The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia

Sanny, Lim; Susastra, A.; Roberts, Courtney C.; Yusramdaleni, R.

doi:10.5267/j.msl.2019.11.036

Cited by 37 publications

(18 citation statements)

References 29 publications

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“…Moreover, consumers should be informed before the interaction that they are interacting with a bot (and not a human) to ensure they can calibrate their expectations accordingly, whether it be regarding the social norms or the wording to use while interacting with the chatbot (Budiu, 2018). The online service should explain why private information is requested and how it will be used to avoid exacerbating technology anxiety and to enhance their user experience (Schaub et al, 2017). This can be done by short privacy notices embedded into the chatbot's interaction flow.…”

Section: Discussionmentioning

confidence: 99%

Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot

Rajaobelina

Tep

Arcand

et al. 2021

Psychology and Marketing

106

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Consumers sometimes describe their experience of interacting with artificial intelligence‐based human‐like chatbots as creepy. This study investigates the antecedents of creepiness (i.e., the chatbot's usability, privacy concerns, and user variables such as technology anxiety and the need for human interaction) and its impact on consumer loyalty. Grounded in the technology paradox, it deepens the understanding of creepiness in light of the theoretical underpinnings of the privacy paradox and privacy cynicism. Presented with the task of obtaining a car insurance quote, 430 consumers participated in a simulation involving interaction with a chatbot, followed by a questionnaire. The findings show that creepiness decreases loyalty and indirectly impacts it through trust and negative emotions. While usability reduces perceptions of creepiness, privacy concerns raised by the interaction with the chatbot increase creepiness, which is positively associated with consumer traits (i.e., technology anxiety and need for human interaction). The main contribution of the research lies in its focus on creepiness, a concept under‐researched in the marketing literature, and which can be seen from the perspective of a coping mechanism for consumers’ privacy concerns. This paper provides practical implications to orient managers in the design and implementation of chatbots, as a promising touch point to build customer loyalty.

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Section: Discussionmentioning

confidence: 99%

Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot

Rajaobelina

Tep

Arcand

et al. 2021

Psychology and Marketing

106

View full text Add to dashboard Cite

show abstract

“…telephone and e-mail in handling multiple enquiries and requests parallel with other customer service channels, thereby increasing an organisation's efficiency and effectiveness (Brintia 2018;Kannan & Bernoff 2019;Sanny et al 2020;Toader et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…This can be achieved by presenting the customer with next-best offers and recommendations, as identified using AI support, thus ensuring that these options are relevant to the customer (Kreutzer & Sirrenberg 2020). Experience of interacting with a chatbot will be further enhanced if it is easy to access 24/7 when other options, for example, customer services are not available (Sanny et al 2020). If a chatbot is unable to answer the query, there is the possibility that 'it' can contact you at a set time suitable to the customer.…”

Section: Key Contributions and Recommendationsmentioning

confidence: 99%

Useful chatbot experience provides technological satisfaction: An emerging market perspective

Lubbe

Ngoma

2021

SA Journal of Information Management

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Background: Chatbots have revolutionised marketing and specifically customer services by delivering 24/7 customer care. This technology saves resources and time, whilst enhancing customer experience that has become a critical differentiating factor in an increasing competitive and hyperconnected landscape.Objectives: This research examined how perceived ease of use (PEOU), perceived playfulness (PP) and perceived usefulness (PU) of chatbots influence customer experience and how these experiences influence users’ satisfaction in an emerging market context.Method: A self-administered questionnaire was distributed to 333 South African millennials and subjected to exploratory factor analysis and multiple regression analysis to test the proposed hypotheses.Results: The results indicate that all the hypotheses suggested have been positive and significant. Whilst experience significantly predicted satisfaction, it is worth noting that PU was the strongest predictor of experience.Conclusion: Marketers should devote time, money and recourses in developing and incorporating self-service technologies, such as chatbots, that are useful for their marketing strategies that will enhance customer experience.

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“…Especially for certain products. Customer satisfaction is an emotional response, when consumers want to buy a product, certainly decide through a series of rational and emotional evaluations (Sanny et al, 2020;Ali et al, 2016;Calvo-Porral, Ruiz-Vega, and Lévy-Mangin, 2018;Hadiansah, 2017;Wang and Chou, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…This research uses several aspects or attributes such as teaching learning, tutoring, and consulting, research and community service, and other tasks beyond the main task as a consideration in judgment Subsequent research that utilizes the Rough Set method, which uses a variable of education, academic position and Department of office is then used to determine the desired rector candidate in accordance with the existing provisions. The results of the study show that education is a major factor in the election of foreign rector which is then supported by the academic position and structural department owned by the lecturer (Chen et al, 2020;Turčínek and Turčínkova, 2015;Dhandayudam and Krishnamurthi, 2013;Shyng et al, 2007;Suchacka and Chodak, 2017;Sembiring and Azhar, 2017;Hadiansah, 2017;Wang and Chou, 2013;Sanny, Susastra, Roberts, and Yusramdaleni, 2020).…”

Section: Introductionmentioning

confidence: 87%

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Istianingsih¹,

Defit²

2021

IJEBA

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Purpose: The research aims to determine how the level of customer satisfaction in a small business is a field of flower arrangement services as well as information that can dug from a collection of customer data. Approach/Methodology/Design: The samples used were ten customers who considered loyal. The method used is a Rough set with a satisfaction attribute consisting of confidence, integrity, pride, and desire. Findings: Based on this decision rule, confidence becomes the most dominant attribute. The results showed that the four dimensions of the emotional bond used as an attribute in the Rough Set process to analyze the level of customer satisfaction that strongly affects is the dimension of Confidence, this is evident from the resulting General Rule. Every decision produced always uses the confidence dimension in the comparison input, meaning the level of customer confidence in the company should be a special concern. In the dimensions of the emotional bonds the first dimensions are constructed and fundamental is the confidence dimension, this dimension indicates the level of customer confidence in the company. Confidence dimensions cannot stand it self yet to build long-term relationships with customers without by other dimensions. Practical Implications: The study will contribute positively to give some information of knowledge that contained on customer satisfaction. It shown the alternative for measuring for satisfaction that can be used for a small business and academic. Originality/Value The results generated in this study opened new avenues for customer satisfaction analysis. We must not limit the analysis of customer satisfaction survey data using conventional statistical methods. The Rough theory Set is an innovative tool for finding Knowledge of customer behaviour patterns.

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The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia

Cited by 37 publications

References 29 publications

Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot

Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot

Useful chatbot experience provides technological satisfaction: An emerging market perspective

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

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