Organisational leadership style, network structure, and knowledge performance in online knowledge community organisations

ABSTRACT

Based on an agent model combined with complex network analysis, this paper investigates the effects of different leadership styles and network structures on organisational learning in online work knowledge community (OWKC). The paper further examines other factors related to the relationship between leadership style and organisational learning, such as environmental change and personnel turnover in OWKC. The results show that the leadership style of differential leader-member exchange (DLMX)) produces higher online community knowledge performance (OCKP) than the leadership style of uniform leader-member exchange (ULMX) in small-world networks, while ULMX outperforms DLMX in scale-free networks. However, with environmental change and in the presence of personnel turnover, ULMX performs better in small-world networks.

KEYWORDS:

• Online knowledge
• organisational learning
• leadership style
• network structure
• information system

Compliance with ethical standards

The authors have declared that no competing interests exist.

Acknowledgments

This research was supported by the National Social Science Foundation of China [Grant No.17BGL025].

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All Data is in this paper.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Notes

1. In the model of Fang, Lee, and Schilling (2010), employees in an organisation learn from each other through interpersonal interaction. The key variable of their model is organisation structure, so they hold the learning rate constant (i.e., $p_{learning}$= 0.3). Similarly, this paper focuses on leadership style and network structure, and the learning process between members is similar to that between individuals in Fang’s model. Therefore, we simulate the model for $p_{1D}$= 0.3 and = 0.3.

2. In our model, we divide employees into two layers: leaders and members. The learning process between leaders and members is different from that between members. According to previous studies (March 1991; Miller, Zhao, and Calantone 2006), there is ‘mutual learning’ between employees and the organisational code. A combination of a high rate of learning by the organisational code with an intermediate rate (i.e., [0.3, 0.7]) of learning from the organisational code produces the superior outcome. The organisational code plays a similar role to leaders. Without loss of generality, we simulate the model for$p_{2D}$= 0.7,$p_{2U}$= 0.7,$p_{3D}$= 0.7, and $p_{3U}$= 0.7.

3. The network sizes we use for our simulations are based on network sizes used in current research on the organisation (Lazer and Friedman 2007; March 1991; Mueller, Bogner, and Buchmann et al. 2015).

4. According to LMX theory, leaders may afford differing treatment to the members they lead (Aleksić, Stanisavljević, and Bošković 2016). Liden and Graen (1980) demonstrate, through empirical research, that leaders interact with ‘in-group’ members more frequently than with ‘out-group’ members. Based on the research of Oh et al. (2016) on DLMX and ULMX, without loss of generality, we simulate the model for$\alpha$= 8, ${\beta }_1$= 5, and ${\beta }_2$= 10. We also simulate different values of these frequencies, finding no substantive change in our results.

5. Fang, Lee, and Schilling (2010) discuss the effect of environmental change and personnel turnover on organisational learning performance. In their model, about 10% of the elements of reality change their values every 200 periods. At the same time, 1% of the organisational members are replaced by new members. They note that the period of environmental change is tuned to allow the organisations sufficient time to learn and adapt to the new environment. That is, the timescale for environmental change is greater than that for organisational learning. Given that the organisational size is 280 in their model while our network size in this section is 100, we adopt a shorter period. Therefore, we assume that 10% of the elements of external environment change their values every 100 periods. In addition, Fang et al. (Fang, Lee, and Schilling 2010) also investigate the effect of different turnover rates (such as 0.001, 0.005, 0.05, 0.1, 0.5, and 1) on learning performance. As we focus on leadership style and network structure, we hold the turnover rate constant. In sum, we simulate the model for = 0.1, $p_n$= 0.1, and = 100.