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An Empirical Examination of Cloud Computing in Humanitarian Logistics

Dara Schniederjans

Assistant Professor of Supply Chain Management

University of Rhode Island

Schniederjans@mail.uri.edu

401-874-4372

zpolat

Assistant Professor of Supply Chain Management

University of Rhode Island

koray@mail.uri.edu

401-874-5750

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1. Introduction

Humanitarian supply chains often require the use of several transport modes, as well as the

involvement of several government and independent non-governmental organizations (NGOs)

(Oloruntoba & Gray, 2006). This along with increasing geographical dispersion leads to

insufficient collaboration and agility in a humanitarian context (Oloruntoba & Gray, 2006). Both

collaboration and agility are vital for humanitarian supply chains in providing relief to

individuals in emergency situations, but also in fostering donor long-term commitment vital for

humanitarian organizations themselves (Scholten et al., 2010). Fundamental to improving

collaboration and agility is the application of supporting information technology (Scholten et al.,

2010).

A growing number of humanitarian organizations are turning to cloud computing technology to

drive logistics and response. For example, the American Red Cross has recently transitioned to

cloud computing to provide a key communication tool for organizing disaster response through

delivery of adequate supplies, the teaching of lifesaving skills, provision of international

humanitarian aid and support for military members and families (Courion, 2012). Companies,

such as Dallas-based non-profit Aidmatrix, work with government, business and humanitarian

organizations by providing software to identify needs, locate resources and route them to

appropriate locations (Microsoft, 2012). The emergence of humanitarian organizations adopting

cloud computing technology could be due to the vast benefits cloud computing may provide with

regard to humanitarian aid supply chains. These benefits include large amounts of computing

power over short periods of time, elasticity and flexibility in location and timing of data and cost

savings (GSN, 2010). Despite these benefits, little to no research has studied the impact of this

information technology on collaboration and how this impacts agility.

Although cloud computing is gradually being adopted, the majority of effective communication

still occurs using media like television, the national weather service, intranet and extranets,

electronic data interchange, and global positioning systems (Richey, Jr., 2009; Richey et al.,

2010). This is perhaps due to the recency of cloud computing used in a supply chain context and

the reluctance to store information into a cloud framework (Armbrust et al., 2010). Current

literature calls for new research in the norms of communication exchanged and quality produced

by these norms (Richey, Jr., 2009). Additionally, a review of humanitarian logistics suggests the

majority of research is analytical and conceptual in nature with only 9% of current research

being empirical (Natarajarathinam et al., 2009). More research needs to empirically examine the

impact of new information technology like cloud computing on collaboration and agility.

Given cloud computing’s potential to improve communication between humanitarian

organizations and their suppliers, as well as the limited empirical research on cloud computing,

this study aims to contribute to humanitarian logistics literature by being the first to clarify the

impact of cloud computing on collaboration and how trust moderates this relationship. It also

examines the ultimate impact on agility, which is vital for humanitarian organizations and

individuals helped by the aid they provide. Based on 105 survey responses from humanitarian

organization supply chain and information systems specialists, these findings further the

development of an empirical model that is supported by resource based view and social capital

theory. In addition, the findings also allow the authors to suggest practical guidelines for

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humanitarian organizations, particularly regarding the effectiveness of cloud computing

technology to enhance collaboration leading to increased agility, which in turn may save lives in

the process.

The remainder of the paper is structured as follows: in the second section we provide a literature

review of each construct in our model. Based on this literature review, using resource based view

and social capital theory we develop and operationalize our model in the third section. We then

present our sampling and data description along with our measurement and structural model

analysis in the fourth section. In the fifth section we include the analysis of our findings and a

discussion of the results. Finally, we discuss the study’s limitations and future research

opportunities in the sixth section.

2. Literature Review

2.1. Cloud computing use and collaboration

Cloud computing derives from research in virtualization, distributed computing, utility

computing, networking web and software services and grid computing (Vouk. 2008). Based on

service level agreements, cloud computing is a large scale, distributed, computing paradigm

where virtualized, dynamically scalable, managed computing power, storage platforms and

services are delivered on demand to customers via the internet (Buyya et al., 2009; Foster et al.,

2008).

According to preliminary research, there are two defining attributes of cloud computing

technology: massively scalable service and on-demand access to information. Massively scalable

service refers to a cloud computing user’s ability to choose from a variety of services offered

(i.e., infrastructure, software and platforms), payment options (i.e., pay-as-you-go, up-front fee

or two tier), as well as how it is delivered (i.e., public vs. private cloud) (Rochwerger et al.,

2009). Each service can be tailored according to a user’s or a supply chain partner’s needs. On-

demand access to information refers to a cloud computing user’s ability to gain access to

information in a fast and efficient manner wherever and whenever needed. Cloud computing

information can be accessed on a variety of platforms, regardless of where a person is.

The cloud computing benefit of information processing can greatly impact collaboration, which

involves forging effective partnerships and developing compliance plans for win-win outcomes

in a supply chain context (Parmigiani et al., 2011). Forging effective partnerships between

humanitarian organizations and their suppliers is a difficult process that requires more than

simply sharing information. Three defining parts of cloud computing technology seem to

enhance levels of inter-organizational relationship formation from traditional information

technology resources, including EDI. These facets are: instant scalability (Bardhan et al., 2010;

Benlian & Hess, 2011; Benlian et al., 2011; Iyer & Henderson, 2010; Mantena, 2012; Marston et

al., 2011; Rhoton, 2011; Vouk, 2008), better utilization of computing resources (Benlian & Hess,

2011; Marston et al., 2011) and better access to information technology in third world countries

(Marston et al., 2011).

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The instant scalability of cloud computing refers to the user being offered a variety of service

and payment options that can be scaled based on primary needs and wants. There are three types

of cloud computing technology: Software as a Service (SaaS), Infrastructure as a Service (IaaS)

and Platform as a Service (PaaS). SaaS provides business applications that are delivered on

demand, usually through a pay-as-you-go service. SaaS benefits include reduced software

complexity and costs that ultimately enhance accessibility to services (Rochwerger et al., 2009).

IaaS provides cloud computing users computer hardware, storage and data center space. With

IaaS companies no longer have to invest in their own data centers and are able to gain

information from the cloud in a matter of minutes from a variety of different media. Lastly, PaaS

delivers integrated software that a developer needs in order to build an application. PaaS offers

software platforms on which systems can run from a variety of locations (Vaquero et al., 2009).

Cloud computing also offers instant scalability in terms of pricing options, including a pay-as-

you-go service, one-time fee or a hybrid of the two (Vouk, 2008). Using cloud computing,

humanitarian organizations can scale services and payment arrangements according to their

needs and also their suppliers’ needs. Since collaboration typically involves consistent

communication and alignment of incentives (Hendricks & Singhal, 2003), cloud computing’s

ability to offer instant scalability in service, pricing options and media, according to an

organization’s and the supply chain partner’s needs, will enhance alignment in terms of

communication and incentives received from cloud computing use.

Cloud computing also provides greater utilization of computing resources. Users have the ability

to request more resources in a shorter period of time with minimal service provider interaction

(Marston et al., 2011; Benlian & Hess, 2011; Iyer & Henderson, 2010). Further, cloud computing

offers mobile interactivity and the ability to share information with supply chain partners using a

variety of different media (Marston et al., 2011; Benlian & Hess, 2011; Iyer & Henderson,

2010). This is dissimilar to web-based EDI applications, which still require a common platform

on either end (Monczka et al., 2011, p. 709). Additionally, cloud computing offers the ability for

users to analyze terabytes of data in a period of minutes, which is a substantial increase in speed

of information flow over traditional information technologies (Marston et al., 2011; Benlian &

Hess, 2011). Given the requirement of consistent information flow for collaboration (Hendricks

& Singhal, 2003) and the need for agility (Kovacs & Spens, 2007), cloud computing benefits for

humanitarian organizations far exceed those of EDI by offering ease of collaboration flow

through greater utilization of computing resources and increased processing speed.

Finally, cloud computing provides greater access to information technology in third world

countries (Marston et al., 2011). With its ability to be scaled to a variety of different media over

traditional EDI (Marston et al., 2011; Monczka et al., 2011), cloud computing delivers IT

services to countries that would traditionally lack resources for IT deployment (Marston et al.,

2011; Benlian & Hess, 2011; Iyer & Henderson, 2010). This is especially useful for improving

collaboration between the United States and suppliers outside of the US.

With the substantial benefits for humanitarian organizations, cloud computing is increasingly

being adopted by humanitarian organizations. One third party organization, NetHope, works with

NGOs to help facilitate collaboration and connectivity through technology to humanitarian

organizations worldwide. It has recently engaged leadership from NGOs’ corporate and

governmental partners to develop a strategy enabling cloud solutions for NGOs worldwide to

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better support cloud computing’s humanitarian efforts (NetHope, 2011). Cloud computing has

already offered solutions giving 30-50%+ benefits in efficiency gains for field workers,

community mobilizers and community health workers (NetHope, 2011). Therefore, we derive

our first hypothesis:

H1. Cloud computing use is positively associated with collaboration between humanitarian

organizations and their suppliers.

2.2. Inter-organizational trust

Despite the benefits that cloud computing provides to humanitarian organizations, the cloud is

still in its infancy compared to more established information technologies like EDI. The recency

of the cloud establishes a level of concern over security issues (Benlian & Hess, 2011; Marston

et al., 2011). Mitigating security concerns is vital to the use of cloud computing in order to

establish adequate levels of collaboration, which often require a high level of trust, long-term

contracts, joint conflict resolutions and the sharing of rewards and risk (Vickery et al., 2003).

Collaboration unlike supply chain integration puts more emphasis on governance through

relational as opposed to contractual means (Cao & Zhang, 2011).

There are various definitions of inter-organizational trust with dimensions including credibility,

goodwill, honesty, integrity, benevolence, etc. In this study we borrow the Zhang et al. (2011)

definition of inter-organizational trust, which is one party’s confidence and belief in the

credibility and goodwill of an object of trust. We borrow this definition primarily because it

incorporates credibility and goodwill, which most scholars agree comprise the dimensions of

trust (Zhang et al., 2011). Further, both dimensions take into account the importance of

dependability, reliability, and acting in the best interests of one another (Baker et al., 1999;

Ganesan, 1994; Johnston et al., 2004; Sako, 1992; Zaheer et al., 1998; Zhang et al., 2011).

Dependable and reliable humanitarian supply chain partners are vital for several reasons.

First, donors to humanitarian aid agencies are increasingly demanding accountability,

transparency and value in return for sponsorship (Scholten et al., 2010). This expectation is also

influencing humanitarian aid agencies to become more professional in their approach to

managing operations (Scholten et al., 2010; Thomas & Kopczak, 2005). There are also various

players in humanitarian supply chains, including, but not limited to, the United Nations, military,

profit seeking organizations and NGOs. Agile supply chains require reduced security risks, while

at the same time delivering speed and efficiency that can prove difficult with complex supply

chains involving various actors (Scholten et al., 2010). A transparent supply chain provides

timely and accurate exchange of information (Scholten et al., 2010). This greater transparency is

also likely to lead to improved systems’ processes (Scholten et al., 2010).

In the majority of empirical studies, inter-organizational trust is seen as a main effect that leads

to positive attitudes, higher levels of cooperation and higher levels of performance (Dirks &

Ferrin, 2001). Various studies have examined the direct effect of trust on workplace attitudes and

performance (i.e. Golembiewski & McConkie, 1975; Jones & George, 1998; Mayer et al., 1995).

Despite this, other studies have shown the moderating role trust plays in various relationships.

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Dirks & Ferrin (2001) posit that trust affects how one assesses future actions of another party.

They also posit that trust affects how one interprets the past or present actions of a party, as well

as the motivations. Both propositions rest on the premise that trust does not have a direct causal

role to an outcome, but rather moderates the effects of causal factors on outcomes based on how

one assesses the future or past behavior of another party. Others have also seen a similar

relationship whereby trust moderates relationships between causal factors and outcomes. Chang

& Wong (2010) suggest that trust moderates the relationship between e-procurement adoptions

and e-marketplace participation. Moreover, Parayitam & Dooley (2009) found cognition-based

trust moderates the relationship between conflict and outcomes.

Trust overall is identified as critical for effective collaboration in a supply chain (Ke & Wei,

2006). While the presence does not guarantee adequate performance (Arino & Abramov, 1997),

when trust is high information sharing can flow freely and create added value (McCarter &

Northcraft, 2007).On the other hand, when trust is low an alliance can exist, but the effectiveness

(in this study defined as agility) will likely be hindered (Kwon & Suh, 2004). Based on this

previous research, we hypothesize

H2. The positive effect of cloud computing use on collaboration will be stronger for

humanitarian organizations and their suppliers with higher inter-organizational trust levels.

2.3. Collaboration and agility

Perhaps even more vital than the relationships between cloud computing use, inter-organizational

trust and collaboration is the ultimate impact on agility. Agility has several definitions, including

a supply chain’s ability to respond to customers unforeseen changes (Sheffi, 2004); responding

rapidly to short-term changes in demand and market turbulence (Lee, 2004; Van Hoek et al.,

2001; Swafford et al., 2006); ability to thrive in constant and unpredictable change (Maskell,

2001; Oloruntoba & Gray, 2006; Zhang, 1999; Swafford et al.,2006); being centered on

customer responsiveness and focused on market turbulence (Van Hoek et al., 2001). All of these

definitions have one commonality: responding quickly to unforeseen changes. This requires

humanitarian supply chains to be flexible in changing levels of output and delivery dates

(Charles et al., 2010; Christopher & Towill, 2000; Slack, 2005; Swafford et al., 2006; Zhang et

al., 2003). It also requires higher levels of responsiveness and effectiveness in delivering the

correct products to the right place, at the right quantity, and during the right time period (Charles

et al., 2010; Supply Chain Council, 2006). Overall, agility is vital for humanitarian

organizations, which need to work with suppliers in order to obtain aid when an emergency

presents itself.

Unfortunately, for individuals in emergency situations agility problems are a concern, given the

increasing complexity of humanitarian supply chains that often involve a number of different

organizations. Some of these organizations include independent operative bodies that specialize

in areas like water and sanitation, medical care and camp management, local and international

NGOs, donors, military and commercial service providers, host governments, United Nations

bodies, etc. (Jahre & Johnson, 2010). This complex network of organizations that comprise

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humanitarian relief show the difficulty in maintaining adequate levels of collaboration required

for adequate responsiveness to emergency situations.

The importance of agility and collaboration is evidenced by the Indian Ocean tsunami of 2004

and the Darfur crisis of 2004/2005 (Jahre & Johnson, 2010). The Indian Ocean tsunami,

considered to be one of the most destructive tsunamis in history, killed more than 150,000

individuals and made millions more homeless in eleven countries (National Geographic, 2005).

The Darfur crisis in 2004/2005, characterized by violence between Sudanese government forces

and rebels protesting the mistreatment of the region’s African groups by the central government,

left hundreds of thousands of people dead and two million homeless (History. Com, n.d.). In both

cases provision relief overlapped, populations were not well served, and numerous predicaments

were encountered in prioritizing the pipeline (Adinolfi et al., 2005; OCHA, 2007; Jahre &

Jensen, 2010).

Humanitarian supply chains are unstable and often break down at the receiving end (Munslow &

Brown, 1999; Stewart, 1998; Byman et al., 2000; Oloruntoba & Gray, 2006). They frequently

lack a coordinated plan (Oloruntoba & Gray, 2006) resulting in inefficiencies, including overuse

of expensive and unsafe air charters, congestion via unplanned deliveries and a lack of inter-

organizational collaboration by information systems (Byman et al., 2000; Long, 1997; Long &

Wood, 1995; Oloruntoba & Gray, 2006). Humanitarian supply chains in comparison to corporate

ones have very short life cycles and need constant communication in order to maintain adequate

agility (Charles et al., 2010). Therefore, an effective information infrastructure would enhance

agility by being responsive to the changing needs of end users and would enable immediate

response to those changes (Oloruntoba & Gray, 2006). Further, the integration of internal

capabilities could enhance supply chain development (Oloruntoba & Gray, 2006) and lead to

greater speed in reacting to unforeseen changes. Therefore, we hypothesize

H3. Collaboration is positively associated with agility between humanitarian organizations and

their suppliers

3. Theoretical Background and Model

3.1. Resource-based view (RBV)

The connection between information technology and collaboration is not new in literature and

has a strong background in RBV (Bharadwaj, 2000; Mata et al., 1995). RBV posits that firms

compete using unique resources that are valuable, rare, difficult to imitate and non-substitutable

by other resources (Barney, 1991; Conner, 1991; Schulze, 1992). These resources in turn can be

used for competitive advantage (Penrose, 1959; Rubin, 1973; Wernerfelt, 1984). While resources

are vital, it is more critical how the firm utilizes them to maximize competitive potential

(Eisenhardt, & Martin, 2000).

In this study we consider cloud computing to be a valuable, rare and difficult to imitate resource

if firms utilize and scale it according to their own and their partner’s needs. As described in the

previous sections, cloud computing offers users massively scalable service and pricing options

that allow humanitarian organizations to scale according to their own and their supply chain

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partners’ needs (Marston et al., 2011). Since collaboration requires mutual incentives (Hendricks

& Singhal, 2003), cloud computing can optimize it through massively scalable services that

perpetuate greater collaborative relationships between supply chain partners.

3.2. Social capital theory/ agency theory

Social capital theory suggests that benefits derived from relationships between entities can

generate intangible and tangible benefits, including those that are social, psychological,

emotional and economic in the short- and long-term (Lin, 2000). Social capital is comprised of

seven dimensions, including: group characteristics, generalized norms, togetherness, everyday

sociability, neighborhood connections, volunteerism and trust, which help to develop both short-

and long-term benefits (Narayan & Cassidy, 2001).

This theory helps define the relationship between collaboration and agility, and the moderating

impact of inter-organizational trust on the relationship. Collaboration typically involves both

continuous communication, as well as an effective platform to collaborate on (Oke & Idiagbon-

Oke, 2010). When communication and platforms are present, other types of social capital

develop, including generalized norms, togetherness, sociability and established connections,

which, according to social capital theory, can lead to a variety of benefits, including agility

(Khan & Pillania, 2008).

Trust is also considered a vital social capital that can lead to a variety of internal and external

benefits (Narayan & Cassidy, 2001). Trust is known to offset risks associated with behaviors

underlying competitiveness, thereby allowing greater benefits of knowledge transfer, joint

learning, and sharing of risks associated with exploiting opportunities in collaboration (Ireland &

Webb, 2007; Inkpen, 2001; Nahapiet & Ghoshal, 1998). Given the recency of cloud computing

and associated security concerns (Armbrust et al., 2010); an adequate amount of inter-

organizational trust can provide a foundation for using cloud computing to perpetuate greater

collaboration.

Both RBV and social capital theory can be used to explain the intricate relationships depicted in

Figure 1, which presents our conceptual model that we will analyze using partial least squares

analysis.

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4. Research Methodology

4.1. Sample design and respondents

The empirical analysis aims to examine the hypothesized relationships between humanitarian

organizations (our unit of analysis) and their suppliers. A mail survey was developed and

implemented to collect data. The sample used in this study consisted of 541 potential

respondents from humanitarian organizations. The sample was drawn from a major United States

network for business professionals. After three rounds of surveys a total of 110 individuals

participated in the survey, resulting in an overall response rate of 20.33%. From the 110

completed surveys, five had to be deleted due to missing data, resulting in 105 usable responses

for further analysis. Respondents’ information on demographics, size and work experience is

provided in Table 1.

Cloud

Computing Use Collaboration Agility

Inter-

organizational

Trust

Figure 1. Conceptual model

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Table 1. Demographics of respondents

Demographic Percentage

Gender

Male 44.8

Female 55.2

Level in organization

President/CEO 4.8

Vice President 14.3

Director 61.0

Manager 20.0

Number of employees

Less than 50 14.3

51-100 11.4

101-200 22.9

201-300 16.2

301-400 7.6

Greater than 401 27.6

Years of work experience in current

organization

0-1 year 2.9

2-3 years 16.2

4-5 years 14.3

6-7 years 19.0

8-9 years 21.9

10 or more years 25.7

In order to guarantee that the respondents were knowledgeable about the topic of our survey, we

surveyed persons directly associated with information technology or supply chain management

activities in the organizations. Titles included information officers, directors of supply, chief and

head of operations, etc. The competency of our sample was also demonstrated by personal

information provided by the respondents (Hartmann & de Grahl, 2012; Kumar et al., 1993). The

majority of our respondents worked at the director level and had more than ten years of work

experience.

4.2. Measurement scales

All measurement scales in this study use both reflective and formative multi-item scales. Face

validity was assessed through pre-tests with five supply chain logistics researchers. The

measurement items and sources are summarized in Table 2 in the appendix.

Collaboration reflects the different dimensions of information sharing, goal congruence,

incentive alignment, collaborative communication and joint knowledge creation. Each item was

measured on a 7-point likert scale adopted from Cao & Zhang (2011). Agility was also measured

from a 7-point Likert scale reflecting the dimensions of demand response, joint planning,

customer responsiveness and visibility. All items were adapted from Braunscheidel & Suresh

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(2009). Following the definition of inter-organizational trust provided by Zhang et al. (2011), we

used items that reflect both credibility and goodwill. All items measuring inter-organizational

trust were adopted from Johnston et al. (2004). Cloud computing was measured to reflect both

the humanitarian organization’s use and reliance. Although we could find no survey questions

pertaining to cloud computing in previous literature, we adapted the 7-point Likert scale

questions from Sanders (2007) and Subramani (2004).

4.3. Results

To test the model presented in Figure 1, partial least squares (PLS) was used employing

SmartPLS (Version 2.0, Ringle et al., 2005). The use of PLS was decided for a couple of

reasons. First, when models contain formative construct components, it is suggested that PLS is

used over component based structural equation modeling (SEM) (Petter et al., 2007). Moreover,

given our small sample size, PLS is a preferable method, because estimates of path coefficients

tend to be more conservative than in covariance-based techniques (Bagozzi & Yi, 1994; Chin,

1998; Hulland, 1999). Our model was assessed in two stages (1) the assessment of the reliability

and validity and (2) the assessment of the structural model (Hulland, 1999).

In order to analyze the statistical significance of the parameter estimates, we used bootstrapping

with 500 resamples and samples of 250 and 1000 to assess the stability of the parameter

estimates. All of the results are consistent across the different bootstrap samples taken. We also

examined the acceptability of the measurement model by analyzing individual item reliability,

convergent validity and discriminant validity (Hulland, 1999).

Individual item reliability is assessed by looking at the loadings of each item with their construct.

The minimum level threshold for item loadings is 0.7 (Krafft et al., 2005; Henseler et al., 2009).

As seen in Table 3, all items in our analysis were well above the 0.7 threshold with the lowest

item loading at 0.896, thus providing results for individual reliability.

Convergent validity suggests that a number of items represent one and only one underlying

construct (Henseler et al., 2009). To assess convergent validity, we assessed each of the

composite reliabilities for each construct all of which had a minimum value of 0.7 (Huber et al.,

2007; Krafft et al., 2005; Henseler et al., 2009). We then assessed the average variance extracted

(AVE) for each construct. All AVE’s above a threshold of 0.5 indicate the construct is able to

explain more than half of the variance of its items (Fornell & Larcker, 1981; Henseler et al.,

2009; Gotz et al., 2010). All of the AVE values for cloud computing use, inter-organizational

trust, collaboration and agility are well above the threshold of 0.5 providing support for

convergent validity.

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Table 3. Item loadings, composite reliabilities and AVE

Construct Item Item loading Composite

reliability

AVE

Cloud computing

use

CC1 0.954 0.9827 0.9045

CC2 0.956

CC3 0.958

CC4 0.963

CC5 0.940

CC6 0.936

Inter-

organizational

trust

T1 0.906 0.9841 0.9115

T2 0.965

T3 0.962

T4 0.975

T5 0.956

T6 0.962

Collaboration C1 0.921 0.9899 0.8669

C2 0.941

C3 0.953

C4 0.934

C5 0.932

C6 0.925

C7 0.909

C8 0.916

C9 0.919

C10 0.937

C11 0.948

C12 0.937

C13 0.931

C14 0.943

C15 0.919

Agility A1 0.946 0.9884 0.8770

A2 0.896

A3 0.958

A4 0.924

A5 0.922

A6 0.901

A7 0.950

A8 0.952

A9 0.942

A10 0.952

A11 0.945

A12 0.896

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Discriminant validity is the extent to which items from one construct differ from those of other

constructs (Hulland, 1999). Discriminant validity is examined using the Fornell-Larcker criterion

and cross-loadings (Henseler et al., 2009). To ensure discriminant validity, the square root of the

AVE should be higher than the correlations with other constructs in the model (Hulland, 1999).

As seen in Table 4, all square roots of the AVE’s are larger than the correlations for each

construct, thus providing support for discriminant validity.

Table 4. Correlation matrix

Cloud computing

use

Inter-

organizational

trust

Collaboration Agility

Cloud

computing use

0.951

Inter-

organizational

trust

0.767 0.955

Collaboration 0.785 0.827 0.931

Agility 0.853 0.860 0.893 0.937

Note: The square root of the AVE is provided in the diagonal of the matrix

To further assess discriminant validity, we also examined the cross loadings. Henseler et al.

(2009) suggests that if an item has a higher loading with another construct than its respective

construct, discriminant validity may be an issue. All items in this study have their highest

loadings on their respective constructs, thus providing support for discriminant validity.

To assess the structural model, we examined the coefficient of determination (R2) of the

endogenous constructs, as well as the path coefficients and their corresponding t-values and

significance. The coefficient for determination suggests the model explains a significant amount

of variance of collaboration (R2=0.748) and agility (R

2=0.811). The PLS results show support for

hypotheses 1 and 3, with positive and significant effects of cloud computing use to collaboration

(0.371, p < 0.01) and collaboration to agility (0.901, p < 0.001). In contrast no support was found

for the moderating impact of inter-organizational trust on the relationship between cloud

computing use and collaboration (0.057, n.s. at p = 0.05 level).

Table 5. PLS analysis results (structural model)

Path Standardized path

coefficient

t-value Significanc

e

H1. cloud computing use →

collaboration

0.371 3.990 p < 0.01

H2. cloud computing use x inter-

organizational trust → collaboration

0.057 0.922 n.s.

H3. collaboration → agility 0.901 48.197 p < 0.001

Note: n.s.: not significant at p = 0.05; t-value were calculated through bootstrapping with 500

resamples and 105 cases

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5. Discussion and Implications

Increasing complexity of humanitarian supply chains due to globalization efforts have led to

organizations having difficulties with both collaboration, as well as agility in getting aid to

individuals in need (Oloruntoba & Gray, 2006). Throughout the paper we used RBV to clarify

the positive association between cloud computing use and collaboration among humanitarian

organizations and their suppliers. We also discussed social capital theory and its ability to

demonstrate the association of inter-organizational trust and its moderating role in the

relationship between cloud computing use and collaboration, as well as collaboration and its

positive association with agility.

Through the literature review, we provided an operationalization of the constructs, which gave

background to the importance of collaboration and agility in a humanitarian context. Our results

offered empirical support that collaboration between humanitarian organizations and their

suppliers positively impacts agility of the supply chain, which is required for individuals in need

to obtain emergency aid. Not only is this necessary for individuals negatively impacted by an

emergency situation, but also for humanitarian organizations to continue, as donor support

depends on the functionality of the humanitarian organization itself (Scholten et al., 2010). Our

empirical results also provided a viable IT solution for increasing collaboration between

humanitarian organizations and their suppliers.

Given the recency of cloud computing use by humanitarian organizations, we hypothesized inter-

organizational trust may moderate the relationship between cloud computing use and

collaboration between humanitarian organizations and their suppliers. Surprisingly, we found no

significant moderating relationship. While security using the cloud is still a concern for many

organizations (Armbrust et al., 2010), this finding may not apply to humanitarian organizations,

which are typically nonprofit (Ingram, 2013). Although competition for donor support is not

foreign to humanitarian organizations, competition is much different between for profit

organizations, whose goal is to generate income for shareholders and non-profits, whose goals is

to serve humanitarian and environmental needs (Ingram, 2013). Thus, security concerns and the

value placed on inter-organizational trust when using information technology to collaborate may

differ between non-profit and for profit organizations.

Although cloud computing is widely being adopted by various humanitarian organizations, we

provide a conceptual model that is both theoretically and empirically supported through the use

of RBV, social capital theory and partial least squares analysis. To the best of our knowledge,

this is the first study that sheds light on how cloud computing use impacts collaboration in a

humanitarian supply chain context. Our results provide empirical support for the positive

association between cloud computing use and collaboration among humanitarian organizations

and their suppliers, as well as the ultimate positive impact on agility. This, in turn, creates a

framework for humanitarian supply chain management scholars to examine agility and how it

may be impacted by information technology such as cloud computing.

6. Limitations and Future Research

15

While this paper provides scholars and practitioners both theoretical and empirical support for

the association between cloud computing use, collaboration and agility in a humanitarian

context, our study is not without certain limitations.

First, even though our sample covers a variety of humanitarian organizations, the data we

gathered was solely from within the United States. By gathering data within the United States,

we limit the generalizability of the results. Future work should incorporate not only humanitarian

organizations within the U.S., but also outside of this country.

Secondly, while our findings help to develop theory in the use of cloud computing in

humanitarian logistics, our small sample size can be seen as a limitation. Future research should

explore surveying a larger sample from a broader international base.

Third, this study found that inter-organizational trust does not moderate the relationship between

cloud computing use and collaboration. It would be interesting to investigate whether this finding

is significant when examining for profit organizations. This would also provide interesting

implications for the development of relationships using cloud computing use between

humanitarian organizations and for profit organizations and their suppliers.

Future research might also incorporate not only collaboration between humanitarian

organizations and their suppliers, but also between humanitarian organizations that use cloud

computing technology. Doing so might further develop a holistic model for optimizing

collaboration and agility in a humanitarian context.

16

Appendix

Table 2. Construct & Items

Cloud computing use CC1→Use of cloud computing technology relative to

industry standard

CC2→Use of cloud computing technology relative to other

humanitarian aid agencies

CC3→Extent to which our organization uses cloud

computing to integrate with our supply chain partners

CC4→Extent to which our organization uses cloud

computing to provide humanitarian aid to end users in

need.

CC5→Reliance on cloud computing technology in

conducting business processes

CC6→Reliance on cloud computing technology in

conducting business with our supply chain partners.

Adapted from Sanders

(2007); Subramani

(2004)

Inter-organizational Trust T1→Our organization feels that it is important not to use

any proprietary information to our supply chain partner’s

disadvantage.

T2→A characteristics of the relationship between our

organization and its supply chain partners is that neither

supply chain partner is expected to make demands that

might be damaging to the other

T3→Our organization feels that our supply chain partner

will not attempt to get its way when it negatively impacts

our organization.

T4→Our organization has strong confidence in our supply

chain partner

T5→Our organization can always rely on another supply

chain partner when it counts

T6→Our organization believes that our supply chain

partner will work hard in the future to maintain a close

relationship with us.

Johnston et al. (2004)

17

Collaboration C1→Our organization and supply chain partners exchange

timely information

C2→Our organization and supply chain partners exchange

accurate information

C3→Our organization and supply chain partners exchange

complete information

C4→Our organization and supply chain partners have

agreement on the goals of the supply chain

C5→Our organization and supply chain partners have

agreement on the importance of collaboration

across the supply chain

C6→Our organization and supply chain partners agree that

our own goals can be achieved through

working toward the goals of the supply chain

C7→Our organization and supply chain partners share

benefits (e.g. saving costs)

C8→Our organization and supply chain partners share any

risks that can occur in the supply chain

C9→Our organization and supply chain partners share

benefits for providing to our end user

C10→Our organization and supply chain partners have

frequent contact on a regular basis

C11→Our organization and supply chain partners have

open and two-way communication

C12→Our organization and supply chain partners influence

each other’s decisions through discussion

C13→Our organization and supply chain partners jointly

search and acquire new and relevant knowledge

C14→Our organization and supply chain partners jointly

assimilate and apply relevant knowledge

C15→Our organization and supply chain partners jointly

identify end user needs

Cao & Zhang (2011)

Agility A1→Our supply chain is able to respond to changes in

humanitarian demand

A2→Our supply chain is able to leverage the competencies

of our partners to respond to humanitarian demand

A3→Our supply chain is capable of responding to

humanitarian demand

A4→Joint planning in our supply chain is important

A5→Information integration in our supply chain is

important

A6→Our organization works with our suppliers to

seamlessly integrate our inter-organization processes

A7→Improving our organization’s level of service in

humanitarian aid is a high priority

A8→Improving our organization’s delivery reliability is a

higher priority

A9→Improving our organization’s responsiveness to

changing humanitarian needs is a high priority

A10→Humanitarian aid demand levels are visible

throughout our organization’s supply chain

A11→Demand in humanitarian aid is accessible throughout

our organization’s supply chain

A12→Inventory levels are visible throughout our

organization’s supply chain

Adapted from

Braundscheidel &

Suresh (2009)

18

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