Input and Output Variables
\r\n\tThe eye is our window to the brain. Vision is the ability to interpret and understand the information that comes in through the eyes. The visual system utilizes brain pathways to process and understand what the eyes sense. The dynamic process of vision is to identify, interpret and understand what the eyes see.
\r\n\tAn image is a sight which has been recreated. It is an appearance which has been detached from the place and time in which it first made its appearance. Sensing is not the same as seeing. The eyes and the nervous system do the sensing, while the mind does the perceiving.
\r\n\t
\r\n\tMedical imaging is the process of using technology to view the human body in the interest of diagnosing, monitoring, and treating medical problems. It is especially beneficial when it comes to detecting cancer. Such a threatening disease requires very early detection to improve the chances of survival. Medical imaging is an extremely important element in medical practice in the world of today. While medical knowledge and discernment forms the basis of diagnoses and decisions, medical imaging plays a vital role in confirming any diagnosis. With scientific advancement and a continued effective use, medical imaging will continue to help with earlier detection of health issues and provide increased preventative care.
\r\n\tThis book intends to provide readers with a comprehensive overview of the latest and most advanced findings in several aspects of ophthalmic pathology, treatment and surgical strategies, ocular imaging, vision sciences, medical images and perception that focuses on the most important developments in these critically important areas. Enough has been achieved already to make it clear that these fields have enormous possibilities for improving the human health.
\r\n\t
With the rapid development of the global economy, more people are living in urban than rural areas, thereby contributing to a significant increase in demand for energy, especially in emerging countries [1] [2] [3] [4]. The realization that fossil fuel resources required for energy generation are becoming scarce and that climate change is related to carbon emissions into the atmosphere has increased interest in energy conservation and environmental protection [5].
Among other recent issues, climate change, energy demand and fluctuations in international oil prices have become the focus of global attention. Renewable energy sources are now the fastest-growing sector of the energy mix and offer significant potential to address issues of energy security and sustainability [6]. All countries seeking to achieve the Kyoto Protocol target of reducing greenhouse gases, have renewable energy as the focus of their energy policy, and some have even become the mainstream of energy development. The energy we currently use is subject to unrestricted exploitation, not only about to run out of the global stock of face time, energy conversion process produce tangible and intangible waste, that have a significant effect on the global environment. To ensure a stable energy supply, enhance our energy supply security, reduce dependence on fossil fuels, and reduce greenhouse gas emissions, developing renewable energy sources has also become our current economic development and environmental resources for the biggest issue.
Thus, in face of the current trends, the demand for energy is rising. In addition to focusing on the power generation efficiency of power plants, we should also consider each unit of electricity efficiency to achieve an effective energy-saving effect in the pursuit of sustainable resource use. Therefore, we adopt a two-stage data envelopment analysis (DEA) [7] and incorporate two sub-processes into a DEA efficiency model to evaluate the level of management performance within renewable energy in the OECD countries. We measure managerial efficiency in two phases: operating efficiency (OE) and the energy density efficiency (DE).
This method is different from those of previous studies that focused primarily on assessing OE [8] [9]. We divide the efficiency of energy plants into two components. Management performance is no longer constrained with production efficiency but constitutes a broader dimension that covers operating activities and the efficiency of energy use. Compared to the traditional single-efficiency model, the sub-processes model is more suitable for evaluating the usage performance because of energy industry characteristics.
This evaluation model is useful for energy managers and current policy-makers. For managers, it provides a more detailed performance evaluation process including two essential operational elements in the energy generation industry; for policy- makers, it offers a complete measurement of efficiency and is based on variable combinations of these two dimensions; policy-makers can identify the most suitable policy (e.g., a subsidy) and develop the most effective strategy.
Taiwan is an island, country that is extremely lacking in energy and is more than 98% dependent on imported energy. Taiwan is also influenced by political and geographical constraints; therefore, the capacity to acquire energy is difficult compared to other countries. Thus, implementation of renewable energy and the abolition of nuclear power generation is a potential policy priority for Taiwan. Seeking the most cost-effective strategy, Taiwan\'s national conditions, if we can use the experience of other countries, will become Taiwan\'s development of a great help. OECD countries including highly and lowly developed countries, especially developing countries, is from the energy consumption, low efficiency and serious pollution to the economic development mode shift to energy efficient, less polluting economic development mode. In this study, we discuss and compare 34 OECD countries’ renewable energy OE and DE by DEA. Finally, we present our conclusions and provide suggestions for renewable energy development in Taiwan. β
Numerous studies related to the efficiency evaluation of renewable power plants have focused primarily on single efficiency and have assisted in the selection of input and output variables used in this study [10] [9]. First, Criswell and Thompson [11] applied DEA with a sample of large-scale commercial power systems for earth in global. They used three input and three output variables are exogenously fixed for the research. Azadeh, Ghaderi and Maghsoudi [12] used data from 25 cities in Iran with six regions within each city. Four types of input variables and two types of output variables were used in their analysis. More recently, Madlener, Antunes and Dias [13] justified the use of DEA logically and systematically in 41 agricultural biogas plants situated in Austria. They used three input and two output variables and identified that DEA offers considerable potential and advantages for seeking accurate evaluate productivity. Iglesias, Castellanos and Seijas [14] evaluated the performance of a group of 57 Spanish wind farms located in the region of Galicia by using three input and two output variables. Azadh, Ghaderi and Nasrollahi [15] measured the efficiency of wind power plants with the lowest possible costs using DEA, with data collected from 25 cities in Iran with 5 regions within each city using DEA with four input and two outputs.
In this section, we propose several hypotheses. Considering Sahelian countries, energy access remained relatively low until recently, despite the abundance of renewable resources such as wind and solar energy. The abundance of renewable resources assumes that access to renewable technologies could increase and improve energy access in remote rural areas [16]. They are compatible with local conditions and resource endowment. Research on regional development specifically related to China’s Western Development Program by the China Energy Strategic Research Group and Fan, Sun & Ren [17] discussed sustainable development issues for economically disadvantaged areas such as the ecological deterioration and sustainable livelihoods of rural households, and suggested reasonable approaches to address energy problems in these areas, such as the use of rich natural resources (endowment), development of renewable energy, and developing a moderate centralized energy supply that considers local energy endowment conditions. Shi [18] supported a similar type of energy development because a region’s unique energy endowments reflect it is energy developmental differences. Chen and Zhu [19] specifically used resource endowment, zoning separation of wind power and solar power resources, the classification results for the preliminary study on China’s energy and economic regionalization. Chen and Zhu argued that there is little evidence on whether the impact of economic development on the electricity mix is affected by energy resource endowments [19]. Marcotullio and Schulz [20] provided evidence of endowment\'s heterogeneity in energy mix transitions across countries. Therefore, we present the following hypotheses:
H1a: Endowment and OE are positively causal related
H1b: Endowment and DE are positively causal related
British Petroleum discussed China and India\'s rapid increase in energy use because they represent approximately one-third of the global population, the expected depletion of oil resources in the near future, and the effect of human activities on global climate change. Bettencourt [2] indicated that as economies and populations continue to grow rapidly, energy and power consumption also increase at the same rate. The Empresa de Pesquisa Energética (EPE) indicated that because of population growth, urbanization and higher income, annual electricity consumption in the residential sector is growing steadily from 4.7% in 2003 up to 6.2% in 2009. The International Energy Agency (IEA) [6] and United Nations (UN) [4] stated that approximately 4.9 billion people (80 % of the global population) lived in developing countries as of 2001. The current annual population growth rate is approximately 1.5 % in developing countries. However, despite the lower living standards and lower per capita energy use in developing countries, total energy use in developing countries is increasing fairly rapidly. Crane and Kinzig [4] indicated that many countries in the pursuit of economic development, the population increase rapidly as the same time, but also face a requirement to increase energy. There is a growing need to implement energy efficiency. Therefore, we present the following hypotheses:
H2a: Population and OE are positively causal related
H2b: Population and DE are positively causal related\n\t\t\t
Because energy efficiency improvement relies on total-factor productivity improvement [21], the technical efficiency (TE) index is computed to analyze the energy efficiencies of economies. The TE index incorporates energy, capital, and labor as multiple inputs for production. They use DEA to find the TE of each economy. Chien and Hu [22] stated that it is possible that capital inputs may increase energy generations. From an economic production perspective, these practices imply that energy savings as well and emission reduction can be achieved by means of factor substitution between energy and capital [16] [23] [24]. This effectively mitigates the dependence of economic growth on energy input and environmental capacity; in other words, it improves the aggregated energy and environmental efficiency (AEEE). Hudson and Jorgenson [25] stated that intensity effects in the industrial sector might depend on three strong interactions. Energy and capital are both, substitutes for labor, whereas capital and energy are complements. In other words, capital and energy can be increased simultaneously. Turner [26] proposed another factor of production that is critical in determining substitution and other effects driving economy-wide responses. Specifically, rebound effects, from increased energy efficiency are capital. Therefore, we present the following hypotheses:
H3a: Capital and OE are positively causal related
H3b: Capital and DE are positively causal related
The renewable energy-developing indicators of an economy are obtained from Renewables Energy Information [5] and have been published by the IEA since 2002. Indicators such as household consumption, capital formation, trade balance, energy imports, and gross domestic product (GDP) are obtained from the world energy development. Anderson and Leach [27] also indicated that if renewable energy technologies supply a significant share of total energy supply, then the energy storage problem must be solved in advance. First, the manner in which GDP affects the promotion of energy policies must be studied. Bettencourt [2] indicated that there seems to be a long way to go to fully use renewable resources. Until the early 1980s, changes in the energy–GDP ratio were the subject of many studies. Questions were raised as to how the ratio would evolve over time if a country experiences different stages of economic development. Understanding such trends provides indicators for how future energy demand would evolve. A number of studies have suggested that as the process of industrialization advances, with agriculture replaced by manufacturing, energy consumption tends to increase more rapidly than GDP, creating an increasing value of the energy–GDP ratio. Among the theories on the relationship between energy consumption (or energy-related environmental indicators) and GDP, the most famous is the environmental Kuznets curve. A recent overview was provided by Ang and Liu [28]. With the GDP measured in common units, comparisons can be made between countries. Cross-country variations in the energy–GDP ratio have been studied for industrialized countries and for developing countries [27] [29].
Therefore, we present the following hypotheses:
H4a: GDP and OE are positively causal related
H4b: GDP and DE are positively causal related
Some researchers have reached an opposing conclusion that energy subsidy reform would produce positive results. Steenblik and Coroyannakis [30] used the computable general equilibrium (CGE) model to simulate the positive effects of removing coal subsidies in Western European countries, such as promoting the industrialization of the power sector and increasing coal production and exports. United Nations [3] concluded that cutting energy subsidies could have significant impacts on residents, although this requires a more in-depth analysis in the future. Conversely, some researchers believed that fossil energy reform would increase energy use efficiency and household income levels. Choi, Roh and Yoon [9] indicated that increase in energy price could improve energy efficiency significantly. Thus, the energy price mechanism is at the core of energy reform, and energy subsidies are crucial determinant of energy prices.
Anderson and Leach [27] showed that energy subsidies in the United States would impede the use of new energy and reduce energy use efficiency. Shah and Larsen [31] showed that if the total energy subsidies worth almost $230 billion in 1990 could be removed, CO2 emissions worldwide would decrease by 9.5%. Using the global coal model, Lam and Shiu [32] analyzed coal subsidy reform in Japan; the results showed that removing the coal subsidies in the power supply and industrial boiler sector would reduce global CO2 emissions by 0.2%. The IEA [5] also indicated that global CO2 emissions would decrease by more than 6% by 2010 if the fossil energy subsidies in the power sector were removed. We use these research data to test and verify these countries, and the relationship between subsidies policy and efficiency. Therefore, Hypotheses 5a and 5b are as follow:
H5a: Verify that causal relationship between subsidy and OE
H5b: Verify that causal relationship between subsidy and DE
We adopted a two-stage DEA [7] to evaluate the level of management performance in renewable energy industries in OECD countries. These two types of efficiency are based on sub-processes that detail the two essential phases of a country’s renewable power plants: outputs provided and use generation. We then followed the approach by Seiford and Zhu [33], who divided the entire production activity into two sub-production processes. Fuel, labor, generating capacity, and operating expenses were the original input variables, whereas total primary renewable energy supply (TPES)/GDP ratio, TPES/population ratio, and grid were final output variables. Medial input variables included electricity-only plants (EOP), combined heat and power plants (CHP) of electricity, and CHP of heat, and heat-only plants (HOP). Figure 1 shows this process.
Two Phases DEA Model
Donthu, Hershberger, and Osmonbekok [10] emphasized the significance of variable selection because the research outcome is heavily dependent on the input and output variables used in the model. Their arguments led researchers to believe that there should be a more rigorous method than those of previous studies for selecting input and output variables for efficiency assessment.
Phase I Input Variables: Selection of input variables is critical task for performance analysis, and the choice of variables depends on the selected methodology and technical requirements, the availability and quality of data, and on countries’ individual socio-economic structures [34]. In this study, we use fuel, labor, generating capacity and operation expenses as our input:
Fuel: According to the IEA, renewable energy is divided into three categories of: (1) hydro fuel; (2) geothermal, solar, tidal, and wind fuel; and (3) combustible renewable energy and waste. The three categories of energy are all different in nature and cost [35]. On this basis, we discuss four renewable power sources: (1) Solar radiation: Glaser [36] provided a critical insight for a new source of solar energy. He proposed that large satellites be placed in geosynchronous orbit around Earth. These solar power satellites (SPS) would continually face the sun. Each SPS would convert a steady stream of sunlight to electric power, transform the electric power to microwave energy, and then transmit the microwaves in a tight beam to a receiver (rectenna) on Earth; (2) Wind speed: Boud and Thorpe [37] and, Bedard et al. [38] suggested that progress ratios from the wind and offshore engineering industries may be expected within the renewable energy industry; (3) Wave energy: Reviews of wave energy technologies are presented by Thorpe [39], among others. Wave energy conversion devices have been classified according to numerous features including their relative location to the shore, the wave mode that energy is captured from, or the device operational type; and (4) Bio-energy: Many studies have stated that the substitution of conventional fossil fuels with biomass for energy production results in a net reduction of greenhouse gas emissions and in the replacement of non-renewable energy sources [40] [41] [42].
Labor: Adjaye [43] and Ghosh [44] indicated that the relationship among output, energy use, and labor employment are built on an econometric framework. From a policy viewpoint, the direction of causality between these variables has important implications. Bettencourt [2] proposed the primary reason for the continued use of labor as an input was because labor cost is a significant cost in many industries. Dugan and Autor [45] and Morey [46] indicated that electric power production is a comprehensive process that includes generation, transmission, distribution, and retailing, involving large amounts of capital, labor, and financial resources.
Generating Capacity: Electric power production is a comprehensive process that includes generation, transmission, distribution, and retailing, involving large amounts of capital, labor, and financial resources [45] [46]. Furthermore, major infrastructure facilities, such as electric power and transport systems, have been improved [21].
Operation Expenses: Many studies on operational processes have been produced within the energy industry [47] a greater energy density of renewable at the design sites schemes increases the importance of efficient operations and maintenance (O&M) planning. Marcotullio and Schulz [20] indicated that controlling the operating costs results in achieving specific renovation and maintenance (R&M) program, adopting better maintenance practices and promoting greater plant utilization.
Electricity-Only Plants (EOP): EOP refers to plants that are designed to produce electricity only [48]. The electric power business is separated into the following four functions: generation, transmission, distribution and retailing. Numerous previous have studies applied DEA to evaluate the performance of electricity generation facilities in many industrialized nations [32] [49] [50] [51].
Heat-only Plants (HOP): HOP refers to plants designed to produce heat only [48]. The names used below for each model originate from the study by Agrell and Bogetoft [52]. The heat output used by Agrell and Bogetoft is the production at the plant and not the quantity sold to heat customers.
Combined Heat and Power Plants (CHP): CHP refers to plants designed to produce heat and electricity, occasionally referred to as co-generation power stations [48]. If possible, fuel inputs and electricity/heat outputs are on a unit basis rather than on a plant basis. However, if data are unavailable on a unit basis, the convention for defining a CHP plant is adopted [5].
Phase II Output Variables in: The results of recent studies have contributed to energy efficiency or environment efficiency evaluation problems that consider total production activity factors. Ramanathan [53] proposed an overall efficiency index that combined energy inputs, desirable outputs and undesirable outputs using DEA to study the relationships among global GDP, energy consumption, and carbon dioxide emissions. The final outputs used in this research, are as follows:
TEPS/GDP ratio: TPES per US. $1000 of GDP. The ratios are calculated by dividing each country\'s annual TPES by their annual GDP expressed in constant prices and converted to US. dollars using purchasing power parities (PPPs) (www.OECD-iLibrary.org). TPES consists of primary energy production adjusted for net trade, bunkers and stock changes.
TPES/Population ratio: TPES includes the sum of the unexplained statistical differences for individual fuels, as they appear in basic energy statistics [48]. TPES per population, ratios are calculated by dividing each country\'s TPES by unit people (www.OECD-iLibrary.org). This ratio can be used to obtain the electricity consumption of residents for each country.
Grid: The energy\'s transport length of line required to moor multiple devices is dependent on the spacing between devices and the array configuration. The length of cable required depends on the array configuration, although groups of devices are typically interconnected in series and each group is connected to a hub [38].
We used the intermediation approach to view the energy industry as intermediaries, and summarized the major input and output variables in Table 1.
This research is based on DEA of operating procedures. Though data collection and literature review on performance measurement of renewable power, we can understand the differences in renewable energy efficiency among 34 OECD countries (Table 2) and provide suggestions for Taiwan. The data obtained for this analysis were gathered from many relevant data resources, including the IEA, Renewable Information, World Bank, and other energy indices of a representative sample from 2007 to 2009. However, the data obtained from Renewable Energy Information [48] are used account for the full range of statistics collected from the Annual Renewables and Waste Questionnaire. This database of annual statistics for OECD countries covers hydroelectricity, solid biofuels, geothermal, renewable municipal waste, wind, gas from biomass, liquid biofuels, solar photovoltaic, solar thermal, tide/wave/ocean, non-renewable municipal waste and industrial waste. It includes EOP and HOP from renewable sources and supply/demand balances of renewable and waste products. The primary data from this system are from IEA annual publications.
\n\t\t\t\tInput Variables (Phase I)\n\t\t\t | \n\t\t|
1. Fuel | \n\t\t\tIEA; Glaser, 1977; Thorpe, 1999; Boud and Thorpe, 2003; Schneider and McCarl, 2003;Owen, 2004; Bedard et al., 2005; Previsic et al., 2005; Dowaki and Mori, 2005; Caputo et al., 2005 | \n\t\t
2. Labor | \n\t\t\tBuonafina, 1992; Adjaye, 2000; Morey, 2001; Ghosh, 2002; Dugan et al., 2002; | \n\t\t
3. Generating Capacity | \n\t\t\tDorian, 1998; Morey, 2001; Dugan et al., 2002 | \n\t\t
4. Operation Expenses | \n\t\t\tKannan and Pillai, 2000; Herman, 2002; AMEC, 2004 | \n\t\t
\n\t\t\t\tMedial Input/Output Variables (Output Phase I and Input Phase II)\n\t\t\t | \n\t\t|
1. EOP | \n\t\t\tOlatubi and Dismukes, 2000; Lam and Shiu, 2001; Nag, 2006; Pombo and Taborda, 2006; Sueyoshi and Goto, 2010; Renewables Information, 2011 | \n\t\t
2. HOP | \n\t\t\tAgrell and Bogetoft, 2004; Renewables Information, 2011 | \n\t\t
3. CHP | \n\t\t\tIEA; Renewable Information, 2011 | \n\t\t
\n\t\t\t\tOutput Variables (Phase II)\n\t\t\t | \n\t\t|
1. TEPS/GDP ratio | \n\t\t\tPPPs (www.OECD-iLibrary.org) | \n\t\t
2. TEPS/Population ratio | \n\t\t\tRenewable Information, 2011; PPPs | \n\t\t
3. Grid | \n\t\t\tHalcrow, 2005; Bedard et al., 2005 | \n\t\t
Input and Output Variables
Note. Source from this study
Data Envelopment Analysis (DEA) is a method for measuring the performance efficiency of decision units, characterizing by multiple input and output variables [8]. The DEA technique uses linear programming to estimate the maximum potential efficiency for various levels of inputs based on each firm’s actual inputs and output. DEA includes two major models, the CCR model, and the BCC model. Charnes, Cooper and Rhodes [54] proposed a model under the assumption of constant return to scale (CRS), called the CCR model. This model is only appropriate when all DMUs are operating at an optimal scale. Banker, Charnes and Cooper [55] extended the CCR model to include the variable returns to scale named the BCC model, which can further decompose the TE into two components: pure technical efficiency (PTE) and scale efficiency (SE). The problem of calculating efficiency can be formulated as a fractional linear programming problem as below:
We utilized the BCC input-oriented model to measure phase I and II to find a maximum output with certain medial output.
\n\t\t\t\tDMUs\n\t\t\t | \n\t\t\t\n\t\t\t\tCountry Name\n\t\t\t | \n\t\t\t\n\t\t\t\tDMUs\n\t\t\t | \n\t\t\t\n\t\t\t\tCountry Name\n\t\t\t | \n\t\t
D1 | \n\t\t\tAustralia | \n\t\t\tD18 | \n\t\t\tJapan | \n\t\t
D2 | \n\t\t\tAustria | \n\t\t\tD19 | \n\t\t\tKorea | \n\t\t
D3 | \n\t\t\tBelgium | \n\t\t\tD20 | \n\t\t\tLuxembourg | \n\t\t
D4 | \n\t\t\tCanada | \n\t\t\tD21 | \n\t\t\tMexico | \n\t\t
D5 | \n\t\t\tChile | \n\t\t\tD22 | \n\t\t\tNetherlands | \n\t\t
D6 | \n\t\t\tCzech Republic | \n\t\t\tD23 | \n\t\t\tNew Zealand | \n\t\t
D7 | \n\t\t\tDenmark | \n\t\t\tD24 | \n\t\t\tNorway | \n\t\t
D8 | \n\t\t\tEstonia | \n\t\t\tD25 | \n\t\t\tPoland | \n\t\t
D9 | \n\t\t\tFinland | \n\t\t\tD26 | \n\t\t\tPortugal | \n\t\t
D10 | \n\t\t\tFrance | \n\t\t\tD27 | \n\t\t\tSlovak Republic | \n\t\t
D11 | \n\t\t\tGermany | \n\t\t\tD28 | \n\t\t\tSlovenia | \n\t\t
D12 | \n\t\t\tGreece | \n\t\t\tD29 | \n\t\t\tSpain | \n\t\t
D13 | \n\t\t\tHungary | \n\t\t\tD30 | \n\t\t\tSweden | \n\t\t
D14 | \n\t\t\tIceland | \n\t\t\tD31 | \n\t\t\tSwitzerland | \n\t\t
D15 | \n\t\t\tIreland | \n\t\t\tD32 | \n\t\t\tTurkey | \n\t\t
D16 | \n\t\t\tIsrael | \n\t\t\tD33 | \n\t\t\tUnited Kingdom | \n\t\t
D17 | \n\t\t\tItaly | \n\t\t\tD34 | \n\t\t\tUnited States | \n\t\t
Country Names of each DMU
Note. Source from this study
First, multi-collinearity analysis was employed to examine the correlation coefficient between input and input variables, and then between output and output variables [56]. We used isotonicity diagnosis to examine positive correlation coefficients between input and output variables [57]. We then used sensitivity analysis to sequentially increase or reduce the input or output variables to examine variation of efficiency [8]. The obtained sensitivity analysis result does not consider the operating expenses because of their highly correlation. Additionally, we also test the rule of thumb issued by Golany and Roll [58]. The four tests are all hold.
Tables 3 and 4 report the BCC efficiency scores of OE and DE for the 34 OECD countries from 2007 to 2009. Table 3 shows the comparison of the main goal in phase I to evaluate how efficiently countries use their resources; in other words, to identify any inefficiency result from PTE or SE. The resource inefficiency 2007, 2008, and 2009 is primarily pure technical efficiency (0.729, 0.704, and 0.727, respectively). In other words, the inefficiency is a result of inappropriate input and output configuration, rather than inappropriate scale. Table 4 shows a comparison of the main goal in Phase II to evaluate how efficiency energy is used to identify inefficiency resulting from PTE or SE. The resource inefficiency during 2007, 2008, and 2009 is primarily scale efficiency (0.439, 0.431, and 0.45, respectively). In other words, the inefficiency is result of inappropriate scale.
Phase 1 | \n\t\t|||||||||
\n\t\t\t | 2007 | \n\t\t\t2008 | \n\t\t\t2009 | \n\t\t||||||
DMUs | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t
Ave. | \n\t\t\t0.666 | \n\t\t\t0.729 | \n\t\t\t0.917 | \n\t\t\t0.5 | \n\t\t\t0.704 | \n\t\t\t0.848 | \n\t\t\t0.597 | \n\t\t\t0.727 | \n\t\t\t0.812 | \n\t\t
SD | \n\t\t\t0.26 | \n\t\t\t0.247 | \n\t\t\t0.148 | \n\t\t\t0.266 | \n\t\t\t0.255 | \n\t\t\t0.176 | \n\t\t\t0.274 | \n\t\t\t0.247 | \n\t\t\t0.183 | \n\t\t
No. of Efficient DMUs Efficient DMUs | \n\t\t\t9 | \n\t\t\t11 | \n\t\t\t11 | \n\t\t\t6 | \n\t\t\t10 | \n\t\t\t7 | \n\t\t\t7 | \n\t\t\t13 | \n\t\t\t7 | \n\t\t
Phase 2 | \n\t\t|||||||||
\n\t\t\t | 2007 | \n\t\t\t2008 | \n\t\t\t2009 | \n\t\t||||||
DMUs | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t
Ave. | \n\t\t\t0.387 | \n\t\t\t0.735 | \n\t\t\t0.439 | \n\t\t\t0.373 | \n\t\t\t0.726 | \n\t\t\t0.431 | \n\t\t\t0.367 | \n\t\t\t0.718 | \n\t\t\t0.45 | \n\t\t
SD | \n\t\t\t0.445 | \n\t\t\t0.293 | \n\t\t\t0.474 | \n\t\t\t0.441 | \n\t\t\t0.291 | \n\t\t\t0.48 | \n\t\t\t0.407 | \n\t\t\t0.285 | \n\t\t\t0.431 | \n\t\t
No. of Efficient DMUs Efficient DMUs | \n\t\t\t9 | \n\t\t\t15 | \n\t\t\t12 | \n\t\t\t8 | \n\t\t\t14 | \n\t\t\t12 | \n\t\t\t7 | \n\t\t\t12 | \n\t\t\t11 | \n\t\t
BCC-efficiency Scores for operating efficiency for each year
Note. Source from this study
We employed the Mann-Whitney U-Test, a non-parameter statistical method, to test the same mean between two groups. The results in Table 4 show that the OE and DE for all cases do not achieve a level of significance (p >.05) for all compared years. Therefore, these 3-years are suitable for the DEA model using 102 DMUs to determine if there is a significant difference between OE and DE (Table 5).
Phase 1 | \n\t\t||||
Case | \n\t\t\tTest Value | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t
Between 2008 and 2007 | \n\t\t\tZ Test | \n\t\t\t-0.21 | \n\t\t\t-0.372 | \n\t\t\t-0.501 | \n\t\t
p-vale | \n\t\t\t0.834 | \n\t\t\t0.71 | \n\t\t\t0.617 | \n\t\t|
Between 20.50.58 and 20.50.57 | \n\t\t\tZ Test | \n\t\t\t-1.349 | \n\t\t\t-0.604 | \n\t\t\t-1.571 | \n\t\t
p-vale | \n\t\t\t0.177 | \n\t\t\t0.546 | \n\t\t\t0.077 | \n\t\t|
Phase 2 | \n\t\t||||
Case | \n\t\t\tTest Value | \n\t\t\tTE | \n\t\t\tPTE | \n\t\t\tSE | \n\t\t
Between 2008 and 2007 | \n\t\t\tZ Test | \n\t\t\t-0.98 | \n\t\t\t-0.234 | \n\t\t\t-0.5.057 | \n\t\t
p-vale | \n\t\t\t0.327 | \n\t\t\t0.815 | \n\t\t\t0.29 | \n\t\t|
Between 20.50.58 and 20.50.57 | \n\t\t\tZ Test | \n\t\t\t-0.433 | \n\t\t\t-0.179 | \n\t\t\t-0.345 | \n\t\t
p-vale | \n\t\t\t0.665 | \n\t\t\t0.858 | \n\t\t\t0.73 | \n\t\t
Results of Mann – Whitney U Test
Note. Source from this study
\n\t\t\t | 2007 | \n\t\t\t2008 | \n\t\t\t2009 | \n\t\t|||
DMUs | \n\t\t\t0E | \n\t\t\tDE | \n\t\t\tOE | \n\t\t\tDE | \n\t\t\tOE | \n\t\t\tDE | \n\t\t
Ave. | \n\t\t\t0.666 | \n\t\t\t0.387 | \n\t\t\t0.5 | \n\t\t\t0.373 | \n\t\t\t0.597 | \n\t\t\t0.367 | \n\t\t
SD | \n\t\t\t0.26 | \n\t\t\t0.445 | \n\t\t\t0.266 | \n\t\t\t0.441 | \n\t\t\t0.274 | \n\t\t\t0.407 | \n\t\t
No. of Efficient DMUs | \n\t\t\t9 | \n\t\t\t9 | \n\t\t\t6 | \n\t\t\t8 | \n\t\t\t7 | \n\t\t\t7 | \n\t\t
Bcc-efficiency score for OE and DE for each year
The Mann-Whitney U-Test is also used to determine if there is a significant difference between OE and DE before and after 2008 (Table 6). The results show that the global financial crisis did not influence OE and DE. Because OE and DE are non-significant, we can assert that the data are consistent and that renewable energy capital investments in each country have a certain proportion; thus, 2008 financial crisis did not have s significant influence on renewable energy development. This implies that the development of renewable energy is crucial. Furthermore, we want to determine if there is a significant difference between OE and DE.
Case | \n\t\t\tTest Value | \n\t\t\tOE | \n\t\t\tDE | \n\t\t\tOEvsDE | \n\t\t
Between 2008 and 2007 | \n\t\t\tZ Test | \n\t\t\t-0.006 | \n\t\t\t-0.98 | \n\t\t\t-2.812 | \n\t\t
p-vale | \n\t\t\t0.995 | \n\t\t\t0.327 | \n\t\t\t0.005** | \n\t\t|
Between 20.50.58 and 20.50.57 | \n\t\t\tZ Test | \n\t\t\t-1.258 | \n\t\t\t-0.5.433 | \n\t\t\t-2.819 | \n\t\t
p-vale | \n\t\t\t0.208 | \n\t\t\t0.665 | \n\t\t\t0.005** | \n\t\t
Results of Mann – Whitney U Test of OE and DE
Note. Source from this study
Tobit regression analysis was conducted to determine whether the efficiency scores are related to characteristics such as GDP, population, capital, endowment and subsidy (Table 7). Furthermore, a dummy variable was included to evaluate the renewable energy subsidies in OECD countries. The function of a regression model can be expressed as:
derived from:
The Tobit regression analysis result shows that endowment, population, and capital all have high positive significance with OE and DE. Thus, H1a, H1b, H2a, H2b, H3a, and H3b are supported. However, GDP has a non-significant negative correlation with OE and DE. Thus, H4a and H4B are rejected. GDP, capital, trade balance, household consumption, and energy imports are critical factors for measuring renewable energy indicators [5]. Our finding in H4a and H4b is that the GDP and OE are negatively correlated, and GDP and DE are also negative correlated. This is potentially because countries did not allocate the use of renewable energy in accordance with GDP degree. For example, compared to poorer countries, wealthy countries must improve the relatively large number of renewable energy use to achieve the target.
Specially, subsidies are significant with OE but not with DE. In other words, the subsidy is positively correlated with OE but negatively correlated with DE. Thus, H5a is supported and H5b is rejected. Some researchers have reached an opposing conclusion that subsidies and OE are positively correlated and that energy subsidy reform would produce positive results. Promoting “subsidy” policies can reduce industrial production costs. However, if they are implemented inefficiently without carefully assessing the cost-efficiency and associated financial risks, a "free rider" phenomenon is created with consequent disadvantages; thus, the subsidies do not have a positive benefit. Therefore, renewable energy subsidies and DE may be negatively correlated [59].
Model | \n\t\t\tPhase 1 | \n\t\t\tPhase 2 | \n\t\t||||
Dependent Variables | \n\t\t\tOE | \n\t\t\tDE | \n\t\t||||
\n\t\t\t | β-value coe. | \n\t\t\tt-value | \n\t\t\tp-value | \n\t\t\tβ-value | \n\t\t\tt-value | \n\t\t\tp-value | \n\t\t
Independent Variables | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
Endowment | \n\t\t\t3.131 | \n\t\t\t-4.7 | \n\t\t\t0.013** | \n\t\t\t-14.294 | \n\t\t\t2.54 | \n\t\t\t0.000** | \n\t\t
Population | \n\t\t\t-0.004 | \n\t\t\t4.43 | \n\t\t\t0.006** | \n\t\t\t0.13 | \n\t\t\t-2.83 | \n\t\t\t0.000** | \n\t\t
Capital | \n\t\t\t0.001 | \n\t\t\t-3.3 | \n\t\t\t0.018** | \n\t\t\t-0.007 | \n\t\t\t2.4 | \n\t\t\t0.001** | \n\t\t
GDP | \n\t\t\t-2.3 | \n\t\t\t0.39 | \n\t\t\t0.961 | \n\t\t\t0.001 | \n\t\t\t-0.05 | \n\t\t\t0.695 | \n\t\t
Subsidy | \n\t\t\t0.17 | \n\t\t\t-0.26 | \n\t\t\t0.017** | \n\t\t\t-0.029 | \n\t\t\t2.42 | \n\t\t\t0.797 | \n\t\t
r2\n\t\t\t | \n\t\t\t\n\t\t\t | 0.337 | \n\t\t\t\n\t\t\t | 0.264 | \n\t\t||
F-value | \n\t\t\t\n\t\t\t | 4.04 | \n\t\t\t\n\t\t\t | 8.69 | \n\t\t||
P-value | \n\t\t\t\n\t\t\t | 0.000** | \n\t\t\t\n\t\t\t | 0.000** | \n\t\t
Estimated Results of the Tobit Regression Analysis
Note. Here is the efficiency scores derived from operating efficiency (OE) and density efficiency (DE). The observation is 102. ** represents significant at 0.05 level and * represents significant at 0.1 level.
Numerous DEA studies have incorporated the concept of production activities with multiple phases. They subsequently divided the DEA model into several sub-processes [60] [55] [33] [61] [62] [19]. Our first finding in this research is that there is a significant difference between OE and DE. In other words, sub-process DEA model is suitable for measuring management performance because of the characteristic of production activities in renewable energy industries [61].
OECD countries in response to United Nations climate Change Framework Convention and the relevant provisions of the Kyoto Protocol, In addition to adjusting the energy supply and demand side policies, and with the greenhouse gas performance of fiscal policy (subsidy) to promote energy conservation and reduce dioxide emissions [63]. Our second finding is that subsidies are positively correlated with OE and negatively correlated with DE. Promoting subsidy policies can reduce industrial production cost. However, if they are implemented inefficiently without carefully assessing the cost-efficiency and associated financial risks, a “free rider” phenomenon is created with consequent disadvantages; thus, the subsidies do not have a positive benefit. Therefore, renewable energy subsidies and DE may be negatively correlated [59].
In our study, we attempted to measure OECD countries’ renewable OE and DE simultaneously, to examine the OECD renewable energy’s promote, employ, and the relevance to the development of research, and provide feasible suggestions for a renewable energy development strategy in Taiwan. For example, because Taiwan is an island country, and resources are difficult to obtain, efforts should be made to actively develop renewable energy technology to replace traditional energy sources. In addition, policy-makers should assess renewable energy subsidy programs, promote renewable energy industry research and development, assist the industry in developing cost-efficient production technologies, and develop a new energy market. Furthermore, strengthen the use of renewable energy demonstration and propaganda work, and to enhance the efficiency of the client to use.
Finally, this study has several limitations that require discussion. First, only 34 OECD samples were selected that could provide the data required to conduct this study. Future research could include more countries, especially developing countries, such as Taiwan, China, and India to achieve more precise results. Second, non-financial data such as output quality and investment of renewable land were not included in our model. These variables are also critical factors for the evaluation of energy industry performance. Future research could include this as an additional evaluation variable. Finally, in this study, we independently tested and verified the two phases of efficiency. However, future research could use a supply chain model that assumes that the two phases of efficiency are dependent and further evaluate the real scores of management efficiency.
Research methodology is the path through which researchers need to conduct their research. It shows the path through which these researchers formulate their problem and objective and present their result from the data obtained during the study period. This research design and methodology chapter also shows how the research outcome at the end will be obtained in line with meeting the objective of the study. This chapter hence discusses the research methods that were used during the research process. It includes the research methodology of the study from the research strategy to the result dissemination. For emphasis, in this chapter, the author outlines the research strategy, research design, research methodology, the study area, data sources such as primary data sources and secondary data, population consideration and sample size determination such as questionnaires sample size determination and workplace site exposure measurement sample determination, data collection methods like primary data collection methods including workplace site observation data collection and data collection through desk review, data collection through questionnaires, data obtained from experts opinion, workplace site exposure measurement, data collection tools pretest, secondary data collection methods, methods of data analysis used such as quantitative data analysis and qualitative data analysis, data analysis software, the reliability and validity analysis of the quantitative data, reliability of data, reliability analysis, validity, data quality management, inclusion criteria, ethical consideration and dissemination of result and its utilization approaches. In order to satisfy the objectives of the study, a qualitative and quantitative research method is apprehended in general. The study used these mixed strategies because the data were obtained from all aspects of the data source during the study time. Therefore, the purpose of this methodology is to satisfy the research plan and target devised by the researcher.
The research design is intended to provide an appropriate framework for a study. A very significant decision in research design process is the choice to be made regarding research approach since it determines how relevant information for a study will be obtained; however, the research design process involves many interrelated decisions [1].
This study employed a mixed type of methods. The first part of the study consisted of a series of well-structured questionnaires (for management, employee’s representatives, and technician of industries) and semi-structured interviews with key stakeholders (government bodies, ministries, and industries) in participating organizations. The other design used is an interview of employees to know how they feel about safety and health of their workplace, and field observation at the selected industrial sites was undertaken.
Hence, this study employs a descriptive research design to agree on the effects of occupational safety and health management system on employee health, safety, and property damage for selected manufacturing industries. Saunders et al. [2] and Miller [3] say that descriptive research portrays an accurate profile of persons, events, or situations. This design offers to the researchers a profile of described relevant aspects of the phenomena of interest from an individual, organizational, and industry-oriented perspective. Therefore, this research design enabled the researchers to gather data from a wide range of respondents on the impact of safety and health on manufacturing industries in Ethiopia. And this helped in analyzing the response obtained on how it affects the manufacturing industries’ workplace safety and health. The research overall design and flow process are depicted in Figure 1.
Research methods and processes (author design).
To address the key research objectives, this research used both qualitative and quantitative methods and combination of primary and secondary sources. The qualitative data supports the quantitative data analysis and results. The result obtained is triangulated since the researcher utilized the qualitative and quantitative data types in the data analysis. The study area, data sources, and sampling techniques were discussed under this section.
According to Fraenkel and Warren [4] studies, population refers to the complete set of individuals (subjects or events) having common characteristics in which the researcher is interested. The population of the study was determined based on random sampling system. This data collection was conducted from March 07, 2015 to December 10, 2016, from selected manufacturing industries found in Addis Ababa city and around. The manufacturing companies were selected based on their employee number, established year, and the potential accidents prevailing and the manufacturing industry type even though all criterions were difficult to satisfy.
It was obtained from the original source of information. The primary data were more reliable and have more confidence level of decision-making with the trusted analysis having direct intact with occurrence of the events. The primary data sources are industries’ working environment (through observation, pictures, and photograph) and industry employees (management and bottom workers) (interview, questionnaires and discussions).
Desk review has been conducted to collect data from various secondary sources. This includes reports and project documents at each manufacturing sectors (more on medium and large level). Secondary data sources have been obtained from literatures regarding OSH, and the remaining data were from the companies’ manuals, reports, and some management documents which were included under the desk review. Reputable journals, books, different articles, periodicals, proceedings, magazines, newsletters, newspapers, websites, and other sources were considered on the manufacturing industrial sectors. The data also obtained from the existing working documents, manuals, procedures, reports, statistical data, policies, regulations, and standards were taken into account for the review.
In general, for this research study, the desk review has been completed to this end, and it had been polished and modified upon manuals and documents obtained from the selected companies.
The study population consisted of manufacturing industries’ employees in Addis Ababa city and around as there are more representative manufacturing industrial clusters found. To select representative manufacturing industrial sector population, the types of the industries expected were more potential to accidents based on random and purposive sampling considered. The population of data was from textile, leather, metal, chemicals, and food manufacturing industries. A total of 189 sample sizes of industries responded to the questionnaire survey from the priority areas of the government. Random sample sizes and disproportionate methods were used, and 80 from wood, metal, and iron works; 30 from food, beverage, and tobacco products; 50 from leather, textile, and garments; 20 from chemical and chemical products; and 9 from other remaining 9 clusters of manufacturing industries responded.
A simple random sampling and purposive sampling methods were used to select the representative manufacturing industries and respondents for the study. The simple random sampling ensures that each member of the population has an equal chance for the selection or the chance of getting a response which can be more than equal to the chance depending on the data analysis justification. Sample size determination procedure was used to get optimum and reasonable information. In this study, both probability (simple random sampling) and nonprobability (convenience, quota, purposive, and judgmental) sampling methods were used as the nature of the industries are varied. This is because of the characteristics of data sources which permitted the researchers to follow the multi-methods. This helps the analysis to triangulate the data obtained and increase the reliability of the research outcome and its decision. The companies’ establishment time and its engagement in operation, the number of employees and the proportion it has, the owner types (government and private), type of manufacturing industry/production, types of resource used at work, and the location it is found in the city and around were some of the criteria for the selections.
The determination of the sample size was adopted from Daniel [5] and Cochran [6] formula. The formula used was for unknown population size Eq. (1) and is given as
where n = sample size, Z = statistic for a level of confidence, P = expected prevalence or proportion (in proportion of one; if 50%, P = 0.5), and d = precision (in proportion of one; if 6%, d = 0.06). Z statistic (Z): for the level of confidence of 95%, which is conventional, Z value is 1.96. In this study, investigators present their results with 95% confidence intervals (CI).
The expected sample number was 267 at the marginal error of 6% for 95% confidence interval of manufacturing industries. However, the collected data indicated that only 189 populations were used for the analysis after rejecting some data having more missing values in the responses from the industries. Hence, the actual data collection resulted in 71% response rate. The 267 population were assumed to be satisfactory and representative for the data analysis.
The sample size for the experimental exposure measurements of physical work environment has been considered based on the physical data prepared for questionnaires and respondents. The response of positive were considered for exposure measurement factors to be considered for the physical environment health and disease causing such as noise intensity, light intensity, pressure/stress, vibration, temperature/coldness, or hotness and dust particles on 20 workplace sites. The selection method was using random sampling in line with purposive method. The measurement of the exposure factors was done in collaboration with Addis Ababa city Administration and Oromia Bureau of Labour and Social Affair (AACBOLSA). Some measuring instruments were obtained from the Addis Ababa city and Oromia Bureau of Labour and Social Affair.
Data collection methods were focused on the followings basic techniques. These included secondary and primary data collections focusing on both qualitative and quantitative data as defined in the previous section. The data collection mechanisms are devised and prepared with their proper procedures.
Primary data sources are qualitative and quantitative. The qualitative sources are field observation, interview, and informal discussions, while that of quantitative data sources are survey questionnaires and interview questions. The next sections elaborate how the data were obtained from the primary sources.
Observation is an important aspect of science. Observation is tightly connected to data collection, and there are different sources for this: documentation, archival records, interviews, direct observations, and participant observations. Observational research findings are considered strong in validity because the researcher is able to collect a depth of information about a particular behavior. In this dissertation, the researchers used observation method as one tool for collecting information and data before questionnaire design and after the start of research too. The researcher made more than 20 specific observations of manufacturing industries in the study areas. During the observations, it found a deeper understanding of the working environment and the different sections in the production system and OSH practices.
Interview is a loosely structured qualitative in-depth interview with people who are considered to be particularly knowledgeable about the topic of interest. The semi-structured interview is usually conducted in a face-to-face setting which permits the researcher to seek new insights, ask questions, and assess phenomena in different perspectives. It let the researcher to know the in-depth of the present working environment influential factors and consequences. It has provided opportunities for refining data collection efforts and examining specialized systems or processes. It was used when the researcher faces written records or published document limitation or wanted to triangulate the data obtained from other primary and secondary data sources.
This dissertation is also conducted with a qualitative approach and conducting interviews. The advantage of using interviews as a method is that it allows respondents to raise issues that the interviewer may not have expected. All interviews with employees, management, and technicians were conducted by the corresponding researcher, on a face-to-face basis at workplace. All interviews were recorded and transcribed.
The main tool for gaining primary information in practical research is questionnaires, due to the fact that the researcher can decide on the sample and the types of questions to be asked [2].
In this dissertation, each respondent is requested to reply to an identical list of questions mixed so that biasness was prevented. Initially the questionnaire design was coded and mixed up from specific topic based on uniform structures. Consequently, the questionnaire produced valuable data which was required to achieve the dissertation objectives.
The questionnaires developed were based on a five-item Likert scale. Responses were given to each statement using a five-point Likert-type scale, for which 1 = “strongly disagree” to 5 = “strongly agree.” The responses were summed up to produce a score for the measures.
The data was also obtained from the expert’s opinion related to the comparison of the knowledge, management, collaboration, and technology utilization including their sub-factors. The data obtained in this way was used for prioritization and decision-making of OSH, improving factor priority. The prioritization of the factors was using Saaty scales (1–9) and then converting to Fuzzy set values obtained from previous researches using triangular fuzzy set [7].
The researcher has measured the workplace environment for dust, vibration, heat, pressure, light, and noise to know how much is the level of each variable. The primary data sources planned and an actual coverage has been compared as shown in Table 1.
Planned versus actual coverage of the survey.
The response rate for the proposed data source was good, and the pilot test also proved the reliability of questionnaires. Interview/discussion resulted in 87% of responses among the respondents; the survey questionnaire response rate obtained was 71%, and the field observation response rate was 90% for the whole data analysis process. Hence, the data organization quality level has not been compromised.
This response rate is considered to be representative of studies of organizations. As the study agrees on the response rate to be 30%, it is considered acceptable [8]. Saunders et al. [2] argued that the questionnaire with a scale response of 20% response rate is acceptable. Low response rate should not discourage the researchers, because a great deal of published research work also achieves low response rate. Hence, the response rate of this study is acceptable and very good for the purpose of meeting the study objectives.
The pretest for questionnaires, interviews, and tools were conducted to validate that the tool content is valid or not in the sense of the respondents’ understanding. Hence, content validity (in which the questions are answered to the target without excluding important points), internal validity (in which the questions raised answer the outcomes of researchers’ target), and external validity (in which the result can generalize to all the population from the survey sample population) were reflected. It has been proved with this pilot test prior to the start of the basic data collections. Following feedback process, a few minor changes were made to the originally designed data collect tools. The pilot test made for the questionnaire test was on 10 sample sizes selected randomly from the target sectors and experts.
The secondary data refers to data that was collected by someone other than the user. This data source gives insights of the research area of the current state-of-the-art method. It also makes some sort of research gap that needs to be filled by the researcher. This secondary data sources could be internal and external data sources of information that may cover a wide range of areas.
Literature/desk review and industry documents and reports: To achieve the dissertation’s objectives, the researcher has conducted excessive document review and reports of the companies in both online and offline modes. From a methodological point of view, literature reviews can be comprehended as content analysis, where quantitative and qualitative aspects are mixed to assess structural (descriptive) as well as content criteria.
A literature search was conducted using the database sources like MEDLINE; Emerald; Taylor and Francis publications; EMBASE (medical literature); PsycINFO (psychological literature); Sociological Abstracts (sociological literature); accident prevention journals; US Statistics of Labor, European Safety and Health database; ABI Inform; Business Source Premier (business/management literature); EconLit (economic literature); Social Service Abstracts (social work and social service literature); and other related materials. The search strategy was focused on articles or reports that measure one or more of the dimensions within the research OSH model framework. This search strategy was based on a framework and measurement filter strategy developed by the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Based on screening, unrelated articles to the research model and objectives were excluded. Prior to screening, researcher (principal investigator) reviewed a sample of more than 2000 articles, websites, reports, and guidelines to determine whether they should be included for further review or reject. Discrepancies were thoroughly identified and resolved before the review of the main group of more than 300 articles commenced. After excluding the articles based on the title, keywords, and abstract, the remaining articles were reviewed in detail, and the information was extracted on the instrument that was used to assess the dimension of research interest. A complete list of items was then collated within each research targets or objectives and reviewed to identify any missing elements.
Data analysis method follows the procedures listed under the following sections. The data analysis part answered the basic questions raised in the problem statement. The detailed analysis of the developed and developing countries’ experiences on OSH regarding manufacturing industries was analyzed, discussed, compared and contrasted, and synthesized.
Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.
Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [9, 10]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.
Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.
Qualitative data analysis used for triangulation of the quantitative data analysis. The interview, observation, and report records were used to support the findings. The analysis has been incorporated with the quantitative discussion results in the data analysis parts.
The data were entered using SPSS 20.0 on Windows 10 and analyzed. The analysis supported with SPSS software much contributed to the finding. It had contributed to the data validation and correctness of the SPSS results. The software analyzed and compared the results of different variables used in the research questionnaires. Excel is also used to draw the pictures and calculate some analytical solutions.
The reliability of measurements specifies the amount to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument [8]. In reliability analysis, it has been checked for the stability and consistency of the data. In the case of reliability analysis, the researcher checked the accuracy and precision of the procedure of measurement. Reliability has numerous definitions and approaches, but in several environments, the concept comes to be consistent [8]. The measurement fulfills the requirements of reliability when it produces consistent results during data analysis procedure. The reliability is determined through Cranach’s alpha as shown in Table 2.
Internal consistency and reliability test of questionnaires items.
K stands for knowledge; M, management; T, technology; C, collaboration; P, policy, standards, and regulation; H, hazards and accident conditions; PPE, personal protective equipment.
Cronbach’s alpha is a measure of internal consistency, i.e., how closely related a set of items are as a group [11]. It is considered to be a measure of scale reliability. The reliability of internal consistency most of the time is measured based on the Cronbach’s alpha value. Reliability coefficient of 0.70 and above is considered “acceptable” in most research situations [12]. In this study, reliability analysis for internal consistency of Likert-scale measurement after deleting 13 items was found similar; the reliability coefficients were found for 76 items were 0.964 and for the individual groupings made shown in Table 2. It was also found internally consistent using the Cronbach’s alpha test. Table 2 shows the internal consistency of the seven major instruments in which their reliability falls in the acceptable range for this research.
Face validity used as defined by Babbie [13] is an indicator that makes it seem a reasonable measure of some variables, and it is the subjective judgment that the instrument measures what it intends to measure in terms of relevance [14]. Thus, the researcher ensured, in this study, when developing the instruments that uncertainties were eliminated by using appropriate words and concepts in order to enhance clarity and general suitability [14]. Furthermore, the researcher submitted the instruments to the research supervisor and the joint supervisor who are both occupational health experts, to ensure validity of the measuring instruments and determine whether the instruments could be considered valid on face value.
In this study, the researcher was guided by reviewed literature related to compliance with the occupational health and safety conditions and data collection methods before he could develop the measuring instruments. In addition, the pretest study that was conducted prior to the main study assisted the researcher to avoid uncertainties of the contents in the data collection measuring instruments. A thorough inspection of the measuring instruments by the statistician and the researcher’s supervisor and joint experts, to ensure that all concepts pertaining to the study were included, ensured that the instruments were enriched.
Insight has been given to the data collectors on how to approach companies, and many of the questionnaires were distributed through MSc students at Addis Ababa Institute of Technology (AAiT) and manufacturing industries’ experience experts. This made the data quality reliable as it has been continually discussed with them. Pretesting for questionnaire was done on 10 workers to assure the quality of the data and for improvement of data collection tools. Supervision during data collection was done to understand how the data collectors are handling the questionnaire, and each filled questionnaires was checked for its completeness, accuracy, clarity, and consistency on a daily basis either face-to-face or by phone/email. The data expected in poor quality were rejected out of the acting during the screening time. Among planned 267 questionnaires, 189 were responded back. Finally, it was analyzed by the principal investigator.
The data were collected from the company representative with the knowledge of OSH. Articles written in English and Amharic were included in this study. Database information obtained in relation to articles and those who have OSH area such as interventions method, method of accident identification, impact of occupational accidents, types of occupational injuries/disease, and impact of occupational accidents, and disease on productivity and costs of company and have used at least one form of feedback mechanism. No specific time period was chosen in order to access all available published papers. The questionnaire statements which are similar in the questionnaire have been rejected from the data analysis.
Ethical clearance was obtained from the School of Mechanical and Industrial Engineering, Institute of Technology, Addis Ababa University. Official letters were written from the School of Mechanical and Industrial Engineering to the respective manufacturing industries. The purpose of the study was explained to the study subjects. The study subjects were told that the information they provided was kept confidential and that their identities would not be revealed in association with the information they provided. Informed consent was secured from each participant. For bad working environment assessment findings, feedback will be given to all manufacturing industries involved in the study. There is a plan to give a copy of the result to the respective study manufacturing industries’ and ministries’ offices. The respondents’ privacy and their responses were not individually analyzed and included in the report.
The result of this study will be presented to the Addis Ababa University, AAiT, School of Mechanical and Industrial Engineering. It will also be communicated to the Ethiopian manufacturing industries, Ministry of Labor and Social Affair, Ministry of Industry, and Ministry of Health from where the data was collected. The result will also be availed by publication and online presentation in Google Scholars. To this end, about five articles were published and disseminated to the whole world.
The research methodology and design indicated overall process of the flow of the research for the given study. The data sources and data collection methods were used. The overall research strategies and framework are indicated in this research process from problem formulation to problem validation including all the parameters. It has laid some foundation and how research methodology is devised and framed for researchers. This means, it helps researchers to consider it as one of the samples and models for the research data collection and process from the beginning of the problem statement to the research finding. Especially, this research flow helps new researchers to the research environment and methodology in particular.
There is no “conflict of interest.”
IntechOpen books are indexed by the following abstracting and indexing services:
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