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RJOAS, 2(122), February 2022 UDC 332; DOI 10.18551/rjoas.2022-02.02
HOW CHATBOTS' SOCIAL PRESENCE COMMUNICATION ENHANCES CONSUMER ENGAGEMENT, BRAND LIKABILITY, INTERACTION SATISFACTION: THE MEDIATING ROLE OF INITIAL CHATBOTS' TRUST
Kumala Chandra, Nuraini Dewi, Helmus Nexio, Masnita Yolanda*
Department of Magister Management, Faculty of Economics and Business, University of Trisakti, Jakarta, Indonesia *E-mail: yolandamasnita@trisakti.ac.id
ABSTRACT
The growing importance of chatbots as interactive communication channels is changing the way businesses approach marketing. Unlike previous chatbot studies that focused on the utilitarian use of chatbots for online customer support, this research not only looks at which aspects of chatbot communication and profile design may drive chatbot effectiveness, but it also looks at the mechanism underlying the messaging and design effects on consumer engagement. Purpose of this study was to investigates the link between social communication tools in the form of chatbots and consumer confidence in chatbots, as well as their impact on customer reaction, with an emphasis on user engagement mediated by the initial trust factor of chatbots. This study used quantitative research confirmatory factors analysis using Structural Equation Modeling (SEM). The sampling method used in this research is purposive sampling with number of samples are 258 respondents in Jabodetabek. The findings showed that social presence communication has a positive influence on intial chatbot trust, initial chatbot trust has a postibe influence on brand likability, user engagement, and interaction satisfaction, and also initial chatbots' trust bringing positive influence on mediating user engagement, interaction satisfaction, and brand likability. AI applications, specifically chatbots in marketing, as an interpersonal communication is an important tools to increase consumer engagement. From this study also can be provides crucial strategic advice to assist marketers use the conversational potential of chatbots for brand communication, based on the empirical findings.
KEY WORDS
Chabots' social presence communication, consumer engagement, brand likability, interaction satisfaction, initial chatbots' trust, artificial intelligence.
Communication is one of the social science disciplines with numerous applications in everyday life. Apart from being essential for an individual's survival, communication is also one of the foundations of today's technological advancements.
The goal of communication technology development is to make it simpler for people to engage with one another in society. On the customer service side, there are factors that influence how people engage and communicate. Promotional activities are, without a doubt, required to build a brand. Promotion is a one-way flow of information or persuasion that can lead to transactions between vendors and purchasers within an organization. To be able to offer items to a larger audience, innovative and engaging promotional activities are required to capture people's attention and ultimately convince them to purchase a product.
The marketing communication mix (Marketing Mix), according to Kotler and Armstrong (2006), consists of eight main communication models: advertising, sales promotion, events and experience, direct marketing, event and experience, public relations and publicity, and interactive marketing.
Interactive marketing is a promotional instrument, according to Kotler and Armstrong (2006). Interactive marketing is an online activity and program used to establish a product image, maintain strong customer relations, distribute goods and services, and sell items by influencing consumers directly or indirectly. Two-way communication and consumer
interaction are hallmarks of interactive marketing, which allows customers to connect directly with brands (Wang, 2021).
The Covid-19 pandemic, which is presently disrupting the globe, has altered all human activity patterns, including communication patterns. The adaptation of physical distancing is one of the patterns of change that individuals must encounter throughout this pandemic. This has an impact on business actors' promotional methods and interactions, as well as their ability to stay in touch with clients without having to meet them face to face in event activities or direct sales. To bridge the gap between brands and customers, business people must be ready to adapt and find successful solutions. When marketers want their brands to stay close to customers, good two-way communication between brand owners and customers is undeniably important. Marketers must now figure out how to keep client interactions going even if they aren't done face to face.
It appears that having a chatbot available is one of the best solutions, and it may be utilized as an option for merchants to continue connecting with consumers in real time. Chatbots play an essential part in conveying a firm's services or goods and demand interaction/engagement with clients, according to the appearance of chatbots as a new invention in the larger community in addressing the present digital business era. A chatbot is a technology that was created utilizing artificial intelligence (AI). Chatbots have been regarded as a big advance in interactive marketing, showing that firms can connect with customers in this new era, resulting in buzz phrases like chatvertising (Books and Flying, 2014) and conversational marketing (Cancel and Gerhardt, 2019).
This research will look at how the existence of chatbots might boost customer responses in numerous areas, including user engagement, interaction satisfaction, and brand likability. Chatbots (Sundar et al., 2016), can represent the promise of electronic media to capture the essence of interpersonal conversation. This study investigates the link between social communication tools in the form of chatbots and consumer confidence in chatbots, as well as their impact on customer reaction, with an emphasis on user engagement mediated by the initial trust factor of chatbots. Explaining the problem's formulation should cover the following points: 1). Problem recognition and its significance; 2). Clear identification of the problem and the appropriate research questions; 3). Coverage of problem's complexity; and 4). Well-defined objectives.
LITERATURE REVIEW
A one-sided interpersonal relationship that television viewers have with media characters is known as parasocial interaction. A relationship of intimacy is formed with media characters through shared experiences that can only be accessed by repeated viewings of the personality or persona (Horton and Richard Wohl, 1956). As time passes, the character's predictability improves. The character is trustworthy. The supporter is devoted. "They 'know' such a persona through direct observation and interpretation of his look, gestures and voice, his speech and behaviour in a variety of contexts" (Horton and Richard Wohl, 1956).
Audience acceptance of the parasocial function might be as simple as watching a television show. Following the conclusion of the show, the spectator will evaluate the character in order to accept, reject, or further understand the notion of parasocial engagement with the persona. If the viewer accepts the relationship, he or she will most likely watch the show again, and if the viewing continues, a parasocial relationship will develop. When the television is switched off, viewers may continue to engage in parasocial connections, much as individuals do in interpersonal relationships when the other is not there. For some people, parasocial engagement may be a viable alternative to interpersonal interactions (Rubin and Rubin, 1985). The link between interpersonal needs and parasocial engagement for the isolated has gained some attention, despite the fact that little research has been done in this area.
When (Horton and Richard Wohl, 1956) initially proposed parasocial contact, one form of television personality they had in mind was newscasters. Later research by (Levy, 1979) established a link between parasocial contact and television news consumption. People who
watched more television news interacted with news personalities in a more parasocial manner. Viewers who enjoyed parasocial interaction increased their exposure to television news so order to have more interactions with the news characters. Parasocial contact, according to (Houlberg, 1984), is a concept distinct from the audience's judgments of a newscaster's professional and physical traits. (RUBIN et al., 1985)discovered significant links between parasocial contact and news affinity as well as perceived news realism. These researchers proposed that parasocial connection, as a sort of intimacy, may be considered a precursor to future media usage. They also suggested that interpersonal relationship development theories be employed to research how these relationships evolve.
Social presence refers to communicators' capacity to "present themselves socially and emotionally as 'real' individuals (i.e., their entire personality)" (Garrison et al., 2000). This variabel is particularly related to study focus of chatbots for marketing communication as chatbot responses can be strategically programmed to project spesific personalities and characters (Tsai et al., 2021). Because chatbot replies may be intentionally programmed to convey various personalities and traits, this construct is especially relevant to the study emphasis on chatbots for brand communication. In the context of communicating with chatbots, (Kim and Sundar, 2012) characterize social presence as "being with an intelligent entity" (p. 244). As a result, the present research looks at how communication tactics for conveying a strong social presence might boost customer engagement. (Rourke et al., 1999) define three communicative techniques, as well as the variables that lead to a high level of social presence.
The first, emotional approach, which displays emotions, feelings, and mood, is seen to be a distinguishing feature of social presence. In online discussions, emotion may be shown through emoticons, comedy, and self-disclosure. The second type, interactive approach, includes communications that clearly acknowledge other communication partners by asking questions, agreeing with, and expressing gratitude and support for them. The third category, cohesive strategy, promotes a sense of interconnectedness and community, as evidenced by phatics (i.e., communications with a purely social function, such as "Great weather today!") and salutations, vocatives (e.g., addressing participants by name), and the use of inclusive pronouns like "we," "our," and "community."
To assess the impact of social presence communication on customer evaluation, this study uses (Rourke et al., 1999), affective, interactive, and cohesive methodologies to construct chatbot conversations with high and low social presence levels. The beneficial impacts of social presence on improving online experiences have been reported in research, such as enriching group learning through social media (Vanek et al., 2018) and inspiring customers to purchase online (Ogonowski et al., 2014). According to studies, the social presence of avatars improves the emotional appeal of the e-service website and the consumer.
H1: The social presence has a positive effect on initial chatbots' trust.
Affective, cognitive, and/or behavioral components of consumer interaction exist in and beyond the social media setting (Hollebeek et al., 2014). User engagement, on the other hand, is described as behavioral manifestations toward a brand that are representations of underlying psychological states that come from a consumer's interaction relationship with a brand (Brodie et al., 2011). We classify the actions of generating, like, and sharing social media material as user engagement activities since they reflect such behavioral expressions toward a brand (Noguti, 2016)and have been used most frequently in recent research (Lee et al., 2018).
In the context of new technology, the antecedents of early trust have gotten a lot of attention (Talwar et al., 2020). (Zhou, 2011), for example, has already examined the impact of information quality, structural assurance, and system quality on first trust. Furthermore, the impact of peripheral (functional consistency) and central (computer monitoring) signals on early trust building was investigated (Silic and Ruf, 2018). Initial trust is stimulated by perceived information and service quality, according to (Talwar et al., 2020), but initial trust is inhibited by perceived ambiguity and perceived asset specificity. Prior research has also revealed that social presence, security perceptions, brand trust, site usability, brand
reputation, trust propensity, website quality, and government assistance all help to foster initial trust development (Ogonowski et al., 2014; Susanto et al., 2013; Zhang et al., 2018).
Furthermore, the trust literature advises using technology theories as a starting point for investigating early trust (Kaabachi et al., 2019; Talwar et al., 2020). For example, when investigating initial trust in the e-commerce environment, theories like UTAUT (Venkatesh et al., 2003), TAM (Davis, 1989), and DOI (Rogers, 1983) are frequently employed as theoretical basis (Chaouali et al., 2016; Kaabachi et al., 2019; Shareef et al., 2017; Zhou, 2011). Thus, in order to prevent overlapping evaluations with other factors from other theories, our study analyzed the most essential variables from the aforementioned connected theories and explored initial trust from this perspective (Mardiana et al., 2015).
H2: The initial chatbots' trust has a positive effect on user engagement.
Fisk et al., 1993; Smith and Bolton, 1998, define interaction satisfaction as an evaluative evaluation of a single encounter. Expectancy disconfirmation is the biggest predictor of satisfaction (Oliver and DeSarbo, 1988). The creation of expectations and the disconfirmation of those expectations through performance comparisons are two processes involved in expectation disconfirmation (Oliver and DeSarbo, 1988). In addition to the impact of disconfirmation, the two components of the expectancy disconfirmation paradigm expectations and performance have separate effects on satisfaction evaluations. Interaction satisfaction is therefore a comparison reaction based on the customer's expectations and the interaction's outcomes (utilitarian and hedonic) (Oliver and DeSarbo, 1988). Interaction satisfaction is defined as an evaluative judgment of a single contact by (Rohm et al., 2013); (Fisk et al., 1993; Smith and Bolton, 1998) in a study of millennials. The largest predictor of pleasure is expectation disconfirmation (Oliver and DeSarbo, 1988). In expectation disconfirmation, two stages are involved: the formation of expectations and the disconfirmation of those expectations through performance comparisons (Oliver and DeSarbo, 1988). The two components of the expectancy disconfirmation paradigm expectations and performance have independent impacts on satisfaction assessments, in addition to the influence of disconfirmation. As a result, customer satisfaction is a comparative reaction based on the customer's expectations and the interaction's outcomes (utilitarian and hedonistic) (Oliver and DeSarbo, 1988).
H3: The initial chatbots' trust has a positive effect on interaction satisfaction.
Along with competence, Keller (2012) emphasizes the importance of trustworthiness and likability in building human brand equity. (Delgado-Ballester and Munuera-Aleman, 2005) that "trust is a critical aspect that builds a relationship and adds to brand equity." (Delgado-Ballester and Munuera-Aleman, 2005) defined trust as "the belief that one party has in another." Customers' good feelings towards a brand can be influenced by the trust (reliability) that has been built in it based on the brand's demonstrated competence and skill (Doney and Cannon, 1997). Information provided from a reputable and dependable source can increase likability, according to the source credibility model developed by (Hovland and Weiss, 1951). As a result, trust is a vital aspect that has a significant influence on consumers' beliefs and opinions, as well as their likability. As a result, when academics possess and display sufficient knowledge and competence, students see them as a reliable and trustworthy source.
H4: The initial chatbots' trust has a positive effect on brand likability.
METHODS OF RESEARCH
Quantitative research with confirmatory factors analysis using Structural Equation Modeling (SEM) is the method employed (Hair et al., 2014). Chatbots' user in Greater Jakarta (Jabodetabek) make up the study's population. Purposive sampling was used for this study, which took place between December 2021 and January 2022. There are 258 respondents who meet the following criteria: (1) Users who domiciled in Jabodetabek area (2) Users who have experience of using chatbot and use it for at least once during last month. The questionnaire employs an interval range of 1-5 from "strongly disagree" to "strongly agree".
Figure 1 - Research Framework
Each question in the questionnaire is a research instrument that must be evaluated with a standard size before being utilized in research. The r-value of table 0.138 was calculated using a total sample of 215 respondents and a significant threshold of 5%. If r count > r table, the research instrument is considered legitimate. Except for indicator x11, which has a computed r-value of 0.020, all indicators have a calculated r-value larger than the r table. As a result, except for statement x11, all indicators in the research are genuine. Table 1 shows the outcome of the validity test. The Cronbach's Alfa test yielded a score of 0.851, which is higher than 0.700, the questionnaire questions utilized in this study are therefore trustworthy. Table 1 shows the results of the reability test.
Table 1 - Validity and Reliability Test Result
Scales and Items
Standardized Cronbach's Estimate Alpha
Social Presence
When I use the chatbots' application, I get a friendly automatic greetings 0.840
The chatbots' application provides automatic messages that make easier to convey customer's needs. 0.772
The chatbot applications is easy to understand. 0.883
The existence of the chatbots' application makes it easy for customers to convey customer's needs. 0.776
Interacting with the chatbot application is very pleasant. 0.873
0.92
Chatbot Initial Trust
Chatbots seem dependable.
Chatbots seem secure.
Chatbots were created to help the client.
Chatbots seem trustworthy.
0.894 0.755 0.935 0.869
0.907
User Engagement
Time appeared to go by very quickly when I was interacting with chatbots' application. I spent more time on chatbots' application than I had intended.
While I was interacting with chatbots' application, I was able to block out most other distractions. While I was interacting with chatbots' application, I was immersed in what I was doing. I lost track of time when I was interacting with chatbots' application.
0.881 0.877 0.788 0.828 0.763
0.921
Interaction Satisfaction I enjoyed my interaction with the website. Interacting with the website is satisfying. Interacting with the website is frustrating. I think the website gave me a headache. Interacting with the website is very awkward.
0.763 0.953 0.904 0.761 0.878
0.934
Brand Likability
Chatbot's brand application is appealing. Chatbot's brand application is good. Chatbot's brand application is pleasant. Chatbot's brand application is favorable. Chatbot's brand application is likeable.
0.822 0.772 0.881 0.808 0.758
0.902
The findings at Table 2 showed that the model matched the data well ( x2 = 695.129, IFI= 0.849, TLI = 0.831, CFI = 0.848, RMR = 0.048, RMSEA = 0.084. All of the theories were shown to be correct.
Table 2 - Goodness of Fit
Goodness of Fit Criteria (cut off value) Indicator Value Conclusion
IFI >90 0.849 Marginal Fit
TLI >90 0.831 Marginal Fit
CFI >.90 0.848 Marginal Fit
RMR <10 0.048 Good Fit
RMSEA <10 0.084 Good Fit
RESULTS OF STUDY
Based on Table 3 found that the characteristic of respondent of this study and details of the demographics of the respondents can be seen in Table 3.
Table 3 - Respondents Characteristic
Characteristic Category Number Percentage (%)
Gender Male 123 47.7
Female 135 52.3
< 20 y.o. 17 6.6
21 - 34 y.o. 137 53.1
Age 35 - 44 y.o. 56 21.7
45 - 54 y.o 31 12.0
>55 y.o. 17 6.6
High School 32 12.4
Education Undergraduate 180 69.8
Graduate 46 17.8
<Rp 2 million 31 12.0
Monthly Income Rp 2 - 10 million 152 58.9
>Rp 10 million 75 29.1
Chatbot Usage Frequency 1 - 5 times a month 152 58 9
More than 5 times a month 106 41.1
Table 3 found that the total of respondents are 258 respondents with 123 persons (47.7%) are male respondents and 135 persons (52.3%) are female respondents. The result also found that 137 persons (53.1%) from 21 - 34 y.o. are the majority respondents based on age characteristics. Based on education found that 180 persons (69.8%) from undergraduate are the majority respondents. Beside that, the result showed that 152 persons (58.9%) with monthly income at Rp 2 -10 million are also the majority respondents. Lastly, the majority respondents in using chatbot in a month is 1 - 5 times a month with number of respondets are 152 persons or 58.9%.
Hypothesis testing was conducted to determine the purpose of this study using SEM analysis. The hypotesis decision is obtained by comparing probability value (p-value) and significance threshold that using in this study. In this study, the significance threshold was used is a= 0.05. The results can be summarized in Table 4.
Table 4 - Hypothesis Test Results
Hypothesis Path Coefficient p-values Conclusion
H1: Social Presence Communication on Initial Chatbot Trust 0.819 0.000 Supported
H2: Initial Chatbot Trust on Brand Likability 0.680 0.000 Supported
H3: Initial Chatbot Trust on User Engagement 1.086 0.000 Supported
H4: Initial Chatbot Trust on Interaction Satisfaction 0.956 0.000 Supported
According to Ogonowski (2014), it was determined that perceived social presence (H1), had positive associations with initial trust. The influence of social presence communication on initial chatbot trust has a positive standardized coefficient value of 0.819 with a significance value of 0.000, which is less than the 5% significance threshold.
The study's second hypothesis has been accepted as well, the influence of initial chatbot trust on brand likability has a positive standardized coefficient value of 0.680 with a significance value of 0.000. It aligns with previous research that has looked at brand trust as both an antecedent and a result of brand likability (Albert and Merunka, 2013).
On research conducted by the author on the H3 is accepted, it shows the influence of initial chatbot trust on user engagement has a positive standardized coefficient value of 1.086 with a significance value of 0.000, this result is supported by the previous findings from (Mostafa and Kasamani, 2021), which stated that initial chatbots' trust has proven to have a beneficial effect on consumer engagement.
Some of the studies proposed that trust precedes satisfaction (Shamsudin et al., 2018) in which they argued that the first customers trust the service providers based on some
factors which have an effect on satisfaction (Abror et al., 2020), the influence of initial chatbot trust on interaction satisfaction has a positive standardized coefficient value of 0.956 with a significance value of 0.000, therefore support H4.
DISCUSSION OF RESULTS
Based the result found that social presence communication has an influence on initial trust. This means that if the community's perceived social presence towards chatbot's increases, the community's initial trust towards chatbots also increases. Social presence is related to what the social and emotional acceptance of the community looks like to the presence of this chatbot, especially on their trust in this chatbot. The indicator that best describes this social presence is chatbots' application is easy to understand. Meanwhile, the indicator that best describes the initial trust is Chatbots was created to help the client. Based on this, it can be concluded that the respondents believe that the existence of this chatbot is very helpful for customers in meeting their needs because this application is very easy to understand.
Another result found that initial chatbots' trust has an influence on brand likability, user engagement, and interaction satisfaction. This means that if the public's initial trust towards chatbot's increases, the community's brand likability, user engagement and interaction satisfaction to chatbot's also increases. It is known that public acceptance of a technology is still very small. People still find it difficult to believe in the emergence of new technologies, including the emergence of chatbot features on various applications in Indonesia. However, with the development of the era and technology, people must be able to understand the existing technology, especially this chatbot. Initial trust in this research relates to what the public trust in the existence of this chatbot application, especially to the brand likability of this chatbot. The indicator that best describes the initial trust is that Chatbots were created to help the client. While the indicator that best describes brand likability is the chatbot's brand application is pleasant. Based on this, it can be concluded that the basis for the emergence of respondents' trust in this chatbot is that the brand of this chatbot application is pleasant. They feel that the available chatbot application brands are fun and help them overcome the problems they are experiencing. This is what underlies them to believe in the chatbot brand.
In addition to the emergence of liking for the available chatbot brands, user engagement is another thing that will appear after the emergence of trust. Consumers will be willing to stick to a product if they feel the product is trustworthy and able to overcome all the problems felt by consumers. The indicator that describes the user engagement is that using time was running very fast when I interact with a chatbot application. From this, it can be interpreted that when a consumer believes in the chatbot application that is able to help them, when operating this chatbot application system they feel the time is running fast because it is fun to operate this application.
Lastly, it would appear the satisfaction of the interactions were did in this chatbot application. Satisfaction is one of the benchmarks in describing a consumer's trust in technology. Satisfaction and trust are interrelated and important for a technology developer to improve the quality of the technology produced. For consumers, they will tend to feel satisfied after feeling trust in technology that can help them in their daily lives. Like the results of this study which found that initial chatbot trust had an effect on interaction satisfaction. The indicator that describes interaction satisfaction is that interacting with this chatbot application is fun. Based on this, it can be said that the emergence of consumer satisfaction with this chatbot application is because they believe that apart from being fun, this chatbot can also help them when transacting.
CONCLUSION
The findings reveal that high social communication messages from chatbots that addressed the user by name, displayed a sense of humor, emoted, utilized emojis, and expressed agreement with the user can indirectly boost user engagement, interaction
satisfaction, and brand likability. This study also successfully prove that there are direct effect of social presence to initial chatbots' trust; direct effect of initial chatbots' trust to user engagement, interaction satisfaction, and brand likability. This report also provides crucial strategic advice to assist marketers use the conversational potential of chatbots for brand communication, based on the empirical findings.
This study adds to the growing body of knowledge in interactive marketing by demonstrating the potential of chatbots, one of the most widely used AI applications in marketing, as an interpersonal communication tool for increasing consumer engagement. This research builds on previous work that evaluates social presence as an audience impression of virtual characters or online communications by adopting a self-reporting Social Presence Scale (in Dutch) that measures the perceived degree of social presence in a CSCL environment. Construction of the items in the scale was inspired by telepresence research (e.g., Lombart and Ditton 1997) and translating them into English. To present a high social presence, turn them into concrete communication methods for chatbots.
As a result, when designing and programming chatbot messages, marketers should use Rourke et al (1999) emotional, interactive, and coherent tactics to portray a high level of social presence so it can also enhance the initial chatbots trust. When conversing with customers, marketers should leverage the affective strategy by building chatbots to communicate feelings, use emoticons, engage in self-disclosure, and use comedy via humorous memes. The interactive strategy should also include actively asking questions rather than waiting for customers to ask them as in traditional Q&A, expressing agreement, appreciating customer comments, and showing appreciation for customers.
This study has several limitations, including the fact that it is a short-time study, therefore the results do not explain an object in the long run. Future researchers should conduct this study over a longer period of time to gain a better understanding of the findings. The findings of this study, which are limited in Jabodetabek area, Indonesia, may can not be applied to other Indonesian cities. Future research should choose a larger geographic area to broaden the scope of the findings. In addition, other variables such as culture can be added because there are still many people who have difficulty understanding, feel uncomfortable with the presence of technology and also people's IT skills are still relatively low.
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