(NCU) Propose and Justify a Research Method and Design

Propose and Justify a Research Method and Design 
BTM 8103, Assignment 8 
DuBose, Justin Z. 
Dr. Robert Levasseur 
29 April 2018 

​E-leadership & Virtual Teams 
E-leadership is an academic field of study that has emerged since the turn of the millennium (Savolainen, 2014) which involves organizational leadership of highly technological structures stretched over different cultures and geographic regions (Avolio, 2014).  These widely dispersed organizational structures led to the advent and implementation of virtual teams (Lilian, 2014).  With this growing organizational structure of dispersed virtual team members comes new, unique, and difficult leadership challenges which are addressed by the e-leader (Hoch and Kozlowski, 2016). 
Liao (2017) defined virtual teams as “a collection of individuals who work on tasks that share varying degrees of interdependence and mutual accountability to accomplish a common goal.”  While virtual teams are dynamic and take many forms, research has highlighted several commons factors which impact how these teams should be led.  For example, Cheshin et al. (2013) found that most teams are partially, rather than exclusively, virtual.  In studying the nature of dispersion amongst virtual teams, Krumm et al. (2013) identified cultural dispersion as the most common dimension of virtual teams.  The organizational e-leader, then, is likely to lead a culturally diverse, partially virtual team. 
In their study of virtual teams, Gilson et al. (2015) identified leadership as one of the most pressing themes in research on virtual teams and considered e-leadership of virtual teams an opportunity for future research.  Hill & Bartol (2016) found that effective e-leadership of virtual teams empowers team members by providing collaboration between e-leader and team member as well as collaboration between fellow team members.  Hill & Bartol (2016) also found that virtual collaboration contributes to team performance, and that team performance is also enhanced when e-leaders interact with individual team members.  Writing about collaboration between e-leader and virtual team members, Liao (2017) notes that current literature does not address the process by which the e-leader interacts with individual virtual team members in a way that builds and maintains relationships. 
Problem Statement 
The research problem addressed in this research study is: given the cultural (Krumm et al., 2013) and geographic (Avolio, 2014) dispersion of virtual teams and the accompanying technological and organizational leadership challenges (Lilian, 2014), what are the effects of periodic personal interaction by the e-leader with individual virtual team members on overall team cohesion and performance?  This problem examines three types of personal interaction between e-leader and virtual team members – face-to-face meetings, professional development, and individual coaching sessions – and their impact on team cohesion and performance.  This study builds upon and develops current literature, specifically Hill & Bartol (2016) and their conclusion that team performance is enhanced further when e-leaders interact with individual team members.  This study also addresses gaps in the existing literature regarding leadership of virtual teams (Gilson et al., 2015) and how e-leaders can positively develop and maintain relationship with individual virtual team members (Liao, 2017). 
Purpose Statement 
The purpose of this study is to examine three types of e-leader/individual virtual team member interaction and the effect of each on virtual team cohesion and performance.  By studying the interactions of face-to-face meetings, professional development, and individual coaching sessions between the e-leader and individual virtual team members, this study will provide e-leaders with research to positively improve their virtual team performance and cohesion between leader and member as well as amongst team members. 
Research Questions 
To adequately address the research problem and fulfill the research purpose, the following research questions are posed and answered throughout this study: 
1.  How do face-to-face meetings between e-leader and individual virtual team members correlate to an increase in team member interactions?   
2. What are the effects of professional development sessions between e-leader and individual virtual team members on team performance? 
3.  In what ways do individual coaching sessions between e-leader and individual team members correlate to improved team performance? 
Research Hypotheses 
In formulating hypotheses for these research questions, existing literature provided some clues as to what this study may conclude.  Hill & Bartol (2016) concluded that team performance is enhanced further when e-leaders interact with individual team members and empower them to grow and succeed.  Liao (2017) likewise concluded that e-leadership is complex and requires additional effort from leaders to achieve results from team members. Based on those conclusions and results, the research hypotheses are as follows: 
H1: Firstly, it is hypothesized that each of these three efforts by the e-leader (face-to-face meetings, professional development, and individual coaching sessions) will positively contribute to team cohesion and performance.   
H2: Secondly, it is hypothesized that both individual coaching sessions and professional development will yield greater results in team cohesion and performance than face-to-face meetings.  This hypothesis is based in part on the work of Hoch & Kozlowski (2016) who concluded that direct relationships between leader and team member positively contribute to team performance.   
H3: Thirdly, it is hypothesized that these direct interactions, which have a specific goal of empowering team members with coaching and professional development from the e-leader, will demonstrate a greater return in cohesion and productivity than the more ambiguous agenda of face-to-face meetings. 
Research Methodology 
Participants in this experimental study are employees of a multi-billion dollar diversified financial services company.  The population sample for this study are twenty-five members of a regionally distributed virtual team working under one regional manager.  For the purposes of this study, this regional manager will be identified and referred to as the e-leader.  These virtual team members are dispersed over a ten-state area and their regular interaction includes a weekly video-conference virtual meeting and a monthly physical meeting with the e-leader.  The weekly virtual meetings last approximately two hours and the monthly physical meetings last two business days.  The purpose of these meetings is to discuss sales numbers and targets from the individual virtual team members to the e-leader and guidance and direction from the e-leader to the individual virtual team members.  Prior to this research study, there were no regular individual interactions between e-leader and individual virtual team members.  The only individual interactions that occurred resulted from either disciplinary action needing to be taken or merit-based awards or promotions being received by team members.  The introduction of a new variable of individual meetings between e-leader and team members into routine business life will provide the basis for this experimental design. 
Research Design 
This study separated team members into five groups based upon geographic location with each team having members distributed over a two-state area and being composed of five team members.  These groups were labeled accordingly with corresponding roman numerals of I, II, III, IV, and V.  During the three-month period of this study, the e-leader met bi-weekly with members of each group for face-to-face meetings, professional development, and individual coaching sessions.  The regional manager employed an administrative assistant to serve as the liaison between the e-leader and individual virtual team members to arrange which type of interaction they preferred.  These interactions were labeled accordingly as f (face-to-face), d (professional development), and c (coaching).  The team members responded to questionnaires to record the type and frequency of individual interaction between the e-leader and individual team members of each of the five virtual teams.  These individual interactions between e-leader and individual team members continued for the duration of the three-month study. 
Justification of Research Methodology & Design 
  This study is not to discover the motivations, emotions, and psychological rationale behind behavioral change but simply to track whether behavioral change results from individual interactions between e-leader and virtual team members.  Mertens (2015) noted that qualitative research utilizes the researcher as the primary means of data collection, whereas quantitative methodology utilizes other means (survey, interview, questionnaire) for data collection.  As this research study will utilize questionnaires from the researcher to the respondents for reporting behavioral change in sales data and personal interactions with fellow team members, the study, including the methodology and design, is quantitative in nature and appropriate for this experimental research. 
Additionally, a quantitative design is suitable because the research is objective and quantifiable in nature based upon data gathered during the study. Cozby (2014) noted that quantitative researchers frequently employ surveys or questionnaires to participants as the preferred method of data collection.  The following section, which explains data collection and analysis, justify both the questionnaire data collection method and the statistical analysis associated with quantitative research.  
Data Collection & Analysis 
Data collection for this study was accomplished through interviews with individual team members in the form of questionnaires.  These questionnaires are digital, and respondents were asked to fill them out and submit them to the researcher online.  This method of data collection by conducting interviews is the most common data collection methodology employed in current literature on e-leadership (Cheong, 2016) ; (Chua, 2017) ; (Kiesenbauer, 2015) ; (Sarros, 2014) ; (Savolainen, 2014).  The interviews for this study take the longitudinal approach which will tracks responses of virtual team members over a three-month period (Mertens, 2015).  The initial interviews were conducted at the outset of the study with two subsequent interviews being conducted once per month for a total of three interviews during a three-month period.   
Questionnaires are comprised of twenty questions, and respondents answered the same questions for each of the three questionnaires they complete.  The questionnaire begins by asking the respondent to identify which virtual team they are on, either I, II, III, IV, or V.  They were then asked to specify the type of individual interaction between team member and e-leader (f, d, or c) and the frequency at which they met.  Team members then responded to closed questions regarding performance standards (financial figures of sales, average response time, numbers and types of services sold) and open-ended questions regarding their own perception of how individual interaction with the e-leader impacted those performance standards.  Did those individual interactions instill confidence?  In what ways were those interactions helpful?  Name one specific interaction that took place during or after those interactions which benefitted you as a member of this team.  Which type of interaction was the most helpful.  Why?  Both the closed and open-ended questions in this section of the questionnaire are designed to measure individual interactions between e-leader and team members and their impact on team performance. 
A subsequent set of questions was then asked to team members regarding team member interactions following the same format.  In this section, team members responded to closed questions regarding interaction and cohesion with other team members (number of daily interactions, length of interaction, virtual or personal interaction) and open-ended questions regarding their own perception of how individual interaction with the e-leader impacted cohesion and team interaction.  Did your individual interactions with the e-leader help resolve team member conflict?  In what ways were those interactions helpful?  Name one specific interaction that took place during or after those interactions which positively contributed to team cohesion.  Which type of interaction was the most helpful.  Why? 
At the end of the three-month period when all questionnaires had been submitted by respondents, data was collected from the three questionnaires for synthesis and analysis by the researcher.  Three charts, one for each questionnaire, will be constructed.  Data was inserted onto the x-axis of each chart by virtual team (I, II, III, IV, or V) and onto the y-axis by type of individual interaction (f, d, or c).  Data received from the administrative assistant regarding the frequency and type of individual interaction by team members was then inserted into the table for analysis.  At this point, our tables are constructed as laid out below, with numbers of virtual team members on each team being aligned with the type of interaction.  Each table represents a one-month period of time based on data reported by the respondents to each of the three questionnaires.   
Questionnaire 1 
I 
II 
III 
IV 
V 

F 
3 
5 
2 
4 
4 

D 
2 
4 
1 
3 
4 

C 
4 
4 
2 
3 
1 
 
Questionnaire 2 
I 
II 
III 
IV 
V 

F 
4 
2 
1 
3 
3 

D 
2 
4 
4 
5 
2 

C 
3 
4 
4 
2 
3 
 
Questionnaire 3 
I 
II 
III 
IV 
V 

F 
2 
3 
1 
4 
2 

D 
5 
3 
1 
4 
3 

C 
4 
4 
4 
1 
3 
 
Once this step is complete, an additional step of analysis was to construct a second and third table with data from the closed and open-ended questions from the questionnaire regarding both team performance as well as team interactions.  The numbers in this second table are the cumulative team numbers for sales and services sold for the observation period and the average response time per team member to resolving customer issues per team member for performance standards.  Likewise, the frequency of team interaction is the cumulative team numbers for interactions with other team members for the observation period and the average length of each interaction as reported by team members for interaction standards.  An example of these two tables is recorded below.  
Questionnaire 1 Performance 
I 
II 
III 
IV 
V 

Sales 
$10,000 
$8,000 
$11,500 
$9,100 
$8,000 

Response time 
14 hours 
10 hours 
7.5 hours 
16 hours 
4 hours 

Services 
6 
5 
7 
6 
5 
 
Questionnaire 1 Interaction 
I 
II 
III 
IV 
V 

Frequency 
10 
7 
14 
12 
18 

Length (min) 
4 
6 
12 
18 
6 

Type 
V 
V 
P 
V 
P 
 
The data from these two tables was synthesized to answer the research questions.  What does the data lead us to conclude about how face-to-face meetings between e-leader and individual virtual team members correlate to an increase in team member interactions?  What does the data lead us to conclude about the effects of professional development sessions between e-leader and individual virtual team members on team performance?  What does the data lead us to conclude about the ways in which individual coaching sessions between e-leader and individual team members correlate to improved team performance?  These questions were answered by this third and final set of tables which would show either an increase in productivity, no change in productivity, or a decrease in productivity from month-to-month over a three-month period.  The same sequence of analysis took place for team interaction and cohesion with closed questions providing the data for interactions and the open-ended questions providing the data for cohesion.   
This final step in data analysis takes the number of cumulative team sales (s) for the observation period and divide it by the number of individual team members who interacted with the e-leader in either face-to-face meetings (Fs), professional development (Ds), or individual coaching (Cs).  Individual Performance (IP) is then calculated by dividing the team numbers for a given data set (sales, response time, and services sold) by the number of team members who experienced individual interaction with the e-leader during that observation period.  Team Performance (TP) numbers are categorized by the type of individual interaction – TP(F), TP(D), or TP(C) – to isolate team performance by the type of individual interaction between team member and e-leader to determine if and to what degree various interactions have on team performance.  This data set is calculated by adding the numbers in each category of meeting type.  An example of this third and final set of tables are below, with data from questionnaire 1 (Q1) and 2 (Q2) followed by a third table which shows the change in data (Δ) from Q1 to Q2.  Data for team interaction and cohesion follow the same format, but are not included here due to space limitations. 
  
Q1 
Team I 
Performance 
Sales 
(10,000) 
response time (14 min) 
services  
sold (6) 
Team Performance 

F (3) 
Fs = 3,333.33 
Fr = 4.667 
Fv = 2 
TP(F) = 3,339.997 

D (2) 
Ds = 5,000 
Fr = 7 
Fv = 3 
TP(D) = 5,010 

C (4) 
Fs = 2,500 
Fr = 3.5 
Fv = 1.5 
TP(C) = 2,505 

Individual Performance 
IP(s) =  $1,111.11 
IP(r) =  
1.55 minutes  
IP(v) =  
0.667 
 
 
Q2 
Team I 
Performance 
Sales 
(12,000) 
response time (14 min) 
services  
sold (7) 
Team Performance 

F (4) 
Fs = 3,000 
Fr = 3.5 
Fv = 1.75 
TP(F) = 3,005.25 

D (2) 
Ds = 6,000 
Fr = 7 
Fv = 3.5 
TP(D) = 6,010.5 

C (3) 
Fs = 4,000 
Fr = 4.667 
Fv = 2.33 
TP(C) = 4,006.997 

Individual Performance 
IP(s) =  $1,333.33 
IP(r) =  
1.55 minutes  
IP(v) =  
0.778 
 
 
Data Change (Q1-Q2) 
Team I 
Performance 
Sales 
 
response time 
services sold 
Team Performance 

F 
ΔFs = -333.33 
ΔFr = -1.167 
ΔFv = -0.25 
ΔTP(F) = -334.747 

D 
ΔDs = 1,000 
ΔFr = 0 
ΔFv = 0.5 
ΔTP(D) = 1,000.5 

C 
ΔFs = 1,500 
ΔFr = 1.167 
ΔFv = 0.83 
ΔTP(C) = 1,501.997 

Individual Performance 
ΔIP(s) =  $222.22 
ΔIP(r) =  
0 minutes  
ΔIP(v) =  
0.111 
 
 
This hypothetical data set led the researcher to conclude that individual performance increases in sales and numbers of services sold but not in response time when the e-leader meets individually with virtual team members for face-to-face-meetings, professional development, and individual coaching.  Overall team performance increases with individual professional development and coaching, but decreases with face-to-face meetings between e-leader and individual virtual team members.  Consequently, the data leads us to reject our original hypothesis of every area – sales, response time, and services sold – being noticeably improved by individual interactions.  However, the data leads us to fail to reject our original hypothesis that individual professional development and coaching sessions would yield greater improvement than face-to-face meetings between e-leader and individual virtual team members.  
Reliability 
To ensure reliability of data and conclusions, this study employed test-retest reliability (Dane, 2011).  Data collected from all questionnaires was verified and tested multiple times to ensure reliability of both data and calculations.  Furthermore, respondents in this longitudinal study were tested and re-tested after one month to ensure that enough time passed so that the memory of the respondents would not impact the reliability of numbers which were being reported on questionnaires (Dane, 2011). 
Validity 
To ensure validity of data and conclusions, several steps were taken by the researcher to include face validity (Dane, 2011) of performance standards.  Research was designed to measure team performance standards of a virtual team in the financial services industry.  Therefore, the measurables chosen to ensure validity were number of financial services sold, the amount of response time for service requests, and the total number of sales in dollars by each team. 
An additional but related step to ensure validity was the use of concurrent validity (Dane, 2011) by the researcher.  This was accomplished by using the existing measurements of individual and team performance – sales numbers, response time, services sold – utilized by the company.  This both validates results to the manager and the company as well as to all in the industry who use such standards to measure results.  
Ethical Considerations 
Dane (2011) notes that research serves “four masters”: the researcher, clients, team members, and administrators.  In this study, to ensure ethical research as well as balancing the interests of each group involved in the research, several ethical considerations were employed by the researcher.  Firstly, participation in the research was voluntary.  The company, the regional manager, and the virtual team members all provided voluntary consent to participate in this study.  Secondly, the identity of the company as well as the individuals were kept anonymous throughout the research.  Additionally, results were measured by team numbers and not by individual.  Therefore, individual performance numbers were not reported in the study.  When individual performance numbers were calculated, the number was the measurable data divided by the total number of team members participating. 
Limitations & Biases 
Limitations of this study include the size of the virtual team (25 members), the singular focus within the business field of financial services, as well as the geographic limitations of the ten-state area.  Additional research could study larger virtual teams, other areas of business beyond financial services, and geographic areas beyond the ten-state area of the eastern United States. 
Steps were taken by the researcher to ensure that biases would not affect data or research conclusions.  Selection bias (Pannucci, 2010) was avoided by including all members of the same virtual team, and data was reported by respondents and verified by the regional manager.   
 
 
References 
 
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