Extending the technology acceptance model to include the IT decision-maker: A study of business management software

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Technovation 28 (2008) 112–121

Extending the technology acceptance model to include the

IT decision-maker: A study of business management software

Blanca Herna´ndez

, Julio Jime´nez, M

a

Jose´ Martı´n

Faculty of Economics and Business Studies, Economics and Business Administration Department, University of Zaragoza, C/Gran Vı´a 2, C.P. 50005 Zaragoza, Spain

Abstract

The implementation of new information technologies (IT) has been a key factor in company development in recent years. Therefore, ﬁrms must be equipped for the correct management of this new resource and effectively confront the challenges posed by its adoption. This paper analyses the acceptance of business management software within the new competitive environment by applying the concepts introduced by the technology acceptance model (TAM). The results obtained show that, in contrast to other studies which analyse employee behaviour, the analysis of the perceptions of the company decision-maker increases the explanatory power (R2¼0.95), thereby avoiding some of the weaknesses inherent in this model. We ﬁnd that, for a greater implementation of the management software, the IT should be useful in the performance of a business function and easy to apply.

Keywords: Technology acceptance model (TAM); Business management software; Decision-maker; Information technologies (IT)

1. Introduction

Today, the new competitive environment is characterised by its demand for continual innovation in the productive systems of businesses, which allows their performance to be improved and their profits to increase constantly (Reicheld, 1993; Howard, 1995). In this innovation process, we highlight the key role of information technologies (IT) as a value-producing variable because of the importance of information as a basic input for any economic activity (Grossman and Helpman, 1991;Doms et al., 1995;Barro and Sala i-Martıń, 1995; Aghion and Howitt, 1998). IT have become an essential tool for the correct development of corporate activity, significantly affecting the various productive systems and leading to the computerisation of their basic functions (Korunka et al., 1997; Doherty and King, 1998).

As a result, the diffusion of new IT has increased in the business world, giving rise to signiﬁcant transformations of

traditional business structures, while innovative systems of relations with trading partners (e-mail, electronic data interchange (EDI), customer relationship manage-ment (CRM), ERP, etc.) have emerged simultaneously (Quelch and Klein, 1996). Nevertheless, some technological systems, although presented as an attractive business opportunity, have not been widely accepted by all companies and their application has witnessed serious failures (Long, 1987;Hornby et al., 1992;Shani and Sena, 1994).

Previous studies have attempted to correctly deﬁne the factors that determine the acceptance of an information technology (Chow, 1967; Bass, 1969; Davis et al., 1989;

Taylor and Todd, 1995;Chau and Hu, 2002a). Most of this research is based on behavioural theories and constructs empirically tested models which explain individuals’ sets of actions on the basis of their beliefs and/or attitudes: theory of reasoned action (TRA), technology acceptance model (TAM), theory of planned behaviour (TPB), or innovation diffusion theory (IDT).

The aim of our paper is to analyse the acceptance of some IT that have appeared in recent years, namely, management software. These technologies, in addition

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to facilitating the execution of basic business functions, allow companies to share information with the agents with whom they interact in the performance of their activity. They can be applied to perform the principal organisational functions: CRM, ﬁnancial accounting, budgetary management and after-sales service. However, in spite of their numerous advantages, they have not been developed as widely as might have been expected, which leads us to wonder about the factors that inﬂuence their acceptance.

To study this, we use a TAM which reflects the acceptance of different IT, establishing a connection between users’ perceptions and their final decisions. In spite of their simplicity, TAM has become widespread in recent years, giving rise to a great number of extensions. Nevertheless, we consider that the basic model of Davis (1989) can provide good estimations as long as the adequate subject is chosen for the analysis (Mathieson, 1991; Straub et al., 1995). Most research on technology acceptance in the business context has concentrated on the point of view of the employee as the end-user, thus ignoring other key company agents responsible for taking decisions about technology (see, for example, Karahanna and Limayem, 2000; Lucas and Spitler, 2000; Horton et al., 2001; Yi and Hwang, 2003). Unlike this approach of the majority of the research, we have considered it more appropriate to focus on the firm as a whole as the IT user. To do so, we have taken the firm’s decision-maker as the subject of analysis. In this way, we have captured decisive aspects in the technology acceptance process that have not been taken into account by other research and might otherwise pass unnoticed (see other similar studies in Lu and Yeh, 1998; Grandon and Pearson, 2004; Carayannis and Turner, 2006).

In the following section, we give a detailed justiﬁcation of the reasons why we focus on the ﬁrm’s decision-maker, describe the model, and formulate the hypotheses to be tested. In Section 3 we explain the methodology and in the fourth section we carry out the relevant empirical analyses. Finally, we present the discussion of the results, the conclusions, the theoretical contributions of our study, and the implications for business.

2. Theoretical approach and hypotheses

The objective of our paper is to analyse the acceptance of management software, using a TAM and focusing, unlike other research, on the point of view of the agent who takes the technological decisions in the ﬁrm. We want to demonstrate the convenience of focusing on the decision-maker and not on the IT user employee in research about the acceptance of technology in ﬁrms. In order to do this, we will describe the differences between the two points of view and the weaknesses that are overcome by using our new approach.

2.1. The point of view of the decision-maker

As proposed in the first papers on technology acceptance (Davis, 1989), most of this research has studied the end-user of IT. Likewise, when his model has been used to predict the acceptance of new technologies in the work-place, the employee, as final user, has been established as the subject of analysis (Mathieson, 1991; Adams et al., 1992;Szajna, 1994, 1996;Chin and Gopal, 1995;Igbaria et al., 1995; Abdul-Gader, 1996). This has been the case of previous studies that have tried to predict the degree of expansion that can be achieved by tools such as e-mail, EDI or ERP (Adams et al., 1992;Amoako-Gyampah and Salam, 2004). We consider that this approach may not be adequate because the perceptions of each employee depend on their specific conditions. Thus, the responses obtained may be influenced by the objectives of the employee (to keep his/her job, the possibility of promotion or achieving greater prestige), which are very different to those of the firm (to maximise profits or long-term market value). Furthermore, the skill, dexterity and knowledge that each employee has previously acquired also conditions his/her interaction with IT.

In most of these case studies, the individuals who comprise the research sample do not take the decision to adopt a speciﬁc tool, because its application in the performance of their activity is determined by the ﬁrm (Hartwick and Barki, 1994;Holland and Light, 1999). At the same time, the fact that employees are obliged to use a certain technology may mean that their perceptions are unreliable predictors of the intention to use and the real employment of an information technology and, conse-quently, the basic TAM hypotheses1are rejected.

As a result, we consider that the focus of study within the business context cannot be the final user, as occurs when analysing the behaviour of the individual consumer, because it is more appropriate to concentrate on the perceptions of the decision-making unit, even though it is not the real user of the IT. Thus, we propose to analyse the perceptions of the firm as a whole, evaluating both the usefulness of management software in the firm’s activity and the difficulties that arise in the adaptation of its traditional structures to new technological advances. In this way, we eliminate the effect generated by the above-mentioned individual circumstances of each employ-ee, allowing us to predict the intention of use and the real application of an IT on the basis of the global perceptions of the firm. Likewise, the deviations derived from the coercive character of the employee’s use can be eliminated since the subject of analysis is the same person who takes the decision to adopt the management software.

1

If the exogenous variables of this model are obtained from the perceptions of the worker, it is difﬁcult to believe that they can explain the intensity of use of the ﬁrm at a global level.

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Thus, through this approach of TAM, a more objective and accurate picture of business reality is obtained (Lu and Yeh, 1998).

2.2. Why TAM?

The current study proposes the application of the TAM, adapting it to the characteristics of the company. TAM is an adaptation of the TRA which is conceived as a general structure designed to explain almost all human behaviour and is based on the importance of an individual’s beliefs for the prediction of his/her behaviour (Fishbein and Ajzen, 1975;Ajzen and Fishbein, 1980). TAM focuses exclusively on the analysis of information technology (Chau, 1996;

Venkatesh, 2000; Mathieson et al., 2001; Childers et al., 2001; Featherman and Pavlov, 2003) and, in contrast to TRA models, establishes a priori two key elements determining technological behaviour2: perceived ease of use (PEOU) and perceived usefulness (PU) (Davis, 1989;

Igbaria, 1993; Fenech, 1998; Gefen and Straub, 1997;

Malhotra and Galleta, 1999; Davis and Wiedenbeck, 2001).

Some TAM research focused on long-accepted IT has introduced new variables to complement the effect of usefulness and ease of use upon the variable to be explained3 (Deci, 1975; Davis et al., 1992; Moon and Kim, 2001;Chen et al., 2002;Ong et al., 2004;Shang et al., 2005). These additional variables have tried to increase the explanatory power of the model, which varies according to the perceptions of the individual included to explain his/her behaviour. In spite of these extensions, the majority of studies in the business environment attain values of between 1% and 45%, with only a few exceeding this ﬁgure, such as the work ofShih (2004b)which displays an R2of 47% (Table 1).

We consider that the low R2of these studies, compared to those obtained for research about the technological behaviour of the individual,4 may be due to an inexact point of departure. Our study tries to demonstrate that a basic TAM model can correctly explain the acceptance level of a technology in the business context as long as the focus and subject proposed are correct. Increasing the number of perceptions may slightly raise the explanatory power. Nevertheless, if the subject proposed is not correct, the model will always have R2that are far from 100%.

The topics of TAM research have been extremely varied, including both the employment of personal computers in the workplace (Moore and Benbasat, 1991; Thompson

et al., 1991;Igbaria et al., 1995) and the acceptance of e-mail as a means of communication (Karahanna and Straub, 1999; Gefen and Straub, 2000). In contrast to these more widespread technologies, the management software dealt with here has hardly been tested empirically because it is so new. Therefore, our research must focus exclusively on the inﬂuence of the basic TAM concepts, as was done in other previous studies with technologies like the Internet, dial-up, e-mail or e-commerce (Davis et al., 1989;Adams et al., 1992;

Subramanian and Nosek, 2001;Szajna, 1994, 1996). PEOU concerns the degree to which an individual considers that the application of a speciﬁc technology does not require additional effort. Thus, this factor is inversely related to the concept of perceived complexity, previously proposed by Rogers (1983, 1995). The other determining factor in a TAM model is PU, which is deﬁned as the degree to which an individual considers that the use of a particular system may improve his/her work performance (Davis, 1989;Lederer et al., 2000). The idea of usefulness leads us to the term ‘‘relative advantage’’ proposed by

Rogers (1983, 1995)as one of the ﬁve key elements of his IDT, which states that an innovation is more rapidly diffused if it is perceived by real and potential users as a source of value.

It would appear, as Davis et al. (1989) assert, that the inﬂuence which a belief such as usefulness may have upon the subject’s intention to use contradicts the TRA. However, extensive theoretical development of this subject, in addition to its empirical demonstration, shows the existence of a direct correlation between the two variables (Triandis, 1977;Brinberg, 1979;Bagozzi, 1982;Davis et al., 1989;Igbaria, 1993; Lin and Lu, 2000; Liaw and Huang, 2003; Cheng et al., 2006). Similarly, Lee et al. (2003)

analyse the importance of the PU–intention relationship in the context of TAM models, observing that, in most cases, this connection is statistically upheld.5

In the current competitive environment, the computer-isation of business management systems is considered an attractive opportunity, i.e. one which permits the company to distinguish itself from the competition and, additionally, to generate extra profits. So, we have considered it interesting to analyse the influence of the PU of manage-ment software on the intention to use them (Fig. 1): H1. The PU of business management software has a positive influence on the intention to use (INT).

With regard to the relationship between the two explanatory concepts (PU and PEOU), it varies according to the environment and the tool analysed. Some research argues that PEOU must have a positive inﬂuence on the perception of usefulness (Agarwal and Prasad, 1999;

Venkatesh, 2000; Liaw and Huang, 2003; O’Cass and Fenech, 2003;Shih, 2004a;Shang et al., 2005;Cheng et al., 2006). Others reject it because they have found no empirical evidence of such a connection (Agarwal and 2_{According to}_{Gefen et al. (2003)}_{, the exclusion of Attitude is consistent}

with most research related to TAM models because this concept does not form part of a more concise version of the model constructed byDavis (1989).

3

The variable to be explained is not only the real use, but also the intention to apply a computer tool or the attitude adopted towards it.

4

Looking at the bibliographical review inSun and Zhang (2006), it can be seen that about 60% of TAM ﬁeld studies and experiments focused on

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Prasad, 1999; Hu et al., 1999; Venkatesh, 1999, 2000;

Venkatesh and Morris, 2000). This latter research usually conﬁrms the direct relationship between ease of use and the intention of the user. In this line, we formulate the following hypotheses (seeFig. 1):

H2. The PEOU of business management software has a positive inﬂuence on the PU.

H3. The PEOU of business management software has a positive inﬂuence on the intention to use (INT).

Finally, it has been proved that the intention of applying a speciﬁc technology directly precedes its subsequent

intensity of use (Agarwal and Prasad, 1997; Chen et al., 2002, among others).

H4. The intension to use business management software (INT) has a positive inﬂuence on the intensity of use (USE).

3. Methodology

Our study sample is comprised of entities belonging to every economic sector, thereby avoiding a concentration on only one productive activity which could produce a bias in the interpretation of the results. The technique employed was a survey, both by traditional post and e-mail, sent to

Table 1

Summary of variables and explained power in TAM studies

Refs. Concept to be explained R2(%) Key variablesa

Davis (1989) Use 31–48 PEOU, PU

Adams et al. (1992)(Study 1) Use 17 PEOU, PU

Adams et al. (1992)(Study 2) Use 15 PEOU, PU

Hubona and Kennick (1996) Use 51 A, AGE, ED, EMP, PEOU, PU, VOL

Igbaria et al. (1995) Perceived use/variety of use 27–26 EX, OS, PEOU, PU, SQ, T

Igbaria et al. (1996) Use 28 E, CX, OS (int+ext.), OU, PEOU, PU, S, SP

Igbaria et al. (1997) Use 28 MS, PEOU, PU, OS (int+ext.), T

Karahanna et al. (1999)(Study 1) Intention to use 38 A, DE, I, PEOU, PU, SN, T, V, VIS

Idem (Study 2) Intention to use 24 A, DE, I, PEOU, PU, SN, T, V, VIS

Karahanna and Limayem (2000) Use 35 IA, MES, PA, PEOU, PU, SI, SOP, SUP,

Lucas and Spitler (2000) Use 20 BS, PEOU, PU, S, SN, SQ

Horton et al. (2001)(Study 1) Use 21 A, INT, PEOU, PU

Idem (Study 2) Use 16 A, INT, PEOU, PU

Chau and Hu (2002b) Intention to use 43 A, C, INT, PBC, PI, PEOU, PU

Achjari and Quaddus (2003) Use 26 C, FNET, INET, PEOU, PU, SE

Shih (2004a) Use 42 A, ACC, IQ, PEOUw, PEOUt, PU, SATIS, SEC, SQ, SVQ

Shih (2004b) Performance 47 A, PEOU, PU, R

Amoako-Gyampah and Salam (2004) Intention to use 29 A, PCR, PEOU, PU, SB, T

Ong et al. (2004) Intention to use 44 G, PEOU, PU, SE

Yi and Hwang (2003) Use 15 INT, E, L, PEOU, PU, SE

Wu et al. (2007) Use 56 EF, FT, IF, PEOU, PU, SE

Source: compiled by authors

a_{A, attitude; ACC, access cost; BS, broker’s strategy; C, compatibility; CX, complexity; DE, demonstrability; E, entertainment; ED, education; EF,}

external factors; EMP, employment category; EX, experience; FNET, formal net; FT, technological factors; G, gender; I, image; IA, informational accessibility; IF, internal factors; INET, informal net; INT, intention to use; IQ, perceived information quality; L, learning; MES, media style; MS, management support; OS (int+ext), organizational support (internal and/or external); OU, organizational use; PA, physical accessibility; PBC, self-control; PCR, project common related to; PEOU, perceived ease of use; PEOUw, PEOU of the Web; PEOUt, PEOU of trading on-line; PI, peer influence; PU, perceived usefulness; R, job relevance; S, skills; SATIS, satisfaction; SB, shared belief in the benefits; SE, self efficacy; SEC, security; SI, social influence; SN, social norms; SOP, social presence; SP, social pressure; SQ, perceived system quality; SUP, support; SVQ, perceived service quality; T, training; USE, use; V, voluntariness; VIS, visibility; VOL, usage volume.

USE OF MANAGEMENT SOFTWARE INTENTION TO USE (INT) USEFULNESS (PU) EASE OF USE (PEOU) H2 H1 H3 H4

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a random sample of 600 companies in Spain. 115 replies were received and, following the reﬁning process, a ﬁnal sample size of 109 valid cases was obtained.

We consider the response ratio to be acceptable, taking into account the difﬁculties which exist in obtaining replies to this type of survey (De Vaus, 1995;Baldauf et al., 1999;

Min and Galle, 2003;Bennet et al., 2005). In line with the objective of our study and as in other similar studies (Lu and Yeh, 1998; Riemenschneider et al., 2003; Carayannis and Turner, 2006), the information refers to the company as a whole and not to each of its employees. Therefore, the questionnaires were sent to the managers who take decisions regarding technology and who were asked to reply on behalf of the company.

With regard to the indicators included in the ques-tionnaire, we have applied those which have most often been employed in previous TAM studies (see the review undertaken byLegris et al., 2003). All the questions were on the 7-point Likert-type scale, where 1 indicates strong disagreement and 7-strong agreement (see Appendix).

4. Results

4.1. Validation of the measurement scales

In order to guarantee the internal consistency and unidimensionality of the scales, we began by analysing the data using initial reliability studies and exploratory factor analyses of ‘‘principal components’’ (Bentler and Wu, 1995).

In the ﬁrst case, the procedure followed to eliminate indicators consisted of suppressing those which displayed an item–total correlation inferior to 0.3 (Nurosis, 1993), or whose exclusion increased the Cronbach’s alpha value, which should exceed the minimum limit of 0.7 (Nunnally, 1978). On the basis of these results, we eliminated the items related to after-sales service software which are present in the intensity of use factor (USE), usefulness (PU) and ease of use (PEOU), thereby obtaining alpha values of 0.722, 0.765 and 0.737, respectively. With regard to the item–total correlation, all cases obtained values above the minimum required.

Similarly, the exploratory factor analyses were per-formed using varimax rotation with Kaiser normalisation (Kaiser, 1970, 1974; McDonald, 1981; Hair et al., 1999) and veriﬁed that the concepts were formed by just one factor. These factors explain 65%, 69% and 66% of the variance for USE, PU and PEOU. Furthermore, their items attain values exceeding 0.7 in all cases.

The second phase of scale validation consisted of carrying out a conﬁrmatory factor analysis. To this end, we applied structural equation modelling,6using the robust maximum likelihood estimation method, since our data do not satisfy the condition of normality (Chou et al., 1991;

Hu et al., 1992;Bentler, 1995).

The goodness of ﬁt was tested using various absolute, incremental and parsimony ﬁt indices, which exceeded in almost all cases the limits established byHair et al. (1999)

(Table 2).

With regard to reliability, the Cronbach’s alpha value (explained above) was complemented by calculating the composite reliability coefﬁcient, which was over the recommended minimum of 0.6 (Bagozzi and Yi, 1988) (Table 3).

Finally, the model’s validity was tested using content validity and construct validity. The former is endorsed by the bibliographical review undertaken in the previous section, which was used to define the concepts tested in the study. In order to test the construct validity, both convergent and discriminant validity had to be analysed (Table 3). Convergent validity was tested using the value of each of its items (as a minimum, it must exceed 0.5 points) and of its significance (Hildebrandt, 1984). Discriminant validity guarantees that the scales represent substantially different concepts, and was obtained by calculating the confidence interval for each pair of factors. It was found that none of these contain the value 1 and, consequently, the four dimensions are significantly different (Peter, 1981;

Anderson and Gerbing, 1988).

In view of the results, the measurement model is adequate and we are therefore able to test the hypotheses posed in this study.

4.2. Testing of the research hypotheses

Following the application of structural equation techni-ques, we observed that the model displays a good ﬁt since the majority of indices reach optimum theoretical values (Hair et al., 1999) (Table 4).

After obtaining the results from the structural model (Fig. 2), we observe that they corroborate the second hypothesis posed, i.e. ease of use positively and signiﬁ-cantly affects the perception of usefulness of management software (standardised value ¼ 0.38).

Our results show that the first and third hypotheses are a reflection of how the usefulness and ease of use of these tools modify the intention to use. Thus, it can be stated that the greater the PEOU of a technology or the greater its PU, the greater will be the intention of applying it in management. That is to say, a company will decide to accept a computerised management system when it considers that its incorporation requires no additional effort and that it can improve efficiency and profits (Fig. 2). With regard to the effect of the two variables, the perception of ease of use has a lower direct weight (0.32 against 0.68 for usefulness) although its overall influence is doubled when the indirect effect exercised through the PU variable is included (increasing from 0.32 to 0.58).7

6

The statistical software used was EQS, version 5.7b.

7

0.58 (overall effect) ¼ 0.328 (direct effect)+0.38 0.68 (indirect effect).

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Finally, the fourth hypothesis, which poses the relation-ship existing between intention to use and the intensity of subsequent use, is veriﬁed (Fig. 2). Thus, the greater the willingness to apply a technology, the greater will be the ﬁnal use the company makes of it.

5. Discussion

In recent years, new IT have expanded signiﬁcantly in the business environment and, consequently, the tradi-tional market structures in which the company operates have undergone drastic changes. The principal objective of our study has been to analyse the technological acceptance of management software, using a TAM and focusing on the decision-maker as the subject of analysis. Because management software is so new and has hardly been analysed previously, our study has only included the two basic concepts of TAM models (as was done in other previous papers with technologies like the Internet, dial-up, e-mail or e- commerce). In spite of using this approach, it is important to emphasise the high explanatory power achieved, since it covers 95% of the existing variations in intensity of use and 74% of those regarding intention to use. These R2exceed that of other studies which include a greater number of variables. This is probably due to the

Table 2

Adjustment ﬁt measures for conﬁrmatory factor analysis

Absolute ﬁt Incremental ﬁt

Adjustment ﬁt measures Optimum value Value Adjustment ﬁt measures

Optimum value Value

pw2 _p40.05 _0.031 _NFI _40.9 _0.958

GFI 40.9 0.936 NNFI 40.9 0.954

MFI 40.9 0.913 CFI 40.9 0.979

RMSR Close to 0 0.076 CFI robust 40.9 0.984

RMSEA o0.08 0.049

Parsimony ﬁt

Adjustment ﬁt measures Optimum value Value

w2/d.f. 1–5 1.94

Table 3

Composite reliability coefﬁcient (CRC) and validity (convergent and discriminant)

I´TEM R2 _Lambda* _CRC _Constructs _{Conﬁdence interval}

Use of management software (USE) USE_1 0.455 0.675 USE_2 0.415 0.644 0.660 (USE–PU) (0.831–0.990) USE_3 0.314 0.560 (USE–PEOU) (0.582–0.874) Perceived usefulness (PU) PU_1 0.544 0.738

PU_2 0.548 0.740 0.733 (USE–INT) (0.457–0.837)

PU_3 0.348 0.590

(PU–PEOU) (0.448–0.796) Perceived ease of use

(PEOU)

PEOU_1 0.490 0.700

PEOU_2 0.486 0.697 0.685 (PU–INT) (0.336–0.746)

PEOU_3 0.301 0.543

(PEOU–INT) (0.228–0.672)

Intention to use (INT)a _INT _0.85 _–

Note: *signiﬁcant to level 0.01.

a

The intention to use factor is comprised of just one indicator, and thus the error variance was set at a conﬁdence level of 85% (Anderson and Gerbing, 1988).

Table 4

Structural model fit indicators Adjustment fit measures Absolute fit Adjustment fit measures Incremental fit p w2 _0.035 _NFI _0.936 GFI 0.900 NNFI 0.918 MFI 0.903 CFI 0.958

RMSEA 0.087 CFI robust 0.965

Parsimony ﬁt

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improvement brought about by our model in using the point of view of the ﬁrm’s technological decision-maker, because the study of the employee’s perceptions in the business context could lead to serious weaknesses.

As stated earlier, the adoption and use of a technology by employees may be due to coercion and, therefore, lack any connection between their level of use and their perceptions (PEOU and PU). However, when the user under study is the decision-maker, the usefulness and ease of use associated with management software are better predictors of the intensity of use. This is because the decision-maker knows the capacities and limitations of the human capital of the firm and, moreover, his/her decisions affect the whole firm. Consequently, we confirm the idea that the analysis of the decision-maker’s perceptions is more appropriate for the business environment. Further-more, this approach attains an explanatory power which doubles that obtained in other studies and verifies the key relations of a TAM.

With respect to the perceptions included in our model, the results obtained show that both ease of use and usefulness are factors which have a positive inﬂuence on the intention to computerise activities and, furthermore, that this intention signiﬁcantly affects the intensity of use. The overall importance of the two variables is similar (0.62 for usefulness and 0.58 for ease of use), due to the fact that the perception of ease of use is reinforced by its indirect effect on intention via the usefulness variable.

These results differ from those obtained byLu and Yeh (1998) who consider that usefulness has a greater weight than ease of use and, thus, conclude that managers must concentrate on the advantages derived from PU. With regard to their study, we do not underestimate the importance of the concept of usefulness but, instead, also emphasise the weight of PEOU on the intention to use and on the ﬁnal use made by the company. This concept directly and indirectly inﬂuences company behaviour and, thus, for correct implementation to be achieved, the importance of the ease of use of the new tools must be transmitted.

We must bear in mind that the indicators concerning after-sales service software have been eliminated from all factors (usefulness, ease of use and intensity of use). This is because their rate of adoption is far below that of other management software. Only 26.61% of companies adopt

this technology, as opposed to 75.23% for CRM, 79.81% for ﬁnancial accounting or 46.80% in the case of budget management. As after-sales service is not a priority for most companies, this software has undergone little devel-opment compared to the other basic functions.

6. Conclusions and implications

The results obtained have important implications for future research and for business strategy.

Firstly, we demonstrate that this simple structure can correctly explain the acceptance level of a technology, without the need of extensions, as long as the focus and subject are correctly proposed. In this way, we show that one of the biggest problems in TAM research, in the business environment, is to establish which subject should be analysed. This aspect, which may seem obvious, has been neglected in much previous research and could be the cause of the limited explanatory power of these studies. Thus, we recommend adapting the initial theory and focusing future research on the analysis of the ﬁrm’s technological decision-maker, no matter who uses the technology later.

The perceptions of the decision-maker take into account the firm’s characteristics and condition the decision that will finally be taken. On the contrary, employees may have perceptions of IT that are far from the intensity of use made by the company and, thus, cause measurement errors that are difficult to rectify. Employees may consider that a particular technology is complex or inefficient, but if they are required to do their ordering via EDI, for example, they must use this application and, thus, such perceptions are not reliable predictors of the behaviour of the global organisation. As a result, our study shows that the TAM applied to the decision-maker provides an adequate vision of the acceptance of the new business management software (CRM, financial accounting and budgeting).

The strategic implications of this study are focused principally on the importance of the managers’ perceptions of ease of use and usefulness for the technological behaviour of the ﬁrm. These perceptions will facilitate the correct implementation of IT.

To guarantee greater acceptance and implementation of the management software, it should be efﬁcient in the performance of a business function and, in addition, easy

0.68** (4.977) 0.38* (2.271) 0.32** (3.002) 0.97** (5.361) USE OF MANAGEMENT SOFTWARE INTENTION TO USE (INT) USEFULNESS (PU) EASE OF USE (PEOU) R2= 0.74 R2= 0.95

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to apply. The executive organs responsible for taking IT decisions know that they must invest resources in technological training and learning to facilitate its diffusion and make it more accessible to their employees. Further-more, it is essential, from the beginning of their imple-mentation, to transmit the saving of effort and the usefulness resulting from the adoption to the employees. In this way, the recognition that certain tools are suitable for the organisation decreases the perceived risk with the result that, during their implementation, efﬁciency is increased.

Finally, with regard to the limitations and future lines of this study, other key variables that are the predecessors of PU and PEOU should be included. Our research has proposed a basic TAM as a ﬁrst step in the study of the acceptance of management software, a technology that has been little studied until now. Nevertheless, once the initial Davis model has been tested and its adequacy veriﬁed, future lines of research must focus on widening the business model analysed. This new research will allow deeper insights into organisational behaviour with respect to technological acceptance in an unstable and highly competitive environment.

Acknowledgements

The authors wish to the following bodies for their help and support: the Ministry of Science and Technology (SEJ2005–05968/ECON); and the Regional Government of Aragon (Grupo Ciber Ref. S-14-3; S-09).

Appendix

Responses of the companies to the different indicators included in the questionnaire are shown inTable A1.

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Table A1 Measurement scale

Item Emp.

analysisa

Perceived ease of use

In general, customer relationship

management software are easy to use in the performance of the activity

PEOU_1 Accepted

In general, accounting software are easy to use in the performance of the activity

PEOU_2 Accepted In general, budgeting software are easy to

use in the performance of the activity

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PEOU_4 Rejected

Perceived usefulness

Customer relationship management software are useful for the performance of the activity

PU_1 Accepted

Table A1 (continued )

Item Emp.

analysisa Accounting software are useful for the

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PU_4 Rejected Intensity of use

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USE_4 Rejected Intention to use

I intend to apply a business management software in the course of my activity in the coming months

INT Accepted

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