Budgeting approaches and employee motivation in the hospitality industry

  • 663 Views
  • 460 Downloads

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

The thorough appraisal of financial tools such as budgeting guides business leaders in effectively planning and controlling business activities for optimum productivity and profitability. Hence, proper budgeting can be used to motivate employees. This study seeks to investigate the functional association between budgetary approaches and employee motivation in the Nigerian hospitality industry. The descriptive research design was employed, and data were garnered via a structured questionnaire (using a five-point Likert) administered to 85 hospitality industry employees spanning accommodation, catering, tourism, and recreation spheres. The paper employed a purposeful sampling technique. Motivation, which is the dependent variable, was determined using the path-goal model. Both descriptive statistics and ordinary least square regression were conducted to gauge the magnitude and direction of the relationship between the variables under consideration. The outcome signposts a significant positive correlation between budgeting style and employee motivation up to 48.9%. Specifically, the participatory budgeting style was shown to boost the motivation of employees to work with set budgets and to pursue company objectives. However, budget standard and success rates were observed to be slightly higher with imposed budgeting. The paper recommends that, while participatory budgeting is good for maintaining a well-motivated workforce, it should be practiced with adequate supervision to avoid having low-performing budgets.

view full abstract hide full abstract
    • Figure 1. Distribution of respondents by a company type
    • Figure 2. Age distribution of respondents
    • Figure 3. Budgeting style and employee motivation on a five-point Likert scale
    • Table 1. Survey sample size
    • Table 2. Respondent demographics
    • Table 3. Model summary
    • Table 4. Regression coefficients
    • Data curation
      Ben-Caleb Egbide, Mercy Agi-Ilochi, Joseph Madugba, Ibrahim Ayomide
    • Formal Analysis
      Ben-Caleb Egbide, Asaolu Taiwo
    • Investigation
      Ben-Caleb Egbide, Mercy Agi-Ilochi, Ibrahim Ayomide
    • Methodology
      Ben-Caleb Egbide, Joseph Madugba
    • Resources
      Ben-Caleb Egbide, Mercy Agi-Ilochi, Joseph Madugba, Asaolu Taiwo, Ibrahim Ayomide
    • Visualization
      Ben-Caleb Egbide, Mercy Agi-Ilochi, Joseph Madugba, Asaolu Taiwo, Ibrahim Ayomide
    • Writing – original draft
      Ben-Caleb Egbide, Mercy Agi-Ilochi, Asaolu Taiwo, Ibrahim Ayomide
    • Conceptualization
      Mercy Agi-Ilochi, Ibrahim Ayomide
    • Supervision
      Joseph Madugba, Asaolu Taiwo
    • Writing – review & editing
      Joseph Madugba, Asaolu Taiwo