extending the theory of planned behaviour as a model to explain post-merger employee behaviour of is...

omputers in
C
Computers in Human Behavior 23 (2007) 240–257

www.elsevier.com/locate/comphumbeh

Human Behavior

Extending the theory of planned behaviour asa model to explain post-mergeremployee behaviour of IS use

Echo Huang a,*, Meng Hao Chuang b

a Department of Information Management, National Kaohsiung First University of Science and

Technology, Taiwan, ROCb Department of Information, Barits International Securities Group, Inc.,

Mega Holdings Corp. Taiwan, ROC 100

Available online 18 November 2004

Abstract

A merger that fails to adequately address technology integration and its consequent impact

on employee IS behaviour is almost doomed from the start. The theory of planned behaviour,

a widely accepted expectancy-value model of attitude–behaviour relationship, suggests an

individual�s behaviour is determined by attitudes toward behaviour, subjective norm, and per-

ceived behaviour control. This paper examines ways of expanding the model in the specific

area of financial merger through inclusion of an additional risk variable. Exploratory results

from field experiments are then presented.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Information systems; Risk; The planned behaviour; Bank mergers; IS use

1. Introduction

The Taiwanese government has been actively promoting mergers among banks

as evident by recent passing of Bank Mergers and Acquisition Act in November

0747-5632/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.chb.2004.10.010

* Corresponding author. Address: 2 Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan, ROC. Tel.:

+886 7 6011000x4119; fax: +886 7 6011042.

E-mail address: [email protected] (E. Huang).

mailto:[email protected]

E. Huang, M.H. Chuang / Computers in Human Behavior 23 (2007) 240–257 241

2000. In addition to tax incentives and lengthening the legal validity deadlines,

the new legislation has also relaxed regulations on mergers between banking

and non-banking entities. When examining existing mergers, two common points

are evident: (1) measurement of the efficiency of post-merger system integration

and diversification has largely been neglected, (2) little is known about employeeperformance of IS usage before consolidation. Bagozzi and Kimmel (1995)

suggested influencing factors of past behaviour on current behaviour are fre-

quency of performing the same target behaviour in the past and recency of those

events.

The theory of planned behaviour (TPB) is designed to predict behaviour. This the-

ory is based on expectancy-value model of attitude–behaviour relationship (Ajzen,

1985; Ajzen & Madden, 1985) and has predicted a variety of behaviours with signif-

icant degree of success. This paper examines ways of expanding the model into thespecific area of post-merger employee system performance through inclusion of var-

iables such as pressure, experience, and security.

The paper is divided into five major sections: The first sections begins with a back-

ground overview; the second briefly describes the challenges of commercial bank

consolidation in Taiwan; the third specifies variables that directly influence employee

behaviour; the fourth presents the research design; and, the fifth section presents an

analysis of the empirical data.

2. Merger and acquisition challenges

Berger, Demsetz, and Strahan (1990) and Kane (2000) identified three main

motivations behind mergers: scale and scope of economy, access to safety net sub-

sidies, and market power. These factors are based on the assumption of wealth-

maximization as the primary motive for the merger. Regardless of whether these

mergers are eventually successful, substantial benefits are presented to customersand shareholders, also the level of competition within the industry becomes more

balanced.

A merger that fails to adequately address technology integration and its conse-

quent impact on employee behaviour is almost doomed from the start. The severity

of failures increases with integration issues; for example, decisions like which branch

automation platform or loan system makes the cut. The acquired bank may have a

better check-processing system that allows it to issue monthly statements with min-

iaturized images of customer checks.Merger architects must evaluate how technology is to be migrated after factor-

ing integration and consolidation issues. The process requires evaluating the bank�sgoal, inventorying resources, evaluating impact on current operation, selling

senior management on system benefits, and consulting outside experts for imple-

mentation.

The challenges of merger and acquisition can be divided into two categories: (1)

Integration issues, which include execution risk, information risk, technology inte-

gration, and rapid integration, (2) Management and Operation issues, which include

ExecutionRisk

TechnologyIntegration

Mergerand

AcquisitionChallenges

RapidIntegration

UninterruptedService

MaximizeOperations

InformationRisk

Combine

Managemen

t

CulturalIssues

DiversificationReduced

Cost

Fig. 1. Friction in mergers.

242 E. Huang, M.H. Chuang / Computers in Human Behavior 23 (2007) 240–257

combined management, cultural problem, diversification, maximize operations,

uninterrupted service, and cost reduction (Guerra, 2001; Murphy, 2002) (shown in

Fig. 1).

3. Literature review

There exists several expectancy-value models of attitude–behaviour relationship

such as the theory of reasoned action (TRA), the technology acceptance model

(TAM), the theory of planned behaviour (TPB), and the decomposed TPB, which

may all be used to explain employee performance patterns. In the following sections,

we investigate various theories to determine an model to rationalize our research

data.

3.1. Theory of reasoned action

TRA is a widely accepted model in social psychology that can explain virtually

any human behaviour (Fishbein & Ajzen, 1975). TRA assumes that human beings

are usually quite rational and make systematic evaluation of information made avail-

able to them. According to TRA, a person�s performance of a specified behaviour is

determined by an intention (l) to perform the behaviour, B = f(l). Next, the intention

is jointly determined by a person�s attitude (A) and subjective norm (SN) with rela-

tive weights estimated by regression coefficients (Bl = wA + wSN). Furthermore, aperson�s attitude toward a behaviour is also determined by his salient belief (bi)

on the consequences of performing the behaviour multiplied by evaluation (ei) of


those consequences (A = Ebiei). Finally, an individual�s subjective norm (SN) is a

multiplicative function of normative beliefs (nbi) and motivation to comply (mci),

SN = Enbimci. A notable aspect of TRA is the assumption that all other factors

influencing behaviour indirectly through attitude, subjective norms, or relative

weights (Davis, Bagozzi, & Warshaw, 1989).

3.2. Technology acceptance model

TAM (Davis et al., 1989) with its TRA theoretical basis (Ajzen, 1991; Fishb-

ein & Ajzen, 1975) is the current favored theory explaining adoption of new IT.

TAM and its relationship to TRA have been discussed extensively in the litera-

ture (Davis et al., 1989; Keil, Beranek, & Konsynski, 1995; Mitchell & Greato-

rex, 1993; Rogers, 1995) and will not be elaborated here. According to TAM,people form intentions to adopt a new behaviour or technology based on their

beliefs and evaluation of the consequences of adoption. Past research has shown

applicability of TAM; PU and PEOU are antecedents of use-intentions, applica-

ble to a wide range of IT services, for both experienced and novice users (Kara-

hanna, Norman, & Chervany, 1999), and across expertise levels (Taylor & Todd,

1995).

When Davis (1989a, 1989b), Davis, Bagozzi, and Warshaw (1992) applied TAM

to IT adoption, they focused on two behavioural beliefs, PU and PEOU. Sincethese two directly affect IT user intentions and social norms are insignificant in

the case of IT adoption, the basic TAM model (Davis, 1989a, 1989b) contains just

these two beliefs as the antecedents of IT acceptance without the mitigating effect

of attitude suggested by TRA without social norms. Later research showed that

social norms might be important only prior to actual use of IT (Keil et al.,

1995). Many subsequent research has adopted this parsimonious TAM model with

just PU and PEOU as predictors of IT or intended IT use. A number of literature

(Adams, Nelson, & Todd, 1992; Agarwal & Prasad, 1997; Gefen & Straub, 1997,2000; Hendrickson, Massey, & TimothyPaul, 1993; Igbaria, Zinatelli, Cragg, &

Cavaye, 1997; Szajna, 1996) has shown that the TAM model is applicable to a

wide range of technology (Gefen, Karahanna, & Straub, 2003; Gefen & Keil,

1998).

3.3. Theory of planned behaviour

The TPB is essentially an extension of the TRA by incorporating an additionalconstruct, perceived behavioural control, to account for situations where an individ-

ual lacks substantial control over target behaviour (Ajzen, 1985; Ajzen & Madden,

1985). According to TPB, an individual�s behaviour can be explained by behavioural

intention, which is jointly affected by attitude, subjective norms, perceived norms,

and perceived behavioural control. The TPB model extended TRA by adding per-

ceived behavioural control as the third factor influencing intention–behaviour rela-

tionship (Ajzen, 1991; Ajzen & Madden, 1985, 1986; Madden, Scholder Ellen, &

Ajzen, 1992).


In addition, TPB postulates that beliefs affect attitudes, subjective norms, and per-

ceived behavioural control. Attitudes are determined by behavioural beliefs (i.e. sali-

ent beliefs about the consequences) multiplied by outcome evaluations. Subjective

norms are determined by normative beliefs (i.e. salient beliefs of how important oth-

ers view the behaviour) multiplied by motivation to comply. Perceived behaviouralcontrol is determined by control beliefs (i.e. salient beliefs of available resources,

opportunities, obstacles, impediments) weighted by the perceived ease of performing

the behaviour.

3.4. Decomposed theory of planned behaviour (decomposed TPB)

Using TPB as the basis, this model decomposes attitude further by incorporating

perceived usefulness and ease of use as mediating variables. Compatibility serves asan antecedent for both perceived usefulness and ease of use. Both TPB and TAM

include attitude as a fundamental determinant of behavioural intention. A review

of nine investigations suggests the significance of effects of perceived usefulness

and ease of use on attitude (Jacoby & Kaplan, 1972; Keil et al., 1995; Mathieson,

1991; Szajna, 1996; Taylor & Todd, 1995). Seven other literature studies reported

the significant impact of compatibility on user technology acceptance (Cooper &

Zmud, 1990; Moore & Benbasat, 1992; Rogers, 1995; Taylor & Todd, 1995; Torna-

tzky & Klein, 1982; Venkatesh & Davis, 2000).

4. Research model

The decomposed TPB model is adopted as the theoretical basis for explaining

how determinants affect system behaviour. In our case, risk is also included, as it

is identified as an essential factor for system use, particularly in banking. Impact

of risk on system behaviour has been reported by many prior studies (Guseman,1981; Jacoby & Kaplan, 1972; Rosenbloom, 2000).

According to TPB, behaviour is determined by the intention to perform the

behaviour. In our behaviour evaluation focus, we test TPB without its intention con-

struct; therefore, behaviour is predicted by four factors: attitude toward the behav-

iour (A), subject norm (SN), perceived behavioural control (PBC), and risk (R).

Identifying different beliefs is a part of the standard methodology when using TPB

(Mathieson, 1991). TPB�s instruments need to be tailored for each group. Therefore,

referring to prior studies, attitude, subjective norms, perceived behaviour control,and risk are decomposed as follows.

4.1. Attitudinal beliefs

Attitudes refer to an individual�s positive or negative disposition when per-

forming a particular behaviour (Davis, 1989a, 1989b; Kane, 2000). In our exam-

ple, an employee�s behaviour is determined by his or her attitude toward IS use.


The described decomposed model resembles the one proposed by Taylor and

Todd (1995), where factors like Perceived Usefulness (PU), Perceived Ease of

Use (EOU), and Perceived Compatibility (COM) influence attitude, defined as

desirability to use the system. Davis et al. (1989) defined EOU as the degree

to which a user expects the target system to be freed of effort. Usefulness isthe user�s subjective probability that using a specific application will increase

job performance within an organization. In our case, an employee�s behaviour

to use IS is affected by factors such as perceived usefulness and perceived ease

of use.

4.2. Subjective norms

Subjective norms refer to an individual�s perception of relevant opinions onwhether to perform a particular behaviour (Salancik & Pfeffer, 1978). Employees

who embrace comparable normative beliefs may vary considerably in the extent

to which they want to comply with these beliefs (Engel, 1998). In our case, supe-

rior�s influences (SI) and peer�s influences (PI) are antecedents to subjective norms.

Superiors and coworkers have a profound effect on an employee�s technology

acceptance as reported by many prior studies (Aydin & Rice, 1991; Compeau &

Higgins, 1995; Fulk, Schmitz, & Steinfield, 1987, 1990). In our case, an employee�sbehaviour to use IS is affected by perceived variables such as superior influence andpeer influence.

4.3. Perceived behavioural control

Perceived behaviour control is a construct unique to TPB and refers to an individ-

ual�s perception of the accessibility of requisite resources or opportunities necessary

for performing a behaviour (Ajzen, 1985; Ajzen & Madden, 1985). Broadly, a con-

trol belief is a perception of the availability of skills, resources, and opportunitiesnecessary for performing the behaviour under discussion (Bandura, 1997; Francik,

Susan Ehrlich, Donna, & Levine, 1991; Taylor & Todd, 1995). In a review of 113

studies, Van den Putte (1993) identified a relatively strong relationship of 0.64.

The addition of perceived behavioural control contributes significantly to prediction

of behavioural intentions. In our case, an employee�s behaviour to use IS is affected

by perceived control variables such as self-efficacy, requisite resources, and technical

support.

4.4. Risk

Risk is a construct that refers to an individual�s perception of pressure or prior

experience for performing a behavior. It also refers to information processing secu-

rity for operating Information System and includes: (1) strategic risk – promised

sales and efficiency gains may not be achieved, (2) infrastructure risk – inadequate

hardware and software will need replacements, (3) vendor risk – new and upgraded


systems may be inadequate or late, (4) information risk – data and information can-

not be optimally used, (5) security risk – data and systems may be exposed to unau-

thorized use or deletion. Mitchell and Greatorex (1993) indicated financial and time

risk showed higher effect on employee in comparison with physical, mental, and so-

cial risks. Compeau and Higgins (1995) found computer anxiety had a significant ef-fect on self-computer use. In our case, an employee�s behaviour to use IS maybe

affected by risk factors such as perceived pressure, past experience, and information

security, please refer to our research model in Fig. 2.

5. Research method

5.1. Measures

The measures used to operationalize the constructs included in the investigated

models were mainly adapted from relevant prior studies, with minor wording

changes to tailor them to the targeted context. Pretests were conducted to ensure that

the instrument possessed acceptable validity. First, 25 respondents from different

branches were asked to evaluate the content validity. Then, the survey was further

tested for reliability, item consistency, ease of understanding, and question sequence

appropriateness.

Complexity

Perceived ease of use

Behaviour

Attitude

Subjective norm

Risk

PerceivedBehavioural

Control

Experience

Information security

Pressure

Technical support

Resources

Self- efficacy

Peer Influence

Superior influence

Perceived usefulness

Fig. 2. Research model.


The instrument�s reliability was assessed using Cronbach�s a. The resulting value

ranged from 0.667 to 0.87, which is acceptable for pretests. Suggestions on the ques-

tion sequence, wording choice, or measures were solicited, leading to some minor

changes to the questionnaire. Based on pretest feedback, several items were removed.

Table 1 lists the final questionnaire items used to measure each construct, togetherwith their reference sources. Subject who had participated in the pretests were ex-

cluded from the subsequent formal study. Items were all measured on a 7-ponit rat-

ing scale with �1 = strong disagree� and �7 = strongly agree.�

5.2. Construct validity and reliability

The four-item behaviour scale obtained a Cronbach a reliability of 0.61. The

three-item attitude and two-item SN scales showed a reliability of 0.97 and 0.72,respectively; the two-item PBC and three-item risk exhibited a reliability coefficient

of 0.66 and 0.65, respectively. These scale reliabilities are considered adequate for

behaviour research at all levels. The two-item PU, EOU and COM, have a values

of 0.86, 0.95, and 0.97, respectively. Two-item SI and CI as are 0.61 and 0.79.

Two-item SE, FR, and TS as are 0.80, 0.83, and 0.81, respectively. The two-item

P, E, and S as are 0.81, 0.71, and 0.91.

6. Results and findings

This section presents the research findings. We first present the demographic pro-

file of respondents, shown in Table 2. Then, ANOVA test variance of two subsam-

ples is shown. Finally, a hierarchical regression model is used to validate the

explanation power of TPB.

6.1. Demographic profile

A total of 224 responses were collected out of 249 sent, with one invalid response.

Thus, the response rate is 89%. Of the 223 responses received, 132 were from old

employees, and 91 from new ones. The samples do not show significant deviation be-

tween the two. Table 2 indicates respondents were predominantly female (75–80%)

key-in staffs (31%), in the age bracket of 26–35 (67–83%). Respondents with a diplo-

ma made up of 59–65% of respondents. The majority of respondents are branch

employees (90–91%).

6.2. Comparison

Table 3 contains a list of factors for respondents. Each item is rated from extre-

mely disagree (1) to extremely agree (7). The 35 firms responding to these questions

were divided into two groups, with ranking shown below. Table 3 displays a signif-

icant difference in variance of behaviour (F = 17.535, p < 0.000). There is also a sig-

Table 1

List of construct indicators

Construct Source

Behaviour

B1 I would have no difficulty telling others about the

results of using back-end system.

Fishbein and Ajzen (1980); Taylor and

Todd (1995, 1996); Riemenschneider

et al. (2002)

B2 I believe I could communicate to others the

consequence of using back-end system.

B3 The results of using back-end system are apparent

to me.

B4 I would have no difficulty explaining why

back-end system may or may not be beneficial.

Attitude

A1 Back-end system is useful in my job. Fishbein and Ajzen (1980); Taylor and

Todd (1995, 1996); Riemenschneider

et al. (2002)

A2 Using back-end system is enhances the quality of

my work.

A3 The advantages of using back-end system

outweight the disadvantages.

Subjective norms

S1 My peers confirm my knowledge and ability to

make use of back-end system.


Todd (1995, 1996)

S2 My superior confirms my knowledge and ability

to make use of back-end system.

Perceived behaviour control

Pb1 I feel that there is no gap between my existing

skills and knowledge and those required by

back-end system.


Todd (1995); Riemenschneider et al.

(2002)

Pb2 Using the current system is entirely within my

control.

Pb3 I have the knowledge and ability to make use of

back-end system.

Risk

R1 When I operate back-end system, I feel unstable. Risk management and control guidance

for securities firms and their superiors

(1998)

R2 I encountered many incorrect messages while I use

back-end system.

R3 I need to take all the responsibility of data security

and accuracy.

Perceived ease of use

E1 Learning back-end system was easy for me. Moore and Benbasat (1991); Davis et al.

(1989); Riemenschneider et al. (2002)

E2 I think back-end system is clear and

understandable.

Perceived usefulness

P1 Using back-end system improves my job

performance.

Moore and Benbasat (1991); Davis et al.


(continued on next page)


Table 1 (continued)

Construct Source

P2 Using back-end system increases my productivity.

Compatibility

C1 Using back-end system is compatible with all

aspects of my work.

Moore and Benbasat (1991); Davis et al.


C2 Using back-end system fits well with the way I

work.

Peer influence

PI1 Peers who influence my behaviour would think

that I should have the knowledge and ability to

use back-end system.

Burnkrant and Page (1988); Shimp and

Kavas (1984)

PI2 Peers who are important to me would think that I

should have the knowledge and ability to use

back-end system.

Superior influence

SI1 My superior who influences my behaviour would

think that I should have the knowledge and ability

to use back-end system.

Burnkrant and Page (1988); Shimp and

Kavas (1984)

SI2 My superiors whom I report would think that I

should have the knowledge and ability to use

back-end system.

Self-efficacy

Se1 Specialized instruction and education concerning

back-end system is available to me.

Compeau and Higgins (1991), Ajzen

(1985), Ajzen and Madden (1985), Ajzen

(1991)

Se2 Formal guidance is available to me in using

back-end system.

Resources

Re1 A specific resource is available only for back-end

system use.


Todd (1995, 1996)

Re2 For making the transition to back-end system, I

felt I need a plenty of related resources (e.g.

printer, manual, training courses, wireless

peripheral equipments, etc.).

Technical support

T1 A specific group is available for assistance with

back-end system difficulties.


Todd (1995, 1996)

T2 For making the transition to back-end system, I

felt I had a solid ‘‘network of support’’ (e.g.

knowledgeable colleagues, support personnel,

consultants, etc).

Pressure

Pr1 I was afraid to make mistakes while using back-

end t system.

Self-defined

Pr2 I feel stressful to use back-end t system.

(continued on next page)


Table 1 (continued)

Construct Source

Experience

Ex1 My prior experience can help me to understand

back-end t system.

Self-defined

Ex2 I have confidence to use back-end t system.

Security

St1 I believe that account-password policy is

important to protect the data processing.

Self-defined

St2 I believe that the privilege policy is necessary to

protect the data processing.


nificant variance in usefulness (F = 37.948, p < 0.000), ease of use (F = 14.687,

p < 0.000), compatibility (F = 24.238, p < 0.000), self-efficacy (F = 18.807,

p < 0.000), and technical resources (F = 17.286, p < 0.000).

6.3. Explaining power

6.3.1. Explaining behaviour

As expected, TPB explains a significant proportion of the variance in Behaviour.Regression results confirmed three of the four factors are significant, and the equa-

tion captures 49% of old group�s Behaviour variance (Adjusted oldR2). The new

group data revealed that Attitude also has a significant effect on Behaviour and

the equation captures 29% variance (Adjusted newR2). The individual determinants

of Behaviour in equation (1a), each of Aold, SNold, and Rold had a very significant

effect on Bold (bA = 0.445, bSN = 0.233, bR = 0.192, p < 0.000), whereas PBCold has

no significant effect. Within new group, Anew had a very strong effect on Bnew

(bA = 0.424, p < 0.000), while SNnew, PBCnew, and Rnew have non-significant effects.Please refer to Table 4 Hierarchical regression test for expected relationships.

6.3.2. Explaining attitude

Regression results confirmed each of the three factors explains a significant vari-

ance (82%) in Attitude (Adjusted oldR2). Individual determinants of Attitude, equa-

tion (2a), Uold, EOUold, and COMold had a very significant effect on Aold

(bU = 0.250, bEOU = 0.255, bCOM = 0.494, p < 0.000). The new group data revealed

that EOU and COM also have significant effects on Anew and the equation captures86% variance (Adjusted newR2), while Unew has nonsignificant effect. Equation (2a),

Table 1, both EOUnew, and COMnew had significant effects on Anew (bEOU = 0.251,

bCOM = 0.595, p < 0.000).

6.3.3. Explaining subjective norms

As theorized, regression models confirmed two factors have significant effects and

the equation explains 17% of the variance of SNold and 29% of the variance of

SNnew. The data revealed that both PIold and SIold have significant effects(bCI = 0.301, bSI = 0.215, p < 0.000). Notice that both regressions predict SN well,

Table 2

Demographic profiles of respondents

Old New

Number Percentage (%) Number Percentage (%)

Gender

Male 33 25 18 20

Female 99 75 73 80

Marriage

Married 61 46 49 54

Unmarried 71 54 42 46

Age

20–25 5 4 6 7

26–30 60 45 37 41

31–35 50 38 26 28

36–40 13 10 15 16

Over 40 4 3 7 8

Division

Branch office 119 90 82 91

Information department 5 4 4 4

Management department 8 6 5 5

Stock company working history

Under 1 yr 20 15 21 23

1–2 yrs 21 16 10 11

2–4 yrs 36 27 20 22

4–6 yrs 32 24 19 21

Over 6 yrs 23 18 21 23

Highest education

High school 21 16 18 20

Diploma 78 59 59 65

Degree 30 23 13 14

Masters 2 2 1 1

Position

IT staff 19 15 15 17

Key-in staff 40 31 28 31

Credit staff 16 12 4 4

Stock staff 8 6 7 8

Audit staff 7 5 3 3

Front-desk clerk 7 5 9 10

Cashier 7 5 4 4

Manager 11 8 11 12

Other 17 13 10 11


although new group model explains slightly more variance than old group model.

Equation (3a), indicates PInew had significant effects (bCI = 0.553, p < 0.000). The re-

sults also imply that new group was slightly more influenced by coworkers� influences

in compare with superior�s influences.

Table 3

ANOVA of two groups perception and behaviours

Factors Old (N = 132) New (N = 91) F-value

Rank Mean SD Rank Mean SD

Information security 1 5.4356 1.0726 1 5.5495 1.0029 0.640

Ease of use 2 4.9508 0.9997 4 4.3462 1.3554 14.687***

Usefulness 3 4.9167 1.0117 8 3.9396 1.3557 37.948***

Compatibility 4 4.7462 1.1026 9 3.9341 1.3503 24.283***

Experience 5 4.7386 0.9235 2 4.5714 0.8582 1.870

Self-Efficacy 6 4.6629 0.9440 6 4.0330 1.2220 18.807***

Peer influence 7 4.4697 1.0203 3 4.5659 1.1235 0.441

Technical support 8 4.4167 1.0192 10 3.8022 1.1735 17.286***

Superiors influence 9 4.2652 0.9173 7 3.9560 0.9934 5.714

Pressure 10 4.1136 1.0782 5 4.1593 0.9941 0.103

Facilitation of resources 11 4.0455 1.2951 11 3.5769 1.3923 6.629

Attitude – 4.7071 1.0264 – 3.7985 1.3616 32.234

Subjective norms – 7.9205 0.0192 – 4.7033 1.0434 3.020

Perceived behaviour control – 4.8409 0.9168 – 4.6374 0.8994 2.696

Risk – 4.5000 0.7510 – 4.4396 0.6124 0.404

Behaviour – 4.7121 0.7175 – 4.2720 0.8438 17.535***

* p < 0.05.** p < 0.01.

*** p < 0.001.


6.3.4. Explaining perceived behaviour control

As expected, regression models confirm three factors captures 48% of variance of

PBCold, and 29% of variance of PBCnew, respectively. The data revealed that SEold

and TSold have significant effects (bE = 0.372, bS = 0.389, p < 0.000), while PPold

has non-significant effect. The effects of individual determinants on PBCnew are

not confirmed.

6.3.5. Explaining risk

As expected, regression models confirmed three factors captures 29% of variance

of Rold, and 25% of variance of Rnew, respectively. The data revealed that Pold and

Eold have significant effects (bE = 0.401, bS = 0.312, p < 0.000), while Sold had non-

significant effect. The Pnew had a significant effect on Rnew was confirmed

(bp = 0.474, p < 0.000).

7. Discussion and conclusion

7.1. Conclusion

Our findings are consistent with the expectancy-value model of attitude–behav-

iour relationship as proposed by Ajzen (1985, 1989, 1991) and Ajzen and Madden

Table 4

Hierarchical regression test for expected relationships

Equation Old group New group

R2 Beta R2 Beta

(1) Behaviour (B)

(a) B = A + SN + PBC + R 0.507 (0.491) 0.321 (0.290)

A 0.445 (5.609)*** 0.424 (4.181)***

SN 0.233 (2.974)** 0.138 (1.303)

PBC 0.051 (0.607) 0.180 (1.888)

R 0.192 (2.926)** �0.116 (�1.260)

(2) Attitude (A)

(a) A = U + EOU + COM 0.829 (0.825) 0.866 (0.862)

U 0.250 (3.966)*** 0.135 (1.714)

EOU 0.255 (4.767)*** 0.251 (3.661)***

COM 0.494 (7.793)*** 0.595 (6.389)***

(3) Subjective norms (SN)

(a) SN = SI + CI 0.188 (0.175) 0.308 (0.293)

SI 0.301 (3.488)*** 0.005 (0.049)

CI 0.215 (2.493)** 0.553 (5.677)***

(4) Perceived behaviour control (PBC)

(a) PBC = SE + FR + TS 0.696 (0.484) 0.240 (0.214)

SE 0.372 (4.157)*** 0.187 (1.278)

FR �0.009 (�0.113) 0.235 (1.808)

TS 0.389 (4.158)*** 0.130 (0.840)

(5) Risk (R)

(a) R = P + E + S 0.310 (0.294) 0.274 (0.249)

P 0.401 (5.297)*** 0.474 (5.121)***

E 0.312 (3.842)*** 0.148 (1.604)

S �0.023 (�0.293) 0.151 (1.643)

Notes. Figures shown are beta coefficients of the OLS regressions, t-values in parentheses. Additional

statistics: olddf = 128, and newdf = 87.* p < 0.05.

** p < 0.01.*** p < 0.001.


(1985, 1986). We have predicted with a significant degree of success various two

subsample behaviours. Also, our results yield insights into the difference between

the two samples. While many of the differences in mean are marginal, five cases

showed statistically significant differences. Respondents with higher scores on

behaviours are more receptive to system in terms of ease of use, usefulness, and

compatibility. This subgroup is also more confident with their self-efficacy andtechnical support.

Our results also yield several insights into the determinants of backend system use

for the old group. Their behaviour can be predicted reasonably well from their atti-

tude, subjective norms, and risk. Compatibility is the primary determinant of atti-

tude, with perceived ease of use and perceived usefulness as the secondary and


tertiary determinants of attitude. Peer�s influence is a major determinant of subjective

norms, while superior�s influence is a significant determinant. Pressure is a major

determinant of risk, with experience as a significant secondary determinant.

However, the results of mental processes can be tangible and physical; the new

joiner performance of daily routine job is predictable, observable, and controllablebased on understand their attitude. Performance of the new group can be predicted

and controlled based on an understanding of their attitude. Our investigation re-

vealed three insights concerning determinants of new backend system users. Their

behaviour can be predicted reasonably well from attitude, with compatibility as a

major determinant of attitude, and perceived ease of use as a significant secondary

determinant.

7.2. Limitation

There are two limitation: (1) The use of only a single company survey restricts us

to a limited pool of respondents, (2) The use of stock backend system without taking

into consideration of front-end system restricts us to fewer system users. Hence, re-

sults obtained may not be generalized to other transaction systems. However, this

research includes all three popular stock backend systems in Taiwan.

7.3. Implication

The findings in this research will facilitate practitioners in formulating measures

to improve new employee performance. Our studies showed that employee attitude

is positively associated with behaviour. Instead of focusing on formal training

courses, superiors should help their staff identify similarity with past system func-

tions, to accelerate understanding of system conversion. Since easy of use, compat-

ibility, and superior�s influence are found to be important factors, practitioners may

also engage a team leader as a guide for system orientation.There are several directions for further research: first, other factors may exist that

can further explain employee behaviour; second, the study of stock backend system

behaviour can be extended to other financial systems; and, finally, the study of stock

backend system can be extended to other industry when facing merger and acquisi-

tion issues.

8. Further reading

Armitage and Conner (2001); Bagozzi (1982); Bagozzi (1983); Chau and Jen-

HwaHu (2001); Chau and Jen-HwaHu (2002); Christie (1981); Clark (1998); Jean

Paul Broonen (2002); Kallgren and Wood (1986); Liao, Yuan Pu, Huaiqing, and

Ada. (1999).


References

Adams, D. A., Nelson, R. R., & Todd, P. A. . (1992). Perceived usefulness, ease of use, and usage of

information technology: a replication. MIS Quarterly, 16(2), 227–247.

Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntaries in the

acceptance of information technologies. Decision Sciences, 28(3), 557–582.

Ajzen, I. (1985). From intentions to actions: A theory of planned behaviour, action control from cognition to

behaviour (pp. 11–39). New York: Springer Verlag.

Ajzen, I. (1989). Attitude, personality, & behavioural. Milton Keynes: Open University Press.

Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision

Processes, 50, 179–211.

Ajzen, I., & Madden, T. J. (1985). Prediction of goal-directed behaviour: attitudes, intentions, and

perceived behavioural control. Journal of Experimental Social Psychology, 20, 453–474.

Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behaviour: attitudes, intentions, and

perceived behavioural control. Journal of Experimental Social Psychology, 22, 453–474.

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: a meta-analytic review.

British Journal of Social Psychology, 40, 471–499.

Aydin, C., & Rice, R. E. (1991). Attitudes toward new organizational technology: network proximity as a

mechanism for social information processing. Administrative Science Quarterly, 36, 219–244.

Bagozzi, R. P. (1982). A field investigation of causal relations among cognitions, affect, intentions, and

behaviour. Journal of Marketing Research, 19, 562–583.

Bagozzi, R. P. (1983). Holistic methodology for modelling consumer response to innovation. Operations

Research, 31, 128–176.

Bagozzi, R. P., & Kimmel, S. K. (1995). A comparison of leading theories for the prediction of goal-

directed behaviours. British Journal of Social Psychology, 34, 437–461.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman.

Berger, A. N., Demsetz, R. S., & Strahan, P. E. (1990). The consolidation of the financial services industry:

causes, consequences, and implications for the future. Journal of Banking and Finance, 23, 135–194.

Burnkrant, Robert E., & Page, Thomas J. (1988). The structure and antecedents of the normative and

attitudinal components of Fishbein�s theory of reasoned action. Journal of Experimental Social

Psychology, 24, 66–87.

Chau, P. Y. K., & Pau, Jen-HwaHu (2001). Information technology acceptance by individual

professionals: a model comparison approach. Decision Sciences, 32(4), 699–720.

Chau, P. Y. K., & Pau, Jen-HwaHu (2002). Investigating healthcare professionals� decisions to accept

telemedicine technology: an empirical test of competing theories. Information & Management, 39(4),

297–311.

Christie, B. (1981). Face to file communication: A psychological approach to information system. New York:

Wiley.

Clark, D. (1998). Megadeal challenges Citicorp to Meld systems successfully series: 3:[1]. American Banker,

163(67), 1–1.

Compeau, D. R., & Higgins, C. A. (1991). A Social Cognitive Theory Perspective on Individual Reactions

to Computing Technology. Proceedings of the International Conference on Information Systems (ICIS).

December 16–18, 187–198. New York.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: development of a measure and initial

test. MIS Quarterly, 19(2), 189–211.

Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: a technological

diffusion approach. Management Science, 36(2), 123–139.

Davis, F. D. (1989a). Perceived usefulness, perceived ease of use and user acceptance of information

technology. MIS Quarterly, 14, 319–340.

Davis, F. D. (1989b). Perceived usefulness, perceived ease of use, and user acceptance of information

technology. MIS Quarterly, 13(3), 319–335.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a

comparison of two theoretical models. Management Science, 35(8), 982–1003.


Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers

in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.

Francik, E., Ehrlich, S., Rudman, D., Cooper & Levine, S. (1991). Putting innovation to work: adoption

strategies for multimedia communication systemsassociation for computing machinery. Communica-

tions of the ACM, 34(12), 52–64.

Engel, P. (1998). Comments on logical and psychological partitioning of mind: depicting the same map.

New Ideas in Psychology, 16(2), 107–113.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: An introduction to theory and

research. Reading, MA: Addison-Wesley.

Fishbein, M., & Ajzen, I. (1980). Understanding attitudes & predicting social behavior. New Jersey:

Prentice-Hall.

Fulk, J., Schmitz, J., & Steinfield, C. W. (1987). On the role of theory in research on information

technology in organizations: an introduction to the special issue. Communication Research, 14(5),

479–490.

Fulk, J., Schmitz, J., & Steinfield, C. W. (1990). A social influence model of technology use. In J. Fulk & C.

Steinfield (Eds.), J. organizations and communication technology (pp. 117–140). Newbury Park, CA:

Sage.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated

model. MIS Quarterly, 27(1), 51–90.

Gefen, D., & Keil, M. (1998). The impact of developer responsiveness on perceptions of usefulness and

ease of use: an extension of the technology acceptance model. The Database for Advances in

Information Systems, 29(2), 35–49.

Gefen, D., & Straub, D. W. (1997). Gender differences in perception and adoption of E-Mail: an extension

to the technology acceptance model. MIS Quarterly, 21(4), 389–400.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease-of-use in IS adoption: a

study of E-commerce adoption. Journal of the Association for Information Systems, 1(8), 1–28.

Guerra, A. (2001). Citigroup – once the deals are signed technology must create the ties that bind. Bank

Systems & Technology, 38(5), A12–A15.

Guseman, D. S. (1981). Risk perception and risk reduction in consumer services. In J. H. Donnelly & W.

R. George (Eds.), Marketing of services (pp. 200–204). Chicago, IL: American Marketing Association.

Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test–retest reliability of perceived

usefulness and perceived ease of use scales. MIS Quarterly, 17(2), 227–230.

Igbaria, Magid, Zinatelli, Nancy, Cragg, Paul, & Cavaye, Angele L. M. (1997). Personal

computing acceptance factors in small firms: a structural equation model. MIS Quarterly, 21(3),

279–305.

Jacoby, J., & Kaplan, L. (1972). The components of perceived risk. In M. Venkatesan (Ed.). Advanced in

consumer research (Vol. 3). Chicago: Association for Consumer Research.

Broonen, P. (2002). Extending the theory of planned behaviour as a model of cognitive and motivational

influences on academic performance. American Institute of Physics Conference Proceedings, 573,

508–513.

Kallgren, C. A., & Wood, W. (1986). Access to attitude-relevant information in memory as a determinant

of attitude–behaviour consistency. Journal of Experimental Social Psychology, 22, 328–338.

Kane, E. J. (2000). Incentives for banking megamergers: What motives might regulators infer from event-

study evidence. Journal of Money, Credit, and Banking, 32(3), 671–701.

Karahanna, E. S., Norman, L., & Chervany, D. W. (1999). Information technology adoption across time:

a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2),

183–213.

Keil, M., Beranek, P. M., & Konsynski, B. R. (1995). Usefulness and ease of use: field study evidence

regarding task considerations. Decision Support Systems, 13(1), 75–91.

Liao, Shaoyi, Yuan Pu, Shao, Huaiqing, Wang, & Ada., Chen (1999). The adoption of virtual banking: an

empirical study. International Journal of Information Management, 19(1), 63–75.

Madden, T. J., Scholder, E., Pamela & Ajzen, I. (1992). A comparison of the theory of planned behaviour

and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3–9.


Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the

theory of planned behaviour. Information Systems Research, 2(3), 173–191.

Mitchell, V.-W., & et Greatorex, M. (1993). Risk perception and reduction in the purchase of consumer

services. The Service Industries Journal, 13(4), 179–200.

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of

adopting an information technology innovation. Information Systems Research, 2(3), 192–222.

Moore, G., & Benbasat, I. (1992). Development of an instrument to measure the perceptions of adopting

an information technology innovation. Information Systems Research, 2(3), 192–222.

Murphy, Patricia A. (2002). Beating the post-merger odds. Bank Director, 12(1), 54–55.

Riemenschneider, C. K., Harrison, D. A, & Mykytn, P. P. Jr., (2002). Understanding IT adoption

decisions in small business: Integrating current theories. Information & Management, 40(4), 269–285.

Rogers, E. (1995). Diffusion of innovations. New York: Free Press.

Rosenbloom, R. S. (2000). Leadership, capabilities, and technological change: the transformation of NCR

in the electronic era. Strategic Management Journal, 21(10/11), 1083–1103.

Salancik, P. (1978). The external control of organizations: A resource dependence perspective. New York:

Harper and Row.

Shimp, T. A., & Kavas, A. (1984). The theory of reasoned action applied to coupon usage. Journal of

Consumer Research, 11, 795–809.

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science,

42(1), 85–92.

Taylor, S., & Todd, P. (1995). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4),

561–570, December.

Taylor, S., & Todd, P. A. (1996). Understanding information technology usage: a test of competing

models. Information Systems Research, 6(2), 144–176.

Tornatzky, L. G., & Klein, R. J. (1982). Innovation characteristics and innovation adoption-

implementation: a meta-analysis of findings. IEEE Transactions on Engineering Management, EM-

29, 28–45.

Van den Putte, B. (1993). On the theory of reasoned action, Unpublished dissertation, University of

Amsterdam, the Netherlands.

Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the technology acceptance model: four

longitudinal field studies. Management Science, 46(2), 186–204.

Echo Huang is an Associate Professor in the Department of Information Management in the School of

Management, National Kaohsiung First University of Science and Technology. Her received her M.S.

degree from the University of Maryland at College Park, Ph.D. degree from the National Cheng Kung

University. Her research focuses on electronic commerce, Internet banking, customer relationship man-

agement, and e-marketing. Her articles on these topics have been published in Electronic Commerce

Research and Applications, Computers in Human Behavior and other academic journals.

extending the theory of planned behaviour as a model to explain post-merger employee behaviour of is...

Documents