Performance indicators of cryptocurrency teams: the effects of team boundary spanning, hierarchical stratification and intra functional diversity
Master thesis, Msc HRM
University of Groningen, Faculty of Economics and Business
Tom C. J. Leeflang Student number: 3523098
Petrus Campersingel 119F, 9713AG, Groningen Email: t.leeflang@live.nl
Supervisor: Thom de Vries
Abstract
Introduction
Most teams within companies do not work optimally without intergroup contact (Ancona & Caldwell, 1992; Gladstein, 1984). Teams can respond to environmental changes and solve complex tasks by sharing information with members outside of their own team, also known as boundary spanning. Marrone (2010) defines boundary spanning as a team’s efforts to establish and manage external linkages, this can occur within an organization (e.g., across marketing and manufacturing teams) or across organizational boundaries (e.g., to external customers, suppliers). More specific team boundary spanning behaviors include representing the team to stakeholders, coordinating task activities with other groups, and seeking information from outside experts (Ancona & Caldwell, 1992). One can look at boundary spanning activities on team level or on individual level. The team behaviors mentioned by Ancona & Caldwell (1992) are fundamental determinants for team performance (Gladstein, 1984).
boundary spanning activities. As can been seen, there is not a clear conclusion about the effects of IFD, nonetheless scholars state that IFD does have a significant impact on boundary spanning activities.
Even though boundary spanning has had some prior attention from scholars, none of the literature took the interaction between both antecedents mentioned into consideration. Therefore, studying this interaction could explain the ambiguity regarding the effect of IFD on boundary spanning behaviors. If a non-ambiguous conclusion about the effect of IFD can be drawn, the relationship between IFD and boundary spanning is better understood. This will assist scholars in future research concerning IFD and boundary spanning with its antecedents. With these insights a detailed model can be made about the effects of IFD and its relationship with boundary spanning. Furthermore, this study will contribute to the advance of models concerning team performance and effectiveness. De Vries et al. (2014) stated that contextual factors have an impact on the relationship between IFD and boundary spanning activities. They recommend that future research on boundary spanning and IFD should focus on the moderating effects between this relationship. Building forth on the research of De Vries et al. (2014), I will evaluate a potential factor that affects the relationship between IFD and boundary spanning activities, namely; hierarchical stratification, see Figure 1 for my conceptual model.
Figure 1. Conceptual model.
Team boundary
spanning
Team performance
A possibility was to study open source-based teams, which are teams where information about them should be accessible to everyone online. Accordingly, teams that are found are not based in just one company or even one country, the research will include a wide range of teams and therefore be more generalizable. A team has a high IFD when its members have been active in multiple functional domains. I hypothesize that a greater amount of boundary spanning is found when a team has a high amount of IFD. In addition to this, I hypothesize that the relationship between IFD and boundary spanning will be affected by hierarchical stratification. The steeper the hierarchy, the more the experience of a wide range of IFD is turned in to effective boundary spanning activities. Therefore, more boundary spanning activities are shown, due to the greater effectivity (DeChurch & Marks, 2006).
Theoretical framework Intra Functional Diversity
A definition for intra functional diversity (IFD) is one from Bunderson (2003), the definition is the distribution of an individual's work history across the different functional specializations that exist in an organization. De Vries et al. (2014) formulated it as follows “the extent to which members have accumulated work experiences across different functional domains relevant for the organization”. Practically, this means that a team with a low extent of IFD has members with experience in a low variety of functional domains. Whereas a team with a high extent of IFD has members with experience in multiple functional domains. For example, if a team has experience in the sectors marketing, finance and sales overall, their IFD is higher when compared to a team that only has experience in the sales sector. However, knowing what IFD is, does not give an answer to how it affects boundary spanning.
Bunderson (2003) explains this effect via two different power bases, namely the expert power base and the referent power base (French & Raven, 1959). An expert power base depends on the extent of knowledge about a topic or perception which is attributed by someone to another person. The other power base is the referent base, which means that a person identifies with another individual or group and therefore wants to be associated with them. In one environment an expert power base is more likely be utilized, whereas in another setting the referent base is more likely to be utilized. For example, when being a specialist the expert power base is most likely to be used, while the referent base is likely to be used when a person has a high amount of experience in multiple functional domains.
teams. The person will also understand their perspective more when in a conflict and this will smoothen the interaction between finance and sales, in this example. That is why a high IFD is desirable, since it contributes to more effective boundary spanning activities (De Vries et al., 2014; Bunderson, 2003). This will in turn lead to a higher organizational performance. However, when IFD is low, boundary spanning activities will be less efficient. Thus, consisting of a lower overall team performance. To conclude, I expect that;
Hypothesis 1. Intrapersonal functional diversity has a positive relationship with the performance of a team, this effect is mediated by the amount of boundary spanning.
Hierarchical Stratification as moderator
There has not been done research towards what kind of effect the hierarchy of a team has on the relationship between IFD and boundary spanning. Therefore, this study is aimed towards the hierarchical stratification of a team and its effects. Bunderson & van der Vegt (2017) defined stratification as the distribution of members across the distinct categories of a vertical difference (e.g., hierarchical level, pay grade, title). In other words, the distribution over the number of vertical layers that are present in a team based on several or one factor(s).
Honig, 2006) to a higher extent than non-managerial function employees. Crawford & Nonis (1996) suggest that boundary spanners should have considerable control over the setting of, and greater flexibility in the ways in which they accomplish their job assignments. This creates the opportunity for managers to utilize their experience (due to their high IFD) and thus engage in boundary spanning behaviors more effectively. Whereas non-managerial function employees have lesser of these opportunities.
On another note, people tend to only pay attention to others who control their outcomes (Fiske, 1993). Since employees with a managerial function rather control the outcomes of others than employees with a non-managerial function do, the outcome of boundary spanning activities have more effect. For example, when a manager engages in boundary spanning activities, others are likely to pay more attention to these behaviors. Because of this, the boundary spanning behaviors are perceived more and are more likely to be reacted to. The opposite is also likely, if a non-managerial function employee engages in these kind of behaviors, others are less likely to perceive these or react to it. In other words, employees with managerial functions have more effective boundary spanning behaviors than employees do without managerial functions. Because of this, the effect of a large amount of IFD on boundary spanning can be negated if an employee does not practice a managerial role.
layers, meaning more managers are appointed. To conclude, when a hierarchy is steep, the more effective boundary spanning behaviors are found when having a high amount of IFD. Whereas a flat hierarchy has less effective boundary spanning behaviors, even though the IFD of a person could be favorable. This because a steep hierarchy contains (more) managers than a flat hierarchy does. Furthermore, because more effective boundary spanning behaviors go along side more engagement in boundary spanning behaviors (DeChurch & Marks, 2006), I expect that;
Hypothesis 2. The relationship between IFD and the amount of boundary spanning is moderated by hierarchical stratification. The steeper the hierarchy is, the stronger this relationship becomes.
Defining Boundary Spanning
behaviors. Instead, a person engaging in these behaviors should have completing team goals in mind. Therefore, I will utilize this definition of boundary spanning, however, applied to team level.
The Effects of Boundary Spanning
There has been evidence that boundary spanning activities enhance performance. For example, Teigland and Wasko (2003) state that internal and external boundary spanning activities facilitate information trading, which in turn increases individual performance. Some studies have been conducted towards the effects on individual performance (Teigland & Wasko, 2003; Caldwell & O’Reilly III, 1982; Agnihotri, Krush, Trainor, & Krishnakumar, 2014), whereas other studies have been conducted towards team performance (Ancona & Caldwell, 1992; Drach-Zahavy, 2011; Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005; Marrone, 2010; de Vries et al., 2014). For example, Ancona & Caldwell (1992) state that there are a few typical activities a team can engage in to be classified as boundary spanning activities. These are: creating a picture of the outside environment (mapping), coordinating tasks and exchanging technical knowledge with other teams within the organization, consulting with outside experts and influencing the external environment. To summarize, when boundary spanning activities are high, team performance will increase. Whilst boundary spanning activities are low, team performance is lower, See Figure 1 for my complete conceptual model. I expect that;
Methods
The data was retrieved via open source-based teams, more specifically cryptocurrency teams. Information about these teams have to be accessible to everyone. These teams have to be transparent otherwise people will not use and buy the respective digital currency. When cryptocurrency teams are not transparent, people are not likely to trust the source, their intentions and the outcomes of the currency. Because of this, information about the team members, their history and the goals of these teams are accessible and were used for this research.
A cryptocurrency is a digital currency where encryption techniques are utilized to regulate the creation and the transfer of funds. However, this is done independently of a central bank, thus making it decentralized (Narayanan, Bonneau, Felten, Miller, & Goldfeder, 2016). Decentralized means that no single entity controls the data and every transaction is transparent for everyone, making fraud almost impossible. Because of this, this technique led to the success of digital cash, whereas other attempts did not prevail. People may also look at how much others own in terms of cryptocurrency value. Bitcoin was, in 2009, the first cryptocurrency made, and remains still one of the most famous. The cryptocurrency market is trending last couple of months, so research towards this topic is and will be relevant. Turpin (2014) even states that governments should study Bitcoin and regulate businesses that exchange in Bitcoin. They should however keep in mind that they should not try to stop or slow the growth of the currency itself (Turpin, 2014).
team, in order to keep the research feasible. Below I will elaborate on how I measured and coded the variables.
Boundary Spanning Activities
For team boundary spanning activities I looked at how many strategic partners a team has. I assumed that the cryptocurrency team in question interacts with its strategic partners, thus engaging in team boundary spanning activities. Also, creating and maintaining partnerships is a form of boundary spanning (Piercy, 2009; Williams, 2002). In order to determine their strategic partners, I had a look at their website and searched there. If they did not mention their partners here, I searched for the corresponding white paper. If that still did not give any valuable information, I looked at the accounts of the CEO and/or the founder(s) of the coin on LinkedIn and Twitter to obtain the data there. If nothing was found still, I assumed they did not have any strategic partners, thus their score for boundary spanning was zero.
Hierarchical Stratification
Following a President or a Chief Operating Officer (COO), who have the second-in-command titles (Hambrick & Cannella, 2004). I have contacted Helfat, Harris, & Wolfson in order to obtain the descriptions of the ranks they made for each title. Unfortunately, they did not have the data in possession anymore. So, in this research I used the following distribution. The highest rank is the CEO of the cryptocurrency. The second highest rank are members with officer in their title function, other than CEO (e.g. COO, CFO; Hambrick, Humphrey, & Gupta, 2015) and titles containing “leader”, “chief” or “manager” (e.g. marketing leader). This was however dependent on the situation. For example, when a team consisted of 5 members, and they had one CEO and four managers, then these managers do not lead any members with a lower hierarchical rank, thus not making them a chief, manager or leader. Then, remaining members are counted as the lowest rank. When this was done for every cryptocurrency, the hierarchical stratification was computed via Blau’s formula (1977): 1 − ∑pk2, where p is the proportion of the team in the kth category (with three different categories in this case). This resulted in an overall score for a team’s hierarchical stratification on a scale ranging from 0 (i.e., all members had the same hierarchical status) to a theoretical maximum of 0.66 (i.e., all team member’s hierarchical status were evenly distributed). A high score means that the hierarchy is steep, implying that the distribution of vertical ranks between employees is skewed. A low score means that there is little to no difference in the distribution of vertical ranks between employees.
Intra Functional Diversity
different domains, namely sales & marketing, manufacturing, distribution, service, personnel/HR, research & development, finance or accounting, administration and general management (Bunderson & Sutcliffe, 2002). To give this a score, I used the formula Blau (1977) suggested: 1 − ∑pi2. Where pi equals the percentage of a group whose dominant functional background is in the ith functional area (with nine different functions in this case). This resulted in an overall score for a team’s IFD on a scale ranging from 0 (i.e., all IFD within a team is based on only one functional domain) to a theoretical maximum of .89 (i.e., a team’s IFD is evenly distributed across all nine domains). A high score means that the team has a low average of functional backgrounds. A low score indicates that the team has a lot of functional backgrounds on average.
Team Performance
because the market shares were too little in order to conduct the analysis properly, specifically AMOS neglects small values.
Control Variables
I also took team size, coin age, average team career length and team gender into consideration. Because I only looked at eight team members per team, sometimes the first eight members mentioned were not representative for the team gender. Therefore, I looked at all the team members, divided the number of women by the total amount of employees. After that, I chose the closest representative proportion and noted that (e.g. 0.10 becomes 0.125, because that is a proportion that is found in the table of eight; 1/8).
Analysis
variance (R2adj) was taken into consideration when determining the best fitting model. Then a latent growth regression was conducted in order to analyze the effects of the moderator and mediator, thus testing the hypotheses.
Even though a latent growth regression consists of an intercept (which represents the first data point measured) and a slope value (which represents the growth or decline of the data points over time), I only focused on the slope value, since the intercept value has no meaning in this research. This because the performance of a team is not dependent on whether a team starts with a high or a low intercept, the slope contains this information. Moreover, the study variables were standardized at their means.
Results Descriptive statistics and model fit indices
Table 1
Pearson zero-order correlations among the study variables
Variable 1 2 3 4 5 6 7
1. Average performance 1
2. Boundary spanning .257* 1
3. Team career length .036 -.165 1
Table 2
Descriptives among the study variables
Variable M SD Max Min
Average performance* 1429.393803 4091.045173 228822.06692 0.0315384615 Boundary spanning -0.519444444 5.272700758 16.02500000 -4.97500000 Team IFD -0.006138889 0.1390294969 0.2737500000 -0.316250000 Stratification 0.0091388889 0.1797344761 0.3312500000 -0.338750000 Team gender 0.0034166667 0.1228889149 0.1150833333 -0.384916667 Coin age 3.011111111 14.18805832 45.60000000 -8.40000000 Team size -1.68056 8.220160 33.275 -12.725
Table 3
Values of the measuring models of fit and additional explained variance per added study variable
Model R2adj ∆R2adj p-value AIC NFI CFI RMSEA 𝜒² ∆𝜒² p-value df
Model 1 0.04 - .34 2594.11 0.63 0.64 0.44 2528.11 - .00 137
Model 2 0.04 0.00 .35 2622.87 0.63 0.64 0.42 2548.87 20.76 .00 152
Model 3 0.10 0.06 .14 2689.27 0.62 0.64 0.40 2619.27 70.40 .00 173
Model 4 0.12 0.02 .32 2716.02 0.62 0.64 0.38 2640.02 20.76 .00 192
Model 5 0.10 -0.02 .14 2731.57 0.62 0.64 0.36 2647.57 7.55 .00 210
Note. Model 1 = Effect of control variables on performance (only slope). Model 2 = Main effect of Intra Functional Diversity (IFD) on performance, Model 3 = Boundary spanning (BS; mediator) and team IFD on performance, Model 4 = BS, IFD and main effect of stratification (moderator) on performance and Model 5 = BS, IFD and interaction between IFD and stratification on performance.
Hypotheses testing
Table 4
Summary of Latent Growth Regression Analysis (N = 90) and effect values when the moderated effect is kept constant in three different categories
Model 5
Variable B SE(B)
Team BS¹ 0.00* 0.00
Team IFD² 2.72 3.88
Stratification² 7.44* 3.00
Stratification * Team IFD² -4.52 19.52
B p-value
Low -0,44 .11
Moderator effect Medium -0,32 .12
High -0,20 .33
Direct 1,63 .35
Discussion Theoretical contributions
Even though the model fits were not good, nor was one of them significant, the results still indicate fruitful findings. First of all, stratification showed a positive effect on boundary spanning, which is in line with literature (Manev & Stevenson, 2001). Furthermore, as indicated in Table 1, boundary spanning showed a positive correlation with performance, which is also in line with other studies (Ancona & Caldwell, 1992; Drach-Zahavy, 2011; Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005; Marrone, 2010; de Vries et al., 2014). Though, this is not representative for the entire study, since the performance value utilized was an average of the thirteen performance indicators. Moreover, IFD showed a positive relationship with team career length, which is logical because the more career length a team has, the more likely it is to have a larger amount of IFD. Lastly, team size correlated negatively with gender. As I obtained the data from the coins, I noticed that a lot of men were involved in these teams, resulting in lower levels of women. This is especially the case when the teams are of larger sizes, as is also indicated in the results. Meanwhile, gender correlated negatively with performance, which means that the more women a team contains, the lower the average performance is. As already indicated, the average performance is used, so this correlation might not be representative.
the less boundary spanning behaviors the person would practice. Which is counterintuitive, according to the theory section. However, stratification still positively influences organizational performance, which is in line with literature. For example, Halevy, Chou, & Galinsky (2011) proposed that hierarchies directly and positively influence performance. Also, according to Cantimur, Rink, & van der Vegt (2016), steeper status hierarchies increased team performance in teams working on tasks with lower complexity. This does not explain the inverted effect of IFD and BS, nonetheless, an effect was found, indicating that stratification might influence IFD and therefore might have an indirect impact on the boundary spanning activities of employees. Lastly, IFD showed a positive, but not significant, relationship with boundary spanning activities. Nonetheless, the fairly positive effect, could indicate a possible consensus about the ambiguous relationship of IFD and boundary spanning behaviors. This might change the way scholars look at the ambiguity of the effects of IFD on boundary spanning. To conclude, this study supports some findings in existing literature. However, a light has been shed on a new perspective concerning the relationship between IFD and boundary spanning. Stratification still might influence the relationship between IFD and boundary spanning, which could lead to a revise of the models regarding IFD. Also, future research could include hierarchical stratification as a variable in the research towards IFD and boundary spanning.
Practical implications
(Dollinger, 1984). Moreover, support has been provided for the utility of boundary spanning behaviors. Managers could implement regulations, so employees engage more in these kinds of behaviors. Furthermore, as Aldrich, & Herker argued (1977), a small organization is able to survive with a simple boundary spanning structure. Perhaps, larger companies could benefit from a structured boundary spanning model with roles and functions.
Limitations
A problem that occurred during this study was that the latent growth function of the performance of several teams did not have a linear slope, which was assumed in the analysis. For example, some teams had a quadratic function instead of a linear one. This skews the fit of the model, because a linear function does not capture quadratic data as good as a quadratic function. Because of this, the fit of the models might have been incorrect. Perhaps other estimates and significance values would have been found when corrected for this bias.
the measurement of stratification, heuristics were used to determine the rank of team members. Even though these ranks are based on other studies and frameworks, these might not have been fully correct and representative for the entire hierarchical stratification within a team. Lastly, this study is probably not generalizable to a broader setting, because the dataset was based on the cryptocurrency industry, which is a specific industry.
Directions for future research
First of all, in order to obtain more reliable results, the model fit has to be increased, in comparison with this study. Other studies could look at the fit of the data when exploring the options with quadratic and cubic functions. Others could also divide the data into categories, for example a linear, a quadratic and a cubic function group. Where the data that has a quadratic function, get paired with a quadratic model. If, eventually, the fit is decent, the analysis could be run again and indicate more reliable findings. Giving a more reliable and representative conclusion on this topic.
Additionally, I would recommend other studies, that are interested in studying this topic, to measure boundary spanning activities on individual level, or via a (semi-)structured interview that could be generalized to team level, as well as stratification and IFD. Not only is this data easier to obtain, but also heuristics do not have to be used, resulting in more reliable data. Furthermore, working with open source-based data might heavily influence the reliability of it. Lastly, other research could focus on other antecedents that might have an impact on boundary spanning, that have not been studied priorly.
Conclusion
References
Agnihotri, R., Krush, M. T., Trainor, K. J., & Krishnakumar, S. (2014). Satisfied and productive boundary spanners: a model of resiliency and customer expectations. Journal of Services Research, 14(2), 57-74.
Aldrich, H., & Herker, D. (1977). Boundary spanning roles and organization structure. Academy of management review, 2(2), 217-230.
Ancona, D. G., & Caldwell, D. F. (1992). Bridging the boundary: External activity and performance in organizational teams. Administrative Science Quarterly, 37, 634-665. Arbuckle, J. L. (2014). Amos (Version 23.0) [Computer Program]. Chicago: IBM SPSS.
Blau, P. M. (1977). Inequality and heterogeneity: A primitive theory of social structure (Vol. 7). New York, NY: Free Press.
Bunderson, J. S. (2003). Team member functional background and involvement in management teams: direct effects and the moderating role of power centralization. Academy Of Management Journal, 46(4), 458-474. doi:10.2307/30040638
Bunderson, J. S., & Sutcliffe, K. M. (2002). Comparing alternative conceptualizations of
functional diversity in management teams: process and performance effects. Academy Of Management Journal, 45(5), 875-893. doi:10.2307/3069319
Bunderson, J. S., & Van der Vegt, G. S. (2017). Diversity and Inequality in Management Teams: A Review and Integration of Research on Vertical and Horizontal Member Differences. Annual Review of Organizational Psychology and Organizational Behavior, 5, 13.1-13.7. Burke, L. A., & Steensma, H. K. (1998). Toward a model for relating executive career
Buyl, T., Boone, C., Hendriks, W., & Matthyssens, P. (2011). Top management team functional diversity and firm performance: The moderating role of CEO characteristics. Journal of management studies, 48(1), 151-177.
Buzzell, R. D., Gale, B. T., & Sultan, R. G. (1975). Market share-a key to profitability. Harvard business review, 53(1), 97-106.
Caldwell, D. F., & O'Reilly III, C. A. (1982). Boundary Spanning and Individual Performance: The Impact of Self-Monitoring. Journal Of Applied Psychology, 67(1), 124-127.
Cantimur, Y., Rink, F., & van der Vegt, G. S. (2016). When and why hierarchy steepness is related to team performance. European Journal of Work & Organizational Psychology, 25(5), 658-673. doi:10.1080/1359432X.2016.1148030
Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1(4), 421-483.
Crawford, J., & Nonis, S. (1996). The Relationship Between Boundary Spanners' Job
Satisfaction and The Management Control System. Journal of Managerial Issues, 8(1), 118-131. Retrieved from http://www.jstor.org/stable/40604093
DeChurch, L. A., & Marks, M. A. (2006). Leadership in multiteam systems. Journal of Applied Psychology, 91(2), 311.
Drach-Zahavy, A. (2011). Interorganizational teams as boundary spanners: The role of team diversity, boundedness, and extrateam links. European Journal of Work &
Dollinger, M. (1984). Environmental Boundary Spanning and Information Processing Effects on Organizational Performance. The Academy of Management Journal, 27(2), 351-368. de Vries, T. A., Walter, F., Van der Vegt, G. S., & Essens, P. J. (2014). Antecedents of
individuals' interteam coordination: Broad functional experiences as a mixed blessing. Academy of Management Journal, 57(5), 1334-1359.
Fiske, S. T. (1993). Controlling other people: The impact of power on stereotyping. American Psychologist, 48(6), 621-628. doi:10.1037/0003-066X.48.6.621
French, J. R., Raven, B., & Cartwright, D. (1959). The bases of social power. Classics of organization theory, 7, 311-320.
Gabele, E., Dunbar, R., & Bresser, R. (1981). The Management of Change. International Studies of Management & Organization, 11(1), 56-74. Retrieved from
http://www.jstor.org/stable/40396886
Gladstein, D. (1984). Groups in context: A model of task group effectiveness. Administrative Science Quarterly, 29, 499–517.
Halevy, N., Chou, E. Y., & Galinsky, A. D. (2011). A functional model of hierarchy: Why, how, and when vertical differentiation enhances group performance. Organizational
Psychology Review, 1(1), 32-52.
Hambrick, D. C. & Cannella, A. A. (2004). CEOs who have COOs: Contingency analysis of an unexplored structural form. Strategic Management Journal, 25(10), 959-979.
Hambrick, D. C., Humphrey, S. E., Gupta, A. (2015). Structural interdependence within top management teams: a key moderator of upper echelons predictions. Strategic
Hayes, A. F. (2013). Mediation, moderation, and conditional process analysis. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach edn. New York, NY: Guilford Publications.
Helfat, C., Harris, D., & Paul J. Wolfson. (2006). The Pipeline to the Top: Women and Men in the Top Executive Ranks of U.S. Corporations. Academy of Management Perspectives, 20(4), 42-64. Retrieved from http://www.jstor.org/stable/4166270
Honig, M. (2006). Street-Level Bureaucracy Revisited: Frontline District Central-Office
Administrators as Boundary Spanners in Education Policy Implementation. Educational Evaluation and Policy Analysis, 28(4), 357-383. Retrieved from
http://www.jstor.org/stable/4121790
Joshi, A., Pandey, N., & Han, G. (2009). Bracketing team boundary spanning: An examination of task-based, team-level, and contextual antecedents. Journal of Organizational Behavior, 30(6), 731-759.
Manev, I. M., & Stevenson, W. B. (2001). Balancing Ties: Boundary Spanning and Influence in the Organization's Extended Network of Communication. Journal of Business
Communication, 38(2), 183-205.
Marks, M. A., Dechurch, L. A., Mathieu, J. E., Panzer, F. J., & Alonso, A. (2005). Teamwork in Multiteam Systems. Journal of Applied Psychology, 90(5), 964-971.
doi:10.1037/0021-9010.90.5.964
Marrone, J. A., Tesluk, P. E., & Carson, J. B. (2007). A multilevel investigation of antecedents and consequences of team member boundary-spanning behavior. Academy of
Management Journal, 50(6), 1423-1439.
Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton, NJ: Princeton University Press.
Piercy, N. F. (2009). Strategic relationships between boundary-spanning functions: Aligning customer relationship management with supplier relationship management. Industrial Marketing Management, 38(8), 857-864.
Teigland, R., & Wasko, M. M. (2003). Integrating Knowledge through Information Trading: Examining the Relationship between Boundary Spanning Communication and Individual Performance. Decision Sciences, 34(2), 261.
Turpin, J. (2014). Bitcoin: The Economic Case for a Global, Virtual Currency Operating in an Unexplored Legal Framework. Indiana Journal of Global Legal Studies, 21(1), 335-368. doi:10.2979/indjglolegstu.21.1.335
Williams, P. (2002). The competent boundary spanner. Public administration, 80(1), 103-124. Williams, P. (2012). The role and competencies of boundary spanners. In Collaboration in
