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The influence of community engagement on seller opportunistic behaviors in e-commerce platform

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Abstract

Firms using e-commerce as platforms need to develop effective mechanisms to govern seller opportunism. This paper attempts to explore the effect of seller community engagement, as an informal mechanism of network governance, on seller opportunistic behaviors. In particular, we identify two types of the seller community established by the e-commerce platforms, which offers infrastructures supporting tightly and loosely coupled relationships among members, respectively. We find that engaging in both types of communities reduces seller opportunistic behaviors. We also show that the intensity of competition positively moderates the relationship between engagement in the communities with tight infrastructure and sellers’ opportunistic behaviors, while deterrence perception exerts a negative moderating effect on the relationship. In contrast, the intensity of competition negatively moderates the relationship between engagement in communities with loose infrastructure and opportunistic behaviors. The findings provide theoretical and managerial implications for opportunism governance in electronic markets.

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Acknowledgements

The authors would like to acknowledge the grants from the National Natural Science Foundation of China (No. 71672150), and the Fundamental Research Funds for the Central Universities (No. JBK18505008).

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Authors and Affiliations

  1. School of Business Administration, Southwestern University of Finance and Economics, No. 555 Liutai Road, Wenjiang District, Chengdu, 611130, China

    Jiaxin Wang, Qinghong Xie & Lili Chen

  2. Sprott School of Business, Carleton University, Ottawa, Ontario, K1S 5B6, Canada

    Shaohan Cai

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  1. Jiaxin Wang
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Correspondence to Qinghong Xie.

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Appendices

Appendix 1: Profile of the sample sellers (N = 370)

Sample characteristics

Frequency

Percentage

Geographic location

Middle China

32

8.65

East China

142

38.37

West China

48

12.98

South China

75

20.27

North China

73

19.73

Number of employees

< 5

135

36.5

5–20

180

48.6

20–50

43

11.6

> 50

12

3.2

Community-owned platform

Taobao

220

59.5

Tmall

81

21.9

JD

23

6.2

Pingduoduo

39

10.5

Amazon China

6

1.6

Others

1

0.3

Age

< 1 year

19

5.1

1–2 years

114

30.8

3–5 years

159

43.0

> 5 years

78

21.1

Product category

Clothing and bags

124

33.5

Beauty

49

13.2

Baby

36

9.7

Electronics

34

9.2

Home

86

23.2

Grocery

19

5.1

Books

10

2.7

Others

12

3.2

Education level

High school or below

56

2.4

Junior college

9

5.1

Undergraduate

290

78.4

Post-graduate or above

15

4.1

Appendix 2: Measurement items and validity assessment

All items are on a seven-point scale (1 = “strongly disagree,” and 7 = “strongly agree”). Engagement in community with tight infrastructure (Koh and Kim [59]; Zhou et al. [90]) CR = 0.84, AVE = 0.51, Cronbach’s α = 0.76.

  1. 1.

    Community members often get information through group discussions or offline meetings in the community. (0.69)

  2. 2.

    We can obtain a lot of information from group discussions or offline meetings. (0.72)

  3. 3.

    In group discussions or offline meetings, community members often share operational information and exchange experience. (0.72)

  4. 4.

    When facing operational problems, community members often seek help from others in the community. (0.72)

  5. 5.

    Members in the seller coalition or specific category group are actively engaged in group discussions or offline meetings. (0.71)

Engagement in community with loose infrastructure (Koh and Kim [59]; Zhou et al. [90]).

CR = 0.85, AVE = 0.53, Cronbach’s α = 0.78.

  1. 1.

    Community members often browse information in the community. (0.72)

  2. 2.

    Community members can get a lot of information from interaction activities in the community. (0.71)

  3. 3.

    Community members often share operational information and exchange experience in the community. (0.78)

  4. 4.

    When facing operational problems, community members often seek help from others in the community. (0.76)

  5. 5.

    Community members are actively involved in interactive activities. (0.68)

Seller opportunistic behaviors (Heide et al. [92]). Community with tight infrastructure: CR = 0.94, AVE = 0.75, Cronbach’s α = 0.92; Community with loose infrastructure: CR = 0.89, AVE = 0.62, Cronbach’s α = 0.85.

  1. 1.

    Community members are more likely to breach rules of the platform after participating in community interactions. (0.85/0.73)

  2. 2.

    Community members are more likely to seek self-interest with dishonest behaviors after participating in community interactions. (0.89/0.81)

  3. 3.

    Community members are more likely to take advantage of rule vulnerabilities to further our own interests after participating in community interactions. (0.86/0.80)

  4. 4.

    Community members are more likely to violate promises after participating in community interactions. (0.86/0.82)

  5. 5.

    Community members are more likely to lie about certain things for getting interests after participating in community interactions. (0.88/0.78)

Competitive intensity (Jaworski and Kohli [91]). Community with tight infrastructure: CR = 0.87, AVE = 0.56; Cronbach’s α = 0.80; community with loose infrastructure: CR = 0.86, AVE = 0.56, Cronbach’s α = 0.80.

  1. 1.

    Competition in our category of products on the platform is cutthroat. (0.79/0.77)

  2. 2.

    There are a lot of sellers selling the same products on the platform. (0.65/0.72)

  3. 3.

    There are frequent price competitions between sellers on the platform (0.77/0.74).

  4. 4.

    There are frequent “promotion wars” between sellers on the platform. (0.77/0.77)

  5. 5.

    Anything (such as products, prices, promotion strategies, etc.) that one competitor can offer, other sellers on the platform can match quickly. (0.76/0.74)

Deterrence perception (Antia and Frazier [85] and Chen et al. [86]). Community with tight infrastructure: CR = 0.89, AVE = 0.62, Cronbach’s α = 0.84; community with loose infrastructure: CR = 0.87, AVE = 0.57, Cronbach’s α = 0.82.

  1. 1.

    If sellers were caught violating rules in the platform, the possibility we would be punished is high. (0.80/0.75)

  2. 2.

    The platform takes stern punitive actions against dishonest sellers. (0.76/0.75)

  3. 3.

    The platform maintains a hard attitude in punishing violations. (0.80/0.75)

  4. 4.

    The platform takes tough measures to punish violators. (0.79/0.79)

  5. 5.

    Previous violations have been severely punished by the platform. (0.77/0.76)

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Wang, J., Cai, S., Xie, Q. et al. The influence of community engagement on seller opportunistic behaviors in e-commerce platform. Electron Commer Res 22, 1377–1405 (2022). https://doi.org/10.1007/s10660-021-09469-w

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  • DOI: https://doi.org/10.1007/s10660-021-09469-w

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