The motivational factors of AI technology that influence milliennials and members of Generation Z in online transactions
Data files
Apr 18, 2025 version files 39.18 KB
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Data.sav
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README.md
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Survey_questionnaire.docx
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Abstract
The motivational factors of artificial intelligence that influence Millennials and members of Generation Z in online transactions were examined through responses from 116 respondents in Abuja Municipal Area Council, Abuja, Nigeria. Of these respondents, 55 were Millennials and 61 were members of Generation Z. Primary data was collected using a self-administered questionnaire based on Gursoy et al.'s (2018) artificial intelligence device use acceptance (AIDUA) and Yang, Lou, and Lan's (2022) modified AIDUA.
The model for this data included anthropomorphism and hedonism motivational factors from AIDUA, as well as utilitarian and interaction convenience motivational factors from Modified AIDUA. Anthropomorphism refers to human-like features and capabilities in artificial intelligence technology during online transactions, while hedonic factors are the enjoyable attributes that enhance the use of artificial intelligence in online transactions. Utilitarian factors focus on the ability of artificial intelligence to perform expected tasks, while interaction convenience factors emphasize the ease and convenience of using artificial intelligence technology.
Notably, millennials and members of Generation Z share some similarities and differences in their online behaviour. Millennials tend to value a less complex lifestyle, while Generation Z values speed and exploration in online transactions. Millennials often use artificial intelligence for leisure and entertainment online, whereas Generation Z tends to use it for shopping or work-related purposes. The research aimed to determine which motivational factors of artificial intelligence technology have the most influence on Millennials and Generation Z in online transactions. Eventually, these data were guided by the following research questions: Do the anthropomorphism, hedonism, utilitarian, and interaction convenience motivational factors of artificial intelligence technology influence Millennials more than members of Generation Z in online transactions?
With these in mind, this study's data were guided by the proceeding null hypotheses: anthropomorphism, hedonism, utilitarian, and interaction convenience motivational factors of artificial intelligence technology have no significant influence on Millennials more than members of Generation Z in online transactions.
Thus, the Mann-Whitney U-test for non-parametric data was used in analyzing the statistical significance of the data at a confidence Interval of 95%, while the p-value of .05 (i.e., p-value >.05 rejects the null hypotheses) was used to determine the significant difference of the motivational factors of artificial intelligence technology among Millennials and members of Generation Z in online transactions. In addition, Gray and Kinnear's (2012) non-parametric effect size estimator determined the practical significance of the difference among both generational cohorts.
The results showed that anthropomorphism and hedonism factors of artificial intelligence have a greater influence on Generation Z than on Millennials in online transactions. On the other hand, utilitarian and interaction convenience factors of artificial intelligence technology had a stronger influence on Millennials than on Generation Z in online transactions. Comparatively, the practical significance of this study's data indicated that anthropomorphism and hedonism factors of artificial intelligence had medium effect sizes on members of Generation Z, while utilitarian factors had small effect sizes on Millennials.
IBM Statistical Package for Social Sciences (SPSS) version 26 was used to analyze the data.
Data.sav
The data set is divided into five sections: A to E.
Section A contains demographic information about the respondents. Section A starts at rows 2-7 in the variable view of SPSS version 26. Row 2 contains the variable name Gen_Cohort, which labels the generational cohort of respondents. This label was coded with values: 1 for Millennials and 2 for members of Generation Z. Row 3 contains the variable name Gender, which was coded with values: 1 for Male and 2 for Female. Row 4 contains the variable name Area_Residence, which labels the respondents’ area of residence in AMAC. The four districts identified in the data were coded as follows: 1 for the Airport Road district, 2 for the Garki district, 3 for the Gwarimpa district, and 4 for the Wuse district. Row 5 contains the variable AI_Encounter, which labels whether the respondent has experienced AI technology in online transactions. The responses were coded as 1 for Yes and 2 for No. Additionally, in row 6, the variable Platform labels the platform on which respondents encountered AI technology in online transactions. The responses were coded as 1 for Facebook, 2 for TikTok, 3 for Instagram, 4 for Company webpage, and 5 for Others. Lastly, row 7 in section A contains the variable Social_Connection, which labels the influence of social connection on millennials and members of Generation Z in accepting AI technology in online transactions. The options were coded as 1 for Yes and 2 for No. All the data in section A are nominal data, which were used for descriptive statistics.
Section B, which starts at rows 9-12, presents the questions and responses of respondents regarding the anthropomorphism and motivational factors of AI technology in online transactions. Row 9-12 contain the variables AI, A2, A3, and A4, which represent labels in column 5: AI has human-like consciousness, AI has human-like intellectual capabilities, AI has human-like emotions, and AI has unique qualities similar to human agents, respectively.
Similarly, section C (rows 14-16) focuses on the hedonistic motivational factors of AI technology in online transactions. The variables in column 5 of the SPSS version 26 interface are named as follows: Hedonism_Fun, Hedonism_Entertaining, and Hedonism_Pleasant for AI is fun to interact with, AI is entertaining, and AI is pleasant, respectively.
Furthermore, section D (rows 18-21) deals with interaction convenience motivational factors of AI technology in online transactions. The variables AI_Effectiveness, AI_Feedback, AI_Recommendations, and AI_Comprehensive_Recommendation represent labels for AI is effective, AI gives immediate feedback, AI recommendations are accurate, and AI provides comprehensive product and service recommendations.
Section E (rows 23-24) focuses on the interaction convenience motivational factors of AI technology in online transactions. The variables AI_Simplier_Approach and AI_Short_Access represent labels for AI has a simple-to-use approach and AI gives simpler access convenience, respectively.
Notably, respondents’ responses to the labels from sections B to E were coded on a 5-point Likert scale ranging from 1= Strongly Disagree to 5 = Strongly Agree. These data are ordinal and were used for inferential statistics.
Lastly, section F contains the transformed data of each item from every section into a single dependent variable. The four items related to anthropomorphism motivational factors in section B were combined into one item in section F, specifically in row 26. The same process was applied to hedonism motivational factors, utilitarian motivational factors, and interaction convenience motivational factors. Therefore, in section F (rows 26-29), the variables anthropomorphism, hedonism, utilitarian, and interaction convenience have been transformed into single items. These items will be used as dependent variables in the analysis of the Mann-Whitney non-parametric U-test for the dataset.
Survey_questionnaire.docx
The survey questionnaire utilized during the data collection process.
References
Field, A. (2013). Discovering statistics using IBM SPSS statistics (5TH ed). Los Angeles: SAGE.
Gray, C. D., & Kinnear, P. R. (2012). IBM spss statistics made simple. New York: Psychology Press.
Gursory, D., Chi, O. H., Lu, L., & Nunkeo, R. (2019). Consumers acceptance of artificial intelligence (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. https://www.doi.org/10.1016/j.ijinformgt.2019.03.608.
Yang, Y., Luo, J., & Lan, T. (2022). An empirical assessment of a modified artificial intelligent device use acceptance model: From the task-oriented perspective. Frontier in Psychology. 1-14. https://doi.org/10.3389/fpsyg.2022.975307.