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Signature Assignment
BTM 7303, Assignment 12 DuBose, Justin Z. Dr. Susan Petroshius 26 December 2017 Introduction This paper serves as the signature assignment for this course on Research Methods. This is a final proposal for a hypothetical research study within the field of e-leadership and virtual teams. Research Problem 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 have 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 must be 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 being addressed in this 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 will examine 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 purposes to 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 & Hypotheses 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? 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, 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. Furthermore, it is predicted 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. 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 & Design 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. 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. 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. 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 Avolio, B., Sosik, J., Kahai, S., Baker, B. (2014). "E-leadership: Re-examining transformations in leadership source and transmission". The Leadership Quarterly, 25(1), 105-131. Retrieved December 26, 2017. Cheshin, A., Kim, Y., Nathan, D. B., Ning, N., & Olson, J. S. (2013). “Emergence of differing electronic communication norms within partially distributed teams”. Journal of Personnel Psychology, 12, 7–21. Retrieved December 26, 2017. Chua, Y.P., & Chua, Y.P. (2017). How are e-leadership practices in implementing a school virtual learning environment enhanced? Computers & Education, 109, 109 –121. Retrieved December 26, 2017. Dane, F.C. (2011). Evaluating research: Methodology for people who need to read research. Thousand Oaks, CA: Sage Publications. Gilson, L. L., Maynard, M. T., Young, N. C. J., Vartiainen, M., & Hakonen, M. (2015). “Virtual teams research 10 years, 10 themes, and 10 opportunities”. Journal of Management, 41(5), 1313–1337. Retrieved December 26, 2017. Hill, N. S., & Bartol, K. M. (2016). “Empowering leadership and effective collaboration in geographically dispersed teams”. Personnel Psychology, 69, 159–198. Retrieved December 26, 2017. Hoch, J. & Kozlowski, S. (2014). “Leading Virtual Teams: Hierarchical Leadership Structural Supports, and Shared Team Leadership”. Journal of Applied Psychology, Vol. 99, No. 3, 390–403. Retrieved December 26, 2017. Kiesenbauer, J. & Zerfass, A. (2015). 'Today's and tomorrow's challenges in public relations: Comparing the views of chief communication officers and next generation leaders'. Public Relations Review, 41(4), 422-434. Retrieved December 26, 2017. Krumm, S., Terwiel, K., & Hertel, G. (2013). “Challenges in norm formation and adherence”. Journal of Personnel Psychology, 12, 33–44. Retrieved December 26, 2017. Liao, C. (2017). ' Leadership in virtual teams: A multilevel perspective'. Human Resource Management Review 27, 648–659. Retrieved December 26, 2017. Lilian, S.C. (2014). 'Virtual teams: opportunities and challenges for e-leaders'. Contemporary Issues in Business, Management and Education, 110, 1251 - 1261. Retrieved November 20, 2017. Mertens, D.M. (2015). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Thousand Oaks, CA: Sage Publications. Pannucci, C.J. & Wilkins, E.G. (2010). 'Identifying and Avoiding Bias in Research'. Plast Reconstr Surg, 126(2), 619-625. Retrieved December 26, 2017. Sarros, J. C., Luca, E., Densten, I., & Santora, J. (2014). Leaders and their use of motivating language. Leadership & Organizational Development Journal, 35(3), 226-240. Retrieved December 26, 2017. Savolainen, T. (2014). Trust-Building in e-Leadership: A Case Study of Leaders' Challenges and Skills in Technology-Mediated Interaction. Journal of Global Business Issues, 8(2), 45-56. Retrieved December 26, 2017.
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Participant Perspectives and the Mixed Methods Approach
BTM 7303, Assignment 11 DuBose, Justin Z. Dr. Susan Petroshius 18 December 2017 What did you think of the length of each survey instrument? Was there a point at which you began to feel tired or paid less attention to the items? The length of each survey instrument was short enough to where I did not lose focus during the process but long enough to gather the desired information. While there was not a certain point at which I began to pay less attention or lose interest, I did notice a psychological shift on the third page of survey questions. This shift, I concluded, was the result of not knowing the number of pages and questions involved before beginning to respond to the survey questions. The uncertainty caused me to rush through the questions at a hurried pace, not knowing how many questions remained and how much time would be required to complete the survey. This affects my potential study in that it has enhanced my understanding of the importance of clearly communicating the length and depth of the survey questions being asked of the respondents. Not only will this keep respondents from losing focus and half-heartedly answering questions, but it will also ensure that my own conclusions are based upon the most thoughtful answers to survey questions. What did you think of the open-ended questions? Were they clear? Did they seem leading? Did they seem concise or too wordy? The open-ended questions were mixed in that some were clear and easy to answer while others were very ambiguous. For example, the first open-ended question in the survey asked me to write a metaphor to describe the climate of Northcentral University. While the question itself called for creativity, and stated as much in the wording of the question, it was nonetheless confusing. If the question was designed to understand my perception of the climate of Northcentral University, it would have been clearer to perhaps ask me to select a number from one to ten. This would have not only been less confusing, but it also would have provided numerical data for the researcher to more objectively understand student perceptions regarding school climate. The second and third open-ended questions were less confusing, even asking for specific examples of what made the student feel a certain way (connected to faculty or other students). I did not understand this to be a leading question, and thought the wording of the question accomplished the goal of forcing the student to be precise with their response. I also felt that the data gathered was likely to be valuable. Did you prefer completing the closed questions (or multiple-choice questions) or the open-ended questions? Why? I personally preferred to complete the closed questions as opposed to the open-ended questions simply due to the clarity and conciseness of the closed questions. For most questions, there were several options dealing with either frequency of events (almost always to almost never) or level of agreement or disagreement with a statement (strongly agree to strongly disagree). There was little ambiguity in the closed questions and the potential responses, though limited, were appropriate for the questions being asked. Which methodology do you believe is most useful in the examination of each variable/construct? Why? Though this survey utilized mixed methodologies, I believe the most useful was the quantitative methodological approach as opposed to the qualitative approach. The qualitative questions, while allowing for a wider variety of responses, are more difficult to categorize and analyze due to the subjective nature of the responses. Quantitative approaches permit each variable to be objectively weighted and quickly and easily categorized by the researcher. In examining each variable within a study, quantitative methodologies provide not only a more expedient examination process but a more useful process as well. How might this experience influence you when designing your own research? This assignment was very helpful in thinking through my own research design. Before beginning this class and, more specifically, completing this assignment, my inclination was toward a qualitative methodology. My thinking process was that it would allow me, the researcher, to get information that would probe deeper than simple numbers and statistics. However, after being on the receiving end of a survey (in addition to the other assignments in this class) which provided both quantitative and qualitative questions, I can see the research benefits of a quantitative approach and how they provide greater benefit to the research than a qualitative approach. My thinking has evolved to conclude that quantitative questions and a quantitative design would be the best approach for my own research study. Reflection on Mixed Methods Design There are certainly benefits to a mixed methodology approach to research design. Firstly, a mixed methods approach to research design allows the researcher to broaden their scope of information and data gathered from the research subjects. Mertens (2015) acknowledges this benefit of a mixed methods approach by saying that mixed methods researchers "seek a common understanding through triangulating data from multiple methods or to use multiple lenses simultaneously to achieve alternative perspectives that are not reduced to a single understanding." For example, in the survey completed this week there were questions aimed at developing an understanding of student perceptions of organizational culture at Northcentral University. Questions were designed to collect both quantitative data (I feel a spirit of community between the faculty and myself while I am working on my coursework) as well as qualitative data (The climate at Northcentral University is like…). This approach allows researchers not only to gather statistical data about the research problem, but also data “outside the box” from those most directly affected by the problem. This mixed methods approach then, combines both the benefits of a quantitative and qualitative approach within the same study. Johnson & Onwuegbuzie (2004) describe this unique benefit of the mixed methods approach to research design by noting that this methodology is exclusively able to “draw from the strengths and minimize the weaknesses of both in single research studies and across studies.” This benefit is only available in a mixed methods approach to research design and eliminates certain drawbacks or shortcomings from an either exclusively quantitative or qualitative research design and methodology. A further benefit to a mixed methods approach to research design is that it serves as a qualifier for the type of research being conducted. Bryman (2006) noted that classifying research as employing a mixed methods approach “conveys a sense of the rigour of the research and provides guidance to others about what researchers intend to do or have done (for example, funding bodies and journal editors).” Similarly, Creswell et. al (2003) suggested that the nomenclature of “mixed-methods” itself brings about certain advantages such as conveying research intentions to prospective readers. While these benefits exist within a mixed methods approach, there are certain drawbacks as well. A mixed methods approach would prove more difficult to the researcher in ensuring that his/her research questions were being directly addressed. Onwuegbuzie & Leech (2006) address this when they say that, “forming research questions is much more difficult in mixed methods studies than in monomethod (i.e., quantitative or qualitative) investigations because it involves the formation of both quantitative and qualitative research questions within the same inquiry.” In other words, a monomethod study ensures that all research questions are designed similarly, but this is not the case in a mixed approach. Survey questions that are all selected by the researcher and are quantitative in nature, for example, provide a greater degree of certainty to the researcher that their research questions are being addressed in a way that benefits the research and helps solve the research problem. This same line of reasoning would also prove true with a research design that is entirely qualitative in nature. However, a mixed methods approach to research design may provide two different types of data which may or may not directly address the questions being asked by the researcher. Similarly, the responses may or may not help solve the research problem. In reflecting on the potential benefits and drawbacks of a mixed methods approach, I have concluded that, while undeniable benefits exist, a quantitative approach is more suitable to the research design I am seeking to employ. A mixed methods approach is not only more tedious for the researcher but, more importantly, as I noted earlier, it allows input from research subjects which may not contribute to answering the research questions or solving the research problem. It is theoretically possible to list qualitative questions in your survey and receive no feedback which is helpful or beneficial. Within a quantitative research design, the researcher maintains control over both the questions and responses to ensure that all feedback received is in line with the intent of the research and positively contributes to answering the research questions and solving the research problem. References Bryman, A. (2006). “Integrating quantitative and qualitative research: How is it done?”. Qualitative Research, Vol 6(1), Feb, 2006. pp. 97-113. Retrieved December 18, 2017. Creswell, J.W. (2003) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd edition). Thousand Oaks, CA: Sage. Johnson, R. & Onwuegbuzie, A. (2004). “Mixed Methods Research: A Research Paradigm Whose Time Has Come” Educational Researcher, v33 n7 p14-26 Oct 2004. 13 pp. Retrieved December 18, 2017. Mertens, D.M. (2015). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Thousand Oaks, CA: Sage Publications. Onwuegbuzie, A. & Leech, N. (2006). “Linking Research Questions to Mixed Methods Data Analysis Procedures” Qualitative Report, v11 n3 p474-498 Sep 2006. 25 pp. Retrieved December 18, 2017. Build A Qualitative Proposal
BTM 7303, Assignment 10 DuBose, Justin Z. Dr. Susan Petroshius 11 December 2017 Introduction to Study This paper is a brief examination of e-leadership of virtual teams. E-leadership is an academic field of study that has emerged since the turn of the millennium (Savolainen, 2014) and one which involves organizational leadership of highly technological structures stretched over different cultures and geographic regions (Avolio, 2014). These highly technological and widely dispersed organizational structures have 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 must be addressed by the e-leader (Hoch & Kozlowski, 2016). Virtual Teams & E-Leadership 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 of these teams that impact how one leads them. 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 in future research. Hill & Bartol (2016) found that effective e-leadership of virtual teams is leadership which 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 enhanced further when e-leaders interact with individual team members. Writing about collaboration between e-leader and virtual team members, Liao (2017) notes that “current literature is silent about how the team leader develops and maintains relationships with individual members on a virtual team”. Research Problem The research problem being addressed in this study is this: how does the e-leader effectively establish and develop relationships with individual members on a virtual team which positively contribute to overall team cohesion and performance? This problem will examine three types of personal interaction – face-to-face meetings, individual coaching, and individual professional training – and their impact on team cohesion and performance. Recent literature has not only highlighted this as a research worthy problem but has also provided a solid research foundation upon which this problem can be developed. Research Purpose The purpose of this study is multi-faceted. Firstly, one purpose is to provide a theoretical contribution to the academic field of organizational leadership by addressing and filling gaps in present literature on the subject. Secondly, another purpose is to provide organizational e-leaders with quantitative data and analysis about leading virtual teams which aims to increase their effectiveness and impact. Thirdly, a further purpose is to better understand virtual team dynamics to increase team cohesion and performance between team members. Research Questions In addressing this research problem, three research questions will be asked and analyzed throughout this study. Each research question addresses one type of personal interaction between e-leader and virtual team member and its effect on team cohesion and productivity. 1. What effect do face-to-face meetings between e-leader and individual virtual team members have on team cohesion and performance? 2. How do individual coaching sessions between e-leader and individual virtual team members impact team cohesion and performance? 3. How does individual professional training by the e-leader contribute to team cohesion and performance? Methodology & Design Researchers in this field have consistently employed quantitative methodology in their studies. Three related studies, for example, all employed surveying virtual team members regarding leadership and collaboration (Hill & Bartol, 2016; Hoch & Kozlowski, 2016 ; Krumm et al., 2013). Each researcher surveyed virtual team members within the business sector, and sample populations varied from 171, 250, and 565 participants. Data collection was accomplished in these studies by surveying virtual team members – two researchers used online surveys (Hill & Bartol, 2016; Krumm et al., 2013) while the third distributed paper surveys and questionnaires to team members (Krumm et al., 2013). Krumm et al. (2013) statistically analyzed data using exploratory factor analysis (EFA) of their 60-item survey to determine key elements of team performance. Hoch & Kozlowski (2016) used confirmatory factor analysis (CFA) of their five categories of competencies for virtual team e-leadership. Hill & Bartol (2016) also used confirmatory factor analysis (CFA) of their five categories of competencies for virtual team e-leadership. CFA was applied to responses on a 1-7 scale to questions broken down into the five categories of core leadership competencies. Research Methodology & Design This study will likewise employ a quantitative methodology by surveying 3 teams of 25 virtual team members from a large national not-for-profit organization. Two online surveys will be distributed and collected, the first at the outset of the study and the second one month into the study. Each survey question is designed to measure the effectiveness of various personal interactions between e-leaders and virtual team members in measurable performance standards and team member ratings of cohesion. Each survey will contain 10 questions relating to team cohesion and 10 questions relating to team productivity. Each response from these three virtual teams (coded A, B, & C) will be recorded from the initial survey, with each section being coded as either c for cohesion or p for productivity. Accordingly, initial surveys will be coded as 1-Ac, 1-Ap, 1-Bc, 1-Bp, 1-Cc, and 1-Cp. This coding system denotes which survey (1), which team (A, B, or C), and which section (cohesion or productivity) is being addressed. This same coding system will be used for the second survey, with a (2) replacing the (1). The three types of personal interaction (face-to-face meetings, individual coaching, and individual professional training) will be employed by the organizational leader with each different team. Thus, team A will receive one month of individual face-to-face meetings with the e-leader, team B will receive one month of individual coaching with the e-leader, and team C will receive one month of individual professional training with the e-leader. The responses from the first and second survey (prior to any individual interaction and after one month of consistent individual interaction) will be compared based on virtual team member responses to determine the impact of effectiveness of each method on team cohesion and productivity. Reliability & Validity Reliability and validity in research are two of the greatest concerns for any researcher. Steps will be taken by the researcher to ensure both reliability and validity of the research conclusions. Reliability will be ensured by maintaining a consistency in the grouping of survey questions; questions measuring cohesion address cohesiveness (team relationships and dynamics, for example) and questions measuring productivity address measure production standards (numbers and metrics, for example). Validity will be ensured by securing a large sample size (75 respondents) and employing objective statistical analysis of the data, such as the utilization of EFA in Krumm et al. (2013) and CFA in Hill & Bartol (2016). Ethical Protections While there are few ethical concerns associated with this study, protecting the individual identity of workers is a consideration of the researcher. In order to maintain anonymity and ensure ethical protections for individual respondents, the coding system mentioned earlier (1-Ac, 1-Ap, 1-Bc, 1-Bp, 1-Cc, and 1-Cp) will be employed throughout the study. In this way, quantitative data is still collected without risking any personal information about those involved in the research while still collecting relevant and substantive data about the subject. Strengths & Challenges As with any study, both strengths and challenges exist within the study. One major strength of this study is the filling of research gaps within existing literature, which not only aids current literature on the subject but also provides a foundation for future research. Another strength is the The major presenting challenge is the subjectivity of the responses from virtual team members surveyed in relation to team cohesion. While survey questions relating to productivity are measurable and objective, questions relating to cohesion are based upon the perceptions of those team members. While this is a valuable and worthy contribution to the study, it nonetheless presents a challenge to the researcher and research itself. References Avolio, B., Sosik, J., Kahai, S., Baker, B. (2014). "E-leadership: Re-examining transformations in leadership source and transmission". The Leadership Quarterly, 25(1), 105-131. Retrieved December 11, 2017. Cheshin, A., Kim, Y., Nathan, D. B., Ning, N., & Olson, J. S. (2013). “Emergence of differing electronic communication norms within partially distributed teams”. Journal of Personnel Psychology, 12, 7–21. Retrieved December 11, 2017. Gilson, L. L., Maynard, M. T., Young, N. C. J., Vartiainen, M., & Hakonen, M. (2015). “Virtual teams research 10 years, 10 themes, and 10 opportunities”. Journal of Management, 41(5), 1313–1337. Retrieved December 11, 2017. Hill, N. S., & Bartol, K. M. (2016). “Empowering leadership and effective collaboration in geographically dispersed teams”. Personnel Psychology, 69, 159–198. Retrieved December 11, 2017. Hoch, J. & Kozlowski, S. (2014). “Leading Virtual Teams: Hierarchical Leadership Structural Supports, and Shared Team Leadership”. Journal of Applied Psychology, Vol. 99, No. 3, 390–403. Retrieved December 11, 2017. Krumm, S., Terwiel, K., & Hertel, G. (2013). “Challenges in norm formation and adherence”. Journal of Personnel Psychology, 12, 33–44. Retrieved December 11, 2017. Liao, C. (2017). ' Leadership in virtual teams: A multilevel perspective'. Human Resource Management Review 27, 648–659. Retrieved December 11, 2017. Lilian, S.C. (2014). 'Virtual teams: opportunities and challenges for e-leaders'. Contemporary Issues in Business, Management and Education, 110, 1251 - 1261. Retrieved December 11, 2017. Savolainen, T. (2014). Trust-Building in e-Leadership: A Case Study of Leaders' Challenges and Skills in Technology-Mediated Interaction. Journal of Global Business Issues, 8(2), 45-56. Retrieved December 11, 2017. Quantitative Design and Data Collection
BTM 7303, Assignment 9 DuBose, Justin Z. Dr. Susan Petroshius 4 December 2017 Introduction to Study This paper is a brief examination of e-leadership and the accompanying communication challenges that twenty-first century e-leaders will encounter. E-leadership is an academic field of study that has emerged since the turn of the millennium (Savolainen, 2014) and one which involves organizational leadership of highly technological structures stretched over different cultures and geographic regions (Avolio, 2014). Savolainen (2014) noted that as the presence and capability of technology increases throughout the workforce, it places new demands on e-leaders in the specific area of communication. Literature Review Recent research has examined these communication challenges in a variety of workplace settings and, while several helpful conclusions have been reached, there are still areas left for further research to uncover. Lilian (2014) studied e-leadership and the implementation of virtual teams to combat communication challenges e-leaders face due to geographic constraints. While virtual teams drastically increased growth potential in economic and organizational ways, it presented an entirely new set of communication challenges. Lilian (2014) also recognized that further research needs to be conducted on leadership of virtual teams to address further communication challenges. Chua (2017) studied e-leadership and communication in the educational realm, and developed an e-leadership model for education based upon interviews with administrators, teachers, students, parents, and software experts which were then coded and analyzed. Chua (2017) concluded that communication was essential to the core competencies of e-leadership, and further recognized that additional research on communication in e-leadership was needed in other locations and areas. Garcia (2014) researched the importance of e-leadership in the quality of virtual education, concluding that communication is essential to effective e-leadership and that virtual teams are an increasingly prevalent reality in virtual organizations. Furthermore, he concluded that virtual teams and their mediums of communication need to be studied more extensively. Jiang (2016) studied the effects of social media on e-leaders and their effectiveness in communication, concluding that a positive link existed between e-leaders use of social media and workplace communication. Further research was needed, however, in the relationship between e-leadership and online interaction. Finally, Kiesenbauer (2015) examined the communication challenges of e-leadership as they relate to public relations. Communication in e-leadership is essential, they concluded, to strengthening internal networks and also shaping the future of the profession itself. Further research is needed, however, in how to develop communications management in practice in the virtual workplace. In order to address these gaps in the existing literature, and build upon recent studies, the issue of effective modes of communication by e-leaders will be explored in this study. Research Problem The research problem being addressed in this study is this: which one mode of technological communication (electronic mail, telephonic conferencing, or video conferencing) employed by organizational e-leaders more frequently results in corresponding action by those within the organization? Research Purpose The purpose of this study is to employ various modes of e-communication by the e-leader to team members within the organization and discover which mode more often produces the desired action of the leader by those team members who receive the e-communication. This purpose will be achieved by noting the imperative communicated by the e-leader and which communication medium was utilized. Then, the frequency with which the desired action was carried out by individual recipients will be noted along with the communication medium by which the imperative was received. Research Questions In addressing this research problem, three research questions will be asked and analyzed throughout this study. Each research question addresses a mode of e-communication from the e-leader and its correlation to the frequency of action taken by organizational team members. What is the correlation between the frequency of action by organizational team members when they are receive communication from the e-leader by electronic mail? What is the correlation between the frequency of action by organizational team members when they are receive communication from the e-leader by telephonic conferencing? What is the correlation between the frequency of action by organizational team members when they are receive communication from the e-leader by video conferencing? Data Collection Method In addressing these research questions, the data collection method used in this research will be sending out surveys to organizational team members. In examining existing literature on the subject, quantitative data is frequently collected by surveys (Jiang, 2016); (Patchanee, 2011); (Verma, 2016). One of the primary benefits of quantitative data collection by surveys is ease of widespread data collection. Verma (2016) conducted online surveys using the “Google Docs” platform. This quantitative instrument was chosen due to the benefit of expediency – results were tabulated instantly – and convenience – large numbers of surveys could be conducted by the researcher without having to travel to physically conduct the surveys. In the case of this research, surveys provide a viable option of quantitative data collection in that each question could be answered by both e-leaders and team members in the study. In this study, surveys will be given to those organizational team members who receive virtual communication from the e-leader. They will be asked to note by which method they received communication, what imperative was communicated, and what action they took as a result of the communication. These surveys will be sent and received electronically by the researcher. This allows for not only more data to be collected by the researcher than interviews or experiments, but also for quicker results. Sample and Population Participants in this research will come from five virtual not-for-profit organizations whose virtual team members are distributed over a multi-state region in the southeastern United States. Twenty workers will be chosen from each organization, constituting a sample size of one-hundred virtual team members and five e-leaders. Each e-leader will be given an imperative to communicate to their virtual team members, and will then record the mode of communication and the imperative communicated. Surveys will subsequently be distributed by the researcher, recorded, and then analyzed. This sample size provides a large enough population to draw conclusions about correlations between e-leader modes of communication and their successful translation to action by virtual team members. Ethical Protections While there are few ethical concerns associated with this study, protecting the individual identity of workers is a consideration of the researcher. In order to maintain anonymity, surveys will be coded by the organization (A, B, C, D, or E) and the number assigned to individual workers (1-100). In this way, quantitative data is still collected without risking any personal information about those involved in the research. References Avolio, B., Sosik, J., Kahai, S., Baker, B. (2014). "E-leadership: Re-examining transformations in leadership source and transmission". The Leadership Quarterly, 25(1), 105-131. Retrieved December 4, 2017. Chua, Y.P., & Chua, Y.P. (2017). How are e-leadership practices in implementing a school virtual learning environment enhanced? Computers & Education, 109, 109 –121. Retrieved December 4, 2017. Garcia, Ingrid. (2015). “Emergent leadership: Is e-leadership importance in the quality of virtual education?” RIED. Revista Iberoamericana de Educación a Distancia. 18, 25-44. Retrieved December 4, 2017. Jiang, H., Luo, Y., & Kulemeka, O. (2016). ' Leading in the digital age: A study of how social media are transforming the work of communication professionals'. Telematics and Informatics 33, 493-499. Retrieved December 4, 2017. Kiesenbauer, J. & Zerfass, A. (2015). 'Today's and tomorrow's challenges in public relations: Comparing the views of chief communication officers and next generation leaders'. Public Relations Review, 41(4), 422-434. Retrieved December 4, 2017. Lilian, S.C. (2014). 'Virtual teams: opportunities and challenges for e-leaders'. Contemporary Issues in Business, Management and Education, 110, 1251 - 1261. Retrieved December 4, 2017. Patchanee, M. & Servaes, J. (2011). 'The media use of American youngsters in the age of narcissism: Surviving in a 24/7 media shock and awe – distracted by everything'. Telematics and Informatics, 28, 66-76. Retrieved December 4, 2017. Savolainen, T. (2014). Trust-Building in e-Leadership: A Case Study of Leaders' Challenges and Skills in Technology-Mediated Interaction. Journal of Global Business Issues, 8(2), 45-56. Retrieved December 4, 2017. Serban, A., Yammarino, F., Dionne, S., Kahai, S., Hao, C., McHugh, K., Sotak, K., Mushore, A., Friedrich, T., & Peterson, D. (2015) “Leadership emergence in face-to-face and virtual teams: A multi-level model with agent-based simulations, quasi-experimental and experimental tests”. The Leadership Quarterly 26 (2015) 402–418. Retrieved December 4, 2017. Verma, P., Mohapatra, S., & Lowstedt, J. (2016). 'Ethics Training in the Indian IT Sector: Formal, Informal, or Both?' Journal of Business Ethics, 123(1), 73-93. Retrieved December 4, 2017. |
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