B2B Technology Adoption in Customer Driven Supply Chains

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Journal of Business & Industrial MarketingB2B technology adoption in customer driven supply chainsAnthony K. Asare Thomas G. Brashear-Alejandro Jun Kang

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B2B technology adoption in customer drivensupply chains

Anthony K. Asare

Department of Marketing, Quinnipiac University, Hamden, Connecticut, USA

Thomas G. Brashear-AlejandroDepartment of Marketing, University of Massachusetts Amherst, Amherst, Massachusetts, USA, and

Jun Kang

Business School, Hunan University, Changsha, China

AbstractPurpose – The purpose of this article is to develop and propose a comprehensive framework that identifies the factors that influence a company’sdecision to adopt business to business (B2B) technologies.Design/methodology/approach – The authors review the literature regarding technology adoption from multiple disciplines including: SupplyChain Management, Logistics, Sociology, Information Systems, Marketing and Economics. A synthesis of the review provides the foundation fordeveloping a comprehensive model of inter-firm technology adoption.Findings – The review and synthesis finds inconsistencies in the theoretical models and constructs used in previous studies of inter-firm technologyadoption. The comprehensive framework presented identifies four major categories of antecedents to technology adoption: characteristics of atechnology, organizational factors, external factors and relationships. The presented model focuses attention on the inclusion of relational factorsthat affect the adoption of B2B technology.Research limitations/implications – An important area that has been ignored in the inter-firm adoption literature is the impact of inter-firmrelationships on technology adoption. This paper emphasizes the importance of inter-firm relationships and identifies power, trust and justice asimportant relationships that influence the adoption of inter-firm technologies.Originality/value – The expanded framework identifies the antecedents of B2B technology adoption, which can be used as a guiding frameworkby both academics and practitioners. The paper also offers directions for future work in the form of propositions.

Keywords Technology adoption, Business to business technology, Inter-firm technology

Paper type Conceptual paper

Introduction

Supply chains are being driven by the customer, and the goal

of supply chains is no longer to improve the material flows of

a small group of selected first tier suppliers but rather to satisfy

the ever-changing needs of the ultimate consumer (Svensson,

2002). Since 2001, The Campbell Soup Company has shifted

its supply chain emphasis from reducing costs to focusing

more on their business customers and end consumers (Clark,

2004). Procter & Gamble also won the Manufacturer of the

Year Award for replacing their traditional cost-cutting supply

chain focus with a Customer-Driven Supply Network that

enables them to focus more on satisfying the changing needs

of their customers (Sowinsky, 2004).

To respond to the ever-changing needs of the customer,

compete with other competitor-led supply networks, andmanage their complex global multilayered partnership

network, channel leaders are increasingly relying on new

information technologies (ITs), particularly collaborativebusiness to business (B2B) technologies (Grover, 1993; Lee

and Qualls, 2010; Zelbst et al., 2010). Examples of these

technologies include Radio Frequency Identification (RFID),

Electronic Data Interchange (EDI), Point of Sale

technologies, Vendor Managed Inventory, Collaborative

Planning Forecasting and Replenishment and Internet-based

technologies. These collaborative B2B technologies are now

even more important than ever because while companies are

trying to rapidly respond to their customer’s needs, they are

also outsourcing their supply source globally, thus pushing

their supply source further away from their point of contact

with their customer (Roy and Sivakumar, 2007). Some

authors suggest that IT is the most important factor in supplychain improvement (Patterson et al., 2003) and a company

such as Walmart has been extremely successful in no small

part due to its ability to use supply chain technologies to create

very sophisticated and efficient supply chains and logistics

networks that enable them to become responsive to consumer

demand (Fries et al., 2010).

The current issue and full text archive of this journal is available on

Emerald Insight at: www.emeraldinsight.com/0885-8624.htm

Journal of Business & Industrial Marketing

31/1 (2016) 1–12

© Emerald Group Publishing Limited [ISSN 0885-8624]

[DOI 10.1108/JBIM-02-2015-0022]

Received 3 February 2015

Revised 3 February 2015

Accepted 3 February 2015

1

http://dx.doi.org/10.1108/JBIM-02-2015-0022http://dx.doi.org/10.1108/JBIM-02-2015-0022http://dx.doi.org/10.1108/JBIM-02-2015-0022


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Although the value of B2B technologies has been widely

accepted in supply chains (Xie and Johnston, 2004),

companies are struggling to get their supply chain partners to

adopt these technologies, and large numbers of these complex

and expensive systems have failed. For example, while 95 per

cent of Fortune 1000 firms implemented EDI (a popular B2B

technology), only 2 per cent of the remaining US businesses

did so even though the largest firms had been aggressively

encouraging the adoption of EDI (Chwelos et al., 2001). Even

among those companies that have adopted B2B technologies,

very few of them are satisfied with the state of their inter-firm

systems, suggesting that substantial barriers exist regarding

the adoption and performance of their supply chain

technologies (Patterson et al., 2003).

While there is extensive literature on technology adoption,

relatively little of it focuses on supply chain or inter-firm

adoption (Xie and Johnston, 2004). The majority of

technology adoption studies focus on technology adoption by

individuals, leaving out an important part of technology

adoption, which is the adoption of technology by

organizations (Rogers, 2003). The existing inter-firmtechnology adoption studies are inconsistent in their choice of

constructs, and as a result, the constructs used differ

considerably between studies. While each study has

contributed cumulatively and explores a portion of adoption,

none of the studies has developed a set of constructs that

comprehensively explains the phenomenon. Grover (1993)

identified the fact that the inter-firm adoption literature was

limited in its ability to focus on the macro level of adoption

and also to provide a core set of constructs. The problem still

remains today. One particularly important area that has been

inadequately covered or ignored in numerous inter-firm

technology adoption studies is the importance of inter-firm

relationships (Damanpour, 1991; Iacovou et al., 1995;O’Callaghan et al., 1992). Relationship variables like trust,

commitment and justice have been identified as very

important influences of inter-firm technology adoption

(Grossman, 2004; Hart and Saunders, 1997), yet most of the

inter-firm adoption studies do not adequately cover them.

Since the adoption of inter-firm technologies in supply chains

are usually initiated by a lead company who needs to convince

the other members to adopt a complex and expensive system,

inter-firm relationships are absolutely important in any effort

to adopt an inter-firm technology (Grossman, 2004).

The goal of this research is to develop and propose a

comprehensive framework to study the adoption of B2B

technologies by supply chain partners. The proposed

Technology Adoption in Supply Chains (TASC) model is

substantially different from the existing supply chain

adoption models and introduces constructs that are new to

the adoption of supply chain literature. The paper borrows

constructs from multiple fields including Supply Chain

Management, Logistics, Sociology, Information Systems,

Marketing and Economics. To better understand the

phenomena, the authors also extensively studied trade

publications and the popular press. The paper also offers

some directions for future work in the form of tentative

propositions.

Literature review

Individual technology adoption models

A preponderance of the technology adoption models found in

the academic literature is used to explain technology adoption

by individuals and not by organizations. However, the existing

inter-firm technology adoption models usually borrow from

these individual-level models and incorporate them intointer-firm adoption contexts. The two most commonly used

models to explain the adoption of technology by individuals

are the Technology Acceptance Model (TAM) and Attributes

of Innovation Model.

Technology acceptance model

TAM is one of the most widely used models to explain

technology user acceptance behavior (Hernandez et al., 2010;

Ma and Liu, 2004) by individuals. The model was introduced

to help identify a small number of fundamental variables that

determine computer acceptance and usage (Davis et al.,

1989). TAM posits that “perceived usefulness” and

“perceived ease of use” are primary determinants of an

individual user’s attitude toward using technology. Theirattitude toward the technology then influences their

behavioral intention to use the technology which in turn

determines whether they will actually use the system (Davis

et al., 1989; Venkatesh and Davis, 2000). Perceived ease of

use refers to the extent to which the user believes that the

system will be free of effort, and perceived usefulness is

defined as the user’s belief that using a system will increase his

or her job performance (Davis et al., 1989; Venkatesh and

Davis, 2000).

According to the model, if individuals perceive a computer

system to be useful and easy to use, they are likely to have a

positive attitude toward the system. The more positive their

attitude toward a system, the more likely they will have a

behavioral intention to use it. Also, the higher the intention touse the system, the more likely they are to actually use it. Since

its introduction, TAM has received considerable empirical

support but has been criticized for ignoring the impact of

social influences on an individual’s decision to use technology.

In an effort to extend TAM to cover social influences and

other relevant predictors of technology acceptance, Venkatesh

and Davis (2000) proposed and empirically tested a new

model that included social factors. This new model, TAM2,

found support for the influence of three social factors:

subjective norms, image and voluntariness, in an individual’s

decision to adopt or reject technology. They also found that

subjective norms have a direct effect on “intention to use” in

mandatory contexts but not in voluntary contexts. The

authors suggested that this explained why previous studies,most of which were conducted in voluntary environments,

found a non-significant role for social factors. In another

study, Brown et al. (2002) also studied the impact of social

influences on TAM. They explored the appropriateness of

TAM in mandatory environments because they anticipated

that the underlying relationships of the traditional technology

adoption models would be different in mandatory

environments. They found that TAM did not adequately

explain technology adoption in mandatory environments.

They also discussed the potential consequences of mandating

technology usage and suggested that mandating technology

B2B technology adoption in customer driven supply chains

Anthony K. Asare, Thomas G. Brashear-Alejandro and Jun Kang


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use in an organization could result in the employees having

low job satisfaction, low loyalty and negative feelings toward

their supervisors and organization. Mandating technology use

could also lead to increased sabotage, unfaithful appropriation

of technology, delay or obstruction to implementation and low

productivity. They recommend that in mandatory settings,

organizations should engender positive attitudes toward the

technology to avoid potentially disruptive attitudes andbehaviors.

Although TAM is a classic model widely used to explain

technology adoption, it has primarily been used to explain

individual user adoption of simple technologies in voluntary

situations. In intra- and inter-firm environments where

technology adoption is sometimes mandated and usually

involves complex technologies, other models and theories,

including the attributes of innovation model, have more

commonly been used to explain technology adoption in more

complex environments.

Attributes of an innovation

Researchers of both individual and organizational technologyadoption have extensively used the attributes of an innovation

model and have found that these attributes usually account for a

large amount of variance in organizational adoption of

innovations (Russell and Hoag, 2004). Borrowing from decades

of diffusion research, Rogers (1983) identified five main

attributes of innovation: relative advantage, compatibility,

complexity, trialability and observability.

Relative Advantage is the degree to which an innovation is

perceived as being better than the existing idea that is being

replaced (O’Callaghan et al., 1992). Past research almost

universally finds a positive relationship between relative

advantage and rate of adoption, and researchers find relative

advantage to be one of the strongest predictors of adoption

(Rogers, 2003). Complexity refers to the degree to which aninnovation is believed to be difficult to understand, use or

implement (Rogers, 2003). Complexity has been widely found

to have a negative influence on adoption and it is believed that

the more complex an innovation, the less likely for it to be

adopted (Sia et al., 2004). Compatibility refers to the degree to

which an innovation is perceived to be consistent with the

adopter’s internal culture, business processes, management

practices and communication protocols (O’Callaghan et al.,

1992). Greater compatibility is usually more preferable

because it presents the adopter with less uncertainty and

allows the interpretation of the innovation in a more familiar

context (Sia et al., 2004). Trialability refers to the degree to

which an innovation can be experienced on a limited basis

before adoption (Rogers, 2003) and is believed to be positivelyassociated with adoption since it helps to reduce uncertainty in

the adoption process (Al-Gahatani, 2003). Observability

refers to the degree to which the results of an innovation can

be easily observed (Venkatesh et al., 2003) and is usually

positively related to its adoption.

Other researchers have modified Rogers’ perceived

attributes of innovation model. Prominent among them are

Moore and Benbasat (1991) who identified two further

constructs – image and voluntariness. Image refers to the

ability for an innovation to enhance the adopter’s social status

in a social system, and voluntariness refers to the adopter’s

perception as to how voluntary the decision to adopt the

innovation is. Tornatzky and Klein (1982) conducted a

meta-analysis of innovation characteristics and found about

30 different characteristics. They identified ten characteristics

that had an effect on technology adoption including Roger’s

five characteristics and an additional five characteristics; cost,

communicability, divisibility, profitability and social approval.

Despite these efforts to modify or improve the five attributes of innovations, most researchers still focus mostly on Roger’s

(1983) five original attributes since they have been well

researched and proven time and again to have a strong

correlation with the decision to adopt an innovation by an

individual.

Just as in the case of the TAM model, a major criticism of

the attributes of innovation literature is that it focuses too

much on innovations for individual adopters and not enough

on innovations by organizations or the larger social system.

Tornatzky and Klein (1982) suggest that future studies should

emphasize the adoption of innovations in organizations.

Inter-firm technology adoptionAlthough TAM and the attributes of innovation model explain

a large amount of the variance in individual technology

adoption, they explain a lot less in organizational and

inter-firm environments since decision making in these

environments is a lot more complex and also introduce a lot

more variables than in individual technology adoption

environments (Damanpour, 1991). Efforts have therefore

been made by inter-firm technology adoption researchers to

create new models that explain the more complex

organizational environment while still including some

elements from the individual adoption models.

O’Callaghan et al. (1992), in their study of EDI in an

inter-firm environment, identify three main factors that influence

technology adoption: relative advantage, compatibility andexternal influences. Relative advantage and compatibility were

borrowed from the attributes of innovation model. The construct

“external influence” is made up of three sub constructs: the

source firm, previous adopters and industry promotion. The

authors found a positive relationship between relative advantage

and the decision to adopt EDI. They however did not find a

significant relationship between external influences and EDI

adoption.

Another inter-firm adoption model was developed by

Grover (1993) to identify factors that facilitate the adoption of

customer-based interorganizational systems (CIOS). The

initial model identified organizational factors, support factors,

policy factors, environmental factors and interorganizational

systems (IOS) factors as the major determinants of anorganization’s decision to adopt a CIOS. After empirically

testing the initial model, the author developed a new model

based on the constructs that he was able to find strong support

for. The new model identified internal push, competitive

need, market assessment, proactive technical orientation and

industry adoptions as the key factors that positively affect the

adoption of a CIOS. Grover (1993) also identified

information intensity, complexity and incompatibility as

impediments to the adoption of a CIOS.

Premkumar and Ramamurthy (1995) studied the role of

interorganizational and organizational factors on an




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organization’s decision to adopt IOS. They identified four

interorganizational f actors that w ere based on a

socio-political framework borrowed from the marketing

literature. These factors were competitive pressure,

transaction climate, exercised power and dependence. They

also identified five organizational factors that were based on IS

research including top management support, internal need, IS

infrastructure, organizational compatibility and the presence

of an internal champion. Their study found support for two

organizational variables, top management and internal need,

and also two interorganizational variables, exercised power

and competitive pressure. Premkumar and Ramamurthy

(1995) also examined the difference between reactive and

proactive firms on three implementation outcomes. Proactive

firms were found to have more external connectivity with their

trading partners, greater extent of adaptation and better

integration of EDI information in their own internal systems.

Hart and Saunders (1997) are one of the few researchers

who focused their research primarily on the influence of

relationships on inter-firm technology adoption. They

developed a theoretical framework that addresses the role thatpower and trust play in EDI adoption and usage. Their model

described the role of power in the persuasion of a trading

partner to adopt EDI. They also looked at the role of trust in

the usage of EDI after its adoption and the relationship

between trust and the type of power exercised. The variables

that they studied include supplier dependence, buyer

dependence, potential power, exercised power, continuity,

level of EDI use and trust. They also identified four

interrelated dimensions of trust: competence, openness,

caring and reliability. Using the case study of a single firm,

the authors illustrate how power and trust can be used to

influence inter-firm technology adoption. The paper also

offers some directions for future work in the form of tentativepropositions.

Russell and Hoag (2004) took a different approach. They

studied the social and organizational influences that affect

people’s acceptance of inter-firm technology designed for use in

an organization. Using case studies, they identified nine social

and organizational variables that influence the adoption of

inter-firm technologies. The variables are relative advantage,

compatibility, complexity, centralization, interconnectedness,

system openness, resource intensiveness, management level

support, breadth of support, formalism and internal champions.

After reviewing the literature on inter-firm technology

adoption, it can be seen that while each has contributed to our

cumulative knowledge and explained part of the adoption

process, no single study incorporates constructs that

comprehensively addresses the major constructs that influence

a company’s decision to adopt inter-firm technologies

(Chwelos et al., 2001). The literature contains several

approaches and operationalizations and a number of

overlapping and divergent models have been used. Efforts

made to explain why organizations adopt inter-firm

technologies have therefore been inconsistent and inadequate.

In the following section, we develop a comprehensive model of

inter-firm adoption building upon the existing literature and

expanding to include relational and environmental variables.

Proposed TASC framework

The authors propose a framework that identifies the

antecedents of TASC. TASC identifies four key determinants

of the adoption of inter-firm technologies (Figure 1):

1 characteristics of technology;

2 organizational factors;

3 external factors; and

4 inter-firm relationships.

Characteristics of technology

The characteristics of the technology being adopted usually

account for a large amount of variance in inter-firm

technology adoption (Russell and Hoag, 2004). The TASC

Model borrows from the attributes of innovation literature and

identifies five key attributes of an innovation that influence its

adoption in a supply chain. They are Relative Advantage,

Complexity, Compatibility, Trialability and Observability.

The model also adds cost, which is a construct that is not

commonly used in the literature to explain inter-firm

adoption.

Relative advantage

Relative advantage has been widely used in the inter-firm

technology adoption literature, and researchers consistently

find the construct to be one of the strongest predictors of

technology adoption (Russell and Hoag, 2004). We define

relative advantage as the degree to which an innovation is

perceived as being better than the idea that it replaces

(O’Callaghan et al., 1992), and the construct has been used in

the literature synonymously with perceived usefulness from

TAM and also perceived benefits (Venkatesh et al., 2003).

Firms are more likely to adopt a technology if they believe it to

be better than the existing technology or methods used in the

firm to perform the same activity (Zablah et al., 2005). Since

Figure 1 Proposed TASC model

Technology

Adoption

Organizational

Characteristics

· Size

· Centralization

· ManagementSupport

· IT Readiness

Characteristics of

Technology

· RelativeAdvantage

· Complexity

· Compatibility

· Testability

· Observability

· Cost

Inter-firm

Relationships

· Power

· Justice

· Trust

External

Environment

· EnvironmentalUncertainty

· CompetitivePressure

· Industry

Support




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past research for the large part finds a positive relationship

between relative advantage and rate of adoption and

researchers find relative advantage to be one of the strongest

predictors of adoption (Rogers, 2003), we propose that:

P1. The perceived relative advantage of the technology

being adopted is positively associated with the intention

to adopt B2B technologies.

Complexity

The technology adoption literature identifies three different

dimensions of complexity: complexity to understand;

complexity to use; and complexity to implement. However,

inter-firm technology adoption researchers rarely use all

dimensions in their definition of complexity. For example, Sia

et al. (2004) focus on complexity to implement while Karahanna

et al. (1999) only emphasize complexity of use. Our definition of

complexity encompasses all three dimensions, and in line with

Rogers (2003) we define complexity as the degree to which an

innovation is difficult to implement, use and understand.

Highly complex technologies are usually seen as a barrier to

technology adoption (Lin and Ho, 2009) since they are usually

difficult to implement, can lead to costly and widespread

disruptions and in general discourage decision makers in an

organization from adopting and implementing a technology.

Complexity has been widely found to have a negative

influence on adoption (Sia et al., 2004), and so we propose

that:

P2. The complexity of the technology being adopted is

negatively associated with the intention to adopt B2B

technologies.

Compatibility

In the individual adoption literature, the compatibility of atechnology is usually determined by how compatible the

technology is with elements of the individual’s social system

(Rogers, 2003). Inter-firm technologies however present a

unique compatibility problem because not only does the

technology have to be compatible with the organization

(organizational compatibility), but it also has to be compatible

with the existing technology systems that it is going to

interface with (systems compatibility). Systems compatibility

is very important in technology adoption and refers to

compatibility between the technology and the organization’s

existing software, hardware, back office computer systems and

other technology systems and resources (Lin and Ho, 2009).

Organizational compatibility, on the other hand, refers to

compatibility between the innovation and the adopter’s

internal culture, business processes and management practices

(O’Callaghan et al., 1992). Too often, organizational

compatibility is ignored during a technology adoption process,

leading to disastrous consequences for the adoption process.

Following O’Callaghan et al. (1992), we classify compatibility

into two separate categories, organizational and system

compatibilities, enabling us to cover both very important

dimensions of compatibility. Since compatibility is positively

related to technology adoption (Premkumar and

Ramamurthy, 1995; Sia et al., 2004), we propose that:

P3a. Organizational compatibility is positively associated

with an organization’s intention to adopt B2B

technologies.

P3b. Systems compatibility is positively associated with an

organization’s intention to adopt B2B technologies.

Trialability

This refers to the degree to which an innovation can beexperienced on a limited basis before adoption (Rogers,

2003). Trials can help the adopter understand how to use the

innovation, thus making it less complex and easier to

understand when they later adopt it (Al-Gahatani, 2003).

They also enable the adopter to find and solve major problems

before rolling out the solution over a larger portion of the

company. Before fully adopting RFID, The Campbell Soup

Company conducted a pilot test in its Texas facility by tagging

over 1,000 cases and 90 pallets, while Unilever North America

conducted an RFID trial to gain operational insights into its

business case (Clark, 2004). Trialability is positively

associated with adoption (Al-Gahatani, 2003), and so w e

propose that:

P4. Trialability of the technology being adopted is

positively associated with an organization’s intention to

adopt B2B technologies.

Observability

This construct has been defined differently by different

authors. While some authors emphasize the demonstrability of

the results of the innovation (Al-Gahatani, 2003; Sonnenwald

et al., 2001), others define it in terms of the visibility of the

innovation itself (Moore and Benbasat, 1991). Although the

visibility of the technology itself is important in individual

technology adoption contexts, it is less important in inter-firm

environments since companies adopt technology because of

what it can do for them and not because of its visibility. Whatis more important to the company is whether the results of the

technology being adopted can be easily demonstrated or

quantified. If the innovation can be directly tied to economic

indicators like increased sales, profitability or return on

investment, it is more likely to be adopted than if it is tied to

indicators that are more difficult to demonstrate. Since

observability is usually positively related to adoption, we

propose that:

P5 . The observability of the results of the technology being

adopted is positively associated with an organization’s

intention to adopt B2B technologies.

Cost of innovation

The cost of an innovation is one of the most important factorsthat affect a firm’s decision to adopt B2B technologies, and the

cost of RFID, for example, is one of the major factors limiting

that technology’s adoption (Frost and Sullivan, 2005; Growe,

2004). Two main types of costs are associated with the

adoption of an innovation: direct and indirect costs. Direct

costs refer to those costs associated with acquiring the

technology, while indirect costs are the costs associated with

using, implementing and maintaining the technology.

Although the cost of an innovation is an important

determinant of whether a technology should be adopted, the

construct is quite often ignored in the technology adoption




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literature, and when it appears in the literature, it is usually

discussed under relative advantage. Tornatzky and Klein

(1982) suggest that cost should be considered separately from

relative advantage and in line with their recommendation we

consider cost as a separate construct. Since the cost of a

product is negatively associated with adoption (Wejnert,

2002), we propose that:

P6 . The cost of a product is negatively associated with an


Organizational factors

The TASC model identifies organizational factors as

important determinants of inter-firm technology adoption,

and the following are some of the organizational

characteristics that TASC identifies as important antecedents

of technology adoption.

Management support

This refers to the extent to which senior executives of an

organization support an innovation. Management supportdoes not refer to mere approval from top management but

requires active and enthusiastic support that can be

transmitted through the whole organization (Grover, 1993).

Support from management is even more important in the case

of inter-firm technology adoption since this type of adoption is

usually expensive, complicated and requires long-term vision

and interaction among trading partners (Premkumar and

Ramamurthy, 1995). Grover (1993) empirically tested the

effects of top management support and found a strong

relationship between management support and the decision to

adopt inter-firm technologies. Premkumar and Ramamurthy

(1995) also found empirical support for the effects of top

management support on inter-firm technology adoption.

Since top management support has been found to have a

positive effect on the adoption of technology by an

organization (Damanpour, 1991, we propose that:

P7 . Management support of a new technology is positively

associated with an organization’s intention to adopt

B2B technologies.

Centralization

This refers to the extent to which decision-making authority is

limited in an organization ( Jaworski and Kohli, 1993; Kirca

et al., 2005). Lower-level managers in different functional

areas are more likely to possess greater knowledge of the

technology, operational-level problems and the business

processes than the higher-level executives (Amami and

Brimberg, 2004). Organizations with decentralized structures

are expected to adopt more innovative and cutting-edge

technologies (Kamaruddin and Udin, 2009). In organizations

where lower-level managers are not empowered to make

important decisions, new ideas and innovations are less likely

to be encouraged. Centralization is usually negatively

associated with organizational innovativeness since the more

centralized the decision making in an organization is, the less

innovative it has been found to be (Rogers, 2003). We

therefore propose that:

P8 . The level of centralization of an organization is

negatively associated with an organization’s intention to


Organizational size

Size is one of the most commonly used measures of an

organization’s innovativeness and has been both positively and

negatively associated with a firm’s decision to adopt atechnology. While large organizations usually have more

resources that they can use to adopt technologies, they are also

less flexible and unable to adapt quickly (Damanpour, 1996).

In spite of the different associations, the positive relationship

between size and organizational innovativeness holds across a

large number of investigations (Damanpour, 1996; Patterson

et al., 2003; Rogers, 2003). Since the positive relationship

between size and organizational adoption of technologies

holds across a large number of investigations, we propose that:

P9 . Organizational size is positively associated with an


IT readiness

This construct is associated with the level of sophistication of IT management (Iacovou et al., 1995). Companies that have

sophisticated IT environments adopt technologies easier than

those with less sophisticated IT environments since

sophisticated IT firms are more likely to have the necessary

expertise and resources in-house to adopt and implement the

technology (Iacovou et al., 1995; Mouzakitis and Askounis,

2010; Qu and Wang, 2011; Zhang and Dhaliwal, 2009).

Grover (1993) found that IS infrastructure and planning

strongly predicted a company’s decision to adopt technology,

and Premkumar and Ramamurthy (1995) also found that the

level of IT sophistication of a company has a positive and

significant relationship with an organization’s decision to

adopt a technology. Since IT readiness is positively associated

with technology adoption (Premkumar and Ramamurthy,

1995), we propose that:

P10 . IT readiness is positively associated with an


External factors

External factors represent factors outside the organization that

can have a significant impact on the organization’s

performance (Sia et al., 2004), and the innovation literature

consistently recognizes that environmental factors influence

technology adoption (Grover and Goslar, 1993). TASC

identifies competitive pressure, environmental uncertainty

and industry support as major external influences affecting

technology adoption.

Competitive pressure

Companies are under pressure to adopt technologies when

their competitors or trading partners have either adopted that

technology or have the capability and desire to adopt it.

Chwelos et al. (2001) found competitive pressure to be the

single most important factor contributing to the adoption of

EDI. According to Premkumar and Ramamurthy (1995),

once their competitors adopt a technology, companies tend to

rush to adopt that technology even if they do not necessarily

need it. In a highly competitive market, companies are




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motivated to adopt innovative technologies to maintain their

customers and strategic flexibility (Huang et al., 2008)

Companies also adopt technologies that their trading partners

request them to for fear that if they are slow to respond to such

requests, they could lose some or all of their business to those

competitors that readily adopt the technology (Kamaruddin

and Udin, 2009). Technology adoption studies have

consistently found a positive relationship between competitivepressure and adoption (Grover, 1993) so we propose that:

P11. Competitive pressure is positively associated with an


Environmental uncertainty

Uncertain environments make companies feel vulnerable and

more willing to adopt technologies that they believe could help

them perform better (Grover and Goslar, 1993). These

vulnerable companies continuously scan the environment,

looking for technologies that could help them perform better.

According to Patterson et al. (2003), during uncertain

environments, companies tend to adopt ITs that enable them

to collaborate more effectively with their trading partners.

Environmental uncertainty motivates companies to adopt

innovative ITs to collect more information for their decisions

(Cegielski et al., 2012). Sia et al. (2004) however unexpectedly

found a negative correlation between environmental

uncertainty and innovation which they attributed to the nature

of the innovation that they studied (distributed work

arrangements). The vast amount of the literature indicates a

positive relationship between environments with high

uncertainty and company’s decision to adopt technologies

(Patterson et al., 2003; Williams, 1994). We therefore propose

that:

P12. Environmental uncertainty is positively associated with

an organization’s intention to adopt B2B technologies.

Industry support

This refers to support from industry associations, availability

of industry-wide standards and other industry-wide initiatives

aimed at managing and promoting the new technology. When

industry associations support the adoption of a technology,

they tend to use multiple means to encourage the use of the

technology in their industry. They help in the creation and

development of standards, provide technology infrastructure

and set up workshops to train their members on how to use the

technology (Lin and Ho, 2009). They also use their numerous

communication resources like industry meetings, publications,

conferences, trade shows, etc. to educate their members on the

value of the technology (Chan and Chong, 2012). According to

Frost and Sullivan (2005), clearly defined industry standardstend to minimize barriers to RFID adoption. Since industry

support is positively associated with technology adoption

(Grover, 1993), we propose that:

P13. Industry support is positively associated with an


Inter-firm relationships

Inter-organizational relationships in supply chains and

distribution channels are an important and key area of

research (Ring and van de Ven, 1994; Zaheer and

Venkatraman, 1995), and yet, surprisingly, most of the

research into TASC does not include the influence of

inter-firm relationships as predictors or influences of the

adoption decision (O’Callaghan et al., 1992; Russell and

Hoag, 2004; Williams et al., 1998). Since the adoption of

technology in supply chains is usually initiated by a lead

company who needs to convince the other members to adopt

a complex and expensive system (O’Callaghan et al., 1992),inter-firm relationships are absolutely important in any effort

to adopt an inter-firm technology (Grossman, 2004). The

authors identify power, trust and justice as important

relationships that influence the adoption of inter-firm

technologies.

Power

Power is defined as the ability of a firm to exert influence on

another firm (Frazier, 1983). Since inter-firm technology

adoption usually involves one company trying to influence the

other to adopt the technology, the amount of power that the

initiating company has is an important factor in the decision to

adopt technology. Power in inter-firm relationships is usually

a function of the level of dependence of the parties involvedand also the way in which the power is exercised (Hart and

Saunders, 1997). Gaski and Nevin (1985) distinguish

between potential and exercised power because firms may

have potential power but yet may not necessarily exercise it.

Power could be exercised in different ways. A persuasive

approach could be used to convince the adopting firms of the

benefits of adopting technology, or a more coercive approach

could be used in which threats and punishments instead of

inducements are used (Hart and Saunders, 1997). While both

persuasive and coercive approaches can influence a firm’s

decision to adopt technology, the coercive approach could

result in long-term damage to the relationship. Although

coercive power could lead to long-term negative outcomes, in

the short term, coercive power like persuasive power couldinfluence trading partners to adopt technology. In general,

partner power has a positive relationship with the adoption of

technologies (Zhang and Dhaliwal, 2009), we therefore

propose that:

P14. The amount of power of the initiating trading partner is

positively associated with an organization’s intention to


Trust

Trust is an essential part of doing business and has been

linked to successful outcomes within firm and inter-firm

environments (Morgan and Hunt, 1994). Trust provides

predictability which creates a sense of security in the exchangerelationship (Andaleeb, 1996) and Morgan and Hunt (1994),

in their seminal study found that trust is positively related to

both cooperation and commitment. A lack of trust among

supply chain partners often results in inefficient and ineffective

performance, and it has been reported that the biggest

stumbling block to the success of strategic alliance formation

is the lack of trust (Kwon et al., 2004).

Trust can be conceptualized as existing when one party has

confidence in another party’s integrity and reliability (Morgan

and Hunt, 1994) with most studies in marketing channels

defining trust as the extent to which a firm believes that its




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exchange partner is credible and/or benevolent (Geyskens

et al., 1999). These definitions lay stress on two dimensions of

trust – credibility and benevolence. Credibility includes two

dimensions: competence-based credibility and honesty-based

credibility. Competence-based credibility arises from the

trustor’s confidence in the trustee’s ability, knowledge and

skill related to a specific task (Cook and Wall, 1980; Mayer

et al., 1995) or influence within a specific domain (Sitkin andRoth, 1993). The second component of credibility is

Honesty-based trust (or integrity), which is the belief that

one’s exchange partner is reliable, stands by its word, fulfills

role obligations and is sincere (Anderson and Narus, 1990;

Dwyer et al., 1987).

Benevolence-based trust is the belief that the exchange

partner is genuinely interested in one’s interests or welfare and

is motivated to seek joint gains. A benevolent partner

subordinates immediate self-interest for long-range group gain

(Anderson et al., 1987) and will not take unexpected actions

that will have a negative impact on the firm (Anderson and

Narus, 1990).

Trust is important in the adoption of collaborative B2B

technologies since the use of inter-firm technologies

introduces collaborations that entail more sharing and access

to important confidential information (Grossman, 2004),

leading to increased vulnerability and interdependence (Hart

and Saunders, 1997). Trust between partners is also necessary

for a company to ensure its partner will commit resource to the

technology adoption and not act opportunistically in this

adoption process (Huang et al., 2008). To manage these

vulnerabilities and uncertainties, it is important for trust to

exist between trading partners (Hart and Saunders, 1997).

Without trust, the trading partners will be reluctant to adopt

technology that will enable their trading partners to access

sensitive trade information. We therefore propose that:

P15a. The level of credibility-based trust is positively associated

with the intention to adopt B2B technologies.

P15b. The level of competence-based trust is positively

associated with the intention to adopt B2B technologies.

P15c. The level of benevolence-based trust is positively

associated with the intention to adopt B2B technologies.

Justice

Perceptions of justice are important to the maintenance and

quality of channel relationships (Gilliland and Manning,

2002; Kumar et al., 1995), and the perceptions of injustice or

unfairness by vulnerable channel partners may result in

hostility toward a partner initiating an activity that is perceivedto be unfair. In inter-firm technology adoption, the adoption

process is frequently initiated by a larger firm asking their

trading partners to adopt a technology that may be of limited

value to the firms being asked to adopt it (Iacovou et al.,

1995). When this happens, the target companies may consider

it unfair and resist the adoption of the technology (Suzuki and

Williams, 1998). To ensure that their trading partners adopt

the technology, the initiating companies frequently threaten

those who are reluctant to adopt the technology with

punishments like fines or termination of their contracts (Hart

and Saunders, 1997).

For fear of the consequences of not adopting the technology,

the target companies may only partially adopt the technology or

even buy the technology at the request of their trading partner

but not implement it (Suzuki and Williams, 1998). Some

companies will use alternative and less-efficient methods in their

back-end systems while making their larger partners believe that

they are using the newly adopted innovation. Because the

companies might not be using the new technology, the tradingpartner will in the long run, not get the efficiencies that they

thought they had planned for. Because companies are being

forced to adopt technologies that they do not find particularly

beneficial to them, issues of fairness and justice are important in

inter-firm technology adoption. Researchers have identified three

distinct dimensions to justice – distributive, procedural and

interactional (Colquitt, 2001; Sindhav, 2001).

Distributive justice. This refers to the perceived justice of

resources received in social exchanges (Brashear et al., 2004).

The literature identifies three categories of distributive justice:

equity, equality and need. Equity is a very important part of

distributive justice since social behavior is affected profoundly by

the belief that the outcomes of what members in a group receive

in an exchange should be proportional to their contributions

(Adams, 1965). Equality, another important aspect of

distributive justice, implies that recipients should receive the

same amount regardless of their inputs (Beugre, 1998; Deutsch,

1975). When using the “equality” rule, distributive justice is said

to occur when every member of a given social group receives the

same outcomes. Need is the third dimension of distributive

justice. When using the “need” rule, the need or welfare of each

recipient determines the distribution of rewards (Beugre, 1998;

Deutsch, 1975). Deutsch (1975) notes that in cooperative

relations when the fostering of personal development and

personal welfare is the common goal, need is likely to be the

dominant principle of distributive justice. Justice is important to

organizations due to its utility as a heuristic in enabling agents toevaluate whether a principal’s request is legitimate. In such a

case, agents can use the perceived justice of the principal as an

indicator of whether the request is legitimate or in the case of

distributive justice, that the past behavior of the partner will

provide some indication of future justice allocations. Based on

the discussions above, we propose that:

P16a. Firms who perceive the allocation of benefits from

adopting inter-firm technology will be distributed

equitably among the partners, are more likely to adopt

the technology.

P16b. Firms who perceive the allocation of benefits from

adopting inter-firm technology will be equal among the

partners, are more likely to adopt the technology.

P16c. Firms who perceive that the allocation of benefits from

adopting inter-firm technology will fulfill their needs,

are more likely to adopt the technology.

Procedural justice. Partners to exchanges are often interested

in the issues of process particularly in situations where process

judgments are important determinants of attitudes and

behavior (Lind and Tyler, 1988). These considerations gave

birth to the research on procedural justice or the justice of the

procedures used to determine outcome distributions and




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allocations. Thibaut and Walker (1975) observed that

disputants in legal procedures viewed the outcome as fair if

they believed that the procedures that had produced them

were fair. Applying procedural justice to an inter-firm

technology adoption context, we propose that:

P17 . Firms who consider requests to adopt inter-firm

technology to be equitable are more likely to adopt thetechnology than those who think it is not.

Interactional justice. Beyond concerns with distributions and

formal procedures, agents and/or participants to exchanges

care about the interpersonal treatment received from a

principal or the interactional justice (Bies and Moag, 1986).

Interactional justice has two components – interpersonal

justice and informational justice (Colquitt et al., 2001).

Interpersonal justice reflects the degree to which agents are

treated with politeness, dignity and respect by authority or

third parties in executing procedures or determining outcomes

(Greenberg and Bies, 1992). Informational justice focuses on

the explanations provided to people that convey information

about why procedures were used in a certain way or whyoutcomes were distributed in a certain fashion (Greenberg and

Bies, 1992). Applying interactional justice to an inter-firm

technology adoption context, we propose that:

P18a. Firms who perceive higher levels of interpersonal justice

in the requests to adopt inter-firm technology are more

likely to adopt the technology.

P18b. Firms who perceived higher levels of informational

justice in the requests to adopt inter-firm technology

will be more likely to adopt the technology.

Conclusion

The goal of this research is to study the reasons why the

adoption of inter-firm technologies in supply chains succeed

or fail, and also to propose a framework for practitioners and

academics, that identifies the antecedents for the successful

adoption of inter-firm technologies. After reviewing the

literature on inter-firm technology adoption, it can be seen

that while each one explains part of the adoption process, most

of the major studies leave out very important constructs that

have been found to have significant influences on company’s

decisions to adopt technologies (Chwelos et al., 2001). The

studies are also inconsistent in their choice of constructs and

as a result are inadequate in their efforts to explain why

companies adopt inter-firm technologies (Grover, 1993).

To solve these problems, the authors propose acomprehensive framework that identifies the antecedents of

successful inter-firm technology adoption. The proposed

framework is substantially different from the existing supply

chain adoption models and introduces constructs that are new

to the adoption of supply chain literature. An important area

that has been ignored in the inter-firm adoption literature and

has been covered by the TASC framework is the impact of

inter-firm relationships on technology adoption. TASC

emphasizes the importance of inter-firm relationships and

identifies power, trust and justice as important relationships

that influence the adoption of inter-firm technologies.

By developing a comprehensive framework that identifies

the antecedents of successful TASC, this paper will help both

academics and practitioners learn more about the factors that

can lead to the success of technology adoption initiatives in

supply chains. This paper will also enable practitioners to

learn more about the things that they can do to improve the

success rates of their inter-firm technology adoption

initiatives, thus making them more competitive.

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B2B Technology Adoption in Customer Driven Supply Chains

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