Research Paper

2

ARTIFICIAL INTELLIGENCE

Literature Review

Saint Leo University

Applied Research DBA-738-DBOL1

Dr. Zachary Smith

06/11/2023

Signature Wanda Jones Dhoray

Antoinette Johnson

Terese Denton

Digital technologies have dramatically changed business practices and consumer buying behaviour. AI technology has become increasingly prevalent in various industries, including e-commerce, retail, and hospitality. Understanding its impact on consumer behaviour is crucial for businesses seeking to optimize their strategies and enhance customer loyalty (Anees et al., 2020). The influence of AI technology on repurchase intention cannot be underestimated, and therefore understanding the complex dynamics between AI and consumer behaviour is vital for any business that wants to remain competitive in the ever-changing market. The study by Nazir et al. (2023) addresses a pertinent and timely research question by investigating the relationship between AI technology and repurchase intention.

Nazir et al. (2023) found that the mediation and moderation approach integrates artificial intelligence technology, consumer engagement on social media, and conversion rate optimization. Nayal et al. (2021) used mediation analysis to examine the underlying mechanisms through which AI technology affects repurchase intention. The approach also helped uncover the mediating variables to explain the relationship between the independent variable (AI technology) and the dependent variable (repurchase intention). This approach provides a deeper understanding of the causal processes and potential mechanisms. Additionally, Nazir et al. (2023) study incorporates moderation analysis, which explores the conditions under which the relationship between AI technology and repurchase intention may vary. By examining potential moderators, such as customer demographics or prior experience with AI, the study can identify boundary conditions and shed light on the contextual factors that may strengthen or weaken the effects of AI on consumer behaviour.

Nazir et al. (2023) suggest satisfying consumer experience is a best practice for examining consumer repurchase intentions in the hospitality industry. Data was collected from 308 hotel customers from different regions of Oman who had an online hotel booking experience, and SmartPLS was used to examine the data and proposed hypotheses. This literature review examines the influence artificial technology has on consumer repurchase intention. This study aims to discover whether artificial intelligence technology positively influences consumer engagement, i.e., on social media and conversion rate optimization. Similarly, consumer engagement on social media and conversion rate positively impacts satisfying consumer experience, increasing customer repurchase intentions.

Theoretical framework

A stimulus-organism-response (SOR) theory describes some hypothesized relationships between AI technology, customer engagement, and customer repurchase intentions, according to Nazir et al. (2022). According to SOR theory, certain environmental stimuli boost an organism's cognitive and emotional abilities. The study by Nazir et al. (2022) suggests that ecological stimuli trigger specific physical responses. Some recent studies, such as Adnan et al. (2021), have also used the SOR model to analyze consumer behaviour in the hospitality industry. However, it is difficult to explain the positive impact of AI-integrated social media platforms on consumer conversion rates, particularly in the hospitality sector, due to the limited empirical evidence. The literature review on AI-based social media platforms examines an integrated framework of factors influencing consumer repurchase intentions. The empirical evidence suggests that AI-integrated social media platforms positively impact consumer repurchase intentions. Moreover, further research is needed to gain a deeper understanding of the impact of AI-integrated platforms on consumer conversion rates.

Methodology

The systematic approaches will be based on secondary data collected by Nazir et al. (2022). Alternatively, secondary data can be collected from personal research or large-scale surveys. Customer surveys, demographics, and behaviour data may be used to answer the research question. Through this systematic approach, patterns and relationships found in the secondary data can be used to help answer the research question and draw meaningful conclusions. The systematic approach can also provide a structured framework to minimize bias, increase the validity of the findings, and enhance the overall quality of the research. The data shows that AI enables hospitality firms to make complicated, critical decisions in a highly competitive and unpredictable environment. AI's global economic contribution is predicted to increase from $20.82 billion in 2020 to $15 trillion in 2030 (The Insight Partners, 2021).

Hypothesis

In discussing how different theoretical perspectives come together to support the research. A hypothesis can be the following:

Hypothesis (H1).

The influence of AI marketing technology in online shopping platforms is conducive to consumer repurchase intentions.

Hypothesis (H2).

The influence of AI marketing technology in online shopping platforms could be more conducive to consumer repurchase intentions.

Comparisons

The studies by Kai et al. (2020) and Nazir et al. (2023) have different focuses and investigate different aspects of customer engagement and consumer behaviour. Kai et al. (2020) focus on the platform differences in gaming, while Nazir et al. (2023) explore the impacts of AI technology on customers' repurchase intentions. However, the two studies immensely contribute to marketing and consumer behaviour by investigating different aspects of customer engagement and the influence of technology. Through the analysis of the two studies, several comparisons can be made.

Kai et al. (2020) focused on a comparative customer engagement analysis of playing games on phones and personal computers. Kai et al. (2020) examined users' capabilities, characteristics, and experiences associated with mobile phones or PCs to determine which factors influenced customer engagement in gaming. The study examines whether the use of mobile phones or PCs significantly influences customer engagement, which influences customer behaviours. Several studies by Foehr & Germelmann (2020) have contributed to the literature on customer engagement, user experience, and buying behaviours. However, the study by Kai et al. (2020) intended to fill the gap by conducting a comparative and comprehensive analysis of the application buyer behaviour on different platforms, such as using PCs and mobile phones, and determining the use based on differentiated customer engagement levels. The data analyzed by Kai et al. (2020) differs in that the study contributes to formulating strategies that focus on customer satisfaction which, in the long run, ensures the growth of competitiveness.

Kai et al. (2020) suggest AI technology can enhance interactions among customers, products, or services in interactive environments and quickly match demands. AI chat robots, content recommendation systems, and consumer feature recognition have become artificial agents for AI marketing activities. For example, Davenport et al. (2020) reported that Amazon takes the lead in using artificial intelligence technology to achieve the retail relocation of people, goods, and stores and extends its artificial intelligence framework.

Data Analysis

The research examined data collection by Nazir et al. (2022) for 12 weeks. The respondents were customers who booked a hotel through online websites at least once over the last six months in three different regions. An ANOVA single-factor test is used for the one-way analysis of variance (ANOVA) to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. The ANOVA single-factor test is essential in analyzing the data because it provides a comprehensive and efficient method to compare the means across multiple groups, identify significant factors and interpret the interactions. Similarly, the single factors test consumer engagement on social media and conversion rate positively influence satisfying consumer experience, leading to increased consumer repurchase intentions. Harman's single-factor test was utilized to test for common method bias in the data to prevent single-source bias. Employing Harman's single-factor test in the analysis is important because it assesses the presence of a common factor in a set of items or variables, thus helping the researcher to detect biasedness, which, if not corrected, can lead to spurious relationships between variables, thus compromising the validity of the study results. Data booking will be linked to booking behaviour.

Conclusion

  Automating business using AI enables hospitality companies to enhance the customer experience by discovering innovative, strategic, and long-term solutions. Silverman (2022) and Nazir et al. (2022), AI also allows hospitality firms to make complicated critical decisions in an unexpectedly unstable and competitive business environment. Chung (2005) suggests analyzing the data to identify patterns and trends. Based on the literature, one can provide recommendations to small business owners on using IT to increase the customer experience. It is essential to consider qualitative data to gain a more complete understanding of customer needs.

Moreover, Silverman (2022) and Chung (2005) note using quantitative data stata tests to analyze collected data is an excellent way to eliminate bias in research. In this way, the results will be presented clearly and understandably. Nazir et al. (2022) note the research can offer recommendations for small business owners to create more effective customer engagement strategies. Kai Kang et al. (2020) note the importance of a plan to monitor the strategy's performance.

Finally, Nazir et al. (2022) suggest consumer habit positively moderates the relationship between satisfying consumer experience and repurchase intention. The study facilitated the understanding of artificial intelligence technology to influence consumer engagement on social media and conversion rate to boost consumer satisfaction and repurchase intention and offers suggestions for developing impeccable service business strategies. The data by Nazir et al. (2022) suggests marketers must consider making posts more interesting through videos, images, and animations, which will satisfy consumers, ultimately boosting their desire to use, share, and generate content on social media platforms for hospitality organizations.

References

Adnan, M., Ahmad, N., Scholz, M., Khalique, M., Naveed, R. T., & Han, H. (2021). Impact of substantive staging and communicative staging of sustainable servicescape on behavioural intentions of hotel customers through overall perceived image: A case of boutique hotels. International Journal of Environmental Research and Public Health,  18(17), 9123.

Anand, B. and Chung, C.A. (2005). Statistical Process Control for the Engineering IT Support Incident Life Cycle. Journal of International Technology and Information Management, 14(2), 83-92.

Anees, R. T., Nordin, N. A., Anjum, T., Cavaliere, L. P. L., & Heidler, P. (2020). Evaluating the Impact of Customer Relationship Management (CRM) Strategies on Customer Retention (A Study of Fast Food Chains in Pakistan).  Business Management and Strategy,  11(2), 117-133.

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, 24-42.

Foehr, J., & Germelmann, C. C. (2020). Alexa, can I trust you? Exploring consumer paths to trust in smart voice-interaction technologies. Journal of the Association for Consumer Research, Vol.5(2), 181-205

Kai Kang, Jinxuan Lu, Lingyun Guo, & Jing Zhao. (2020). How to Improve Customer Engagement: A Comparison of Playing Games on Personal Computers and Mobile Phones. Journal of Theoretical & Applied Electronic Commerce Research, 15(2), 76–92. https://doi-org.10.4067/S0718-18762020000200106

McClave, J., Benson, P. G., and Sincich, T. (2022). Statistics for Business and Economics (14th ed.). New York, NY: Pearson.

Nayal, P., Pandey, N., & Paul, J. (2021). Examining m-coupon redemption intention among consumers: A moderated moderated-mediation and conditional model.  International Journal of Information Management,  57, 102288.

Nazir, S., Khadim, S., Asadullah, M. A., & Syed, N. (2023). Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach. Technology in Society, 72, 102190.

Silverman, D. (2022) Doing qualitative research (6th. Ed.). Sage Publications, Inc. ISBN-978-1-5297-6901-2; ISBN-978-1-5297-6900-5 (pbk).

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