The Impact of Path-Goal Leadership Styles on Work Group Effectiveness and Turnover Intention
Bibliographic record
Abstract
Globalization and the demand for a skilled, educated, and expeditious workforce pressure organizations to leverage their diverse workforces to gain competitive advantage (Harris, 1996). Throughout the next decade, the U.S. workforce is forecasted to become even more diverse, with 75 percent of the immigrant population arriving in the United States from Asia and Latin America, with only five percent coming from Canada and Europe. Women and minorities were projected to represent 70 percent of the U.S.'s 2008 workforce (Lockwood, 2005). To maintain financial competitiveness in this diverse landscape, organizational leaders must embrace the leadership styles that are most effective in motivating the diverse groups in which many employees work. Diversity in work groups can generate significant benefits for organizations, including enhanced innovation, creativity, and productivity (Valentine, 2001). Capturing these benefits takes the right type of leadership style and skills (Jung and Sosik, 2002; Silverthorne, 2001; Waldman et al., 2001; Kim and Organ, 1986; House, 1971; Fiedler, 1967). Despite recognition that an appropriate leader can enhance a work group's performance, increase group members' job satisfaction, and reduce turnover intentions, there is scant research assessing the impact of specific leadership styles on diverse work group effectiveness and turnover intention (Duemer et al., 2004). To help fill this gap, we analyze the relationships among three Path-Goal leadership styles, diversity, work group effectiveness and work group members' turnover intention. The following section discusses the important literature about diverse work groups, work group effectiveness, turnover intention, and Path-Goal leadership styles. Then, the methods and results of our data collection and analysis are presented. Finally, the conclusions and implications of this study's findings for organizational leaders and the fields of leadership and management are explained. REVIEW OF THE LITERATURE Work Groups and Work Group Diversity Work groups are comprised of individuals who are interdependent and/ or interact with each other to complete tasks and projects that contribute to organizational productivity, innovation, and creativity. The exchange of information and know-how among work group members as they achieve common goals generates social bonds that enhance productivity and organizations' financial performance (Gil et al., 2005; Blanchard and Miller, 2001; Beck et al., 1999; Anakwe and Greenhaus, 1999; Nonaka and Takeuchi, 1995). Diverse work groups exist when members' individual attributes differ (Mannix and Neale, 2005; Hobman et al., 2004, 2003). Researchers often focus on two dimensions of group member diversity. The first is dissimilarity, which includes explicit characteristics such as age, race/ethnicity, and gender, and the second is dissimilarity, which includes relative characteristics such as functional background, educational background, and seniority (Hobman et al., 2004, 2003; Chattopadhyay, 2003; Chatman and Flynn, 2001; Williams and O'Reilly, 1998). When individuals interact with people whom they perceive as different, they tend to classify themselves and those people into social categories (Cox and Nkomo, 1990). Research has found that, early in the life of a work group, members focus on the visible aspects of diversity such as gender, race/ethnicity, and age. As group members interact, they redirect their attention to other members' non-visible features such as personality, education, expertise, values, and communication styles (Cunningham and Sagas, 2004; Hobman et al., 2004, 2003; Salomon and Schork, 2003; Richard et al., 2002; Caudron, 1994). Employees with more perceived value/informational dissimilarity with their leaders tend to be less satisfied with them and have weaker organizational attachment that those with high perceived similarity (Lankau et al. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".