MEASURING THE DELONE AND MCLEAN MODEL OF INFORMATION SYSTEMS SUCCESS APPLIED TO BANKING SECTOR OF TURKEY

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1 MEASURING THE DELONE AND MCLEAN MODEL OF INFORMATION SYSTEMS SUCCESS APPLIED TO BANKING SECTOR OF TURKEY 1 BUSRA KUTLU, 2 ASIL ALKAYA 1,2 Adnan Menderes University,Adnan Menderes University busra.kutlu@adu.edu.tr, asil.alkaya@adu.edu.tr Abstract Up to this time, measuring information systems (IS) success is most debatable and important issue on the scope of information systems. While new systems and technologies occur, perceptionsneed to be updated about the whole information systems concept.in this study, it is intended to find factors which evaluate information systems success of banking sector which is called as net benefits on this article. This research creates Delone and McLean model that measures information systems success and along with this model, job performances increase has been aimed. The variables of information systems success will be examined and looked for their relationships and interactions. Index Terms Delone and McLean s IS success model, User Satisfaction, Banking, Quality. I. INTRODUCTION Organizations generally invest on information systems if systems increase their budgets. Because of the changeable structure of the economies, organizations compete with each other and always try to reduce the costs. Therefore, information systems investment decision must be examined in detail. Before investing, possible returns and costs must be calculated elaborately. Likewise other service organizations, banks have been evolving since last two decade. As bank sector expands, numbers of banks are continuously increasing and along with growing technology, banks services change. While banks and managers invest in large volumes to fit all the changes, investments of returns remain as a continuous debate. Researchers generate a lot of frameworks and models to evaluate information system success but assessing information systems is tough issue both for organizations and researchers. There are many factors that must be considered while measuring the information systems success. In this point, the most important issue is choosing right factors and assessing them. The Delone and McLean Model is implemented in order to provide a general and comprehensive definition of IS success that covers different perspectives of evaluating information systems, DeLone and McLean reviewed the existing definitions of IS success and their corresponding measures, and classified them into six major categories. Thus, a multidimensional measuring modelis created with interdependencies between the different success categories toimprove job performance. This model measures information systems success and aimsto try to present some necessary clues to create successful systems for software and system developers which are core factors of information systems.that must go around systemic and humanistic factors together. In this paper, variables of information systems success shall be examined by looking for their relationships and interactions. Themodel consists of six interrelated dimensions of IS success: information, system and service quality, (intention to) use, user satisfaction, and net benefits. The model can be interpreted as follows: A system can be evaluated in terms of information, system, and service quality; these characteristics affect the subsequent use or intention to use and user satisfaction. As a result of using the system, certain benefits will be achieved. The net benefits will (positively or negatively) influence user satisfaction and the further use of the information system. II. INFORMATION SYSTEMS CONCEPT In an organization, information systems; access, process, stores and control the information to support forecasting, planning, controlling, coordinating, and making a decision. Information systems present necessary information to managers to make them their decision on an issue and by this means, they makes the organization be under their control (Hardcastle, 2008). In an organization or in society, in terms of operational activities as access, process, distribute and usage of information, in terms of management as support to decisions about management, information system is multidisciplinary field and relatable with technology and even non-separable. İnformation systems use technology in large scale because of its flexibility, reliability and its speed. This discipline has been improved with evolving information technology and increasing technology usage areas (King and Lyytinen, 2006). Information systems are made according to an organization s targets, strategies and plans. They can be called as teleological systems. III. INFORMATION SYSTEMS SUCCESS AND MEASUREMENT 8

2 Information systems success is both the user s satisfaction with the functionality of the system and the data output and the importance of high technical quality and cost effectiveness. As organizations seek to achieve return on investments from IS, they must assess and understand what factors lead to the success of IS applications. The complex, multidimensional, and interdependent nature of IS success requires careful attention to the definition and measurement of each dimension of this dependent variable. It is important to measure the possible interactions among these success dimensions in order to isolate the effect of various independent variables with one or more of these dependent success dimensions (DeLone and McLean, 2003). IV. RESEARCH BASED ON DELONE AND MCLEAN MODEL The IS success model (DeLone and McLean, 1992) explains the impact of IS at the individual and organizational levels. The research model used in this paper builds on the individual level constructs of system quality, information quality and service quality, and their subsequent impact on user and user satisfaction. A. User and User Satisfaction Before using IS, some judgments and feelings about system are occurred by a user and user satisfaction depends upon how much user s expectation has been met by the IS that he used. When user satisfaction wanted to be measured, the decision about which measures will be taken must be investigated carefully. (Seddon and Kiew, 1994 ;Torkzadeh, 1988 ; DeLone and McLean, 2003). There are some important measures which are respectively; satisfaction of software and hardware, graphical interface and information and user complaints about system (Seddon et al., 1999). Use is the most contradictory factor in IS field and that can be defined as taking advantage from information systems applications individually and organizationally. In IS field, there is a continuous debate which is about the scope of use. It is not a necessary criterion that requires to be measured according to some researchers but at the same time it is put forward that if use is voluntary in an organization, it can be taken as a measure to evaluate IS success. There can t be find any important information when ISs use is mandatory ( Seddon and Kiew, 1994). The hypothesis is as follows: H 1 : User situation for banking will positively B. System Quality System quality variable means accessing to the system easily, having ability of answering to user s problems and needs in time, meeting users expectations, easiness of using the system, reliability of the system and easiness of adaptation to it. System quality includes five basic dimensions which are flexibility, reliability, being on time, accessibility and ability of being integrate. According to the model, it attempts to test the following hypothesis: H 2 : System quality of banking will positively contribute to user. H 3 : System quality of banking will positively C. Information Quality Information quality means measuring the quality of information which is an output of the system. This variable needs to be understandable, accurate and right and it must support the user when some targets have been aimed to reach by users (Wang and Wang, 2009).So that leads to the following hypothesis: H 4 : Information quality of banking will positively contribute to user. H 5 : Information quality of banking will positively D. Service Quality Service quality was added to the variables that measure IS success. Technical service s ability of problem solving and their attitude to the users when a trouble is occurred about the system and increasing learning level of workers by the time are the measures of service quality (Skok et al., 2001). H 6 : Service quality of banking will positively contribute to user. H 7 : Service quality of banking will positively V. METHODOLOGY AND SAMPLING Bankers are chosen to apply the survey on, because they are intensively using information systems every day, accordingly the survey applied on bankers will give us more trustable data. Survey method is used as data collecting tool to determine factors that measure information systems success. Study is conducted via Google survey tool and face to face interviews. 126 voluntary-participating bankers fulfilled the survey. Participants were asked to respond to the statement using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Demographic features (gender, age, education, experience of information systems, experience of sector and experience of current career position) are examined plus to factors that measure information systems success. For detection of demographic features nominal and ordinal scales is used. 9

3 Table I. Demographic Features (independent variables) effect dependent variable (net benefits) question is tested with multiple regression analyze. Cronbach salfa coefficient is used for reliability analyze. Coefficient alpha in the interval of 0.00 α<0.40 is considered as not reliable, 0.40 α<0.60 indicates low level of reliable, 0.60 α<0.80 is accepted as fairly reliable and 0.80 α<1.00 is considered as highly reliable. In this study Cronbach alpha coefficient is 0.97 and it is accepted as highly reliable. E. Factor Analysis KMO scale is used to see propriety of the sample size for factor analysis KMO is used to see propriety of the sample size for factor analysis. Namely, KMO is conducted to test sample adequacy and is wanted to be above %60. Table II. KMO and Bartlett's Test According to the KMO results, data seems as suitable for factor analysis. According to analysis results, we obtain four factor whose eigenvalues are upper than 1 and that explain % of variance. Henceforth, these four factors indicate new variables that will be used on the next steps of analysis. New factors may be renamed according to factors size and their meanings. At the beginning of the factor analysis, five success factors that measure net benefits has been considered by the study but according to rotated component matrix table, factors numbers are reduced six to five. New factors are information systems quality, information quality, service s quality and user satisfaction respectively. The analysis of the retrieved survey data is based on SPSS. Firstly, Cronbach s Alfa coefficient is used to examine reliability of the survey. Secondly, Factor analyze has been applied to the items to reveal significant and valid factors. How obtained factors F. Regression Analysis Regression analysis investigates the relationship between dependent variable(s) and independent variable. In this survey, dependent variable is net benefits and independent variables are Information systems quality, information quality, services quality and user satisfaction respectively. According to the table; sig=0.00 and F is equal to 23,37and this result implies that the model we draw is significant and System s quality, information s quality, service s quality and users satisfaction 10

4 statistically explains % 42.1 of variance of net benefits. Table III. Rotated Component Matrix Table IV. Analysis of Variance On the Table VI below, coefficients of variables are located. According to that, information system s quality seems insignificant variable while other independent variables are significant and having positive effect on net benefits. Users satisfaction variable explains most of the variance of net benefits while information quality and service quality explain less of it. In addition, VIF values must be smaller than ten for non-multicollinearity, so it can be said multicollinearity isn t seen on the model drawed. As a result the equation is; CONCLUSION At the end of the analyses, it is seen that five major factors reduced to four factors. Use variable is extracted and by the regression analysis Information system s quality variable is considered as meaningless and doesn t contribute to model. Namely, Information system success (net benefits) depends on three independent variables as information quality, service quality and user satisfaction. Most of these results match with the findings on the literature. Use factor has been always contradictory variable on the literature. It has been seen on this study either. But the fact we found: information systems quality is not a factor of IS success, is not commonly accepted idea in the literature. It can be explain as; in Turkey, IS users (bankers) don t care about information systems quality because they accept it is already perfect designed by the programmers and they ignore the reality of arrangeable structure of systems and so they avoid to think about system quality because of their complex perceptions. Small advice to managers who wants to build up an information system on its business place, for measuring effect of IS s In Turkey, they must notice service quality, user satisfaction and information quality (output of the systems quality). 11

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