Beyond the Word-of-Machine Effect - Does Social Influence Impact Preferences for AI Recommender Apps?
(2024) BUSN39 20241Department of Business Administration
- Abstract
- Abstract
Purpose: This study investigates the impact of social influence on consumer preferences for product recommendations made by an AI or a human within the cosmetics industry, specifically in light of hedonic or utilitarian goals.
Methodology: This quantitative research follows a deductive approach, using an online self-completion survey for data collection. A total of 659 valid responses were analyzed using statistical methods to test the proposed hypotheses, with respondents randomly assigned to scenarios emphasizing either hedonic or utilitarian goals, leaning onto a study design
conducted by Longoni and Cian (2022).
Theoretical Perspective: The study is grounded in the Word of Machine Effect theorized by Longoni and Cian... (More) - Abstract
Purpose: This study investigates the impact of social influence on consumer preferences for product recommendations made by an AI or a human within the cosmetics industry, specifically in light of hedonic or utilitarian goals.
Methodology: This quantitative research follows a deductive approach, using an online self-completion survey for data collection. A total of 659 valid responses were analyzed using statistical methods to test the proposed hypotheses, with respondents randomly assigned to scenarios emphasizing either hedonic or utilitarian goals, leaning onto a study design
conducted by Longoni and Cian (2022).
Theoretical Perspective: The study is grounded in the Word of Machine Effect theorized by Longoni and Cian (2022) and the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (2003, 2012). Furthermore, the thesis engages with different concepts of the customer journey.
Findings: The results confirm that goal orientation (hedonic vs. utilitarian) significantly influences the preference for AI or human recommenders. Hedonic and utilitarian goals are both associated with a preference for human recommenders. However, utilitarian goals are associated significantly stronger with AI recommenders than hedonic goals. Furthermore,
social influence was found to also significantly influence the choice of recommender, with a higher level of social influence associated with a decrease in the odds of choosing AI recommenders.
Implications: This study makes significant theoretical contributions to marketing research, particularly in understanding technology acceptance in the FMCG-cosmetics industry. It is the first to integrate the social influence factor from the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Word of Machine Effect in the novel context of
cosmetics-AI-recommendations. Due to a negative association between social influence and AI recommender choice, this study challenges existing UTAUT assumptions and emphasizes the importance of context-specific factors. Additionally, the research provides empirical evidence on consumer’s utilitarian motivations regarding AI recommender systems, confirming the Word of Machine Effect's applicability to the cosmetics industry.
Originality: This research uniquely expands the Word of Machine Effect, which associates hedonic or utilitarian goals of consumers with their different preferences for AI- or human recommender apps, with the novel influence factor of social influence. Furthermore, it tests the mentioned associations in an FMCG environment, namely the cosmetics industry, which
is a use case currently very close to reality. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9167089
- author
- Sorle, Michaela LU and Haldenmayr, Ina Katja LU
- supervisor
- organization
- course
- BUSN39 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- AI recommendations, social influence, Word of Machine Effect, UTAUT, hedonic, utilitarian, cosmetics industry, FMCG, customer journey, consumer behavior.
- language
- English
- id
- 9167089
- date added to LUP
- 2024-06-25 13:27:03
- date last changed
- 2024-06-25 13:27:03
@misc{9167089,
abstract = {{Abstract
Purpose: This study investigates the impact of social influence on consumer preferences for product recommendations made by an AI or a human within the cosmetics industry, specifically in light of hedonic or utilitarian goals.
Methodology: This quantitative research follows a deductive approach, using an online self-completion survey for data collection. A total of 659 valid responses were analyzed using statistical methods to test the proposed hypotheses, with respondents randomly assigned to scenarios emphasizing either hedonic or utilitarian goals, leaning onto a study design
conducted by Longoni and Cian (2022).
Theoretical Perspective: The study is grounded in the Word of Machine Effect theorized by Longoni and Cian (2022) and the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (2003, 2012). Furthermore, the thesis engages with different concepts of the customer journey.
Findings: The results confirm that goal orientation (hedonic vs. utilitarian) significantly influences the preference for AI or human recommenders. Hedonic and utilitarian goals are both associated with a preference for human recommenders. However, utilitarian goals are associated significantly stronger with AI recommenders than hedonic goals. Furthermore,
social influence was found to also significantly influence the choice of recommender, with a higher level of social influence associated with a decrease in the odds of choosing AI recommenders.
Implications: This study makes significant theoretical contributions to marketing research, particularly in understanding technology acceptance in the FMCG-cosmetics industry. It is the first to integrate the social influence factor from the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Word of Machine Effect in the novel context of
cosmetics-AI-recommendations. Due to a negative association between social influence and AI recommender choice, this study challenges existing UTAUT assumptions and emphasizes the importance of context-specific factors. Additionally, the research provides empirical evidence on consumer’s utilitarian motivations regarding AI recommender systems, confirming the Word of Machine Effect's applicability to the cosmetics industry.
Originality: This research uniquely expands the Word of Machine Effect, which associates hedonic or utilitarian goals of consumers with their different preferences for AI- or human recommender apps, with the novel influence factor of social influence. Furthermore, it tests the mentioned associations in an FMCG environment, namely the cosmetics industry, which
is a use case currently very close to reality.}},
author = {{Sorle, Michaela and Haldenmayr, Ina Katja}},
language = {{eng}},
note = {{Student Paper}},
title = {{Beyond the Word-of-Machine Effect - Does Social Influence Impact Preferences for AI Recommender Apps?}},
year = {{2024}},
}