Productivity Performance Assessment of a Firm: A Case Study of Infrastructural Construction Company in Nigeria

: Productivity measures how efficiently production inputs, such as labour and capital, are being used in an economy to produce a given level of output. Productivity growth constitutes an important element for modeling the productive capacity of economies. It also allows analysts to determine capacity utilization, which in turn allows one to gauge the position of economies in the business cycle and to forecast economic growth. The objective of the study is to analyze the impact of productivity, while using the inventory of the construction Company in Nigeria to investigate, measure, plan and control the productivity and performance of a firm. Data used for this study was generated through the firm's annual reports and financial statements. Multiple Linear regression Model developed was used to predict accurately the productivity level of the firm. In order to check the significant and the adequacy of the model developed, the coefficient of correlation(R), coefficient of determination (R 2 ) and adjusted R 2 were determined, with an R 2 value of 0.983 and an adjusted R 2 values of 0.932 obtained indicating that the model is adequate. The result of the study shows that the degree of association and correlation of the data is meritorious. Investigation revealed that factors in both external and internal work environment as well as the firm's policies are unfavorable to the enhancement of labour productivity.

The term productivity has been recognized for its contribution to operational, organizational, industrial, and national competitiveness.Productivity implies how well the resources are utilized for goods and service generation (and, from the national perspective, the wealth generation).It is crucial to the welfare of the industrial firm as well as for the economic progress of the country.High productivity refers to doing the work in a shortest possible time, with least expenditure on inputs without sacrificing quality and with minimum wastage of resources (Taj and Lismar, 2006).According to (Anyanwu, 2004, Udo-Aka, 1983).There is no universal definition of the term, productivity.It has been defined by Economists as the ratio of output to input in a given period of time.In other words, it is the amount of output produced by each unit of input Business Managers, on the other hand, see productivity not only as a measure of efficiency, but also connotes effectiveness and performance of individual organizations (Anyanwu, 2004).For them, productivity would incorporate quality of output, workmanship, adherence to standards, absence of complaints, customer satisfaction, etc (Udo-Aka, 1983).According to (Adekoya, 1987) The administrator is more concerned with organizational effectiveness, while the industrial engineer focuses more on those factors which are more operational and quantifiable, work measurement and performance standards.Productivity can be computed for a firm, industrial group, the entire industrial sector or the economy as a whole.It measures the level of efficiency at which scarce resources are being utilized.Higher or increasing productivity will, therefore, mean either getting more output with the same level of input or the same level of output with less input.The least controversial definition of productivity is that it is a quantitative relationship between output and input (Iyaniwura andOsoba, 1983, Antle andCapalbo, 1988).This definition enjoys general acceptability because of two related considerations.One, the definition suggests what productivity is thought of to be in the context of an enterprise, an industry or an economy as a whole.Two, regardless of the type of production, economic or political system, this definition of productivity remains the same as long as the basic concept is the relationship between the quantity and quality of goods and services produced and the quantity of resources used to produce them (Prokopenko, 1987).Eatwell and Newman (1991) defined productivity as a ratio of some measure of output to some index of input use.Put differently, productivity is nothing more than the arithmetic ratio between the amount produced and the amount of any resources used in the course of production.This conception of productivity goes to imply that it can indeed be perceived as the output per unit input or the efficiency with which resources are utilized (Samuelson and Nordhaus, 1995).Today the term productivity has acquired a wider meaning.Originally, it was used only to rate the workers according to their skills.The person who produced more either faster or harder were said to have higher productivity.Subsequently, emphasis was laid to improve the hourly output by analyzing and improving upon the techniques applied by different workers.A system of measurement was then evolved to compare the improvement made in relation to the rate of output and in order to improve productivity further, machines were introduced (Afaha, 2014).Manufacturers of machines started incorporating new features, with the help of latest technological developments.Computers have now become powerful tools towards improving productivity (Singh, 2000;Khan, 2003).Productivity measurement and analysis have gained more recognition from researchers and higher acceptance from practitioners over the past three decades (Mohamed, 1996).It has evolved from merely linking individual and accounting-related to more comprehensive information that contains both financial and non-financial information.The need to improve productivity measurement is apparent in both manufacturing and service industries (Medori, 2000).Caves et al. (1982) observed that efficiency of transformation of inputs to outputs is largely dependent on the skill of the workforce.Skill is one of the main inputs of a production process.Publications by Scott and Pisa (1998) recognize and analyze the need for a coherent, systematic methodology for productivity measurement and analysis at the factory level.There are three important measures which are defined in monetary rather than physical units: throughput, inventory and operational expenses.The first should be maximized and the last two should be minimized.Productivity measures the capability to meet the demand and not the sales.Consequently, attempts to measure the output in terms of units sold in a shop, mixes both a production measure and a demand measure in a way that makes it difficult to quantify (Abdel-wahab, 2008).Many organizations have used productivity measurement as a primary tool for communicating future directions, establishing functional and project accountability, defining the roles and responsibilities, allocating the limited resources, monitoring and evaluating the activities, establishing the targets and benchmarks, and initiating necessary changes to ensure continuous improvement.This work is geared towards studying the productivity and performance of a firm, evaluate and measure its productiveness, determine the impact of productivity on the nation's economy, and identify critical factors limiting productivity in an organization using Multiple Linear regression analysis.

MATERIALS AND METHODS
The processes of Multiple linear regression analysis was the statistical tools and techniques employed for the analysis of data.The mathematical approach for this study is enumerated in model formulation of this research work.This study was carried out using Data obtained from Infrastructural Construction Company in Nigeria.The data obtained from the company's annual reports and financial statements was converted into productivity ratios and subjected to various mathematical manipulations to measure and determine the relevance of productivity on the firm's performance, and the various factors affecting it.Consequently, the multiple linear regression analysis methods was chosen because of its ability to group data into dependent and independent variables.In the proceeding sections, we explained the mathematical platform on which the multiple linear regression model was developed, how Productivity of the firm was computed, how data was visibly manipulated and the way this translates into interpretable results.
Model Formulation: From the above data in table 1, the Total productivity ratio represents the dependent variable y, while the labour, material, capital, energy, and other expenses productivity ratios are the domain of independent variables x1, x2, x3, x4 and x5 respectively.In order to develop the multivariate normal equations, we need to formulate tables from the derived data's in table 1.
To develop the multivariate normal equations using the dependent variable y and the domain of independent variables x1, x2, x3, x4 and x5.Thus: The above is widely used in industries the model is of the form To solve equation (1) we need to develop a set of normal equation by multiplying the model by independent variable and sum.Thus, To solve for bo, b1, b2, b3, b4, and b5 we proceed as follow: Equations ( 3) to ( 8 We can transform the above equations to matrix form as follows; To test for the adequacy of the multivariate model, we use the equation 10; Model Adequacy: The above results are then tabulated to obtain the model adequacy.
Where R 2 is the coefficient of determination and R is the coefficient of correlation.
Coefficient of determination is a measure of the extent to which the dependent variable is able to account for the observed variability in the independent variable, while coefficient of correlation is a measure of the degree of association between the two variables.
Where y, yi and y are estimated mean value, actual and predicted values of output response (y) respectively.Since it is always possible to increase the value of R 2 by adding more repressor variables, therefore the adjusted R 2 value is computed using equation ( 11).
Where n is the total number of observations and p is the number of regression coefficients.

RESULT AND DISCUSSION
The obtained inventory of the Infrastructural construction company in Nigeria is shown in Table 1.    9. Results obtained is presented in Table 5.The above results are then tabulated to obtain the model adequacy.The coefficient of determination, R 2 shows that the dependent variable y is able to account for 98.3% of the observed variability in the independent variables.
To determine the coefficient of correlation R = 0.983 = 0.99 From the coefficient of correlation R, the model is meritorious.
The adjusted R 2 (R 2 adj) computed from equation (10) = 1 -5-1/5-6(1 -0.983) = 0.932 The Data used for the analysis is presented in Table 1.Which is the inventory of the section of the construction company.The productivity ratios and Productivity measures of the firm was computed and the results shows that productivity of a firm could be measure using total productivity rather labour, partial or multifactor, Total factor productivity as presented in Table 2 and Table 3,That do not accurate measures the productivity of the firm.Productivity analysis of the company from 2011 to 2015 in order to measure the productivity level of the company using 2011 as a base year.From the analysis of results there was an increase in total productivity by 11 percent in 2012 and radial decrease in the total productivity in 2013 and 2014 by 4 percent and 7 percent respectively.From The result, radial decrease in total productivity of the company could be as a result of internal and external factors that facilitate the productivity growth.2015saw an increase in Productivity with a small margin of 2 percent.While the multiple linear regression model developed was able to predict accurate the productivity of the firm in future.The result shows an increase in productivity of the firm in 2011 to 2015, In order to determine the adequacy of the model developed the coefficient of correlation(R) and the coefficient of determination (R 2 ) were computed with an R 2 value of 0.983 obtained and the Adjusted R 2 value of 0.932 was also obtained indicating that the

Table 2 :
Inventory of the Infrastructural Construction Sector in Nigeria

Table 5 :
Total Productivity

Table 7 :
Material Productivity

Table 6 :
Energy Productivity