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ADRP 1 is one of the most important doctrinal documents for Army Soldiers. ADRP 1, titled “The Army Profession” defines professionalism and service for all Soldiers. Chapter 1 of ADRP 1 is the most important chapter in ADRP 1 because it forms the very basis for the remainder of the publication. Titled “The United States Army Profession”, this chapter defines our profession as a calling, Army Soldiers as the keepers of the public trust, and ties our professionalism to our values and ethics. Each of these three characteristics establish the very foundation of Army professionalism and, consequently, form the most important chapter of ADRP 1. Chapter 1 begins by referring to Army Professionalism as a calling. Chapter 1 opens with a quote by former Chief of Staff of the Army General Raymond T. Odierno. General Odierno noted that the Army Profession is “a noble and selfless calling founded on the bedrock of trust.” 1 Paragraph 1-7 expands upon General Odierno’s quote by noting that the Army profession is more than a career. “Professionals are intrinsically motivated by the value of the service they render to society. Thus, a profession is far more than a job; it is a calling – a way of life.” 2 In establishing the Army profession as a calling – a way of life – this chapter sets the tone for the rest of the doctrine. Chapter 4, for example, is titled “Honorable Service – Our Noble Calling”, which builds upon this definition established in chapter 1. Chapter 1 also establishes another foundational element of ADRP 1: Army Soldiers are the keepers of the public trust. General Odierno’s quote highlights the fact that our calling is sustained by a bedrock of trust. The word “trust” is used nearly two- hundred times throughout ADRP 1, and this word is defined and conceptualized in 1 Headquarters, Department of the Army. ADRP 1: The Army Profession. Washington, D.C, 2015. 2 Headquarters, Department of the Army. ADRP 1: The Army Profession. Washington, D.C, 2015. 1 chapter 1. Paragraph 1-9 notes that trust between Army professionals and the American public is not only earned, but must be “continually reinforce[d]” and is “essential for the autonomy granted by our society...to exercise discretion in fulfilling our role within the defense community.” 3 Without this trust as defined in chapter 1, there would be no need for the subsequent chapters of ADRP 1. Without this trust, there is no ADRP 1 or Army Profession. Finally, chapter 1 ties our professionalism to our values and ethics. As with calling and trust, the definition established in chapter 1 for professional values and ethics is built upon and utilized throughout ADRP 1. These values and ethics not only impact the individual but the entire Army culture. Paragraph 1-6 notes that “a professional ethic reflects laws, values, and beliefs deeply embedded within the profession’s culture. The professional ethic binds individual members together in a common moral purpose to do the right thing for the right reason in the right way.” 4 Without this embodiment of Army values and ethics by the professional culture and professional individual Soldier, the entire doctrine disintegrates. Furthermore, paragraph 1-29 links our professional values and ethics to our mission and purpose. “Consistently demonstrated, the characteristics of the Army Profession reflect American values, the Army Ethic, and our approach to accomplishing our mission in support and defense of the Constitution.” 5 Chapter 1 is the most important chapter of ADRP 1. This is primarily due to the above definitions and concepts which are established in chapter 1 and are built upon throughout the remainder of the publication. Without these ideas as conceptualized in 3 Headquarters, Department of the Army. ADRP 1: The Army Profession. Washington, D.C, 2015. 4 Headquarters, Department of the Army. ADRP 1: The Army Profession. Washington, D.C, 2015. 5 Headquarters, Department of the Army. ADRP 1: The Army Profession. Washington, D.C, 2015. 2 chapter 1, the rest of the doctrine would have no foundation. These concepts comprise the core of our profession and, without which, the Army culture and the individual Soldier lose their foundation. 3 BIBLIOGRAPHY Headquarters, Department of the Army. ADRP 1: The Army Profession. Washington, D.C, 2015.
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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 Avolio, B., Sosik, J., Kahai, S., Baker, B. (2013). E-leadership: Re-examining transformations in leadership source and transmission. The Leadership Quarterly, 25(1), 105-131. doi: 10.1016/j.leaqua.2013.11.003 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. doi: 10.1027/1866-5888/a000076 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. doi: 10.1016/j.compedu.2017.02.012 Cozby, P. C. (2014). Methods in behavioral research (12th ed.). Boston, MA: McGraw Hill Higher Education. Dane, F.C. (2011). Evaluating research: Methodology for people who need to read research. Thousand Oaks, CA: Sage. 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. doi: 10.1177/0149206314559946 Hill, N. S., & Bartol, K. M. (2016). Empowering leadership and effective collaboration in geographically dispersed teams. Personnel Psychology, 69, 159–198. doi: 10.1111/peps.12108 Hoch, J. & Kozlowski, S. (2014). Leading Virtual Teams: Hierarchical Leadership Structural Supports, and Shared Team Leadership. Journal of Applied Psychology, 99(3), 390–403. doi: 10.1037/a0030264 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. doi: 10.1016/j.pubrev.2015.05.013 Krumm, S., Terwiel, K., & Hertel, G. (2013). Challenges in norm formation and adherence. Journal of Personnel Psychology, 12, 33–44. doi: 10.1027/1866-5888/a000077 Liao, C. (2017). Leadership in virtual teams: A multilevel perspective. Human Resource Management Review 27, 648–659. doi: 10.1016/j.hrmr.2016.12.010 Lilian, S.C. (2014). Virtual teams: Opportunities and challenges for e-leaders. Contemporary Issues in Business, Management and Education, 110, 1251 - 1261. doi: 10.1016/j.sbspro.2013.12.972 Mertens, D.M. (2015). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Thousand Oaks, CA: SAGE. Pannucci, C.J. & Wilkins, E.G. (2010). Identifying and Avoiding Bias in Research. Plastic and Reconstructive Surgery, 126(2), 619-625. doi: 10.1097/PRS.0b013e3181de24bc 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. doi: 10.1108/LODJ-06-2012-0073 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 from www.globip.com/globalinternational.htm INFORMATION PAPER
ATSC-TD2018 27 April 2018 1. Purpose: This paper is an information paper on the key points of ADRP 1, The Army Profession. ADRP 1 describes the importance of trust, defines professional service, and explains military expertise and how it is essential to providing support as well as providing readiness. 2. Importance of trust. Trust is one of the most frequently addressed key points throughout ADRP 1. Chapter 1 describes trust as one of the essential characteristics of the Army Profession. Paragraph 1-15 notes that without trust the American people will never “grant us the autonomy we need to accomplish our mission in the right way”. Chapter 3 says that trust is the foundation of our relationship with the American people. Paragraph 3-6 notes that as professionals we “are responsible for reinforcing the Army culture of trust”. Trust is also addressed in chapter 6 as being necessary to the American people trusting its military professionals to steward resources. Trust is further addressed in chapter 7 as essential to our espirit de corps, itself an essential characteristics of our profession. 3. Defining professional service. The title itself of ADRP 1 highlights the truth that the Army is a profession. Thus, one of the key points throughout ADRP 1 is the defining of professional service. A key component of professional service is its link to the Army Ethic. Chapter 2 notes that our very identity as professionals is in our ethical service. As Army professionals, we serve through moral principles that guide our every action. Paragraph 1-14 notes that we serve honorably and provide the American people a service which they cannot provide for themselves. Paragraph 4-15 ties this service we provide to the American people to our oath of office. “By our oath of office…we voluntarily agree to live our lives, even at the risk of injury or death, in honorable service to the American people”. Chapter 6 adds that, because we are professionals, our readiness is sustained over the long-term. Because we are professionals, we remain ready, on behalf of the American people, to serve. 4. Military expertise. Finally, because we are Army professionals, we are experts in our trade. Chapter 5 explains that our military expertise is essential to our role as Army professionals. Paragraph 5-8 links our expertise to the trust the American people have in us. It says that “we apply our military expertise with the autonomy granted by the American people”. Our expertise is supporting the Constitution, the American people, and our national interests. Paragraph 1-26 summarizes our expertise as “the ethical design, generation, support, and application of landpower”. Additionally, this paragraph notes that Army professionals are lifelong learners so as to continually develop their expertise. 5. Summarizing key points. In conclusion, the key points of ADRP 1 are best summarized by the following statements. It is imperative that we establish and maintain trust with the American people as Army professionals. Professionalism is our identity as servants to our country and people. Professionalism is embodied in our readiness as well as our moral and ethical principles. Our professionalism is directly linked to our oath of office. As Army professionals, we are experts and lifelong learners in our trade. We apply our expertise only through the trust that the American people have in us as professionals. Prepared by: CH (CPT) Justin DuBose Approved by: MAJ Virginia Emery Formulate Hypothetical Research Designs
BTM 8103, Assignment 7 DuBose, Justin Z. Dr. Robert Levasseur 22 April 2018 Introduction In this paper, two hypothetical design plans, quantitative and qualitative, are under examination. Each hypothetical design is nested within the field of e-leadership and, more specifically, virtual teams. Both the academic field of e-leadership as well as recent research in the area of virtual teams are introduced so that each hypothetical design is placed in an appropriate context. The appropriateness of each hypothetical design plan is then discussed as well as the applicability of each design plan to the field of study. The purpose, methodology, and participants of each hypothetical research design are different, but all examine e-leadership of virtual teams. E-leadership Scholars have defined e-leadership as the style of leadership by those leaders who mainly use technological mediation in their work as leaders (Savolainen, 2014). The need for this technological mediation could be the result of either cultural or geographic challenges (Avolio, 2014). Due to these technological, cultural, and geographic challenges, e-leaders face the unique dilemma of communicating with their workforce from remote locations rather than in-person (Mackenzie, 2010). Chua (2017) noted that e-leadership was the exercise of social influence by means of information and communication technology for the purpose of producing change in performance and behavior in individuals and organizations. Relevance of study As the modern workforce continues to change, with older generations continuing to work and younger generations matriculating into the workforce, organizational leaders face the problem of leading an increasingly diverse workforce (Al-Asfour, 2014). Additionally, as the modern workplace increases in the development and implementation of technology and broadens its geographic footprint, organizational leaders will be pressed to hone their expertise as technological leaders (Al-Asfour, 2014). Furthermore, research has concluded that the technological work environment has created additional need for research in this field. Recent research on e-leadership has been conducted in a variety of fields including schools (Chua, 2017; Clark, 2017), the government sector (El Khouly, 2014), communication professionals (Jiang, 2016) and Information Technology (IT) professionals (Verma, 2016). Each researcher, while acknowledging the contribution of their study to their field, noted the need for additional research in other areas and locations where e-leadership was utilized. Hypothetical Qualitative Design The research problem being addressed in this hypothetical qualitative design is: what are the critical imperatives of e-leadership in developing and influencing deep, meaningful relationships between organizational e-leaders and followers? Several recent studies have recognized this gap in e-leadership literature and have communicated the need for further research in these areas (Avolio, 2014; Mackenzie, 2010; Patchanee, 2011; Lilian, 2014; Chua, 2017). The purpose of this qualitative proposal, therefore, is to examine the nature of relationship dynamics in the virtual workplace between e-leader and follower and establish an effective leadership theory and methodologies for the e-leader to develop intimacy and trust in both individual relationship and organizational culture. The research questions being addressed in this hypothetical qualitative design are: 1. How do e-leaders of virtual teams effectively cultivate a culture of intimacy and trust? 2. What methods of digital communication are most effective in developing relational intimacy between e-leaders and workers? 3. What are the potential effects of these technologies on the leadership dynamic in virtual teams? Population & Sample For this hypothetical qualitative design, face-to-face interviews will be conducted with 100 virtual team members and 10 e-leaders from 3 different organizations. Lilian (2014) recently researched this issue of e-leadership of virtual teams and concluded that relational and leadership dynamics are greatly impacted by virtual communication as opposed to personal, face-to-face communication. This highlights the need for further research to be conducted within these organizational populations and samples. Data Collection Data will be collected by face-to-face interviews with virtual team members. This data collection methodology is preferable for this study over electronic surveys or electronic or telephonic interviews primarily because it allows the researcher to gather more data for field notes including facial expressions, non-verbal communication, tonality when responding, and additional personal interaction that would unable to be gathered or observed using electronic or other data collection methodologies. Each of these benefits are appropriate to a qualitative study as it examines the subjective responses of team members. Data Analysis Since the data will be collected in the form of-face-to-face interviews with these workers, the data analyzed will be the responses to the researcher’s questions. Additionally, other field notes gathered by the researcher in the form of non-verbal communication will be gathered and compiled. Once all the data has been collected and categorized from these interviews, the researcher will begin to “code” the data, combing through the categories to form larger, thematic categories (Creswell, 2013). These major categories will then undergo further analyzation during which process the elements or factors which caused these major categories to emerge will be extracted (Creswell, 2013). Hypothetical Quantitative Design The research problem being addressed in this hypothetical quantitative design is: 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. The purpose of this quantitative research is to provide organizational e-leaders with quantitative data and analysis about leading virtual teams which aims to increase their effectiveness and impact. 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? Data Collection This hypothetical study will survey 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. Data Analysis 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. Appropriateness of each design In each of the above hypothetical designs, the design – whether qualitative or quantitative – is suited to the research question and purpose. Cozby (2014) best described these differences and which design is most appropriate for different types of research. Qualitative research is subjective in nature and uses individual interpretations of research participants; qualitative researchers will often conduct interviews as their method of data collection (Cozby, 2014). The hypothetical qualitative design is appropriate because the research is subjective in nature, seeking to understand the individual responses of virtual team members. Similarly, the hypothetical 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. This is because quantitative research generally requires a larger sample size of specific data since statistical analysis forms the basis of the conclusions (Cozby, 2014). Cozby (2014) summarized these differences in research methodology by saying, “qualitative researchers emphasize collecting in-depth information on a relatively few individuals or within a very limited setting; quantitative investigations generally include larger samples” (p. 117). References Al-Asfour, A. & Lettau, L. (2014). Strategies for leadership styles for multi-generational workforce. Journal of Leadership, Accountability, and Ethics, 11(2), 58-69. Avolio, B., Sosik, J., Kahai, S., Baker, B. (2013). E-leadership: Re-examining transformations in leadership source and transmission. The Leadership Quarterly, 25(1), 105-131. doi: 10.1016/j.leaqua.2013.11.003 Brooks, R. (2010). The development of a code of ethics: An online classroom approach to making connections between ethical foundations and the challenges presented by Information Technology. American Journal of Business Education, 3(10), 1-13. doi:10.19030/ajbe.v3i10.483 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. doi: 10.1016/j.compedu.2017.02.012 Cozby, P. C. (2014). Methods in behavioral research (12th ed.). Boston, MA: McGraw Hill Higher Education. Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage. El Khouly, S., Ossman, M., Selim, M., & Zaghloul, M. (2014). Impact of e-leadership on leadership styles within the Egyptian government sector. Competitive Forum, 12 (1), 131 –140. 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. doi:10.1016/j.tele.2015.10.006 Lilian, S.C. (2014). Virtual teams: Opportunities and challenges for e-leaders. Contemporary Issues in Business, Management and Education, 110, 1251 - 1261. doi: 10.1016/j.sbspro.2013.12.972 Mackenzie, M.L. (2010). Manager communication and workplace trust: Understanding manager and employee perceptions in the e-world. International Journal of Information Management, 30, 529-541. doi:10.1016/j.ijinfomgt.2010.04.001 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. doi:10.1016/j.tele.2010.09.005 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. 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. doi:10.1007/s10551-014-2331-4 Apply Exploratory Research
BTM 8103, Assignment 5 DuBose, Justin Z. Dr. Robert Levasseur 8 April 2018 What is naturalistic observation? How does a researcher collect data when conducting naturalistic observation research? In conducting research, researchers employ a variety of research approaches. One common approach is naturalistic observation. Naturalistic observation occurs when “the researcher goes into the field to observe the phenomenon in its natural state” (Trochim, 2016, p. 62). In this research approach, the researcher is simply observing and taking field notes on the researched phenomenon. Cozby (2014) noted that this research approach originated in the field of anthropology but is commonly and widely used in the field of social sciences today. While employing naturalistic observation, the researcher does not attempt, in any way, to influence the phenomenon or any other variable and its relationship to the phenomenon (Cozby, 2014). Once the researcher has satisfactorily recorded their field notes, these notes and other data are often “subsequently coded and analyzed for major themes” (Trochim, 2016, p. 62). Why are the data in naturalistic observation research primarily qualitative? When conducting research, the collected data is primarily qualitative in nature. This is because such data “are the descriptions of the observations themselves rather than quantitative statistical summaries” (Cozby, 2014, p. 120). In other words, each of these naturalistic observations, as recorded by the researcher, are descriptions of behavior and relationships as opposed to any form of statistical analysis. Thus, naturalistic observation is not only beyond the scope of statistical analysis but also a subjective interpretation of events, behaviors, and relationships. The data gathered from naturalistic observation forms “a complex picture of the problem or issue under study. This involves reporting multiple perspectives, identifying the many factors involved in a situation, and generally sketching the larger picture that emerges” (Creswell, 2013, p. 186). Distinguish between participant and nonparticipant observation; between concealed and nonconcealed observation. When a researcher is conducting naturalistic observation, they are either engaging in participant observation or nonparticipant observation. In participant observation, the researcher places themselves inside the observed environment, thus placing themselves in the role of participant. One benefit from participant observation is that it places the researcher in closer and more direct proximity to the objects of their observation. This benefit allows them greater observation opportunities, which is likely reflected in more detailed field notes about their research subjects. Cozby (2014) notes, however, that there are problems with participant observation. Primarily, he suggested, the problem “is that the observer may lose the objectivity necessary to conduct scientific observation. Remaining objective may be especially difficult when the researcher already belongs to the group being studied or is a dissatisfied former member of the group” (p. 121). Nonparticipant observation occurs when the researcher remains outside of the realm of observation. In nonparticipant observation, the researcher is in no way involved as a participant in the research but retains status as an outsider who observes the environment from a distance. While this likely allows the researcher greater objectivity in their research, it also keeps them at a greater distance from the phenomenon which they are observing. Similarly, researchers will either conduct concealed or nonconcealed observation during their research. Concealed observation occurs when the researched subjects are not aware of the presence of the researcher (Cozby, 2014). Conversely, nonconcealed observation occurs when those research subjects are aware of the presence and purposes of the researcher in their environment. Like participant and nonparticipant observation, concealed and nonconcealed observation each have their own benefits and drawbacks. Cozby (2014) noted that the primary concern in deciding between concealed and nonconcealed observation was research ethics. Concealed observation allows for a greater likelihood that research participants will not alter their behavior due to the presence of the researcher. However, it also creates ethical dilemmas for the researcher depending upon the nature of their research. Henle & Hubbell (1938), for example, conducted a concealed observation of college students by hiding underneath their beds. Their intent was to discover what these college students discussed in private, but their concealment highlights obvious ethical concerns of accessing dormitory rooms and hiding underneath beds without the knowledge of approval of those students. What is systematic observation? Why are the data from systematic observation primarily quantitative? While naturalistic observation is one research technique utilized by researchers, systematic observation is a differing research technique. In systematic observation, researchers meticulously and systematically observe specific behaviors or relationships in a more controlled setting. Systematic observation can occur either in a naturalistic setting or a laboratory setting (Cozby, 2014). Regardless of the setting, systematic observation leads to data which is quantitative in nature because the research observations themselves are quantifiable (Cozby, 2014). One example of systematic observation is Bakeman & Brownlee (1980) and their study of child behavior in social settings. They placed children in groups and systematically observed the number of instances in which they played alone, played alongside other children, or played with other children. The resulting data was quantitative in that the data was quantifiable by the number of occurrences in which various children played either alone, alongside other children, or with other children. What is a coding system? What are some important considerations when developing a coding system? Each researcher must develop a coding system to classify observed data. This is true of both naturalistic and systematic observation. A coding system for research is a methodological approach to categorizing data. Each coding system considers the observations and field notes of the researcher and breaks them down further into categories and/or themes (Trochim, 2016). Cozby (2014) identified four main considerations of researchers when constructing a coding system for their data: equipment, reactivity, reliability, and sampling. In developing a coding system, researchers must consider the equipment used in gathering the observations and its suitability to the research. Did the researcher use a pencil and paper when an audio recorder would have been more appropriate? Ramirez-Esparza, Mehl, Alvarez-Bermúdez, & Pennebaker (2009), for example, observed the social behaviors of Mexicans and Americans and, when using audio equipment, received more detailed information which allowed for more accurately coded data. This, in turn contributed to more precise and accurate conclusions. Reactivity refers to the reaction of the observed subjects, and reliability refers to accuracy of the measurement tool used to collect data (Cozby, 2014). To increase reliability, multiple researchers collect data (Cozby, 2014). Finally, sampling refers to the particular size and scope of observed subjects selected by the researcher and the ability of the researcher to project the observed behaviors of that population onto a larger, broader population (Cozby, 2014). Each of these four characteristics are imperative to the development of an accurate coding system. What is a case study? When are case studies used? What is a psychobiography? A case study is “an observational method that provides a description of an individual. This individual is usually a person, but it may also be a setting such as a business, school, or neighborhood” (Cozby, 2014, p. 125). One type of case study is a psychobiography. “A psychobiography is a type of case study in which a researcher applies psychological theory to explain the life of an individual, usually an important historical figure” (Cozby, 2014, p. 125). Researchers use case studies and psychobiographies to develop theories of behavior and to research phases of childhood development (Trochim, 2016). What is archival research? What are the major sources of archival data? Archival research is research which uses “previously compiled information to answer research questions” (Cozby, 2014, p. 126). In archival research, researchers typically rely on existing sources of data rather than observing and collecting original data (Cozby, 2014). Major sources of archival data include various public records, existing reports on a given subject, and other information contained in databases (Cozby, 2014). Cozby (2014) noted two major problems associated with archival research. “First, the desired records may be difficult to obtain: They may be placed in long-forgotten storage places, or they may have been destroyed. Second, we can never be completely sure of the accuracy of information collected by someone else.” (Cozby, 2014, p. 128). What is content analysis? Content analysis is similar to archival research in that it is “the systematic analysis of existing documents” (Cozby, 2014, p. 128). More specifically, however, content analysis refers to the process of coding information gathered from existing documents whereas archival research refers to a particular approach to research. Researchers undergoing archival research would use content analysis in their research of archived data to define and delineate categories for the information and content discovered in the archived data. References Bakeman, R. & Brownlee, J.R. (1980). The strategic use of parallel play: A sequential analysis. Child Development, 51, 873-878. doi:10.2307/1129476 Cozby, P. C. (2014). Methods in behavioral research (12th ed.). Boston, MA: McGraw Hill Higher Education. Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage. Henle, M. & Hubbell, M.B. (1938). “Egocentricity” in adult conversation. Journal of Social Psychology, 9, 227-234. doi:10.1080/00224545.1938.9921692 Ramírez-Esparza, N., Mehl, M. R., Alvarez-Bermúdez, J. & Pennebaker, J. W. (2009). Are Mexicans more sociable than Americans? Insights from a naturalistic observation study. Journal of Research in Personality, 43, 1-7. doi:10.1016/j.jrp.2008.09.002 Trochim, W., Donnelly, J., & Arora, K. (2016). Research methods: The essential knowledge base (2nd ed.). Mason, OH: Cengage. |
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