Effectiveness of learning and growth performance metrics in the Nepalese telecommunications industry for organizational success

  • 71 Views
  • 2 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The primary use of financial-based performance metrics to assess an organization’s success might be misleading. The application of non-financial performance metrics could improve organizational success and longevity. This study aimed to examine the effectiveness of learning and growth performance metrics for organizational success in the Nepalese telecommunication industry. The quantitative research approach was utilized for collecting, presenting, and analyzing data obtained during a survey. The two major telecommunications service providers in Nepal, Ncell and Nepal Telecom, were taken as sample organizations, and their employees were the study’s respondents. The study revealed that two latent learning and growth performance metrics, namely ‘organizational culture and alignment’ having seven observable variables (β = 0.229, t = 3.419, p < .05) and ‘information capital’ having four observable variables (β = 0.079, t = 1.193, p < .05) were significant for organizational success. In contrast, one latent metric, ‘human resources’ having seven observable variables (β = 0.047, t = 0.708, p > .05), was insignificant. The overall explanation of the observed non-financial performance metrics to the organizational success of the Nepalese telecommunication industry was approximately 6%. A better learning and growth environment helps an organization generate, acquire, share, and integrate information to build resources and capabilities. In addition, non-financial performance metrics help organizations connect business performance with strategy, allowing them to be competitive.

view full abstract hide full abstract
    • Figure 1. Hypothesized paths of the study model
    • Figure 2. The outcome of the standardized hypothesized paths
    • Table 1. Questionnaire structure
    • Table 2. The mode of delivery and questionnaire return
    • Table 3. Reliability, validity, and CMB insights
    • Table 4. Occupational and personal characteristics of the respondents
    • Table 5. Descriptive statistics and correlation matrix
    • Table 6. Regression insights
    • Conceptualization
      Rewan Kumar Dahal
    • Data curation
      Rewan Kumar Dahal
    • Formal Analysis
      Rewan Kumar Dahal
    • Investigation
      Rewan Kumar Dahal
    • Methodology
      Rewan Kumar Dahal
    • Project administration
      Rewan Kumar Dahal
    • Resources
      Rewan Kumar Dahal
    • Software
      Rewan Kumar Dahal
    • Supervision
      Rewan Kumar Dahal
    • Validation
      Rewan Kumar Dahal
    • Visualization
      Rewan Kumar Dahal
    • Writing – original draft
      Rewan Kumar Dahal
    • Writing – review & editing
      Rewan Kumar Dahal