Path Goal Leadership Style And Performance Of Manufacturing Small And Medium Enterprises In Nairobi County, Kenya

Abstract

The purpose of this study was to determine the influence of path goal leadership style on organizational performance of manufacturing Small and Medium Enterprises (SMEs) in Nairobi County, Kenya. This study considered five research questions: - How does directive leadership style influence organizational performance of manufacturing SMEs in Nairobi? How does supportive leadership style influence organizational performance of manufacturing SMEs in Nairobi? How does participative leadership style influence organizational performance of manufacturing SMEs in Nairobi? How does achievement oriented leadership style influence organizational performance of manufacturing SMEs in Nairobi? How do environmental contingency factors moderate the relationship between path-goal leadership styles and organizational performance in manufacturing SMEs in Nairobi? Positivism research philosophy and descriptive correlational research design were adopted in the study. The target population comprised of managers/owners from 425 SMEs listed members of the Kenya Association of Manufacturers based in Nairobi County. Stratified random sampling technique and Yamane (1967) formula were used to draw a sample size of 369 managers/owners of the manufacturing SMEs. 367 managers/owners participated in the study, giving a response rate of 99%. Data was analyzed using descriptive and inferential statistics. Descriptive statistics covered were mean, standard deviation, skewness and kurtosis while inferential statistics used for data analysis included: - pearson’s correlation, chi-square and binary logistic regression. Statistical Package for Social Sciences (SPSS) version 22 was used as the data analysis tool. Data was presented in figures and tables. On the first research question, Binary Logistic Regression (Cox and Snell R squared) revealed that 13.7 % of the probability of organizational performance was explained by directive leadership style. The parameter estimates indicated that directive leadership style positively and significantly predicted organizational performance, β= 3.435, p= .000, p < 0.05. Therefore, the null hypothesis; “Directive leadership style does not influence organizational performance in manufacturing SMEs in Nairobi” was rejected. Leaders of manufacturing SMEs need to give clear instructions, schedule work and set standards for employees to perform better. In regard to the second research question, binary logistic regression (Cox and Snell R squared) showed that 12 % of the probability of organizational performance was explained by supportive leadership style. The parameter estimates indicated that supportive leadership style positively and significantly predicted organizational performance, β= 4.114, p= .000, p < 0.05. Based on the results, the study rejected the null hypothesis; “Supportive leadership style does not influence organizational performance in manufacturing SMEs in Nairobi.” Leaders of manufacturing SMEs need to support, care and recognize employee efforts to boost morale and employee engagement. On the third research question, binary logistic regression (Cox and Snell R squared) revealed that 6.9 % of the probability of organizational performance was explained by participative leadership style. The parameter estimates indicated that participative leadership style positively and significantly predicted organizational performance, β= 2.901, p = .000, p < 0.05. As a result, the null hypothesis; “Participative leadership style does not influence organizational performance in manufacturing SMEs in Nairobi.” was rejected. For optimum performance, leaders of manufacturing SMEs need to include employees in the decision making process by communicating effectively, consulting and delegating assignments. In regard to the fourth question, binary logistic regression (Cox and Snell R Squared) showed that 5.9 % of the probability of organizational performance was explained by achievement oriented leadership style. The parameter estimates indicated that achievement oriented leadership style positively and significantly predicted organizational performance, β= 2.450, p= .000, p < 0.05. Hence, the study rejected the null hypothesis; “Achievement oriented leadership style does not influence organizational performance in manufacturing SMEs in Nairobi.” Leaders of manufacturing SMEs therefore, need to set challenging goals, encourage feedback and offer incentives to boost organizational performance. On the final research question, binary logistic regression (Cox and Snell R Squared) indicated that the environmental contingency factors explains 16.5 % of the probability of the relationship between path-goal leadership style and organizational performance. The parameter estimates showed that the coefficient of leadership style by environmental contingency factors were; directive leadership (- 2.935, p =.112), supportive leadership ( .427, p =.717), participative leadership (-.4.645, p = 0.046), achievement oriented leadership style (2.586, p = 0.118) .Therefore, the study failed to reject the null hypothesis that environmental contingency factors do not significantly moderate the relationship between path goal leadership style and organizational performance in three study variables (directive, supportive and achievement oriented leadership styles). The study rejected the null hypothesis in participative leadership by environmental contingency factors. Leaders of manufacturing SMEs need to define and instruct employees on the tasks to be performed and build a cohesive team. The study concluded that path goal leadership style significantly influenced organizational performance in manufacturing SMEs. Hence, the study recommends that leaders in manufacturing SMEs need to apply path goal leadership style to enhance organizational performance. This study therefore recommends further studies to be conducted to examine the influence of path goal leadership style on organizational performance of manufacturing SMEs in Kenya at large, the inclusion of demographic variables, the use of a longitudinal approach and other data analysis methods could provide new and diverse insights in the SME sector.