應用於供應鏈管理上之新興軟體技術的選擇與運用策略研究

科技部補助專題研究計畫成果報告

期末報告

應用於供應鏈管理上之新興軟體技術的選擇與運用策略研究

中　華　民　國　104　年　10　月　07　日

中 文 摘 要 ： 資訊科技的發展近況，引發了學界與產業界的關注；新興資訊科技 如雲端運算、巨量資料分析與行動應用等的蓬勃發展，不僅提供了 企業營運的新工具，也衝擊了如企業供應鏈管理等的企業管理層面 。然而，學者與企業管理者也持續對於應用這些新興資訊科技的實 際價值提出議論。因此，本研究將探討新興資訊科技應用對於供應 鏈績效的直接與間接影響。本研究運用「資源→策略→績效」模型 ，建構出以三種競爭策略（成本領導、差異化與聚焦）中介於新興 資訊科技應用影響供應鏈績效的研究架構，並透過企業問卷調查與 資料分析以進行各項研究假說的實證，及論述其管理意涵。 中 文 關 鍵 詞 ： 供應鏈管理、新興資訊科技、雲端運算、巨量資料、行動應用、競 爭策略、資源基礎觀點

英 文 摘 要 ： Information technology in recent developments has invoked tremendous attentions from both academics and industries. The emerging information technology such as cloud

computing, big data analytics and mobile application not only serves as a new tool for operations of enterprises, but also impacts management arenas of enterprises such as supply chain management. As considerable discussions about the real value of applying the emerging information

technology in academics and industries, this study researches direct and indirect impacts on applying the emerging information technology to the supply chain performance. By adoption of the

resource→strategy→performance model, this study builds a research framework in which three competitive strategies of firms cost leadership, differentiation and focus

-mediate the effect of applying emerging information technology on supply chain performance. Empirical survey and analysis of enterprise data is conducted to test our hypotheses, and managerial implications of findings are also further elaborated.

英 文 關 鍵 詞 ： Supply chain management, Emerging information technology, Cloud computing, Big data, Mobile application, Competitive strategy, Resource-based view

I

摘要

II

Abstract

Information technology in recent developments has invoked tremendous

attentions from both academics and industries. The emerging information technology such as cloud computing, big data analytics and mobile application not only serves as a new tool for operations of enterprises, but also impacts management arenas of enterprises such as supply chain management.

As considerable discussions about the real value of applying the emerging information technology in academics and industries, this study researches direct and indirect impacts on applying the emerging information technology to the supply chain performance.

By adoption of the resource→strategy→performance model, this study builds a research framework in which three competitive strategies of firms - cost leadership, differentiation and focus - mediate the effect of applying emerging information technology on supply chain performance. Empirical survey and analysis of enterprise data is conducted to test our hypotheses, and managerial implications of findings are also further elaborated.

Keywords: Supply chain management, Emerging information technology, Cloud

computing, Big data, Mobile application, Competitive strategy, Resource-based view

III

Table of Contents

2.1 Emerging Information Technology ... 3

2.2 Supply Chain Management ... 6

2.3 The Impact of Emerging Information Technology on Firms ... 6

3. Hypotheses Development... 9

3.1 The Impact of Information Technology on Supply Chain Performance ... 9

3.2 The Role of Competitive Strategy ... 10

4. Research Method ... 16

4.1 Survey Instrument ... 16

4.2 Sample and Data Collection ... 21

4.3 Reliability and Validity ... 22

5. Results ... 24 6. Discussion... 27 6.1 Managerial Implications ... 27 6.2 Study Limitations ... 27 6.3 Further Research ... 28 References ... 29

IV

List of Figures

Figure 1 Conceptual model ... 12

V

List of Tables

Table 1 Research variables ... 16

Table 2 Items used in the survey... 19

Table 3 Profile of the sampling firms ... 22

Table 4 Descriptive statistics and reliability and validity test ... 24

Table 5 Construct correlation... 25

1

1. Introduction

1.1 Research Background

Due to the high competitiveness of globalization in recent years, the meticulousness of

enterprise internationalization and business integration, and the rapid development of information

technology, business environments are experiencing tremendous change. For enterprises,

customers' requirements need more rapid response and fulfillment. In order to respond promptly to

the changing internal situations and external environments, enterprises need to interact quickly

between vendors of upper, middle and lower streams by means of information technology to form a

highly efficient supply chain organization.

In the meantime, the booming development of various emerging information technologies

such as cloud computing, big data and mobile application, are expected to affect enterprises’

managerial performance, including the supply chain performance. As the most noticed trends of

information technology development in recent years (Gartner, 2014, 2015; IDC, 2014, 2015;

IEEE, 2014, 2015), these emerging information technologies have attracted attentions as possible

sources to strategic advantages for firms (Porter & Heppelmann, 2014). Their influence on

economy and society has also attracted the attention of governments and companies worldwide.

The adoption of emerging information technologies can provide strategic advantages for

company managers and may even bring tremendous changes to the future market (Iansiti &

Lakhani, 2014). Therefore, aligning with the development trends of emerging information

technologies has become critical when companies develop their organizational strategies. Thus,

this study focuses on the managerial impact of innovative information technologies that emerge

in recent years (Gartner, 2015; IEEE, 2015; Porter & Heppelmann, 2014).

1.2 Research Motivation

The recent trends of information technology development center around the evolution of

2

applications (Gartner, 2015; IDC, 2015; IEEE, 2015). These emerging information technologies

(EIT) have also invoke tremendous attention from the academics (Iansiti & Lakhani, 2014; Porter

& Heppelmann, 2014). Facing this flourishing of emerging information technologies, how

enterprises react to these technologies becomes an important issue. The EITs are innovative

technologies and still under development. Their impacts on supply chain performance have not

been thoroughly realized, and deserved further investigation.

By now there is few rigorous research regarding the impact of the emerging information

technology on firm’s management activities and performance. Since supply chain management is

critical to firm’s operation and financial outcome (Qrunfleh & Tarafdar, 2014), this study will focus

on the influence of the emerging information technology on firm’s supply chain performance.

1.3 Research Goals

This study will explore the link between emerging information technologies and supply chain

performance, and investigate the mediators and moderators in the link. In this study, the sources→

strategy→performance scheme of competitive strategy (Day & Wensley, 1988; Venkatraman, 1989)

will be employed as a research framework. Empirical data from quantitative survey questions will

be collected and analyzed to realize the research hypotheses.

Through the research process and results, this study expects to clarify the effect of emerging

information technologies on supply chain performance, and also to identify possible mediators and

3

2. Literature Review

2.1 Emerging Information Technology

Recently, the emerging information technologies have attracted attentions as possible

sources to strategic advantages for firms (Porter & Heppelmann, 2014). Their influence on

economy and society has also attracted the attention of governments and companies worldwide.

Following the structure of the “new technology stack” depicted in Porter and Heppelmann

(2014), this study will focus on the emerging software technology embedded in the stack.

(1) Cloud

The innovation of cloud has made a major impact on the products, services and business

models of the IT software and hardware industries (Armbrust et al., 2010; Sultan, 2013; Vouk,

2008). Cloud computing has therefore become an emerging concept and technology that has

drawn attention from the IT software and hardware industries. The scope of the industry as well

as the fact that it spans both the enterprise and consumer markets has led to much discussion on

its future business potential (Graham, 2011; Iyer & Henderson, 2010; Katzan, 2009).

Nevertheless, cloud computing technologies and business models as well as the new products,

services, competition and alliances that arise as a result offer an emerging market that is well

worth monitoring (Helland, 2013).

Currently there are two main tracks of software technology in cloud infrastructure

management, virtualization technology and software defined network (SDN) technology.

Virtualization software is commonly used in the management and deployment of computing

infrastructure such as virtualizing server and storage devices. The technology has also been

extended to fields in desktop virtualization, application virtualization and internet virtualization

(Sotomayor et al., 2009).

On the other hand, rapid implementation of cloud related infrastructure and flexible

management of cloud data center has gain significance during the cloud development. Software

4

computing resources. Kim and Feamster (2013) discussed the concept and advancement of

software defined network as a promising technology for the management flexibility of data center

networking devices.

(2) Analytics and Big Data

“Big data” refers to the technology applied in big, immediate and manifold structured and unstructured information. It helps companies store, transform, transmit and analyze huge amounts

of information (McAfee & Brynjolfsson, 2012). It also provides advanced business analytics,

develops business intelligence and leads to gains in business values (Chang et al., 2014).

As the rapid growth of cloud computing, electronic commerce, social media, internet of things

and mobile devices, data volume grew explosively, and makes companies all over the world started

to pay attention to big data related technology (Kwon et al., 2014). Big data technology means to

use computing processes such as storing, transforming, streaming, transferring and analyzing to

handle structural or non-structural data that are dynamic, massive and variable, for the business

benefits (Jacobs, 2009). The use of big data is to perform instant and complex analysis to massive dynamic data, and support companies’ decision-making in a short period of time. The rise of big data has provided new opportunities for future ICT industries and data scientists (Jelinek & Bergey,

2013).

Chen et al. (2012) describe the evolution of Big Data technology. They use business

intelligence and analytics (BI&A) as a unified term, and treat big data analytics as a related field.

They argue that the evolution of Big Data technology is characterized by BI&A 1.0, BI&A 2.0

and BI & A 3.0. Data management and warehousing is considered the foundation of BI&A 1.0.

BI&A 2.0 systems require the integration of scalable techniques in text mining, web mining,

social network analysis, and spatial-temporal analysis with those existing DBMS-based BI&A 1.0

systems. BI&A 3.0 integrate Big Data technology with mobile applications, such as mobile BI,

mobile and sensor-based content, location-aware analysis, person-centered analysis,

context-relevant analysis and mobile visualization of data.

5

The Connectivity and Smart Mobile Applications software are used to establish exchange of

information in a mobile and ubiquitous way (Iansiti & Lakhani, 2014; Porter & Heppelmann,

2014). In order to provide ubiquitous mobile computing, infrastructure of wireless

communication network need to be constructed first. Currently, various networking infrastructure

are under development, such as IoT (Internet of Things) (Atzori et al., 2010) and wireless sensor

network (Piran et al., 2011).

Near Field Communication (NFC) (Leong et al., 2013; Tan et al., 2014) evolved from

Radio-Frequency Identification (RFID) (Ilie-Zudor et al., 2011) and interconnection technology.

In the past, non-contact chips were always produced as card applications. In recent years, chips

have been embedded into mobile devices for greater convenience. Mobile devices have therefore

been turned into a payment tool that allows downloading and payment of services in any public

setting and can also be used for exchanging data on mobile devices. This development extends

the possible applications of smart, connected products and the product clouds (Porter &

Heppelmann, 2014).

(4) Identity and Security

Porter & Heppelmann (2014) describes identity and security as “Tools that manage user

authentication and system access, as well as secure the product, connectivity, and product cloud layers.” This description of identity and security comprises cloud security and device security.

Cloud security software technology has two dimensions. One dimension is the adoption of

IT security technology, products or services by businesses to improve the security of cloud services. This is known as “Security for the Cloud” (Kalloniatis et al., 2013). The other dimension is the use of cloud computing by IT security vendors to strengthen, expand or transform their existing IT security technologies and services. This is known as “Security as a Service”. Examples include the collection of real-time virus data, updating virus definitions through the cloud, using cloud data centers for correlation analysis, reducing the load on terminal

computers and blocking malicious attacks before they can enter the corporate network. The goal

6

protect businesses from the IT security risks associated with the adoption of cloud architecture

(Subashini & Kavitha, 2011).

Device security involves Mobile Device Management (MDM) and Mobile Data Protection

(MDP). One controls physical mobile devices while the other secures data saved in mobile

devices, including user authority and privacy. BYOD (Bring Your Own Device) is currently the

main issue for many enterprises adopting mobile devices (Hancke et al., 2010; Shin et al., 2012).

2.2 Supply Chain Management

The goal of supply chain management (SCM) is to facilitate the efficient and effective

movement of products, services, finances, and information from a provider to a consumer. SCM is

not only essential for effective production, distribution, and logistical performance of today’s

companies, but can also influence their strategic posit (Vijayasarathy, 2010). Understanding how

firms can profit from their supply chain management is highly important for both management

practitioners and academics (DeGroote & Marx, 2013).

Prior research has characterized SCM as fundamentally reshaping the enterprise competition

and evolving as a part of firm’s new dominant logic (Wagner et al., 2012). Investigators have argued that the firm’s practices for leveraging associations with supply chain can be fundamental to sustaining a competitive advantage in the market (Qrunfleh & Tarafdar, 2014).

2.3 The Impact of Emerging Information Technology on Firms

In spite of the continuing development of the emerging information technology, research

regarding firms’ adoption, impact analysis and business strategy perspectives is still at its

embryonic stage (Porter & Heppelmann, 2014).

Gupta et al. (2013) exploit possible adoption considerations with cloud computing

technology. Factors that may influence adoption decisions of cloud computing, such as

7

& van de Bunt-Kokhuis, 2012). Garrison et al. (2012) investigated possible success factors for

deploying cloud computing. Cloud computing also introduces new risk into information

technology adoption. Subashini and Kavitha (2011) presented a survey of the different security

risks which pose a threat to the cloud.

Waller and Fawcett (2013) discuss potential impact of big data technology on firms. In their

paper, data science, predictive analytics and big data are collectively referred to as DPB. They

argue that data science is the application of quantitative and qualitative methods to solve relevant

problems and predict outcomes. Data scientists need deep domain knowledge and a broad set of

analytical skills. Predictive analytics is a subset of data science. Although predictive analytics is

related to many long-standing quantitative approaches, it stands as distinct from each. Predictive

analytics attempts to quickly and inexpensively approximate relationships between variables

while using deductive mathematical methods to draw conclusions. Chiang et al., (2012) argue that

the current state of the analytics software industry makes it difficult and cumbersome to conduct

analyses without a deep perspective of the underlying systems and technologies. They advocate

that BI&A should be an interdisciplinary area that integrates data management, database systems,

data warehousing, data mining, natural language processing, text mining, network analysis, social

networking, optimization, and statistical analysis.

The impacts of emerging connectivity and mobile application technology on firms are also

noticed. As mobile application software on mobile phones and tablets become more popular,

more development and deployment tool will be linked to the design and development of mobile

application software (Serrano et al., 2013). This trend will see more software developers release

innovative applications that link apps to SoLoMo (Social, Location based, Mobile) services,

creating new business opportunities for firms.

In view that the added value created by hardware products is limited, firms have gradually

transformed into investing in the development of mobile application software. In order to satisfy

8

the key source of competition for firms to differentiate their products (Mills et al., 2014; Watson

9

3. Hypotheses Development

3.1 The Impact of Information Technology on Supply Chain Performance

The possible impact of information technology (IT) on supply chain performance has been

an important topic to both academics and practitioners. For enterprises, the adoption of

information technology is geared to facilitate and enhance information processing and exchange.

The timely and accurate flow of information is a necessity for successful supply chain operations.

Therefore, information technology use in a supply chain is expected to produce positive results on

performance. For example, Wu et al., (2006) investigate the impact of information technology on

supply chain capabilities drawing from the resource-based view of IT. The findings suggest that

IT advancement and IT alignment are able to facilitate the development of supply chain

capabilities.

Zhang et al. (2011) conduct a systematic review of literature on the period of 1995 to

mid-2010 and find that a majority of papers confirm a positive relationship between IT and

supply chain performance.

For more recent research, DeGroote and Marx (2013) investigate the impact of information

technology on supply chain agility measured by the ability to sense and respond to market

changes. Their data are collected from supply chain executives at 193 U.S. manufacturing firms.

The results suggest that IT improves the supply chain’s ability to sense market changes by

improving the adequacy, accuracy, accessibility, and timeliness of the information flows among

members of the supply chain. Furthermore, IT also increases the supply chain’s ability to respond

to market changes by reducing the cost, and improving the quality and timeliness of developing

and executing coordinated plans to respond to market changes throughout the supply chain. Since

the information technology this study will focus on is the emerging information technology. It is

therefore hypothesized:

H1. There is a significant association between emerging information technology and supply

10 3.2 The Role of Competitive Strategy

Despite the research results of the significant impacts of information technology on supply

chain performance, it is argued that information technology alone does not completely determine

supply chain performance. Business strategic thinking also plays an important role towards supply

chain performance. For example, Huo et al. (2014) survey 604 Chinese manufacturers and find

competitive strategies significantly influenced the effectiveness of supply chain practices,

including internal, process and product integration.

Porter’s framework for competitive strategy is one of the most widely accepted business competition models (Miller, 1988). Porter’s research in industrial economics suggests three

generic strategies of competing above average rates of return: cost leadership, differentiation and

focus (Porter, 1980, 1985). He argues that to succeed in business, a firm needs to adopt one or

more of the three generic competitive strategies, and that the firm’s strategic choice ultimately

determines its competitiveness and profitability (Miller & Dess, 1993).

In the meantime, the resource-based view (RBV) (Wernerfelt, 1984) of the firm attribute

superior firm performance to firm resources. Firms build competitive advantage by utilizing unique

sets of resources and strategies (Barney, 1991). Resources enable firms to conceive and implement

strategies, improving effectiveness (Barney, 1991). In contrast, strategies are the ways in which

firms relate to their environment (Porter, 1980, 1985). Strategies therefore impact the link between

resources and performance (Edelman et al., 2005).

Day and Wensley (1988) extend the work of Porter (1980) by introducing the sources→

positions→performance (SPP) framework of competitive advantage. In addition to acknowledging

the performance impact of positional advantages in terms of superior customer value (e.g.,

differentiation) and lower relative costs (e.g., cost leadership). Their framework embraces elements

from the resource-based view (Wernerfelt, 1984) by arguing that organizational capabilities are the

11

Bharadwaj (2000) develops the concept of Information Technology as an organization

resource by drawing on the resource-based view of the firm. The study empirically examines the

association between IT resource and firm performance and finds the relationship to be positive and

significant.

Koo et al. (2004) investigate Porter’s competitive strategies in electronic virtual markets. In

their research framework, the resource of a firm is on-line business capability. Porter’s framework

of competitive strategies is used to compare the market strategies of on-line firms and firms

operating concurrently in the traditional and electronic markets. They also examine the connection

between the competitive strategies and business performance in electronic markets.

Edelman et al. (2005) provide a schematic representation of the resources → strategy →

performance model. They examine the relationship between firm resources, strategies, and

performance in a cross-section of small firms. They model firm strategy as a mediating variable by

arguing that mediation tests specify the existence of a significant intervening mechanism (e.g., firm

strategy) between an antecedent variable (e.g., firm resources) and the consequent variable (e.g.,

firm performance).

Reimann et al. (2009) draw from the sources→positions→performance framework and build a

research model in which two strategic postures of firms - differentiation and cost leadership -

mediate the effect of CRM on firm performance. Their results reveal that CRM does not affect firm

performance directly. Rather, the CRM-performance link is fully mediated by differentiation and

cost leadership.

In this research, the emerging information technology is viewed as sources of competitive

advantage to supply chain performance (DeGroote & Marx, 2013; Dong et al., 2009; Vijayasarathy,

2010). The basic premise of our study is that the relationship between emerging information

technology use in the supply chain and supply chain performance can be affected by three key

12

Figure 1 Conceptual model

(1) Cost Leadership

With a cost leadership strategy, a firm can improve its competitive stance by lowering its

manufacturing and marketing costs. A lower cost structure can improve profitability and market

share.

The advent of the emerging information technology has complicated the market dynamics,

making it harder to predict how firms will compete in the market (Porter & Heppelmann, 2014).

By now there are few studies of how companies compete in a market in terms of EST can be

related with cost leadership strategy. However, previous studies have shown that the integration

of information technology with supply chain management can reduce the cost of information

exchange between parties in the supply chain, and thus impacting the supply chain performance

(Dong et al., 2009; Qrunfleh & Tarafdar, 2014).

Therefore, we propose that emerging information technology indirectly affects firm

performance by increasing efficiency and driving down costs, implying that emerging information

technology significantly affects a firm’s cost leadership position, leading to superior supply chain

performance. It is thus hypothesized:

H2a: There is a significant association between intention use of emerging information

technology and position of cost leadership strategy.

Emerging Information Technology Supply Chain Performance Competitive Strategy

13

H2b: There is a significant association between cost leadership strategy position and supply

chain performance.

H2c: Cost leadership mediates the relationship between emerging information technology and

supply chain performance.

(2) Differentiation

On the other hand, a firm may pursue a strategic advantage by differentiating its products

and services from those offered by competitors. By providing unique and innovative products and

services with creative marketing, a firm can establish strong brand recognition and customer

loyalty.

With a differentiation strategy, a firm seeks to be unique in the way it offers products and

services to customers, because it is viewed as valuable by customers (Porter, 1980). Porter and

Millar (1985) further envisioned the potential of information technology as a driving force behind

differentiation strategies. Many believe that firms can also enhance value by creatively exploiting

differentiated market segments and channels. Differentiation strategies in market allow firms to

charge premium prices for products and services deemed unique by customers (Porter, 2001).

Adoption of information technologies may enable a firm to obtain in-depth information about

its suppliers and customers and then use this knowledge to adapt its offerings to meet the needs of

its supply chain operations in a better way than does its competitors. Therefore, the emerging

information technology is linked to the business strategy of differentiation, which enables firms to

achieve superior performance. This link is consistent with the sources→positions→performance

framework; emerging information technology is as the source that allows firms to achieve a

differentiated position, and in turn drives firm’s supply chain performance (Day and Wensley

1988).

H3a: There is a significant association between intention use of emerging information

14

H3b: There is a significant association between differentiation strategy position and supply

chain performance.

H3c: Differentiation mediates the relationship between emerging information technology and

supply chain performance.

(3) Focus

An alternative strategy, focus, is generally held to be appropriate for small firms or

entrepreneurs with few resources, because it allows them to compete with larger firms and

position themselves based on other strategic strengths (Wright, 1987).

With a focus strategy, a firm concentrates its efforts on a specific market segment (Porter,

1980). This strategy is generally held to be appropriate for small firms with few resources,

because it allows them to compete with larger firms and position themselves based on other

strategic strengths (Wright, 1987). How this strategy would play in an IT based market is not

clear. Further, there have been no extensive empirical studies of the behavior and performance of

firms in the market competing on the basis of information technology. Koo et al. (2004) examine Porter’s competitive strategies in electronic virtual markets and find on-line firms incline to differentiation strategy, whereas click-and-mortar firms prefer strategies based on focus strategy.

Both on-line firms and click-and-mortar firms are pioneer adopters of emerging information

technologies such as cloud computing and big data analytics. We thus hypothesize:

H4a: There is a significant association between intention use of emerging information

technology and position of focus strategy.

H4b: There is a significant association between focus strategy position and supply chain

performance.

H4c: Focus mediates the relationship between emerging information technology and supply

15

Drawing from these hypotheses proposed above, the research framework is illustrated in

Figure 2.

Figure 2 Research framework Emerging Information Technology Differentiation Supply Chain Performance Cost Leadership Market Focus H1 H2c H3c H4c H2a H2b H3a H4a H3b H4b

16

4. Research Method

4.1 Survey Instrument

The survey instrument is developed with questions derived from the literature on emerging

information technologies, Porter’s competitive strategies, resource → strategy → performance

framework, and supply chain performance discussed above. We operationalize the study variables

using multi-item reflective measures on a seven-point scale (Jarvis et al. 2003).

Table 1 summarizes the independent and dependent variables, which are further elaborated

below.

Table 1 Research variables

Construct Operational Definition Supporting Literature

Emerging information technology

A firm’s intention to adopt the emerging information technology

Armbrust et al. (2010); Cegielski et al. (2012)

Cost leadership

A firm’s posture of competition based on lower cost of operation and resource relative to the firm’s competitors.

Koo et al. (2004) Reimann et al. (2009) Oltra and Luisa Flor (2010)

Differentiation

A firm’s ability to compete by being unique within their industry in a number of perspectives.

Koo et al. (2004) Reimann et al. (2009)

Focus

A firm’s ability to compete by targeting specific groups of buyers, product lines, product lifecycle, or geographic areas.

Koo et al. (2004) Kim et al. (2004)

Supply chain performance

A firm’s assessment of the efficiency and effectiveness of its supply chain.

Wu et al. (2006) Vijayasarathy (2010) DeGroote and Marx (2013)

17

(1) The “Emerging information technology” construct

Emerging information technology is measured with five items that reflected the firm’s

intention to adopt the technologies. These items are based on technology forecast reports of famous

market research institutions and academic literature. Global market research firms such as Gartner

(2014, 2015) and IDC (2014, 2015) publish top technology trend predictions annually. Academic

society such as the IEEE Computer Society also announces annual top 10 technology trends (IEEE,

2014, 2015). Following the structure of the “new technology stack” depicted in Porter and

Heppelmann (2014), this study will focus on the emerging information technology items in the

technology stack.

We choose to measure intention rather than actual adoption of the technology due to the

relative newness of the emerging information technology as a business tool. In the literature, there

is extensive support for the notion that measuring intention, when placed in context with respect to

time, is an accurate proxy for action (Cegielski et al., 2012).

(2) The “Cost leadership” construct

The cost leadership construct is measured with three items that reflect the extent to which a

firm deploys a cost oriented strategy. First of all, cost leadership means the generation of higher

margins than competitors by achieving lower operation costs. Firms with a cost leadership

strategy often have highly stable product lines and a strong emphasis on profit and budget

controls (Miller, 1988). Secondly, cost leadership is often reflected in the price competitiveness

(Koo et al., 2004). The third item is the economic scale. A firm can gain a cost advantage with

economies of scale or superior manufacturing processes (Porter, 1980, 1985). Generally, larger

firms with greater access to resources are more likely to take advantage of cost leadership

strategies, whereas smaller firms are often forced to compete with highly differentiated products

18

(3) The “Differentiation” construct

The differentiation construct is measured with four items that reflect the extent to which a firm

deploys a differentiation strategy.

First of all, differentiation entails being unlike or distinct from competitors, e.g., by

providing superior information, prices, distribution channels, and prestige to the customer (Porter,

1980). Differentiation prevents a business from competitive rivalry, insulating it from competitive

forces that reduce margins (Phillips et al. 1983).

Extending Porter’s competitive strategy framework, Miller distinguished differentiation strategies based on innovation from those based on operational and marketing efficiency (Miller,

1988; Wright, 1987). These form the two items included in the construct.

Differentiation strategies based on innovation can create a dynamic market environment in

which it is hard for competitors and customers to predict and react. This unpredictability could

give the innovator a substantial advantage over its competitors (Miller, 1987). We further

distinguish innovation as new markets and new business models. Differentiation strategies based

on innovation can create a dynamic market environment or a distinct business model in which it

is hard for competitors and customers to predict and react. This unpredictability could give the

innovator a substantial advantage over its competitors (Miller, 1987).

(4) The “Focus” construct

We base our measure of the focus construct on the research construct of Koo et al. (2004),

and Laosirihongthong et al. (2010).

A firm may obtain a strategic advantage by choosing to become specialized and focus on a

niche market instead of competing broadly in the whole market. The choice of market segment

for focusing can be based on dimensions such as prices, customer groups, product functions and

19

market orientation, customer focus and product focus in the case business. We measure the focus

construct with four items which reflect four different market and product segmentations

(5) The “Supply chain performance” construct

Supply chain performance served as the dependent variable and is measured by seven items.

Respondents will be asked to rate their supply chain performance over the past few years relative to

their competitors on a seven-point scale.

Beamon (1999) proposed a framework for measuring supply chain performance. The

framework included the measurement of resources, output and flexibility as the strategic goals of

SCM. The key measuring variables include cost, activity time, customer responsiveness and

flexibility. Neely et al. (1995) discuss design issues in supply chain performance measurement

systems. The metrics and measures are also discussed in the context of supply chain

activities/processes such as plan, source, make/assemble, and delivery/customer (Gunasekaran et

al., 2001; Gunasekaran et al., 2004).

This study will focus specifically on supply chain effectiveness (the achievement of specific

objectives) and efficiency (the means by which specific objectives were attained), which have

been recognized as direct and observable goals of SCM practice. Firms in a supply chain achieve

operational efficiency and effectiveness by lowering costs, reducing inventory, promoting

flexibility, ensuring on-time deliveries, and minimizing shortage of critical resources. These

objectives relate to both parties in a buyer–supplier relationship, and therefore, can determine the

performance of the overall supply chain.

Table 2 presents the items used to measure each of the independent and dependent variables.

Table 2 Items used in the survey

Variable Item

Intention of emerging information technology adoption (1 – to no extent; 4 – to some extent; 7 – to a great extent)

20

EIT2: IoT sensing

EIT3: IoT big data analytics

EIT4: IoT user interface

EIT5: IoT mobile app

EIT6: IoT security and privacy

Cost Leadership strategy orientation (1 – strongly disagree; 7 – strongly agree)

CL1: Developing products or services with lower cost

CL2: Delivering products or services with lower price

CL3: Providing products or services in large quantity or scale

Differentiation strategy orientation

(1 – strongly disagree; 7 – strongly agree)

DF1: Differentiating products and services based on operational efficiency

DF2: Differentiating products and services based on innovation

DF3: Delivering products or services with superior functionality in current market

DF4: Delivering products or services with innovative business model

Focus strategy orientation

(1 – strongly disagree; 7 – strongly agree)

MF1: Focusing in a niche market segment

MF2: Focusing in first to market position

MF3: Focusing in market position as a fast follower

MF4: Focusing in a mature market segment

Supply Chain Performance

21

SCP1: Delivering products or services on time

SCP2: Reducing lead time

SCP3: Responding to changes of customer requirement

SCP4: Avoiding lack of critical resources

SCP5: Inventory and logistics flexibility

SCP6: Reducing cost of the whole supply chain management

SCP7: Reducing inventory cost

4.2 Sample and Data Collection

Empirical data to test the hypothesized relationships is obtained by using a survey of large

Taiwanese companies. An online questionnaire developed in accordance with Table 2 above is

implemented as the survey instrument. It is pre-tested in an iterative manner among a sample of 15

executives and supply chain managers. The questionnaire items is revised on the basis of results of

the expert interviews and refined through rigorous pre-testing to establish content validity. The

pre-testing focuses on instrument clarity, question wording and validity. During the pre-testing,

members of the sample are invited to comment on the questions and wordings of questionnaire. The

comments of these individuals then provide a basis for revisions to the construct measures.

After the pre-testing and revision, data of this research will be collected through an online

survey of supply chain executives and managers. China Credit Information Service, LTD (CCIS)

provides comprehensive data of the Top 5,000 list of the largest companies in Taiwan, and 2,300

firms are selected randomly as the sample of this study.

Table 3 shows the profile of the sampling list. The list for this research provides the names,

addresses, and e-mail addresses of the 2,300 randomly chosen members. Survey invitations are sent

to firms in the list by e-mail. Follow-up letters are sent approximately one month after the initial

22

Table 3 Profile of the sampling firms

Industry Frequency %

High-tech manufacturing 946 41%

Other manufacturing 332 14%

IT and Telecom services 450 20%

Other services 301 13%

Retail and wholesale 271 12%

Total 2,300 100%

4.3 Reliability and Validity

The reliability of the survey instrument will be tested using Cronbach’s alpha (Cronbach,

1951) as a test of internal consistency for the Emerging Information Technology, Cost Leadership,

Differentiation, Focus, and Supply Chain Performance constructs. Cronbach’s alpha tests the

interrelationship among the items composing a construct to determine if the items are measuring a

single construct. Nunnally and Bernstein (1994) recommend a threshold alpha value of .7. Cicchetti

et al. (2011)suggest the following reliability guidelines for significance: r < .70 (unacceptable), .70 ≤ r < .80 (fair), .80 ≤ r < .90 (good) and r > .90 (excellent).

Content validity (Straub, 1989) refers to the extent to which the instrument measures what it is

supposed to measure. Most of the measures used in the study are adopted from published studies on

the topics. Although basing the study on the established literature provided a considerable level of validity, the study’s validity will be further improved by pilot-testing the instrument with a panel of experts comprising 15 executives and supply chain managers.

In order to assess convergent and discriminant validity, the items used to measure Emerging

Information Technology, Cost Leadership, Differentiation, Focus, and Supply Chain Performance

23

sphericity and the Kaiser–Meyer–Olkin measure of sampling adequacy are tested for satisfactory,

24

5. Results

This study will investigate the impact of emerging information technology on supply chain

performance, mediated by the firm’s position of competitive strategy. The empirical results are

expected to demonstrate that emerging information technology such as cloud computing, big data

and mobile connectivity (Iansiti & Lakhani, 2014; Porter & Heppelmann, 2014) influences the

supply chain performance such as reducing cost, increasing flexibility and shortening time. The

results also will verify the mediating role of Porter’s theory of generic strategy (Porter, 1980, 1985)

on the link between emerging information technology and supply chain performance. Finally, these

results will also make a solid case for the the sources→strategy→performance framework of

competitive strategy (Day & Wensley, 1988; Venkatraman, 1989).

Table 4 summarizes the descriptive statistics, reliability and validity tests.

Table 4 Descriptive statistics and reliability and validity test

Construct Item Mean SD Cronbach’s

alpha Cronbach’s alpha if item deleted Factor loading on single factor Emerging EIT1 4.12 1.554 0.886 0.870 0.782 Information EIT2 4.58 1.564 0.860 0.830 Technology EIT3 4.45 1.619 0.861 0.824 EIT4 3.67 1.479 0.865 0.805 EIT5 4.71 1.554 0.869 0.784 EIT6 4.99 1.500 0.873 0.765 Cost Leadership CL1 4.46 1.414 0.719 0.724 0.732 CL2 3.72 1.521 0.596 0.824 CL3 3.60 1.460 0.557 0.842 Differentiation DF1 4.55 1.371 0.905 0.893 0.854 DF2 4.39 1.375 0.857 0.921 DF3 4.31 1.579 0.889 0.866

25 DF4 4.21 1.456 0.870 0.895 Market Focus MF1 4.52 1.379 0.852 0.806 0.839 MF2 4.31 1.485 0.812 0.834 MF3 4.32 1.311 0.835 0.792 MF4 4.22 1.372 0.792 0.864 Supply Chain Performance SCP1 4.51 1.460 0.931 0.926 0.795 SCP2 4.61 1.330 0.918 0.858 SCP3 4.94 1.338 0.917 0.866 SCP4 4.55 1.330 0.921 0.832 SCP5 4.55 1.396 0.917 0.868 SCP6 4.42 1.465 0.922 0.828 SCP7 4.64 1.338 0.920 0.847

Table 5 summarizes the correlation among different factors.

Table 5 Construct correlation

Construct EIT CL DF MF SCP Emerging Information Technology EIT 1 Low Cost CL 0.324** 1 Differentiation DF 0.356** 0.647** 1 Market Focus MF 0.421** 0.663** 0.864** 1 Supply Chain Performance SCP 0.362** 0.622** 0.650** 0.759** 1 **p < 0.01

26

Table 6 Tests of hypothesized model

Hypothesis Coefficient p-value Test Result VIF Mediated

H1 EIT → SCP 0.362*** 0.000 Supported

H2a EIT → CL 0.324*** 0.000 Supported

H2b CL → SCP 0.622*** 0.000 Supported

H2c EIT → SCP 0.180** 0.002 Supported 1.118 Partial

CL → SCP 0.564*** 0.000 Supported

H3a EIT →DF 0.356*** 0.000 Supported

H3b DF →SCP 0.650*** 0.000 Supported

H3c EIT → SCP 0.150** 0.009 Supported 1.145 Partial

DF →SCP 0.596*** 0.000 Supported

H4a EIT →MF 0.421*** 0.000 Supported

H4b MF → SCP 0.759*** 0.000 Supported

H4c EIT → SCP 0.052 0.304 Rejected 1.215 Complete

MF → SCP 0.737*** 0.000 Supported

27

6. Discussion

This study investigates the impact of emerging information technology on supply chain performance, mediated by the firm’s position of competitive strategy. Managerial implications and possible avenues for further research are elaborated as follows.

6.1 Managerial Implications

Supporting these research hypotheses, the critical insight we obtain from our empirical

results is that the link between emerging information technology and supply chain performance is

fully mediated by the strategies of firms. In other words, the link between emerging information

technology and supply chain performance is not direct, but rather indirect. On the basis of this

finding, we conclude that prior research was incomplete in that it focused exclusively on the

direct effect of emerging technology. By adopting a mediational structure in this study, we isolate

the specific processes by which emerging information technology links to supply chain

performance. To the best of our knowledge, this is the first empirical study to investigate critical

mediators in the technology-performance link as well as to examine emerging information

technologies in the context of business strategies.

6.2 Study Limitations

Although this study reports meaningful implications toward the development of

multidimensional measures of factors that influence supply chain performance, the validity of an

instrument cannot be firmly established on the basis of a single study. Especially, all cases used

for tests were collected in firms located in Taiwan. Taiwan is a relatively speedy and competitive

arena for accepting new information technology, but it has difference dependent on industry.

Especially, the manufacturing and retail industries have shown front-running features. It is

appropriate for SCM practitioners and academicians to interpret our findings as a guide model,

28 6.3 Further Research

As has been implied, there is a need for further research efforts focused on accumulating

empirical evidence for the validation and assessment of measurement properties and data

surmounting the limitations of the present study. Such research is needed to address how other

variables relate to supply chain performance. Wearable interface in IoT, for example, has received

inadequate attention in MIS and technology innovation theories. Further research could also

investigate the relative importance of the factors impacting each stage of the supply chain

process. These efforts should involve studies identifying the organizational factors which affect

such independent variables as process fit, information quality, and system support. Also, special

attention should be focused on data collected in various industries or specific context over an

extended period. The analysis of such data may enable conclusions to be drawn about both more

29

References

Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., & Rabkin, A. (2010). A

view of cloud computing. Communications of the ACM, 53(4), 50.

doi:10.1145/1721654.1721672

Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks,

54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010

Barney, J. (1991). Firm Resources and Sustained Competitive Advantage Journal of Management,

17(1), 99-120. doi:10.1177/014920639101700108

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of

Personality and Social Psychology, 51(6), 1173-1182. doi:10.1037/0022-3514.51.6.1173

Beamon, B. M. (1999). Measuring supply chain performance. International Journal of Operations

& Production Management, 19(3), 275-292. doi:10.1108/01443579910249714

Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Quarterly, 24(1), 169-196.

Cegielski, C. G., Allison Jones‐Farmer, L., Wu, Y., & Hazen, B. T. (2012). Adoption of cloud computing technologies in supply chains. The International Journal of Logistics

Management, 23(2), 184-211. doi:10.1108/09574091211265350

Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67-80. doi:10.1016/j.dss.2013.08.008

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.

Chiang, R. H. L., Goes, P., & Stohr, E. A. (2012). Business Intelligence and Analytics Education, and Program Development. ACM Transactions on Management Information Systems, 3(3), 1-13. doi:10.1145/2361256.2361257

Cicchetti, D. V., Koenig, K., Klin, A., Volkmar, F. R., Paul, R., & Sparrow, S. (2011). From Bayes through marginal utility to effect sizes: a guide to understanding the clinical and statistical significance of the results of autism research findings. J Autism Dev Disord, 41(2), 168-174. doi:10.1007/s10803-010-1035-6

Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. doi:10.1007/BF02310555

Day, G. S., & Wensley, R. (1988). Assessing advantage: a framework for diagnosing competitive superiority. Journal of Marketing, 52(2), 1-20.

DeGroote, S. E., & Marx, T. G. (2013). The impact of IT on supply chain agility and firm performance: An empirical investigation. International Journal of Information Management,

30

33(6), 909-916. doi:10.1016/j.ijinfomgt.2013.09.001

Dong, S., Xu, S. X., & Zhu, K. X. (2009). Research Note—Information Technology in Supply Chains: The Value of IT-Enabled Resources Under Competition. Information Systems

Research, 20(1), 18-32. doi:10.1287/isre.1080.0195

Edelman, L. F., Brush, C. G., & Manolova, T. (2005). Co-alignment in the resource–performance relationship: strategy as mediator. Journal of Business Venturing, 20(3), 359-383. doi:10.1016/j.jbusvent.2004.01.004

Garrison, G., Kim, S., & Wakefield, R. L. (2012). Success factors for deploying cloud computing.

Communications of the ACM, 55(9), 62. doi:10.1145/2330667.2330685

Gartner. (2014). The Top 10 Strategic Technology Trends for 2014. Retrieved from

https://www.gartner.com/doc/2667526

Gartner. (2015). Research Guide: The Top 10 Strategic Technology Trends for 2015. Retrieved from https://www.gartner.com/doc/2966917

Graham, M. (2011). Cloud Collaboration: Peer-Production and the Engineering of the internet. 67-83. doi:10.1007/978-90-481-9920-4_5

Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347. doi:10.1016/j.ijpe.2003.08.003

Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management,

21(1/2), 71-87. doi:10.1108/01443570110358468

Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861-874. doi:10.1016/j.ijinfomgt.2013.07.001

Hancke, G. P., Markantonakis, K., & Mayes, K. E. (2010). Security Challenges for User-Oriented RFID Applications within the Internet of Things. Journal of Internet Technology, 11(3), 307-313.

Helland, P. (2013). Condos and clouds. Communications of the ACM, 56(1), 50. doi:10.1145/2398356.2398374

Huo, B., Qi, Y., Wang, Z., & Zhao, X. (2014). The impact of supply chain integration on firm performance. Supply Chain Management: An International Journal, 19(4), 369-384. doi:10.1108/scm-03-2013-0096

Iansiti, M., & Lakhani, K. R. (2014). Digital Ubiquity - How Connections, Sensors, and Data Are Revolutionizing Business. Harvard Business Review, November, 91-99.

IDC. (2014). IDC Predictions 2014: Battles for Dominance — and Survival — on the 3rd Platform (244606). Retrieved from

IDC. (2015). IDC Predictions 2015: Accelerating Innovation — and Growth — on the 3rd Platform (252700). Retrieved from

31

IEEE. (2014). Top Technology Trends for 2014. Retrieved from

http://www.computer.org/portal/web/membership/Top-10-Tech-Trends-in-2014

IEEE. (2015). Top Technology Trends for 2015. Retrieved from

http://www.computer.org/portal/web/membership/Top-Tech-Trends-for-2015

Ilie-Zudor, E., Kemény, Z., van Blommestein, F., Monostori, L., & van der Meulen, A. (2011). A survey of applications and requirements of unique identification systems and RFID techniques. Computers in Industry, 62(3), 227-252. doi:10.1016/j.compind.2010.10.004 Isoherranen, V. (2011). Analysis of Strategy Focus vs. Market Share in the Mobile Phone Case

Business. Technology and Investment, 02(02), 134-141. doi:10.4236/ti.2011.22014

Iyer, B., & Henderson, J. C. (2010). Preparing for the Future: Understanding the Seven Capabilities of Cloud Computing. MIS Quarterly Executive, 9(2), 117-131.

Jaccard, J., Wan, C. K., & Turrisi, R. (1990). The Detection and Interpretation of Interaction Effects Between Gontinuous Variables in Multiple Regression. Multivariate Behavioral Research,

25(4), 467-478.

Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36. doi:10.1145/1536616.1536632

Jelinek, M., & Bergey, P. (2013). Innovation as the strategic driver of sustainability: big data knowledge for profit and survival. IEEE Engineering Management Review, 41(2), 14-22. doi:10.1109/emr.2013.2259978

Kalloniatis, C., Mouratidis, H., & Islam, S. (2013). Evaluating cloud deployment scenarios based on security and privacy requirements. Requirements Engineering, 18(4), 299-319. doi:10.1007/s00766-013-0166-7

Katzan, H. J. (2009). Cloud Software Service: Concepts, Technology, Economics. Service Science,

1(4), 256-269.

Kim, E., Nam, D.-i., & Stimpert, J. L. (2004). The applicability of Porter's generic strategies in the digital age: assumptions, conjectures, and suggestions. Journal of Management, 30(5), 569-589. doi:10.1016/j.jm.2003.12.001

Kim, H., & Feamster, N. (2013). Improving Network Management with Software Defined Networking. IEEE Communications Magazine, February, 114-119.

Koo, C. M., Koh, C. E., & Nam, K. (2004). An Examination of Porter's Competitive Strategies in Electronic Virtual Markets: A Comparison of Two On-line Business Models. International

Journal of Electronic Commerce, 9(1), 163-180.

Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information

Management, 34(3), 387-394. doi:10.1016/j.ijinfomgt.2014.02.002

Laosirihongthong, T., Tan, K. C., & Kannan, V. R. (2010). The impact of market focus on operations practices. International Journal of Production Research, 48(20), 5943-5961. doi:10.1080/00207540903232797

32

Leong, L.-Y., Hew, T.-S., Tan, G. W.-H., & Ooi, K.-B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems

with Applications, 40(14), 5604-5620. doi:10.1016/j.eswa.2013.04.018

Lin, A., & Chen, N.-C. (2012). Cloud computing as an innovation: Percepetion, attitude, and adoption. International Journal of Information Management, 32(6), 533-540. doi:10.1016/j.ijinfomgt.2012.04.001

McAfee, A., & Brynjolfsson, E. (2012). Big Data- The Management Revolution. Harvard Business

Review, October, 1-9.

Miller, A., & Dess, G. G. (1993). Assessing Porter’s (1980) model in terms of generalizability, ccuracy, and simplicity Journal of Management Studies, 30(4), 553-585.

Miller, D. (1987). The structural and environmental correlates of business strategy. Strategic

Management Journal, 8(1), 55-76.

Miller, D. (1988). Relating porter’s business strategies to environment and structure: analysis and performance implications. Academy of Management Journal, 31(2), 280-308.

Mills, L. A., Knezek, G., & Khaddage, F. (2014). Information Seeking, Information Sharing, and going mobile: Three bridges to informal learning. Computers in Human Behavior, 32, 324-334. doi:10.1016/j.chb.2013.08.008

Neely, A., Gregory, M., & Platts, K. (1995). Performance measurement system design.

International Journal of Operations & Production Management, 15(4), 80-116.

doi:10.1108/01443579510083622

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3 ed.). New York: McGraw-Hill. Oltra, M. J., & Luisa Flor, M. (2010). The moderating effect of business strategy on the relationship

between operations strategy and firms' results. International Journal of Operations &

Production Management, 30(6), 612-638. doi:10.1108/01443571011046049

Piran, M. J., Murthy, G. R., & Babu, G. P. (2011). Vehicular Ad Hoc and Sensor Networks: Principals and Challenges. International Journal of Ad hoc, Sensor & Ubiquitous

Computing, 2(2), 38-49. doi:10.5121/ijasuc.2011.2204

Porter, M. E. (1980). Competitive Strategy. New York: Free Press. Porter, M. E. (1985). Competitive Advantage. New York: Free Press.

Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, 79(2), 63-78.

Porter, M. E., & Heppelmann, J. E. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review, November, 64-88.

Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage. Harvard

Business Review, 63(4), 61-78.

Qrunfleh, S., & Tarafdar, M. (2014). Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International Journal of Production Economics,

147, 340-350. doi:10.1016/j.ijpe.2012.09.018

33

performance: the mediating role of business strategy. Journal of the Academy of Marketing

Science, 38(3), 326-346. doi:10.1007/s11747-009-0164-y

Serrano, N., Hernantes, J., & Gallardo, G. (2013). Mobile Web Apps. IEEE Software,

September/October, 22-27.

Shin, S., Kim, K., Kim, K.-H., & Yeh, H. (2012). A Remote User Authentication Scheme with Anonymity for Mobile Devices. International Journal of Advanced Robotic Systems, 1. doi:10.5772/50912

Sotomayor, B., Montero, R. S., Lorente, I. M., & Foster, I. (2009). Virtual infrastructure management in private and hybrid clouds. IEEE Internet Computing, 13(5), 14-22.

Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-169. Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud

computing. Journal of Network and Computer Applications, 34(1), 1-11.

doi:10.1016/j.jnca.2010.07.006

Sultan, N. (2013). Cloud computing: A democratizing force? International Journal of Information

Management, 33(5), 810-815. doi:10.1016/j.ijinfomgt.2013.05.010

Sultan, N., & van de Bunt-Kokhuis, S. (2012). Organisational culture and cloud computing: coping with a disruptive innovation. Technology Analysis & Strategic Management, 24(2), 167-179. doi:10.1080/09537325.2012.647644

Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307. doi:10.1016/j.tele.2013.06.002

Venkatraman, N. (1989). The concept of fit in strategy research - Toward verbal and statistical correspondence. Academy of Management Review, 14(3), 423-444.

Vijayasarathy, L. R. (2010). An investigation of moderators of the link between technology use in the supply chain and supply chain performance. Information & Management, 47(7-8), 364-371. doi:10.1016/j.im.2010.08.004

Vouk, M. A. (2008). Cloud Computing – Issues, Research and Implementations. Journal of

Computing and Information Technology, 16(4), 235-246. doi:10.2498/cit.1001391

Wagner, S. M., Grosse-Ruyken, P. T., & Erhun, F. (2012). The link between supply chain fit and financial performance of the firm. Journal of Operations Management, 30(4), 340-353. doi:10.1016/j.jom.2012.01.001

Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of

Business Logistics, 34(2), 77-84.

Watson, C., McCarthy, J., & Rowley, J. (2013). Consumer attitudes towards mobile marketing in the smart phone era. International Journal of Information Management, 33(5), 840-849. doi:10.1016/j.ijinfomgt.2013.06.004

34

171-180.

Wright, P. (1987). A Refinement of Porter’s generic strategies. Strategic Management Journal, 8, 93-101.

Wu, F., Yeniyurt, S., Kim, D., & Cavusgil, S. T. (2006). The impact of information technology on supply chain capabilities and firm performance: A resource-based view. Industrial

Marketing Management, 35(4), 493-504. doi:10.1016/j.indmarman.2005.05.003

Zhang, X., Pieter van Donk, D., & van der Vaart, T. (2011). Does ICT influence supply chain management and performance? International Journal of Operations & Production