Study on the Effects of the Relationship between Contingency Variables and BSC System Property Variables on BSC System Performance

Transcription

1 International DSI / Asia and Pacific DSI 2007 Full Paper (July, 2007) Study on the Effects of the Relationship between Contingency Variables and BSC System Property Variables on BSC System Performance Joon-Woo Song 1), Chi-Hoon Lee 1), Jong-Sung Park 1), Jung-Hoon Lee 2) 1) Graduate Student, Graduate School of Information, Yonsei University, Seoul, Korea 2) Assistant Professor, Graduate School of Information, Yonsei University, Seoul, Korea (jhoonlee@yonsei.ac.kr) Abstract Compared to the functions provided by the BSC system, previous research studies on BSC system did not take into consideration the organizational environments or were unable to reflect its theoretical structure. In this study, the interaction between the relationship of contingency variables and BSC system property variables that affects the BSC system were observed from a contingency theory perspective and a empirical analysis was conducted through questionnaire surveys. The results showed that an organization s decentralization, task structured, interrelationship of task and CEO interests interacted with the BSC system property variables. Through this, concrete implementation and utilization methods of the BSC system that fits the needs of a company were presented. 1. Introduction In recent years, companies are developing their core competencies in order to acquire a sustainable competitiveness while adapting to the rapidly changing business environment. In addition, by accurately analyzing a wide array of company information and finding issues to be resolved, companies are actively seeking a strategic enterprise management technique that can maintain a sustainable competitiveness. From this point, BSC(Balanced Scorecard) evolves from the limits of performance management based on finances towards performance management that includes future-oriented potential value perspectives. This management innovation framework that aims at strategic alignment and strategy execution tool is being welcomed by many today. The BSC system, based on the fundamental concept of BSC, is an information system that makes strategic decision-making for companies easier(bsc Collaborative, 2000). It integrates objectives and index data (Missroon, 1998) and analyzes them (Silk, 1998), while providing an automated base for efficient execution of strategic communication between organizations (McCann, 2000) and cooperation (Silk, 1998). Through this, it provides reliability, accuracy and timeliness. However, there were few research studies on presenting a plan for implementing and using the BSC system and has the common limitations that they do not consider the circumstances of the organization or that they do not properly reflect its theoretical structure. Contingency theories were first developed from organization theories in the 1960s and first began from the fact that an effective organization and management method in specific circumstances may not be effective in different environments (Fiedler, 1964). This was a theory that examined the features for an organization to effectively adapt to changes in various environments (Lawrence and Lorsch, 1967) and provided theoretical structures and methodologies of empirical studies for performance of information systems that were applied to information systems (Umanath, 2003; Weill and Olson, 1989). In particular, when considering the fact that many Korean companies that use the BSC system reveal many differences in utilizing it due to their unique environmental characteristics (Kim et al., 2003), contingency theories can be recognized as a very useful 1

2 perspective in studies for revealing the performance factors of the BSC system. In order to provide a concrete suggestion for executing and using the BSC system from a contingency theory perspective in this study, the relationship between the contingency variable that affects the BSC system performance and the BSC system property variables are found through empirical analysis procedures. In this study, a research approach of the contingency theory is reviewed, the contingency variable and BSC system property variables are set taking into consideration the BSC system user environment and BSC system s characteristics, and hypothesis on the effects of the relationship between these variables on BSC system performance variables are set by referring to preceding studies. A survey was conducted on users in Korean companies that have implemented the BSC system in order to verify the research model and find the results. 2. Theoretical Background The basic concepts for contingency theories are environment, organizational structure, strategy, task characteristics and organization size. Because an organization s performance enhances when the compatibility between variables are higher (Weill and Olson, 1989), approach methods of contingency theory perspectives were defined focusing on the concept and analysis method of its compatibility level. Based on studies of organizational structures of contingency theory perspectives, Van de Ven and Drazin (1985) defined three approach methods as shown in Table 1. In here there are three different approaches: selection, interaction and systems accordance with different concepts of fit and different analysis methods are discussed. Table 1 Definition of the Approach Method of Van de Ven and Drazin (1985) Approach Concept of fit (Initial) Analysis Method Selection Interaction Systems Fit is assumed premise underlying a congruence between context and structure. Fit is the interaction of pairs of organizational contextstructure factors; it affects performance. Fit is the internal consistency of multiple contingencies and multiple structural characteristics; it affects performance characteristics. Correlation analysis, Regression analysis MANOVA analysis Deviations from ideal-type designs Venkatraman (1989) supplemented the empirical analysis method for this approach method and based on studies for organizational strategies from a contingency theory perspective, six approach methods were defined as seen in Table-2. Table 2 Definition of the Approach Method of Venkatraman (1989) Approach Concept of fit Analysis Method ANOVA analysis Fit as moderation Interaction Moderated regression analysis Subgroup analysis Fit as mediation Intervention Path Analysis AOVA Analysis, Fit as matching Matching Deviation score analysis Residual analysis 2

3 Fit as gestalts Fit as profile deviation Fit as co-variation Internal congruence Adherence to a specified profile Internal consistency Cluster analysis Factor analysis The calculation of deviation as a eucledian distance in an n-dimensional space MDE Second-order factor analysis (confirmatory) Umanath (2003) implemented above approach methods and synthesized the demonstrative study results on studies of information system performance from a contingency theory perspective and defined the following approach methods. Table 3 Definition of the Approach Method of Umanath (2003) Approach Concept of fit Analysis Method Fit as Congruence Fit as contingency Fit as holistic configurations Assessment of relationship between variable pairs with no explicit evaluation of impact on a criterion variable Assessment of relationship between variable pairs on the basis of impact on an explicit criterion variable Assessment of multivariate co-variation among variables of different domains to tease out holistic configurations Test simple correlation between pairs of variables Mediation Model Moderation Model : Multiplicative, Deviation score Pattern analysis : the distance of a target configuration from a multidimensional normative profile of an externally specified ideal type In Umanath s (2003) definition, Fit as Congruence approach methods do not take information system performance such as criterion variables into consideration and therefore, is not parallel to the objective of this study. In addition, because Fit as holistic configuration approach methods selects samples for ideal types problems occur with its validity. This point will have an even greater effect in BSC system fields, which have very lacking preceding studies. When taking criterion variables into consideration, fit as contingency approach methods can be sub-grouped into mediation models and moderation models depending on the relationship between contingency variables and information system property variables. Because mediation models limit information system performance to the one-sided effect on contingency variables, it does not reflect the interaction between the two variables. Based on this argument, this study has selected Umanath s (2003) fit as contingency approach method and moderation model to design the research model. When examining past studies on information system Performance from a contingency theory perspective based on Umanath s (2003) approach methods, fit as congruence approach methods that concentrate on the relationship on contingency variables and information system property variables were the main type of researches in the beginning (Cerullo, 1980; Chenhall and Morris, 1986; Ein-Dor and Segev, 1982; Ewusimensha, 1981; Huber, 1981; Macintosh, 1981; Gordon and Miller, 1976; Gordon, Larker and Tuggle, 1978; Gordon and Narayanan, 1984; Sathe and Watson, 1987). Since the late 80s, fit as contingency and fit as holistic configuration approach method studies that reflect the standard variable were reported (Gul and Chia, 1994; Kim, 1988; Kim and Umanath, 1992; Nicolaou, 2000; Nidumolu, 1995; Raymond, 1990; Tait and Vessey, 1988; 3

4 Zeffane and Cheek, 1995). Most of these studies reflect the internal and external features of an organization, task characteristics, CEO characteristics and user characteristics for contingency variables, while calculated information, implementation and operation types are reflected for the information system property variables. 3. Research Design 3.1 Research Model and Hypothesis Based on result of empirical research as the below, this study predicts relationship between contingency variable and BSC system property variables and examines the multiplicative relationship of the interaction of the two variables in the fit as contingency approach method defined by Umanath (2003) and moderation model. Fig. 1 Research Model Decentralization of an organization deepens discrimination between departments and makes communications more difficult (Galbraith, 1973), while also causing problems for establishing a plan for the organization (Adriaans, 1993). Therefore, decentralized organizational structures must enhance information sharing (Zeffane and Cheek, 1995) and the need for all-inclusiveness (Sathe and Watson, 1987), and requires an information system that can summarize and integrate different types of information (Chenhall and Morris, 1986). Hypothesis 1. Relationship between organizational centralization and BSC system property variables. Hypothesis 1-1. Organizational centralization will be positively related to the BSC system performance. Hypothesis 1-2. Interaction between organizational centralization and BSC system property variables. (a: source approachability, b: range of information, c: the processing level of information) will have an effect on BSC system performance. Organizations that have a low level of task structuring have a high difficulty level in task, and because it is difficult to understand the procedures to achieve a goal, the need for external, future and summary information through information systems increases (Gordon et al., 1978). Hypothesis 2. Relationship between task structuralization and BSC system property variables. Hypothesis 2-1.Task structuralization will be positively related to the BSC system performance. Hypothesis 2-2. Interaction between task structuralization and BSC system property variables. (a: Source approachability, b: range of information, c: the processing level of information) will have an effect on BSC system performance. Reciprocal connections of task increases the uncertainty of performing task and also increases the amount of 4

5 information to be handled (Galbraith, 1973, Tushman and Nadler, 1978), and therefore, it requires information to be found and integrated from various information systems (Goodhue, 1995) and an information system that can provide external and future information (Chenhall and Morris, 1986). Hypothesis 3. Relationship between Interrelationship of task and BSC system property variables. Hypothesis 3-1. Interrelationship of task will be positively related to the BSC system performance. Hypothesis 3-2. Interaction between Interrelationship of task and BSC system property variables. (a: Source approachability, b: range of information, c: the processing level of information) will have an effect on BSC system performance. Because information systems of the organization, which is a great point of interest for the CEO, must provide information for making decisions, there is a need to integrate and find outside and future information (Cerullo, 1980; Gorry and Morton, 1971). Hypothesis 4. Relationship between CEO interests and BSC system property variables. Hypothesis 4-1. CEO interests will be positively related to the BSC system performance. Hypothesis 4-2. Interaction between CEO interests and BSC system property variables. (a: Source approachability, b: range of information, c: the processing level of information) will have an effect on BSC system performance. 3.2 Setting and Measurement of Variables (1) Contingency Variables In this study, contingency variables are set focusing on the BSC system user environment, to be more precise, the organizational properties that must be critically considered for companies with BSC system. For companies that have implemented BSC system, the method for selecting and cascading their strategic objectives are critical decisions. The objectives set from the top down have the strength that it can reflect the intention of the CEO, but it may not cascade to lower levels of the organization. In contrast, strategic objectives set from the bottom up have the advantage that it can increase the amount of participation from members of the organization, but because it has the weakness to align the company s visions and strategic objectives, we can presume that BSC and an organization s level of centralization have a close relationship. Since BSC is known as a framework for management innovation technique based on the assumption that what cannot be measured, cannot be managed, and it has the certain characteristics of all business activities of the company being quantitatively measured and evaluated (Kaplan and Norton, 1996, Niven, 2005), we can predict that task is transformed towards a strategic direction that makes the range and objective clearer. Companies using BSC integrates and clarify task achievements of members of its organizations through managing key performance indicators (KPIs) and tends to align cooperation s business objectives and critical success factors (CSFs) between hierarchical levels of the organization (Kaplan and Norton, 1996). In effect, we can presume that it transforms to create a higher dependency of interrelationships between other organization members and departments. The chief directors of a company using BSC are the highest person in charge and sponsor for using BSC system, and there is a need for in-depth understanding of the how BSC system contributes to achieve business objectives through robust performance managements (Niven, 2005). In effect, CEO interests and leaderships are essential in companies using BSC and we can presume that it can be applied identically with the field of BSC systems that can improve the execution levels of strategic objectives. When summing up the above discussions, the contingency variables were set for the organization s centralization, task structuralization, inter-relationship of task and CEO interests, which were measured using a 5-point Likert scale. The organization s centralization at this point used the reverse coding of measured items of verified documents in the organization s decentralization. 5

6 Table 4 Operational Definitions and Measurement Items of Contingency Variables Variables Operational Definition and Measurement Items Reference Degree of concentrate decision-making authorities in Organizational centralization Task structuralization Interrelationship of task CEO interests the upper-part of the organization (Degree of reflecting member s opinion; Degree of discretionary power in decision; Degree of member s participation in management goal) Degree of definitude and standardization of task objective and boundary (Degree of definitude of task goal; Degree of definitude of task boundary; Degree of general repetition of task) Degree of resource depending and sharing between department (Amount of related task to another department; Degree of demanding aid of another department; Degree of decision demanding of another department; Degree of output demanding of another department) Degree of CEO s understanding and interest in BSC System (Degree of CEO s understanding; Degree of CEO s interest; Degree of CEO s expectation ) Chenhall and Morris (1986) Gordon and Narayanan (1984) Gorry and Morton (1971) Van de Ven et al. (1976) Sanders and Courtney (1985) McCann and Ferry (1979) Chau and Tam (1997) (2) BSC System Property Variable In order to measure the functions of the BSC system from a user s standpoint in this study, the estimated information properties were set as BSC system property variables. The BSC system user can assign the appropriate CSFs and KPIs and communicate performance information of their department with other members, departments or with the entire company. In addition, corporate resources related to the task can also be shared. BSC systems include qualitative information and it provides not only red/green interfaces that can manage and evaluate past Performance, but also simulation and prediction information based on OLAP platforms for strategic learning. Depending on the use of the information, the BSC system can integrate and summarize performance data and provide visual analysis information in various forms such as graphs or causal relationships. Summing up the above discussions, the BSC system property variables were set up using the Source approachability of information, range of information and the processing level of information in order to measure the 5 point Likert scale. 6

7 Table 5 Operational Definitions and Measurement Items of BSC System Property Variables Variables Operational Definition and Measurement Items Reference Degree of approaching and sharing information source by Source approachability of information Range of information Processing level of information BSC System. (Degree of Approaching and sharing in department; Degree of Approaching and sharing between departments; Degree of Approaching and sharing in company) Range of getting various information type of information by BSC System. (Range of qualitative information; Range of past information; Range of forecasting information) Degree of getting processed and integrated information by BSC System. (Degree of analysis; Degree of Integrating, Degree of summary; Degree of visibility) Culnan (1983) Keen (1991) Gorry and Morton (1971) Gordon and Narayanan (1984) Gul and Chia (1994) Gorry and Morton (1971) Gordon and Narayanan (1984) Gul and Chia (1994) (3) BSC System Performance Variable Normally in order to measure performance of a specific information system, a subjective variable known as user satisfaction and an objective variable called system usage amount are widely used. However, in this study, based on results from research studies that user amounts are appropriate only in voluntary user environments (Lucas, 1978) and taking into consideration that the BSC system is more characterized as a non-voluntary user environment, the BSC system performance variables were set as user satisfaction levels and measured using the 5 point Likert scale. Table 6 Operational Definitions and Measurement Items of BSC System Performance Variables Variables Operational Definition and Measurement Items Reference User satisfaction Degree of meeting user s information demand and expectation by BSC System (Degree of reliability; Degree of exactitude; Degree of easy of accessing data; Degree of compatibility with task; Understanding possibility of information) Doll and Torkzadeh (1988) Raymond (1982) Bailey and Pearson (1983) Baroudi & Orlikowski (1988) 7

8 3.3 Data Collection Method In this study, with the help of the cooperation from BSC system suppliers and consulting firms, we acquired a list of 47 large-size companies with 300 employees or more that use the BSC system. The survey were distributed to users in 35 of these companies that were confirmed to actually use this system. Five surveys per selected companies for a total of 175 surveys were sent via and fax, and a return rate of 77% from 135 replies was recorded. Excluding 18 replies in which the survey data were insufficient or poorly answered were excluded in data analysis and a total of 119 data were decided to be used in the final data analysis. The data analysis was conducted using the SPSS WIN 12.0 statistics package. When examining the demographic features of the data used in the final analysis, it is evident that most of repliers are managers or high position and have used the BSC system for six months or more as shown in Table 7. Table 7 Demographic Features of Samples Property Frequency % Clerk Deputy Director Position Director Vice Manager Manager Executive Less than 6 months month 1 year Period of using BSC System 1 2 years years More than 3 years Manufacturing Finance Construction Business Type Trade Service Information Technology Shipping Education Analysis 4.1 Descriptive Statistics CEO interest and processing level exceeded the average value at 3.4, which explained the high level of interest from CEOs on BSC systems and can be understood that there is an amount of use of BSC system integration and analysis functions. However, the average score for organization centralization was 2.902, which suggests that companies using BSC systems have selected a relatively decentralized performance setting. 8

9 Table 8 Descriptive Statistics (N = 119) Variables Average Organizational centralization Contingency Variables Task structuralization Interrelationship of task CEO interests Source approachability of information BSC System Variables Range of information Processing level of information BSC System Performance Variables User satisfaction Reliability Verification In this study, Cronbach s alpha was used to review reliability according to internal consistencies. As seen in table 9, all measured values have 0.6 or more (that is, Cronbach s alpha value), which is a common requirements for social science studies. Since most of the variables score 0.7 or higher, they have a relatively high reliability. Table 9 Variable Reliability Analysis Results Variables Items Cronbach s alpha organizational centralization Contingency Variables task structuralization interrelationship of task CEO interests BSC System Variables Source approachability of information range of information processing level of information BSC System Performance Variables user satisfaction Validity Verification 9

10 In this study, factor analysis were conducted for the structural validity, or in other words convergent validity and discriminant validity between the measured items, and were evaluated by its factor load and Eigen value as per VARIMAX rotation methods. Factor loads show what variables are most related to which factors and when they are 0.5 or higher, they are usually deemed as an important variable. The eigenvalue represents how much each factor explains existing variable information to what extent, and normally the value must exceed 1.0. Since the items for factor analysis result measurements scored above 0.5 for factor loads and above 1.0 for eigenvalues, which well explains each variable, in this research, it was confirmed that the set variables acquired convergent validity and discriminant validity. Therefore, these were used for hypothetical verification and table 10, 11 and 12 have shown that all data variables were valid. Table 10 Factor Analysis Results of Contingency Variables Measurement Items Component 1 Component 2 Component 3 Component 4 Degree of demanding aid of another department Amount of related task to another department Degree of decision demanding of another department Degree of output demanding of another department Degree of reflecting member s opinion Degree of member s participation in mgt. goal Degree of discretionary power in decision Degree of CEO s expectation Degree of CEO s interest Degree of CEO s understanding Degree of definitude of task goal Degree of definitude of task boundary Degree of general repetition of task Eigenvalue Table 11 Factor Analysis Results of BSC System Property Variables Measurement Items Component 1 Component 2 Component 3 Degree of analysis Degree of summary Degree of integrating Degree of visibility Degree of Approaching and sharing between department Degree of Approaching and sharing in company Degree of Approaching and sharing in department Range of qualitative information Range of past information

11 Range of forecasting information Eigenvalue Table 12 Factor Analysis Results of BSC System Performance Variable Measurement Items Component 1 Degree of reliability.795 Degree of exactitude.710 Degree of compatibility with task.701 Degree of easy of accessing data.693 Understanding possibility of information.534 Eigenvalue Research Hypotheses Verification In order to verify the multiplicative model relationship hypothesis, this research study decided use Baron and Kenny s moderated regression analysis process (1986). The moderated regression analysis falls under the hierarchical regression analysis, and follows the below 3 steps expressed in regressive form. Step 1. Step 2. Step 3. Y = β 0 + β 1X1 + ε Y = β 0 + β 1X1 + β2 X2 + ε Y = β 0 + β 1X1 + β2x2 + β3 X3 + ε Y: BSC system performance, X1: Contingency variable, X2: BSC system property variable X3: X1 and X2 interaction variable ( X1 multiplied by X2), β 0 : Regression constant β 1, β 2, β 3 : Regression coefficient, ε : Difference from average Through these procedures, we found that the interaction variable in step 3 is statistically noticeable and when explanation (R 2 ) was increasing, we can say there is a moderating effect. However, multicollinearity issues that can occur in multiple regression analysis were reviewed using tolerance and VIF (Variance Inflation Factor), and we found that there were no problems with verifying all hypotheses. (1) Verification of Research Hypothesis 1 The relationship between the organization s centralization and BSC system property variables and its effects on BSC system performance were verified in hypothesis 1-1 and The analysis results showed that organizational centralization have negative affection to user satisfaction(p<0.001) and interaction variables of the organization centralization s source approachability of information, range of information and processing level of information were all statistically noticeable (p<0.05) as shown in table 13. The regression explanation (R2) could be seen increasing, which illustrated a positive moderating effect. Therefore it was rejected for use in hypothesis 1-1, but accepted for use in hypothesis

12 Table 13 Verification of Research Hypothesis 1 Independent variables β t R 2 p Tolerance VIF 1-1 Organizational centralization (X 1 ) *** - Organizational centralization (X 1 ) 1-2(a) 1-2(b) 1-2(c) Source approachability of information (X 2 ) Organizational centralization (X 1 ) Range of information (X 2 ) Organizational centralization (X 1 ) Processing level of information (X 2 ) * * * Dependent variables: User satisfaction, * p<0.05, ** p<0.01, *** p<0.001 (2) Verification of Research Hypothesis 2 The relationship between the task structuralization and BSC system property variables and its effects on BSC system performance were verified in hypothesis 2-1 and 2-2. The analysis results showed that task structuralization have positive affection to user satisfaction (p<0.01) and interaction variables of only the task structuralization and source approachability of information were statistically noticeable (p<0.05) as shown below. The regression explanation (R2) could be seen increasing, which showed a negative moderating effect. Therefore it was accepted for use in hypothesis 2-1, and partially selected for use in hypothesis 2-2. Table 14 Verification of Research Hypothesis 2 Independent variables β t R 2 p Tolerance VIF 2-1 Task structuralization (X 1 ) ** - Task structuralization (X 1 ) 2-2(a) Source approachability of information (X 2 ) *

13 2-2(b) 2-2(c) Task structuralization (X 1 ) Range of information (X 2 ) Task structuralization (X 1 ) Processing level of information (X 2 ) Dependent variables: User satisfaction, * p<0.05, ** p<0.01, *** p<0.001 (3) Verification of Research Hypothesis 3 The relationship between the interrelationship of task and BSC system property variables and its effects on BSC system performance were verified in hypothesis 3-1 and 3-2. The results of data analysis showed that interrelationship of task have positive affection to user satisfaction (p<0.01) and interaction variables on only the interrelationship of task and range of information were statistically noticeable (p<0.05) as shown in table 15. The regression explanation (R2) could be seen increasing, which showed a positive moderating effect. Therefore it was selected for use in hypothesis 3-1, and partially selected for use in hypothesis 3-2. Table 15 Verification of Research Hypothesis 3 Independent variables β t R 2 p Tolerance VIF 3-1 Interrelationship of task (X 1 ) ** - Interrelationship of task (X 1 ) 3-2(a) 3-2(b) 3-3(c) Source approachability of information (X 2 ) Interrelationship of task (X 1 ) Range of information (X 2 ) Interrelationship of task (X 1 ) Processing level of information (X 2 ) * Dependent variables: User satisfaction, * p<0.05, ** p<0.01, *** p<0.001 (4) Verification of Research Hypothesis 4 This relationship between the CEO interest and BSC system property variables and its effects on BSC system performance were verified in hypothesis 4-1 and 4-2. Results showed that CEO interest have positive affection to user satisfaction (p<0.001) and interaction variables on only the CEO interest and processing level of information were statistically noticeable as shown in table 16. The regression explanation (R2) could be seen increasing, which showed a positive moderating effect. Therefore, it was selected for use in hypothesis 4-1, and partially selected for use in hypothesis 4-2. Table 16 Verification of Research Hypothesis 4 13

14 Independent variables β t R2 p Tolerance VIF 4-1 CEO interests (X 1 ) *** - 4-2(a) 4-2(b) 4-3(c) CEO interests (X 1 ) Source approachability of information (X 2 ) CEO interests (X 1 ) Range of information (X 2 ) CEO interests (X 1 ) Processing level of information (X 2 ) Dependent variables: User satisfaction, * p<0.05, ** p<0.01, *** p< Conclusion This research study empirical analyzed the relationship between the contingency variable and BSC system property variables that affect the BSC system performance through interaction from a contingency theory perspective to find results. A summary of the research results are as follows. First, companies that focus decision-making authorities in the upper-part of the organization needs to emphasize the source approachability of information, range of information and processing level of information for implementing and using the BSC system. In addition, the results address the issue of decreasing participation from other members of the organizations caused by centralization could be resolved by utilizing information from the BSC system. Secondly, companies that have a clear range and objective of their operations need to restrict the source approachability of the BSC system information. We can realize that the vast information sharing through the BSC system can actually semi-forcefully increase the complexity of business operations. In particular, when comparing preceding research results related to information system performance from a contingency theory perspective, the clear differences become very obvious. Thirdly, companies that have a high dependency on exchange of resources between departments have a need to stress the BSC system range of information. Difficulties with increasing information to be handled and uncertainties of operations caused by inter-relationships between departments could be resolved through a wide array of information from the BSC system. Fourthly, companies with a high CEO interest on the BSC system needs to emphasize the processing level of BSC system information. This can be understood that CEOs prefer summarized and comprehensive information. In setting contingency variables, this study did not explore the range of variables found through previous research studies and only reflected the internal circumstances of an organization. This can be deemed as a research limitation in the fact that the rapidly changing company situations cannot be reflected in various ways. This study is significant because it makes an empirical analysis of BSC system performance on a theoretical platform and that is presents a concrete suggestion for implementing and using the BSC system that fits company circumstances. In addition, it can be used as a foundation for developing various contingency variables and BSC system property variables in the futures, or become a cornerstone for empirical analysis research for finding BSC system performance through different approach methods. 14

15 References [1] Bailey, J. E. and Pearson, S. W.; Development of a Tool for Measuring and Analyzing User Satisfaction, Management Science, Vol. 29(5), pp , [2] Baron R. M. and Kenny, D. A.; The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations, Journal of Penality and Social Psychology Vol. 51(6), pp , [3] Baroudi J.J. and Orlikowski, W. J.; A Short form measure of user satisfaction and notes on use, Journal of Management Information System, Vol. 4(4), pp 44-59, [4] Bergeron F., Raymond, L. and Rivard, S.; Fit in strategic information technology management research: an empirical comparison of perspectives, The International Journal of Management Science, Vol. 29, pp , [5] Cerullo M.J.; Information Systems Success Factor, Journal of Systems Management, Vol. 31(12), pp 10-19, [6] Chau P. Y. K. and Tam, K. Y.; Factor Affecting the Adopting of Open System: An Exploratory Study, MIS Quarterly, Vol. 21(1), pp 1-25, [7] Chenhall R. and D. Morris.; The Impact of Structure, Environment, and Interdependence on the Perceived Usefulness of Management Accounting System, The Accounting Review LXI, Vol. 61(1), pp 16-35, [8] Culnan M. J.; Environmental Scanning: The Effect of Task Complexity and Source Accessibility on Information Gathering Behavior, Decision Sciences, Vol. 14(2), pp , [9] Doll W.J. and Torkzadeh, G., The measurement of end-user computing satisfaction, MIS Quarterly, Vol. 12(2), pp , [10] Ein-Dor P. and Segev, E.; Organizational Context And MIS Structure: Some Empirical Evidence, MIS Quarterly, Vol. 6(3), pp 55-68, [11] Ewusi Mensah K.; The External Organizational Environment and Its Impact on Management Information Systems, Accounting, Organizations and Society, Vol. 6(4), pp , [12] Fiedler F. E.; A Contingency Model of Leadership Effectiveness, Advances in Experimental Social Psychology Advances in Experimental Social Psychology, Vol. 1, pp ,

16 [13] Galbraith J. R.; Organization Design, Addison-Wesley Publishing Co., [14] Goodhue D. L.; Understanding User Evaluations of Information Systems, Management Science, Vol. 41(12), pp , [15] Gordon L. A. and Miller, D.; A Contingency Framework for the Design of Accounting Information Systems, Accounting, Organizations and Society, Vol. 1(1), pp 59-69, [16] Gordon L. A., Larcker, D. F, and Tuggle, F. D.; Strategic Decision Processes and the Design of Accounting Information Systems: Conceptual Linkages, Accounting, Organization and Society, Vol. 3(3-4), pp , [17] Gordon L. A. and Narayanan, V. K., Management accounting systems perceived environmental uncertainty and organization structure: An empirical investigation, Accounting, Organization and Society, Vol. 9(1), pp 33-47, [18] Gorry G. A. and Morton, S.; A Framework for Management Information Systems, Sloan Management Review, Vol. 12(1), pp 55-70, [19] Gul F. A. and Chia, Y. M.; The Effect of Management Accounting Systems, Perceived Environment Uncertainty and Decentralization on Managerial Performance: A Test of Three-way Interaction, Accounting, Organization and Society, Vol. 19(4-5), pp , [20] Huber G. P.; The Nature of Organizational Decision Making and the Design of DSS, MIS Quarterly, Vol. 5(2), pp 1-10, [21] BSC Collaborative Inc.; BSC Functional Standards Release 1.0a, [22] Kaplan R. S. and Norton, D. P.; Linking the Balanced Scorecard to Strategy, California Management Review, Vol. 39(1), pp 53-79, [23] Keen P. G. W.; Shaping the Future: Business design through Information Technology, Harvard Business School Press, [24] Kim K.; Organizational Coordination and Performance in Hospital Accounting Information Systems: An Empirical Investigation, The Accounting Review, Vol. 63(3), pp ,

17 [25] Kim K. and Umanath, N. S.; Structure and perceived effectiveness of software development units: a task contingency analysis, Journal of Management Information Systems, Vol. 9(3), pp , [26] Lawrence P. and Lorsch, J.; Differentiation and Integration in Complex Organizations, Administrative Science Quarterly, Vol. 12(1), pp 1-30, [27] Lawson, R. and Hatch, T.; Automating the Balanced Scorecard, CMA Management, [28] Lucas R. L.; On the Size Distribution of Business firms, Bell Journal of Economics, Vol. 9(2), pp , [29] Macintosh N. B.; A Contextual Model of Information Systems, Accounting, Organizations and Society, Vol. 6(1), pp 39-52, [30] Marr, B. and Neely, A.; Automating the balanced scorecard : selection criteria to identify appropriate software applications, Measuring Business Excellence, Vol. 7(3), pp 29-36, [31] McCann M.; Turning vision into reality, Management Accounting, Vol. 78(1), pp 36-37, [32] McKeen J. D. and Guimaraes, T.; Successful Strategies for User Participation in Systems Development, Journal of Management Information Systems, Vol. 14(2), pp , [33] Missroon, A. M.; Automating the balanced scorecard methodology, Midrange Systems, Vol. 11(44), [34] Nicolaou A. I.; A contingency model of perceived effectiveness in accounting information systems: Organizational coordination and control effects, International Journal of Accounting Information Systems, Vol. 1(2), pp , [35] Nidumolu S., The effect of coordination and uncertainty on software project performance: residual performance risk as an intervening variable, Information Systems Research, Vol. 6(3), pp , [36] Nidumolu S., Inter-organizational information systems and the structure and climate of seller-buyer relationships, Information and Management, Vol. 28, pp , [37] Niven P.R., Balanced scorecard step-by-step: Maximizing performance and maintaining result, John Wiley & Sons, Inc., [38] Raymond L., Organizational Context and Information Systems Success: A Contingency Approach., Journal 17

18 of Management Information Systems, Vol. 6(4), pp 5-20, [39] Sanders G.L., and Courtney, J. F., A field study of organizational factors influencing DSS success, MIS Quarterly, Vol. 9(1), pp 77-93, [40] Sathe V., and Watson, D., Contingency theories of organization structure, Management Planning and Control, [41] Silk S., Automating the balanced scorecard, Management Accounting, Vol. 11, pp 38-44, [42] Tait P., and Vessey, I., The effect of user involvement on system success: a contingency approach, MIS Quarterly, Vol. 12(1), pp , [43] Tushman M.L., and Nadler, D. A., Information Processing as an Integrated Concept in Organization Design, Academy of Management Review, Vol. 3(3), pp , [44] Umanath N.S., The concept of contingency beyond "It depends" : illustrations from IS research stream, Information & Management, Vol. 40, pp , [45] Van de Ven A.H., and Drazin, R., The Concept of Fit in Contingency Theory, Research in Organizational Behavior, Vol. 7, pp , [46] Venkatraman N., and Camillus, J. C., Exploring the Concept of "Fit" in Strategic Management, Academy of Management Review, Vol. 9(3), pp , [47] Venkatraman N., The Concept of Fit in Strategy Research: Toward Verbal and Statistical Correspondence, Academy of Management Review, Vol. 14(3), pp , [48] Weill P. and Olson, M. H., An Assessment of the Contingency Theory of Management Information Systems, Journal of Management Information Systems, Vol. 6(1), pp 59-85, 1989 [49] Zeffane R., and Cheek, B., The differential use of written, computer-based and verbal information in an organizational context?an empirical exploration, Information and Management, Vol. 28(2), pp ,