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., … & Teller, A. (2016). Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education.

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the ‘good society’: the US, EU, and UK approach. Science and engineering ethics24(2), 505-528.

Frank, B. (2021). Artificial intelligence-enabled environmental sustainability of products: Marketing benefits and their variation by consumer, location, and product types. Journal of Cleaner Production285, 125242.

Kaplan, A. (2020). Retailing and the ethical challenges and dilemmas behind Artificial Intelligence. In Retail Futures. Emerald Publishing Limited.

Deng, G., Zhang, J., Ye, N., & Chi, R. (2020). Consumers’ human nature and their shopping channel choices in the emerging artificial intelligence era: based on Xunzi’s humanity hypothesis. International Marketing Review.

Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management29(7), 739-756.

Jain, P., & Aggarwal, K. (2020). Transforming marketing with artificial intelligence. International Research Journal of Engineering and Technology7(7), 3964-3976.

Flanagan, E. (2021). The human need for recognition. Philosophy, Psychiatry, & Psychology28(1), 27-29.

Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research58(3), 263-267.

Schweyer, A. (2019). The Impact and Potential of Artificial Intelligence in Incentives, Rewards, and Recognition.

Chowdhary, K. R. (2020). Fundamentals of artificial intelligence. New Delhi: Springer India.

Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management60, 102383.

Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 1-26.

Chiu, Y. T., Zhu, Y. Q., & Corbett, J. (2021). In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations. International Journal of Information Management60, 102379.

Rzepka, C., Berger, B., & Hess, T. (2021). Voice Assistant vs. Chatbot–Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks. Information Systems Frontiers, 1-18.

Buck, C., Ifland, S., Stähle, P., & Thorwarth, H. (2021). Raiders of the Lost Ark—A Review About the Roots and Application of Artificial Intelligence. International Journal of Innovation and Technology Management18(08), 2150045.

Hernández-Orallo, J. (2017). Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement. Artificial Intelligence Review48(3), 397-447.

Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Justin, R. T., & Langlotz, C. P. (2021). Regulatory frameworks for development and evaluation of artificial intelligence–based diagnostic imaging algorithms: summary and recommendations. Journal of the American College of Radiology18(3), 413-424.

Brown, D. G., & Samuelson, F. W. (2019). Pitfalls and Opportunities in the Development and Evaluation of Artificial Intelligence Systems. In Artificial Intelligence in the Age of Neural Networks and Brain Computing (pp. 139-159). Academic Press.

Hanisch, L. (2020). Detecting Vague Requirements with Machine Learning.

Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line.

Forrest, E., & Hoanca, B. (2015). Artificial intelligence: Marketing’s game changer. Trends and innovations in marketing information systems, 45-64.

Marinchak, C. L. M., Forrest, E., & Hoanca, B. (2018). The impact of artificial intelligence and virtual personal assistants on marketing. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 5748-5756). IGI global.

Rodgers, W., & Nguyen, T. (2022). Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways. Journal of Business Ethics, 1-19.

Ninness, C., & Ninness, S. K. (2020). Emergent virtual analytics: Artificial intelligence and human-computer interactions. Behavior and Social Issues29(1), 100-118.

Weller, S. C., Vickers, B., Bernard, H. R., Blackburn, A. M., Borgatti, S., Gravlee, C. C., & Johnson, J. C. (2018). Open-ended interview questions and saturation. PloS one13(6), e0198606.


Don't have time to write this essay on your own?
Use our essay writing service and save your time. We guarantee high quality, on-time delivery and 100% confidentiality. All our papers are written from scratch according to your instructions and are plagiarism free.
Place an order

Cite This Work

To export a reference to this article please select a referencing style below:

Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Copy to clipboard
Need a plagiarism free essay written by an educator?
Order it today

Popular Essay Topics