Available online at www.sciencedirect.com
Procedia - Social and Behavioral Sciences 58 (2012) 1565 – 1575
8th International Strategic Management Conference
Evaluating the intended use of Decision Support System (DSS) by applying Technology Acceptance Model (TAM) in business organizations in Croatia Zelimir Dulcica, Dino Pavlicb, Ivana Silicc, b* Faculty of Economics, University of Split, 21000 Split, Croatia
Abstract Since the beginning of the expansion of information systems, people have been considering using them for making business decisions. Decision Support System (DSS) is a computer technology solution that can be used to support complex decision making and problem solving. In order to produce a high quality business decisions, managers have to be equipped with wide range of relevant information which makes the process of decision making even more complex. In situations like this, use of DSS can be a logical solution. The aim of this paper is to investigate the intended use of DSS within medium and large business organizations in Croatia by applying Technology Acceptance Model (TAM). While many models have been proposed to explain and predict the use of information systems, TAM has been the only one which has acquired the most attention of information systems community. In order to identify the influencing factors on using DSS by managers of medium and large business organizations in Croatia, online questionnaire was distributed to the target sample and the obtained data from questionnaires was analyzed through the SPSS statistical software. The study indicates the importance of perceived usefulness and perceived ease of use as core factors which influence on the perception of using DSS to support management decision process. Keywords: Decision Support System, Technology Acceptance Model, acceptance of DSS, management decision making, business organizations in Croatia th
-review under under responsibility responsibilityof ofthe The8th 8 International InternationalStrategic Strategic © 2012 Published by Elsevier Ltd. Selection and/or peer-review Management Conference Conference Open access under CC BY-NC-ND license. Management 1. Introduction Nowadays, strategic decision-making is undoubtedly the most attractive area of management research (Papadakis et al., 1998). This type of decisions is usually complex and uncertain (Kivijarvi et al., 1993). The importance and power of using information systems to support the decision making process is being increasingly recognized among managers (Rode, 1997). Global, technological and environmental changes have a significant influence on business processes and this makes it almost impossible to exist without some kind of IT support. The most important reason for implementing and using information systems to support decision making process is to gain a competitive advantage and efficiency when talking about business processes (Garaca, 2009). Decision Support System (DSS) is an information system designed to support the business and organizational decisionmaking process. System uses a database documents, stored knowledge, and built models and procedures to display the
* Corresponding author. Tel: +385 99 299 3992 Email address:
[email protected] 1877-0428 © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the 8th International Strategic Management Conference Open access under CC BY-NC-ND license. doi:10.1016/j.sbspro.2012.09.1143
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different views of the requested information (Imamagic, 2010). DSS has the ability to interact with the user, and can also be u properties are: (1) they are designed specifically for the decision making process; (2) provide an interactive help with deciding, but do not automate the decisionDSS categorization includes the following systems (Power et al., 2000): data based (data warehouses, GIS - Geographical Information Systems), model based (OLAP), knowledge based (data mining), documents based (web search engines) and communications systems (GDSS Group Decision Support System, different groupware tools as teleconferencing and distant whiteboards). Looking back in the past, since the early 1970-s, DSS significantly improved (Shim et al., 2002). It has evolved from Electronic Data Processing (EDP) to Management Information System (MIS) and finally to DSS as information technology applications that are designed to assist specific types of problems and decision makings (Gustavsson, 2009). At first, DSS concept has been defined by Gorry and Scott Morton (1971), who connected management activity categories (Anthony, 1965) and different types of decision making (Simon, 1960). Over the past four decades, DSS changed its definition to narrower and wider, while other IT systems developed in order to assist specific types of problems (Shim et al., 2002). First theoretical studies of organizational decision making were conducted at the Carnegie Institute of Technology during late 1950s and early 1960s (Keen et al., 1987). The technology behind DSS takes advantage of the opportunities that the World Wide Web presents, especially the rapid dissemination of information to decision-makers. Process of strategic decision making using DSS is more efficient primarily when considering the impact of World Wide Web (Shim et al., 2002). Further IT innovations and growth of the Internet will result in increased performance of DSS which will be given to a wider range of potential clients including smaller organizations, offered at lower prices (Shim et al., 2002). IT and DSS usage in the developed world has been well adopted, while in developing countries such as Croatia, this type of research still presents a relatively new area (Kamel, 1995; Rose et al., 1998). The aim of this paper is to investigate the intended use of DSS by medium and large business organizations in Croatia influenced by various factors. The theoretical framework of this research uses widely tested Technology Acceptance Model (TAM) (Boudreau et al., 2001; DSS. The TAM was A Technology Acceptance Model for Empirically at the Sloan School of Management at Massachusetts Testing New EndInstitute of Technology (Chuttur, 2009). However, the origins of the TAM came from Ajzen and Fishb they introduced TRA Theory of Reasoned Action. Davis (1985) presented conceptual model for technology acceptance (Actual System Use). TAM explains a majority of the variation in the possibility of adoption of an information system (Gardber et al., 2004; Khalifa et al., 2003; Koufaris, 2002; Bhattacherjee et al., 2004; Chau et al., 2002), which can be explained through items used in a questionnaire. Money and Turner (2004) presented a revised TAM which is used as a revised TAM since this research is not investing any impact that external variables have on perceived usefulness or perceived ease of use of DSS, but the effect those two factors have on the intended and actual use of DSS, which was also proposed in researches by Gustavsson (2009) and Rigopoulos (2007). In order to identify the influencing factors on using DSS by managers of medium and large business organizations in Croatia, online questionnaire was distributed to the target sample and the obtained data from questionnaires were analyzed through the SPSS statistical software. The paper provides the insight into the theoretical framework in the following, second section. The results of the research including hypotheses, internal consistency of measurement scales, correlation and regression analysis of the evaluated theoretical constructs took place in the third section. The fourth section, the conclusion with the overview of the results and comments, finalizes this paper.
2. Theoretical framework and hypothetical research model 2.1. Technology acceptance model (TAM)
Framework to -
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intention to use a particular informat
TAM has been used in many papers in order to investigate the acceptance of different technologies (Cases, 2010). The number of researches and academics that still use TAM proves its general acceptance as valid tool. It presents an attractive tool due to its ease of use and implementation. More than 700 authors quot but it has been partly changed from one study to another. TAM explains and predicts systems use in terms of its two constructs: Perceived Usefulness and Perceived Ease of Use (Elbeltagi et al., 2005), which are influenced by external variables. If managers have an opinion about using DSS, many external variables have the influence on their perception e.g. situational involvement, prior use, argument of change (Jackson et al., 1997); internal computing support, external computing training (Igbaria et al., 1997); level of education, prior similar experiences, participation in training (Agarwal et al., 1999); subjective norms, job relevance (Venkatesh et al., 2000). Some of the authors who investigated influencing variables do not link them to TAM (Elbeltagi et al., 2005). Also, there is no certain template about considering exact external variables (Legris et al., 2003). Several authors that used TAM in order to test the acceptance factors of different technologies did not investigate external variables (Bajaj et al., 1998; Hu et al., 1999; Davis et al., 1989; Szajna, 1996; Taylor et al., 1995). The aim of this paper is to investigate the intended use of DSS, and we found Money and Turner's revised TAM the most suitable variation for the purpose of this research. Money and Turner's (2004) TAM presents relationship among its 4 constructs: Perceived Usefulness, Perceived Ease of Use, Behavioural Intention to Use and Actual System Use of a target system. This revised TAM model dropped Attitude Towards Using construct because 2004). Also, External Variables construct was dropped since revised TAM is not investigating the impact of external variables, and as it was previously noted - that is also not the aim of this research. As already mentioned as a part of the introduction, the future observed within the business perspective will offer changes and surprises. However, certain trends can be observed in this uncertain environment and one of them is the rise of DSS users and capabilities (Shim et al., 2002). Furthermore, the question itself about defining the factors which influence on the intended use of DSS imposes the solution. In order to identify and research those factors, a questionnaire with four main constructs was completed by management of medium and large business organizations in Croatia. The mentioned four constructs composed of Money and Turner's revised TAM, and are presented by Figure 1 below.
Perceived Usefulness Behavioral Intention to Use
Actual System Use
Perceived Ease of Use
Fig 1. Money and Turner Revised Technology Acceptance Model (TAM), (Money and Turner, 2004)
2.2. Perceived usefulness of DSS (C1) Managers and employees are forced to improve their work performance, which makes Perceived Usefulness an important factor when considering advantages that particular system usage brings to its users. Davis (1985) defined perceived usefulness as a degree which refers to individual beliefs that using a particular system would improve and increase someone's job performance within an organizational context. Bandura (1982) also defined perceived usefulness as the extension to self-efficiency in which operations are associated with valued outcomes. In his further studies, Davis
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(1989) developed measurement scales for perceived usefulness and perceived ease of use, based on psychology proposed six questions as a tool for measuring Perceived Usefulness. These questions include items related to productivity, job performances, effectiveness and usefulness. Number of authors such as Keil, Szajna, Agarwal, Chau, Gefen and others added extra items which are related with the quality of new technologies, control of work, speed of the tasks performed and critical job aspects support in order to measure Perceived Usefulness (Legris et al., 2003). Responses obtained from these questions can be analyzed and used as a degree which (Chuttur, 2009).
2.3. Perceived ease of use of DSS (C2)
make him or her free of mental and physical effort (Davis et al., 1989). Tornatzky and Klein (1982) studied how innovation characteristics are connected with its adoption. They found that the complexity of innovation is the most important out of three factors which indicate the level of innovation acceptance. Money and Turner (2004) also found that it is expected that the user is going to use a system which is easy to use, rather than useful system. If it is complicated to use a certain system like DSS, its performance benefits will be fewer than the difficulties that a system usage will bring. Most frequently used items for measuring Perceived ease of use are related with the ease of operating level, rigidness and flexibility of system and effort needed to learn and use the system (Legris et al., 2003). In order to measure Perceived ease of use, authors like Hu, Dishaw, Venkatesh, Szajna and Keil have changed and adapted these questions to their researches (Legris et al., 2003). 2.4. Behavioral intention to use of DSS (C3) When predicting behavioral intention to use DSS, it is important to consider both perceived usefulness and perceived to Bandura's, emphasized the importance of both determinants for perceived behavior. Instead of the term perceived usefulness, he used information quality and associated cost of access in his work, which are similar to perceived ease of use. One of the main findings of TAM was that perceived usefulness and perceived ease of use have a direct influence on behavioural intention (Chuttur, 2009). Also, each of the two main constructs has an individual influence on Behavioral intention to use the system. Within their exploratory study, Schultz and Slevin (1975) realized that perceived usefulness is positively statistically correlated with the predicted use of DSS. Later, Robey (1979) confirmed the work of Schultz and Slevin. Behavioral intention to use directly effects the Actual system use, and that is also one of the reasons why this factor is presented within TAM, and not TRA (Teo et al., 2008). 2.5. Actual system use of DSS (C4) Determinants of Behavioral intention to use have a direct impact when predicting Actual system use (Tao, 2009; Hu et al., 2003). Generally, Actual use of a certain system can be obstructed by additional variables like time or money (Porter et al., 2006). It is important to realise the distinction between these two constructs. Arning and Ziefle (2007) explained tituderal-accep -acceptance includes affective and cognitive component, while behavioral-acceptance refers to the actual system use. According to this, actual system use can only be partly described by behavioral intention to use, and the connection between these two constructs should not be ignored. 3. Research results 3.1. Research hypothesis and Rigopoulos, we basically applied similar hypothesis as a part of our framework in order to realize the potential support of management decision making process by DSS in Croatia. Based on the theoretical framework outlined in the previous chapter, and the illustrated TAM model, following research hypotheses were formed: H1: Perceived ease of use of DSS is positively correlated to the perceived usefulness of DSS.
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H2: Perceived usefulness and perceived ease of use of DSS are positively correlated to the behavioral intention to use DSS. H3: Perceived usefulness and perceived ease of use of DSS are positively correlated to the actual use of DSS. H4: Behavioral intention to use DSS is positively correlated to the actual use of DSS. The aim of this research is to identify the influencing factors on using DSS by managers of medium and large business organizations in Croatia. In order to test the hypotheses, online questionnaire was distributed to the target sample. Detailed information about the conducted research is given and followed by the results of the internal consistency of measurement scales. The correlation and regression analysis of the evaluated theoretical constructs are presented with regards to the research results. 3.2. Research implementation The research included examining the potential of DSS within medium and large business organizations in Croatia. It was conducted on a sample of 156 companies within 10 territorial subdivisions from different industries presented graphically in Figure 2.
Fig.2. (a) Sample allocation on territorial subdivisions of the Republic of Croatia; (b) Industries of the research sample
42% of the business organizations that participated in this research had more than 250 employees. Even though the questionnaire was sent to different management departments, the respondent structure shown it was completed mostly by the owners (22%) and general managers (21%). Standard 5-point Likert scale was used for the questionnaire, with gradation presented below: 1. 2. 3. 4. 5.
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
Statistical analysis was performed using SPSS program package, while the questionnaire was delivered online. Further on, the questionnaire which was adopted from research conducted by Davis (1989) and used in this research is presented in detail. Perceived usefulness of DSS is surveyed using the following statements: Using DSS gives greater control over our work. Using DSS improves our work performance. Using DSS enables us to accomplish tasks more quickly.
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Using DSS supports critical aspects of our work. Using DSS improves our work efficiency. Using DSS improves the quality of our work. Using DSS makes it more convenient to accomplish our strategies and goals. Using DSS demonstrates our inventiveness to our business partners. Overall, I find DSS useful for our work. Perceived ease of use of DSS is surveyed using the following statements: Using DSS is simple. Using DSS is easy to understand. Using DSS is intuitive. Using DSS is flexible. Using DSS does not require a lot of effort. Using DSS does not require studying the manuals. Using DSS is easy to predict. Overall, I find DSS easy to use. Behavioral intention to use DSS is surveyed using the following statements: I think that using DSS is a good idea. I think that using DSS is beneficial to us. I think that by using DSS we would achieve certain strategic advantages. I intend to use DSS periodically in the future. I intend to use DSS routinely and regularly in the future. I intend to fully integrate our work with DSS. I intend to recommend the use of DSS to our business partners. Overall, I have a positive perception towards using DSS. Actual use of DSS is surveyed using the following statements: I use DSS periodically. I use DSS routinely and regularly. Our work is fully integrated with DSS. I often recommend DSS to our business partners. Descriptive statistics of the obtained results is presented within Table 1. Mean values of the surveyed business organizations score the maximum value at the perceived usefulness of DSS, but also at the behavioral intention to use DSS. When talking about the lowest mean values, they are noticed at the actual use of DSS. Table 1. Descriptive statistics of theoretical constructs
mean and standard deviation (S denotes number of statements within a single construct)
S
Minimum
Maximum
Mean
Std. Deviation
C1
9
1,89
5,00
3,5912
,77259
C2
8
2,00
5,00
3,3846
,65954
C3
8
2,00
5,00
3,5337
,73799
C4
4
1,00
5,00
3,2131
,89794
3.3. Internal consistency of measurement scales In statistical studies, internal consistency is a common measure based on correlations between different particles of the same construct measuring whether several statements suggested by the same construct provide similar results. Internal consistency ( ) is usually measured by Cronbach's alpha test - statistics calculated from the correlation between items ranging between zero and one. It is generally accepted rule that the value of from 0.6 to 0.7 is acceptable, while 0.8 or more indicates very good reliability. High reliability (0.95 or more) is not necessarily desirable since it may indicate that some items of the same construct could be fully redundant (Cronbach, 1951). The goal in designing a reliable instrument for the achievement of relevant results is that the items are well-connected and internally consistent, but also that every item contributes with unique information.
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suggests that the items are well connected and
for each of the four theoretical constructs.
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Table 2. Internal Consistency of the constructs examined - Cronbach's alpha test
S
Cronbach's alpha test
C1
9
0,951
C2
8
0,918
C3
8
0,932
C4
4
0,880
3.4. Correlation analysis of the tested model Correlation analysis was carried out on gross results of measurement scales. According to the results of the correlation analysis, the positive correlation of the following constructs was proven to be significant: Perceived ease of use of DSS and perceived usefulness of DSS Perceived usefulness and perceived ease of use of DSS and behavioural intention to use DSS Perceived usefulness and perceived ease of use of DSS and the actual use of DSS Behavioral intention to use DSS and the actual use of DSS Table 3. Pearson correlation matrix for the theoretical constructs examined
C1 1
C2 ,712** ,000 156 1
Pearson Correlation Sig. (2-tailed) N 156 Pearson Correlation ,712** C2 Sig. (2-tailed) ,000 N 156 156 Pearson Correlation ,924** ,775** C3 Sig. (2-tailed) ,000 ,000 N 156 156 Pearson Correlation ,581** ,794** C4 Sig. (2-tailed) ,000 ,000 N 156 156 **. Correlation is significant at the 0.01 level (2-tailed). C1
C3 ,924** ,000 156 ,775** ,000 156 1 156 ,734** ,000 156
C4 ,581** ,000 156 ,794** ,000 156 ,734** ,000 156 1 156
0,924
Perceived Usefulness 0,734
Behavioral Intention to Use
0,712
Perceived Ease of Use
Fig. 3. Pearson correlation model 1
0,775
Actual System Use
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0,581
Perceived Usefulness
Actual System Use
0,712
Perceived Ease of Use
0,794
Fig.4. Pearson correlation model 2
Figures 3 and 4 show Pearson correlation between the examined theoretical constructs which was found significant for all the hypotheses that were established within this research. 3.5. Regression analysis of the tested hypothetical model With regards to the regression analysis, parts of the hypothetical model were considered separately as sub-models. Regression analysis was carried out on gross results of measurement scales. The four sub-models with their regression analysis - R2 and standard beta coefficients are presented below: to the perceived usefulness of DSS (R2=0,507) ral intention to use DSS (R2=0,881) 72) of DSS to the actual use of DSS (R2=0,631) 2 The correlation of behavioral intention to use DSS to =0,631) R-square or coefficient of multiple determination represents the dependent variable variance share explained by correspondent independent variable is increased by 1. Group test of the regression models' significance (p-value of the empiric F-ratio in the ANOVA table) was 0.000, which means that the regression models are statistically significant. Overall, the results of regression analysis confirmed the findings of the previously presented correlation analysis, and once again found the suggested correlations significant. 4. Conclusion This paper used revised TAM model (Money and Turner, 2004; Rigopoulos and Askounis, 2007) to evaluate how end-users' perceived usefulness and perceived ease of use determines their behavioral intention and actual use of DSS. The research conducted within this paper made a valuable contribution to the DSS domain by investigating users' intentions to accept and use DSS as a part of the decision making process. The importance of this research is also imposed by the fact it was conducted within business organizations, while many similar researches made use of students in controlled environment, and therefore, results obtained from those studies can not be generalized to the real world since the sample of students may have different motivation. Although the sample of students may reduce the costs of the research, it is no doubt that DSS implementation process is more relevant for business organizations (Legris et al. 2003). This research determined that the adopted hypotheses proposed by Money and Turner (2004) were found significant as can be seen from the statistical analysis in the previous sections. Furthermore, positive correlation for all of the tested hypotheses was demonstrated: H1: Perceived ease of use of DSS is positively correlated to the perceived usefulness of DSS. H2: Perceived usefulness and perceived ease of use of DSS are positively correlated to the behavioral intention to use DSS. H3: Perceived usefulness and perceived ease of use of DSS are positively correlated to the actual use of DSS.
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H4: Behavioral intention to use DSS is positively correlated to the actual use of DSS. by how use intention to use the system is more determined by the perceived usefulness than by the perceived ease of use. It is interesting to point out that the similar findings were found significant within the research performed by Davis et al. from 1989, where perceived usefulness predicted intentions to use the system whereas perceived ease of use was secondary and acted through perceived usefulness (Davis et al. 1989). However, when analyzing the actual system use, this research proved the opposite the users already using DSS found perceived ease of use as a more relevant factor than its perceived usefulness contributed to their work. This can be explained by the fact that the users actually using the system are not using it voluntarily, but are forced to use the system that is already owned by the company. When considering these mandatory settings, perceived ease of use may have more impact on system acceptance than perceived usefulness. This was also found significant by a research performed in 2002 by Brown and colleagues carried out to replicate TAM in the banking industry (Brown et al. 2002). Furthermore, this conclusion also suggests that the influence of some factors varies at different stages of the DSS implementation process. It also points out that although the results mostly are convergent, there are situations where they are conflicting. Overall, TAM was indeed proven to be a very popular model for explaining and predicting system use (Chuttur, 2009). Until now, there have been an impressive number of studies on TAM, and the research results have been, over the years, generally consistent. It was acknowledged that the effectiveness of any change process relies on the interdependence between the technology, the organizational context, and the change model used to manage the change (Orlikowski et al. 1997). This supports the suggestion that it may be difficult to increase the predictive capacity of TAM if it is not integrated into a broader model that should also include organizational and social factors (Legris et al. 2003). It was also determined that an effective IS implementation tends to follow a pattern where the management proceeds with disjoint periods of intensive implementation, rather than with continuous improvement (Orlikowski et al. 1993). The usage potential of DSS is still not a fully explored area, and it was not possible to analyze all of the relevant aspects in this paper. Nevertheless, the presented research and facts clearly show the trends and frameworks for the expansion of DSS in future. References Agarwal, R. and Prased, J. 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