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MODELING THE RELATIONSHIP BETWEEN FIRM IT CAPABILITY,

COLLABORATION, AND PERFORMANCE

by

Nada R. Sanders

Wright State University

and

Robert Premus

Wright State University

Advancements in the capability of information technology (IT) have rapidly changed the face

of industry over the past decade. Functions that have been particularly impacted, at least from a the-

oretical perspective, are supply chain management (SCM) and logistics. Supply chain manage-

ment, founded on collaboration between supply chain partners (Golicic, Foggin, and Mentzer 2003;

Narasimhan and Jayaram l998; Stank, Keller, and Daugherty 2001), is intended to bring performancebenefits to the organization. Consider the recent collaborative relationship between Sears and

Michelin which has resulted in a 25% reduction in inventories for both companies (Steerman 2003).

Similarly, General Motors’new collaborative relationship with its suppliers has reduced vehicle devel-

opment cycle times from four years to eighteen months (Gutman 2003). This type of collaboration

is only made possible through the sharing of large amounts of information along the supply chain,

including operations, logistics, and strategic planning data.

Information sharing provides firms with forward visibility, improving production planning, inven-

tory management, and distribution. This collaboration is facilitated by the existence of an efficient

and effective information technology (IT) system. Information technology (IT), which allows for

the transmission and processing of information necessary for synchronous decision making, can be

viewed as the backbone of the supply chain business structure (Grover and Malhotra l999; Kent and

Mentzer 2003). For this reason the literature often refers to IT as an essential enabler of SCM activ-

ities (Mabert and Venkataramanan l998).

Advancements in IT capabilities have significantly improved the extent of internal and exter-

nal organizational information sharing. IT capability has been positively linked to firm perfor-

mance (Bharadwaj 2000; Kearns and Lederer 2003) and shown to have the potential of providing

a significant competitive advantage to firms (Earl l993; Ives and Jarvenpaa l99l; Kathuria, Anan-

darajan, and Igbaria l999). Similarly, firm collaboration has been shown to have a positive impact

on performance (Stank, Keller, and Daugherty 2001). However, the collective relationship between

JOURNAL OF BUSINESS LOGISTICS, Vol. 26, No. 1, 2005 1



IT capability, firm collaboration, and firm performance has not been fully documented. Given the

large expenditures IT efforts require and the prolific nature of information technology, calls havebeen made for more empirically based research to provide a greater understanding of the role of IT

in organizations (Lewis and Talalayevsky l997; McAfee 2002).

The purpose of this research is to contribute to this knowledge by proposing and testing a

model of the relationship between firm IT capability, extent of internal and external organizational

collaboration, and firm performance. The model and constructs used are directly derived from the

literature. The findings of the study have important implications for managers as they evaluate

investment in information technologies.

PREVIOUS RESEARCH

The impact of IT on the firm as a whole has been studied at length (Brynjolfsson and Hitt

l996; Davenport l992). This research has ranged from studying the alignment of specific IT appli-

cations with the organizational competitive priorities (Kathuria, Anandarajan, and Igbaria l999) to

comparisons of the effectiveness of specific IT applications (Raghunathan l999). IT research relat-

ing to topics within the SCM domain has been primarily strategic in nature. Studies have suggested

that significant differences in SCM strategies and practices can be expected based on higher IT

usage and capability. Studies by Bowersox and Daugherty (l987; l995) identify information tech-

nology as one of the most common factors associated with advanced supply chain practice. Clin-ton and Closs (l997) used the Bowersox and Daugherty (l995) typology to relate firm practice to

strategy specifications. Their findings confirm differences in the supply chain strategies of firms based

on information technology use.

The importance of IT in SCM is further documented through the extended enterprise model,

developed by Bowersox and Daugherty (l995), Bowersox, Closs, and Stank (l999), and modified

by Edwards, Peters, and Sharman (2001). The model identifies key attributes of firms moving

toward world-class logistics, with an integrated IT system identified as a key component of this frame-

work. The highest level firms within this framework are those that operate seamlessly across bound-

aries due to IT capability that enables information to flow freely in real time.Although research supports the idea of IT as an enabler of SCM activities and documents its

role in supply chain strategy, studies have not directly associated higher IT usage with greater

involvement in specific SCM practices. SCM practices encompass a spectrum of activities, some

internal and some external to the firm, all with the primary goal of creating value to the end-customer

(Copaciano l997; Kahn and Mentzer 1996). This is accomplished through coordination of activities

between linked firms, and should result in reduced costs due to the elimination of operational dupli-

cation and resource waste (Andraski l998; Stank, Keller, and Daugherty 2001). This requires engag-

ing in collaboration that is both internal and external to the organization (Stank, Keller, and Daugherty

2001).

2 SANDERS AND PREMUS



Internal organizational collaboration requires cross-functional planning, coordination, and

sharing of integrated data bases. External organizational collaboration requires sharing of informationacross the full range of supply chain participants, as well as sharing of internal cross-functional

processes (Schrage l990). Higher levels of both internal and external organizational collaboration

are expected to increase coordination of operations and logistics processes between organizations.

Further, higher levels of coordination are expected to contribute to improved organizational per-

formance (Bowersox and Daugherty 1995; Sheombar l992).

In a London School of Economics survey, CEOs rated IT as the firm’s top strategic tool (Com-

pass Group l998). However, the CEOs asserted that the source of competitive advantage was not tech-

nology per se, but superior information sharing that these systems offer. Although research supports

the idea of information technology as an enabler of SCM activities and documents its role in sup-

ply chain strategy, studies have not documented the relationship between IT capability, collabora-

tion, and firm performance.

CONCEPTUAL MODEL AND RESEARCH HYPOTHESES

We propose a conceptual model, shown in Figure 1, of the relationship between firm IT capa-

bility, internal and external collaboration, and firm performance. We define IT capability as tech-

nological capability used to acquire, process, and transmit information for more effective decision

making, relative to competitive standards (Grover and Malhotra l999). Our model shows IT capa-bility as a factor influencing the organization’s internal and external collaboration, two distinct

collaboration constructs. Internal collaboration is a construct defined as an affective, mutually

shared process where two or more departments work together, have mutual understanding, have a

common vision, share resources, and achieve collective goals (Schrage, l990). External collabora-

tion is defined similarly to internal collaboration, with the exception that the focus of collaboration

is between two or more firms, rather than departments.




FIGURE 1

PROPOSED CONCEPTUAL MODEL

The relationship between IT and internal and external collaboration has been assumed by past

studies (Raghunathan l999), though it has not been directly tested. Studies have, however, tested the

relationship between IT and other constructs related to collaboration, such as relationship com-

mitment (Kent and Mentzer 2003). For example, a study by Kent and Mentzer (2003) found a

strong and positive relationship between investment in information technologies and relationship

commitment between channel partners. Other researchers have demonstrated that IT can decrease

coordination costs (Clemons, Reddi, and Row 1993; Clemons and Row l992), and is expected to bring

about increased coordination (Vickery et al. 2003). These studies collectively support the development

of our first two hypotheses that assume a positive impact of IT on both internal and external col-

laboration:

H1: Firm IT capability (IT) has a direct and positive impact on internal collaboration (IC).

H2: Firm IT capability (IT) has a direct and positive impact on external collaboration (EC).

A review of the IT literature reveals mixed results with respect to the impact of IT on firm

financial performance (Hu and Plant 2001). A study by Hitt and Brynjolfsson (l996) finds that the

inconsistencies observed among various studies can be attributed to variations in methods andmeasures used in the analyses. Most recent studies, however, have found support for the direct

impact of IT on firm financial performance (Bharadwaj 2000; Kearns and Lederer 2003; San-




thanam and Hartono 2003). Based on these studies, we can expect IT capability to be significantly

and positively related to firm performance, defined as success relative to specifically set businessgoals (Kearns and Lederer 2003). This leads to the third hypothesis:

H3: Firm IT capability (IT) has a direct and positive impact on firm performance (FP).

The proposed model further posits that external collaboration directly impacts internal col-

laboration, which in turn directly impacts firm performance. The relationship between internal and

external collaboration in the current model is consistent with the latest studies. Subramani (2004)

argues that the collaboration and association suppliers develop with buyers are in fact directly con-

strained by communication within the supplier firm. In effect, internal communication serves to medi-

ate buyer-supplier collaboration. Stank, Keller, and Daugherty (2001) also found external collaboration

to directly influence internal collaboration. Similarly, a recent study of the impact of IT on supply

chain collaboration (Vickery et al. 2003) assumes that collaboration within the firm and between firms

are equally subsumed under the construct of supply chain integration. The authors argue that the var-

ious internal functions comprising a company are essentially a part of the supply chain. The proposed

model tests this relationship and leads to our next hypothesis:

H4: External collaboration (EC) has a direct and positive impact on internal collabora-

tion (IC).

Finally, higher levels of coordination are expected to contribute to improved organizational

performance (Bowersox and Daugherty 1995). Stank, Daugherty, and Ellinger (l999) provideempirical support for a link between internal integration and customer service performance. Their

study found significant differences in the elements of customer service performance for firms with

higher levels of integration. Similarly, Stank, Keller, and Daugherty (2001) found internal collab-

oration to impact firm performance. This leads us to the final hypothesis:

H5: Internal collaboration (IC) has a direct and positive impact on firm performance (FP).

RESEARCH METHODOLOGY

The Sampling Procedure

Data for this study were obtained from a survey of U.S. manufacturing firms. The survey

instrument was initially pre-tested by six executives and five academics. The individuals were

asked to review the questionnaire for readability and ambiguity (Dillman 2000). Based on results

of the pre-test, minor changes were made to select questionnaire items. The instrument was then mailed

to 2,000 U.S. industrial companies. As the information targeted was strategic in nature, the survey

instrument was sent to the highest ranking officer of primarily large manufacturing companies with

annual sales in excess of $4.5 billion. This is supported by a study by Phillips (l981) that indicates

high ranking informants tend to be more reliable sources of information than low ranking. Thedatabase was purchased with these criteria specified from American Business Lists. The current study




focused on large firms typically seen as leaders in innovative practices, such as ITand SCM. We also

hoped to prevent firm size from having a confounding effect on the findings.

A variation of Dillman’s total design method was used (Dillman 2000) in order to ensure

adequacy of response. The initial mailing included a cover letter and the survey instrument, with the

latter designed to be merely folded and returned, with postage pre-paid. Reminder postcards were

sent approximately ten days following the initial mailing, followed by a second survey mailing

approximately 30 days later. Those that had already responded were told to ignore the mailing.

Twenty incomplete responses were discarded. The mailings yielded 245 usable responses, for a

response rate of 12.3%.

Although the response rate is low, the rate is similar to other surveys sampling senior officers

(Wisner 2003). The typical respondent to the survey held the title of President, CEO, Vice President,

or Director of Procurement and Purchasing, as shown in Table 1. Even with a low response rate, 245

responses from senior officers can provide valuable insight, provided that non-response bias is not

an issue. Accordingly, the analysis that follows and all reported statistics are based on a sample of

245 manufacturing firms. Specific demographic information of the sample is shown in Table 2.

TABLE 1

PROFILE OF SURVEY RESPONDENTS

Respondent Title Frequency Percentage

1. President 19 8

2. CEO 29 12

3. Vice President 76 31

4. Director 93 38

5. Senior Manager 11 5

6. Other 17 6

245 100%




TABLE 2

SAMPLE DEMOGRAPHIC

Industry Sub-Sector Frequency Percentage

1. Miscellaneous Manufacturing 61 25

2. Electrical/Electronic Equipment 44 18

3.Chemical 37 15

4. Fabricated Metal 27 11

5. Rubber and Plastic 27 116. Computer/Electronic Equipment Manufacturing 13 5

7. Machinery Manufacturing 7 3

8. Transport Equipment Manufacturing 6 2

9. Apparel Manufacturing 3 1

10. Food Manufacturing 2 1

11. Furniture and Related Product Manufacturing 2 1

12. Beverage Manufacturing 2 1

13. Wood Product Manufacturing 2 1

14. Paper Manufacturing 2 1

15. Other 10 4

245 100%

Test for Non-Response Bias

A concern with any survey methodology is the adequacy of the response sample. One method

to assess non-response bias is to test for significant differences between early and late respondents

(Armstrong and Overton l977). In order to ensure adequacy of our data, the first and second waveof respondents were compared. T-tests were performed on all fifteen questionnaire items used in this

study, with no significant differences found between the two samples. Further, chi-square differences

were calculated between respondents and non-respondents for annual sales revenues (2 = 4.54,

p > 0.05), number of employees (2 = 6.23, p > 0.05), and industry (2 = 6.82, p > 0.05), and found

to be insignificant. These results collectively suggest that non-response bias is not present in the data

(Sabherwal l999; Teo and King l997).




Measure Development and Purification

Measures for the factors were derived from the literature and are shown in Table 3. These

measures were developed based on procedures outlined by Churchill (l979) and DeVellis (l99l).

In this section we describe the procedure used to purify the measurement scales and ensure scale

adequacy.

TABLE 3

VARIABLE AND FACTOR LISTING

Factors and Scale Items1 Standardized Standard t-Value

coefficient error

F1: Firm IT capability (IT); ␣ = .874

Please indicate your firm’s…

IT1: IT capability relative to industry standard 0.712 0.021 11.32*

IT2: IT capability relative to key competitors 0.732 0.029 11.21*

IT3: IT capability relative to key customers 0.719 0.023 10.43*

IT4: use of information networks with key suppliers 0.722 0.022 12.12*

F2: Internal Collaboration (IC); ␣ = .738Please indicate the extent to which your firm …

IC1: uses cross-functional collaboration in strategic planning 0.429 0.029 16.23*

IC2: utilizes integrated database for information sharing 0.528 0.023 16.19*

IC3: shares operations information among departments 0.531 0.027 16.01*

F3: External Collaboration (EC): ␣ = .842

Please indicate the extent to which your firm …

EC1: shares operations information with suppliers 0.632 0.031 14.56*

EC2: shares cross-functional processes with suppliers 0.741 0.035 15.71*

EC3: engages in collaborative planning with suppliers 0.716 0.041 14.23*

EC4: shares cost information with suppliers 0.676 0.032 15.65*

F4: Firm Performance (FP): ␣ = .762

Please indicate your firm’s…

MP1: cost improvement relative to performance goals 0.734 0.027 12.36*

MP2: product quality improvement relative to performance goals 0.628 0.028 13.45*

MP3: new product introduction time relative to performance goals 0.612 0.036 11.51*

MP4: delivery speed improvement relative to performance goals 0.548 0.031 11.38*

1All developed using 5-point Likert scale; where 1 = strongly agree, 5 = strongly disagree; exception are

IT1-IT4 where 1 = significantly above standard; 3 = comparable to standard; 5 = significantly below standard;

*Significance at the p ≤ 0.01 level




An important measure of scale adequacy is scale reliability. Scale reliability is the percent of

variance in an observed variable that is accounted for by the true score of the latent factor or under-lying construct (DeVellis l99l). When scale reliability is high, then all variables that measure a sin-

gle factor share a high degree of common variance. Scale reliability can be measured in different

ways. However, a commonly employed statistic is Cronbach’s coefficient alpha, which measures

the degree of inter-item correlation in each set of items and indicates the proportion of the variance

in the scale scores that is attributable to the true score. Alpha levels below 0.7 are considered unac-

ceptable (DeVellis, l99l).

Prior to purification of scales, the alpha levels in this study indicated that some values were below

the acceptability level. Purification of scales was then performed through the use of confirmatory

factor analysis. Scale items exhibiting insignificant factor loadings or high residuals were then

identified and eliminated from the factor measurement (DeVellis, l99l). Following the purification

process alpha levels ranged from 0.738 to 0.874.

Construct Measures

Table 3 shows the four model factors and the multiple variables used to measure each factor.

The scale items used to measure these factors are derived from past studies and are described in this

section.

Factor 1 measures firm IT capability. The development of scale items for this factor needed totake into account the definition of IT, as it can have different meanings to different constituency groups.

We defined information technology as technology used to acquire, process, and transmit informa-

tion for more effective decision making (Grover and Malhotra l999). Four scale items were used to

evaluate firm IT capability: IT capability relative to industry standards, relative to key competitors,

relative to key customers, and the level of information networks used with key suppliers. The scale

items are comparable to those used in a study by Kent and Mentzer (2003). While the scale items

in the Kent and Mentzer (2003) study asked respondents whether the company is a “leader” or “on

the leading edge” of information technology, our study asks how the company compares relative to

industry standards and competition in the use of information technology.Factor 2 measures internal organizational collaboration. Three scale items measure this factor:

cross-functional collaboration in strategic planning, utilization of an integrated database for infor-

mation sharing, and sharing of operations information among departments. These variables measure

the existence of collaboration and support for information sharing. The first variable, in particular,

focuses on collaboration at the strategic level. The importance of aligning functional strategies

with those of the firm and making them part of the overall strategy has been well documented in the

literature (Hayes and Wheelwright l984; Hill 2000; Skinner l969). True strategy formulation requires

active participation of functional strategies in order to ensure that the functional strategies are

aligned with the overall business strategy (Hill 2000). The scale items used here are comparable tothose used to measure internal collaboration in past studies (Stank, Keller, and Daugherty 2001).




A fourth scale item was initially introduced for Factor 2, but eliminated during the scale purifi-

cation process. This item measured the direct role of SCM in the strategic planning process of thefirm. Direct SCM involvement in strategic planning is considered a necessary component of mean-

ingful SCM implementation. Internal collaboration is largely driven by SCM, which relies on the

close integration of internal functions within the firm, such as procurement, logistics/distribution,

product design/development, manufacturing, and marketing (Vickery, Calantone, and Dröge l999).

Therefore, it was surprising that this variable was eliminated.

Factor 3 measures the degree of external collaboration with suppliers, a key element of SCM

(Choi and Hartley l996; Lambert, Emmelhainz, and Gardner l999; Tan, Kannan, and Handfield

l998; Zaheer, McEvily and Perrone l998). SCM enhances competitive performance through inter-

nal cross-functional collaboration that is linked with the functions of suppliers and channel mem-

bers (Monczka et al. l998; Vickery, Calantone, and Dröge l999). Differences in collaboration exist

depending on the type of information being shared and the nature of the collaboration process. The

current study considered four variables to measure external collaboration: sharing of operations and

planning information; sharing of cross-functional processes; participation in collaborative net-

works with multiple suppliers; and sharing of cost information. Again, the scale items are compa-

rable to those used to measure external collaboration in past research (Stank, Keller, and Daugherty

2001).

The last factor considered, F4, measures firm performance. Firm performance has been mea-

sured in numerous ways in the past literature (Handfield and Nichols l999; Narasimhan and Das l999;

Wisner 2003). These measures typically include the four basic competitive priorities of cost, qual-

ity, dependability, and flexibility (Buffa l984). More recently innovation has become an added

dimension (Ward, Leong, and Snyder l990). However, some researchers have suggested that world-

class manufacturers tend to simultaneously pursue multiple performance objectives, rather than

merely focusing on one measure. Following the approach of multiple performance objectives,

taken in previous studies (Narasimhan and Das 2001), we treat firm performance as a composite con-

struct composed of multiple measures. These measures include cost, quality, delivery, and new

product introduction time. These scale items are not new and have been used in past studies

(Narasimhan and Das 2001; Scannell, Vickery, and Dröge 2000).

RESULTS

Evaluation of the proposed model was made using structuring equation modeling, following

the two step approach recommended by Anderson and Gerbing (l988). The first step involved the

development of an acceptable measurement model through the use of confirmatory factory analy-

sis. At this first stage the latent factors of interest are identified, and the relationship between the

observed variables and their respective latent factors is tested. The relationships between the

observed variables and latent factors are specified by the researcher and all factors are allowed tointercorrelate.




All SEM analyses were conducted using EQS (Bentler l997). Table 4 presents results of the mea-

surement model. As recommended, multiple fit criteria are considered in order to rule out measurementbiases (Hu and Bentler l999). The fit indices considered are those most commonly recommended

for this type of analysis (Bagozzi and Yi l988; Byrne l994). All the indices were within the recom-

mended range, including ratio of chi-square to degrees of freedom (2 /df = 1.92), root mean square

error of approximation (RMSEA = 0.05), root mean square residual (RMR = 0.04), goodness of fit

index (GFI = 0.96), normed fit index (NFI = 0.95), comparative fit index (CFI = 0.96), and incre-

mental fit index (IFI = 0.96). Collectively these statistics lead us to judge the overall measurement

model fit as satisfactory (Byrne l994).

TABLE 4

FIT STATISTICS FOR MEASUREMENT MODEL

Overall Fit Measures

Model Acceptable

Fit Statistic Notation Value Value

Chi-Square to Degrees of Freedom 2 /df 1.92 ≤ 2.0

Root Mean Square Error of Approximation RMSEA 0.05 ≤ 0.06

Root Mean Square Residual RMR 0.04 ≤ 0.05

Goodness of Fit Index GFI 0.96 ≥ 0.95

Normed Fit Index NFI 0.95 ≥ 0.95

Comparative Fit Index CFI 0.96 ≥ 0.95

Incremental Fit Index IFI 0.96 ≥ 0.95

Standardized coefficients, standard errors, and t-values for variable items are shown in

Table 4. All the coefficients are found to be significant at the p < 0.01 level. The table also shows

coefficient alpha values for each factor following the purification process. The coefficient alphas

ranged from 0.738 to 0.874, all in the acceptable range (DeVellis l99l).

Convergent validity

In order to perform meaningful analysis of the causal model, measures used need to display

certain empirical properties. The first of these is convergent validity, which is the degree to which

individual questionnaire items measure the same underlying construct. One way to test for convergent

validity is to evaluate whether the individual item’s standardized coefficient from the measure-

ment model is significant, namely greater than twice its standard error (Anderson and Gerbing

l988). An analysis of Table 3 reveals that coefficients for all items greatly exceed twice their




standard error. Also considering that coefficients for all variables are large and significant provides

evidence of convergent validity for the tested items.

Discriminant Validity

In addition to convergent validity, to ensure adequacy of the measurement model it is impor-

tant to measure that groups of variables intended to measure different latent constructs display dis-

criminant validity. Discriminant validity addresses the extent to which individual items intended to

measure one latent construct do not at the same time measure a different latent construct (DeVellis

l99l). We test for discriminant validity in two ways. First, inter-factor correlations are computed for

all factors and shown in Table 5. Very high inter-factor correlations, say approaching 1.00, indicate

that the items are measuring the same construct, although significant inter-factor correlations may

be observed between theoretically related constructs. An analysis of Table 5 reveals the inter-

factor correlations to be quite low.

TABLE 5

CORRELATION RESULTS

Firm IT Internal External Firm

Capability Collaboration Collaboration Performance

Firm IT Capability 1.00

Internal Collaboration .294 1.00

External Collaboration .258 .362 1.00

Firm Performance .320 .241 .372 1.00

In addition to the simple inter-factor correlation analysis, discriminant validity was further eval-

uated through a confidence interval test. Aconfidence interval of plus or minus 2 standard errors was

computed around the correlation estimates between the factors and determined whether this inter-val includes 1.0. In our test, none of the confidence intervals contained 1.0, demonstrating dis-

criminant validity (Anderson and Gerbing l988)

Structural Model Test Results

Figure 2 presents the results of the structural model tested. Overall model fit indices are as

follows: ratio of chi-square to degrees of freedom (2 /df = 164.05/85 = 1.93), root mean square

error of approximation (RMSEA = 0.05), root mean square residual (RMR = 0.04), goodness of

fit index (GFI = 0.98), normed fit index (NFI = 0.96), comparative fit index (CFI = 0.97), and

incremental fit index (IFI = 0.97). A comparison of these values against those recommended inthe literature suggests that the model is satisfactory (Hu and Bentler l999). All paths are statistically

significant at the 0.05 level.




FIGURE 2

STRUCTURAL MODEL

*All coefficients are significant at p ≤ 0.05

H1: ␥ = .42 * H5: = .46 *

+ +

H3: ␥= .36 *

+

H4: = .35 *

H2: ␥= .34* +

+

F1:IT Capability

F2:Internal

Collaboration

F3:External

Collaboration

F4:Firm

Performance

IC1 IC2 IC3

IT1

IT2

IT3

IT4

MP2 MP3 MP4

EC1 EC2 EC3

MP1

EC4

␥

␥




TABLE 6

THE GOODNESS OF FIT OF THE STRUCTURAL EQUATION MODEL

Overall Fit Measures

Model Acceptable

Fit Statistic Notation Value Value

Chi-Square to Degrees of Freedom 2 /df 1.93 ≤ 2.0

Root Mean Square Error of Approximation RMSEA 0.05 ≤ 0.06

Root Mean Square Residual RMR 0.04 ≤ 0.05

Goodness of Fit Index GFI 0.98 ≥ 0.95

Normed Fit Index NFI 0.96 ≥ 0.95

Comparative Fit Index CFI 0.97 ≥ 0.95

Incremental Fit Index IFI 0.97 ≥ 0.95

This serves as the basis of evaluation of the hypotheses:

H1: Firm IT capability (IT) has a direct and positive impact on internal collaboration (IC). This

hypothesis is supported, as the parameter estimate (.42) is significant and substantial.

H2: Firm IT capability (IT) has a direct and positive impact on external collaboration (EC).

This hypothesis is supported, as the parameter estimate (.34) is significant.

H3: Firm IT capability (IT) has a direct and positive impact on firm performance (FP). This

hypothesis is supported, as the parameter estimate (.36) is significant.

H4: External collaboration (EC) has a direct and positive impact on internal collaboration

(IC). This hypothesis is supported, as the parameter estimate (.35) is significant.

H5: Internal collaboration (IC) has a direct and positive impact on firm performance (FP). This

hypothesis is supported, as the parameter estimate (.46) is significant.

We note that in addition to the proposed model, alternative structural models were presented

and tested during the analysis. However, the presented model proved to have the best fit based on

the discussed measures. Our study documents that IT has both a direct and indirect impact on firm

performance, with the indirect effect of IT on performance being significant (.25) at the p ≤ 0.05 level.

Note that we did not hypothesize a direct relationship from external collaboration to firm

performance based on the literature review. The Lagrange Multiplier (LM) test for omitted paths

did not indicate that such a relationship would be significant. Therefore, the relationship between

external collaboration and firm performance is found in our study to be indirect, through internal

collaboration. This is further supported by the computed indirect effect of external collaboration onfirm performance, which was significant (.16) at the p ≤ 0.05.This finding is important as it supports




the argument of Subramani (2004) that internal collaboration constrains the benefits of external

collaboration.

DISCUSSION AND IMPLICATIONS

The purpose of this paper was to propose and test a model of the relationship between firm IT

capability, internal and external collaboration, and firm performance. A number of important find-

ings emerge that have both theoretical and managerial implications. First, a significant contribution

of this study is the empirical test of theoretical assumptions in the extant literature of IT influence

on collaboration and firm performance. IT is shown to have a significant direct impact on perfor-

mance and a significant impact on both internal and external collaboration. This finding under-

scores the important role IT plays in the organization.

This research also suggests that collaboration is not synonymous with information technology.

Rather, information technology is a separate construct that promotes both internal and external

collaborative relationships. This is noted as occasionally companies presume that having informa-

tion technology in place automatically assumes that collaboration exists. Collaboration is a result

of human interactions which can only be supported by IT, but not replaced. This is an important point

for managers as they consider funding for various IT initiatives. Based upon the findings of this study,

IT efforts that particularly promote collaboration should be given greater consideration.

Another important finding relates to the significant impact of internal collaboration on per-formance. Although this finding is not new, it does validate and further confirm the important role

internal collaboration serves. The significant impact of internal collaboration on performance sug-

gests that companies should invest in strategies that promote cooperation and integration across the

functions of the organization. As IT is shown to promote internal collaboration, companies should

also consider investing in these types of information technologies.

Last, the research model supports the finding that external collaboration influences internal

collaboration, which in turn impacts performance. This supports previous findings. This finding, how-

ever, should not be surprising. By engaging in external collaboration, companies automatically

force higher levels of internal collaboration. Benefits of collaboration appear to be synergistic in nature.They help members of the organization access information in a timely manner, process relevant infor-

mation efficiently, and make informed decisions both internally and across enterprises.

LIMITATIONS AND FUTURE RESEARCH

A number of limitations of the current study need to be noted, as well as directions for future

research. Our study did not compare the impact of specific information technologies, an area that

should be a consideration in future research. For example, one functional classification of IT is

provided by Barki, Rivard, and Talbot (l993), where IT is aggregated into six categories: transac-

tion processing systems, decision support systems, interorganizational systems, communication

systems, storage and retrieval systems, and collaborative work systems. A simpler classification of




IT is provided by Kendall (l997), where IT is divided into two categories: production-oriented

information technologies and coordination-oriented information technologies. Regardless of clas-sification, it can be assumed that some of these technologies have a more direct impact on collab-

oration than others. Future research should elaborate on our initial findings to consider the impact

of specific information technologies on collaboration and firm performance.

Some studies suggest that there is a recommended sequence in using information technology

in order to achieve supply chain integration (Narasimhan and Kim 2001). These studies suggest that

in order for information technology to be implemented successfully there needs to be coordination

and a functional relationship between the stage of supply chain integration and the utilization of IT.

As collaboration is an integral part of supply chain integration it seems that this would have an impact

on collaboration as well. It also suggests the complexity of this issue. The model tested in our cur-

rent study is somewhat simplistic. Future studies should consider expanding this relationship to include

the stage of supply chain integration.

Our research considers collaboration to be comprised of two constructs, one for internal and

one for external collaboration. In fact, collaboration can be viewed in stages, from simple information

sharing to true collaboration (Sabath and Fontanella 2002). Future research should consider the rela-

tionship between specific types of information technologies and their linkage to specific collabo-

ration needs. This type of work could potentially provide interesting findings for use of information

technology in business.

Our study also has limitations due to the nature of empirical data that it is based on. First, the

random errors inherent in this type of data may cause differences in the scale results using confir-

matory factor analysis, with differing data sets. Scale development, purification, and validation, is

an ongoing process that needs to be developed longitudinally and across multiple data sets. Con-

sequently, future research will need to reexamine the measures used in this study.

A second limitation of our study relates to characteristics of the sample upon which the hypothe-

ses are tested. Larger studies on broader samples would evaluate whether these results are truly gen-

eralizable. Also, IT capability is pervasive throughout all types of industries. Our study was limited

to primarily large manufacturing firms, increasing the validity of the measures. Similar measuresneed to be developed for a broader range of firms, as well as firms in the service environment.

Despite the discussed limitations, our research provides support for the positive influence of

IT on capabilities on collaboration and firm performance. Some authors have questioned the tan-

gible benefits associated with the large and rapidly growing expenditures toward IT, and have

encouraged empirical research to shed light on this issue (McAfee 2002). Our study attempts to pro-

vide such empirical evidence by elucidating the role of IT in organizations.




CONCLUSION

The capability of information technology (IT) and it use have dramatically increased over the

recent past. Investments in IT are important decisions for companies as they involve large capital

expenditures. The literature has viewed IT as an enabler of internal and external firm collaboration,

which is the foundation of supply chain management. Firm collaboration and information sharing,

in turn, are expected to improve firm performance. Our study tested a model of the relationship

between firm IT capability, internal and external collaboration, and firm performance, using

empirical data. Our findings show that firm IT capability impacts performance both directly and by

having a positive impact on internal and external collaboration. Internal collaboration is shown to

have a strong direct impact on performance and, in turn, is impacted by external collaboration.

These findings underscore the importance for companies to promote internal collaboration and

invest in information technologies that facilitate it.

ACKNOWLEDGMENT

This study was funded by a research grant from the Department of Defense, Defense Acqui-

sition University, the Naval Post Graduate School, Monterey, California.

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ABOUT THE AUTHORS

Nada R. Sanders is Professor of Operations Management and Logistics at the Raj Soin

College of Business at Wright State University. She received her Ph.D. and MBA from The Ohio

State University. Dr. Sanders is author of numerous articles that have appeared in journals such as

Journal of Business Logistics, Decision Sciences, Sloan Management Review, Omega, Interfaces,

International Journal of Production and Operations Management, and Journal of Behavioral Deci-

sion Making. She has written chapters for books and encyclopedias, and is co-author of the book Oper-

ations Management . Her research interests include supply chain management, outsourcing, business

forecasting, and the role of information technology on supply chain organizations.

Robert Premus is Professor of Economics at Wright State University. He earned his doctor-ate in economics and industrial organization from Lehigh University and has authored numerous schol-

arly papers and reports on the economics of innovation and economic growth. His primary teaching

fields are in macroeconomics, economic growth, and monetary economics. Dr. Premus has fre-

quently served as a speaker and consultant to companies and government agencies on issues of

technology, innovation, and economic growth.


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