DOI: 10.5061/dryad.8cz8w9h3h
title: The motivational factors of AI technology that influence milliennials and members of Generation Z in online transactions

description: 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 &gt;.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.
description: # The motivational factors of AI technology that influence milliennials
 and members of Generation Z in online transactions 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](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](https://doi.org/10.3389/fpsyg.2022.975307).