Market orientation and innovation behaviour: how do service employees benefit from their uniplex and multiplex intrafirm network centrality?

ABSTRACT

Intrafirm networks enable service employees to transform market orientation behaviours into innovation behaviours. Few studies, however, have investigated how network centrality in intrafirm networks can moderate this relationship. This paper investigates how service employees can leverage their intrafirm network popularity in three types of social networks: advice, friendship, and multiplex networks. The findings of a multi-source, multilevel study among 1175 service employees embedded in 60 firms demonstrate the important role of multiplex-network centrality. Employees who have a central position in multiplex networks (with overlapping friendship and advice ties) can tap into the complementarity of the assets rooted in friendship and advice networks, allowing them to more effectively convert market orientation into innovation behaviours. Our study demonstrates the importance of investigating multiplex relationships next to uniplex relationships in order to better understand the relative effects of different network types. Direct implications are given to encourage employees’ MO and innovation efforts.

KEYWORDS:

• Network centrality
• friendship network
• advice network
• multiplex network
• popularity
• market orientation

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Notes

¹ Based on the meta-analysis of Fang et al. (2015), we selected in-degree centrality rather than brokerage as source of social capital. Their results show that in-degree centrality is more strongly relate to job performance (and career success) than brokerage. We acknowledge that network structure (network density) may also determine the ability of service employees to convert MO behaviour into innovation behaviour, as both in-degree centrality and brokerage may provide employees with structural advantage (Grosser, Venkataramani, and Labianca 2017; Wong and Boh 2014). We will partially assess the role of such network structures in our robustness section. We thank an anonymous reviewer for highlighting this issue.

² Advice networks are also based on shared interests and trust, but they tend to derive from the sharing of professional values and cognitive-based trust rather than from personal values and affect-based trust (Gibbons 2004).

³ The multi-informant setup allows to cross-validate some of the survey results. Both front-line managers and front-line employees filled in the same scales. The two groups showed no significant mean differences to the same statement for each item and showed a similar pattern, thereby providing evidence that the scales are consistent and valid across groups. We used structural equation modelling to assess the invariance of the relationships across front-line employees (N = 701) and front-line managers (N = 153), and find compared with the full sample (N = 1175) similar fit indices for the combined model: CFI = .88/.87, TLI = .88/.85, RMSEA = .058/.047. After establishing full configural invariance (all lambdas are significant for each group), also the measurement weights (Δχ²(29) = 17.78, p = .95) as well as its intercepts (Δχ²(29) = 32.17, p = .56) of the items appeared to be invariant across the two groups. This provided additional evidence that the items measure the same thing and to the same degree across groups.

⁴ We applied a full-information maximum likelihood (FIML) approach since using a list-wise deletion approach would discard the bulk (48%) of our sample. The results of the sample with missing values (N = 1175) are compared with those of the list-wise deletion approach (N = 568) and demonstrate high similarity in terms of fit (CFI = .89/.89, TLI = .88/.88, RMSEA = .058/.071) and for each corresponding standardised loading. In Table 3, we refer to the results of the larger sample.