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This study aims to examine the mediating role of resource capacity on the effect of administrative capacity on local level development.

Data is obtained from three Metropolitan, Municipal and District Assemblies (MMDAs) in the Central Region of Ghana using a survey. A structural equation modelling that includes a formatively measured construct (administrative capacity) is used in the analyses.

Leadership, human resource management, financial resource management, technical and adaptive capacities are supported as formative constructs of administrative capacity. More so, it is established that administrative capacity has an effect on resource capacity. Also, administrative capacity and resource capacity each has an effect on local level development. The mediation analysis shows that resource capacity mediates the effect administrative capacity has on local level development.

Administrative capacity is a necessity for local governments as enhancing it together with the resource capacity of MMDAs are vital if local level development is to be achieved. In this regard, administrative and resource capacities of local governments must be pursued by both the central government and decentralised governments for the latter to achieve its primary mandate.

To the best of our knowledge, this is the first study to empirically ascertain leadership, human resource management, financial resource management, technical and adaptive capacities as the dimensions of administrative capacity using a structural equation model. Given that there is a lack of consensus on the dimensions of administrative capacity, this study contributes significantly to the discourse. The study also highlights the need to pursue and enhance administrative and resource capacities for local level development.

Theeuwes and Adriaansen (1994), among others, have asserted that activity-based costing (ABC) is inappropriate for operational decision-making. In this article, ABC is modified to reflect separate flexible and committed cost driver rates for an activity. This enables the model to reflect the difference in the behavior of an activity’s flexible and committed costs needed for operational planning decisions. The modified ABC facilitates determining the resources required to produce the product mix developed from the firm’s strategic plan and the excess capacity that will result. The modifications made to ABC aid in determining an optimal product mix when the firm has excess capacity, while the traditional ABC may not. Equally important, it facilitates measuring the financial implications of the resource allocation decisions that comprise the firm’s operational plan. As the operational plan is implemented, operational control is used to ensure that it is performed in an efficient and effective manner. The modified ABC enables the firm’s managers to compute the different types of deviations that arise from using flexible and committed resources at the unit, batch, and product levels of the firm’s operations. This aids in understanding problematic aspects of the firm’s operations and identifying where management resources are needed to improve operational efficiency.

Health care is an example of an organization where the needs of potential clients are much greater than the capabilities of the service delivery system. The implementation of any medical procedure, as well as the provision of any service, just like the manufacturing of any product, can be decomposed into a series of tasks. The purpose of this paper is to propose a model for measuring the effectiveness of quality assurance tasks in health-care delivery processes.

The authors analyze a system of factors that affect the implementation of tasks in a process. In their considerations, they have focused on four areas of science that describe conditions that are related to the implementation of tasks: Scheduling as a methodology for allocating resources to perform tasks; Capacity planning as a methodology for assigning values to given resources expressed by the number of tasks that can be executed with the resources; Queueing theory, used as a methodology for describing phenomena in which not all planned tasks are performed within the prescribed specification limits; and Quality management, as a methodology to ensure appropriate conditions for completing tasks (CCTs), where CCT is a representation of parameters of casual relationship between variables.

The authors show that the effectiveness of executing any scheduled tasks in the process is determined by the difference between the capacity of resources allocated (at a given time interval) and the number of tasks planned to be carried out at that time. The CCT conditions determine the level of capacity of the fixed amount of resources. It is shown that their deviation from the reference CCT specification may cause the nominally correct amount of resources be either too small (causing queue formation and longer wait time in hospitals) or too large to contribute to the waste in the system by creating idle capacity.

The scope of application of the model is wide. It covers tasks performed with different degrees of uncertainties regarding the capacity of resources. It applies in all areas of health care where unlike manufacturing, the services delivered and the tasks performed in the health-care delivery system are seldom identical. Every patient is treated differently than the one waiting next in line. The workloads are pre-arranged in the order they are needed and completed in accordance with the FI-FO (first in-first out) principle. The model presented in this paper makes it possible to better understand the mechanism of effectiveness and efficiency improvement and the role of humans as a specific carrier of capacity.

As most of the health-care organizations are still stuck in the soft side of quality assurance, there has been little research conducted to test the applicability of well-known productions/operation management methodologies and theories benefitting health-care systems. The formulation of a reference point of CCT in this study is to serve as a stabilizing control point with the same connotation as that of a central reference line in the statistical process control chart. The correct capacity planning is needed to determine with a high degree of probability of success in implementation of all tasks to assure quality all the time.

This study aims to improve the performance of geothermal energy. Therefore, this research requires a deep examination of the determinant factors that affect the performance of geothermal energy; the results of this study are expected to increase the outcomes that can be enjoyed by the people of Indonesia.

This research uses quantitative approach. Data are obtained via questionnaires. The population in this study is all stakeholders of the national geothermal energy policy throughout the region. The stakeholders in question are the Community Care for Energy and the Environment (MPEL), using a sample of 400 respondents. The variables used were human resource capacity (X1), political resource capacity (X2), economic resource capacity (X3), social resource capacity (X4), performance of geothermal energy policy (Y1) and geothermal energy policy outcomes (Y2). Data analysis used to solve hypothetical model built in this research is partial least square.

While human resource, political resource, economic resource and social resource capacities affect the performance of geothermal energy policy, those capacities directly affect the performance of geothermal energy policies. On the other hand, the results of the indirect effect test show that with the mediation of good geothermal energy policy, it will be seen that the effect of human resource capacity, political resource capacity, capacity of economic resources and the capacity of social resources to the utilization of geothermal energy. The utilization of geothermal energy cannot be directly felt by the community without the support of the formulation of geothermal energy policy or unless it is supported by high human resources, political resources, economic resource and social resource capacities.

No previous research has comprehensively examined the effect of human resource, political resource, economic resource and social resource capacities on geothermal energy policy and its implications for the outcomes of geothermal energy policy.

The subjects considered in this research paper form the basic prerequisites for improving the effectiveness of organizational development. The author assumes that the effectiveness of any process depends on the size of the reserve of capacity of the resources used in the process (for details, see Appendix). The purpose of this paper is to determine the conditions for the effectiveness of improvement projects.

The author has constructed an algorithm (called ABC) to control the capacity of resources. With its help, the author defines the strategy for improvement in an organization. The basic principle of this strategy is continuous improvement, and tactics to control resources refers to the ABC algorithm.

The author shows that continuous improvement is a prerequisite for the effectiveness of this strategy. It is shown that the features of this strategy reflect sustainable development of the organization. Along with the algorithm ABC, it forms a basis for the sustainable development strategy. The author shows conditions for effectiveness and explains how the strategy shapes the sustainable development of the organization. The author also explains why commitment of top management is necessary, what it is, and how it can benefit the organization.

This effort is symbolized by the involvement of resources in projects conducted to improve organizational functions. The result of the capacity growth is increased ability to confront new challenges.

The author claims that the strategy for sustainable development is shaping the conditions for a gradual increase in the capacity of the resources used in the improvement process. The growth in resource capacity in the repeatedly run improvement projects can serve as a model of organizational learning. The learning process requires effort.

This study aims to examine the potential impact of external complementary resources on inbound open innovation and whether transformative capacity acts as a mediator in the process. If small and medium enterprises (SMEs) are to successfully implement inbound open innovation, they require injections of external complementary resources.

Structural equation modeling was used to conduct confirmatory factor analysis to evaluate measurement model, while ordinary least squares regression analysis was used to test the hypotheses; research data are collected using surveys of 200 Taiwanese-owned SMEs in mainland China.

Empirical results indicate that when SMEs seek to implement inbound open innovation, technological complementarity is the most important factor in resource complementarity. In addition, transformative capacity has a significant mediating role on the relationship between resource complementarity and inbound open innovation.

The contribution of this paper lies in explaining the role played by transformative capacity in the process of inbound open innovation for SMEs through empirical analysis.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

Quality management (QM) principles define the values of the organizational culture that makes people focus on quality assurance and improvement. However, the formulation of the principles of the ISO 9000 standard published in 2000–2015 is more like a list of things to remember that result from the development of QM methodologies, rather than systematic guidelines to follow. Their ambiguity does not facilitate understanding and can lead to activities that do not bring benefits to the organization. The purpose of this paper is to formulate such a system of directives that when implemented allows achieving the desired level of effectiveness of the organization’s processes with increased efficiency, and some measures can be assigned that show the progress of implementation.

The resource capacity reserve was adopted as the main factor ensuring the effectiveness of the planned tasks. A systemic analysis of relationships between the QM principles and the categories of Deming’s system of profound knowledge was carried out. Some factors referred to in the principles have been distinguished. The conditions necessary to achieve their positive impact on the capacity of organization’s resources have been formulated in terms of some directives for management processes. The interaction of the components of variability in organization processes with the factors was discussed.

Eight directives for effectiveness management (EM) have been established. They define independent dimensions in the management tactics for the development of the organization’s resources necessary to ensure and increase the effectiveness of the organization’s processes. The implementation of the tactics defined by EM directives, assuming randomness in processes, consists in the demonstration of care for trust within the organization, organization development and confidence in the organization of external partners.

Two key QM functions, quality assurance and improvement, are implemented in management processes in order to provide a balance between available capacity and tasks allocated to the organization’s processes, and increase the available capacity by eliminating gaps in knowledge used to control resources in the processes. The EM directives show the role of the organization’s knowledge and stakeholder commitment in providing the necessary complement to the capacity available in the organization. The confidence in the organization is shown as a key premise for access to the commitment of stakeholders. They also show that QM functions would be unnecessary or trivial if one could eliminate random variation from processes in the organization.

Effective procurement of infrastructure is linked to the attainment of the sustainable development goals set by the United Nations. While the capacity of organisations is generally thought to be related to organisational performance, there is a lack of empirical insights concerning the contribution of procurement capacity of public organisations towards the attainment of procurement objectives in infrastructure procurement. Thus, it is unclear which aspects of the capacity of public procurement organisations contribute the most to the attainment of procurement objectives in the procurement of infrastructure. This research sought to address this gap.

The research used a survey of public procurement professionals which yielded 590 responses.

Exploratory factor analysis of 23 organisational capacity items revealed three components of organisational procurement capacity: “management of the procurement process”; “human and physical resources”; and “financial resources and management”. Multiple regression modelling of the relationship between the components and the attainment of 12 procurement objectives further reveals that there is a significant positive relationship between the three components and all the objectives. However, “management of the procurement process” emerged as the greatest contributor to the attainment of seven objectives, whereas “human and physical resources”, and “financial resources and management” were the greatest contributor to the attainment of one objective and four objectives, respectively.

The study provides strong empirical justification for investment in the development of procurement capacity of public agencies involved in procurement of infrastructure. Furthermore, procurement capacity development of specific capacity components can be prioritised based on the relative contribution of capacity components to the attainment of desired procurement objectives. This should be useful to government policymakers as well as multilateral organisations that fund infrastructure and procurement reforms in various countries.

Discusses the difficulties of measuring, defining and managing capacity; and develops a model which splits it into two components (resource and ability) plus several sub‐components, and recognizes the interfaces between them. Illustrates and defines the sub‐components and identifies three states of capacity loading: resource‐loaded (over‐resourced), ability‐loaded (e.g. over‐qualified staff) and even‐capacity (i.e. resources compatible with ability). Asserts that the relative capacities of firms within an industry form a “capacity curve” with ability‐loaded small firms, medium firms at even capacity and large firms resource‐loaded. Analyses 1994‐1997 data for the US electronics and electrical equipment industry to plot its capacity curve, explains the methodology used, and shows how the regression model can be applied to individual firms within the industry to improve capacity management. Recognizes the limitations of the study.