Enhancing e-platform business by customer service systems: a multi-methodological case study on Ali Wangwang instant message’s impacts on TaoBao

Abstract

E-commerce is very popular nowadays and e-platforms like TaoBao are crucially important. Ali Wangwang, as a subsidiary of TaoBao, is providing important functions to the e-customer services. Motivated by the importance of the e-customer service in managing customer relations and enhancing sales, this paper aims to investigate how to enhance e-platform business operations by improving e-customer service systems. Specifically, we adopt a multi-methodological case study approach to examine Ali Wangwang instant message (an e-customer service) and explore its impacts on the e-platform TaoBao. Our empirical research via surveys shows that Ali Wangwang plays an important role in customers’ online purchase decision making; moreover, the response time and communication efficiency are the two critical factors that need to be improved for Ali Wangwang. Based on our empirical findings, we then develop an analytical model to study the optimal response time and communication convenience level of an e-platform seller under both the exogenous and endogenous price cases. Finally, we propose three recommendations, including facilitating the communication channel, improving existing functions and developing new functions, and enhancing operations by setting appropriate response time and communication convenience level, for the e-platform to improve its e-customer service systems. We believe this paper will help the e-platform provide better customer service, which will further help build a successful e-commerce business.

Appendices

Appendix 1: Deep interview questions

1.1 Customers who have used TaoBao before

• 1. Do you use Ali Wangwang when purchasing products in TaoBao?

• 2. You also can see the detail descriptions on the products’ page, why do you use Ali Wangwang to ask questions?

• 3. In what situation will you use Ali Wangwang? As long as you plan to buy products in TaoBao or just some specific types of products?

• 4. What types of questions will you ask the servers?

• 5. Can these questions be solved?

• 6. What are the problems you faced when using Ali Wangwang?

• 7. Are you willing to use TaoBao without Ali Wangwang?

• 8. Give some suggestions about Ali Wangwang.

1.2 For people who never use TaoBao before

• 1. Will you try to communicate with sellers when you purchase on the Internet? Describe the situation.

• 2. Do you feel it is necessary to change the way you are used to in using the instant message tool?

• 3. Is it convenient for you to do online shopping without any instant message tools?

• 4. Have you worried about the picture you have seen during purchasing is quite different from the real product?

• 5. If you want to change your product choice, do you think using an instant message tool could improve your efficiency?

• 6. If the online buying platform you used promotes a kind of instant message tool, will you try to use it? Why?

Appendix 2: The process of deep interview

Interviewee

Interviewees were randomly selected from people who had experiences in using Ali Wangwang. The randomly selecting was done at the university gate. Interviewers stood in front of the gate and asked people who would like to take this interview. In this deep interview, 20 people were interviewed. Some of them are from mainland of China and the other are from Hong Kong. The results reflected by interviewees were similar from the 13th to 20th interviewees. “There is no new or relevant information emerges with respect to the newly constructed theory” (Given 2008). So deep interview was stopped at the 20th interviewee.

Interview method

Deep interviews were done through face-to-face talk, QQ, Wechat and telephone. Interviewers talked directly to the interviewees and found more information about their use of Ali Wangwang.

Interview time

From 10:00 to 20:00, 10–20 min for each interview.

Preparation

• (i) Outline interview questions.

• (ii) Chocolate as a gift: interviewees were the volunteers and a box of chocolate would be sent as a surprising gift to them after the interview.

• (iii) Digital voice recorder, paper, pen.

Interview questions

Eight questions are prepared in the deep interview (see Appendix 1). Interviewers can ask more related questions according to the specific situation. Deep interview is flexible. Firstly, we need to know whether people would use Ali Wangwang when they shopped in TaoBao. Secondly, we need to know the reason why they would use Ali Wangwang. Therefore, we can know which function of Ali Wangwang is used by people. Thirdly, we continue to find out the efficiency or effectivity when people using Ali Wangwang. Fourthly, problems of using Ali Wangwang will be asked.

Interview content selection

Twelve effective interview contents have been selected and key variables have been summarized. There are 5 females and 7 males in the 12 selected interviewees. They are aged from 21 to 33 years old and 6 of them are from Hong Kong while other 6 are from mainland China.

Appendix 3: The analysis results of questionnaire

See Tables 2, 3, 4, 5, 6, 7 and 8.

Table 2 The analysis results for whether buyers use Ali Wangwang before trading on TaoBao

Table 3 The analysis results for examining the number of extended function used

Table 4 The analysis results for buyers’ satisfaction score of Ali Wangwang

Table 5 The analysis results for evaluation of Ali Wangwang’s influence in TaoBao

Table 6 The analysis results for whether buyers would continue to shop in TaoBao without Ali Wangwang

Table 7 The analysis results for evaluation of Ali Wangwang’s limitation

Table 8 The analysis results for buyers’ suggestion on improvement of Ali Wangwang

Appendix 4: Mathematical proofs

Proof for Lemma 2

From the following derivations,

$$ \begin{aligned} \frac{{\partial \pi_{T} }}{\partial t} & = - \alpha \theta (p - m) + \gamma (1 - t);\quad \frac{{\partial^{2} \pi_{T} }}{{\partial t^{2} }} = - \gamma ;\quad \frac{{\partial^{2} \pi_{T} }}{\partial t\partial p} = - \alpha \theta \\ \frac{{\partial \pi_{T} }}{\partial p} & = \alpha (a + m - \theta t + \beta c_{i} - 2p);\quad \frac{{\partial^{2} \pi_{T} }}{\partial p\partial t} = - \alpha \theta ;\quad \frac{{\partial^{2} \pi_{T} }}{{\partial p^{2} }} = - 2\alpha , \\ \end{aligned} $$

we derive the Hessian Matrix of the e-platform seller’s profit function as follows:

$$ H = \left( {\begin{array}{*{20}c} {\frac{{\partial^{2} \pi_{T} }}{{\partial t^{2} }}} & {\frac{{\partial^{2} \pi_{T} }}{\partial t\partial p}} \\ {\frac{{\partial^{2} \pi_{T} }}{\partial p\partial t}} & {\frac{{\partial^{2} \pi_{T} }}{{\partial p^{2} }}} \\ \end{array} } \right) = \left( {\begin{array}{*{20}c} { - \gamma } & { - \alpha \theta } \\ { - \alpha \theta } & { - 2\alpha } \\ \end{array} } \right) $$

To ensure the existence of the optimal solution, $H$ must be negative definite. We thus have $- \gamma < 0$ and $2\alpha \gamma - \alpha^{ 2} \theta^{ 2} { = }\alpha (2\gamma - \alpha \theta^{2} ) > 0$, which implies that $\gamma > \frac{1}{2}\alpha \theta^{2}$.

Solving the first-order conditions $\frac{{\partial \pi_{T} }}{\partial t} = 0$ and $\frac{{\partial \pi_{T} }}{\partial p} = 0$ yields

$$ p_{E}^{*} = \frac{{(a + m + \beta c_{i} - \theta )\gamma - \alpha \theta^{2} m}}{{2\gamma - \alpha \theta^{2} }} $$

and

$$ t_{E}^{*} = \frac{{(a + m + \beta c_{i} )}}{\theta } - \frac{{2p_{E}^{*} }}{\theta }. $$

$\hfill\square$

Proof of Lemma 3

From Lemma 2, we rewrite $p_{E}^{*}$ and $t_{E}^{*}$ as follows

$$ p_{E}^{*} = \frac{{(a + m - \theta )\gamma - \alpha \theta^{2} m}}{{2\gamma - \alpha \theta^{2} }} + \frac{{\beta \gamma c_{i} }}{{2\gamma - \alpha \theta^{2} }} = \Delta_{p} + \Omega_{p} c_{i} , $$

and

$$ t_{E}^{*} = \frac{a + m}{\theta } + \frac{{\beta c_{i} }}{\theta } - \frac{2}{\theta }(\Delta_{p} + \Omega_{p} c_{i} ) = \Delta_{t} + \Omega_{t} c_{i} , $$

where $\Delta_{p} = \frac{{(a + m - \theta )\gamma - \alpha \theta^{2} m}}{{2\gamma - \alpha \theta^{2} }}$, $\Omega_{p} = \frac{\beta \gamma }{{2\gamma - \alpha \theta^{2} }}$, $\Delta_{t} = \frac{{a + m - 2\Delta_{p} }}{\theta }$ and $\Omega_{t} = \frac{{\beta - 2\Omega_{p} }}{\theta }$.

To satisfy $p_{E}^{*} \ge m$ and $0 \le t_{E}^{*} \le 1$, we have $a \ge m + \theta - \beta c_{i}$ and $\gamma \ge \alpha \theta (a - m - \beta c_{i} )/2 \ge \alpha \theta^{2} /2$. Then, the profit function of $\pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} )$ can be expressed as

$$ \pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} ) = \alpha (p_{E}^{*} - m)(a - p_{E}^{*} - \theta t_{E}^{*} + \beta c_{i} ) - \frac{1}{2}\gamma (1 - t_{E}^{*} )^{2} - \lambda c_{i} . $$

Putting $t_{E}^{*} = \Delta_{t} + \Omega_{t} c_{i}$ and $p_{E}^{*} = \Delta_{p} + \Omega_{p} c_{i}$ into the profit function $\pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} )$, we have

$$ \begin{aligned}\pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} ) &= \alpha (\Delta_{p} - m + \Omega_{p} c_{i} )(a - \Delta_{p} - \theta \Delta_{t} + (\beta - \Omega_{p} - \theta \Omega_{t} )c_{i} ) \\&\quad- \frac{1}{2}\gamma (1 - \Delta_{t} - \Omega_{t} c_{i} )^{2} - \lambda c_{i} . \end{aligned} $$

Taking the first order derivative of $\pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} )$ with respect to $c_{i}$, we have

$$ \frac{{\partial \pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} )}}{{\partial c_{i} }} = \bar{\lambda }_{i} - \lambda , $$

where $\bar{\lambda }_{i} : = \alpha \left[ {(\Delta_{p}{-} m {+} \Omega_{p} c_{i} )(\beta - \Omega_{p} - \theta \Omega_{t} ) + \Omega_{p} (a - \Delta_{p} {-} \theta \Delta_{t} {+} (\beta - \Omega_{p} - \theta \Omega_{t} )c_{i} )} \right] + \gamma \Omega_{t} (1 - \Delta_{t} - \Omega_{t} c_{i} ).$

It obvious that if $\lambda < \bar{\lambda }_{i}$, then $\frac{{\partial \pi_{T} (t_{E}^{*} ,p_{E}^{*} |c_{i} )}}{{\partial c_{i} }} > 0$.$\hfill\square$