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Supportive Leader as a Predictor Change Readiness


Affiliations
1 Assistant Professor, Department of Psychology, School of Humanities and Social Sciences, Sharda University, Greater Noida - 201310, Uttar Pradesh, India
     

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Although technological innovations in the oil and gas sector have upgraded the extraction of hydrocarbons, the ever-expanding demand of crude oil and natural gas has pulled in exorbitant investment to increase extraction of hydrocarbons, these investments are has been observed to be in growing trend. The industry is constantly facing technological changes. The present research suggests that in order to make the implementation of new technologies successful, leader needs to prepare the employees for change thus steer the organization for success. With help of two separate questionnaires, data was collected from 146 employees of Oil & Gas exploration and production organizations. Exploratory factor analysis was computed in SPSS 20 to find the underlying construct of the two questionnaires. Further, the extracted factors were correlated. With Smart PLS 3, the relation between supportive leader and change readiness was computed. Reliabilities of both the questionnaires are more than 0.85. Positive correlation (0.463) was found between the two variables. PLS_SEM 3 confirmed the relationship between supportive leader and change readiness (t = 8.168 and R square = 0.252).The study concludes that subordinates who receive support and benefits from their leaders are ready to accept change. Conclusions from the study can further be hypothesized for future researches. This study suffers from the usual limitations of survey research method. Including more variable that would increase the explained variance in change readiness can enhance the scope of research. The present study shall help organizations to recognize supportive leaders and their role in effective change management and acceleration of the same.

Keywords

Supportive Leader, Subordinate, Change Readiness, Exploratory Factor Analysis, PLS-SEM
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  • Supportive Leader as a Predictor Change Readiness

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Authors

Toran (Kapoor) Talwar
Assistant Professor, Department of Psychology, School of Humanities and Social Sciences, Sharda University, Greater Noida - 201310, Uttar Pradesh, India

Abstract


Although technological innovations in the oil and gas sector have upgraded the extraction of hydrocarbons, the ever-expanding demand of crude oil and natural gas has pulled in exorbitant investment to increase extraction of hydrocarbons, these investments are has been observed to be in growing trend. The industry is constantly facing technological changes. The present research suggests that in order to make the implementation of new technologies successful, leader needs to prepare the employees for change thus steer the organization for success. With help of two separate questionnaires, data was collected from 146 employees of Oil & Gas exploration and production organizations. Exploratory factor analysis was computed in SPSS 20 to find the underlying construct of the two questionnaires. Further, the extracted factors were correlated. With Smart PLS 3, the relation between supportive leader and change readiness was computed. Reliabilities of both the questionnaires are more than 0.85. Positive correlation (0.463) was found between the two variables. PLS_SEM 3 confirmed the relationship between supportive leader and change readiness (t = 8.168 and R square = 0.252).The study concludes that subordinates who receive support and benefits from their leaders are ready to accept change. Conclusions from the study can further be hypothesized for future researches. This study suffers from the usual limitations of survey research method. Including more variable that would increase the explained variance in change readiness can enhance the scope of research. The present study shall help organizations to recognize supportive leaders and their role in effective change management and acceleration of the same.

Keywords


Supportive Leader, Subordinate, Change Readiness, Exploratory Factor Analysis, PLS-SEM

References





DOI: https://doi.org/10.15410/aijm%2F2020%2Fv9i2%2F155637