CONCLUSION AND RECOMMENDATIONS
6.1 Conclusion
6.1.1 Quantitative Findings
The chosen framework can guide researchers to choose a theory and apply an essential set of beliefs that establishes a set of practices and a worldview to create a holistic trove of data. This study has tried to choose acceptable analytical tools to see the actual analysis queries in order to analyze causative relationships among the foremost variables of policy performance once the research drawback has been narrowly made public. The investigation was then influenced by the literature review and conjointly the designed instruments to urge data that are capable of objective examination and express an assortment of analyses and knowledge reflected inside the tested hypotheses.
Conceptual Framework
Figure 6.1 Conceptual Framework
Policy integration problems in the management of cross-cutting issues in the political domain transcend the boundaries of established policy fields and often do not correspond to the institutional responsibilities of individual departments. Inside the policy literature, several disciplines address policy integration, although not endlessly bearing this specific term. A spread of different connected terms are used, like policy coherence, cross-cutting political, combined decision-making, policy consistency, holistic government, joined-up government and, above all, policy co-ordination. These ideas unite at developed intervals of structured theories, like those of inter-
Number of Agencies (NA)
H1+
H2+
H3+
H4+
H5+
H6+
H8+
H9+
H7+
H10+
H11+
Stability of Government Policy (SGP) Medium Term Plan (MP)
Medium-term Plan
Policy Performance Indicators (PPI)
Effective management of
Integration Budgeting Plan
(EIB)
Leadership: (LS)
Diversity of Agencies (DA) Complexity of Policy (CP)
Long Term Plan (LP)
Collaborative Resource (CR)
Flexibility and openness to changing circumstances of collaboration (FOC)
Strategic Planning (SP)
organizational co-operation and co-ordination, collaboration, intergovernmental management and network management. Thus, the abstract framework unfolds.
This model has aimed to utilize the relation between Policy Factors and Inter- organizational Factors with the effectives of the Integration Budgeting Plan in order to find the significant factors for budget management. In order to be effective, the creation of the budget should be a collaborative approach in order for the organization and its individuals to succeed. Establishing a cohesive, cooperative decision-making plan that evaluates alternatives and determines that a financial model can be a manifestation of the integrated approach should be considered when preparing annual budgets or forecasts. Government organizations, as well as private industry, continue to search for improvements to the budget process. Budgeting is a fairly simple concept. IBP budgeting means that employees and subordinates decide what priorities and projects should be included when preparing the budget (Baiocchi & Ganuza, 2014). An open structure of budget planning to decide on procedures that regulate effective communication also contributes to a successful budget plan. This framework aims to see an increasing predictability of resource flows and the criteria by which funding decisions are made, these are the objectives of the IBP approach. In many developing countries, the resource allocation process is plagued by uncertainty, much of which is self-inflicted. A common tendency to make overly optimistic revenue projections is one example of how governments themselves increase the uncertainty of resource flows.
In the quantitative information analysis, primary information was accumulated through structured form surveys so hypotheses could be tested on the premise of the helpfully collected information. The data, obtained within structured form surveys, were fed into an SPSS package and therefore the transformation of variables was applied to create them usable for SPSS. MLR is employed to work out the connection among independent variables and investigate the information to check the hypotheses.
MLR serves as a vital tool for developing information that involves recognition of variables, and therefore the progress of a theoretical model. Eventually, the hypotheses were developed to support the theoretical model. MLR is helpful for locating relationships between 2 continuous variables. One is the predictor or variable, and the alternative is the response or variable. The relationship between 2 variables is claimed to be settled if one variable is accurately expressed by the opposite.
Table 6.1 Hypothesis Findings
Hypothesis Result
H1: Strategic planning impacts effective management of the Integration Budgeting Plan
Rejected H2: Long term plan impacts effective management of the
Integration Budgeting Plan
Accepted H3: Medium term plan impacts effective management of the
Integration Budgeting Plan
Accepted
H4: Policy Performance Indicators impacts effective management of the Integration Budgeting Plan
Accepted H5: Stability of Government policy impacts effective
management of the Integration Budgeting Plan
Rejected H6: Complexity of Policy impacts effective management of
the Integration Budgeting Plan
Accepted H7: Leadership impacts effective management of the
Integration Budgeting Plan
Accepted H8 Number of agencies impacts effective management of the
Integration Budgeting Plan
Accepted H9: Diversity of agencies impacts effective management of
the Integration Budgeting Plan
Accepted H10: IBP Resource impacts effective management of the
Integration Budgeting Plan
Accepted H11: Flexibility and openness to changing circumstances of
collaboration impacts effective management of the Integration Budgeting Plan
Rejected
The findings point out 11 independent variables, there are 8 variables that are statistically significant with the P value is less than 0. 05. Figure 6. 2 portrays the path diagram of the model structure along with the output for hypothesized relationships in the new proposed model of effective management for the Integration Budgeting Plan.
According to Figure 6.2, there are 8 significant factors extracted from original budgeting ground theories. Nonetheless, the proposed model can be classified into 2 groups of variables, which are: the policy and performance budgeting factor, and the inter-organizational factor. As a result of multiple regression analysis, the result of variables which are statistically significant appears to explain up to 82 percent (R2 = 0.819). From the findings, EIB in Thailand relies on long term and medium term plans which influence budgeting implementation. It refers to the idea that budgeting planning will have an effect on how the policy is designed, in this case, narcotics problems.
Second, policy performance indicators refers to its goals, an implicit causal theory about how to impact the problem situation. Third, complexity of policy refers to major elements of government policy formulation. Leadership can inspire the teams to accomplish effective budgeting policy. All variables of policy implementation by IBP are examined and statistically significant at P < 0.05, as shown in Table 6.2.
Figure 6.2 Proposed Model
Number of Agencies (NA)
.175
.174
.062 .206
.254 .321 .126
.210 Medium Term Plan (MP)
Medium-term Plan
Policy Performance Indicators (PPI)
Effective management of
Integration Budgeting Plan
(EIB)
Leadership (LS)
Diversity of Agencies (DA) Complexity of Policy (CP)
Long Term Plan (LP)
Collaborative Resource (CR)
New Equation of IBPI
EIB = - 266+.206 CP +.210 CR +.321 DA +.254 NA +.175 LP+.174 MP +.126 LS +.062 PPI
F = .589 P = 0.00 R2 = .819 Adjust R2 = .813
This new finding of a new model, which makes a case for the effectiveness of integrated budget management in Asian nations (EIBT), is essential. It is potentially a different answer for the Budget Bureau and different government agencies. The MLR as a prognostic analysis, multiple statistical regression is employed to clarify the connection between one continuous variable, that is, that the effects of IBP, and eight freelance variables. EIBT is able to forecast the effects or impacts of changes. This analysis helps government to know what proportion the effects of IBP can modify once the new budget policy changes over every twelve months.