organizational size and it innovation adoption - a meta-analysis (i &m 2006)

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Organizational size and IT innovation adoption: A meta-analysis

Gwanhoo Lee a,*, Weidong Xia b

a Kogod School of Business, American University, Washington, DC, USAb Carlson School of Management, University of Minnesota, Minneapolis, MN, USA

Received 8 September 2005; received in revised form 31 August 2006; accepted 17 September 2006

Available online 13 November 2006

Abstract

Organizational size has long been considered to be an important predictor of IT innovation adoption. However, empirical results

on the relationship between them have been disturbingly mixed and inconsistent. Through a meta-analysis of 54 correlations

derived from 21 empirical studies, we attempted to explain and resolve these mixed results by synthesizing across studies the effects

of organizational size on IT innovation adoption and by examining the effects of six moderators on the relationship. The results

suggested that, although a positive relationship generally existed between them, the relationship was moderated by five variables:

type of IT innovation, type of organization, stage of adoption, scope of size, and type of size measure. This suggested that the mixed

empirical results from previous studies can be explained by a lack of consideration of moderators.

# 2006 Elsevier B.V. All rights reserved.

Keywords: IT innovation adoption; Organizational size; IS department size; Meta-analysis; Moderating effect

1. Introduction

IT innovation has profound impacts on organizations.

As such, much research has been done to understand

organizational factors that influence it [20,28,66,70]. In

particular, organizational size has been studied as a

determinant of IT innovation adoption [25,39]. IT

innovation was defined as administrative or operational

idea, practice, or object perceived as new by an

organizational unit and whose underlying basis was IT

[41]. IT innovation adoption is an on-going organiza-

tional process rather than an isolated event [78]. As such,

it normally has multiple stages [12]. Organizational size

has been defined as the organization’s resources,

transaction volumes, or workforce size [37]. It is

therefore a surrogate for total and slack resources that

represent the organization’s economies of scales [44,45].

Despite past research, a consistent relationship

between organizational size and IT innovation adoption

has not been established. Researchers have found

positive, negative, and non-significant effects [19,67].

Two important factors contribute to the mixed and

inconsistent findings. First, studies often failed to

control important contextual variables that might

influence the direction and strength of the effect: a

uniform relationship may not exist between organiza-

tional size and IT innovation adoption and therefore,

studies may produce misleading conclusions if they use

organizational size without taking into consideration

important contextual variables. Second, organizational

size has been operationalized in various ways and they

capture different aspects/dimensions of it.

We intended to shed light on the mixed and

inconsistent findings through a meta-analysis of past

empirical studies. Meta-analysis overcomes inherent

www.elsevier.com/locate/imInformation & Management 43 (2006) 975–985

* Corresponding author. Tel.: +1 202 885 1991;

fax: +1 202 885 1992.

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

0378-7206/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2006.09.003

mailto:[email protected]

http://dx.doi.org/10.1016/j.im.2006.09.003

http://dx.doi.org/10.1016/j.im.2006.09.003

mailto:[email protected]



limitations and biases that may be present in individual

studies and discovers patterns of relatively invariant

relations and causalities. It also helps identify mod-

erators that explain and resolve mixed empirical

findings.

Our objectives were therefore to examine:

the cumulative, aggregate effect of organizational size

on IT innovation adoption;

the moderating effects of contextual variables on the

relationship;

the moderating effect of size measures on the

relationship.

2. Theoretical background

2.1. IT innovation adoption

IT innovations enable organizations to improve

productivity and quality and facilitate interorganiza-

tional collaboration and transactions [69]. IT innova-

tions are adopted as organizational responses to change

in internal and external environments, or as preemptive

actions to influence the environments.

IT innovation adoption has been studied from many

theoretical perspectives, including Rogers’ diffusion of

innovation theory [26], absorptive capacity [6], orga-

nizational learning [21], an evolutionary perspective

[50,51], push–pull theory [61], the tri-core model[27,68], network externality [2,3,30], institutional

theory [8,46], power perspective [32], and resource

dependence theory [33]. These theoretical perspectives

have provided complementary or alternative views on

organizational adoption of IT innovations.

Commonly studied determinants of IT innovation

adoption include environmental, organizational, and IS

factors, as well as innovation characteristics. Environ-

mental factors, in turn, include environmental uncer-

tainty, industry sector, and industry competitiveness

[7,10,56]. Organizational factors include firm size, top

management support, centralization, formalization, andthe presence of a champion [59,62]. IS factors include

IS department size, organization’s technology experi-

ence, IS investment, IS infrastructure, and the role of IT

in the organization [29,81]. Finally, innovation char-

acteristics include relative advantage, compatibility,

complexity, and cost [11,57].

2.2. Organizational size and IT innovation adoption

Our review of past IT innovation adoption research

suggested that organizational size, including both the

firm and IT department size, was one of the most

commonly studied determinants of IT innovation

adoption. Organizational size is considered important

for its structure and process: larger organizations tend to

be associated with greater differentiation [5], greater

formalization [58], more decentralized managerial

decision-making authority [31], greater task specializa-tion [4], and more complex forms of communications

[34]. These characteristics can profoundly influence the

processes in which organizations adopt innovation.

Many past studies on organizational innovation

suggest that organizational size should positively affect

organizations’ capability to adopt innovations, partly

because large organizations have more complex and

diverse facilities that contribute to the adoption [47,73].

Small businesses, on the other hand, suffer from

resource poverty [72] with tight IT budgets, a lack of in-

house IS personnel and expertise, and short-rangemanagement perspectives, resulting in more barriers to

IT innovation adoption. For example, a study of

executive information systems (EIS) suggested that

large organizations are more likely to adopt them [64].

However, the Schumpeterian hypothesis originally

stated that organizational size is negatively associated

with innovativeness [74]. Small organizations can be

more innovative because they have greater flexibility/

adaptability. Innovation often requires close collabora-

tion and coordination [43]. This can be achieved more

easily in small organizations [35].In addition to the overall firm size, IS department

size has been used as an important determinant of IT

innovation adoption; large IS departments tend to have

more technical resources and skills [23]. IS department

size represents IS resources, professionalism, and

capabilities that directly affect IT innovation adoption.

2.3. Moderators of the size–adoption relationship

Previous meta-analysis studies of organizational

innovation suggested that different research contexts

may influence the relationships between organizationalfactors and innovation adoption [9,14]. By taking into

consideration important moderators, research efforts

may explain the mixed empirical findings. We therefore

examined six moderators.

2.3.1. Type of innovation

As suggested by Downs [17], researchers have

distinguished product from process innovations [38,75].

We distinguished IT product innovations from IT

process innovations. The former refer to new IT

products or services that are internally developed or

G. Lee, W. Xia / Information & Management 43 (2006) 975–985976



externally purchased to meet user needs. These do not

necessarily require changes in IT or business processes.

Examples include computers and relational database

software [15,79]. In contrast, the latter refer to new

practices of IT or IT-enabled business processes that

organizations use to generate products and/or deliver

services. They do not necessarily require developmentor purchase of new IT products or services. Examples

include object-oriented programming and software

development practices such as those proposed in the

capability maturity model (CMM) [82].

While the distinction between IT product and

process innovation is useful, some IT innovations

cannot be classified into one type because they include

both components. For example, in Fuller and Swanson’s

study, information centers were considered as both. In

our meta-analysis, we categorized IT innovations with

both product and process innovation elements as mixedtypes.

2.3.2. Industry sector

The need for adopting IT innovations may vary across

industry sectors due to their levels of inherent informa-

tion intensity [55]. Furthermore, there are considerable

differences in the underlying dimensions of technology

and structure across industry sectors [42]. Therefore,

organizational size can significantly affect IT innovation

adoption in one sector while it has a weaker effect on

another one. Pavitt et al.[49] found that the size–adoptionrelationship was not uniform across different industries.

In our meta-analysis, we examine industry sector,

manufacturing versus service, as a moderator that could

have influenced the relationship.

2.3.3. Type of organization

Different types of organizations have different

incentives for and ways of adoption [52,65]. In

particular, non-for-profit organizations differ from

for-profit organizations in terms of environmental

demands, structural characteristics, managerial roles,

and decision-making processes. With an incentive toincrease market competitiveness, large for-profit orga-

nizations may utilize their resources for this purpose.

Some studies have suggested that the correlation

between organizational size and innovation adoption

was lower for non-for-profit organizations [1,16]. In our

meta-analysis, we incorporated type of organization,

non-for-profit or for-profit, as a moderator.

2.3.4. Stage of adoption

Cooper and Zmud [13] proposed six distinct stages in

the adoption and diffusion of IT innovation: initiation,

adoption, adaptation, acceptance, routinization, and

infusion. Such stages have been identified as an

important factor and the direction and strength of the

size–adoption relationship may differ across different

stages [54].

An increase in size appears to have a positive effect

on the adoption and implementation stages because of the increased availability of resources, capabilities, and

knowledge. However, post-adoption stages may depend

more on such factors as user perception of the

innovation, user participation in the innovation process,

and overall IS maturity of the organization than on the

scale of resources and capabilities. Therefore, organi-

zational size appears to have different effects on

different adoption stages [18,80]. As such, our meta-

analysis examined stage of adoption, adoption or post-

adoption, as a moderator.

2.3.5. Scope of size

Whereas firm size is a surrogate variable of an

organization’s overall resources and assets, IS depart-

ment size taps into IT-specific resources and capabilities

that directly affect IT innovation adoption [60].

Therefore, IS department size may be a better predictor

of IT innovation adoption than firm size. Our meta-

analysis examined the scope of size, firm or IS

department size, as a moderator.

2.3.6. Type of size measureSize measures tap into different aspects of organiza-

tional size, such as the resources that an organization

possesses (e.g., asset measures), transaction volumes

(e.g., sales volume or units produced), or work–force/

personnel size (e.g., number of employees) [24]. Use of

different size measures can lead to different results.

Thus discussions of organizational size and IT

innovation adoption may not be appropriate unless

the type of size measure is specified. We therefore

examined type of size measure, personnel or non-

personnel, as a moderator.

3. Research method

3.1. Study selection

We used ABI/INFORM to search for relevant

research in major IS and management journals

published in the time period from 1980 to 2004. The

keywords used in the search included combinations of

innovation, information technology, information sys-

tems, adoption, diffusion, implementation, post-imple-

mentation, post-adoption, infusion, routinization, and

G. Lee, W. Xia / Information & Management 43 (2006) 975–985 977



usage. The criteria for selecting studies in our meta-

analysis were:

(1) the focus was on IT innovation, not other types;

(2) the level of analysis was organization, not individual

or industry;

(3) the dependent variables included adoption, imple-mentation, diffusion, infusion, and usage;

(4) the study reported zero-order correlation estimates

for relationships between organizational size vari-

ables and innovation adoption variables.

We obtained 54 correlations of the size–IT innova-

tion adoption relationship, derived from 21 journal

publications. Multiple correlations from a single study

were included only if each correlation represented a

distinct combination of size and adoption variables.

Table 1 shows the distribution of studies by journal typeand time period. This indicated that research examining

the relationship between organizational size and IT

innovation adoption had been steadily increasing since

1980. The time period from 2000 to 2004 thus

represented a relatively strong portion of the total

research sample. In terms of journal type, most studies

were published in IS journals.

3.2. Coding of the studies

We independently coded moderators of each study.Each variable was coded as one of its pre-defined

dichotomous categories. When a combination of the

categories for a given moderator was used, the variable

was coded as ‘‘mixed’’. When information about a

moderator was not available, the variable was coded as

‘‘N/A (not available)’’.

Using this scheme, type of innovation was coded as

IT product, IT process, or mixed; industry sector was

coded as manufacturing, service, or mixed; type of

organization was coded as for-profit, non-for-profit,

or mixed; stage of adoption was coded as adoption,

post-adoption or mixed; scope of size was coded as

firm or IS department; type of size measure was coded

as personnel, non-personnel, or mixed. Adoption

stage included decision, diffusion, and implementa-

tion, whereas post-adoption stage included infusion,

routinization, and usage. Personnel size referred to the

number of employees of the organization, and non-personnel size was used for annual sales, size of

assets, or capacity.

After independently completing the coding, our

results were compared. The inter-rater reliability for the

initial codes was 89.7%, indicating a high level of

agreement between us. All differences in the initial

coding were resolved by discussion.

3.3. Meta-analysis procedure

The first step of our meta-analysis procedure was toaccumulate correlation coefficients across all studies.

Next, a mean correlation on the size–adoption

relationship, weighted by sample size, was calculated.

Then, an observed variance among correlations and the

variance due to sampling error were obtained. A 95%

confidence interval for the mean correlation was

computed based on the mean correlation, the number

of the correlations, and the variance due to sampling

error [36]. If a confidence interval did not include zero,

the size–adoption relationship was considered to be

statistically significant. In contrast, if the confidenceinterval included zero, the mean correlation was not

considered to be statistically different from zero, thus

indicating no significant effect of size on adoption.

To determine whether a testing of moderating effects

was warranted, the percentage of observed variance

explained by sampling error was computed. According

to Peters et al. [53], moderators should be introduced if

sampling error accounts for less than 60% of the

observed variance.

If the sampling error analysis indicated the need for

introducing moderators for the size–adoption relation-

ship, the effects of the moderators were tested.The total research sample was divided into subgroups

based on the code of a given moderator. Then, the same

meta-analysis procedure used for the total sample was

used to estimate a weighted mean correlation, an

observed variance, a sampling error variance, and

a 95% confidence interval for each subgroup. A

t -approximation test of the difference between the

mean correlations of a pair of subgroups for a

given moderator was conducted [76]. A significant

t -approximation value (t 0-value) indicated a significant

moderating effect.


Table 1

Distribution of empirical studies included in the meta-analysis

Journal type Time period Total

1980–1989 1990–1999 2000–2004

IS journals 2 5 3 10

Interdisciplinary

journals

2 3 3 8

Management

journals

0 2 1 3

Total 4 10 7 21




Table 2

Coding and correlation results for individual studies

Study Innovation Type of

innovation

Industry

sector

Type of

organization

Stage of

adoption

Scope

of size

Size

measure

Sample

size

Correlation

DeLone [15] Computer PD MFG FP ADPT FIRM P 55 0.056

Eder andIgbaria [19] Intranet PD Mixed FP ADPT FIRM P 281 0.030Mixed FP POST FIRM P 281 0.070

Fichman [20] OO programming PC N/A FP POST IS Mixed 608 0.330

N/A FP ADPT IS Mixed 608 0.280

N/A FP Mixed IS Mixed 608 0.360

RDB PD N/A FP Mixed IS Mixed 608 0.390

CASE Mixed N/A FP Mixed IS Mixed 608 0.440

Fichman and

Kemerer [21]

OO programming PC N/A FP Mixed FIRM P 608 0.150

N/A FP Mixed IS Mixed 608 0.390

Fletcher et al. [22] DB marketing PC SVC FP POST FIRM P 46 0.056

SVC FP POST FIRM P 46 0.151

SVC FP POST FIRM P 46 0.036

SVC FP POST IS P 46 0.010

Fuller and

Swanson [23]

Information centers Mixed Mixed Mixed POST FIRM P 26 0.070

Mixed Mixed POST FIRM P 25 0.380

Mixed Mixed POST IS P 27 0.260

Gremillion [25] Computer system PD SVC NFP POST FIRM NP 66 0.090

SVC NFP POST FIRM NP 66 0.160






SVC NFP POST FIRM P 66 0.050

Liberatore and

Breem [39]

Digital imaging

technology

PD SVC FP ADPT FIRM P 137 0.266

Liberatore and

Pollack-

Johnson [40]

Project management

software

PD Mixed Mixed POST FIRM P 240 0.047




Nystrom et al. [48] Imaging technology PD SVC FP ADPT FIRM NP 70 0.570

Rai [59] CASE (front-end) Mixed Mixed Mixed ADPT IS P 140 0.320

CASE (back-end) Mixed Mixed Mixed ADPT IS P 108 0.310

Rai and Bajwa [60] EIS PD Mixed Mixed ADPT FIRM P 43 0.280

Mixed Mixed ADPT IS P 42 0.330

Raymond [63] Information systems PD MFG FP POST FIRM P 34 0.280

MFG FP POST FIRM P 34 0.020

MFG FP POST FIRM P 34 0.310

Seyal and

Rahman [67]

E-commerce Mixed SVC FP ADPT FIRM P 95 0.190

Teo et al. [69] Financial EDI Mixed Mixed FP ADPT FIRM P 222 0.157

Mixed FP ADPT IS P 222 0.177

Thong [71] Information systems PD Mixed FP ADPT FIRM P 166 0.364

Mixed FP POST FIRM P 120 0.472

Thong and

Yap [72]

IT PD Mixed FP ADPT FIRM P 166 0.272

Wang and

Cheung [77]

E-business Mixed SVC FP ADPT FIRM P 137 0.310



4. Results

4.1. Coding and correlation results for individual

studies

Table 2 shows focal IT innovations, coding of

moderators, sample size, and correlations of the size–

adoption relationship for the individual studies in our

sample. A variety of IT innovations were studied,

ranging from IT product innovations to IT process

innovations and IT mixed innovations. IT product

innovations were examined in more studies (11) than IT

mixed innovations (5) and IT process innovations (4).More studies examined a combination of manufacturing

and service sectors (10) than either service sector (7) or

manufacturing sector (2). More studies investigated for-

profit organizations (14) than non-for-profit organiza-

tions (6). Adoption stage was more often studied (11)

than post-adoption stage (6). Three studies examined

both adoption stage and post-adoption stage. A majority

of studies investigated firm size (12), four studies

examined IS department size and five studies examined

both firm size and IT department size. Personnel size

measure (16) was used more frequently than non-

personnel size measure (2). Three studies used bothpersonnel and non-personnel size measures.

Sample size ranged from 25 to 638 across studies

with a median sample size of 116. Correlations of the

size–adoption relationship ranged from 0.300 to

0.570: 41 correlations were positive and 13 correlations

were negative, confirming mixed, inconsistent effects.

4.2. Size–adoption relationship for the total sample

Using the overall sample and 54 correlations, we

analyzed a cumulative correlation of the size–

adoption relationship. Results are shown in Table 3.

The cumulative sample size of all studies was 9419.The mean correlation was 0.2265 and the observed

variance was 0.0370. The variance due to sampling

error was 0.0052. The 95% confidence interval for the

mean correlation did not include zero (0.2073,

0.2457). Therefore, the analysis results of the

cumulative correlations across studies revealed a

significant, positive relationship between organiza-

tional size and IT innovation adoption. However, the

observed variance explained by sampling error was

only 14%. With the major portion of the observed

variance unexplained, this suggested that a test of moderators on the size–adoption relationship was

warranted.

4.3. Moderating effects

We conducted further meta-analyses for all sub-

groups based on the coding schemes used for each of the

six moderators. For each subgroup, Table 4 shows the

number of correlations, sample size, mean correlation,

observed variance, variance due to sampling error,

percentage of the observed variance explained by

sampling error, and 95% confidence interval of thecorrelation. Subgroups showed a wide range of number


Table 2 (Continued )

Study Innovation Type of

innovation

Industry

sector

Type of

organization

Stage of

adoption

Scope

of size

Size

measure

Sample

size

Correlation

Yap [79] Computer PD SVC FP ADPT FIRM NP 638 0.420

Zmud [81] Software practices PC Mixed Mixed ADPT IS P 49 0.300

PC Mixed Mixed ADPT IS P 49 0.230

PC Mixed Mixed ADPT IS P 49 0.110PC Mixed Mixed ADPT IS P 49 0.250



Zmud [82] Software practices PC Mixed Mixed POST IS P 47 0.320

PC Mixed Mixed POST IS P 47 0.150

Note: type of innovation: PD (product), PC (process); industry sector: MFG (manufacturing), SVC (service); type of organization: FP (for-profit),

NFP (non-for-profit); stage of adoption: ADPT (decision/diffusion/implementation), POST (infusion/routinization/usage); scope of size: FIRM

(firm), IS (IS department); type of size measure: P (personnel), NP (non-personnel).

Table 3

Meta-analysis results for the total sample

Number of correlations 54

Sample size 9419

Mean correlation 0.2265

Observed variance 0.0370

Sampling error variance 0.0052

Explained variance (%) 14

95% confidence interval (0.2073, 0.2457)



of correlations (4–38), sample size (157–4798), and

mean correlation (0.0063 to 0.3269). Furthermore,

results indicated that the introduction of moderators

significantly increased the percentage of observed

variance explained by sampling error for the manu-facturing sector subgroup and for the non-for-profit

organization subgroup. However, the other subgroups

still showed an inadequate percentage of the observed

variance explained by sampling error.

To assess the significance of the effects of the

moderator variables, we used t -approximation tests that

examine the differences in mean correlations between

subgroups of the corresponding moderator. Results are

shown in Table 5.

For all three subgroups categorized by type of IT

innovation, the 95% confidence intervals for the mean

correlation were positive and did not include zero,suggesting a positive relationship between organiza-

tional size and IT innovation adoption for all three

groups. As indicated by significant t 0-values, correla-

tions for the IT process innovation subgroup and the IT

mixed innovation subgroup were significantly higher

than that for the IT product innovation subgroup. We

found no significant difference in the mean correlations

between the IT process innovation subgroup and the IT

mixed innovation subgroup. These results indicated that

the strength of the size–adoption relationship differed

for different types of IT innovation.

The two subgroups by industry sector showed

positive size–adoption relationships as their 95%

confidence intervals were positive and did not include

zero. Furthermore, although the mean correlation of the

size–adoption relationship for the service sector wasslightly higher than that for the manufacturing sector,

the difference was not statistically significant. This

indicated that industry sector was not a significant

moderator of the size–adoption relationship.

The for-profit organization subgroup showed a

significant positive size–adoption relationship, whereas

the non-for-profit organization subgroup showed a non-

significant one. We found a significant difference in the

mean correlations between the for-profit organization

subgroup and the non-for-profit organization subgroup.

These results suggested that the type of organization

moderated the direction and strength of the relationshipbetween organizational size and IT innovation adoption.

The 95% confidence intervals for both the adoption

stage subgroup and the post-adoption stage subgroup

were greater than zero, indicating significant and

positive effects. The mean correlation for the adoption

stage subgroup was significantly higher than that for the

post-adoption subgroup. These results indicated that

size was a more significant predictor of IT innovation

adoption in the early stages (e.g., adoption decision and

implementation) of the innovation adoption lifecycle

than in the later stages (e.g., infusion and usage).


Table 4

Meta-analysis results for subgroups by moderators

Subgroup by

moderator

No. of

correlations

Sample

size

Mean

correlations

Observed

variance

Sampling

error variance

Explained

variance (%)

95% confidence

interval

Type of innovation

Product 27 4197 0.1714 0.0495 0.0061 12 (0.1420, 0.2009)

Process 17 3612 0.2633 0.0202 0.0041 20 (0.2328, 0.2937)Mixed 10 1610 0.2874 0.0273 0.0053 19 (0.2424, 0.3323)

Industry sector

Manufacturing 4 157 0.1429 0.0185 0.0185 100 (0.0096, 0.2762)

Service 17 1789 0.1594 0.0720 0.0091 13 (0.1140, 0.2048)

Type of organization

For-profit 27 7132 0.2802 0.0271 0.0032 12 (0.2587, 0.3016)

Non-for-profit 8 528 0.0063 0.0217 0.0154 71 (0.0922, 0.0797)

Stage of adoption

Adoption 22 3424 0.2277 0.0377 0.0058 15 (0.1958, 0.2596)

Post-adoption 27 2955 0.1021 0.0335 0.0090 27 (0.0662, 0.1379)

Scope of size

Firm 34 4798 0.1297 0.0404 0.0069 17 (0.1018, 0.1576)

IS organization 20 4621 0.3269 0.0136 0.0035 25 (0.3011, 0.3527)

Type of size measure

Personnel 38 4546 0.1094 0.0297 0.0082 28 (0.0805, 0.1382)

Non-personnel 9 1170 0.2575 0.0590 0.0068 11 (0.2038, 0.3112)



Both the firm size subgroup and the IS department

size subgroup revealed positive 95% confidence

intervals. The mean correlation of the size–adoption

relationship for the IS department size subgroup was

significantly higher than that for the firm size subgroup.

This result indicated that scope of size was an important

moderator of the strength of the size–IT innovation

adoption relationship and that IS department size was a

stronger predictor of IT innovation adoption than firmsize.

Finally, the 95% confidence intervals for both the

personnel size subgroup and the non-personnel size

subgroup were positive and did not include zero,

indicating significant, positive size–adoption relation-

ships. The difference in the mean correlations between

the personnel size subgroup and the non-personnel size

subgroup was significant only at the 0.1 significance

level. The results thus suggested that type of size

measure was a marginal moderator of the size–IT

innovation adoption relationship and that non-personnel

size measure was a marginally stronger predictor of ITinnovation adoption than personnel size measure.

5. Discussion and conclusions

5.1. Limitations of the research

Care must be taken in interpreting our findings

because of some limitations of our research. Some

moderator subgroups included only a few correlations

of the size–adoption relationship. The statistical power

to detect moderating effects for these subgroups was

relatively low. Therefore, non-significant moderating

effects found for those subgroups with a few correla-

tions need to be accepted only tentatively. For example,

the manufacturing sector subgroup included only four

correlations and the non-personnel size subgroup

included nine. Although we found an insignificant

moderating effect of industry sector and only a

marginally significant moderating effect of type of size

measure, it is therefore premature to draw definiteconclusions.

We excluded a number of past studies because they

did not report zero-order correlation coefficients of the

size–adoption relationship. Also, some studies in our

sample did not provide information about its industry

sector. As a result, our findings were based only on data

available in our sample.

5.2. Implications for research and practice

Our work has important implications for IT

innovation adoption research. The synthesis of thecumulative correlations across empirical studies

revealed that organizational size had a positive effect

on IT innovation adoption: larger organizations gen-

erally adopt more IT innovations than do smaller

organizations.

We examined six moderators that would influence

the relationship between organizational size and IT

innovation adoption. Type of innovation, type of

organization, stage of innovation adoption, and scope

of size were found to be significant moderators of the

size–adoption relationship. Type of size measure was


Table 5

t -Approximation tests for moderating effects

Moderator and subgroups Result t 0-Value

Type of innovation

Product vs. process Product < process 3.624**

Product vs. mixed Product <mixed 3.861**

Process vs. mixed Not significant 0.614

Industry sector

Manufacturing vs. service Not significant 0.829

Type of organization

For-profit vs. non-for-profit For-profit> non-for-profit 6.916**

Stage of adoption

Adoption vs. post-adoption Adoption > post-adoption 5.790**

Scope of size

Firm size vs. IS department size Firm size < IS department size 8.746**

Type of size measure

Personnel vs. non-personnel Personnel < non-personnel 1.949y

* p < 0.05, ** p < 0.01, y p < 0.1.



found to be only a marginal moderator. More

specifically, the positive effect of organizational size

on IT innovation adoption was stronger for IT process

innovations, IT mixed innovations, for-profit organiza-

tions, adoption stages, IS department size, and non-

personnel size measures. However, industry sector was

not found to be a significant moderator.Furthermore, it is worth noting that only the non-for-

profit organization subgroup showed a non-significant

effect while all other subgroups showed positive effects.

This result indicated that organizational size may not be

an advantage in adopting IT innovations for non-for-

profit organizations.

In sum, our results suggest that the direction and

strength of the relationship between organizational size

and IT innovation adoption depends on type of

innovation, type of adoption organization, adoption

stage, scope of size measure, and type of size measure.This research also has important implications for

practice. Managers should be aware that the effect of

their organization’s size on IT innovation adoption

depends on the specific contexts under which the IT

innovation is being adopted and on how size is

measured. Managers need to strategize their approach

to adopting an IT innovation based on various

moderators specific to the innovation. Managers can

use our results to predict the likelihood of the adoption

of an IT innovation by analyzing moderators and by

assessing the size of their organization. Importantly,there is no one-size-fit-all relationship between orga-

nizational size and IT innovation adoption. In order to

increase IT innovation adoption success, managers need

to understand and effectively manage those important

moderators before they make adoption decision. It is

important to distinguish adoption decisions regarding

IT process innovations from those for IT product

innovations. While organizational size does not affect

IT product innovations, managers must recognize the

advantages and disadvantages of their size in the

adoption of IT process innovation.

Another important finding was that organizationalsize had different effects on IT innovation adoption,

depending on the specific stages of the adoption

process. While large organizations tend to have

advantage in the early stages, they face critical

challenges in the latter ones. As such, to achieve

success, managers in large organizations should under-

stand and effectively manage the later adoption stages.

Managers should conduct benchmarking analyses

with comparable samples based on both appropriate

size measures and contextual variables in order to assess

the effectiveness and performance of IT innovation

adoption. With this improved understanding, managers

can assess desirable levels of managerial intervention

and investment required for IT innovation to be adopted

successfully.

Acknowledgements

This research was funded by research grants from the

UPS (United Parcel Service) Foundation, Center for

Information Technology and Global Economy in Kogod

School of Business at American University, and the

University of Minnesota.

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Gwanhoo Lee is an assistant professor of

information technology and UPS scholar at

the Kogod School of Business, AmericanUniversity, where he is also an assistant

director of the Centerfor Information Tech-

nology and Global Economy. He earned his

doctorate in management information sys-

tems from the University of Minnesota. His

research focuses on IS project manage-

ment, IT-enabled organizational agility,

global software teams, outsourcing and offshoring, technology adop-

tion, strategic role of IT, and interorganizational systems. He has been

working on collaborative research projects involving companies such

as 3M, A.G. Edwards, Cargill, McDonald Food, Marriott, Medtronic,

Pillsbury, St. Paul Cos, and the World Bank. His research has been

published in Journal of Management Information Systems, Commu-

nications of the ACM, European Journal of Information Systems,Information Technology & People, and conference proceedings such

as ICIS, HICSS, and AMCIS.

Weidong Xia is an assistant professor in

the Carlson School of Management at the

University of Minnesota. His research

focuses on IT strategy, organizational cap-

abilities and business alignment; IT project

complexity and flexibility; IT adoption

decisions. His writings have been published

or areforthcoming in a numberof academic

journals and international conferences

including: Communications of the ACM,Decision Sciences, European Journal of Information Systems, Inter-

national Journal of Career Development, Journal of Management

Information Systems, MIS Quarterly, Journal of Statistics and Man-

agement Systems, Journal of End-User Computing, and International

Conference on Information Systems. At the University of Minnesota,

he has taught nine different courses at PhD, Executive, MBA, and

undergraduate levels. He has served as MIS undergraduate and MBA

program coordinators. He has been working closely with executives

from companies such as 3M, A.G. Edwards, Ahold, Cargill, McDo-

nald’s, Medtronic, Musicland, Northwest Airlines, Pillsbury,

ShopNBC, St. Paul Cos., Target, Union Pacific, US Bank and US

Cellular on issues related to his research areas. He received his

doctorate in information systems from the University of Pittsburgh.


organizational size and it innovation adoption - a meta-analysis (i &m 2006)

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