An Information Processing Model of Information Systems Impact on Interorganizational Coordination

Paul W. Forster

School of Business and Management

Hong Kong University of Science and Technology

John L. King

School of Information

University of Michigan

Barrie R. Nault

Haskayne School of Business

University of Calgary

November 21, 2002

Acknowledgements: We wish to thank Barb Marcolin, Vicky Mitchell and Ron Murch at

the University of Calgary, seminar attendees at the University of Calgary Haskayne School of

Business, and Kevin Kobelsky at the Leventhal School of Accounting at the University of

Southern California for their feedback on earlier drafts of this paper.

Please do not cite or circulate without permission from the authors.

Copyright @ 2002 Paul W. Forster, John L. King and Barrie R. Nault. All rights

reserved.

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An Information Processing Model of Information Systems

Impact on Interorganizational Coordination

Abstract

There has been much speculation that information technology can be used to enhance

coordination across organizations. The research on the matter is less than convincing, in part due

to the absence of models that capture the complexity of the phenomenon, and in part due to

problems of measurement. In this research we use the framing device of the information

processing model of organization, extended to interorganizational operations . We test the

information processing model in the interorganizational context of the scheduled air cargo

industry. Using data from a survey of US forwarders, we find that interactions between several

dimensions of information processing requirements and information processing capabilities

significantly explain variance in operational performance. In particular we find that the

information systems variables interact with task variability, task analyzability, buyer

independence and supplier independence on the dependent variable, on-time performance. The

findings provide partial support for the proposed information processing model and suggest that

information systems do enhance interorganizational coordination, although many challenges

remain in fully explicating the ways in which this occurs.

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I. Introduction

There has been much speculation that information techno logy can be used to enhance

coordination across organizations. The research on the matter is less than convincing, in part due

to the absence of models that capture the complexity of the phenomenon, and in part due to

problems of measurement. In this research we use the framing device of the information

processing model of organization, extended to interorganizational operations, to explore the role

of information systems in the US scheduled air cargo industry. Evidence is provided that

information systems do enhance interorganizational operational performance, although many

challenges remain in fully explicating the ways in which this occurs.

The Information Processing Model of Organization

March and Simon (1958) launched the information processing model of organizations by

postulating that organizations are a natural response to uncertainty. This view has been

expanded upon since from a number of perspectives (e.g. Cyert and March, 1963; Williamson,

1975: Ouchi, 1980). One of the most influential commentators on the subject has been Galbraith

(1973, 1977, 1980), whose views inspired the work of Bensaou & Venktraman (1995) who

extended the information processing model to the interorganizational level. The work of

Galbraith and Bensaou and Venktraman form the basis of the work reported here.

In this study we use the information processing model to examine the use of information

systems to coordinate interorganizational tasks. The problem of coordination is in essence a

problem of information. Coordination requires decision-makers to determine how to allocate

resources to a task (Casson, 1997). In general, complex tasks can be decomposed into

interdependent subtasks. Decision-makers in various subtasks require information in order to

decide how to allocate resources available to them to the segment of the task for which they are

responsible. Coordination becomes a problem of how to effectively communicate relevant

information between decision-makers involved in the performance of interdependent subtasks.

The more complex the overall task, the greater the problem of coordinating the various subtasks

(Thompson, 1967). An implicit assumption of the information processing model is that

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organizational tasks evolve towards increasing complexity and therefore firms continually face

more severe problems of coordination and communication.

The key concept in the information processing view of organization is uncertainty, the

difference between the amount of information available and the amount of information required

to perform the task at the desired level of performance (Galbraith, 1973). This difference

determines the information processing requirements of the task. In this study we identify

uncertainty arising from the nature of the task, the environment, and the relationships between

buyers and suppliers. Uncertainty is manifested as exceptions that occur during task execution.

Higher levels of uncertainty result in more exceptions. Exceptions are resolved by decision-

makers who allocate resources in order to resolve the exception and continue the task at the

desired level of performance. Organizations use their information processing capabilities to

move relevant information to decision-makers in order to manage these exceptions.

Organizations that can match information processing requirements to information processing

capabilities will perform better than those that cannot.

When there is no uncertainty associated with the performance of a task, then all the

required information can be known before task execution. Coordination can take place in

advance with all decisions made up front, resources allocated and performance levels set. Little

to no information need be exchanged between decision-makers during execution. Highly

standardized processes such as mass production are a case in point. Procedures are highly

standardized and during task execution the system is buffered from sources of uncertainty that

might throw the system off kilter. Few exceptions arise, and system is not robust. When

exceptions arise, performance degrades.

When uncertainty is high, then coordination takes place during task execution. When

exceptions are detected information is exchanged between decision-makers to information

decisions about where and how to allocate resources. The higher the uncertainty encountered

during task execution, the higher the number of exceptions and the greater the information

processing requirements.

Information is provided to decision-makers through various organizational mechanisms,

some with greater information processing capabilities than others. To attain a desired degree of

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performance, organizations match their information processing capabilities to their information

processing requirements. Galbraith suggested a continuum of organizational structures of

increasing information processing capabilities. At one end of his continuum are rules and

regulations that enable a high degree of pre-processing of information. At the other, lateral

relations enable ad hoc communication directly between decision-makers. Information systems

are essential in both the pre-processing and processing of information at both ends of the

continuum, although for various reasons more attention has been devoted to building systems to

support rule-based, highly regulated task domains (e.g., accounting).

Much production and nearly all commerce between enterprises occurs through

interorganizational coordination of tasks. An interorganizational task is executed through the

execution of subtasks in individual organizations. The information processing view of

organization can be extended to the interorganizational level by transitivity. To achieve a

desired degree of performance, the multiple organizations involved in interorganizational tasks

respond by matching organizational structures and technologies to information processing

requirements. Interorganizational information systems (IOS) act as information processes that

can match types of information processing requirements among the organizations. This is

captured in Figure 1.

Figure 1 An information processing model of IOS impact on performance

Task Uncertainty Variability Analyzability Environmental Uncertainty Dynamism Relationship Uncertainty Trust Buyer independence Supplier independence

IOS Breadth Standardized

“Fit”

Information processing requirements

Information processing capabilities

Operational Performance

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The IOS Research Tradition

IOS have been described as information and communications technologies that share

information across the boundaries of organizations (Kumar and van Dissel, 1996). The most

frequently studied instance of IOS are Electronic Data Interchange (EDI) systems. These are

usually proprietary systems operating within industry subdomains such as the automobile supply

chain. Much of this research IOS has focused on performance impacts of these IOS. The

competitive advantage among firms has been a major area of IOS work (Porter and Millar, 1985;

Johnston and Vitale 1988; Konsynski and McFarlane, 1990; Jelassi and Figon, 1994; Powell and

Dent-Micallef, 1997). Other performance-related work focuses on operational issues such as

control of inventories and inventory costs (Kekre and Mukhopadhyay, 1992; Jelassi and Figon,

1994; Mukhopadhyay et al., 1995), and the frequency of errors and discrepancies in operations

(Riggins and Mukhopadhyay, 1994; Srinivasan et al., 1994). These studies draw on a wide

variety of theories: transaction cost economics, game theory, organizational theory, political

economy and resource-based theories. The assumption that IOS can increase coordination

between firms is usually not explicitly addressed in this work, but it is implicit in the intellectual

legacies invoked by the authors.

The results of this performance-oriented research vary from no findings of relationship

between IOS use and operational performance (Venkatraman and Zaheer, 1990; Powell and

Dent-Micallef 1997) to some evidence of positive IOS performance impacts on performance

(Kekre and Mukhopadhyay, 1992; Riggins and Mukhopadhyay, 1994; Srinivasan et al., 1994;

Mukhopadhyay et al., 1995). Where positive performance impact is found, it is associated with

shorter production time, lower inventories, and fewer exceptions. Relatively few of these studies

explore moderating factors, but Srinivasan et al. (1994) found that the degree of operational

complexity moderated the impact of IOS use on performance. A case study by Hart and Estrin

(1991) suggests that the type of uncertainty moderates the effectiveness of IOS, dependence,

strategy, resource allocation and trust. Clemons and Row (1993) and Jelassi and Figon (1994)

found that bargaining power can influence the effectiveness of IOS, and suggested that inter- firm

relationships are an important moderator of IOS impacts on operational performance.

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These studies taken together suggest that IOS can and sometimes do positively influence

effective coordination and thus operational performance, though the impact of IOS might be

moderated by the character of interorganizational relationships. None of these studies deals

extensively with the question of whether IOS affect performance by enhancing

interorganizational coordination, but this would stand to reason because this explanation is well

grounded in the literature, and coordination is a key focus of efforts to improve

interorganizational relationships. This view is well supported by Galbraith (1973, 1977, 1980)

and Bensaou & Venkatraman (1995).

Following the lead of Galbraith and Bensaou and Venkatraman, we propose a model in

which high performing organizations match information processing requirements to information

processing capabilities in order to efficiently reduce uncertainty (Tushman and Nadler, 1978).

The model is tested against data from the U.S. scheduled air cargo industry, a complex global

network of freight forwarders and airlines. The analysis is tested using a partial least squares

methodology to assess interactions between information processing capabilities and information

processing requirements. Six of twelve hypothesized interactions are significant. The findings

indicate that relationship between highly standardized IOS and operational performance is

moderated by task variability, task analyzability and supplier independence. The relationship

between a breadth of IOS and operational performance is moderated by task variability, task

analyzability, and buyer independence.

This article proceeds in the following way. Section 2 describes the information

processing model. Research hypotheses are proposed in Section 3. Section 4 briefly describes the

air cargo industry that is the context for this study. Section 5 provides a description of the

methods. Section 6 describes our findings. The discussion is found in Section 7. Section 8

addresses the contribution of the study.

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II. Information Processing Model

Dimensions of Information Processing Requirements

Uncertainty, through exceptions, drives information processing requirements. We

identify dimensions of task, environment, and key relationships as sources of uncertainty for

interorganizational tasks.

Task Uncertainty: Task variability and task analyzability are sources of task uncertainty.

They influence the amount and nature of the information required during task execution to

resolve exceptions.

Task variability refers to the frequency with which unanticipated events occur during the

execution of the interorganizational task requiring nonroutine procedures to be used in the

execution of the task (Bensaou and Venkatraman, 1995; Keller 1994). Tasks that are

unpredictable increase exceptions during task execution and increase information processing

requirements. As task variability increases, the behavior of critical elements of the task become

increasingly unpredictable and information requirements of decision-makers increase in order to

coordinate the task at the desired level of performance.

Task analyzability is the extent to which there is a “known procedure that specifies the

sequence of steps to be followed in performing a task” (Bensaou and Venkatraman, 1995:1475).

Keller (1994) uses the dimension of task unanalyzability to reflect the ambiguity of a task.

Interorganizational tasks that are analyzable lend themselves to preplanning, have fewer

exceptions during execution and have lower information requirements. Tasks that are not

analyzable cannot be preplanned but require constant management during execution, increasing

information requirements.

Environmental Uncertainty: Lawrence and Lorsch (1967) observe that firms do not

operate in isolation from their environments, and that environmental complexity influences

internal uncertainty. The greater the instability of the general environment, the greater the

uncertainty facing decision-makers (Tushman and Nadler, 1978). When the environment is

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stable, firms can preplan and reduce much of the information that is required during the

execution of their activities. When the environment is unstable it can influence

interorganizational operations and increase the frequency of exceptions.

There are many potential sources of environmental uncertainty, however we center our

attention on environmental dynamism as the key source of uncertainty that, as Bensaou and

Venkatraman (1995) suggest, is the dimension of environmental uncertainty on which there

seems to be more agreement. Dynamism reflects the extent to which task-relevant characteristics

of the environment are changing. Where the environment is changing, cause-and-effect

relationships between the environment and the firm become unclear (Daft and Lengel, 1986).

Inter-firm Relationship Uncertainty: While task uncertainty happens close to the

production line, relationship uncertainty happens at the institutional level of the organization

(Thompson, 1967). Task uncertainty influences the inputs to the organizations and is under the

control of organizations. Relationship uncertainty is outside the control of any one organization

in the system. In this external environment, relationships can change independent of one

organization. Trust and independence are two dimensions we identify as sources of relationship

uncertainty.

Kumar and van Dissel (1996) view IOS as a technical manifestation of inter- firm

relationships. As such, the quality of the relationship is reflected in the effectiveness of the IOS.

The inter- firm relationship can be described by interdependence between firms. The level and

nature of interdependence influences the potential and source for conflict. IOS create power

shifts between organizations, which can lead to conflict that can diminish the positive

coordinating effects of IOS.

IOS provide a vehicle for facilitating changes in inter- firm relationships (Bakos and

Brynjolffson 1993; Clemons, Reddi and Row 1993; Clemons and Row 1993). In turn, the inter-

firm relationship affects the willingness of firms to share information with each other and thus

influences the effectiveness of IOS.

In contrast, Malone and Rockhart (1993) assert that IOS can mitigate the uncertainty

created in low trust situations by making remote decision makers more effective, controlling and

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monitoring remote decision makers, and by socializing remote decision makers and building

loyalty.

Trust is defined by Zaheer et. al. (1998) as the expectation that an actor (1) can be relied

on to fulfill obligations, (2) will behave in a predictable manner, and (3) will act and negotiate

fairly when the possibility for opportunism is present. A common theme in descriptions of

interorganizational trust is that distrust between partners creates conditions for opportunism.

From a transaction cost perspective, trust reflects a calculated decision by a party to the

transaction about the risks of opportunism. From an institutional perspective, institutional

arrangements (e.g. regulations, professions, laws, rules) produce the trust that supports complex

economic systems. Distrust leaves a party vulnerable, requiring more information to reduce their

uncertainty about their partner’s future behavior. The exchange of reliable and accurate

information is one facet of trusting relationships, in which partners share rather than withhold

information (Mishra, 1996). That is, information sharing is an element of trust.

Independence: Emerson (1962) argues that one party’s power “resides implicitly in the

other’s dependency” (p. 32). Whereas dependence suggests the power of one party to control or

influence the other’s decisions, independence suggests one organization makes choices without

control or influence of the other. Where independence is high, incentives may be misaligned

giving rise to decisions that create exceptions for other organizations, exceptions that increase

information processing requirements.

However, dependency has its dangers as well. As organizations become more

interdependent, inter- firm relations increase in their significance as a source of uncertainty.

Kumar and van Dissell (1996) argue “the closer the coupling or interdependency, the greater the

intentional or accidental harm one unit can inflict upon the other” (p.283).

Dimensions of Information Processing Capabilities

IOS, in general, enable coordination between organizations. In doing so, they enable the

management of exceptions as they arise during task execution. The ability to manage exceptions

is manifested in operational performance.

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Not all IOS share information in the same way. In particular we hypothesize that highly

standardized IOS and a breadth of IOS provide different kinds of coordinating capabilities and

that these IOS will be effective in different contexts.

Standardized IOS (S-IOS): S-IOS is defined by the extent to which standardized formal

business documents are exchanged between organizations. Standardized documentation requires

agreement on standard operating procedures. Deviations from those procedures are costly

(Brousseau, 1994). Firms with greater S-IOS make greater use of electronic connections for

formal business documents such as quotes, invoices, waybills, and payments. We associate S-

IOS with preprocessing of a task, not for the exchange of information for the resolution of

exceptions during task execution. This represents an investment in IOS for preplanning activities.

This category of IOS captures EDI and EDI- like systems, with standardized documents for

specific types of task.

Breadth of IOS (B-IOS): B-IOS is defined as the diversity or breadth of different IOS

used between organizations. Firms that have a wider variety of electronic connections with their

partners have greater B-IOS and consequently greater opportunities for the exchange of task

critical information. B-IOS supports both exchange of information for preplanning and exchange

of information during task execution such as for monitoring and problem resolution.

At a high level of analysis, greater intensity of use of either will provide greater

information processing capability than lower intensity of use. That is, all other things being

equal, the firm that has a broader array of electronic communications for coordination and more

standardization in its electronic communications will have higher information processing

throughput than another. At a more detailed level of analysis, however, the second dimension of

information processing capability, S-IOS, can constrain the level of information processing

capability because of its limiting capabilities.

IOS is often used to monitor interorganizational operations and provide decision-makers

with tools to manage exceptions before they critically impair performance or to quickly respond

to exceptions when they arise. We distinguish two basic strategies for managing exceptions

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during task execution. First is the use of IOS to routinely monitor interorganizational tasks in

order to anticipate exceptions and reduce their information requirements. This use of IOS is a

feedforward control system, where the information is used as an early warning system to make

decisions early and thus lower the number of exceptions that arise. This strategy relies on IOS to

provide timely and accurate information updates and requires performance benchmarks against

which system status can be compared. A second strategy is to use IOS to manage exceptions

only after they have been detected. Feedback control systems use information after an exception

to rapidly respond to exceptions as they arise. In this case, the system assists to identify the

decision-makers and provide a communication mechanism for processing the information

required to resolve an exception.

In feedback control systems, greater S-IOS limits the range of information and

procedures that can be brought to bear on an exception since by its nature, standardization relies

on the avoidance of exceptions. And in many contexts such as the one we study – international

scheduled air cargo – exceptions are unavoidable. Exceptions occur for a variety of reasons that

are beyond the reach of a feedforward control strategy, and therefore are unpredictable.

Operational Performance

Coordination, as the allocation of resources to the performance of a task is reflected in a

variety of performance measures. Although financial performance can follow effective

coordination of all the tasks in which an organization is engaged, this is problematic in the study

of interorganizational tasks. First, an operational performance measure can eliminate the

accounting-based problems and to eliminate the interpretation of poor performance based on

overly costly IT. Second, the impact of IOS on the performance of a particular task can be

obscured by other tasks in which the organization is engaged. At the interorganizational level

this problem compounds. In this study we hold that effective coordination of an

interorganizational task is best reflected in measures of operational performance.

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III. Research Hypotheses

Our model hypothesizes that the fit between information processing requirements and

information processing capability will predict performance. There are different interpretations of

the appropriate statistical representation of the concept of “fit” (e.g. Schoonhoven, 1981; Drazin

and Van de Ven, 1985; Venkatraman, 1989). We employ a “moderation” which argues that the

variation in performance is explained by the interaction between information processing

capabilities and information processing requirements.

High task variability increases uncertainty by creating unanticipated and novel events

during task execution. Variable tasks require IOS that are flexible and can provide support for ad

hoc communications between decision-makers during task execution. More standardized IOS

that support a limited range of procedures will not effectively support high task variability.

Therefore:

H1a: For higher levels of S-IOS, higher levels of task variability are associated with

lower operational performance.

H1b: For higher levels of B-IOS, higher levels of task variability are associated with

higher operational performance.

Highly analyzable tasks can be clearly articulated and embedded in standard operating

procedures. Information standards are closely coupled with the work (Brousseau, 1994). S-IOS

benefits from the standardization of information and will perform well under these circumstances

enabling smooth exchange of preprocessing information. B-IOS, as a communications channel

for ad hoc information during task execution will not perform well.

However, where there is low task analyzability, and the task is not analyzable, S-IOS will

not perform well because the task is not easily standardizable. B-IOS perform well as they

support a wider range of shared information during task execution. Therefore:

Hypothesis 2a. For higher levels of S-IOS, higher levels of task analyzability are

associated with higher operational performance.

Hypothesis 2b. For higher levels of B-IOS, higher levels of task analyzability are

associated with lower operational performance.

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High environmental uncertainty, or dynamism, reflects greater potential for change in

inputs, demand levels, industry structure, introducing uncertainty throughout the performance of

an interorganizational task. Under such change, a greater variety of electronic communications

preserves options whereas standardized formal electronic communications may inhibit flexibility

to respond to changing demands. Therefore:

Hypothesis 3a. For higher levels of S-IOS, higher levels of environmental uncertainty are

associated with lower performance.

Hypothesis 3b. For higher levels of B-IOS, higher levels of environmental uncertainty are

associated with higher performance.

In general, higher levels of trust reduce concern of a partner’s decision-making and future

behavior. Higher trust makes it more likely that IT-based investments in electronic

communications will be made and used to enhance performance. S-IOS, where behavior during

task execution is predictable, is appropriate for high trust situations. Where trust is low, B-IOS is

desirable to monitor task execution. Therefore:

Hypothesis 4a. For higher levels of S-IOS, higher levels of trust are associated with

higher levels of operational performance.

Hypothesis 4b. For higher levels of B-IOS, higher levels of trust are associated with

higher levels of operational performance.

When buyers or suppliers are dependent, their behavior is predictable, uncertainty arising

from the partnership is less and exceptions are few. However, where suppliers or are

independent, their behavior becomes less predictable. Coordination between organizations is

hindered because the decision-makers in different organizations take independent actions. Where

decisions are made independently of one another, information from others is not acted upon, and

the best one organization can do is monitor the other’s actions. Thus, while IOS may share

information, this information is ineffective at increasing coordination. We anticipate when firms

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are highly independent, neither S-IOS, nor B-IOS will be effective at increasing coordination.

That is, there are some types of uncertainty for which IOS do not provide matching information

processing capabilities. Therefore:

Hypothesis 5a. For higher levels of S-IOS, higher levels of buyer independence are

associated with lower levels of performance.

Hypothesis 5b. For higher levels of B-IOS, higher levels of buyer independence are

associated with lower levels of performance.

Hypothesis 6a. For higher levels of S-IOS, higher levels of supplier independence are

associated with lower levels of performance.

Hypothesis 6b. For higher levels of B-IOS, higher levels of supplier independence are

associated with lower levels of performance.

IV. International Scheduled Air Cargo

We test the information processing model against data from the export activities of the

U.S. scheduled air cargo industry1. Air cargo is representative of the problems of global

coordination in logistics and distribution. The industry is driven by increasing demands from

global supply chains to provide time-dependent movement of materials in order to reduce

uncertainty. Scheduled air cargo moves approximately 60% of world air cargo. However,

vertically integrated express carriers, with a 12% share of world air cargo, have been growing

since 1991 at a 21% growth rate far outstripping the growth of scheduled air carriers (Boeing,

2002) and providing superior time-definite performance. Forwarders and airlines in the

international scheduled air cargo industry are attempting to use IOS in order to increase

interorganizational coordination and boost on-time performance.

1 Of $300B USD in world scheduled airline revenues (IATA, 1998), the global air cargo industry accounts

for an estimated $40B USD. US-international accounts for approximately 25% of global air cargo, or $10B USD.

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Figure 2 Scheduled air cargo

Scheduled air cargo shipments have a common element of high value-to-weight ratio but

are heterogeneous in nature. Valuables, electronics, dangerous goods, live animals, emergency

parts, oversized and overweight shipments in addition to containerized freight are standard fare.

In the scheduled air cargo configuration, the provision of on-time cargo delivery is

provided through the coordinated efforts of fo rwarders and scheduled airlines that carry both

passengers and cargo (Figure 2). Forwarders package, document, and transport shipments to the

scheduled airlines (e.g. Lufthansa, KLM) that fly the shipments in the bellies of passenger

aircraft to the destination airport where agents of the origin forwarder move the shipments to the

consignee.

The forwarder plays a critical role in the carriage of air cargo. The forwarder typically

selects an airline for transport, books the shipment, plans routing and transhipments, and

arranging surface movement at source and destination. The forwarder has the expertise to assist

in the preparation of complicated documentation for specialized shipments and international

transport. Forwarders can also provide expertise in the areas of packaging, insurance, customs

clearance and international payments. When shipments are consolidated with other shipments

with a common destination, the forwarder assumes the identity of indirect carrier, accepting legal

responsibility for shipments.

Task: The task of moving international shipments is a complex activity. The

heterogeneity of the types of goods moved by air, the complexity of international shipments, and

the involvement of multiple organizations such as forwarders, airlines, customs, and destination

agents all affect the uncertainty that surrounds the execution of air cargo.

Heterogeneity of goods increases variability of the task by increasing the diversity of

procedures and practices that must be maintained in order to meet the physical handling needs of

Shipper Forwarder Airline(s) Agent Consignee

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different types of shipments. The demands on the expertise required of the forwarder also

increases with the diversity of the shipments. The complexity and uncertainty of the shipment

task is reflected in the number of unplanned shipment delays directly affecting on-time

performance.

Environment: International air cargo is a highly regulated industry subject to many

environmental factors that influence demand for its products including: growth in trade in high

value goods, domestic and internationa l regulation of air cargo, fuel prices, and competing

modes of transportation.

Inter-firm Relations Between Forwarders and Airlines: In contrast to the integrated air

cargo providers who have no interorganizational relations to manage, the relationship between

forwarders and carriers is critical to the production of on-time air cargo shipments.

Forwarders tender the majority of export cargo to scheduled airlines. They purchase

cargo space from the airlines and survive on the difference between the price they receive from

the shipper or consignee and the cost of cargo space. In doing so, forwarders have an incentive to

hide information from their airline suppliers. The information asymmetries inherent in the

industry create barriers to relationships of trust associated with effective use of IOS.

IOS Use Between Forwarders and Airlines:

The key functions of IOS in air cargo are the sharing of standardized documents such as

the air waybill and other supporting documents, electronic queries and booking, and electronic

tracking and tracing. These features are available through Cargo Community Systems, value-

added networks, or proprietary systems. Several EDI standards are supported in different

regions.

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V. Methods

Survey Design

While both forwarders and airlines are responsible for the delivery of an air cargo

shipment, in the context of international shipping, the forwarder plays a crucial role as a third-

party carrier. In its primary function as consolidator, the forwarder is the carrier, and is held

legally accountable for performance. 2 We chose the forwarder as the focal organization in the

forwarder-airline dyad, and sampled the membership of Cargo Network Services (CNS), a

wholly owned subsidiary of the International Air Transport Association. 3

The survey design was supported by seventeen semi-structured interviews with

knowledgeable executives representing forwarders, airlines, associations, and third party

information providers. Observation of air cargo operations was conducted at a major airline hub.

Content validity of all items was addressed through survey walkthroughs by knowledgeable

researchers and through two survey pretests. A first pretest was conducted with senior executives

(owner, president or general manager) of three forwarders and four airlines. Approximately one

month later a second pretest was conducted for the revised questionnaire with executives from

four forwarders, two third party information systems providers, and two airlines. The purpose of

each pretest was to evaluate each measure with respect to comprehensibility, consistency of

meaning, and the respondents’ ability to respond accurately. The interviews helped to clarify

questions, improve instructions and scales and correct terminology.

The initial mailing (cover letter, survey, and postage paid envelope) went to 1,433

forwarders of the approximately 1,500 forwarders operating in the U.S. CNS estimates that its

membership accounts for upwards of 90% of all US-origin scheduled air cargo. Therefore, the

sampling frame closely approximates the entire population of U.S. air cargo forwarders. A total

of 203 firms responded with one unusable survey for an effective response rate of 14.1%.

2 The consignee is usually the party paying for the shipment in commercial transactions. 3 Cargo Network Services membership accounts for 90-95% of all scheduled air cargo revenues in the US.

CNS provided support for the survey mailing and for automated fax reminders from the researchers. Returned surveys were mailed directly to the researchers in the enclosed prepaid envelopes.

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Non-response bias occurs when non-respondents differ systematically from respondents

on key characteristics and is a threat to external validity. 1998 sales figures available from CNS

were examined revealing no significant differences between respondents and non-respondents.

Tests comparing the first 40 and last 40 respondents revealed no statistically significant

differences on all model measures and on measures of firm size (revenues, tonnage, waybills),

limiting concerns of non-response bias (Kanuk and Berenson, 1975).

The demographics in Table 2 reveal that the senior executives (owner, CEO, cargo

manager) have significant experience in the business of air cargo. Executives had been with their

company for an average of 13 years. The average forwarding firm had 433 employees, operated

in 8 branches, and handled 63,907 house air waybills for revenues of USD $26.7 million. 93% of

responding firms were privately held, while 7% were publicly held.

Design of Measures

Where possible, measurement items were based upon previously validated measures.

Otherwise new items were created. In some cases it was necessary to adapt the measures to the

particular context of air cargo. Table 1 describes the survey items and measurement scales.

Measures of Information Processing Capability

IOS are a source of information processing capability. While most empirical IOS studies

have used a single measure to represent IOS use such as EDI, in this study we employ two

measures of IOS.

S-IOS is a measure of the use of standardized formal exchange of electronic documents.

Documentation plays a critical role in the processing of shipments in international operational

processes. The air waybill is the basic document containing financial and operational

information. Complete documentation would reflect the supporting export documentation in

addition to the air waybill. The two questions were worded not to ask for a particular standard of

communication, such as EDI, but to capture the degree of use of electronic documentation (Table

1).

B-IOS is a formative construct composed of items measuring key IOS in the air cargo

industry. Cargo Community Systems, EDI, electronic tracking and tracing, and Internet use

19

together reflect a diversity of interorganizational information processing capabilities associated

with operational performance. Cargo Community Systems provide broad functionality such as

price search, booking, tracking and e-mail functions through a variety of forwarder, carrier, and

third party networks. Electronic tracking and tracing provide specific mechanisms for locating

shipments through a combination of barcoding, scanning and information networks. These three

items were measured on a dichotomous scale (Table 1). Web use was captured on a trichotomous

scale representing no web use, web use, and electronic commerce respectively. Their

combination reflects the diversity of IOS capabilities available to the forwarder in coordinating

their operations.

Both constructs, B-IOS and S-IOS are formative constructs, formed by their respective

items.

Measures of Information Processing Requirements

Task variability refers to the frequency with which unanticipated events occur during the

execution of the interorganizational task. In our measures of task variability we focus on the

frequency of exceptions during the execution of the shipment task. In the preliminary factor

analysis, several items cross- loaded on other factors at higher loadings than their associated

component. These items were removed and as a result, one item, the most direct measure of the

construct, was retained.

Task analyzability refers to how well the task in question is understood. Two items were

retained for this construct. The first question is consistent with the measures in earlier studies

(Bensaou and Venkatraman, 1995). The second question reflects an issue that if task

performance is difficult to measure then it is difficult to know how to modify steps in the task in

order to change the performance level.

Environmental dynamism reflects the extent to which the task environment is stable. Two

of six items were retained for this construct focusing on the stability of products and product

demand.

20

Trust refers to the expectation that the suppliers (airlines) can be relied on to fulfill their

obligations in the performance of the task (Anderson and Weitz, 1992). We use a three-item

scale for trust reflecting dealings at the organizational level and not at the interpersonal level.

Independence: The independence scale is based upon Heide (1994) and reflects the ease

with which a supplier or buyer can be replaced. We create measures for buyer independence and

supplier independence. Three items measure buyer independence (the independence of the

forwarder from the airline). Four items measure supplier independence (the independence of the

airline from the buyer). Note that the items for supplier independence reflect the forwarder’s

perception of its airline partners’ attitudes towards their relationship with the forwarder.

Measure of Operational Performance

International on-time performance is the dependent variable for this study. The measure

for performance is a multi- item scale of the percent of shipments that are available to the

consignee at destination at various time intervals. As there is no standard measure of on-time

performance in this industry, these items are an informed assessment of when shipments arrive

across all company destinations and all carriers. The senior air cargo executives interviewed in

the pretest maintained very close ties with operations and felt themselves knowledgeable about

their firm’s on-time performance.

The items are reduced to a single measure for the average international waiting time by

calculating a weighted average. The result scale was non-normal and a logarithmic

transformation was applied for the final measure.

Control

We use a control for firm size to partial out organizational size effects. Many private

forwarders were reluctant to provide revenue figures, our control. Calls to respondents resulted

in several new revenue figures. A regression was then used to estimate firm size for those

forwarders for whom complete house, master and direct air waybill data was available. The

regression had an adjusted R2 of .72 and replaced 28 missing values. The scale was non-normal

and a logarithmic transformation was applied for the final measure.

Descriptive statistics for the final set of items in the analysis are presented in Table 3.

21

Partial Least Squares (PLS) Analysis

A partial least squares technique (PLSGRAPH v3.0) for interaction term analysis was

employed to evaluate the model. PLS is a causal modeling technique that supports the inclusion

of latent variables and includes measurement error, making it a superior alternative to traditional

techniques (Hulland, 1999).

Moderating effects can be modeled through various techniques including covariance-

based techniques as employed in LISREL and regression-based techniques such as moderated

multiple regression and PLS. PLS is better suited for our analysis for several reasons. First,

information processing capability constructs in the models are formative and cannot be modeled

adequately using covariance-based tools. PLS can incorporate both formative and reflective

indicators.4 Second, traditional techniques such as moderated multiple regression cannot account

for measurement error in exogenous constructs which reduces the ability to detect moderating

effects (McClelland and Judd, 1993). Third, PLS places fewer demands on the distributions of

the measurement items and is suited to studies with smaller sample sizes (Chin et al., 2001).

We assess the PLS model in two stages: the measurement model is examined to assess

the reliability and validity of measures, and; the structural model is examined to assess the nature

of the relationships between latent variables (Hulland, 1999). Throughout the discussion of the

methods and results all items have been standardized or centered consistent with recommended

strategies for assessing interactions (Chin et al., 2001).

Measurement Model Evaluation

We conducted a factor analysis in PLS in order to examine the properties of the scales

more closely. The loadings from this analysis were then used in calculations of internal

consistency and average variance extracted.

Item reliability was assessed by an examination of factor loadings to see if items are

correlated with their associated constructs. The principal components analysis is provided in

4 Formative constructs have indicators that form or cause the creation or change in the construct. Whereas

reflective constructs are those where the indicators reflect the same underlying concept. (Chin, 1998)

22

Table 4. At this stage three items in task variability and three items in environmental dynamism

were examined and removed for crossloading on other constructs at the same level or higher than

their associated construct. Each removed item was first examined again for its content validity.

The remaining items load on unique components.

Convergent validity can be assessed by an examination of the loadings in Table 5. The

loadings were obtained from a PLS analysis using reflective main effects and the performance

measure. This was done without the control with no relationships specified between exogenous

constructs. The loadings were used in the subsequent calculations of internal consistency and

average variance extracted. The one item was retained with its loading of .64 as it was deemed

to be a measure consistent with the underlying construct.

Reliability measures are reported in Table 5. As a measure may have unacceptable

convergent validity and still be reliable, reliability was assessed after determination of

convergent validity (Steenkamp and van Trijp, 1991). All Cronbach alpha values exceeded the

generally accepted level of .70. Because Cronbach’s alpha does not estimate reliability within the

context of the causal model we used another measure of internal consistency suggested by

Fornell and Larcker (1981). The values for internal consistency are all above the suggested

minimum of .70.

Discriminant validity is demonstrated when a construct shares more variance with its own

measures than it shares with other constructs in the model. First, the average percentage of

variance extracted (AVE) for each construct exceeds .50 (Fornell and Larcker, 1981) indicating

that the variance accounted for by each construct exceeds the variance accounted for by

measurement error (Hair et al., 1998). Second, the square root of the AVE is larger than the

correlations between constructs (Barclay et al., 1995) as seen in Table 6.

The validity measures indicate strong evidence of item reliability, convergent validity

construct reliability and discriminant validity. Overall, these statistics indicate that the properties

of the measurement model are sufficiently strong to support interpretation of the structural

estimates.

23

Structural Model Evaluation

Interactions are tested in PLS using a two-stage technique described by Chin et al. (2001)

and employed previously in the IS literature (Sarkar et al., 2001; Chwelos et al., 2001). In the

first stage, all formative and reflective constructs are modeled against the dependent variable and

the construct scores are saved. Using these saved scores, scores for the interaction terms are

created by multiplying the scores for capabilities and requirements (B-IOS times TASKVAR, B-

BIOS times TASKAN, etc.).

In the second stage, these saved construct scores are used as individual items in a model

with both main and interaction effects. In this stage, a control was applied to all constructs

(performance, main and interaction effects) to partial out the effects of firm size.

The results of the analysis are reported in Table 7. The table provides path coefficients

and t-statistics for the main and interaction effects. As PLS does not any distributional

assumptions, traditional parametric tests are invalid. To assess the significance of the estimates,

PLS uses a bootstrapping method with replacement to estimate standard errors. Bootstrapping

with 200 resamples is used.

VI. Results

The results of the analysis are contained in Table 7. For both B-IOS and S-IOS there are

three separate significant interactions with information processing requirements variables. For

each information processing capability variable, half the possible interactions with information

processing requirements variables significantly explain variance in operational performance.

Three hypotheses are supported (H1a, H1b, H5b) and three hypotheses are contradicted (H2a,

H2b, H6a).

Supported Hypotheses

H1a: Task variability positively moderates the relationship between S-IOS and

operational performance (ß=0.147, p<.10) indicating that higher levels of S-IOS and higher

levels of task variability are associated with higher waiting time. The interaction supports the

24

hypothesis that higher levels of S-IOS and higher levels of task variability are associated with

lower operational performance.5

H1b: Task variability negatively moderates the relationship between B-IOS and

operational performance (ß=-0.135, p<.05) indicating that high use of B-IOS and high task

variability are associated with lower waiting time. This supports the hypothesis that higher levels

of B-IOS and higher levels of task variability are associated with higher levels of performance.

H5b: Buyer independence positively moderates the relationship between B-IOS and

operational performance (ß=0.149, p<.10) indicating that higher use of B-IOS and higher buyer

independence are associated with higher waiting time. This supports our hypothesis that for

higher levels of B-IOS, higher levels of buyer independence are associated with lower levels of

performance.

Contradicted Hypotheses

H2a: Task analyzability positively moderates the relationship between S-IOS and

operational performance (ß=0.153, p<.05) suggesting that higher levels of task analyzability and

high levels of S-IOS are associated with higher waiting time. This finding contradicts our

hypothesis that for higher levels of S-IOS, higher levels of task analyzability are associated with

higher operational performance.

H2b: Task analyzability negatively moderates the relationship between B-IOS and

operational performance (ß=-0.169, p<.05). Higher levels of B-IOS and higher levels of task

analyzability are associated with lower waiting time. This finding contradicts our hypothesis that

for higher levels of B-IOS, higher levels of task analyzability are associated with lower

operational performance.

H6a: Supplier independence negatively moderates the relationship between B-IOS and

operational performance (ß=-0.159, p<.05). Higher levels of S-IOS and supplier independence

are associated with lower waiting time. This finding contradicts our hypothesis that for higher

5 Because the dependent variable is the average waiting time for a shipment to arrive, lower wait time

indicates higher performance.

25

levels of S-IOS, higher levels of supplier independence are associated with lower operational

performance.

Unsupported Hypotheses

Six hypotheses were not supported. Environmental dynamism (H3a, H3b), trust (H4a,

H4b), buyer independence for S-IOS (H5a), and supplier independence for B-IOS (H6b) were

not supported.

VII. Discussion

Support for the information processing model

The test of the information processing model is the presence of interactions, not main

effects. Six of twelve possible interactions were found to be significant in the analysis. Five of

the interactions are “pure” moderators (without significant main effects) indicating that these

effects are only present as interactions (Sharma, 1981). We conclude that the fit between

information processing requirements and capabilities is important in explaining operational

performance, at least providing partial support for Galbraith’s information processing model at

the interorganizational level.

Task variability

We conjectured that task variability is a moderator of IOS impacts. This is supported by

the significant interactions for H1a and H1b. We argued that where task variability is high,

exceptions arise during execution increasing information requirements. S-IOS, as a technology

that favors preplanning and exchange of standard documents, does not enable this type of ad hoc

exchange and therefore fails to support resolution of the exceptions and performance suffers. The

interaction for H1a supports this argument.

B-IOS provides an array of communication technologies. We associate B-IOS with the

coordination of tasks that are not preplanned. Exceptions arise during task execution for which

26

B-IOS provides flexible communications for their resolution. Highly variable interorganizational

tasks create exceptions that require information to be exchanged during task execution on an ad

hoc basis. Having available a breadth of IOS technologies enables communications across

organizations to resolve these exceptions and maintain high levels of performance. The

interaction for H1b supports this argument.

Task analyzability

We conjectured that task analyzability is a moderator of IOS impacts. This is supported

by significant interactions for H2a and H2b. However, our argument that high task analyzability

lowers exceptions favoring S-IOS but not B-IOS was not supported and the opposite was found.

An examination of the items for task analyzability has led us to this interpretation of the findings.

We conjecture that task analyzability as we have implemented it in the study does not necessarily

mean task “standardizability”. High task analyzability, especially for complex tasks such as

international air cargo, may mean that a task is well understood. However, it does not mean that

the task can be preplanned. Instead, exceptions still arise, albeit well understood exceptions.

Therefore B-IOS is more appropriate for their resolution.

Buyer and supplier independence

In the model, independence is a moderator of IOS impacts. We find two of four possible

interactions are significant which partially supports the role of independence as moderator.

We had hypothesized that where independence is high for either buyers or suppliers this

leads to a lack of coordination between organizations for which neither S-IOS nor B-IOS can

improve. H5b supports this argument, but given the findings of H6a this explanation is wanting.

H6a is contradicted and indicates that S-IOS improves performance with higher

independence. In the context of air cargo, where large and influential airlines dwarf smaller

forwarders, buyer and supplier independence do not have similar effects. Where a weak buyer is

independent and using an IOS, their independence leads to more exceptions and ineffective use

of any IOS. Where a powerful supplier is independent and using an IOS, their independence

influences forwarders to comply with their needs and leads to standardization and enforced use

of IOS. In this case, supplier independence does not increase exceptions, but results in unilateral

27

determination of standards, which in turn reduces exceptions enabling S-IOS to be effective in

increasing coordination. This suggests that buyer and supplier independence need to be

considered together in assessing IOS impacts.

Differential impacts of dimensions of IOS

The findings support the argument that S-IOS is a technology that constrains exchange of

information required to resolve exceptions, while B-IOS enables information exchange and

resolution of exceptions. The findings for S-IOS and B-IOS highlight the differential impact of

dimensions of IOS on operational performance. The relationship between high S-IOS use and

high operational performance is moderated by low task variability, low task analyzability, and

high supplier independence. The relationship between high B-IOS and high operational

performance is moderated by high task variability, high task analyzability, and low buyer

independence.

VIII. Contribution

Contribut ion

The findings support the general configuration of the proposed model drawn from

Galbraith (1977) and extended to the interorganizational level by Bensaou and Venkatraman

(1995). Our work supports the investigation of the nature of task and relationship uncertainty as

moderators of IOS impacts, dimensions of IOS as information processing capabilities, and the

use of operational performance as a measure of interorganizational coordination.

The study finds that the conditions for effective use of IOS depend not only on the

context but on dimensions of the technology. Some technologies such as S-IOS constrain

exchange of information during task execution, while others such as B-IOS enable exchange.

While these dimensions are exploratory, they suggest that further research along this trajectory is

warranted.

28

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Table 1 Measurement Items

INFORMATION PROCESSING CAPABILITIES Retained Items X1 X2

Standardized IOS (S-IOS) Standardized transmission of electronic documentation What percent of outbound shipments have: 1. Electronic air waybills 2. Completely electronic documentation

0,1,2 0,1,2

B1 B2 B3 B4

Breadth of IOS (B -IOS) Breadth of information technologies Does your company currently use: 1. Cargo Community System 2. Electronic data interchange (EDI) 3. Electronic tracking and tracing 4. Internet (web use + e- commerce use)

0,1 0,1 0,1 0,1,2

INFORMATION PROCESSING REQUIREMENTS Retained Items V1

Task Variability (TASKVAR) Frequency of exceptional and novel events which require different methods for performing the task. To what extent do the following statements characterize your company’s air cargo operations? 1. Operational problems frequently arise for which there are no standard solutions. 2. Most operational problems are routine and have routine solutions. 3. The number of exceptional (non-routine) problems we encounter are rising. 4. Percent of total shipments considered to be routine shipments.

5-point*

A1 A2

Task Analyzability (TASKAN) Extent to which there is a known procedure that specifies the sequence of steps to be followed in performing the task To what extent do the following statements characterize your company’s air cargo operations? 1. There are well-established practices and procedures to guide agents in preparing and managing air cargo shipments 2. Operational performance is easy to measure

5-point 5-point

E1 E2

Environmental Dynamism (ENVDYN) The extent to which task -relevant characteristics of the environment are changing. To what extent do the following statements accurately describe your company's environment? 1. Changes in the products offered by our competitors are hard to predict 2. Changes in product demand are hard to predict 3. Competing in this industry today is more difficult than ten years ago 4. Prices charged by airlines are hard to forecast 5. There are good opportunities for growth in our company’s primary markets

5-point 5-point

T1 T2 T3

Interorganizational Trust (TRUST) Degree of trust that exists between actors (forwarders and carriers) To what extent do these statements reflect your company’s dealings with airlines? 1. The airlines we deal with adhere to agreements, verbal and written. 2. Our information relationship is open and sharing. 3. Airlines are fair in their dealings with our company.

5-point 5-point 5-point

34

BD1 BD2 BD3

Buyer (Forwarder) Independence (BUYIND) Extent to which the buyer (forwarder) is dependent on the supplier (airline). To what extent do these statements reflect the relationship your company has with its airline partners? 1. If we stop shipping with one airline we can easily switch to another 2. There are many competitive airlines we could use for our shipments 3. Our operations can easily be adapted to a new airline

5-point 5-point 5-point

SD1 SD2 SD3 SD4

Supplier (Airline) Dependence (SUPIND) Extent to which the supplier is dependent on the buyer. To what extent do these statements reflect the relationship your company has with its forwarder partners? 1. If we stopped dealing with an airline they can easily find another forwarder to replace our business 2. It is relatively easy for an airline to find other forwarders 3. Finding other forwarders would not have a negative impact on the price the airlines can charge 4. If the relationship with our company was terminated, it would not hurt an airline’s operations

5-point 5-point 5-point 5-point

OPERATIONAL PERFORMANCE WT

What percent of (international) shipments are available to the consignee at the destination airport at the following times? Just before or just at the scheduled delivery time Within 4 hrs of the scheduled delivery time Within 12 hrs ” Within 24 hrs ” Within 48 hrs ” Within 72 hrs ” Over 72 hrs ” Weighted score is the average waiting time (WT) for a shipment by the consignee.

% % % % % % %

CONTROL RV Gross revenues from air cargo products and services in 1998 (Thousands USD) $USD (000) * Responses were recorded by circling a number a 5-point scale anchored by “to no extent” and “very great extent”

Table 2 Respondent Demographics

Forwarder Descriptive Statistics

165 1 1,382,850 26,665 153,170

160 1 1,369,788 17,863 118,766199 1 60 17 12

188 1 100 50 32

154 0 6,325,000 63,907 534,423154 0 675,000 8,155 56,820154 0 75,000 1,976 8,236194 1 60,000 433 4,358110 1 10,800 150 1,093

165 0 250 7 31

172 0 390 8 39

192 0 100 10 22

190 0 100 22 33

Gross Revenue$USD thousandsMetric TonnesYears in air cargo% Total revenue inair cargo

Air CargoBusiness

House AWBsMaster AWBsDirect AWBs

Air Waybills

FTE EmployeesAir cargo FTEInformationSystems empl.Air cargo branches

OrganizationSize

% Gross revenuefrom specializedfreight% Gross revenuefrom time-definiteservices

Products

N Min Max MeanStd.Dev.

35

Table 3 Descriptive Statistics

Min Max Mean S.D. Skewness Kurtosis A1 1 5 3.95 0.88 -0.53 -0.15 A2 1 5 3.76 0.93 -0.64 0.36 V1 1 5 2.64 1.12 0.35 -0.65 E1 1 5 2.64 1.09 0.27 -0.57 E2 1 5 2.79 1.09 0.29 -0.43 T1 1 5 3.81 0.92 -0.40 -0.46 T2 1 5 3.25 1.06 -0.31 -0.33 T3 1 5 3.42 0.93 -0.29 -0.09

BD1 1 5 3.76 1.03 -0.72 0.15 BD2 1 5 3.60 1.10 -0.59 -0.14 BD3 1 5 3.93 1.04 -1.06 0.86 SD1 1 5 3.50 1.14 -0.44 -0.49 SD2 1 5 3.68 1.01 -0.36 -0.56 SD3 1 5 3.34 1.16 -0.24 -0.51 SD4 1 5 3.30 1.29 -0.20 -0.98

X1 0 2 0.49 0.73 1.13 -0.20 X2 0 2 0.33 0.63 1.72 1.66 B1 0 1 0.31 0.46 0.84 -1.30 B2 0 1 0.36 0.48 0.60 -1.65 B3 0 2 0.94 0.72 0.09 -1.03 B4 0 2 0.94 0.72 0.09 -1.08 RV 0.69 14.14 6.86 2.29 0.24 0.67 WT 0 4.41 1.91 1.04 0.07 -0.78

Table 4 Matrix of loadings and crossloadings*

SUPIND B-IOS BUYIND TRUST S-IOS ENVDYN TASKAN TASKVAR V1 0.01 -0.06 -0.02 0.03 -0.03 0.07 0.01 0.88 A1 0.04 0.12 0.06 0.10 -0.04 0.08 0.87 0.10 A2 -0.03 -0.11 0.13 0.09 -0.07 -0.14 0.85 -0.09 E1 0.02 -0.08 0.06 -0.05 0.06 0.88 -0.13 0.03 E2 0.05 -0.00 -0.16 -0.04 -0.03 0.88 0.08 0.05 T1 0.14 0.16 0.13 0.74 0.02 -0.02 0.10 0.01 T2 -0.10 -0.06 -0.16 0.82 0.17 -0.00 0.04 0.03 T3 -0.16 -0.11 0.08 0.79 -0.15 -0.08 0.05 0.01 BD1 0.14 0.03 0.77 0.13 -0.16 -0.03 -0.01 0.19 BD2 0.03 0.02 0.91 0.07 0.03 0.02 0.07 -0.10 BD3 0.18 -0.14 0.73 -0.21 -0.03 -0.13 0.22 -0.16 SD1 0.79 -0.03 0.07 -0.06 0.03 -0.01 -0.03 -0.34 SD2 0.85 -0.09 0.05 0.02 -0.09 0.08 -0.04 -0.11 SD3 0.77 -0.02 0.04 -0.06 -0.04 0.05 0.14 0.26 SD4 0.76 -0.06 0.18 -0.04 0.00 -0.04 -0.03 0.12 B1 -0.10 0.75 -0.06 0.04 -0.07 -0.16 0.05 -0.03 B2 0.13 0.67 0.07 -0.09 0.09 -0.01 0.07 -0.21 B3 -0.12 0.68 -0.12 0.07 0.15 -0.01 0.01 0.16 B4 -0.08 0.77 0.06 -0.03 0.07 0.08 -0.11 -0.00 X1 -0.02 0.32 -0.03 0.06 0.85 0.00 -0.06 -0.04 X2 -0.07 -0.04 -0.09 -0.02 0.90 0.03 -0.05 -0.00 * Principal components analysis with Varimax rotation

36

Table 5 Reflective Construct Statistics

Item Loading

t-stat

Cronbach

Alpha6 Composite Reliability7 AVE8

Task Analyzability A1 0.867 ** 2.681 .729 0.877 0.781 A2 0.901 *** 3.594

Environmental Dynamism E1 0.642 * 2.261 .722 0.814 0.696 E2 0.990 *** 3.594

Interorganizational Trust T1 0.893 *** 3.740 .736 0.830 0.622 T2 0.790 *** 4.262 T3 0.667 ** 2.660

Buyer Independence BD1 0.790 *** 4.284 .806 0.878 0.706 BD2 0.887 *** 4.771 BD3 0.840 *** 4.524

Supplier Independence SD1 0.760 *** 6.368 .795 0.860 0.606 SD2 0.839 *** 7.963 SD3 0.798 *** 7.602 SD4 0.712 *** 7.415 * Indicates the item is significant at the p<.05 level; ** p<.01 level; *** p<.001 level

6 Cronbach’s alpha (1951). As per Nunnally (1978) this should exceed .7 indicating acceptable reliability

levels. 7 A measure of internal consistency. suggest this measure should be greater than .70 (Fornell and Larcker,

1981). 8 This is a measure of discriminant validity. Fornell and Larcker (1981) suggest that the average percentage

of variance extracted should be greater than .50.

37

Table 6 Inter-construct Correlations *

1 2 3 4 5 Task

Analyzability 0.884

Environmental Dynamism -0.039 0.834

Trust

-0.249 0.053 0.789

Buyer Independence -0.157 0.019 0.019 0.840

Supplier Independence

-0.029 -0.031 0.027 0.212 0.779 * The diagonal elements are square roots of AVE; off-diagonal elements are interconstruct correlations.

38

Table 7 Results - Main and Interaction Path Coefficients and t-statistics

Control Info. Proc. Capability

Main Effects (Information Processing Requirements)

Interactions (Information Processing Capability x Requirements)

RV S-IOS TASKVAR TASKAN ENVDYN TRUST BUYIND SUPIND x TASKVAR x TASKAN x ENVDYN x TRUST x BUYIND x SUPIND

S-IOS H1a H2a H3a H4a H5a H6a

Path coefficient -0.227 -0.093 0.057 0.063 0.022 -0.108 0.119 0.151 0.147 0.153 0.026 0.005 0.100 -0.159

t-stat -2.548 -1.082 0.711 0.740 0.246 -1.295 1.302 1.715 1.560 1.936 0.275 0.061 1.208 -1.988

1-tail sig (df=201) 0.006 0.140 0.239 0.230 0.403 0.098 0.097 0.044 0.060 0.027 0.392 0.476 0.114 0.024

R2 main R2 interaction

0.145 0.222

ü

û

-- -- -- û

B-IOS H1b H2b H3b H4b H5b H6b

Path coefficient -0.250 -0.103 0.089 -0.070 -0.030 -0.154 0.058 0.132 -0.135 -0.169 0.036 0.036 0.149 0.024

t-stat -2.873 -1.329 1.066 -0.737 -0.339 -1.866 0.597 1.422 -1.800 -2.058 0.384 0.394 1.409 0.263

1-tail sig (df=201) 0.002 0.106 0.139 0.206 0.365 0.027 0.268 0.081 0.035 0.025 0.352 0.328 0.075 0.403

R2 main R2 interaction

0.084 0.195

ü

û

-- -- ü

--

ü= supported; û= contradicted; -- = not supported