國立虎尾科技大學 |

Exploring the Behavioral Intentions to Use AI-Based Chatbots for Apparel Shopping.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Exploring the Behavioral Intentions to Use AI-Based Chatbots for Apparel Shopping./
作者:	Myin, Mon Thu A.
面頁冊數:	1 online resource (216 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Fashion. -
電子資源:	click for full text (PQDT)
ISBN:	9798382767345

Exploring the Behavioral Intentions to Use AI-Based Chatbots for Apparel Shopping.
Myin, Mon Thu A.

Exploring the Behavioral Intentions to Use AI-Based Chatbots for Apparel Shopping. - 1 online resource (216 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Greensboro, 2024.

Includes bibliographical references

In the retail industry, AI chatbots have played a vital role by offering 24/7 customer services, enhancing sales through prompt and accurate responses to customers' questions, and providing personalized product recommendations based on customers' preferences (Ashfaq et al., 2020). Despite the significant impact of AI chatbot technology on the apparel retail industry, its coverage is still nascent in existing apparel and retail literature. Specifically, the lack of studies has hindered our understanding of consumers' antecedents (reasons for and reasons against) and consequences (willingness to buy and eWOM) regarding attitudes toward using AI chatbots, along with the moderating effect of technology familiarity. To address this gap, this dissertation developed and tested a conceptual model of the potential antecedents and consequences of consumers' attitudes toward using AI chatbots. Specifically, three primary objectives of the study are: (1) to examine relationships between reasons for (perceived chatbot service quality) factors, reasons against (perceived chatbot barriers) factors, and attitudes toward using AI chatbots; (2) to investigate relationships between consumers' attitudes toward using AI chatbots and their behavioral intentions as measured in terms of willingness to buy apparel with the help of AI chatbots and eWOM; and (3) to examine the moderating role of technology familiarity on the relationship between reasons for (perceived chatbot service quality) factors, reasons against (perceived chatbot barriers) factors, and attitudes toward using AI chatbots.Data were collected from 717 participants through a self-administered questionnaire distributed on Amazon Mechanical Turk (MTurk), an online panel. After careful screening, the final sample consisted of 632 usable responses for statistical analysis. Among participants, 35.1% were female, 64.9% were male. The majority (32.1%) were aged between 26 and 30. In addition, the largest proportion of participants identified as White (90.2%). A total of 58 measurement items were adapted from previous studies and assessed using a 5-point Likert-type scale. The two-step approach, as outlined by Anderson and Gerbing (1988), was employed using Mplus version 8 to establish both measurement and structural models. The confirmatory factor analysis (CFA) was employed first. After the measurement model was established, the path analysis was performed to test all hypothesized relationships using the structural equation model (SEM).Results showed that reasons for factors, such as responsiveness, reliability, and assurance positively influenced attitudes toward using AI chatbots. Conversely, there were no positive relationships between most reasons against factors, such as usage barrier, risk barrier, value barrier, image barrier, and attitudes toward using AI chatbots. While this study identified a significant positive relationship between tradition barrier and attitudes toward using AI chatbots, the result did not align with the proposed hypothesis. Therefore, this study highlighted the significance of responsiveness, reliability, and assurance as important factors influencing consumers' adoption of AI chatbots for apparel shopping. In contrast, this study suggested that all five barriers, namely usage barrier, risk barrier, value barrier, image barrier, and tradition barrier, may not be important factors in either rejecting or accepting AI chatbots for apparel shopping. Furthermore, the results revealed that attitudes toward using AI chatbots positively influenced both willingness to buy apparel with the help of AI chatbots and eWOM. Thus, the study suggested that consumers with positive attitudes toward AI chatbots are more likely to use them and share favorable reviews and comments about AI chatbots on social media and other online platforms. Subsequently, consumers' positive reviews may encourage other online shoppers to engage with AI chatbot services offered by apparel brands. The results also indicated a positive moderating effect of technology familiarity on the relationship between reliability and attitudes toward using AI chatbots. In addition, the moderating effect of technology familiarity on the relationship between tradition barrier and attitudes toward using AI chatbots was negatively significant.This dissertation provides significant contributions to the literature by developing and testing a research model that investigates the antecedents and consequences of attitudes toward using AI chatbots for apparel shopping. Moreover, the findings also provide empirical evidence for the moderating role of technology familiarity on attitudes toward using AI chatbots. Practical implications are also provided. Additionally, this dissertation addresses limitations and suggests future research directions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382767345Subjects--Topical Terms:

866133
Fashion.
Subjects--Index Terms:

AI chatbotsIndex Terms--Genre/Form:

554714
Electronic books.

Exploring the Behavioral Intentions to Use AI-Based Chatbots for Apparel Shopping.
LDR:06290ntm a22004097 4500 001 1148010
005 20240916070002.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798382767345
035 $a (MiAaPQ)AAI30996508
035 $a AAI30996508
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Myin, Mon Thu A. $3 1473876
245 1 0 $a Exploring the Behavioral Intentions to Use AI-Based Chatbots for Apparel Shopping.
264 0 $c 2024
300 $a 1 online resource (216 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Watchravesringkan, Kittichai Tu.
502 $a Thesis (Ph.D.)--The University of North Carolina at Greensboro, 2024.
504 $a Includes bibliographical references
520 $a In the retail industry, AI chatbots have played a vital role by offering 24/7 customer services, enhancing sales through prompt and accurate responses to customers' questions, and providing personalized product recommendations based on customers' preferences (Ashfaq et al., 2020). Despite the significant impact of AI chatbot technology on the apparel retail industry, its coverage is still nascent in existing apparel and retail literature. Specifically, the lack of studies has hindered our understanding of consumers' antecedents (reasons for and reasons against) and consequences (willingness to buy and eWOM) regarding attitudes toward using AI chatbots, along with the moderating effect of technology familiarity. To address this gap, this dissertation developed and tested a conceptual model of the potential antecedents and consequences of consumers' attitudes toward using AI chatbots. Specifically, three primary objectives of the study are: (1) to examine relationships between reasons for (perceived chatbot service quality) factors, reasons against (perceived chatbot barriers) factors, and attitudes toward using AI chatbots; (2) to investigate relationships between consumers' attitudes toward using AI chatbots and their behavioral intentions as measured in terms of willingness to buy apparel with the help of AI chatbots and eWOM; and (3) to examine the moderating role of technology familiarity on the relationship between reasons for (perceived chatbot service quality) factors, reasons against (perceived chatbot barriers) factors, and attitudes toward using AI chatbots.Data were collected from 717 participants through a self-administered questionnaire distributed on Amazon Mechanical Turk (MTurk), an online panel. After careful screening, the final sample consisted of 632 usable responses for statistical analysis. Among participants, 35.1% were female, 64.9% were male. The majority (32.1%) were aged between 26 and 30. In addition, the largest proportion of participants identified as White (90.2%). A total of 58 measurement items were adapted from previous studies and assessed using a 5-point Likert-type scale. The two-step approach, as outlined by Anderson and Gerbing (1988), was employed using Mplus version 8 to establish both measurement and structural models. The confirmatory factor analysis (CFA) was employed first. After the measurement model was established, the path analysis was performed to test all hypothesized relationships using the structural equation model (SEM).Results showed that reasons for factors, such as responsiveness, reliability, and assurance positively influenced attitudes toward using AI chatbots. Conversely, there were no positive relationships between most reasons against factors, such as usage barrier, risk barrier, value barrier, image barrier, and attitudes toward using AI chatbots. While this study identified a significant positive relationship between tradition barrier and attitudes toward using AI chatbots, the result did not align with the proposed hypothesis. Therefore, this study highlighted the significance of responsiveness, reliability, and assurance as important factors influencing consumers' adoption of AI chatbots for apparel shopping. In contrast, this study suggested that all five barriers, namely usage barrier, risk barrier, value barrier, image barrier, and tradition barrier, may not be important factors in either rejecting or accepting AI chatbots for apparel shopping. Furthermore, the results revealed that attitudes toward using AI chatbots positively influenced both willingness to buy apparel with the help of AI chatbots and eWOM. Thus, the study suggested that consumers with positive attitudes toward AI chatbots are more likely to use them and share favorable reviews and comments about AI chatbots on social media and other online platforms. Subsequently, consumers' positive reviews may encourage other online shoppers to engage with AI chatbot services offered by apparel brands. The results also indicated a positive moderating effect of technology familiarity on the relationship between reliability and attitudes toward using AI chatbots. In addition, the moderating effect of technology familiarity on the relationship between tradition barrier and attitudes toward using AI chatbots was negatively significant.This dissertation provides significant contributions to the literature by developing and testing a research model that investigates the antecedents and consequences of attitudes toward using AI chatbots for apparel shopping. Moreover, the findings also provide empirical evidence for the moderating role of technology familiarity on attitudes toward using AI chatbots. Practical implications are also provided. Additionally, this dissertation addresses limitations and suggests future research directions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Fashion. $3 866133
650 4 $a Home economics. $3 568501
653 $a AI chatbots
653 $a Apparel shopping
653 $a Behavioral reasoning theory
653 $a Consumer behaviors
653 $a Positive attitudes
655 7 $a Electronic books. $2 local $3 554714
690 $a 0338
690 $a 0200
690 $a 0800
690 $a 0386
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of North Carolina at Greensboro. $b School of Business & Economics: Consumer, Apparel, and Retail Studies. $3 1195425
773 0 $t Dissertations Abstracts International $g 85-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30996508 $z click for full text (PQDT)