VOL. 3, NO.11 Nov, 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

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1 A Fuzzy Logic Approach for Evaluation of Government Performance In ICT Projects Implementation 1 Adewole Kayode S., 2 Ajiboye Adeleke R., 3 Isiaka Rafiu M, 4 Babatunde Ronke S. 1, 2 Department of Computer Science, University of Ilorin, Ilorin. 3, 4 Department of Computer, Library and Information Science, Kwara State University, Malete. 1, 2 {adewole.kayode, ajibraheem@unilorin.edu.ng} 3, 4 {abdulrafiu.isiaka, ABSTRACT Information and communication technology (ICT) has become one of the core elements of managerial reform for creating the best efficiency and comparative advantages. However, the entire citizens both in the developed and developing countries are still facing a critical issue concerning what criteria should be used for evaluating and assessing the successfulness of the performance of their past and present government in ICT projects implementation. In this paper, we present a model developed using fuzzy logic approach for evaluation of government performance in ICT projects implementation in a North Central State in Nigeria. The results obtained from the proposed fuzzy logic model provide a reasonable solution for evaluator or decision maker to conduct a comprehensive assessment of government and for establishing a more meaningful method for evaluating government performance in ICT projects implementation. However, the model is recommended to be used for evaluating governance in other developing countries and/or for other types of project with little or no modification. Keywords: Information communication technology (ICT), model, evaluation, fuzzy logic. 1. INTRODUCTION Local, State and Federal governments in Nigeria are major providers of essential services such as: education, emergency services, law enforcement, judicial administration, Rail transportation, Road construction/maintenance and setting up of regulatory agencies, to mention just few. Generally Technology is the invention of tools and techniques that enable man to create new ways of living and new ways of governance [1]. Information and Communication Technology (ICT) consists of all technical means (the Technology) used to handle information (Processed Data) and aid communication (Interpretation and Exchange of Information), it includes; computer, network hardware, communication middleware as well as necessary software to enhance a process [1]. Electronic Evaluation (e-evaluation) involves assessing the strengths and weaknesses of programs, policies, and public agencies to improve their effectiveness through the use of computer on network of networks (Internet). Governments do make several promises prior to elections, it is therefore necessary to evaluate the level of fulfillment of these promises and make recommendations that could lead to its sustenance. An important area in which fuzzy-set theory can be applied is performance evaluation. This is however, the goal of this paper. Fuzzy logic models provide a reasonable solution to these common situations, which may easily be converted into human linguistic forms and subjective constructs. Fuzzy logic is a problem solving methodology that provides a simple way of drawing definite conclusions from vague and imprecise information [2]. Fuzzy set theory is applied to complement the framework in order to capture fuzziness in the form of inconsistencies and vagueness coming from subjective judgments by the evaluator or decision makers. Governance is the process of decision making and the process by which decisions are implemented or not implemented [3]. Therefore, level of information and communication technology (ICT) project implementation by the government is the focus of this paper. Though researchers have focused considerable attention on how e- government can help the government or the public agencies in improving their services, there has been considerably less focus on examining the long-term sustainability and effectiveness of these projects [4],[5]. Appraising government performance provides a useful and important tool to address the need for credible information, well-grounded decision making, and governmental transparency. Within a government context, the legitimacy of evaluation can be seen as deriving from the structure of the government it serves and from the functions it fills [6]. The importance of ICT is so enormous; this accounts for why successive governments intervene and invest heavily on ICT related projects. To determine the merit, quality, and usefulness of these interventions, credible information is needed about what the program or policy in question has achieved and at what cost [6]. According to [6] "such information is crucial if government officials are to ensure that the chosen interventions are working, that taxpayers money is being spent wisely, and that the government is accountable to the public for the interventions and their results". Government performance measures differ from those of private-sector because the purpose of a government is quite different from that of a business. The primary purpose of a for-profit business is generally recognized as being to increase the wealth of its owners, 1487

2 and its primary focus as being on generating a return on investment. For governments, the primary purpose is to provide services and to make and enforce laws and policies that enhance or maintain the well-being of their citizens [7]. Government has structures for its operations and every government agency has their mandates. It is therefore acceptable to access government via its operational arms, such as the Ministries, Agencies and Parastatals [3]. This paper therefore presents a better way of evaluating the government performance in the application of technology to governance. This initiative will however be a reference for researchers, subsequent administration and the general public at large. 2. ICT AND E-GOVERNANCE E-governance can be described as the way in which the public sector uses ICTs to improve accountability, transparency, effectiveness, public service delivery and citizen participation in decision-making process [8]. It is also the process of transformation of the relationship of government with its constituents; the citizens, the businesses and between its own organs, through the use of tools of information and communication technology (ICT). The purpose to adopt ICTs is to give an opportunity to citizens, so they can get involve in decision making process. Information and communication technology (ICT) is seen as posing great potential for development in developing countries. However, it has also been substantiated that developing countries require to tietogether ICT in order to support development. ICT is believed to bring great prospects for developing economies and their communities. Since the so-called knowledgebased economy is driven by ICT, governments of developing countries need to make substantial investment in all sectors to ensure all its key sectors instigate growth and development [9]. E-Governance has potential to provide all government information and services online to the public and private sector, an e-governance initiatives and innovations will ensure a more democratic, transparent and accountable framework for the public and private apparatus to operate in. The governments of developing countries, therefore, need to play a crucial role in establishing a suitable environment for e-governance [9]. This however has pointed out the need for a platform to be developed for e-evaluation of ICT projects implementation by the government of the developing countries most especially in Nigeria. The figure below shows the elements of an e- Governance architecture: Fig 1: Key elements of an e-governance Architecture Adapted from [10]. 3. EXISTING E-GOVERNMENT EVALUATION APPROACHES In order to investigate citizens' perspective in evaluating government performance towards ICT project implementation, it is required that an appropriate research approach is chosen which considers the main focus of this paper. Government investment on delivering e-government services is usually huge. Many developed and developing countries have put considerable financial resources, estimated to be greater than 1 percent of GDP, behind the development of e-government. In order to make such investments worthwhile, government should have the ability to justify these investments, which typically requires evaluation [11],[12]. The most commonly used evaluation approaches are traditional ones [13]. They include return on investment (ROI), cost/benefit, payback period, and present worth. Using traditional approaches can be problematic in evaluating information systems investment in general and e-government investment in particular. The problems in these approaches include the limited definition of stakeholders, targeting only direct tangible costs and benefits, and they are based on accounting and financial instruments [12],[13]. These approaches was also criticized by [14] stating that they are based on narrow technical and accounting terms, ignoring human and organizational components of information system users. These evaluation approaches run the risk of not identifying all the hidden costs and intangible benefits generated from system users [15]. In the work of [16], a model for evaluating e- government services with citizen-centric approach was developed and tested. The model can also serve as a tool for understanding why e-government portals succeed or fail to help citizens find the information they required [12]. Another approach for evaluating e-government portals that takes into account the social and political context of the information and its value for citizens was proposed by [17]. The function of this model is to evaluate the openness of e-government portals which the researchers described as a social-technical toolkit that contains three different parts: internal information 1488

3 characteristics, elements to capture the social and political context of the information; and assumptions about the roles of citizens and government. According to [17], the sociotechnical toolkit assumes that online information are part of the social world, which is delivered by people who hold certain values, assumptions, goals, and power relationships. Therefore judging the openness of the e-government website content requires capturing data about not only the information, but also about the social and political context of that information, including value of the information to various stakeholders, and the types of citizen participation facilitated by the information. Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) theory to evaluate citizen adoption of e-government initiatives were proposed by [18]. Their study identified seven factors that influence the citizen's perspective of e-government services. These factors include: perceived usefulness, relative advantage, compatibility, perceived ease of use, image and trust in the Internet and in government. However, this paper presents a model for evaluation of government performance in ICT projects implementation using fuzzy logic. 4. METHODOLOGY 4.1 Fuzzy Set Theory Fuzzy set theory is a powerful problem-solving methodology with a myriad of applications in embedded control and information processing. Fuzzy Logic was introduced by Zadeh in The aim of Zadeh was to give us a language, with syntax and local semantics, in which we can translate our qualitative knowledge about the problem to be solved. Fuzzy provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions [19]. Fuzzy-set theory has been used over the past 10 years in numerous scientific and engineering applications, primarily in control systems and pattern recognition. Application of fuzzy-set theory in the social sciences, however, has been a much slower process. Fuzzy-set theory represents a generalization of classical (crisp) set theory. A fuzzy set is defined by a function that ranges between 0 and 1, which assigns the degrees of membership to each element in a set. Intuitively, the degree of membership represents the extent to which an expert's judgment places an element in a set. An element can belong to more than one set with different degrees of membership. This allows a gradual transition among adjacent sets. Thus, it allows us to view concepts of possibility and vagueness separate from probabilistic or random uncertainty. The set-theoretic approach also allows the non-linear integration of different domains [20]. The figure below shows the input to output fuzzification process: Fig 2: Input to Output Fuzzification process adapted from [21] Composition of Fuzzy Set A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one [22]. To understand a fuzzy set, let X be a space of points (universe), with a generic element X denoted by, then a fuzzy set A in the universe X is characterized by a membership function which associates with each point in X a real number in the interval [0, 1] such that: A = (1) Where at represent the "grade of membership" of in a fuzzy set A. The generic element may have full membership in A when, or partial membership when or non-membership when. For instance, fuzzy linguistic variable performance can be categorized as low, average, outstanding. Each category is called a linguistic modifier. Each of these linguistic modifiers is linked to a numeric value on a particular scale. Example on this is shown in figure 3. In this figure, three fuzzy sets are used to characterize government performance (low, average and outstanding) on a scale of 0 to 6 with each linguistic modifier having membership value from 0 to 1. The fuzzy linguistic variable "performance" in this example represents government performance. Scale with number 2 represents the highest level of low performance with a membership value of 0.3, while scale number 3 defines low performance with a membership value of 0.1 or average performance with membership value of 0.3. This implies that scale number 3 describes government performance that is 30% average and 10% low. The three fuzzy sets are as follow: 1489

4 Low_performance = {0 1.0, , , , , , , , , , , , } Average_performance = {0 0.0, , , , , , , , , , , , } Outstanding_performance = {0 0.0, , , , , , , , , , , , } The evaluator can therefore combine any of the linguistic modifiers with the linguistic variable (performance) to derive overall performance evaluation. Thus, according to [22], the nearer the value of to unity, the higher the grade of membership of in a fuzzy set A. Performance Low Average Outstanding Membership value Fig 3: Three fuzzy sets for performance rating Operations on Fuzzy Sets The notations of inclusion, union, intersection, complement, relation, convexity are some of the operations that can be extended to fuzzy sets [22]. For instance, let A and B represent two fuzzy sets with membership functions and respectively. The two fuzzy sets A and B are equal written as A = B, if and only if for all in X. In our example, A B since for all. The complement of a fuzzy set A is denoted by and is defined by The complement of a set is computed by subtracting each element of the set from its maximum possible value [2], in our example: The union of A and B with respective membership functions and is a fuzzy set C, written as C =, that is,. The union operation of fuzzy sets is carried out by applying the Max function to the elements of the two sets. More formally we have: (2) (3) In our example,. Intersection is done by applying Min function to the elements of the two fuzzy sets. That is, C =, written as,, thus,. Implication function is used to decide if A is true, to what extent that implies that B is true? The implication operation is done by computing, known as Kleene-Dienes implication [9], such that: In our example,. According to [22], some of the algebraic operations that can be extended to fuzzy sets include: product, sum, absolute difference, convex combination and so on. The operations that can be carried out on fuzzy sets are not limited to those operations described in this paper. However, some of these operations were selected to clarify how the proposed fuzzy logic model for government performance evaluation was derived. 4.2 Data Collection and Analysis The researchers designed a form for collating relevant data regarding the availability and usability of ICT tools for services delivery to citizens and businesses in the major sectors, agencies and parastatals under the Ministries of Education, Finance, Health, Agriculture and Natural (4) 1490

5 Resources, Energy, Planning and Economic Development, Sports and Youth Development, Housing and Urban Development, Works and Transport, Environment and Forestry, Local Government and Chieftaincy Affairs, Culture and Tourism, Water Resources. The form is shown in table 1. Interview was conducted with government Table 1: Data collection form officers across some of the Ministries in a North Central State in Nigeria. However, the metrics used for performance evaluation as well as the weight of each of these metrics was defined through an expert's ideas as shown in table 2. Ministries, Sectors and Parastatals Education & SUBEB (M1) Finance (M2) Health (M3) Agriculture and Natural Resources (M4) Energy (M5) Planning and Economic Development (M6) Sports and Youth Development (M7) Housing and Urban Development (M8) Works and Transport (M9) Environment and Forestry (M10) Local Government and Chieftaincy Affairs (M11) Culture and Tourism (M12) Water Resources (M13) Web Presence (e1) Automated Internal Operations (e2) Functional MIS unit (e3) Internet Access (e4) Computer Systems for Offices (e5) ICT Training (e6) 5. PROPOSED FUZZY LOGIC MODEL FOR GOVERNMENT PERFORMANCE EVALUATION The proposed model takes into consideration thirteen (13) Ministries (M1,M2,..., M13) and six (6) metrics (e1, e2,..., e6) for the evaluation as shown in table 1. The model, however, used fuzzy logic approach in order to provide a reasonable solution for the evaluator or decision maker to conduct a comprehensive assessment of government and for establishing a more meaningful method for evaluating government performance in ICT projects implementation. We therefore, form a fuzzy set Y which takes values in a universe of discourse W in the interval of [0,1], such that: Y = (5) as shown in table 2. Table 2: Evaluation metrics and their membership grades Evaluation Metrics Representation- (w) Membership Grade - fy(w) Web presence e1 1 Automated internal e2 0.5 operations Functional MIS unit e3 0.4 Internet access e4 0.7 Computer systems for e5 0.9 offices ICT training e6 0.8 From the table, each evaluation metric is assigned a membership grade between 0 and 1. This signifies performance grade for the evaluator. Each 1491

6 evaluation metric is assigned a qualitative judgment to determine the degree of government performance for the selected metric category. These qualitative judgments are called linguistic variables and are shown in table 3: These linguistic variables therefore formed another fuzzy set Z which takes values in a universe of discourse W in the interval of [0,1], such that: Table 3: Linguistic variables employed for the qualitative judgments Membership Grade Representation Linguistic variables 0.10 VP Very poor 0.30 P Poor 0.40 BA Below average 0.60 A Average 0.70 SAA Slightly above average 0.90 AA Above average 1.0 E Excellence Z = (6) The next step is to assess the performance of each Ministry by each evaluation metric that is based on the fuzzy opinion of the evaluator or decision maker, as shown in table 4: Table 4: Government performance rating across ministries M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 e1 A BA AA BA SAA AA A VP BA A AA SAA BA e2 AA A A BA BA A SAA P A AA AA AA VP e3 E SAA A SAA AA BA BA SAA SAA A SAA A P e4 SAA AA SAA A P SAA P A AA SAA BA SAA A e5 E E VP AA AA A SAA A A VP BA BA VP e6 A A P P BA A A AA P A A A BA The results of these decisions however constitute thirteen (13) different fuzzy sets with membership functions. For instance, from table 5, the fuzzy set and membership function of the first Ministry (M1) is: Table 5: Government performance rating across the Ministries M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 e e e e e e

7 We established a fuzzy implication relation between a specific performance metric and performance of government in each Ministry. According to [2] the fuzzy implication relationship is established by taking the complement of the performance metric which will create a minimum performance value assigned to all the Ministries. The Max function is then applied to each Ministry's performance set and the complement of the evaluation metric set as shown below: M M RESULTS AND DISCUSSION The table below shows the summary of the results of overall evaluation of government performance across the thirteen Ministries in ICT project implementation. The information contain in the table clearly revealed that overall government performance in Ministries 1 and 6 is 60% with average performance rating; Ministries 2, 11 and 12 is 40% with government performance that is below average; Ministries 4, 5, 7 and 9 is 30% with poor performance rating while Ministries 3, 8, 10 and 13 is 10% with very poor performance rating. The fuzzy logic model presented therefore provides a flexible way of evaluating government performance and also allows the decision maker to introduce a wide range of linguistic variables and modifiers into the model for the purpose performance evaluation. Table 7: Overall Government performance across the Ministries The final step in our model is to combine various performance of each Ministry in order to arrive at the final evaluation. This is done by applying the Min function to the set derived for each Ministry in the fuzzy set union operation above. The result of this operation is show in table 6: Table 6: Overall numerical performance of government across the Ministries Ministry Performance Rating M1 0.6 M2 0.4 M3 0.1 M4 0.3 M5 0.3 M6 0.6 M7 0.3 M8 0.1 M9 0.3 M M Ministries Education & SUBEB (M1) Finance (M2) Health (M3) Agriculture and Natural Resources (M4) Energy (M5) Planning and Economic Development (M6) Sports and Youth Development (M7) Housing and Urban Development (M8) Works and Transport (M9) Environment and Forestry (M10) Local Government and Chieftaincy Affairs (M11) Culture and Tourism (M12) Water Resources (M13) Fuzzy Performance Rating Average Below average Very poor Poor Poor Average Poor Very poor Poor Very poor Below average Below average Very poor 7. CONCLUSION AND RECOMMENDATION This paper revealed the capability of fuzzy logic model to evaluate the performance of government in any aspect of governance. To actually demonstrate this, a model was developed in this work and applied to thirteen Ministries in a North Central State in Nigeria in order to evaluate the performance of government in ICT projects implementation across the selected Ministries. The result shows that our model gives the decision maker and general public a clear and flexible method of evaluating performance where the available data to be used for evaluation is based on vagueness and uncertainty. The model is suitable for evaluating any other organs or components of government and governance, it is therefore recommended for such performance evaluation with little or no modification. 1493

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