A non-parametric algorithm for computing information technology ...

icccbe 2010

© Nottingham University Press Proceedings of the International Conference on  Computing in Civil and Building Engineering  W Tizani (Editor)

A non-parametric algorithm for computing information technology induced productivity in the construction value chain Yahuza Kassim & Jason Underwood

University of Salford, Salford, United Kingdom

Benny Raphael

National University of Singapore, Singapore

Abstract Several studies have suggested that information technology (IT) resources offer strategic advantage to organisations and enhance their competitiveness. However, none of the studies have been able to define precisely a model for measuring information technology business value. Furthermore, attempts to quantify IT business value have led to inconsistencies and paradoxes. Some of these problems are attributed to the lack of theoretically structured framework in the previous studies. This paper presents a methodology for evaluating IT business value in the construction industry by adopting three paradigms of process-based, microeconomics-based, and resource-based views as the theoretical framework to model the relationship between IT and the performance of engineering and construction firms. The ITBV is modelled using a non-parametric technique known as data envelopment analysis (DEA). DEA identifies the surface that represents the most efficient data points in terms of maximum output produced using minimum input. For the purpose of this study, the output variables are parameters that measure the performance of a firm, while the input variables measure the level of usage of IT resources. An algorithm for evaluating the efficiency of engineering and construction processes is developed using DEA and illustrated through the use of an empirical example. The paper eliminates the problems associated with parametric approach to evaluating ITBV and provides a comprehensive theoretical framework in modelling the ITBV. Keywords: data envelopment analysis, business value, productivity, construction firms

1

Introduction

For decades, attempts to establish empirical evidence of productivity gains from IT investments produced what was termed ‘productivity paradox’ in some cases (Brynjolfsson 1993). Some of the reasons ascribed to the equivocal results of the previous IT business value research include difficulties associated with modeling and measurements, and choice of variables among others (Oh and Pinsonneault, 2007). To mitigate these draw backs, Kassim et al.,(2009) proposed a model for evaluating IT business value in construction organisations through a web of intermediate levels of construction project activities, in line with the value-chain analysis suggested by Porter (1985) and organisation resource-based view (RBV) and core competence (Barney, 1991). This paper extends the model by presenting an algorithm for evaluating the productive efficiency of construction processes using Data Envelopment Analysis (DEA). Thus, the paper presents a procedure for evaluating the impact of utilization of IT resources as factors of production on the productive efficiency of the construction value chain.

The rest of the paper is structured into the following sections: literature review, proposed theoretical framework, data envelopment modelling, methodology, and conclusion.

2

Literature review

IT resources improve productivity in organisations by supporting the delivery of organisation's value chain and cause sustainable improvement in its competitive position (Bharadwaj, 2000). These resources are termed as IT infrastructure (ITI) that include both tangible and intangible assets of the organisation, and are conceptualized in four dimensions: shared technical components, IT Human competence, IT application, and business process (Bhatt, 2000). As indicated above, some reasons for the inconsistencies include the lack of contextualisation of the studies based on business specifics, and absence of a comprehensive accepted framework used in the previous work. Therefore, multiple paradigms are deployed in extending Kassim’s (2009a; 2009b) model from which the DEA algorithm is presented.

2.1

Theoretical framework of the IT business value model

The contribution of IT to the improvement of various measures of an organisation’s performance metrics such as productivity, profitability, cost, differentiation and market share is variously termed by different researchers as “IT business value”, “strategic value of IT”, “strategic advantage”, “competitive weapons”, and “IT-dependent strategy” (Melville et al.,2004; Piccoli and Ives, 2005; Oh and Pinsonneault, 2007). In this study, IT business value is defined as the impact of deploying and utilizing IT resources in the construction organisation value chain on its performance metrics including contract growth, cost, schedule, profitability, safety, and stakeholders. Diverse conceptual models and frameworks have been adopted at different levels of analysis in the study of the impact of IT investments on organisational performance using theoretical paradigms from economics, strategy, accounting, and operations research, philosophy, and sociology ( Piccoli and Ives, 2005) without necessarily integrating them in a single study. Therefore, this study adopts three paradigms (Qing and Jing, 2005) of process-view, resource-based view, and microeconomic-view to evaluate IT induced productive efficiency in construction organisations. Using the process-based view a typical construction value chain is proposed consisting of five primary activities: strategic planning, engineering design, procurement, construction and start up and operation & maintenance. Each of these activities are further broken down at the process level, e.g. strategic planning includes market research, bidding process, contract strategy, and manpower planning. These sub activities are referred to as work functions (WFs) (El-Mashaleh et al., 2006). The level of IT utilisation and integration in executing the WFs is used to measure the extent of business application as an IT business application resource. The resource-based view (RBV) proposed that the deployment and exploitation of valuable, rare resources, and capabilities contributes to an organisation’s competitive advantage, which in turn also contributes to its performance (Barney, 1991). To satisfy this requirement complementary resources are incorporated in the model as independent variables. The economic view of IT value is that of input in the production function of a firm and there is a substituting effect between IT and other production factors (Dewan and Chung-ki, 1997). The concept allows estimation of the measure of IT resources usage as an economic production function.

2.2

IT induced productivity

There is no single universally accepted definition of productivity. As a concept, productivity defines the relationship between inputs and outputs based on context. Thus, productivity here is conceived to refer to the measure of efficiency in the utilization of IT and none IT resources as inputs in the

construction organisations’ processes (Crawford and Vogl, 2006). The microeconomic production theory provides a framework for investigating the productivity impact of utilizing IT resources as production factors using the business value of IT concept (Dasgupta et al., 1999). The theory is mainly based on the conceptual use of the Pareto-optimal frontier of production possibility sets to define the production function (Charnes et al., 1985). Thus, a prerequisite for measuring productive efficiency is the setting up of a certain production frontier against which a production unit is evaluated (Lin and Shao, 2006). The frontier specifies the relationship between inputs and outputs in the production process and indicates the maximum output levels obtainable by employing a certain combination of input resources. The main categories of techniques used for structuring such frontiers are non-parametric and parametric, with non-parametric being the DEA.

3

Data envelopment model of ITBV

DEA is a non-parametric frontier approach that uses mathematical programming to construct piecewise linear convex production frontiers. The technique was first proposed by Charnes et al. (1978) and later extended by Banker et al. (1984). DEA is used for measuring and evaluating performance and in productivity management. It has been successfully applied for the evaluation of efficiencies and the identification of benchmarking organisations (Wöber, 2007). The original DEA model was introduced by Charnes, Cooper and Rhodes thus the name CCR model was coined from the initials of the authors’ surnames. The model proposed that the relative efficiency of a decision making unit (DMU) can be evaluated as the maximum of a ratio of sum of weighted outputs to sum of weighted inputs, subject to the condition that the same ratio for all enterprises must be less than or equal to one (Charnes et al., 1978). The term 'envelopment' reflects the fact that DEA measures efficiency within a production possibility set which ‘envelops’ all input-output correspondences. Consider N number of construction organisations (DMUs) each utilizing sets of IT and complimentary organisational resources as input vector x ∈ ℜ m+ to execute construction projects involving engineering design, procurement and construction activities leading to the project performance outcome as output vector y ∈ ℜs+ including measure of productive efficiency on cost, schedule performance, contract growth and profitability. The observed ordered pair (x, y) is regarded as a feasible production plan; while the collection of all feasible production plan forms production possible sets (Φ) such that Φ ≡ ( x, y ) | x can produce y . Using the output oriented model the efficiency of the jo construction organisation under consideration ( coo ) could be evaluated by solving the following linear programming problem:

{

}

S

M

S

M

r =1

i =1

r =1

i =1

max ∑ μ r y rjo s.t.:∑ α i x ij = 1 and ∑ μ r y rj − ∑ α i x ij ≥ 0 , μ r ,α i ≥ ε > 0 ,∀i, j,r

(1)

xij,yrj>0 are indexes of IT and other resources and performance metrics as inputs and outputs of the jth construction organisation respectively, αi and μr are the inputs and outputs weights. xio and yro represent the variables for coo . Equation (1) maximizes the weighted output of the j0 organisation

subject to the constraint that weighted inputs equal one. The optimal vector weights μ*, α* represents that weights that will provide the organisation under consideration with the highest efficiency rating possible while maintaining feasibility for the remaining N-1 organisations in a given N sample groups. The values of μ* and α* may vary for each organisation as unit evaluated. The equation needs solving N times, once for each organisation.

4

DEA algorithm for computing ITBV

The following actions are the steps required while undertaking evaluation of comparative efficiency of the given set of construction organisations using DEA (Golany and Roll, 1989; Thanassoulis,

2003): (1) definition and selection of the organisations. (2) identification of the input-output variables (3) construction of the production possible sets (PPS) (4) establishment of the type of efficiency to be assessed (5) determination of organisations’ sample size (6) determination of a DEA model to apply (7) solving the linear program for all organisations (8) presentation and analysis of the outcome.

4.1

Selection of the organisations to be evaluated

There are always differences in the way organisations are managed that may lead to different decision making. Therefore, while the objectives of DEA analysis include identifying the differences in the performances of the units assessed, there is the requirement to have the units to be homogenous (Farrell, 1957). 4.1.1 Strategic grouping The homogeneity of the operating enterprise to be assessed using DEA could be ensured by conducting strategic group analysis. A strategic group consists of those rival firms with similar competitive approaches and positions in the market. They provide an intermediate frame of reference between viewing an industry as a whole and considering each firm separately (Flavian and Polo, 1999; Dikmen et al., 2009). 4.1.2 Organisations’ sample size The next step is to determine the size of the comparison group. A large population size of the organisations will tend to increase the probability of capturing high performance organisations which would determine the efficiency frontier. However, a rule of thumb is that the number of sampled firms should be at least twice the sum of the number of inputs and outputs variables (Ali eta al., 1988; Bowlin, 1987).

4.2

Inputs and outputs variables

One of the fundamentals for the assessment of comparative efficiency by DEA is the construction of the production possible set (PPS) containing all input-output level ‘correspondences’ which are capable of being observed. Correspondence of inputs and outputs in this context is based on a relationship of exclusivity and exhaustiveness between the two sets of variables (Thanassoulis, 2003). The initial list of the variables to be considered for assessing organisational performance should be as wide as possible. Every dimension, the changes which may affect the organisations to be evaluated, should be included in the initial list. The input variables should capture all resources and the output variables all the outcomes having a bearing on the type of efficiency being assessed. In addition, contextual factors impacting the transformation of inputs to outputs should also be reflected which in our case include complimentary organisational resources. The initial set of potential input-output variables can be refined using a combination of statistical test and/or sensitivity analysis (Boussofiane, et al., 1991; Thanassoulis, 2003).

4.3

Solving a DEA model

All linear program (LP) formulations are a function of a particular organisation about which we need to determine its efficiency classification; we say that the LP “scores” this organisation. The procedure based on solving one LP for each of the organisations using the entire data set will be referred to as standard. This is a formal statement of the standard procedure (Ali, 1993; Dulá 2008). 1 For j = 1 to N Do (for all the sampled organisations) 2 Initialize j*←j 3 Define x0 ←xj, y0 ←yj (in the example below, xj represents ITI and yj the EFY and PEF) 4 Solve equation (2) for θ* s* and λ* to score Coj* 5 Increase j←j+1 for j