The predictive power of machine learning models suggests that there may exist complex interaction patterns among email indicators not captured by regression model. Therefore, we performed clustering analysis to further reveal top performers’ email communication patterns. K-means clustering was employed to explore the underlying types of top performers based on their email communication indicators. The optimal number of clusters was determined by the “elbow” criterion [81], which balances the number of clusters (interpretability of clusters) and between-group variance maximization (explanatory power of clusters). The results suggest that three clusters of top performers emerged in the sample. In order to test the robustness of the clustering result, we also performed clustering analysis using the Gaussian mixture model based on the Expectation Maximization algorithm [82]. The Kappa inter-rater agreement coefficient between the two sets of clustering results is 0.954 (p<0.001) indicating strong consistency. Therefore, we
27
results, we conducted dimension reduction using principal component analysis (PCA) and plotted top performers on the coordinates of the first two principal components (Figure 5). The values of the three cluster centers in the dimension of each email indicator are presented using radar charts as the representative email communication profiles of the three kinds of top performers.
Figure 6 The email communication profile of the three types of top performers
Note: The values in the radar charts are measured in terms of standard deviations from the sample mean.
As shown in Figure 6, three kinds of top performers have rather distinct email communication profiles, which can be interpreted as: “networkers”, ‘influencers”, and “positivists”. Type 1 top performers locate at the center of the whole network with high network centrality values. Their superior social capitals in the email communication network enable them to act as the relational leaders that facilitate information diffusion in the organization. Type 2 top performers are only slightly higher in network centrality than average but have much higher influence and complexity indicator values. This suggests that their emails introduce more novel information (complexity) and tend to be followed by coworkers (influence). Thus, they can be regarded as the opinion leaders of the organization. Type 3 top performers stand out for their strongly positive sentiment, low emotion fluctuation and high responsiveness (low ego nudges) to coworkers’ emails. They appear to be the emotional leaders that emit positive power and strengthen cohesiveness in the organization. The versatility of top performers’ email communication profiles provides
two fundamental implications for email communication competence.
First, not all top performers have the same pattern of email communication. There appears no unified set of optimal email communication pattern, and various patterns can be associated with top performance.
This can be understood with respect to the fact that different roles, such as relational leaders (“networkers”), opinion leaders (“influencers”) and emotional leaders (“positivists”), are needed to fulfill different organizational functions [83, 84]. This finding also implies the need to rethink the definition of email communication competence, which may be better operationalized as the combination of the strength in several communication dimensions instead of a unified construct.
Second, not all favorable communication behaviors (e.g. high responsiveness, positive sentiment) reside in one kind of top performers. This, on the one hand, reflects the inherent trade-off between the focus on different communication behavior given an individual’s limited time and energy. For example, it may be hard for an individual to be highly responsive (type 3) and at the same time keep introducing novel and influential ideas (type 2). On the other hand, as shown in Figure 6, the three kinds of top performers are rather specialized in a few dimensions of email indicators and are average (or even below average) in other dimensions. This can also be viewed as top performers’ adaptation to organizational needs instead of being dominant in every respect.
6. Conclusion
This study reveals the email communication indicators that are predictive of individuals’ work performance. The panel regression models provide interpretable results that top performance is associated with central network position, positive sentiment, low emotionality, high complexity, more adoption of influential words by coworkers and high responsiveness in email communication. The machine learning models that allow more complex interactions among independent variables indicate the high predictive power of email indicators with the best model achieving over 80% accuracy. The cluster analysis results further reveal that the top performers can be generally classified into three types that have advantages in different dimensions.
For theory development, the findings provide implications for operationalizing the construct of email
29
communication style of top performers further implies that email communication competence might be better defined as a combination of several supportive factors instead of a unified construct. For management practices, the identified email indicators can be used to suggest improvements in email communication skill development training. Furthermore, as fine-grained email communication data becomes increasingly available, the analyses performed in this study can also be replicated in different organizations to understand individuals’ contributions to organization communication.
The implications of this study should be viewed with respect to the following limitations, which leave room for improvements in future studies. First, the data are from only one company. Although the fact that the company operates internationally alleviates this limitation to some extent, caution should still be taken when generalizing the findings to other organizational settings. Future studies to test the robustness of the findings in a broader range of cultural and organization environments are needed. Second, because of privacy restrictions we cannot directly observe the email contents, which may provide additional information. Third, the findings of this study cannot support causal interpretation. Future research may use causal inference techniques, such as natural experiments, to precisely estimate the effects of influential email indicators.
Appendix
Appendix Table 1 Email communication indicators in existing studies
Indicators Definition and calculation method Related research1 Outcome
variables Aral and Van Alstyne [42] Worker
productivity
Closeness centrality The extent of the person’s affinity with co-workers Ahuja et al. [41] Individual performance Aral and Van Alstyne [42] Worker
productivity Degree centrality Number of co-workers the person communicates Ahuja et al. [41] Individual
31
with. performance
Aral and Van Alstyne [42] Worker productivity
Contribution Messages sent Number of emails the person sent Wasiak et al. [18] Team performance Aral and Van Alstyne [42] Individual
performance Appendix Table 1 Email communication indicators in existing studies (continued)
Indicators Definition and calculation method Related research1 Outcome
variables Contribution Balance of contribution The extent to which the person’s communication is
balanced in terms of emails sent and received
Eckmann et al. [54] Group cohesion
Lim and Teo [39] Work attitude
Content Sentiment Positivity and negativity of communication Wasiak et al. [18] Team performance
Gloor et al. [13] Job turnover
Gloor [57] Communication
behavior
Emotionality Deviation from neutral sentiment Francis et al. [40] Communication
behavior
Gloor et al. [13] Job turnover
Lim and Teo [39] Work attitude
Complexity The complexity of a person’s email contents Erhardt et al. [4] Team learning Kanawattanachai and Yoo [50] Team
performance
Gloor et al. [13] Job turnover
Aral et al. [36] Individual
performance Influence The influence of a person’s email contents on
co-workers’ email contents Dynamics Ego responsiveness The person’s responsiveness to emails Erhardt et al. [4] Team learning
Gloor et al. [13] Job turnover
Eckmann et al. [54] Group cohesion Alter responsiveness The responsiveness of the person’s co-workers Eckmann et al. [54] Group cohesion
Gloor et al. [13] Job turnover
Network position rotation
The extent to which the person frequently changes network position from central to peripheral, and communication using subjective measures (e.g. [3]) are not included.
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