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Usability and responsiveness of artificial intelligence chatbot on online customer experience in

e-retailing Ja-Shen Chen

College of Management, Yuan Ze University, Taoyuan, Taiwan

Tran-Thien-Y Le College of Management, Yuan Ze University, Taoyuan, Taiwan and School of Economics, Can Tho University, Can Tho, Vietnam, and

Devina Florence College of Management, Yuan Ze University, Taoyuan, Taiwan

Abstract

Purpose –The rapid evolution in artificial intelligence (AI) has redefined the customer experience and created huge opportunities for companies to interact with customers using chatbots. This study explores the role of AI chatbots in influencing the online customer experience and customer satisfaction in e-retailing. Design/methodology/approach – A research model based on the technology acceptance model and information system success model is proposed to describe the interrelationships among chatbot adoption, online customer experience and customer satisfaction. Personality is a moderator in the model. The authors used a quantitative approach to collect 425 useable online questionnaires and Statistical Product and Service Solutions (SPSS) and SmartPLS to analyze the measurement model and proposed hypotheses. Findings – The usability of the chatbot had a positive influence on extrinsic values of customer experience, whereas the responsiveness of the chatbot had a positive impact on intrinsic values of customer experience. Furthermore, online customer experience had a positive relationship with customer satisfaction, and personality influenced the relationship between the usability of the chatbot and extrinsic values of customer experience. Originality/value –This research extends understanding of the online customer experience with chatbots in e-retailing and provides empirical evidence by showing that extrinsic and intrinsic values of online customer experience are enhanced by chatbot adoption.

Keywords Chatbot adoption, Online customer experience, Customer satisfaction, Personality, e-retailing

Paper type Research paper

Introduction The world today is becoming more digital and transforming dramatically as a result. The growing number of online consumers and the rapidly changing business environment push e-retailing to differentiate itself by providing superior customer service and a better customer experience. These changes, which companies enact to remain competitive, have brought about changes in the roles of chatbots (Sousa and Rocha, 2019; Belanche et al., 2019; Davenport et al., 2020). In the past couple of years, chatbots have become so well integrated into the online customer experience that customers can hardly discern whether they are interacting with a chatbot or a human (An, 2018; Luo et al., 2019). The rapid evolution in artificial intelligence (AI) has redefined the customer experience and created huge opportunities for companies to interact with customers using chatbots (Sidaoui et al., 2020; Hollebeek et al., 2021; Kumar et al., 2019; De Cicco et al., 2020). E-commerce capabilities integrated within chatbots have become one of

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This work was partially supported byMinistry of Science and Technology, Taiwan with grant number: MoST 107-2410-H-155 -037 -MY3.

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0959-0552.htm

Received 21 August 2020 Revised 31 August 2020 27 September 2020 14 March 2021 5 April 2021 Accepted 14 April 2021

International Journal of Retail & Distribution Management Vol. 49 No. 11, 2021 pp. 1512-1531 © Emerald Publishing Limited 0959-0552 DOI 10.1108/IJRDM-08-2020-0312

the fastest growing uses of AI, and an increasing number of businesses are now using AI chatbots (hereafter, “chatbots”) in customer interactions (Okuda andShoda, 2018;An, 2018) and influencing the online customer experience (Sidaoui et al., 2020; Wirtz et al., 2018).

It is increasingly important for retailers to adopt technology to provide customer support services that meet individuals’ evolving needs (Huang and Rust, 2021; Lu et al., 2019). Chatbots can have significant effects by addressing problems and deficiencies in e-retailing as well as mitigating the impersonal nature and risks associated with online purchasing. They improve efficiency and effectiveness by substituting for and complementing frontline employees through technology-mediated learning, which makes them critical for advancing frontline experiences in service encounters (Lu et al., 2019). For example, retail chatbots provide gamified ways to shop through a conversational interface that captures and holds users’ attention, encouraging customers to browse, providing them with information about products and attempting in numerous ways to upsell their purchases (Przegalinska et al., 2019).

Furthermore, the spread of the coronavirus disease 2019 (COVID-19) virus has challenged the business world in multiple ways. Retailers have been forced to make swift changes in their operations, and chatbots have emerged as viable and scalable solutions.With social distancing measures forcing the shuttering of many brick-and-mortar stores, the online customer experience has become key in promoting retention, making sales, as well as improving lead generation through automated customer services (McLean and Osei-Frimpong, 2019).

Although the potential of chatbots in e-retailing has been acknowledged (e.g. De Cicco et al., 2020; Chopra, 2019), problems continue to hinder their growth (e.g. lack of expertise in chatbot development, lack of awareness of context; Pricilla et al., 2018). Previous research has explored the use and importance of chatbots in service encounters and the behavioral outcomes of chatbots (Chung et al., 2020; Youn and Jin, 2021). At the same time, few studies go in depth into what factors effectively and efficiently enrich the functionality of chatbots with customers (Stoeckli et al., 2020). For instance, retailers are continuously investing substantial amounts in e-commerce applications but are not concerned about the success of their e-commerce systems. This not only means that some technology-based products and services never reach their full potential or are rejected but also results in financial losses and dissatisfaction (Przegalinska et al., 2019). The few studies on chatbots have focused mainly on a business management perspective, neglecting to ascertain essential constructs in chatbot value from the customer’s perspective (Hu et al., 2018). Therefore, there is still a gap in the dimension of value proposition concerning the online customer experience with chatbot adoption in e-retailing. In addition, the moderating effects of the user’s personality on chatbot adoption remain underexplored.

Because businesses’ adoption of chatbots lags behind customers’ openness to using this technology, understanding the individual motives behind chatbot adoption is essential for creating better e-commerce technologies (Chopra, 2019). This paper enriches the knowledge on chatbot adoption for online customer experience and customer satisfaction in e-retailing research. We propose a conceptual model (Figure 1) that examines the relationships among chatbot adoption, online customer experience, and customer satisfaction and tests the moderating role of personality in these linkages.

In particular, we address the following research questions: (1) To what extent chatbot adoption in usability and responsiveness can improve customer experience and the consequence of customer satisfaction in e-retailing? (2) Does customer personality play a moderating role in the influence of chatbot adoption on the online customer experience?

Literature review and hypothesis development Chatbot adoption AI is likely to substantially change both marketing strategies and customer behaviors including chatbots (Davenport et al., 2020. Kumar et al., 2019; Wirth, 2018). A chatbot is a

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computer program that mimics human conversation using natural language capabilities; chatbots commonly act as virtual assistants on the Internet (Fryer et al., 2019). There have been a few studies discussed about the use of chatbots in business (e.g. Belanch�e et al., 2019; Sotolongo and Copulsky, 2018; Vassinen, 2018; De Cicco et al., 2020). For example, Belanch�e et al. (2019) discussed aboutAI on financial technology (FinTech) and identified key drivers of robo-advisor adoption among customers. De Cicco et al. (2020) applied a social relationship perspective to the design of chatbots addressed to younger consumers. The focus of this study was on conversational chatbots as customer service platforms. Such platforms can create advanced dialog systems that deliver realistic and engaging natural language interfaces. During interactionswith a customer, a chatbot needs to understand the customer’s request, maintain and update the customer state, as well as ask clarifying questions while keeping the customer engaged (Alt et al., 2019; Quintino, 2019), particularly conversational interfaces allow people to direct devices and programs through natural dialog (Sotolongo and Copulsky, 2018). Equipped with AI, chatbots play a vital role in facilitating engagement because of their conversational, data-driven and predictive nature (Sands et al., 2021). They serve three key functions: search support for information retrieval, navigational support for product discovery and essential decision support for recommendations (Agichtein et al., 2020). Their benefits range from giving accurate information, credible advice, information on current trends and opportunities for customization to saving time (Chung et al., 2020). Conversational commerce is a subset of e-commerce that uses neurolinguistic programming, either text messaging or voice recognition systems (Kraus et al., 2019). Chatbots in the e-retailing service field often serve a search or decision support function, providing convenient, personal, unique, interactive and engaging customer service interactions or encounters. They help build crucial customer relationships and decrease customers’ uncertainty and anxiety, allowing for a more efficient use of time and a better understanding of products or services (Quintino, 2019). For example, the chatbot can recognize the customer and communicate with the customer to provide information like “whether a certain product is available?”, “is the product in promotion?”, “what the related (cross sell/up sell) products will the chatbot suggest?”, “how much will these products cost in total?” and “when will the products deliver to me?”

To better understand how customers perceive interactions with chatbots, we draw on the technology acceptance model (TAM) (Davis, 1989) and the information system (IS) success model (DeLone and McLean, 2004). TAM proposes that perceived ease of use and perceived usefulness drive users’ behavioral intentions to adopt and use technology. Perceived usefulness and perceived ease of use generally contribute to usability, which reflects users’ expectations of technology-based applications (Flavi�an et al., 2006). In the IS success model,

Personality

H3(+)

H1(+)

H6(+)

H2(+)

H4(+)

H5(+)

H7 H8

Usability

Responsiveness

Extrinsic values

Intrinsic values

Customer Satisfaction

Chatbot Adoption Online Customer Experience

Direct effect

Moderating effect

Figure 1. Proposed model

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usability is perceived as a valuable, desired characteristic of the quality of an e-commerce system, and responsiveness (i.e. a service attribute) is perceived as the predominant role of customer service. By integrating these two theories, we introduce two dimensions—namely, usability and responsiveness—to measure chatbot adoption in e-retailing.

Online customer experience The importance of experience in the growth of online shopping has been recognized (Rose et al., 2012). It is crucial in forming customers’ perceived expectations of e-retailing (Pappas et al., 2014). Customer experience in e-commerce is a psychological state manifested as a subjective response to or total perceptions accompanying service delivery (Rose et al., 2012; Zhang et al., 2017). According to Petre et al. (2006), customers’ experience with e-commerce extends beyond their interaction with a website to influence their perceptions of value and service quality. Mathwick et al. (2001) suggested assessing the retail shopping experience based on extrinsic as well as intrinsic values that go well beyond the traditional mix of price and quality. The extrinsic aspect focuses on economic outcomes (e.g. efficiency and economic value) and is instrumental in nature (Wei et al., 2016). In contrast, the intrinsic aspect emphasizes the consumption of fun, enjoyment and playfulness rather than their consequences (Mortimer et al., 2019; Wei et al., 2016).

Chatbots and the online customer experience Usability and responsiveness are two elements of a chatbot. Usability is a quality or attribute that represents how easy a human–computer interface is to use to achieve a specified goal effectively, efficiently and satisfactorily (Petre et al., 2006). Usability metrics (Finstad, 2010) for user experience also point out the factors of ease of use. Furthermore, a survey conducted by Userlike in 2019 showed that customers perceive companies with chatbots as innovative rather than cheap because of their ability to initiate conversation or present the functionality of products or services on e-commerce websites (Joyce and Kirakowski, 2015). By providing credible advice, chatbots canmake customers feel that communication is personalized to their needs, which shows that credibility is essential to meeting customers’ needs without creating other problems (Prentice et al., 2019).

Responsiveness is readiness to help a customer by offering accessible services instantly to bring about convenience (Chung et al., 2020; Van den Broeck et al., 2019). The fact that chatbots reply quickly, are easy to contact and are available when needed (Roy et al., 2018) makes customers feel comfortable and valued as well as provides themwith the enjoyment of chatting with a chatbot (Chung et al., 2020). Responsiveness in this research also refers to response time (Tiwana, 1998) or the timeliness of a service (DeLone and McLean, 2004; Molla and Licker, 2001). The more responsive a chatbot is, the more innovative the company is perceived to be by customers.

Furthermore, we propose evaluating the online customer experience of chatbot adoption by two dimensions, namely, extrinsic values and intrinsic values. Extrinsic values of online customer experience include the convenience, time savings and efficiency that have been acknowledged as functional outcomes of using a technology (Kokkinou and Cranage, 2013). In regard to chatbots in e-retailing, the relevance of the web content is essential to initiating transactions as well as ensuring that customers return to a site regularly. Chatbots play a role in customizing assistance through direct chats or messages to customers in e-retailing (Chung et al., 2020). This customization enhances the online customer experience (Rose et al., 2012), as users expect a highly personalized system from digital assistants (Kraus et al., 2019). Convenience in the customer experience helps save time and effort for customers, whether it is cognitively, emotionally or physically, during the purchase of a product or the use of a service (Islam et al., 2019; Roy et al., 2018). Therefore, based on the literature regarding

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experiential values pertinent to chatbot adoption in e-retailing, the following hypothesis is derived:

H1. The usability of a chatbot positively affects extrinsic values of online customer experience in e-retailing.

Also, the fact that chatbots reply quickly, are easy to contact and are available when needed (Roy et al., 2018) makes customers feel comfortable and valued (Chung et al., 2020). Through their quick response they help customers talk to them personally, resolve problems and gather additional information on services or goods. Therefore, we propose the following:

H2. The responsiveness of a chatbot positively affects extrinsic values of online customer experience in e-retailing.

Intrinsic values of customer experience in the online environment include feelings of accomplishment, independence, confidence, novelty and enjoyment that prior studies have found to increase one’s intention to accept technology (e.g. Meuter et al., 2005). Customers also find it fun to chat with chatbots. Fun is crucial to the customer experience, as it increases value perceptions and intentions for customers to adopt digital tools (Go and Sundar, 2019). In addition, Chung et al. (2020) emphasized that interactions must be smooth, accurate and complete to evoke positive perceptions of understanding and relevant communication. Providing customers with specific, clear and easy-to-read information, along with providing comprehensive discussion, increases the likelihood of a customer feeling valued and comfortable (Go and Sundar, 2019). Especiallywhen chatbots are aware of the context during a conversation, customers may think that they are talking to them personally (Roy et al., 2018). From these arguments, we propose Hypothesis 3 as follows:

H3. The usability of a chatbot positively affects intrinsic values of online customer experience in e-retailing.

Finally, because chatbots reply quickly, are easy to contact and are available when needed, customers feel comfortable and valued (Zarouali et al., 2018; Chung et al., 2020) and receive benefits from interacting with the chatbot with little effort (Prentice et al., 2019; Roy et al., 2018). In addition, because customer care is available 24 h a day via the chatbot, consumers would enjoy to get the benefits to post their requests regardless of the standard operating hours (Adamopoulou and Moussiades, 2020). Moreover, responsiveness is one of the important chatbot quality attributes and could significantly improve customer support chatbot systems and have fun to chat with (Li et al., 2021). Thus, we propose Hypothesis 4 as follows:

H4. The responsiveness of a chatbot positively affects intrinsic values of online customer experience in e-retailing.

Customer satisfaction Customer satisfaction is often perceived as key to the success and long-term competitiveness of a company (Irfan et al., 2019). Molla and Loicker (2001) defined customer satisfaction as a reaction and feeling related to customer experience in e-commerce. A good experience with online shopping influences future intentions and affects customers’ sense of trust, causing them to perceive the seller as reliable (Pappas et al., 2014). In addition, M€unster and Haug (2017) discussed how convenience facilitates the relationship between managerially important constructs, such as customer satisfaction and repurchase intentions.

According to Kang (2006), interactions with chatbots that meet customer expectations are most likely to result in customer satisfaction because of the chatbots’ capacity to search for information and identify products that meet customer needs (Sanny et al., 2020). Among the factors of customer satisfaction in online shopping, Kraus et al. (2019) pointed to

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recommendation accuracy, convenience, customization and process efficiency as prominent characteristics of chatbots that are positively linked to customer satisfaction. The user’s perception of elapsed time is also a significant factor in satisfaction, as users expect systems to be fast, efficient and reliable. Because important values in chatbot customer support services play an essential role in achieving higher customer satisfaction, we propose the following:

H5. Perceived extrinsic values of online customer experience have a positive effect on customer satisfaction with the chatbot in e-retailing.

Once customers begin to have fun and enjoy the chatbot, they feel more satisfied. Gallarza et al. (2017) suggested entertainment, aesthetics, ethics and spirituality as four intrinsic values and tested overall perceived value, satisfaction and loyalty. While studying gamification features and intrinsic need satisfaction, Xi and Hamari (2019) suggested measuring customer satisfaction by the satisfaction of intrinsic needs (autonomy, competence and relatedness needs). In addition, customer experience of social support, social presence and flow were suggested to affect community engagement and word-of-mouth intention (Zhang et al., 2017). Thus, we propose the following:

H6. Perceived intrinsic values of online customer experience have a positive effect on customer satisfaction with the chatbot in e-retailing.

The moderating role of personality Personality has been used as an explanatory tool in the IS literature to predict human beliefs and behavior (Da Cunha and Greathead, 2007). It is a direct driver of individual behavior that determines patterns of interaction with the environment and influences customers’ use of mass media and online behaviors (Judge et al., 1999). Although the role of personality in Internet use is rapidly evolving, it is a critical driver of customer experience (Correa et al., 2010). Here, we focus on extraversion and openness to experience given their relevance to the implementation of chatbot technology in e-retailing. This choice is supported by a study conducted by Islam et al. (2017) that proved that extraversion and openness to experience have strong positive associations with customer engagement in online brand communities. Openness to experience and extraversion therefore are positively related to acceptance and use of new technology. Previous studies have shown that personality is a major deciding factor in social interaction—in this case, with a chatbot. Thus, it can be deemed as having a positive effect on customers’ adoption of chatbots in e-retailing, as it can influence the willingness of an individual to try out new technology (Da Cunha and Greathead, 2007). From these arguments, we propose Hypothesis 7 as follows:

H7. Personality moderates positively the effects of the usability of a chatbot on extrinsic values of customer experience in e-retailing.

Pratt and Chudoba (2006) highlighted the positive relationship between extraversion and users’ initial acceptance of a system. In contrast, openness to experience has a positive impact on organisations’ innovative use of technology. People who are open to experiences are more unconventional, curious, imaginative, adventurous and flexible in seeking out new things and experiences and engage in more meaningful use of social media (Correa et al., 2010; Jadin et al., 2013). Furthermore, they aremore inclined to follow online brand communities, blog and engage in various online activities (Tsao, 2013). Previous research by Amiel and Sargent (2004) and Correa et al. (2010) linked extraversion to high use of the Internet, social networks and social apps. Customers who are open to experiences or extraverted are more likely to have broader minds and ranges of interests, to be more tolerant of different perspectives and to seek out opportunities to learn something new or different. Thus, when it comes to interactingwith chatbots, these customersmay feel comfortable and valued and even think of

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the chatbot as innovative and fun, which allows them to receive benefits with little effort. Therefore, Hypothesis 8 is proposed as follows:

H8. Personality moderates positively the effects of the responsiveness of a chatbot on intrinsic values of customer experience in e-retailing.

Methods Instrument design and pilot testing Astructured questionnaire was developed in linewith the literature and the use of chatbots in e-retailing. Existing scales were adapted, modified and extended for this study. Usability was measured with nine items adapted from Rose et al. (2012) and Finstad (2010). Responsiveness was measured with four items adapted from Roy et al. (2018) and Chung et al. (2020). The extrinsic values scale included six items adapted from Rose et al. (2012) and Chung et al. (2020), whereas the intrinsic values scale contained three items adapted fromRoy et al. (2018). Customer satisfaction was assessed with four items adapted from Rose et al. (2012) and Pappas et al. (2014). Personality was measured with a four-item scale adapted from John and Srivastava (1999) and Islam et al. (2017). All constructs used a five-point Likert-type scale, with scores ranging from 1 5 strongly disagree to 5 5 strongly agree.

To ensure accuracy, a cover letter was provided to explain the purpose of the study and asked respondents to recall their own experience of using chatbot in e-retailing. Participants had to complete two screening questions on awareness and use of chatbots at the beginning of the questionnaire before they were eligible to participate in the study. The preliminary survey consisted of six parts, namely, chatbot awareness and use, demographics, chatbot adoption, online customer experience, customer satisfaction and personality.We conducted a pilot test with an online convenience sample to check the wording and reliability of the preliminary questionnaire. After receiving feedback from the pilot test, we revised the questionnaire for the final data collection. The respondents to the pilot test were not included in the final sample. The final items are presented in the Appendix.

Sample and data collection For the main data collection, recruitment of participants and administration of online questionnaires were conducted with Amazon Mechanical Turk (MTurk). MTurk was chosen to take advantage of the interactive nature of the Internet and for its inclusion of diverse respondents in online panels (Harrigan et al., 2017). In addition, it is able to recruit a large number of participants in a relatively inexpensive, expeditious manner (Follmer et al., 2017). Moreover, data collected through this method provides proper samples, inclusively with advantages over other samplingmethods (Kees et al., 2017; Peterson andMerunka, 2014), and it has been widely used (Buhrmester et al., 2011; Mason and Suri, 2012).

We received a total of 501 responses to the survey. We cleaned up the data set by excluding caseswithmissing data and outliers, yielding a final sample of 425 valid responses, which amounted to a useable response rate of 85%. Table 1 presents the sample profile. The samplewas composed ofmoremen (63.3%) thanwomen (36.7%). In terms of age, themajority of the respondents were young. Those ages 20 to 29 accounted for 55.5% of the sample, followed by respondents ages 30 to 39, who accounted for 29.9% of the sample. Regarding country of origin, the respondents were mainly from the United States (51.5%), followed by Asia (41.4%) and Europe (3.1%); other countries included Canada, India and countries in South America (4%). Furthermore, a significant number of respondents were working (83.8%) and most of the respondents (79.4%) had a bachelor’s degree.

Data analysis Statistical Product and Service Solutions (SPSS) and SmartPLSwere used to test and analyze the hypotheses. SmartPLS was chosen as the better option for this study because of the

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formative constructs of the structural equationmodel. SmartPLS also offers a pathmodel that is able to describe the relationships between variables and indicators, thus providing advantages in terms of relationship specifications and model complexity. Finally, because it does not require distributional assumptions, it is more flexible in terms of data requirements (Hair et al., 2011).

Results Measurement model We conducted exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in SPSS and SmartPLS to test the hypotheses aswell as to examine the reliability and validity of the constructs. The average variance extracted (AVE), composite reliability and Cronbach’s alpha (α) were then obtained.

We used EFA to estimate the validity of the items and the adequacy of the constructs. We used principal component analysis and a rotated component matrix, as the components were a linear combination of indicators. There was a total of 30 items on the scale. After running cleaned items from the EFA through the rotated component matrix in SPSS, we removed six

Characteristic n %

Gender Male 269 63.3 Female 156 36.7

Age (in years) Younger than 20 1 0.3 20–29 236 55.5 30–39 127 29.9 40–49 38 8.9 50 or older 23 5.4

Country of origin United States 219 51.5 Europe 13 3.1 Asia 176 41.4 Other 17 4

Vocation University student 56 13.2 Company employee 356 83.8 Other 13 3.1

Highest level of education Doctorate 7 1.7 Master’s degree 62 14.6 Bachelor’s degree 295 69.4 Other 61 14.4

Income per month ($NTD) No income 5 1.2 Less than $10,000 146 34.4 $10,001–$20,000 82 19.3 $20,001–$30,000 47 11.1 $30,001–$40,000 50 11.8 $40,001–$50,000 53 12.5 More than $50,000 42 9.9

Table 1. Sample profile

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items from the scale because of low factor loading, failure to load on the predicted construct, and cross-loading. The six omitted items included four from the usability scale, one from the intrinsic values scale and one from the personality scale. Thus, 24 items remained on the scale to estimate the constructs.

We then used CFA to test themulti-indicator constructs aswell as to confirm the reliability and validity of the measurement model. All constructs were subjected to CFA to verify the adequacy of the measurement model. Table 2 summarizes the properties of the measurement model. Cronbach’s alphas (range 5 0.634–0.871) were all above the acceptable threshold of 0.6–0.7, fulfilling the conditions specified by Griethuijsen et al. (2014). Hence, internal consistency was high. Moreover, the AVE ranged from 0.570 to 0.779, exceeding the threshold of 0.5 and thus establishing the convergent validity of the measurement model.

Detailed results for means, standard deviations, AVE and correlation coefficients for each variable can be seen in Table 3. None of the inter-item correlations (reported below the diagonal in Table 3) exceeded the square roots of the AVE for the corresponding factors. These results indicate satisfactory discriminant validity, as it falls into the acceptable range of 0.6–0.7 (Griethuijsen et al., 2014). Hence, the measures were both valid and reliable.

Common method bias Common method bias poses a potential threat to the reliability and validity of items and may significantly impact covariation among latent constructs (Podsakoff et al., 2003). The simple

Item Factor loading Cronbach’s alpha Composite reliability AVE

Usability (U) 0.861 0.899 0.643 U1 0.786 U3 0.800 U7 0.816 U8 0.825 U9 0.781 Responsiveness (R) 0.749 0.841 0.570 R1 0.751 R2 0.672 R3 0.811 R4 0.779 Extrinsic values (E) 0.871 0.904 0.611 E1 0.807 E2 0.728 E3 0.690 E4 0.841 E5 0.829 E6 0.781 Intrinsic values (I) 0.717 0.876 0.779 I2 0.893 I3 0.873 Customer satisfaction (S) 0.852 0.901 0.694 S1 0.823 S2 0.804 S3 0.826 S4 0.876 Personality (P) 0.634 0.802 0.575 P1 0.784 P2 0.731 P4 0.760

Note(s): (1) AVE 5 average variance extracted. (2) All factor loadings were significant at the 0.05 level

Table 2. Measurement properties

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Harman’s single-factor test is the most common test used to examine common method variance (Podsakoff et al., 2003). We performed the test in SPSS using principal component analysis, in which all items used to measure the independent, moderating and dependent variables were entered. EFA was then conducted with the extraction factor fixed to a single factor. The first factor accounted for 44.4% of the variance, which indicates that no common method bias existed, and the data were acceptable for validating the proposed research model.

Structural model and hypothesis testing The path coefficients for the research constructs are expressed here in a standardized form. As shown in Table 4, there were significant positive effects of the usability of the chatbot on extrinsic values of customer experience (β 5 0.385, t 5 4.063, p < 0.001) and of the responsiveness of the chatbot on intrinsic values of customer experience (β5 0.279, t5 2.155, p < 0.01). Hence, Hypotheses 1 and 4 were supported, whereas Hypotheses 2 and 3 were not supported. Furthermore, customer satisfaction was positively affected by extrinsic values of customer experience (β 5 0.637, t 5 10.036, p < 0.001) as well as by intrinsic values of customer experience (β 5 0.309, t 5 4.470, p < 0.001). Thus, Hypotheses 5 and 6 were supported.

Figure 2 shows the results of partial least squares estimation of the direct effects. Bootstrapping was used to approximate the normality of the data and determine the significance of the structural paths using t-tests. The percentile bootstrap procedure using the partial least squares algorithm in this study was performed on 1,000 samples. Taking the explained variance (R2) into account to determine the predictive power of the model, the results showed a significant portion of the variance in extrinsic values (R2 5 0.713) and intrinsic values (R2 5 0.539) of customer experience and customer satisfaction (R2 5 0.756).

The moderating effects model tested the extent to which personality moderates the main effects of chatbot adoption and customer experience values. Regarding the two significant

Mean SD AVE U R E I S P

U 3.708 0.825 0.643 0.802 R 4.079 0.667 0.570 0.577** 0.755 E 3.695 0.872 0.611 0.687** 0.596** 0.782 I 3.899 0.795 0.779 0.646** 0.645** 0.645** 0.883 S 3.891 0.804 0.694 0.791** 0.641** 0.737** 0.720** 0.833 P 4.085 0.733 0.575 0.475** 0.482** 0.489** 0.398** 0.507** 0.758

Note(s): (1) AVE 5 average variance extracted, U 5 usability, R 5 responsiveness, E 5 extrinsic values, I5 intrinsic values, S5 customer satisfaction, P5 personality. (2) Intercorrelation coefficients are below the diagonal and square roots of the AVE are on the diagonal. (3) **p < 0.01

Hypothesized path Path coefficient t Result

H1 Usability → Extrinsic values 0.385*** 4.063 Supported H2 Responsiveness → Extrinsic values 0.066 0.621 Not supported H3 Usability → Intrinsic values 0.249 1.924 Not supported H4 Responsiveness → Intrinsic values 0.279** 2.155 Supported H5 Extrinsic values → Customer satisfaction 0.637*** 10.036 Supported H6 Intrinsic values → Customer satisfaction 0.309*** 4.470 Supported

Note(s): (1) H 5 hypothesis. (2) ***p < 0.001; **p < 0.01

Table 3. Means, correlations

and square roots of the AVE for each construct

Table 4. Direct effects

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direct effects presented in the previous section, personality had moderating effects on only the link between usability and extrinsic values of customer experience. As shown in Table 5, the usability 3 personality interaction had a positive and significant effect (β 5 0.232, p < 0.05). Thus, of the moderating hypotheses (Hypotheses 7 and 8), only Hypothesis 7 was supported. Furthermore, this effect increased the R2 value for personality with usability from 61.6% to 64.3%. The two-way unstandardized moderation graph in Figure 3 further shows the moderating effects of personality on the link between usability and extrinsic values of customer experience.

Multigroup analysis A multigroup analysis was performed to examine whether there is difference between countries of origin of the respondents. Using country of origin as the grouping variable, we chose the two largest groups, United States (51.5%) and countries in Asia (41.4%) to proceed with the multigroup analysis. Accordingly, Table 6 showed the results of the moderating effect of the country group (USA and Asia) in one of six hypotheses. Specifically, the effect of the usability of chatbot on extrinsic values within the USA group is higher than it in the Asia group, at the significance of 0.1%.

Personality

0.249

0.385***

0.309***

0.066

0.279***

0.637***

Usability

Responsiveness

Extrinsic values

Intrinsic values

Customer Satisfaction

Chatbot Adoption Online Customer Experience

R2 = 0.713

R2 = 0.539

Direct effect

Moderating effect

R2 = 0.756

Note(s): ***p < 0.001; *p < 0.05

Hypothesis Path Main effects Interaction Result

H7 U → E 0.538*** 0.525*** Supported P → E 0.084 0.108* U 3 P → E 0.232* R2 0.616 0.643

H8 R → I 0.979*** 0.943*** Not Supported P → I 0.133 0.146 R 3 P → I 0.063 R2 0.422 0.429

Note(s): (1) ***p < 0.001; *p < 0.05. (2) H 5 hypothesis; U 5 usability; E 5 extrinsic values; R 5 responsiveness; I 5 intrinsic values; P 5 personality

Figure 2. Direct effects

Table 5. Moderating effects

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Discussion and conclusions Discussion of the findings This study examined the use of chatbots in e-retailing from the customer’s perspective to determine how chatbot adoption affects the customer experience and how the customer experience in turn affects customer satisfaction. The moderating effects of personality on chatbot adoption were also examined to observe whether customers will use a chatbot based on the benefits that they receive from the new technology.

There were three main findings. First, the usability of the chatbot significantly influences extrinsic values of online customer experience. Chatbots that score high on usability provide a customized experience, solve customers’ problems, make customers feel comfortable and valued, and cause customers to view retail businesses that adopt chatbots as innovative. Also, the responsiveness of the chatbot has robust positive effects on intrinsic values of customer experience, such that customers can receive benefits with little effort and acquire additional information from the chatbot. By contrast, the impact of usability on intrinsic values (t 5 1.925) and the impact of responsiveness on extrinsic values (t 5 0.621) were not significant, although the former approached significance. Possible explanations for this may

5

4.5

4

3.5

3

2.5

2

1.5

1

Low Personality

High Personality

High UsabilityLow Usability

Ex tr

in sic

v al

ue s

Moderator

Hypothesis

Country

Result

Path coefficient

Path coefficient difference

USA Asia

H1 Usability → Extrinsic values 0.758 0.392 0.366*** Supported H2 Responsiveness → Extrinsic values 0.084 0.514 �0.430 Not supported H3 Usability → Intrinsic values 0.403 0.294 0.109 Not supported H4 Responsiveness → Intrinsic values 0.421 0.408 0.014 Not supported H5 Extrinsic values → Customer

satisfaction 0.642 0.676 �0.034 Not supported

H6 Intrinsic values → Customer satisfaction

0.305 0.260 0.045 Not supported

Note(s): ***p < 0.001

Figure 3. Moderating effects of

personality on the relationship between

usability and extrinsic values of customer

experience

Table 6. Multigroup analysis

(USA and Asia)

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be that customers found it difficult to use the chatbot while navigating or searching the e-retailing site, which made it a time-consuming effort to receive assistance or resolve their problems. Second, perceived extrinsic values of customer experience positively influence customer satisfaction. Specifically, customers are pleased and satisfied when they can receive customized conversation, get their problems solved, as well as feel comfortable and valued when interacting with a chatbot. In addition, when customers think that the company adopting the chatbot is innovative, they will recommend it to others. Similarly, perceived intrinsic values of customer experience positively influence customer satisfaction. It could be that customers are satisfied when they receive benefits as well as additional information from using the chatbot with little effort, and this motivates them to recommend the chatbot to others. This empirical evidence indicates that positive customer experiences of the usability and responsiveness of a chatbot create a high level of customer satisfaction. Third, customers’ personality—in terms of being sociable, being excited by new ideas and having broad interests—moderates the relationship between the usability of the chatbot and extrinsic values of customer experience. Nevertheless, the results show nomoderating effects on the relationship between the responsiveness of the chatbot and intrinsic values of customer experience.

Furthermore, the multigroup results suggest that there is a relevant difference in the influence of the usability of chatbots on extrinsic values, depending on the participants’ country. In this respect, the effect of chatbot usability on external value seems to be stronger for the USA group (path coefficient 5 0.758) than the Asia group (path coefficient 5 0.392). This would suggest that specific to this study, the USA group shows a stronger linkage of extrinsic values (e.g. helps to resolve my needs) from the usability of a chatbot (e.g. is easy to use and effortless) in comparison to the Asia group.

Theoretical and managerial implications:. This study offers several implications to the research. First, it integrates the TAM and IS success model to build a research model analyzing the effects of chatbot adoption on the online customer experience and customer satisfaction in e-retailing. By doing so, it highlights the development of chatbots through usability and responsiveness, as expressed via the positive associations between usability and improved extrinsic values and between responsiveness and intrinsic values in online customer experience. Second, this research extends understanding of the online customer experience and customer satisfaction with chatbots in e-retailing by showing that extrinsic and intrinsic values of online customer experience are enhanced by chatbot adoption, which in turn facilitates customer satisfaction in the online environment. Third, the current research provides empirical evidence of moderating effects of personality on chatbot adoption. In this way, it extends the literature on the moderating role of customer traits on the use of emerging technology in e-retailing.

This study also has several valuable implications for managers. Online retail businesses should identify which elements of usability and responsiveness in chatbots, alongwithwhich extrinsic and intrinsic values in customer experience, best fit their business model. Assessing the suitability of their retail business for using chatbots is vital to adopting chatbots that will meet their target customers’ needs with measurable results. Critical factors in a suitability assessment may include repetitive procedures, the requirement for human interaction, the complexity of the current process and the ease of using and controlling a chatbot via conversation.

In contrast, crucial factors in assessing the viability of the use of chatbots in an e-retailing include technological compatibility, data availability and firm readiness. Online retail businesses should be able to define their business objective for chatbot applications, business systems, the team designing and running the chatbot, as well as the requirement for regular access to back-end bot data and analysis to be able to provide customized services. Assessing value and setting expectations with customers is critical, as problems may arise even when a

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chatbot functions spectacularly. For instance, the chatbot may fail to understand its limits and thus fail to guide customers through the right experience. To provide a better human– machine interactive environment, online retail businesses should use a hybrid approach that relies on both humans and chatbots to assist customers. By doing so, they can provide the best user experience and handle customers’ inquiries promptly and correctly, which may prevent frustration and dissatisfaction among customers.

Furthermore, because chatbots can learn from previous interactions, providing them with relevant information such as product knowledge, process flows and customer journey maps (Van den Broeck et al., 2019) will enable them to answer queries automatically. This will enhance the usability and responsiveness of the chatbot and improve the customer experience. Online retail businesses can even offer guidance to customers by teaching them how to interact with the chatbot through tutorials or frequently asked questions (FAQs) that will facilitate chatbot use and bring about a more positive overall experience. In addition, they need to ensure that they have the scale to adopt the chatbot or the potential to enhance their chatbot value proposition so that adequate amounts of technology and resources are allocated appropriately to ensure that the chatbot functions efficiently and effectively. If they do not, the chatbot will be counterproductive, resulting in a negative customer experience. Hence, with clear use cases, it is easier for an online retail business to develop specific solutions and scale resources and policies to create a seamless customer experience.

Limitations and future research There are several limitations to the current study. First, this study did not examine thoughtfully the consequences of online customer shopping experience/satisfaction. There are several other perspectives of online customer satisfaction which could have been explored, such as online convenience (Duarte et al., 2018) that could receive significant interferences from the usability and responsiveness of chatbot adoption. Future study may try to explore on this issue. Second, chatbot style could eventually lead to different results in terms of its adoption, whichmay also build up another line of research to explore in the future. Third, even thoughMTurk offers the chance to enroll participants who are more accustomed to online new technologies, most study participants were 20–39 years old. Future studies should evaluatewhether outcomes differ for age groups unfamiliar with chatbots. In addition, applying the same research model to other industries, such as banking, airline, news media, telecom or healthcare. Fourth, the study is an empirical research to conduct and confirm the model and hypotheses proposed. An exploratory study is suggested to conduct and analyze, for example, what and how chatbot features are better appreciated by customers. Fifth, our study provided a general cover letter and asked the respondents to recall their own experience of using chatbot in e-retailing. Given the heterogeneity of the user experience (UX) design, future study may ask more specific AI-based service interactions to replicate or enhance the present work. Finally, the factors chosen for the personality construct may not have been the most effective ones. Future researchers should test other Big Five personality traits (Judge et al., 1999), which may have more significant effects on chatbot adoption.

References

Adamopoulou, E. and Moussiades, L. (2020), “Chatbots: history, technology, and applications”, Machine Learning with Applications, Vol. 2 December, p. 100006.

Agichtein, E., Hakkani-T€ur, D., Kallumadi, S. and Malmasi, S. (2020), “ConvERSe’20: the WSDM 2020 workshop on conversational systems for e-commerce recommendations and search”, Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 897-898.

Artificial intelligence

chatbot

1525

Alt, R., Ehmke, J.F., Haux, R., Henke, T., Mattfeld, D.C., Oberweis, A., Paech, B. and Winter, A. (2019), “Towards customer-induced service orchestration-requirements for the next step of customer orientation”, Electronic Markets, Vol. 29 No. 1, pp. 79-91.

Amiel, T. and Sargent, S.L. (2004), “Individual differences in Internet usage motives”, Computers in Human Behaviour, Vol. 20 No. 6, pp. 711-726.

An, M. (2018), “Artificial intelligence is here - people just don’t realize it”, available at: https://blog. hubspot.com/news-trends/artificialintelligence-is-here#LINK3m.

Belanch�e, D., Casal�o, L.V. and Flavi�an, C. (2019), “Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers”, Industrial Management and Data Systems, Vol. 119 No. 7, pp. 1411-1430.

Buhrmester, M., Kwang, T. and Gosling, S.D. (2011), “Amazon’s mechanical Turk: a new source of inexpensive, yet high-quality data?”, Perspectives on Psychological Science, Vol. 6 No. 1, pp. 3-5.

Chopra, K. (2019), “Indian shopper motivation to use artificial intelligence”, International Journal of Retail and Distribution Management, Vol. 47 No. 3, pp. 331-347.

Chung, M., Ko, E., Joung, H. and Kim, S.J. (2020), “Chatbot e-service and customer satisfaction regarding luxury brands”, Journal of Business Research, Vol. 117 No. 9, pp. 587-595.

Correa, T., Hinsley, A.W. and de Z�u~niga, H.G. (2010), “Who interacts on the Web?: the intersection of users’ personality and social media use”, Computers in Human Behaviour, Vol. 26 No. 2, pp. 247-253.

Da Cunha, A.D. and Greathead, D. (2007), “Does personality matter?”, Communications of the ACM, Vol. 50 No. 5, pp. 109-112.

Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2020), “How artificial intelligence will change the future of marketing”, Journal of the Academy of Marketing Science, Vol. 48 No. 1, pp. 24-42.

Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-340.

De Cicco, R., e Silva, S.C. and Alparone, F.R. (2020), “Millennials’ attitude toward chatbots: an experimental study in a social relationship perspective”, International Journal of Retail and Distribution Management, Vol. 48 No. 11, pp. 1213-1233.

DeLone, W.H. and McLean, E.R. (2004), “Measuring e-commerce success: applying the DeLone and McLean information systems success model”, International Journal of Electronic Commerce, Vol. 9 No. 1, pp. 31-47.

Duarte, P., e Silva, S.C. and Ferreira, M.B. (2018), “How convenient is it? Delivering online shopping convenience to enhance customer satisfaction and encourage e-WOM”, Journal of Retailing and Consumer Services, Vol. 44, pp. 161-169.

Finstad, K. (2010), “The usability metric for user experience”, Interacting with Computers, Vol. 22 No. 5, pp. 323-327.

Flavi�an, C., Guinal�ıu, M. and Gurrea, R. (2006), “The role played by perceived usability, satisfaction and consumer trust on website loyalty”, Information and Management, Vol. 43 No. 1, pp. 1-14.

Follmer, D.J., Sperling, R.A. and Suen, H.K. (2017), “The role of MTurk in education research: advantages, issues, and future directions”, Educational Researcher, Vol. 46 No. 6, pp. 329-334.

Fryer, L.K., Nakao, K. and Thompson, A. (2019), “Chatbot learning partners: connecting learning experiences, interest and competence”, Computers in Human Behaviour, Vol. 93, pp. 279-289.

Gallarza, M.G., Fayos Gard�o, T. and Calder�on Garc�ıa, H. (2017), “Experiential tourist shopping value: adding causality to value dimensions and testing their subjectivity”, Journal of Consumer Behaviour, Vol. 16 No. 6, pp. 76-92.

Go, E. and Sundar, S.S. (2019), “Humanizing chatbots: the effects of visual, identity and conversational cues on humanness perceptions”, Computers in Human Behaviour, Vol. 97, pp. 304-316.

IJRDM 49,11

1526

Griethuijsen, R.A.L.F.V., Eijck, M.W.V., Haste, H., den Brok, P.J., Skinner, N.C., Mansour, N., Gencer, A.S. and BouJaoude, S. (2014), “Global patterns in students’ views of science and interest in science”, Research in Science Education, Vol. 45 No. 4, pp. 581-603.

Hair, J.F., Sarstedt, M., Ringle, C.M. and Mena, J.A. (2011), “An assessment of the use of partial least squares structural equation modeling in marketing research”, Journal of the Academy of Marketing Science, Vol. 40 No. 3, pp. 414-433.

Harrigan, P., Evers, U., Miles, M. and Daly, T. (2017), “Customer engagement with tourism social media brands”, Tourism Management, Vol. 59, pp. 597-609.

Hollebeek, L.D., Sprott, D.E. and Brady, M.K. (2021), “Rise of the machines? Customer engagement in automated service interactions”, Journal of Service Research, Vol. 21 No. 1, pp. 3-8.

Hu, T., Xu, A., Liu, Z., You, Q., Guo, Y., Sinha, V., Luo, J. and Akkiraju, R. (2018), “Touch your heart: a tone-aware chatbot for customer care on social media”, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-12.

Huang, M.H. and Rust, R.T. (2021), “Engaged to a robot? The role of AI in service”, Journal of Service Research, Vol. 24 No. 1, pp. 30-41.

Irfan, W., Siddiqui, D.A. and Ahmed, W. (2019), “Managing in-store logistics: perspective from Pakistani small and medium retail stores”, International Journal of Retail and Distribution Management, Vol. 47 No. 4, pp. 350-367.

Islam, J., Rahman, Z. and Hollebeek, L.D. (2017), “Personality factors as predictors of online consumer engagement: an empirical investigation”, Marketing Intelligence and Planning, Vol. 35 No. 4, pp. 510-528.

Islam, J.U., Hollebeek, L.D., Rahman, Z., Khan, I. and Rasool, A. (2019), “Customer engagement in the service context: an empirical investigation of the construct, its antecedents and consequences”, Journal of Retailing and Consumer Services, Vol. 50, pp. 277-285.

Jadin, T., Gnambs, T. and Batinic, B. (2013), “Personality traits and knowledge sharing ‘in online communities’”, Computers in Human Behaviour, Vol. 29 No. 1, pp. 210-216.

John, O.P. and Srivastava, S. (1999), “The big five trait taxonomy: history, measurement, and theoretical perspectives”, Handbook of personality: Theory and research, Vol. 2, pp. 102-138.

Joyce, M. and Kirakowski, J. (2015), “Measuring attitudes towards the Internet: the general Internet attitude scale”, International Journal of Human-Computer Interaction, Vol. 31 No. 8, pp. 506-517.

Judge, T.A., Higgins, C.A., Thoresen, C.J. and Barrick, M.R. (1999), “The big five personality traits, general mental ability, and career success across the life span”, Personnel Psychology, Vol. 52 No. 3, pp. 621-652.

Kang, G.D. (2006), “The hierarchical structure of service quality: integration of technical and functional quality”, Managing Service Quality: International Journal, Vol. 16 No. 1, pp. 37-50.

Kees, J., Berry, C., Burton, S. and Sheehan, K. (2017), “Reply to ‘amazon’s mechanical Turk: a comment’”, Journal of Advertising, Vol. 46 No. 1, pp. 159-162.

Kokkinou, A. and Cranage, D.A. (2013), “Using self-service technology to reduce customer waiting times”, International Journal of Hospitality Management, Vol. 33, pp. 435-445.

Kraus, D., Reibenspiess, V. and Eckhardt, A. (2019), How Voice Can Change Customer Satisfaction: A Comparative Analysis between E-Commerce and Voice Commerce, Vol. 14, Internationale Tagung Wirtschaftsinformatik, Siegen, pp. 23-27.

Kumar, V., Rajan, B., Venkatesan, R. and Lecinski, J. (2019), “Understanding the role of artificial intelligence in personalized engagement marketing”, California Management Review, Vol. 61 No. 4, pp. 135-155.

Li, L., Lee, K.Y., Emokpae, E. and Yang, S.B. (2021), “What makes you continuously use chatbot services? Evidence from Chinese online travel agencies”, Electronic Markets, pp. 1-25, doi: 10. 1007/s12525-020-00454-z.

Artificial intelligence

chatbot

1527

Lu, L., Cai, R. and Gursoy, D. (2019), “Developing and validating a service robot integration willingness scale”, International Journal of Hospitality Management, Vol. 80, pp. 36-51.

Luo, X., Tong, S., Fang, Z. and Qu, Z. (2019), “Frontiers: machines vs. humans: the impact of artificial intelligence chatbot disclosure on customer purchases”, Marketing Science, Vol. 38 No. 6, pp. 937-947.

Mason, W. and Suri, S. (2012), “Conducting behavioral research on Amazon’s Mechanical Turk”, Behavior Research Methods, Vol. 44 No. 1, pp. 1-23.

Mathwick, C., Malhotra, N. and Rigdon, E. (2001), “Experiential value: conceptualization, measurement and application in the catalog and Internet shopping environment*”, Journal of Retailing, Vol. 77 No. 1, pp. 39-56.

McLean, G. and Osei-Frimpong, K. (2019), “Chat now. . . Examining the variables influencing the use of online live chat”, Technological Forecasting and Social Change, Vol. 146, pp. 55-67.

Meuter, M.L., Bitner, M.J., Ostrom, A.L. and Brown, S.W. (2005), “Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies”, Journal of Marketing, Vol. 69 No. 2, pp. 61-83.

Molla, A. and Licker, P.S. (2001), “E-commerce systems success: an attempt to extend and respecify the Delone and MacLean model of IS success”, Journal of Electronic Commerce Research, Vol. 2 No. 4, pp. 131-141.

Mortimer, G., Lings, I. and Drennan, J. (2019), “Examining customer-oriented positive deviance intentions of retail employees”, International Journal of Retail and Distribution Management, Vol. 47 No. 8, pp. 826-854.

M€unster, A. and Haug, A. (2017), “Design variables and constraints in fashion store design processes”, International Journal of Retail and Distribution Management, Vol. 45 No. 2, pp. 122-142.

Okuda, T. and Shoda, S. (2018), “AI-based chatbot service for financial industry”, Fujitsu Scientific and Technical Journal, Vol. 54 No. 2, pp. 4-8.

Pappas, I.O., Pateli, A.G., Giannakos, M.N. and Chrissikopoulos, V. (2014), “Moderating effects of online shopping experience on customer satisfaction and repurchase intentions”, International Journal of Retail and Distribution Management, Vol. 42 No. 3, pp. 187-204.

Peterson, R.A. and Merunka, D.R. (2014), “Convenience samples of college students and research reproducibility”, Journal of Business Research, Vol. 67 No. 5, pp. 1035-1041.

Petre, M., Minocha, S. and Roberts, D. (2006), “Usability beyond the website: an empirically-grounded e-commerce evaluation instrument for the total customer experience”, Behaviour and Information Technology, Vol. 25 No. 2, pp. 189-203.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in Behavioural research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.

Pratt, R. and Chudoba, K. (2006), “Is extraversion the next predictor of system adoption? Effects of personality traits on system acceptance”, Paper Presented at the, Academy of Management Meeting, Atlanta, GA.

Prentice, C., Han, X.Y., Hua, L.L. and Hu, L. (2019), “The influence of identity-driven customer engagement on purchase intention”, Journal of Retailing and Consumer Services, Vol. 47, pp. 339-347.

Pricilla, C., Lestari, D.P. and Dharma, D. (2018), “Designing interaction for chatbot-based conversational commerce with user-centered design”, 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), IEEE, pp. 244-249.

Przegalinska, A., Ciechanowski, L., Stroz, A., Gloor, P. and Mazurek, G. (2019), “In bot we trust: a new methodology of chatbot performance measures”, Business Horizons, Vol. 62 No. 6, pp. 785-797.

Quintino, A.R.P. (2019), “The impact of chatbot technology attributes on customer experience: an example in telecom”, Doctoral dissertation.

IJRDM 49,11

1528

Rose, S., Clark, M., Samouel, P. and Hair, N. (2012), “Online customer experience in e-retailing: an empirical model of antecedents and outcomes”, Journal of Retailing, Vol. 88 No. 2, pp. 308-322.

Roy, S.K., Shekhar, V., Lassar, W.M. and Chen, T. (2018), “Customer engagement Behaviours: the role of service convenience, fairness and quality”, Journal of Retailing and Consumer Services, Vol. 44, pp. 293-304.

Sands, S., Ferraro, C., Campbell, C. and Tsao, H.-Y. (2021), “Managing the human–chatbot divide: how service scripts influence service experience”, Journal of Service Management, Vol. 32 No. 2, pp. 246-264.

Sanny, L., Susastra, A.C., Roberts, C. and Yusramdaleni, R. (2020), “The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia”, Management Science Letters, Vol. 10, pp. 1225-1232.

Sidaoui, K., Jaakkola, M. and Burton, J. (2020), “AI feel you: customer experience assessment via chatbot interviews”, Journal of Service Management, Vol. 31 No. 4, pp. 745-766.

Sotolongo, N. and Copulsky, J. (2018), “Conversational marketing: creating compelling customer connections”, Applied Marketing Analytics, Vol. 4 No. 1, pp. 6-21.

Sousa, M.J. and Rocha, �A. (2019), “Skills for disruptive digital business”, Journal of Business Research, Vol. 94, pp. 257-263.

Stoeckli, E., Dremel, C., Uebernickel, F. and Brenner, W. (2020), “How affordances of chatbots cross the chasm between social and traditional enterprise systems”, Electronic Markets, Vol. 30, pp. 369-403.

Tiwana, A.B. (1998), “Interdependency factors influencing the World Wide Web as a channel of interactive marketing”, Journal of Retailing and Consumer Services, Vol. 5 No. 4, pp. 245-253.

Tsao, W.-C. (2013), “Big Five personality traits as predictors of internet usage categories”, International journal of management, Vol. 30 No. 4, p. 374.

Van den Broeck, E., Zarouali, B. and Poels, K. (2019), “Chatbot advertising effectiveness: when does the message get through”, Computers in Human Behaviour, Vol. 98, pp. 150-157.

Vassinen, R. (2018), “The rise of conversational commerce: what brands need to know”, Journal of Brand Strategy, Vol. 7 No. 1, pp. 13-22.

Wei, W., Torres, E. and Hua, N. (2016), “Improving consumer commitment through the integration of self-service technologies: a transcendent consumer experience perspective”, International Journal of Hospitality Management, Vol. 59, pp. 105-115.

Wirth, N. (2018), “Hello marketing, what can artificial intelligence help you with?”, International Journal of Market Research, Vol. 60 No. 5, pp. 435-438.

Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S. and Martins, A. (2018), “Brave new world: service robots in the frontline”, Journal of Service Management, Vol. 29 No. 5, pp. 907-931.

Xi, N. and Hamari, J. (2019), “Does gamification satisfy needs? A study on the relationship between gamification features and intrinsic need satisfaction”, International Journal of Information Management, Vol. 46, pp. 210-221.

Youn, S. and Jin, S.V. (2021), “In AI we trust? ‘The effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging’ feeling economy”, Computers in Human Behavior, Vol. 113, p. 106721.

Zarouali, B., Van den Broeck, E., Walrave, M. and Poels, K. (2018), “Predicting consumer responses to a chatbot on Facebook”, Cyberpsychology, Behaviour, and Social Networking, Vol. 21 No. 8, pp. 491-497.

Zhang, M., Hu, M., Guo, L. and Liu, W. (2017), “Understanding relationships among customer experience, engagement, and word-of-mouth intention on online brand communities”, Internet Research, Vol. 27 No. 4, pp. 839-857.

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Appendix

About the authors Ja-Shen Chen is a professor of College of Management in Yuan Ze University, Taiwan. He holds MS and PhD both in Decision Sciences from Rensselaer Polytechnic Institute, NY. His research interests include service innovation, customer relationshipmanagement and e-business management. He has published a number of research articles including ones appeared in Journal of Service Research, Information and Management, Industrial and Marketing Management, Internet Research, International Journal of Contemporary Hospitality Management and OMEGA. He also served as a trustee board member of Shangri-La Hotel in Taiwan and now actively associates with industries as a consultant or a principal project investigator. Ja-Shen Chen is the corresponding author and can be contacted at: [email protected]

Questionnaire item

Usability (U) (Range of factor loadings from CFA 5 0.78–0.83, α 5 0.86) U1 Learning to navigate through e-commerce websites is simple with assistance from the chatbot

* U2 Searching with assistance from the chatbot saves me time U3 The chatbot makes e-commerce websites easy to use and effortless

* U4 The chatbot is able to initiate conversation for further discussion (e.g. by offering suggestions or presenting the functionality of products or services on e-commerce websites)

* U5 The chatbot provides customers with specific, preferred information * U6 The chatbot provides clear, easy-to-read information

U7 The chatbot provides a complete solution to my problems U8 The chatbot is aware of the context during a conversation U9 The chatbot is able to solve my problems

Responsiveness (R) (Range of factor loadings from CFA 5 0.67–0.81, α 5 0.75) R1 The chatbot replies quickly R2 Getting in contact with the chatbot is easy R3 The chatbot is always available when I need it R4 The chatbot provides credible advice

Extrinsic values (E) (Range of factor loadings from CFA 5 0.69–0.84, α 5 0.87) E1 The chatbot makes me feel that it is talking to me personally as a customer E2 The chatbot helps resolve my needs without creating other problems E3 I feel more comfortable talking with a chatbot than a human E4 The chatbot makes me feel valued as a customer E5 I think that a company is innovative if it uses a chatbot E6 The chatbot helps me gather additional information on goods or services

Intrinsic values (I) (Range of factor loadings from CFA 5 0.87–0.89, α 5 0.72) * I1 I like it when the chatbot helps me customize my e-commerce experience to my own liking

I2 I enjoy getting the benefits from using the chatbot with little effort I3 The chatbot is fun to chat with

Customer satisfaction (S) (Range of factor loadings from CFA 5 0.80–0.88, α 5 0.85) S1 I am pleased with using the chatbot S2 I am satisfied with the pre-purchase experience of using the chatbot (e.g. product search, quality of

information on products or services, product comparison) S3 I am satisfied with my overall experience using the chatbot S4 I would recommend that others use the chatbot

Personality (P) (Range of factor loadings from CFA 5 0.73–0.78, α 5 0.63) P1 I get excited by new ideas P2 I have wide interests

* P3 I am unconventional P4 I am sociable

Note(s): (1) CFAs 5 confirmatory factor analysis (2) Asterisks indicate items excluded from the data analysis

Table A1. Construct measures

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Tran-Thien-Y Le is a lecturer of School of Economics, Can ThoUniversity, Vietnam. She received her MBA degree from University of Economics in Ho Chi Minh City, Vietnam. She is currently a PhD candidate of College of Management in Yuan Ze University, Taiwan. Her research interests include customer experience and customer behavior in service marketing.

Devina Florence received her MBA degree from College of Management, Yuan Ze University. Her research interests include service innovation and customer behavior in service marketing.

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