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 defined network is the new technology after virtualization software to dissolve hardware

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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.

(3) Connectivity and Smart Mobile Applications

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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 is real-time protection. The improved cloud IT security technology and service can also help

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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).