a conceptual framework on the adoption of negotiation support systems

A conceptual framework on the adoption of negotiation support systems

John Lim*

School of Computing, National University of Singapore, Kent Ridge, Singapore, Singapore 119260

Received 1 November 2002; revised 28 January 2003; accepted 10 February 2003

Abstract

An exploratory study was conducted to identify factors affecting the intention to adopt negotiation support systems (NSS) by managers and

executives. Drawing from past literature, the Theory of Planned Behavior and the Technology Acceptance Model provided basis for

analyzing our results. Overall, subjective norm and perceived behavioral control emerged as strongest determinants of intention to adopt

NSS. Further probing of subjective norm revealed organizational culture and industrial characteristics to play significant roles. A new

conceptual framework is proposed which would be of both theoretical and practical importance.

q 2003 Elsevier Science B.V. All rights reserved.

Keywords: Negotiation support systems; Adoption intention; Technology Acceptance Model; Theory of Planned Behavior; Framework

1. Introduction

Negotiations have become increasingly important and

inevitable in today’s business. As computer and communi-

cation technologies become more advanced and easily

available, using computers to aid negotiations has become

viable, especially as the issues negotiated become more

complex. This has led to the emergence of negotiation

support systems (NSS), a specialized class of group support

systems designed to help negotiators achieve optimal

settlements. A number of commercial NSS packages are

available for sale. However, practical usage of NSS in

organizations has been minimal. This phenomenon causes

the motivation for this study, which is to identify factors

affecting business managers’ and executives’ intention to

adopt NSS.

In information systems research on user behavior,

intention models from social psychology have been

frequently used as the potential theoretical foundation for

research on the determinants of user behavior [1,2].

Among these theories are the Theory of Planned Behavior

(TPB) [3,4] and the Technology Acceptance Model (TAM)

[5,6]. As TPB and TAM are both viable and popularly

employed explanatory mechanisms for IT adoption and

their explanatory powers vary depending on the technology

[7,8], the current paper makes use of them as theoretical

basis for the context of NSS. Section 2 discusses the

technology as well as reviews the two adoption models.

Section 3 describes the analysis of data collected. Section 4

discusses the implications of the findings. In Section 5, a

new conceptual framework is presented.

2. Negotiation support systems and adoption models

Bui et al. [9] described negotiations as complex, ill-

structured, and evolving tasks requiring sophisticated

decision support. However, weak information processing

capacity and capability, cognitive biases and socio-

emotional problems often hinder the achievement of

optimal negotiations [10–13]. As a result, much interest

has been generated to provide computer support for

negotiations. This leads to NSS, a special class of group

support systems designed to support bargaining, consensus

seeking and conflict resolution [9]. Conceptually, NSS

consist of decision support systems (DSS), which are

networked [14]. The DSS component helps to refine

negotiators’ objectives, and at the same time, provides a

tactful forum for expressing them [15]. It supports the

analysis of subjective preference and/or external objective

data. NSS also provide modeling techniques (based on

regression analysis, multi-criteria decision making, and

game theory) to generate integrative solutions or viable

strategies [16–18]. This information processing capability

and capacity, as well as the identification of potential

0950-5849/03/$ - see front matter q 2003 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0950-5849(03)00027-2

Information and Software Technology 45 (2003) 469–477

www.elsevier.com/locate/infsof

* Tel.: þ65-6874-6773; fax: þ65-6779-4580.

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

http://www.elsevier.com/locate/infsof

settlements, enhance easy interpretation and objective

evaluation of issues and outcomes.

Much of NSS research has focused on the design and

implementation of NSS, as well as the building blocks of

NSS (i.e. the DSS and communication components)

[15–17,19–22]. Another key area addresses the modeling

and representation of negotiation problems [23–26]. Some

of these efforts have involved inter-disciplinary research

and collaboration, ranging from management science to

cognitive/behavioral sciences to applied artificial intelli-

gence, and neural networks [24,27–29]. They cover a

variety of domains, including static buyer/seller nego-

tiations, dynamic scenario management [30], and electronic

mobile marketplace [31]. Empirical research on NSS has

shown that computer-aided negotiations generally yielded

higher joint outcomes and greater satisfaction; in short, NSS

help to improve the negotiation process as well as the

negotiation outcome [13,32,33]. Despite these positive

results, widespread adoption of NSS is not observed. This

paper seeks to understand what factors are dominant in

affecting peoples’ intention to adopt NSS by employing two

established adoption models.

The TPB [3] postulates three conceptually independent

determinants of intention: attitude, subjective norm and

perceived behavioral control. Attitude refers to the extent

to which a person evaluates the behavior (i.e. adoption of

NSS) favorably or unfavorably. A person will have a

favorable attitude towards this behavior if he or she

believes that doing it will have largely positive

consequences. On the other hand, if a person perceives

mostly negative outcomes from performing the behavior,

then he or she will view the behavior unfavorably.

According to TPB, attitude towards NSS adoption is an

additive function of the products of behavioral belief and

outcome evaluation of that belief. Subjective norm refers

to the perceived social pressure to, or not to, adopt NSS.

It is determined by normative beliefs, which are

concerned with the likelihood of important referent

individuals or groups approving or disapproving of

performing the behavior. Subjective norm regarding

NSS adoption is an additive function of the products of

normative belief about each referent and motivation to

comply with that referent. Perceived behavioral control

refers to the perceived ease or difficulty of performing

the behavior and is dependent on second-hand infor-

mation, experiences of acquaintances and friends, and

anticipated assistance and impediments. Specifically, an

individual’s perceived control increases as he or she

perceives greater resources and opportunities, and

anticipates fewer obstacles and impediments. Perceived

behavioral control over NSS adoption is an additive

function of the products of control belief and perceived

power of that belief. In general, the more favorable the

attitude and the subjective norm, and the greater the

perceived behavioral control, the stronger would be an

individual’s intention to adopt NSS. Examples of

applying TPB to IS topics include [7] (use of spreadsheet

software), [8] (use of Computing Resource Center), and

[34] (adopting IT in small businesses).

The TAM [5] postulates two determinants, perceived

usefulness and perceived ease of use. Perceived usefulness

is defined as the extent to which a person believes that using

NSS will improve his or her job performance within an

organizational context. Perceived ease of use refers to the

degree to which a person expects the usage of NSS to be free

of effort. This model also invokes the attitude concept, and

predicts that adoption intention (of NSS) is a function of

perceived usefulness and attitude, which in turn is

determined by perceived usefulness and perceived ease of

use. Examples of TAM studies pertaining to IS include [35]

(executive information system), [7] (spreadsheet software),

[8] (Computing Resource Center), [36] (email), and [37]

(Word, Excel). Other TAM studies have focused on the

relationship between usage and perceived usefulness and

perceived ease of use [38–40]. Whereas the above adoption

models can be and have been applied to studying various

types of IT, in the current paper they are employed as

theoretical basis for understanding NSS adoption.

3. Data analysis

An exploratory study was conducted. Questionnaires,

adapted from Harrison et al. [34] (for measuring items

related to TPB) and Davis [38] (for measuring items

related to TAM), were sent to managers and executives.

The target involved a representative sample of firms

located in Singapore, characterized by an open economy

and typical of a developed city. The major industries

touched on were manufacturing, services, and commerce.

Collected data were subject to validity and reliability

tests; if constructs are valid, tests such as factor analysis

will yield relatively high correlations between measures

of the same construct and low correlations between

measures of constructs that are expected to differ. Three

principal component factor analyses with promax rotation

were performed (an oblique rotation was chosen because

the factors might be correlated). The first factor analysis

was performed on measures pertaining to behavioral,

normative and control beliefs; these are the product terms

for attitude, subjective norm, and perceived behavioral

control, respectively. The result of the factor analysis is

shown in Table 1. Factor loadings, except for two items,

were greater than 0.55. From the factor analysis, six

factors were identified: three sets of behavioral beliefs,

one set of normative beliefs, and two sets of control

beliefs. For behavioral beliefs, the first set pertains to the

perceived advantages of using NSS, the second set the

perceived disadvantages of adopting NSS, and the third

set the direct costs associated with using NSS in the

organization. Control beliefs are divided into computer-

related and non-computer-related factors. The items in

J. Lim / Information and Software Technology 45 (2003) 469–477470

these factors were utilized in regression analyses for

subjective norm and perceived behavioral control to be

discussed later. The second factor analysis was conducted

for measures of intention, attitude, subjective norm and

perceived control (see Table 2). All the items loaded into

their respective factors with loadings of 0.67 and greater.

Reliabilities varied between 0.72 and 0.95.

The third factor analysis was conducted for measures of

perceived usefulness and perceived ease of use (see Table 3).

All the items loaded into their respective factors with loadings

of 0.66 and greater. Reliabilities ranged from 0.87 to 0.96.

Echoing the two adoption models, two regression

equations were tested which highlighted subjective norm

and perceived behavioral control as significantly strong

Table 1

Pattern matrix for measures of behavioral, control and normative beliefs

Construct Item Factor

1 2 3 4 5 6

Behavioral beliefs I (Perceived advantages) Improve information access 0.96 20.10 20.01 0.17 0.00 0.01

Improve communication 0.94 0.00 0.00 0.00 0.00 0.00

Increase speed 0.93 0.00 0.00 0.01 20.01 0.01

Improve efficiency 0.89 0.00 0.01 0.01 20.01 0.01

Better service 0.79 20.01 0.12 0.00 20.01 0.01

Reduce costs 0.77 0.13 0.00 20.22 0.18 20.14

Easier to use 0.69 20.01 0.21 20.01 0.00 0.00

Control beliefs I (Non-computer-related) Training 20.01 0.84 0.22 0.16 0.01 0.00

Employees’ support 0.01 0.76 0.27 0.00 20.01 0.17

Time 20.20 0.75 0.26 0.10 0.12 20.13

Financial assets 0.00 0.68 20.23 0.34 20.19 0.01

Normative beliefs Suppliers/vendors 0.00 0.15 0.83 20.12 0.00 20.01

Customers/clients 0.01 0.15 0.77 20.19 0.00 20.12

IT specialists 0.01 0.01 0.72 0.14 0.01 0.16

Other employees 0.19 0.17 0.67 20.01 0.00 20.01

Control beliefs II (Computer-related) Compatibility with software 0.00 0.22 20.01 0.93 0.00 20.16

Compatibility with hardware 0.12 0.25 0.00 0.91 0.00 20.01

Behavioral beliefs II (Perceived disadvantages) More downtime 20.01 0.12 0.01 0.00 0.80 0.00

Integration problems 0.00 20.30 20.11 0.10 0.71 0.01

Employees’ resistance 20.11 20.42 0.38 0.24 0.55 0.00

Less organized 0.15 0.21 20.33 20.22 0.45 20.34

Reduced info security 0.00 0.24 20.29 20.12 0.45 0.41

Behavioral beliefs III (Direct costs) Higher training costs 0.00 0.00 0.01 20.01 0.12 0.87

High costs to develop 0.00 0.00 20.12 20.17 0.00 0.82

Table 2

Pattern matrix for measures of intention, attitude, subjective norm and perceived behavioral control

Construct Item Factor a

1 2 3 4

Attitude Positiveness of attitude 0.93 0.12 0.00 20.20 0.87

Effectiveness 0.86 0.19 20.12 20.01

Goodness 0.85 0.00 0.00 20.01

Helpfulness 0.70 20.28 0.00 0.33

Wiseness 0.67 20.12 0.17 0.13

Intension Commitment 0.00 0.94 20.01 0.01 0.95

Certainty 0.00 0.94 0.00 0.01

Likelihood 0.01 0.88 0.10 0.00

Perceived behavioral control Degree of being under control 0.00 20.21 0.86 0.00 0.72

Simplicity to arrange 0.00 0.20 0.83 0.00

Easiness 0.00 0.12 0.70 0.14

Subjective norm Strong approval of important people 20.01 0.01 0.00 0.92 0.91

Approval of important people 0.00 0.01 0.00 0.92

J. Lim / Information and Software Technology 45 (2003) 469–477 471

determinants of intention to adopt NSS. Further stepwise

regression analyses were therefore performed on subjective

norm and perceived behavioral control, whose respective

product terms were obtained from an earlier factor analysis

reported in Table 1. Table 4 shows that subjective norm was

significantly influenced by endorsement from customers, IT

specialists and (other) employees. For perceived behavioral

control, the only significant factor was employees’ support

for the organizational adoption of NSS.

Exploratory analysis was performed by separating the

data according to countries of origin (Western vs. Asian

countries). From the results, Asian countries’ data set

showed subjective norm to be the only significant factor

ðp , 0:001Þ influencing intention; in contrast, the only

significant factor found in Western countries’ data set was

perceived behavioral control ðp , 0:05Þ: The data was also

analyzed based on three major industries identified:

manufacturing (33%), services (13%), and commerce

(11%). For all three-regression analyses, subjective norm

emerged as a significant predictor. In addition, perceived

behavioral control was significant for the manufacturing

industry.

4. Implications of findings

Exploratory analysis performed on subjective

norm showed that it was significantly influenced by

customers/clients, IT specialists, and other employees in

the organization. An organization’s dependence on its

trading partners has often affected its decision making on

various aspects of inter-organizational collaboration [41,

42], such as the adoptions of NSS and electronic data

interchange (EDI). In particular, organizations are depen-

dent on their customers and clients, as they are the revenue

generators in the organization’s value chain. For example,

suppliers of major US automobile companies were required

to adopt EDI systems if they wanted to continue doing

business with these customers, who had proactively adopted

inter-organizational systems [43]. Similarly, NSS are inter-

organizational systems1 that are designed to support

business negotiations (such as contract or even merger

Table 3

Pattern matrix for measures of intention, attitude, perceived usefulness and perceived ease of use

Construct Item Factor a

1 2 3 4

Perceived usefulness Accomplish tasks faster 0.97 20.14 0.00 0.11 0.96

Improve job performance 0.97 0.00 0.00 0.00

Enhance effectiveness 0.94 0.00 0.00 0.00

Increase productivity 0.91 0.00 0.01 0.01

Easier to do job 0.88 0.11 0.00 20.01

Useful 0.66 0.36 0.00 20.15

Perceived ease of use Easy to use 0.00 0.93 0.01 0.00 0.94

Easy to become skillful 20.01 0.93 0.12 20.12

Easy to learn to operate 0.00 0.89 20.01 0.01

Flexible to interact with 0.00 0.85 20.14 0.01

Clear and understandable interaction 0.12 0.81 0.00 0.00

Easy to do what they want 0.01 0.76 0.00 0.12

Attitude Negative/positive 20.15 0.00 0.92 0.00 0.87

Ineffective/effective 0.01 0.00 0.79 0.13

Bad/good 0.12 20.01 0.79 0.00

Harmful/helpful 0.01 0.00 0.77 20.14

Foolish/wise 20.01 0.01 0.76 0.00

Intention Certainty 0.00 0.00 0.00 0.96 0.95

Commitment 20.01 0.01 0.00 0.95

Likelihood 0.01 21.37 0.00 0.91

Table 4

Stepwise regression for subjective norm and perceived behavioral control

(based on TPB)

Function Regression coefficients Correlation coefficients

1. SN ¼ Customers þ Suppliers þ IT specialists þ Employees

Customers 0.10* 0.54***

IT specialists 0.13** 0.55***

Employees 0.21*** 0.61***

2. PBC ¼ Financial resources þ Time þ Software

compatibility þ Hardware compatibility þ Employees þ Training

Employees 0.57*** 0.29**

*p , 0:05; **p , 0:01; ***p , 0:001:

1 Inter-organizational systems refer to telecommunication-based

computer systems that are used by two or more organizations to support

data, information and applications sharing among users in different

organizations [62,63]. A recent form, for example, is the continuous

replenishment program (CRP) [64].


negotiations) between organizations. Therefore, customers

are a considerable driving force behind organizations’

intention to adopt NSS. On the other hand, an organization’s

suppliers and vendors are relatively dependent on the

organization and consequently they will have less influence

on the organization’s NSS adoption intention and decision.

Prior IS research has stressed the importance of user

participation [38]. For instance, Baroudi et al. [44] found

user participation and user information satisfaction to be

positively correlated with system usage. Barki and Hard-

wick [45,46] found that user participation was a determinant

of user attitude. The non-IT employees (end users) who

participate in the development process of a NSS are likely to

develop beliefs that the new system is good, important and

personally relevant. Through participation, employees may

be able to influence the design of this system. In turn, they

may develop feelings of satisfaction and ownership, as well

as a better understanding of the new system and how it can

help them in their job. Thus, the significance of employees

(both IT specialists and other employees) in influencing

subjective norm is consistent with, and lends further support

to, the importance of employee participation in system

adoption and acceptance.

While perceived behavioral control was presumed to be

affected by financial cost, implementation time, software

and hardware compatibility, employee’s support and

training, exploratory analysis showed employees’ support

for the organizational adoption of NSS as the only

significant factor. Although NSS were relatively novel,

past experience with other new systems might be able to

provide managers with an estimation of employees’ support

(or resistance) level. Hence, in the case of NSS, employees’

support may be the most crucial factor in determining the

perceived behavioral control of adopting the new technol-

ogy. This also further strengthens the importance of

employees’ influences (i.e. subjective norm) in system

adoption. Moreover, employees’ support is likely to reduce

resistance to change, which has been found to be a major

inhibitor for EDI adoption [47].

The findings of this study have important implications for

practitioners, including managers and executives, as well as

marketers of NSS technology. For management people, the

findings indicate that theyshouldbeawareof thesocial factors

that may hinder the successful adoption of a new IT such as

NSS. They should encourage active participation from

referent groups who play critical roles in influencing

organizational decisions; for example, views and positions

of IT specialists should be consulted in the case of system

acquisition, and end users’ feedbacks should be accommo-

dated in the case of system development or user acceptance

testing. In fact, analysis showed that most respondents

possessed a strong desire to comply with their referent groups.

Thus, iforganizations perceive that their referentgroups are in

favor of their NSS adoption, they will very likely intend to

adopt NSS. Special attention should be paid to customer-

s/clients, IT specialists, and other employees. Information

about the new IT could be disseminated to them to highlight

the importance of adopting such an IT. Similarly, marketers

should actively seek out these referent groups and create an

awareness of NSS among them. In particular, a product or

technology champion to educate the users on the new

technology and facilitate its adoption would help to garner

user support for its adoption [43]. Existence of a champion has

been found to be an important factor in IT adoption [48], and

particularly in inter-organizational systems and EDI adoption

[49,50]. Therefore, a champion to promote NSS would help to

generate favorablesocialpressureandencourage itsadoption.

Furthermore, practitioners should also take note of the

resources required for any successful implementation of

NSS. They should first identify the resources available in

their organizations and then evaluate the feasibility of an

adoption. Moreover, as perceived behavioral control,

besides actual control, carries a substantial weight in

influencing adoption intention, information on the required

resources for adopting NSS should be readily available to

any potential adopters to help them assess and improve their

perceived behavioral control. Particular attention should be

focused on increasing employees’ support for NSS adop-

tion. To heighten employees’ support, management could

involve the participation of employees in the installation of

a NSS in the organization. A special customized introduc-

tion process aimed at making users feel comfortable with

the new system will also help to foster greater employee

support.

Baronas and Louis [51] found that special training during

the system implementation process would help to restore or

enhance an employee’s sense of control over his work,

which might have been threatened during such process. This

will in turn cause employees to be more satisfied with the

system and positive in their interaction with system

implementers. Consequently, user acceptance of NSS will

be greater. In fact, correlational analysis between employ-

ees’ support (control beliefsupport £ perceived powersupport)

and training (control belieftraining £ perceived powertraining)

revealed a strong positive correlation (r ¼ 0:69; p , 0:001),

suggesting that employees’ training may influence per-

ceived behavioral control indirectly through employees’

support.

5. Conceptual framework

Based on empirical analysis, this section puts fourth a

conceptual framework regarding the adoption of NSS (see

Fig. 1). In this framework, subjective norm and perceived

behavioral control are posited to influence the intention to

adopt NSS. This linkage, however, is moderated by

organizational culture and industry characteristics. In

other words, the extent to which subjective norm and

perceived behavioral control affect adoption

intention depends on the specific conditions assumed by

organizational culture and industry characteristics. Further,


subjective norm is conceived to be affected by three

exogenous variables: customers’ endorsement, IT special-

ists’ support, and employees’ support; the last variable also

impacts perceived behavioral control. Whereas these

exogenous variables have been dwelt upon in Section 4,

the following deliberates on the two proposed moderators.

5.1. Organizational culture

Based on his cross-country study on culture, Hofstede

[52] categorized countries with four different character-

istics—individualism/collectivism, uncertainty avoidance,

power distance and masculinity/femininity.2 These four

value dimensions, which distinguish national value systems,

also affect individuals and organizations. According to

Hofstede [53], prevalent value systems, which form a part of

an organization’s culture, encompass a national component

reflecting the nationality of the organization’s founders and

dominant elite. He proposed that founders of organizations,

being unique individuals belonging to a national culture, are

likely to incorporate their national values into their

organizational culture, even if the organizations spread

internationally. For example, there is something American

about IBM the world over, something Swiss about the Red

Cross. Therefore, it is conceivable that individuals who join

the organization will subsequently go through processes of

selection, self-selection and socialization to assimilate

themselves to the organizational culture [53]. It is also

noted that survey respondents were instructed to answer

from the organization’s perspective. Correspondingly, the

organizational culture may be reflected in the responses.

Among the four cultural dimensions, the individualism/-

collectivism component may provide particular insight into

the behavior of organizations with respect to subjective

norm. Individualism/collectivism relates to the self-concept

of ‘I’ or ‘we’. In an individualist society, individuals see

themselves as I and are motivated by self-interest and self-

actualization. Tasks enjoy higher priorities over relation-

ships. On the other hand, in a collectivist society,

individuals see themselves as part of we and are motivated

by group interests and relationships. It is evident from

Hofstede’s work that Asian countries were largely collecti-

vist societies, and Western countries were relatively

individualistic. A Pearson correlational analysis showed a

negative correlation (r ¼ 20:18; p , 0:10) between sub-

jective norm and individualism index scores as reported by

Hofstede, thus confirming the role of organizational culture

in the proposed framework.

The influence of organizational culture can be further

deliberated by examining an organization’s value tradeoffs

between relationships and tasks. Asian organizations are

likely to be collectivist and relationships with employees

and trading partners are placed above other considerations.

In a collectivist organization, people think in terms of we

(work group, organization) and ‘they’ (the others). Accord-

ingly, social influences will have a greater impact on

organizational decisions in collectivist organizations. The

employer–employee relationship in these organizations

comprises a moral component, with a tendency for

employers to protect employees and their welfare. Thus,

organizational members’ opinions matter and if they

disapprove of NSS adoption, the organization’s intention

to adopt will be visibly weakened. In the business arena,

harmonious and lasting relationships with business partners

are highly regarded. Hence, if business partners disapprove

of the organization’s NSS adoption, it is unlikely that the

organization will adopt NSS to avoid jeopardizing painstak-

ingly built relationships.

In contrast, Western organizations are characterized as

individualistic. Individualistic organizations may be more

concerned with task-oriented issues such as productivity and

efficiency. In these organizations, employers may see

employees as ‘a factor of production’ and part of a ‘labor

market’. Moreover, opinions of employees regarding a

system considered useful by top management may not carry

much weight, relatively speaking. In business relationships,

task considerations take precedence over personal relation-

Fig. 1. Conceptual framework on the intention to adopt NSS.

2 Notwithstanding the use of Hofstede’s conceptualizations here, its

limitations and oppositions are noted [65].


ships. Thus, employees’ and trading partners’ approvals or

disapprovals may be of secondary importance in these

organizations.

5.2. Industry characteristics

An organization’s intention to adopt a system may also

be affected by the nature of the business. From the

exploratory analysis on the manufacturing sector, subjective

norm and perceived behavioral control emerged as signifi-

cant predictors of intention. On the other hand, only

subjective norm was important for the commerce and

services sector. Manufacturing companies represent

machine bureaucracies, which are characterized by stan-

dardization, functional structural design and large size [54].

These structures are generally associated with mass

production technology in which repetition and standardiz-

ation dictate the products, process and distribution. In other

words, the manufacturing environment symbolizes stability

and few changes in working procedures and policies. As a

result, if employees perceive that the introduction of NSS

will bring about revolutionary changes to existing work

practices (as in using NSS to negotiate), the new system will

be met with strong resistance.

Moreover, the implementation of large-scale just-in-time

(JIT) systems by these organizations to streamline their

operations requires that the introduction of NSS does not

disrupt the operations of existing tightly coupled systems.

These factors make perceived behavioral control an

important component of organization’s intention to adopt

NSS. Other factors such as computer anxiety may also

determine the perceived ease of adopting NSS. Thus,

successful implementation of NSS in these organizations

requires sound strategies to ensure compatibility and

smooth integration with other systems, as well as minimal

changes to current work practices. Special training pro-

grams will also help to alleviate employees’ resistance to the

new information technology.

On the other hand, the services industry is dynamic and

uncertain because they involve customer participation [55].

The information processing involved and the task activities

performed vary in response to customers’ requirements and

wants [56]. The environment faced by the commerce

industry is also competitive and constantly changing.

Thus, to achieve or maintain a competitive advantage,

organizations in these environments are constantly scanning

and implementing new technologies [49]. Firms that fail to

do so may quickly lose their competitiveness. As Grover

and Goslar [49] found in their study, environment

uncertainty was positively correlated with IT contribution.

Correspondingly, firms in uncertain environment view IT as

a potential solution to their business challenges. These

organizations tend to ignore the perceived ease or difficulty

of implementing NSS in their consideration of adopting

NSS, so long as important business partners agree with

the adoption. In other words, perceived behavioral control

may not be an important factor for these organizations.

In sum, industry characteristics play a crucial role in

influencing intention. For organizations where tasks and

processes are repetitive and unchanging, the introduction of

a new system may be viewed as a threat to current work

practices by employees. On the other hand, organizations in

dynamic industries often see IT as business solutions to help

them achieve a competitive advantage. Therefore, organiz-

ations in these industries will be more willing to adopt

innovative technologies. Nonetheless, for organizations in

both stable and dynamic industries, generating positive

perceptions in referent groups will help to facilitate the

adoption of new technologies. In addition, perceived

behavioral control is also important to organizations in

non-dynamic industries. To alleviate potential resistance

from users, special training programs should be formulated

to assure users and build their confidence. For example,

sessions can be organized to address and resolve the fears

and stress associated with the system implementation.

Hands-on sessions can also be held to dissipate feelings of

threats and uncertainties among potential users. Through

these efforts, employees’ support for system adoption will

increase. In turn, increased employee support will help to

improve organizations’ perceived behavioral control. Cor-

respondingly, organizations’ intentions to adopt the new

system will be strengthened.

5.3. Other implications

This study examines the adoption intention of a relatively

new technology in organizations; this is in contrast to many

past studies of adoption, which focused on more familiar

technologies. It is conceivable that the level of exposure—

the degree to which users have knowledge of and/or

experience with the system, may influence the magnitude

of adoption intention. Low exposure to the system will lead

to unfamiliarity with the system, which in turn will lead to a

weaker relationship among the variables and low prediction

power. On the other hand, high exposure will result in

system familiarity and subsequently, stronger variable

relationship and higher prediction power.

In fact, the degree of exposure is closely related to prior

experience, which has been found to be an important

determinant of behavior [57,58]. As Eagly and Chaiken [59]

proposed, knowledge gained from past behavior would help

to shape intention. This is because experience makes it

easier to remember acquired knowledge [60,61]. Past

experience also brings out non-salient events, ensuring

their consideration in the formation of intentions [57].

6. Concluding remarks

Using the TPB and the TAM as a starting point, a study

was performed on the adoption intention for NSS, which


identified subjective norm and perceived behavioral control

to be the key determinants. Further analysis suggested

organizational culture and industrial characteristics to play a

moderating role. A new conceptual framework encompass-

ing the above elements was put forth, and should help

provide future research directions. As companies become

increasingly globalized, adoption research must be

approached with organizational culture as a major aspect

of its design. As well, the inter-relationship between

organizational culture and industry characteristics should

provide interesting research topics of practical values.

Lastly, case studies can be conducted to take into account

the full complexity of the framework, and provide holistic

understanding of the issues at hand.

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