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A Meta-Analysis of Research onInformation Technology Implementation

in Small Business

G. Premkumar

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To cite this article: G. Premkumar (2003): A Meta-Analysis of Research on Information TechnologyImplementation in Small Business, Journal of Organizational Computing and Electronic Commerce,

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JOURNAL OF ORGANIZATIONAL COMPUTINGAND ELECTRONIC COMMERCE 13(2), 91121 (2003)

A Meta-Analysis of Research onInformation Technology Implementationin Small Business

G. PremkumarCollege of Business

Iowa State University

The small business sector is one of the fastest growing sectors of the economy. Thefirms in this sector are becoming increasingly dependent on information systems (IS)for their operations. Traditional research in IS has primarily focused on large corpora-tions. The problems, opportunities, and management issues encountered by smallbusiness in the IS area are unique, and research is too limited to provide useful guide-lines. This study compares the research literature on IS implementation and researchon IS in small business, examines the commonality and differences, and identifiesresearch gaps. An overall research framework is developed to review the research inthe two areas and determine areas of opportunity. As a follow-up of this analysis, aresearch model is developed to explore the factors influencing the adoption of com-

puter-mediated communication technologies in small business. The model incorpo-rates some of the innovation factors that are identified as potential gaps in the earlieranalysis. The research model evaluates the impact of 6 factorsperceived usefulness,cost, compatibility, top management support, competitive advantage, and sizeon theadoption of computer-mediated communications technologies. A telephone interviewwas used to collect data from 207 firms. The results of data analysis reveal that com-petitive advantage, top management support, and size are important determinants ofadoption of computer-mediated communication technologies.

IT adoption, innovation adoption and diffusion, IT implementation,small business, communication technologies

1. INTRODUCTION

Computer and communication technologies play a critical role in the transforma-tion of our economy to a digital economy. Most research studies have focused oninformation technology (IT) implementation in a large corporate setting, and verylittle research has focused on use of information and communication technologies

The author acknowledges the support provided by the Rural Development Initiative at Iowa StateUniversity and James Quinn for help in the collection of data.

Correspondence and requests for reprints should be sent to G. Premkumar, 300 Carver Hall, Collegeof Business, Iowa State University, Ames, IA 50011. E-mail: [email protected]


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in the small business sector and the issues related to IT implementation and use insmall businesses. Traditional theories on organizations and information systemsmay not be directly applicable to the small business sector [1]. Researchers in small

business have been pointing this out for more than two decades, as highlighted byDandridge [2] in his title, Children are not little grown-ups: Small business needsits own organizational theory. Afew studies have pointed out that IS theories andpractices developed for large businesses may not be suitable for small ones [3, 4].Small firms are different from large firms in a number of ways. For example, insmall businesses, decision making is centralized in one or two persons, bureaucra-cies are minimal, standard procedures are not well laid out, there is limited long-term planning, and there is greater dependence on external expertise and servicesfor information systems (IS) operations. The problems, opportunities, andmanagement issues encountered by small business in the IS area are unique, and

research is too limited to provide useful guidelines. This study attempts to reviewthe research in this area and identify the research gaps.Organizations, both big and small, have become critically dependent on infor-

mation systems for their daily operations. Traditionally, small businesses havebeen the slowest in adopting modern information technologies [5]. However, inrecent years, they have invested a significant amount of resources in IT implemen-tation. Investment in IT as a proportion of sales revenue for small firms is compa-rable to that for large firms [6]. A recent study indicated that small firms wereexpected to spend as much as $90 billion in information technology products andservices [6]. Case studies and the trade press highlight the benefits of IT for small

businesses including cost reduction, improved profitability, better customer serv-

ice, enlarged market scope, and tighter interorganizational relationships with theirtrading partners. Information communication has become the predominant driv-ing force in the Internet age. Computer-mediated communication technologiessuch as e-mail, the Web, interorganizational systems (IOS), and electronic datainterchange have dramatically changed business processes. The latest trend towardintegrated supply chain management has highlighted the importance of commu-nication technologies and IOS for businesses [7]. Research on the adoption of thesecommunication technologies by small businesses is very limited.

The main objectives of this study are:

To develop a research framework for IT implementation and map priorempirical studies to that framework. To analyze past research on IT in the small business area, identify major find-

ings, and map to that research framework. To examine major differences between the two research streams and identify

potential areas for future research. To present the results of an empirical study that examines one of the research

areas.

The primary objective of the article is an analysis of past research, and thenext three sections focus on that analysis. The article is organized as follows.

Section 2 presents the research framework for evaluating research on IT adoptionand implementation; Section 3 uses the same framework to map IT research in

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small business; Section 4 compares the two research streams; Section 5 presentsthe research model and the research methodology for the empirical study; andthe last section presents the results and discussion.

2. IT IMPLEMENTATION RESEARCH

Since the focus of this research is to develop a framework for IT implementation insmall business, we will borrow from other researchers who have done extensivemeta-analysis of IT/IS implementation research in corporate settings [811].Researchers have emphasized the similarity between IT adoption and innovationadoption/diffusion, and borrowed heavily from the diffusion of innovation litera-ture [11, 12]. Information systems were considered as new technology adoptions in

organizations, and the field of diffusion of innovation adoption provided a rich setof theory and findings that could be directly applied to study IT adoption.Since IT implementation is a very broad field, it is necessary to define the scope of

this analysis. There are two broad areas of study, organizational and individualadoption/diffusion of IT [9]. This study focuses only on organizational adoption/diffusion of IT. Innovation adoption/diffusion studies have used various stage mod-els, ranging from a simple two-stage model of initiation and implementation [12] toa six-stage model of initiation, adoption, adaptation, acceptance, routinization, andinfusion [13]. This study includes all stages of IT use in its review of past studies, butfocuses on the adoption stage for its research model. Studies examine IT implemen-tation from various perspectives including factors influencing adoption/diffusion

[8], political undercurrents that cause irrational behavior and roadblocks to imple-mentation outcome [14], and processes used for implementation and their impact onimplementation success [15, 16]. Since the focus of this study is to evaluate the fac-tors influencing IS implementation in a small business context, only studies from thefactors research stream in IS implementation research will be used for the review.Although the earlier review [8] did not differentiate between information systemsand management science models, this study will only consider studies dealing withinformation systems.

2.1 Research Framework

The study proposes a research framework, based on Leavitts [17] organizationalmodel, for reviewing and comparing the research on IT implementation with IT insmall business. The organizational model identifies four major domains in anorganizationtask, technology, people, and structurethat work within the contextof an external environment. The model has been refined and used in various IT stud-ies [8, 10, 1820]. Kwon and Zmud [8], in their review of IT implementation, identi-fied five broad categories of factorsindividual, structural or organizational, techno-logical, task, and environmental factors. The MIT study on information technologiesin the 1990s was patterned using Leavitts organizational model [19]. That study

included a fifth domain, external socioeconomic and technical environment, in itsframework because the external environment plays a very critical role in influencing

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the four internal domains. Alavi and Joachimsthaler [10] used a similar framework,along with the environment domain, in their meta-analysis of research on decisionsupport systems. Hence, we propose using these five domains in our framework.

The five major domains are illustrated in Figure 1. We have slightly renamedthese domains to be consistent with contemporary research in the IT area. Forexample, strategy and structure are combined to reflect the broader context of theorganizational domain. Although strategy and structure are important constructsin organizational theory and strategic planning, they have been combined into asingle organizational domain in recent IT studies [8, 10].

The five major domains are individual, task, innovation/technology, organiza-tion, and environment. The domains can be considered as different layers of theenvironment that influence the design and use of information technology. The pri-mary purpose of information technology in an organization is to enable people to

complete various work-related tasks. Hence, at the core of the framework we havethe individual and the task that needs to be completed. The technology domainprovides the tools and information to aid the individual in his or her task, andtherefore it is shown at the next layer. Because the technology is implemented inan organization, various characteristics of the organization influence IT imple-mentation. Often the decision to use a particular technology may be taken by aperson who may not be actually using the system to complete the task.Organizations can be considered as a collection of individuals working to accom-plish a business objective with a common set of rules, procedures, and value sys-tems. We need to differentiate the organization from the individual environmentsince the collective vision and belief system may not be the same as that of the

individuals within the organization. Hence, the organizational environment iscaptured as a separate domain. Organizations react to external environment andtheir decision making on innovations is influenced by the strategic necessity tocompete in the marketplace using resources from the internal and the externalenvironment. Hence, the external environment is shown as a separate domain.

Table 1 presents a summary of major studies on IT implementation thatappeared in leading IS research journals including MIS Quarterly, Information

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Environment

Organization

Technology/Innovation

Task

Individual

Figure 1. Research framework.


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95

Table 1Review of Research in Information Systems (IS) Implementation

Variable Description References

EnvironmentCompetition Competition in terms of price, [30, 25, 21]

scope, quality, etc.

Power/interfirm dependence Power of trading partners, economic [23, 59, 21, 72, 24]

dependence on partner

Vertical interaction and climate Extent of interactions with [25, 24]

customers/suppliers

Supplier incentives Incentives through price, service [25]

support, etc., from the innovation

supplier to adopt the innovation

Organization

Top management support Extent of commitment and resource [39, 73, 21, 40, 24]

support from top management forthe innovation

Product champion Existence of a high-level individual [30, 74, 26]

to sell the innovation within the

organization

Size Organizations size in number of [75, 27]

employees or sales revenue

Communication channels Channels available and extent of use [76, 28]

of various channels

Centralization Level of centralization of decision [30, 29]

making in organization

Formalization Use of formal procedures for [30, 29]

operation and rule observance

Technology

Relative advantage Perceived usefulness of the new [35, 32, 33, 77, 58]

(perceived usefulness) technology

Complexity (ease of use) Level of ease of use [33, 32, 77, 58, 13]

Compatibility Organizational compatibility with [32, 33, 77, 58]

beliefs and value systems and

technical compatibility with the

task and work practices

Trialability Ability to experiment before adoption [32, 77, 58]

Observability Ability for others to observe results [32, 77, 58]

of innovation

Cost Relative cost to benefits of the [33, 23]

technology

Individual

Education Level of education [39, 30]

Job tenure Length of service in firm [30, 39]

User Involvement Extent of user participation in [4143]

analysis and design

IS expertise Prior experience in IS [42, 40, 43]

Cognitive style Cognitive and decision-making style [38, 78, 79]

of individual

User training [42, 43, 37]

Computer self-efficacy Usage [80, 81]

(continued)


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Systems Research, Journal of MIS, Decision Sciences, andManagement Science. Theyare listed under the five domains. A detailed analysis of the factors studied in eachdomain is now presented.

2.2 Environmental Domain

In the early 1980s IS were primarily used to support back-end operations.Therefore, environmental factors were not considered critical for research on ITimplementation. In the 1990s the use of IS to gain competitive advantage and the

growth in IOS spanning organizational boundaries highlighted the impact ofenvironmental factors on IT implementation. IS are catering no longer to just aninternal audience, but also to the firms customers, suppliers, and other tradingpartners. Hence, it is not surprising that external environmental factors areincreasingly being studied in IT implementation. Studies have examined theimpact of competitive advantage in initiating the implementation of IOS [21].Many studies on IOS have examined the role of interorganizational dependenceand power on adoption of these systems because in most cases the powerful trad-ing partner in the dyad influences IT implementation [2224]. Researchers havealso examined the role of incentives and the transaction climate between the trad-

ing partners in facilitating the implementation of IOS [25].

2.3 Organizational Domain

Since most IT implementation is in an organizational context, researchers haveexamined the impact of various organizational factors on IS implementation. Theorganizational factors that have been extensively studied are degree of top man-agement support, existence of a product champion, organizational size, commu-nication channels, centralization, and formalization [8, 21]. There are other factors,such as user training and user involvement, which are individual factors but are

measured at the organizational level, that have been found to be related to ITimplementation. The importance of top management support and commitment

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Table 1 (Continued )

Variable Description References

TaskTask autonomy/responsibility Personal control over tasks [40]

Task structurability Level of structure to task [40]

Task interdependence [40, 45]

Task standardization [40]

Task uncertainty Deterministic, probabilistic, random [43, 20]

decisions

Task complexity Availability of well defined [43, 20]

procedure to solve tasks

Note. IS = information systems.


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cannot be overemphasized. This support sends strong signals within the organiza-tion, reduces any political roadblocks, and ensures adequate resources for imple-mentation [21]. Product champions play a critical role in marketing the innovation to

the decision makers, developing an implementation plan, facilitating resource allo-cation, and removing roadblocks to implementation [26]. The impact oforganiza-tional size on IT implementation is mixed. Although some studies have found it to

be important, others have not found it to have a significant effect. It seems likethere is a threshold level beyond which size is not an important factor [27]. Largerorganizations have more resources and IS expertise to facilitate IT implementation,

but could be bogged down with more bureaucratic practices and resistance tochange that smaller organizations may not have. A few studies have examined theuse of various communication channels to get information for adoption, but thesehave not been examined in terms of their impact on adoption/diffusion [28].

Centralization andformalization, two variables extensively studied in early researchon IT implementation [29, 30], have not found much support in recent IS research[21]. Organizations have become too complex to fit neatly into the categoriesdefined by centralization and formalization.

2.4 Technology Domain

Innovation adoption research has focused heavily on evaluating the impact of var-ious innovation/technology characteristics on adoption/diffusion. Tornatzky andKlein [31], in their meta-analysis of innovation research, identified three variables

compatibility, complexity, and relative advantageto be consistently related toadoption. These characteristics have been extensively used in IT implementationresearch [21]. Recent studies have also included other variables such as cost, trial-ability, and observability [32, 33]. Compatibility of an innovation is the degree towhich an innovation is perceived as consistent with the existing values, past expe-riences, and needs of the receivers [34]. Compatibility in the context of IS includes

both organizational and technical compatibility; that is, the innovation should becompatible with the organizations values and belief systems as well as with thework practices and system interfaces. It has been found to be positively related toadoption [13]. Relative advantage is the degree to which an innovation is perceived

as better than the idea it supersedes [34]. Although some researchers have criti-cized this as a catch-all variable [31], it has been extensively used in IT adoptionstudies and found to be positively related to IT adoption [23, 32, 33]. Complexity isthe degree to which an innovation is perceived as relatively difficult to under-stand and use [34]. Ease of use of computer systems has been extensivelystudied in IT implementation and has been found to be positively related toadoption [23, 32, 33]. Recent studies have used the technology acceptance model(TAM) and examined the impact of ease of use (or complexity) and perceivedusefulness (or relative advantage) on IT adoption and usage [3537]. Trialabilityis the degree to which an innovation may be experimented with on a limited

basis[34] and observability is the degree to which the results of an innovation are

visible to others [34]. The last two factors, trialability and observability, havebeen problematic for researchers in terms of articulation as well as measurement



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[32]. Some studies have found the cost effectiveness of the innovation to be asignificant variable [23, 33].

2.5 Individual Domain

Factors in the individual domain have always taken a secondary role in studiesdealing with organizational-level IT implementation. Since the technology isimplemented across multiple individuals, it becomes difficult to clearly delineatethe effect of individual characteristics on IT implementation. Studies on individ-ual factors such as demographics have been more useful in studyingindividual-level IT implementation and in the design of systems and user inter-faces. Various demographic factors such as education, job tenure, cognitive style,

and IS expertise and experience have been examined [30, 3840]. Many studieshave examined the relationship between user involvement and system success[41], but the results have been inconclusive. Whereas some studies considered itas an individual variable, others operationalized it at the organizational level.Similarly, user training has also been conceptualized at the user and the organiza-tional level by different studies depending on the context of the study [42, 43].

2.6 Task Domain

Factors in the task domain, like individual factors, have been studied to only a

limited extent. Most studies have been in the context of decision support system(DSS) research, examining the impact of task structure, task autonomy, task uncer-tainty, and task interdependence on DSS implementation [10, 20, 40, 44]. Therehave been some studies examining the tasktechnology fit [45].

The use of a single IS across multiple tasks makes it difficult to clearly delineatethe effects of task factors on organizational-level studies of IT implementation.Task factors are very valuable for determining the fit among the system, the user,and the task characteristics. However, these are of interest for studies on systemdesign rather than for studies on IT implementation.

3. RESEARCH ON IT IMPLEMENTATION IN SMALL BUSINESS

Research on IT adoption and implementation in small firms has been rather lim-ited, with most studies focusing on various factors influencing system usage anduser satisfaction. A detailed review of research in leading management informa-tion system (MIS) journals (Journal of MIS, Information Systems Research, MISQuarterly, Decision Sciences, Information and Management) for the last 10 years wasperformed to identify all articles related to IT adoption and implementation insmall business. A summary of the major studies in this area is provided in Table 2.A casual observation of the research indicates that the studies are widely diver-

gent and not comprehensive enough to create a cumulative research tradition. Animportant question to answer is whether this research field has adequate research

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99

Table 2

Information Systems Research in Small Businesses

Study Dependent Variable Independent Variable Results

Igbaria et al. Individual system Direct: perceived usefulness Perceived usefulness, ease

(1997) usage and ease of use; indirect: of use, external training,

internal and external external support, and top

computing support, management support are

internal and external training, significant

and top management

support

Harrison Intention to adopt Attitude, subjective norm, Intention related to all three

et al. (1997) perceived behavioral control independent variables

Thong et al. User satisfaction, Top management support, Top management support

(1996) organizational consultant effectiveness, related to organizational

impact, overall IS vendor support impact, IS effectiveness;effectiveness consultant effectiveness

related to all three

(satisfaction, impact,

effectiveness); vendor

support related to all

three

Palvia et al. Usageyes/no, Size, education, managers Sales, number of employees,

(1994) amount of use IS skills and IS experience, education, IS experience,

(hr/day), number training, age of business, skills, age of business,

of software number of years of years of computing at

systems used computing at home, number home are significant

of years of computing,

profitability

Lai (1994) Usage Size, age of business, IS IS experience, IS rank, age

experience, in-house of business related to

development, MIS ranking computer use

Cragg and Growth Top management

King (1993) involvement, relative

Case study advantage were positive

relationships and

inadequate resources

was a negative

relationship

Raymond DSS satisfaction Type of application, origin, Task autonomy, experience,

and Begeron tool type, task variety, task application type, tool,

(1992) autonomy, training, and origin significant

experience

Soh et al. System usage Use of consultant Firms with consultant have

(1992) more system usage

Yap et al. Satisfaction Vendor support, consultant All variables significant

(1992) effectiveness, experience, except number of

resources, top management applications and

support, user participation, expertise

number of applications IS

expertise

(continued)


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100

Table 2 (Continued )

Study Dependent Variable Independent Variable Results

Raymond Use (offline Size, organizational maturity Size, maturity, time(1990) and online), (formalization), resources, horizon, sophistication

satisfaction, planning time horizon related to satisfaction;

IS sophistication sophistication related to

offline usage; size and

time horizon related to

online usage; resources

and maturity related to

sophistication

Delone Use (time and IS experience, age of operations, Top management, in-house

(1988) frequency), impact training, top management, operation, IS knowledge

(success score involvement, number of associated with computer

importance score) complaints, planning, control, successapplications (in-house/

external), operations

(in-house/external)

Raymond User comprehension, Computer training, computer Training related to

(1988) user participation, experience comprehension and usage

usage (batch and (batch and online);

online) experience related to

user participation

Montazemi IS satisfaction Presence of system analyst, Systems analyst, level of

(1988) level of analysis, user analysis, involvement,

involvement, computer literacy, online, and

literacy, proportion of online decentralization aresystems (IS sophistication), related to satisfaction

proportion of specialized

software, duration of CBIS,

decentralization

Raymond Satisfaction, IS experience, development In-house operation, greater

(1985) utilization (in-house or packaged), applications, online

processing (in-house or applications, rank related

external), IS sophistication to satisfaction; in-house

(number of applications, operation, greater

interactive applications), rank, applications, online

location (remote or urban) applications, rank are

related to utilizationDelone (1981) Size IS experience, in-house/ Less IS experience, more

external, resources (budget externa l support, and

as proportion of sales) smaller resources are

associated with smaller

firm size

Note. IS = Information system; MIS = management information system; DSS = decision supportsystem; CBIS = computer-based information system.


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potential for a separate research stream. This study attempts to answer this ques-tion by reviewing existing studies, comparing them with IT implementationresearch, and evaluating whether there are potential areas for research.

Most studies on IT implementation in small business have used satisfaction orsystem usage as the primary dependent variable. Surprisingly, very few studieshave examined the adoption of new information technologies by small businesses.A study on IT adoption by Harrison et al. [46] examined use of the theory ofplanned behavior to evaluate the relationship among attitude, subjective norms,and perceived control, and the intention to adopt IS.

Raymond [4, 5, 47] conducted a series of studies on computer usage in smallbusiness. He examined the factors influencing user satisfaction and system uti-lization using data from a survey of 464 small manufacturing firms [4]. He foundthat IS sophistication in terms of number of in-house, online, and total applica-

tions and the rank of the top IS executive are related to user satisfaction andutilization. He also found that various IS-related factors such as IS experience,in-house operation, larger number of IS applications, and lower MIS rank wererelated to the firm size. The relationship of organizational factors with the twodependent variables also varied based on size. Raymond [5] examined the impactof computer training and experience on usage, user comprehension, and user par-ticipation. Training was found to be related to usage, highlighting the importanceof training users. He also examined the influence of size, resources, time frame,maturity, and the mediation effect of IS sophistication on user satisfaction andusage [47]. IS sophistication and time frame have a direct effect on both depend-ent variables, and the effect of size, time frame, and maturity is mediated by IS

sophistication.Montazemi [48], in an empirical study of 83 firms, found that user involvement,

IS expertise (literacy), IS sophistication in terms of online systems, and detailedrequirement analysis were all related to IS satisfaction. Delone [49] surveyed 98small businesses and found that involvement of top management, in-house oper-ation, and IS expertise/knowledge were related to computer success, a joint indexof computer use and impact. He did not find any relation between computer suc-cess and external support, supporting the findings of Raymonds [4] study.

Yap et al. [50] empirically examined the relationship between a large number ofenvironmental and organizational variables and system satisfaction. They found

that vendor support, consultant effectiveness, experience, top management sup-port, and user participation had a significant relationship with satisfaction.Raymond and Bergeron [51] studied the impact of a few task-related variables andfound task autonomy to be related to satisfaction in the context of use of DSS insmall businesses. Cragg and King [52] used case studies to examine IS growth inorganizations and found that whereas top management involvement facilitatedgrowth, resource constraints inhibited growth. Palvia et al. [53] found that size,education, IS experience, and age of business were related to system use. Lai [54]also found that IS experience and age of business were related to usage.

Igbaria et al. [44] surveyed 358 small business users and found, using TAM, thatperceived usefulness and ease of use had a significant impact on system usage.

They also found that top management support and external support had an indi-rect impact through these two variables on system usage. Harrison et al. [46] used



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TAM and the theory of planned behavior model to find that IT adoption is relat-ed to attitude, subjective norm, and perceived behavioral control.

In summary, an analysis of Table 2 indicates that researchers have been study-

ing IT implementation in small business on a regular basis starting from 1981when computers were being introduced in small business in a limited fashion.We have had a wide range of studies examining the relationships between vari-ous dependent and independent variables. User satisfaction and system usageare the two most common dependent variables in many studies. A wide rangeof operationalization has been used on these two variables. Usage has beenmeasured with a variety of objective measures including frequency of use,length of use, and range of software used, and as a dichotomous variable(yes/no). A few studies have used subjective measures of usage. Studies haveused their own measures for user satisfaction, even though a few validated

measures such as that of Ives and Olson [41] were available. Perhaps these meas-ures, developed for corporate settings, were not appropriate in a small businessenvironment.

Most studies have used a contingency framework of a wide range of inde-pendent variables without a clear development of theory specific to small busi-ness. There have been limited attempts to validate theories. Only recently havestudies used well-tested theories in the context of small business and found themto be equally applicable [44, 46]. It should be noted that IT implementationresearch in corporate settings was also developing during this period and newdevelopments in the IT area have contributed to a state of flux in IT research. Inspite of limitations, these studies have contributed significantly to our under-

standing of IT implementation in small business by identifying critical factorsfor successful implementation. Some of these factors are IS expertise, availabilityof an IS consultant, sufficient firm size, vendor support, perceived usefulness, easeof use, appropriate level of IS education, resources, and training. A detailed com-parison of the two research streams is provided in the next section.

4. COMPARISON OF RESEARCH

Table 3 compares and maps IT research in small business with similar variables

identified in IT implementation.A cursory analysis of the table indicates many empty cells where IT research in

small business is nonexistent. Although some empty cells may indicate the irrele-vance of the factors in the context of small business, other empty cells may indi-cate the potential for future research. A detailed analysis of the different domainsis now provided.

4.1 Environment

There is very limited research on the impact of environmental factors on IT insmall business. Only one variable, vendor support, was examined in prior stud-ies. This variable bears resemblance to the variable supplier incentives in IT

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implementation, which examines the incentives provided to the firm by the sup-plier for adopting the innovation. Interestingly, although earlier studies indicatedthat external support is not a significant variable and in-house support and oper-

ations lead to greater usage and satisfaction [4, 49], later studies indicated thatexternal support is a significant variable influencing system satisfaction and usage[44, 55]. It is not clear whether the significance of vendor support is a recent


Table 3

Mapping Research in Small Business with Information SystemImplementation Research

Variable Study

Environment

Competition

Power/interfirm dependence

Vertical interaction and climate

Supplier incentives [44, 55, 53, 82, 83, 49]

Vendor support [4, 75]

Organizational

Top management support [44, 55, 52, 50, 49]

Products champion

Size [75, 47, 53]

Communication channelsCentralization

Formalization

IS sophistication/IS experience [4, 4850]

Rank of IS [4, 54]

Age of business [54, 53]

Resources [50, 52]

IS Support [47, 39]

Technology

Perceived usefulness (Relative advantage) [46, 44]

Ease of use (complexity) [44]

Compatibility

TrialabilityObservability

Cost

Individual

Education [53]

Job tenure

User involvement [48]

IS expertise [4, 5, 47, 54, 53, 48]

Cognitive style

User training [5, 51]

Task

Task autonomy responsibility [51]

Task structurabilityTask interdependence

Task standardization

Task uncertainty

Task complexity

Note. IS = information systems.


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phenomenon. Perhaps the increased sophistication of IS and the scarcity of IS staffis forcing small businesses to depend on external support for their IS operations.Newer communication technologies and innovative IT service models are increas-

ing the significance of this variable. The ability to provide some IT services overthe Web through application service providers may make outsourcing a com-pelling alternative.

The lack of research on environmental variables does not necessarily indicatethat they are not relevant for small businesses. Factors such as competition,power, and interfirm dependence have been researched in the last 10 years in thecontext of IOS and have been found to be significant in influencing IOS adoption[21, 24]. Anecdotal evidence in the trade press has documented the pressureexerted by large firms on small trading partners to adopt new communicationtechnologies such as electronic data interchange (EDI). The lack of studies spe-

cific to small businesses may have resulted in many of the empty cells in this cat-egory. Perhaps the initial focus on development of internal information systemsmay have motivated users and researchers to ignore the external factors, whichcome into greater prominence with systems focused on external constituentssuch as suppliers and customers. This is an area that has great potential for futureresearch.

4.2 Organization

The variables in the organizational domain seem to be the primary focus of many

studies in small business. Among the organizational factors in Table 3 we noticethat centralization, formalization, and product championing have not beenresearched in small businesses. The first two are not very relevant for small busi-nesses because most decision making is centralized in a few senior people andsmall firms do not normally have very rigid rules and procedures. The concept ofproduct champion may be more relevant in large organizations, where a numberof people have to be sold on the technology before the adoption decision. Decisionmaking in small firms is less complex and more centralized, and therefore may notrequire extensive championing within the organization.

There are a few variables that are more popular in small business research

compared to IS implementation research. They are IS expertise/experience, ageof business, rank of IS executive, resources, and level of IS support. It is surpris-ing that the rank of the IS executive is a common research variable. Given thatsmall businesses tend to have a flatter organizational structure, one would notexpect the rank of the IS executive to be a major variable. Perhaps this signifiesthe existence and significance of a separate IS department and thereby indirectlyindicates the sophistication of the IS function. Many small businesses start withpaper-based systems or a simple computer system and therefore may not have aformal IS department. Hence, they may feel constrained in terms of internal ISexpertise when it comes to evaluating and adopting new technologies. Hence,they are more likely to get these services from external vendors rather than devel-

oping in-house expertise. The importance of vendor support confirms thattrend.

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4.3 Technology

Very few studies have examined the impact of technology innovation characteris-

tics on IT in small business. The variables in this category are primarily derivedfrom the IT adoption literature. Unfortunately, there have not been many studiesthat have examined IT adoption in the specific context of small business and per-haps this may be a reason for the empty cells in this category. The closest study toIT adoption is by Harrison et al. [46], who examined the impact of attitudes (or

behavioral beliefs), subjective norms (or normative beliefs), and perceivedcontrols on intention to adopt IT. The category of behavioral beliefs of what thetechnology can do to the organization is very similar to perceptions of relativeadvantage, a variable that is found in IT adoption literature to be a predominantdeterminant of adoption. Various studies using TAM have found perceived use-

fulness to be a significant variable influencing IT adoption [35, 37].Compatibility and complexity are two critical variables in IT adoption that needto be studied in more depth in the small business area. Small firms, due to lack ofa formal structure and greater flexibility, may have fewer organizational compati-

bility problems with new technologies. On the other hand, lack of in-house ISexpertise and large IS staff may make new ITs seem more complex and difficult toimplement, thereby inhibiting adoption. TAM models employ ease of use, which isvery similar to complexity, in the specific context of system interface. Igbaria et al.[44] found it to be a significant variable. Another important factor is the cost ofinnovation, since typically small firms have very limited budgets. Trialability andobservability have had mixed results in IT adoption and it is not clear whether the

results would be any different in the small sector.

4.4 Individual

Among the individual factors, education and user training have been found to berelated to IS implementation. Some of the variables such as user involvement andIS expertise can be considered in either the organizational or the individual cate-gory, depending on the measurement context. They can be considered as organi-zational variables if IS expertise is measured as a variable based on overall

assessment of expertise within the organization. It is an individual variable if it isused to measure an individuals IS expertise and relate it to his or her satisfactionor usage.

Researchers studying organizational-level IS implementation normally use asingle respondent from a firm to answer questions about organizational-levelvariables, and therefore do not analyze in depth the individual variables and theireffect on usage. The respondent, normally a senior IS executive, reflects the orga-nizational perspective on IS implementation by aggregating the various charac-teristics of the organization. If however, one needs to do a microlevel study (e.g.,study system usage in depth), one needs to study individual organizations withmultiple respondents within the same firm (to control for organizational and envi-

ronmental factors). Some studies do not differentiate between organizational- andindividual-level adoption.



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4.5 Task

Task characteristics have not been extensively researched in the small business

area. The primary research stream that contributed much of the findings in thisarea is the research on decision support systems, an area which has not beenextensively researched in the small business field. The focus on organizational-level analysis in most studies has reduced the emphasis on individual and taskcharacteristics.

In summary, based on the analysis of research in small business and comparingit with studies in IS implementation, we can infer the following. There has beenvery limited research on adoption of IT. From Table 3 we notice that very littleresearch has been done on the impact of technology factors on IT adoption. Thisarea has great potential as evidenced by the number of studies in the traditional

IT literature on IT adoption. Similarly, research on the impact of environmentalcharacteristics has also been limited. The need for interconnectivity with sup-pliers, customers, and other trading partners brings a new dimension to theinfluence of external forces on IT adoption. Hence, research on the influence ofexternal forces on IT adoption will bring some new insights to the process of ITadoption in small business.

5. EMPIRICAL STUDY

The extensive analysis of past research on IT implementation identified some of the

research gaps in the small business area. This empirical study addresses one of themany research gaps identified in the analysis. Because IT adoption in small busi-ness has not been extensively studied, this empirical study focuses on IT adoption.Although the impact of innovation characteristics has been widely studied in tra-ditional IT adoption/diffusion studies, the reviews of past studies reveal that suchresearch is surprisingly limited in the small business context. Hence, this studyproposes to examine the impact of innovation characteristics on IT adoption.Widespread adoption of computer-mediated communication technologies by largefirms motivated us to study its adoption in small businesses.

5.1 Computer-Mediated Communication Technologies

The dramatic growth of the Internet has created a shift from using computers as apure computing device to using them as a combination of communication andcomputing devices. The proportion of time an end-user spends in using the com-puter to communicate or receive information has steadily increased in the last5 years and in some cases exceeds the time spent on computing. Although com-puters have been adopted extensively in large firms to better manage complexoperations and facilitate communication, their use in small businesses has yet to

be empirically examined. Hence, this study provides an opportunity for under-

standing the changing nature of computing devices and the factors influencingtheir adoption.

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The characteristics of communication among firms vary along many dimen-sions including the richness of the medium [56], the type of information com-municated, the mode of communication (synchronous or asynchronous), the

need for further processing of communicated information, and the geographiclocation of the parties [57]. A variety of communication channels are availableincluding face to face, telephone, fax, U.S. mail, online data access, e-mail, EDI,and Internet access. The last four communication technologies can be termedcomputer-mediated communication technologies. EDI provides computer-to-computer communication of business transaction communications without anyhuman intervention. It may use a direct line or a value-added network forcommunication. Typically, it is used for communicating standard businesstransactions such as purchase orders, sales invoices, and so on, using prede-fined format standards. E-mail enables communication of both structured and

unstructured information, and is normally between two end-users. Wherease-mail and EDI are asynchronous in their communication characteristics, onlinedata access enables a user to directly retrieve and enter information in anotherfirms computer, normally using a direct line for communication. Such systemsare common among firms that are tightly linked with a common IS and areoften seen in parentsubsidiary or franchiserfranchisee links. However, this istypically limited to only a few proprietary applications. Internet access pro-vides the opportunity for two firms to communicate using open standards.Although primarily focused on information dissemination, the growth ine-commerce has triggered the use of the Internet for a wide variety of commu-nication requirements. Each channel has its own advantages and disadvan-

tages. For example, whereas a face-to-face communication would be appropri-ate for rich unstructured communication, EDI would be a better choice forstructured communication that needs to be further processed in a computer(e.g., a sales invoice). E-mail is very useful for asynchronous communication.Internet access using the Web provides a richer channel for disseminating awide variety of information.

5.2 Research Model

The research model examines the influence of seven variables on the use of com-puter-mediated communication technologies. The innovation factors that havebeen found to have a major impact on adoption of innovation are perceived use-fulness, compatibility, ease of use, cost, trialability, and observability [32, 35, 57,58]. The last two variables, trialability and observability, have exhibited inconsis-tent results [32, 58], and therefore are not included in this study. Studies on diffu-sion of innovation [31] and IT adoption [58] have found perceived usefulness to

be a significant determinant of innovation adoption. In the context of small busi-ness, Harrison et al. [46] and Igbaria et al. [44] found that belief about the useful-ness of the technology influences the intention to adopt. Some small firmsembrace these technologies as a strategy to automate their operations and reduce

dependence on labor, which is a scarce and expensive resource for small busi-nesses. Hence, we can expect that firms with greater perceived usefulness for



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these technologies are more likely to use it. Compatibility is another factor that isfound to consistently influence adoption of innovations. Studies on EDI adoptionhave found that small trading partners resist adoption of communication technolo-

gies such as EDI because it is not compatible with their paper-based systems [33].Many small businesses that use a paper-based process or a simple computer-basedsystem may find that computer-mediated communications technologies create sig-nificant changes in processes and are incompatible with their business practices andvalue systems. In general, firms that perceive compatibility of these technologieswith their value systems and business processes are more likely to use them.

Ease of use of technology has been found to be a significant factor influencingadoption of innovation [32, 33, 35, 58]. Small businesses are often concerned aboutimplementing complex technologies [44]. They normally do not have extensive in-house IS expertise for experimenting with new technologies or training their

employees in these technologies. Hence, ease of use can have a positive influenceon a small businesss decision to adopt the technology. The cost of an innovationhas been found to be a significant deterrent to adoption [12]. However, someresearchers have also found, especially in the context of computers, that cost is nota significant factor [53]. In many cases, it can be expected that small businesses,normally constrained in financial resources, find cost to have a significant nega-tive influence on adoption of new technologies.

Studying the use of new technologies without considering the organizationaland the environmental context does not provide a complete understanding of thedecision process. Hence, we included a select set of environmental and organiza-tional factors in the research model to better describe the adoption of IT. The

research model includes two organizational variables, organizational size and topmanagement support, and one environmental variable, competitive advantage,which have been found to be important in prior research. Top management sup-port has been found to be a significant factor in most studies on IT adoption [21].In small firms it becomes more important because decision making is mostly cen-tralized in a few key persons in the organization [50]. Their vision on the use of ITinfluences what innovations are adopted. Many studies in small businesses [50,52, 55] have found top management support to be critical for IT implementation.Organizational size is another important variable that has been extensivelyresearched in IT adoption/diffusion. Although our studies only focused on small

firms, we wanted to determine whether size is still an important determinantamong small firms. Results from prior studies on the impact of organizational sizehave been mixed. Whereas Raymond [47] and Palvia et al. [53] found it to be a sig-nificant variable, Lai [54] did not find it to be an important variable. It can beargued that small firms have relatively simple operations, which may not requireextensive use of IT. On the other hand, they are nimble at trying new technologiesand may not face the same level of resistance to change that is faced by large firms.However, within the small business sector it is not clear whether size will be amajor issue. We believe that larger firms within the small business sector are morelikely to implement new technologies. Recent studies on IT implementation inlarge firms have highlighted the significance of competitive advantage, particular-

ly in the context of communication technologies that facilitate IOS [21, 24]. Evensmall firms may use these technologies if they are suppliers to large firms whoinsist on using these technologies for doing business with them [59]. Firms that

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perceive the possibility of gaining competitive advantage from using these tech-nologies are more likely to adopt them.

5.3 Measurement

The variables were measured using multi-item indicators. All the items, exceptsize, were measured using a 5-point Likert-type scale ranging from strongly dis-agree to strongly agree. A single item was used to determine whether the respon-dents adopted one of the four communications technologies, online data access,e-mail, Internet access, or EDI.

Perceived usefulness was measured using three items, adapted fromPremkumar et al. [33], that assessed the perceived benefits of these communica-

tions technologies to the firm. Ease of use had to be dropped from the researchmodel due to measurement problems. Process compatibility was assessed by twoitems that determined whether these technologies were compatible with the firms

beliefs, value systems, and work practices. Cost was measured by two items thatdetermined the cost of implementing the technology relative to its benefits. Topmanagement support was assessed using three items that determined the level ofsupport for the technology. Competitive advantage was measured by three itemsthat assessed the strategic necessity of these technologies for competing in the mar-ket. Size of the firm was determined by the logarithm of its sales revenue.

5.4 Data Collection

The data were collected from 207 small business firms. This study used a tele-phone survey to collect information from the owner or a senior officer in the com-pany. Telephone survey, although more expensive and time-consuming, is veryeffective in eliciting information from the respondents because the data collectionprocess is interactive and there are opportunities for both sides to clarify anydoubts. The richer communication medium helps to collect more reliable data.However, telephone interviews are constrained by time and therefore questionshave to be carefully crafted with minimum redundancy to reduce the burden on

the interviewee. In our study each interview lasted 20 to 30 min. The respondentswere informed that their participation was voluntary and the information theyprovided was confidential. The multi-indicator items for each construct wereframed into questions and structured along with other questions into a single sur-vey that blended various aspects of our study. The questions were pilot-testedwith a few small businesses. The questions were modified to remove ambiguitiesand restructured to provide continuity in the telephone conversation. The inter-viewers were experienced personnel who had been trained in telephone surveyand had done many similar surveys. The researchers discussed in detail withthem the questions in the survey.

A random sample of 311 small business firms was chosen in the midwest

region. We considered firms with fewer than 100 employees as small businesses.A letter was mailed to each firm informing them of the study and asking for theirparticipation. Then these firms were contacted over the telephone. Firms were



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unreachable if they could not be contacted after trying multiple times on differentdates. Of the 311 firms, more than 50 were not reachable due to various reasonsincluding not answering the phone or business closure or wrong phone number.

The telephone interview was completed with 207 firms, resulting in a responserate of 81% for firms that were reached by phone.

5.5 Sample Characteristics

The characteristics of the sample are shown in Table 4. The sample included firmsof varying sizes and from several industry sectors.

5.6 Validity and Reliability

The constructs were tested for two psychometric properties, validity and reliabil-ity, to ensure that the measurement was accurate and sound. Whereas validityassesses the degree to which the items measure the theoretical construct, reliabil-ity assesses the stability of the scale based on an assessment of the internal con-sistency of the items measuring the construct [60]. Validity was assessed throughcontent, convergent, and discriminant validity. Content validity assesses whetherthe measurement covers the complete domain of the construct; convergent valid-ity evaluates whether all the items measuring the construct cluster together toform a single construct; and discriminant validity measures the degree to which a

concept differs from other concepts and is indicated by a measure not correlatingvery highly with other measures from which it should theoretically differ [60].

Content validity was established through the extensive process of item selec-tion and refinement in the development of the instrument. Convergent and dis-criminant validity were evaluated using confirmatory factor analysis availablethrough the CALIS procedure in the SAS program. Convergent validity isassessed using the measurement model shown in Figure 2. The significance of the

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Table 4Sample Characteristicsa

Number of Firms Percent

Industry

Manufacturing and construction 21 10.0

Retail sales and wholesale trade 62 31.0

Service 54 26.0

Professional 34 16.0

Finance, insurance, or real estate 23 11.0

Other 13 6.0

Annual sales revenue

Less than $0.5 million 94 45.4

$0.5 to $1 million 83 40.0

Greater than $1 million 30 14.6

aN= 207.


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overall model ensures that the measures load on a priori-defined constructs. It isalso useful to check whether the individual factor loadings of the measures aresignificant. The results of the measurement model are shown in Table 5.

The results indicate that the overall fit of the model is very good. Because thechi-square value is dependent on sample size, the ratio of chi-square to degrees offreedom is normally used and a value below 2 is considered as a good fit. In our

case the chi-square value was below 2. The probability level associated with thechi-square statistic indicates the probability p of attaining a large chi-square valuegiven that the hypothesized model is supported. The higher the value ofp, the bet-ter is the fit and a value ofp .1 is considered an indication of satisfactory fit [61].Exclusive reliance on chi-square can be problematic [62]. Therefore, other fitindexes are considered to evaluate the model fit. The goodness-of-fit index wasabove .9 and the root mean square residual was very low. The Bentler and Bonnetindex, another fit index, is normally expected to be above .9 [63]. In our case it was.92, indicating that the model is a good fit. Factor loading is another indicator ofconvergent validity. All the factor loadings are significant at p .001, indicatingthe items are significantly loading on the construct.

Discriminant validity, which assesses whether the measures of different con-structs are different from each other, is tested by evaluating whether the correlation


Pu1

Pu2

Pu3

Perceived

Usefulness

Cst1

Cst2

Cost

Co1

Co2

Compat

ibility

Mgt1

Mgt2

Mgt3

Mgmt.

support

com1

com2

com3

Competitve

Advantage

Figure 2. Confirmatory factoranalysis.


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between any two constructs is significantly different from unity [64]. This can betested by comparing two models, an unconstrained model where the correlation isfree and a constrained model where the correlation is fixed to one. The differencein chi-square between these two models is also a chi-square variate with degrees

of freedom equal to one [61, 65]. A significant chi-square difference implies that theunconstrained model is a better fit for the data, supporting the existence of dis-criminant validity between the two constructs [66]. Table 6 presents the results ofpairwise testing of discriminant validity of the five constructs. Each construct iscompared with every other construct, resulting in 10 unique comparisons. Theresults indicate that the chi-square difference is significant for all the comparisontests, thereby providing support for the discriminant validity of the constructs.

The reliability of the constructs was assessed using Cronbachs alpha. Theresults in Table 7 indicate that four of the five variables have alpha values greaterthan the cutoff value of 0.7 suggested by Nunnally [67]. The value for cost is .64.

The slightly lower value can be attributed to the fact that Cronbachs alpha tendsto be lower for constructs with few items. Because Cronbachs alpha is basedon the restricted assumption of equal importance of all indicators, researchershave used other measures of reliability such as composite reliability [68, 69].Composite reliability considers the ratio of nonrandom variation associated withall measures of a subscale to total variation in all these measures.

(Li)2

composite reliability (Li)

2 + (1 Li2)

Normally a value above .6 is considered acceptable [68]. The composite relia-

bility for all our constructs were above .6, thereby indicating that our measuresexhibit adequate reliability.

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Table 5

Convergent Validity

Variable M SD ML Estimate T value

PU1 2.24 0.82 0.56 8.8*

PU2 2.76 0.98 0.66 8.5*

PU3 2.17 0.86 0.56 8.26*

Cst1 3.03 1.03 0.89 9.09*

Cst2 2.85 0.98 0.54 6.36*

CO1 3.12 1.04 0.73 9.48*

CO2 3.36 0.98 0.83 11.56*

Mgt1 2.50 0.81 0.59 9.52*

Mgt2 3.03 0.95 0.54 7.07*

Mgt3 2.63 0.98 0.65 8.51*

Com1 3.11 1.11 0.90 11.79*

Com2 2.83 1.06 0.98 14.32*Com3 3.58 0.87 0.43 6.49*

Note. Goodness of fit0.921; root mean square residual0.04; 2 (55) 93.66, Bentler index .92.*p .001.


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6. RESULTS AND DISCUSSION

Because the research model uses a dichotomous dependent variable and continu-ous independent variables, we decided to use a logit model to empirically validatethe research model. Logit analysis is a preferred technique because it does notassume equal variancecovariance matrices across groups and multivariate nor-mality of the variables [70]. Also, the output from the analysis is very similar toregression and is therefore easier to draw inferences. Logit uses a binomial prob-

ability function for the dichotomous dependent variable and estimates whether itis one way or the other using an odds ratio. Unlike regression, where we try tominimize the squared deviations, in logit we maximize the likelihood of a firmadopting the innovation. We can also assess the models predictive power using aclassification table, which is very similar to a classification table in discriminantanalysis. It determines the number of cases correctly and incorrectly classifiedusing the logit model. The significance of the independent variables is assessedusing the Wald statistic (similar to the t statistic in regression).

The results of the logit analysis are shown in Table 8. The results indicate thatthe goodness of fit of the overall model is very good. The Cox and Snell R2 valueis also sufficiently large, indicating that the overall model is significant. Among

the independent variables top management support, size, competitive advantage,and compatibility are significant determinants of adoption of computer-mediated


Table 6

Discriminant Validity

Constrained Unconstrained

Model Model

Test 2 df 2 df Difference p

Perceived usefulness with cost 28.46 5 4.57 4 23.89 .001

Perceived usefulness with compatibility 34.5 5 0.91 4 33.5 .001

Perceived usefulness with management support 49.32 9 18.72 8 30.6 .001

Perceived usefulness with competitive advantage 72.97 9 13.49 8 59.48 .001

Cost with compatibility 11.93 2 1.84 1 10.09 .001

Cost with management support 35.35 5 13.51 4 21.84 .001

Cost with competitive advantage 35.81 5 4.99 4 30.82 .001

Compatibility with management support 35.20 5 8.4 4 26.8 .001

Compatibility with competitive advantage 55.30 5 3.31 4 51.99 .001Management support with competitive advantage 42.10 9 9.69 8 32.41 .001

Table 7

Variable Statistics

Variable M SD Reliability Composite Reliability

Perceived usefulness 2.388 0.73 .71 .62

Cost 3.171 0.90 .75 .69

Compatibility 3.212 0.85 .78 .75

Management support 2.731 0.73 .70 .62

Competitive advantage 2.918 0.88 .64 .83


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communication technologies. The classification table, a measure of predictivepower of the model, shows that the predicted frequency is very good with anoverall accuracy of 80.3%.

6.1 Discussion

The most significant variable was competitive advantage. Communication tech-nologies, unlike other innovations, are interdependent innovations, which requirea firms business partners to also adopt the innovation. The utility of the innova-tion increases as more people adopt it. For example, the utility of e-mail increaseswhen one has more people to interact with using e-mail. Hence, there is someincentive for the early adopter to market the innovation among its trading part-

ners to increase the value of the innovation they adopted. Very soon it becomes astrategic necessity to have the innovation in order to compete in the marketplace.Whereas large firms realized the potential of communication technologies someyears ago, smaller firms are now realizing the strategic necessity of adopting thesetechnologies to survive in their business. The electronic integration driven by sup-ply chain and similar initiatives is making it imperative for even small firms toadopt these technologies to participate in the chain. For example, a small truckingfirm with very limited need for computer-mediated communications supportinstalled EDI to continue their business with a large shipper.

Another important factor in this study, which is consistently found to be criticalin IS implementation research, is top management support. In very small firms, the

primary decision maker is the owner of the business, and his or her vision forthe use of these technologies determines the level of support for adoption of the

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Table 8

Logistic Regression Resultsa

Variable Regression Coefficient Standard Error Wald Statistic Significance

Perceived usefulness 0.0016 0.338 0.00 .996

Cost 0.075 0.230 0.10 .743

Compatibility 0.428 0.253 2.85 .091

Management support 0.701 0.333 4.42 .035

Competitive advantage 1.15 0.284 16.39 .0001

Size 0.232 0.097 5.65 .017

Classification Table

Predicted

Observed Adoption No Adoption Yes Percent Correct

Adoption no 119 13 90.15

Adoption yes 26 40 60.61

Overall 80.30

aModel: 2(6) 97.13, p .0001, Cox and Snell R2 .388. Hosmer and Lemeshow goodness-of-fit:2(8) 6.745, p .564.


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innovation. For example, we came across two large farming operations on twoextreme ends of IS sophistication. One owner did not see any benefit from thetechnology, considered it as a nuisance, provided little support for IT, and there-

fore used it primarily for simple accounting. The other firm was totally automat-ed with computers being used to measure the feed, the weight gain of herds, andsophisticated forecasting techniques to forecast the herd output on a daily basis.They had electronic links with their customers to receive orders electronically andused the Internet to monitor prices. The owner claimed that IT has helped them to

be 10% to 20% more profitable than their competitors.The results indicate that size still plays an important role and larger firms in the

small business category have a greater propensity to adopt communication tech-nologies. In our study we had some really small firms who did not perceive much

benefit from these technologies.

Process compatibility was only marginally significant at the .09 level. Theincompatibilities in terms of business processes can be a significant deterrent toadoption. Cost was not a major factor in determining adoption. Probably the lowcost of hardware and software has reduced the impact of this variable. The price ofhardware has been declining for many years, and in recent years the price of soft-ware has also declined considerably to make it very affordable. Further, the cost ofestablishing communication links through a service provider has also come downdue to competition in the telecommunications industry. However, it should benoted that firms still face many hidden costs, as in the case of EDI, where costsincrease significantly if EDI has to be integrated with other internal IS applications.

It was surprising that perceived usefulness was not a significant factor in pre-

dicting adoption. Most prior studies on IS implementation had found it to besignificant. One possible explanation for the lack of significance is the overwhelm-ing influence of the competitive advantage variable. Some small firms are adopt-ing communications technologies because it has become a strategic necessity forsurviving in the business rather than because they perceive any direct benefitfrom using the technology. We can argue that the technology required to survivein the business is in itself a key market benefit of adoption. An examination of themean values of these variables in the two categories indicates that it is higher inthe adopter category. Although stories in the trade press and studies on IOS havefound these variables to be critical, we have not seen any study specifically in the

small business sector.

6.2 Future Research Directions

The review of prior research identified many new factors that need to be studied.In the environmental domain researchers can examine the impact of power of trad-ing partners and economic dependence on them on adoption of IT. The growth insupply chain management and IOS that tightly integrate a small firms operationswith a larger suppliercustomer makes this a critical variable influencing adoptionand diffusion of IT. Another interesting variable in the environmental domain is

the external support for adopting these technologies. Whereas studies in 1980sindicated that vendor support was not related to IS use or satisfaction, more recent



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studies [44, 55] indicated that external support is a significant variable. The short-age of IT personnel in the marketplace and the popularity of outsourcing areprompting many small firms to outsource their IS operations. We need to empiri-

cally examine whether this is a significant factor. Literature on outsourcing in largefirms has been inconclusive on the benefits of outsourcing [71]. Future studies couldexamine outsourcing in the context of small businesses.

It is surprising that some of the innovation factors were not significant in thisstudy. More research has to be conducted because these results are inconsistentwith some of the traditional IS research. One major reason for the lack of signifi-cance may be due to our focus on communication technologies. Small businesses,

being more reactive, are forced to adopt these technologies by the early adopters,who may be bigger and have greater power. Therefore, traditional notions of eval-uating the benefits of these technologies and their compatibility with existing sys-

tems may not be relevant in these contexts.The review identified many research opportunities in the individual and taskdomains. Future studies could examine the impact of many of these factors in thecontext of both individual- and organizational-level IS implementation.

This study examined only the adoption stage. Future studies could examine thediffusion of these technologies within the organization. Given that the impetus foradoption was from outside, it would be interesting to examine whether these tech-nologies diffuse into newer applications. Studies on EDI adoption have found thatmany small firms use a PC just as a window or a post box to link with their trad-ing partners without integrating the data in their internal applications. However,we have also noticed that the increasing demand and sophistication of these IOS

makes it necessary for small firms to integrate the IOS data in their internal appli-cations. The order data has to be linked up with the invoice and payment data forthese IOS systems to integrate the order-processing cycle across two partners.

7. CONCLUSION

One of the primary objectives of this study was to review the research literature onIS adoption and implementation in small business, examine the commonalities anddifferences with the traditional IS literature, and identify research gaps in the small

business area. A research framework, adapted from an organizational model, wasused to identify five major domains that influence IT adoption and use in organi-zations. The research on IS implementation was reviewed and the studies mappedto the research framework. A similar analysis was conducted for studies in thesmall business sector. A comparison of the studies in the two areas revealed thegaps in small business research and potential opportunities for future research.Most studies focused on factors having an impact on system usage or satisfaction.Very few studies examined the adoption of IT in small businesses and many fac-tors in the technology domain were not studied.

A research model was developed to explore the factors influencing the adoptionof computer-mediated communication technologies in small business. It incorpo-

rated some of the innovation factors that were identified as potential gaps in ourearlier analysis. The research model evaluated the impact of six factorsperceived

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usefulness, cost, compatibility, top management support, competitive advantage,and sizeon the adoption of computer-mediated communications technologies.The variables were measured using multi-item indicators and the data were collect-

ed using a large-scale field study. A telephone interview was used to collect datafrom 207 firms on various aspects of their organization, their use of communicationstechnologies, and the factors influencing their adoption of these technologies. Theresults of statistical analysis of the data reveal that competitive advantage, top man-agement support, and size were important determinants of adoption of communi-cation technologies.

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