Discussion
Communication plays a vital role in any institution. It is through communication that a business is able to engage its staff, management and consumers in achieving the set organization’s goals. Studies have shown that groups are more capable of making more sustainable decisions than individuals (Attila Ambrus, 2009). There is, therefore, the need for businesses that value group decisions to monitor communication.
In this discussion we focus on two businesses in scenario A and B. Both of the business use Leximancer analytical to analyze communication in both business. In scenario A the management aims to select the most influential members in terms of communication to help in making important decision. In Scenario B the company aims to gauge whether the management offers sufficient support to members through organization network analysis. The discussion focuses on the importance communication for group decisions (Smith, K. J & Tischler, R. J. 2015).
The following is a discussion of the criteria that was used in selecting the six clusters and the role played by vital nodes. The summary also covers what the clusters represent in the organization communication. The discussion follows structured questions.
Table 1
Custer numbers and node identifiers in each cluster
Cluster Number |
Cluster Identifier |
1. |
19,52,14,39,36,76,35,04,13,41,78,40,74,73,58,61 |
2. |
80 ,10 54,26,59,68.34,07,32,53 |
3. |
32,12,53,70,30,29,38,69,72,07 |
4. |
59,26,68,15,80,71,75,,08,24,81,66,54 |
5 |
37,31,50,47,44,45,26,59,15,80,10 54,66 |
6 |
37,31,50,47,44,45,26,59,15,80,10 54,66 |
Table 2
Room’s identifiers for each cluster and members selected to attend meeting.
Rooms |
Custer key |
Members Selected to Attend |
A |
1. |
19 and 35 |
B |
2. |
80 and 54 |
C |
3. |
32 and 07 |
D |
4. |
59 and 68 |
E |
5. |
37 and 66 |
F |
6. |
36 and 52 |
The type of the clusters was based on direct or indirect connection to start nodes in each cluster. Each of the six clusters was based on start nodes 19, 80,07,59,37, and 36 respectively. Boundary nodes and other nodes that were connected to more than one start node were included in multiple clusters. Dangling nodes and unconnected nodes were not included in the clusters. A single node was chosen to represent nodes in dangling paths based on its centrality. The size of the clusters was dependent on the number of nodes connected to the hub. The selected clusters represent the flow of information among a network of individuals in a larger network of staff. (Mansi, V. R, 2018).
In the selection of the clusters, key nodes in each cluster were selected first. According to Xue li (2018) in organization network analysis, key nodes are nodes with the highest degree centrality, clique centrality, and Eigenvector centrality. Degree centrality is measured by the number of direct interconnections with other nodes. Other cluster nodes were then selected following their direct or indirect connection of the key node. This mode selection allows the key node to be the start node and the furthest connected nodes to mark cluster ends. Individual nodes could belong to more than one cluster provided connection exist. However, a single node could only be the hub of one cluster. Each cluster represents a group of staff that share information that are connected to single staff member either directly or indirectly thus creating a pool of information (Stasser, G. & Titus, W, 2005).
Dangling nodes were not included in the clusters. A dangling node is a node that has an inlet but does not have an outlet (Ipsen & Selee, 2008). Dangling nodes are not involved in the process of propagating information in the network. Any tacit information that dangling nodes contains already exist in the cluster. This is because dangling nodes are fed by a member of a cluster. Including dangling nodes in the cluster would be duplication of the emails included in the cluster. Nodes in dangling paths, however, were represented by one member of the dangling sub-cluster. Dangling paths are a network of directly or indirectly connected nodes to the hub through a single path. This was based on the fact that nodes dangling paths share information among themselves but only one node in the dangling path shares information with the rest of the network.
Scenario A
Unconnected nodes were not included in the cluster. Unconnected nodes represent the staff members that did not share or receive emails from any members of the cluster. Unconnected nodes were not connected directly or indirectly to any of the six cluster start nodes. Two or more unconnected nodes that shared emails among each other were also not included in the clusters. Unconnected nodes did not share new information with the rest of the network thus were not vital. It is also worthy to note that that unconnected not did also not receive any information from the clusters. They are therefore the least enlightened members of the staff members in the group under study in terms of shared information. Unconnected nodes represent members that were not involved in the daily activities of the business hence not useful in the decision-making process of the business (Hicks Patrick, 2013).
The nodes 19, 35, 80, 54 32, 0768, 66, 59,37,36,52 were selected to represent the six cluster of nodes. Nodes 19, 35, 80, 54, 68,59,36,52 were selected based on the degree of centrality. These were the nodes that were most interconnected nodes in each cluster. However, nodes 37 & 66 and 32 &07 were selected based on an eigenvector centrality and close clique centrality. Eigenvector centrality measures the importance of a node by the nodes it is attached to, a node is said to be of high importance if it is connected to a high scoring node (Nielsen, M. A. 2004). 66 is connected to 54 making it key. In close clique centrality, the importance of a node is measured by the importance of the networks it is connected to. 07 is connected to both the vital clusters with start nodes and 59 and 19.
The selected nodes the most important nodes based on different measures of centrality (Opsahl, T, Agneessens F & Skvoretz. J, 2010). They represent a pool of information of all the nodes under study collected through communication, in our case through emails. These were the most informed members in terms of shared information thus suited to give valuable input during the meeting. (Barney J. B, 2009)
The service staff members are involved in diverse forms of communication with different parties in the business. Based on the sizes of the concepts, the most significant form of communication is formal communication. Employees engage in formal communication with both the management and customers. In the Leximancer map, informal communication is represented by open communication. Staff members use open communication to among themselves and with customers. They most likely talk about their work experiences and the challenges they encounter while working. Open communication with customers is most likely about the customers’ opinion about the product.
According to the map, there is little direct communication between the management and customers. Management depends on staff members to link them to customers. Lack of communication between the managers and customers may affect the business negatively on two fronts (Boran, 2009). First and foremost, management lacks insight on how to better improve business services due its unfamiliarity with the service consumers. Secondly, customers may lose trust in the business. Consumers do not have a platform to air their grievances in case of harassment or poor services from staff members. Furthermore, customers cannot contribute their opinions on how the services offered can be improved to better suit their needs. The managers, therefore, do not deserve an incentive. The organization should alternatively use incentive funds to create a link between the two parties such as setting a direct line for customers to managers.
Scenario B
There is no proper support from the management to the staff. In any business, employees are a responsibility of management. The role management is to oversee, delegate and support staff members. Effective management of staff can only be achieved through nurturing and guiding of staff members in their duties as they develop their skills. Support from management creates a sense of importance in the employees (Barney, J. B., & Wright, P. M, 2008). A supportive management gives room for employees to openly communicate with managers and contribute ideas for the business without any fear. Lack of focus from the staff team can be attributed to the lack of support from the management. Due to this disconnect, management fails to recognize the personal strengths of its staff and the problems the face. Holland (2015) talks about the importance of monitoring staff and their communication.
The organization should not reinforce the role of management, alternatively, a self-managed team of employees should be introduced. A self-managed team is a group of employees with well-defined duties in an institution. The staff has the power to make its own decisions based on their own expertise and experiences (Humphrey, S. E. et al 2011). Since the teams deal with customers directly, self-managed teams are better suited to make sustainable decisions. Self- managed teams are also more flexible than using a management system since staff members operate on their own convenience and based on their personal strengths. On the side of the organization, self-managed teams reduce the cost of operations (Flory, 2009). According to the Leximancer map, staff engage in more open communication with the customers as opposed to management. This implies that using a self-managed team would improve customer satisfaction by allowing them to voice their opinions and grievances to a party that can adapt to their needs (Kaliannan, 2015).
Lack of links between open communication, staff and management concepts in the map indicates that there is no open the team and management. Open communication refers to when employees are allowed to freely express their thoughts good or bad without fear of retribution from the management. Open communication allows all members of the organization to exactly know what role they are supposed to play in order to achieve the set organization’s goals. In the case under study, the management and the team lacks cohesiveness as indicated by lack of open communication. This leads to lack of focus in the organization.
Customer service communication should be changed to one of greater openness. Customers should be allowed to give their honest reviews about the services offered by the organization Open communication would increase customers trust and satisfaction. Furthermore, the company’s services would improve thus attracting more customers.
Staff members play a role in communicating results. The nature of the results they may communicate are the sales results. Communicating results to the management allow the management to compare, analyze and make decisions based on the results or response to the product.
The management also communicates sales results to the entire organization. Staff members are able to gauge their overall contribution to the business and gauge it with previous periods.
CASE A
The management should offer the key member nodes a permanent role in the management. Key nodes members have cultivated good relationships with other members of staff. These members could utilize these relationships to help the company to frequently involve many staff members in the decision-making process of the company.
The business should also ensure all members of staff are engaged in business decisions by creating an enabling environment. (Beugré, C. D, 2006.).
CASE B
The organization should put strategies in place to shift customer communication to be more openness. As discussed above customers input would prove to be of tremendous value to the company. This could be achieved by actively engaging customers to contribute their opinions and creating a forum for reviews
The company should introduce a self-managed team. A self-managed team would benefit both the business and the customers in more than one way as discussed above.
Conclusion
For any business to achieve sustainable growth group decisions are of key importance. Group decisions, however, cannot be made without proper communication. Though there are many barriers obscuring good communication within an institution, communication can be monitored. Emerging powerful data analysis techniques that have been developed over the recent past allows business to monitor different aspects of their businesses for the smoother running of operations and better decision making. In scenario A, the company utilizes the Leximancer analytical software to round up the most connected members of staff to help shape the organization. The management values group decisions and engaging the staff members in an open communication for sustainable decision. In scenario B the management utilizes Leximancer analytical to monitor communication between customers, staff and management. It is a high time for businesses to adapt data analytics to give an insight into the communication between different parties in the organization for more sustainable decisions
References
Boran, F., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems With Applications, 36(8), 11363-11368. doi: 10.1016/j.eswa.2009.03.039
Beugré, C. D. (2006.). Organizational Conditions Fostering Employee Engagement: The Role of “Voice”. Handbook of Employee Engagement. doi:10.4337/9781849806374.00021
Flory, M. (n.d.). Managing a Self-Managed Team. Next Generation Business Handbook, 186-199. doi:10.1002/9780470172223.ch12
Hicks Patrick, J., Steele, J., & Spencer, S. (2013). Decision Making Processes and Outcomes. Journal Of Aging Research, 2013, 1-7. doi: 10.1155/2013/367208
Holland, P. J., Cooper, B., & Hecker, R. (2015). Electronic monitoring and surveillance in the workplace. Personnel Review, 44(1), 161-175. doi:10.1108/pr-11-2013-0211
HUMPHREY, S. E., HOLLENBECK, J. R., MEYER, C. J., & ILGEN, D. R. (2011). Personality Configurations in Self-Managed Teams: A Natural Experiment on the Effects of Maximizing and Minimizing Variance in Traits. Social Journal of Applied Psychology, 41(7), 1701-1732. doi:10.1111/j.1559-1816.2011.00778.x
Ipsen, I. C., & Selee, T. M. (2008). PageRank Computation, with Special Attention to Dangling Nodes. SIAM Journal on Matrix Analysis and Applications, 29(4), 1281-1296. doi:10.1137/060664331
Kaliannan, M., & Adjovu, S. (2015). Effective Employee Engagement and Organizational Success: A Case Study. Procedia – Social And Behavioral Sciences, 172, 161-168. doi: 10.1016/j.sbspro.2015.01.350
Liebowitz, J. (2005). Linking social network analysis with the analytic hierarchy process for knowledge mapping in organizations. Journal Of Knowledge Management, 9(1), 76-86. doi: 10.1108/13673270510582974
Mansi, V. R. (2018). Leadership Communications, Dialogue, and Communications Areas: New Paths for Employee Communications. Strategic Employee Communication, 147-154. doi:10.1007/978-3-319-97894-9_12
Nielsen, M. (2004). Optical Quantum Computation Using Cluster States. Physical Review Letters, 93(4). doi: 10.1103/physrevlett.93.040503
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245-251. doi:10.1016/j.socnet.2010.03.006
Philip Chen, C., & Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. doi:10.1016/j.ins.2014.01.015
Smith, K., & Tischler, R. (2015). Electronic Monitoring in the Workplace. Employment Relations Today, 42(1), 73-79. doi: 10.1002/ert.21491
Sotiriadou, P., Brouwers, J., & Le, T. (2014). Choosing a qualitative data analysis tool: a comparison of NVivo and Leximancer. Annals Of Leisure Research, 17(2), 218-234. doi: 10.1080/11745398.2014.902292
Stasser, G., & Titus, W. (1985). Pooling of unshared information in group decision making: Biased information sampling during discussion. Journal Of Personality And Social Psychology, 48(6), 1467-1478. doi: 10.1037//0022-3514.48.6.1467
Xue, H., Li, T., Luo, X., & Tian, Z. (2018). Identifying Key Nodes of Network Based on Subjective-Objective Weighting Method for Structural Holes. 2018 10Th International Conference On Intelligent Human-Machine Systems And Cybernetics (IHMSC). doi: 10.1109/ihmsc.2018.10191