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Task-oriented vs. social-oriented: chatbot communication styles in electronic commerce service recovery

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Abstract

Chatbots are being increasingly utilized for service recovery in e-commerce. However, chatbot communication styles in service recovery and their impacts on consumer satisfaction remain understudied. In this study, we conducted a scenario-based experiment to explore the appropriate communication styles for chatbots and to identify the underlying mechanisms that influence consumer satisfaction in service recovery. Our findings reveal that a social-oriented chatbot is more effective in delivering service recovery responses compared to a task-oriented chatbot. Interacting with social-oriented chatbots enhances consumers’ service recovery satisfaction by increasing their cognition-based trust and affect-based trust. Importantly, we also find that social-oriented chatbots outperform task-oriented chatbots in service tasks that vary in terms of complexity and for consumers with different relationship orientations. Our study contributes to chatbot designs by providing theoretical and practical guidance for online retailers to design appropriate communication styles for chatbots in service recovery.

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Acknowledgements

This research was supported by the "new liberal arts program" of the Ministry of Education in China (2021090003); the "BUPT Excellent Ph.D. Students Foundation" (No. CX2022154); the basic research project supported by Beijing University of Posts and Telecommunications (No. 2022RC21); and National Natural Science Foundation of China (No.72201038).

Funding

This work was funded by Ministry of Education in China (Grant No. 2021090003), Beijing University of Posts and Telecommunications (Grant No. 2022RC21; Grant No. CX2022154), National Natural Science Foundation of China (Grant No. 72201038).

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

    Siran Wang

  2. School of Modern Post, Beijing University of Posts and Telecommunications, Beijing, China

    Qiang Yan & Lingli Wang

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  1. Siran Wang
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Correspondence to Lingli Wang.

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Appendices

Appendix 1

Table 11 provides examples of answers in the first pretest.

Table 11 Examples of answers in the first pretest
Full size table

Appendix 2

Conversation materials A

Scenario Description You bought a coat from an online store and waited for the package for over seven days. However, you did not receive the parcel. Therefore, you contacted the store, expressed your dissatisfaction, and hoped to get a refund. You are greeted by a chatbot. The conversation begins in the following manner: 

Fig. 2
figure 2

Task-oriented chatbot (conversation material A)

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Fig. 3
figure 3

Social-oriented chatbot (conversation material A)

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P.S. In this scenario, Fig. 2 shows the dialogues between a consumer and a task-oriented chatbot while Fig. 3 shows the dialogues between a consumer and a social-oriented chatbot.

Conversation materials B

Scenario Description You bought a coat at an online store and waited for the package for over seven days. However, you did not receive a parcel. You checked the logistics information and found that the parcel has been signed for but the receipt address was not yours. To address this, you contacted the store, expressed your dissatisfaction, and hoped to understand the reason for the wrong delivery. In addition, you are seeking a solution. You are greeted by a chatbot. The conversation begins in the following manner: 

Fig. 4
figure 4

Task-oriented chatbot (conversation materials B)

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Fig. 5
figure 5

Social-oriented chatbot (conversation materials B)

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P.S. In this scenario, Fig. 4 shows the dialogues between a consumer and a task-oriented chatbot while Fig. 5 shows the dialogues between a consumer and a social-oriented chatbot.

Appendix 3

We conducted a series of one-on-one online interviews among 30 participants recruited from a popular social media platform in China. 15 males and 15 females were recruited and each of them received a compensation of 10 RMB after an interview.

Before the formal interviews, we first asked the participants to answer questions regarding their own relationship orientation using a five-point Likert scale (1: “strongly disagree”; 5: “strongly agree”). Items used for measuring relationship orientation are listed in Table 5. Among 30 participants, 17 were classified as exchanged-oriented individuals (with a total score of less than 30) while 13 were classified as communal-oriented individuals (with a total score of more than or equal to 30).

To ensure that the participants have experienced recovery services provided by chatbots, we began our interview with the following question (Q1: Have you ever encountered a chatbot when you hope to get help from online retailers after a service failure?). The results indicated that every participant had previously engaged with chatbots after suffering from service failures. Then, we presented the participants with questions pertaining to their expectations regarding service recovery responses delivered by chatbots (Q2: Please talk about your feelings when interacting with the chatbot. Q3: Please describe your ideal service chatbot in service recovery contexts. Q4: When you interact with chatbots in service recovery contexts, what is your opinion about emotional support from chatbots, and what is your opinion about the need for efficient service recovery from chatbots? Q5: When you are handling complex service tasks, how would you expect the way the chatbot provides the services? Q6: When confronted with simple tasks, how would you expect the way the chatbot provides the services?).

The findings from the interview revealed participants’ inclinations when interacting with chatbots in service recovery. The majority of participants (24 out of 30) expressed a preference for chatbots that exhibit empathic cues and offer emotional support. Most participants emphasized that emotional support and empathic responses from chatbots have the potential to “build positive attitudes toward solving problems”, “enhance my willingness to communicate with the chatbot”, and “alleviate the negative emotions”. Conversely, some participants conveyed an aversion to chatbots that only focus on delivering efficient and concise responses, because such kind of chatbots is perceived as “showing unsatisfying service attitudes”, “less flexible”, and “exacerbating consumers’ negative emotions” in service recovery. In addition, when encountered with simple tasks, a subset of participants (9 out of 24) believed that the significance of empathic responses from chatbots in service recovery might be diminished.

Furthermore, the outcomes of our interviews did not reveal evident differences in preference between exchange-oriented participants and communal-oriented participants concerning chatbot-delivered emotional support and efficiency in the service recovery context. Among the 17 exchange-oriented participants, 13 participants expressed a preference for empathic responses from chatbots as opposed to concise responses. Similarly, among the 13 communal-oriented participants, 11 participants exhibited a preference for empathic responses over concise responses from chatbots in service recovery. These results provide valuable insights that help explain why hypotheses H4a and H4b were not supported in this research. Table

Table 12 Examples of answers in the additional interview
Full size table

12 presents a few examples of answers from participants.

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Wang, S., Yan, Q. & Wang, L. Task-oriented vs. social-oriented: chatbot communication styles in electronic commerce service recovery. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09741-1

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