Post-reforms training needs of front-line managers at Indian power distribution companies: A middle managers perspective

Abstract

The Indian power distribution companies (DISCOMs) adopted new technologies and commercial practices. The present study identifies the competency areas in which frontline managers (FLMs) need training in the Indian power distribution sector. A literature review was conducted to identify gaps. This process was then used to develop the questionnaires. Following a pilot study data involving 78 middle managers from 4 DISCOMs, a study was undertaken. Stratified probability proportional to size (PPS) sampling was used. The collected data was tabulated and analyzed using statistical package for social sciences (SPSS). The study identified five technical, eight managerial, and four commercial areas of competencies and skills in which DISCOMs' FLMs need training. Technical-Investigate energy consumed and billed, investigate reoccurrence of same fault, reconfiguring network, distributed generation, and installing right switchgears at right location. Managerial-Unbiased judgement, identify work related issues, conclude maximum from given info, align staff, design work, allocate tasks, break old habits, anticipate consequences, resolve conflicts, ensure cordial environment, respect rules and ethics, make decisions, amicable solutions. Commercial-Understand tariffs, provide constructive feedback, understand audit, use software for commercial solutions, and maximize revenue. There is a need for institutionalizing training process at DISCOMs and training needs assessment should be given prior importance for FLMs' competency. The research also suggests that the competent FLMs also lead to customer satisfaction. Cronbach alpha was 0.73 confirming internal consistency (reliability) of data. Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.87, and Bartlett's test of sphericity was significant at 0.00. Validity of questionnaire was established using data reduction technique. Questionnaires were tested for convergent (average loading of all extracted components was greater than the minimum acceptable value of 0.7) and discriminant validity (variance extracted between all components was greater than the correlation square between them).