Barriers that Prevent Implementation of IoT within Small Businesses

Executive Summary
The purpose of this study is to develop timely and informed answers to a series of guiding research questions and subquestions to identify the risks and barriers that are associated with Internet of Things implementations. These types of studies are important today because the Internet of Things is changing the manner in which companies of all sizes and types operate their businesses, and current trends indicate that these implementations are increasing exponentially. The Internet of Things allows conventional computer-based networks to incorporate data collected from everyday objects to provide real-time analyses and new applications for these technologies are being developed every day. Indeed, there are already more mobile devices in use around the world than there are people, and most authorities agree that the Internet of Things deployments will continue to increase well into the foreseeable future. Chapter one of the study provides the rationale in support of this research together with the guiding research questions and subquestions followed by a review of the relevant literature in chapter two. Chapter three provides a detailed description of the study’s qualitative methodology and chapter four presents the results of the data analysis. Finally, chapter fives provide a summary of the research together with answers to the study’s guiding research questions and subquestions.
The Implementation of Internet of Things (IoT): A Case Study of the Barriers that Prevent Implementation of IoT within Small Businesses
Chapter 2: Literature Review
Chapter Introduction
The review of literature will be critical to establishing the current scope of innovation related to the IoT as well as its potential for expansion, especially into use by SMEs. To this end, this chapter describes more fully the study’s guiding Unified Theory of Acceptance and Use of Technology theoretical/conceptual framework followed by an overview of the Internet of Things (IoT) and an analysis of current trends that are having an impact on its proliferation. In addition, an examination of the risks that are associated with the implementation of IoT is followed by an analysis of other barriers that prevent the implementation of IoT by small businesses. Finally, a summary of the research and the key findings that emerged from a synthesis of the relevant literature conclude the chapter.
Theoretical/Conceptual Framework
The theoretical/conceptual framework that will be used in this study was originally developed by Venkatesh, Morris, Davis, and Davis (2003) by integrating a number of disparate elements from previous models to create a more accurate approach to understanding how and why technology-related decisions are made by organizations today. The Venkatesh et al. (2003) theoretical model draws on a number of key elements from eight previous theories or models in order to produce a more robust overview concerning the effects of different motivational factors on the technology-related decision-making process to identify how and why end users actually use different technologies.
The rationale in support of this more comprehensive model included identifying similarities between earlier models that could be applied in novel ways to gain fresh applicable insights that can be applied to similar situations involving the uptake of new technologies. This comprehensive model was termed the Unified Theory of Acceptance and Use of Technology (UTAUT) which was regarded as highly relevant for the purposes of this study as well. As noted in the introductory chapter, the topics selected for the questionnaire described further in chapter 3 below were based upon the UTAUT and were designed to explicate the respective levels of acceptance of technology acceptance and willingness to adopt innovations by stakeholders . Moreover, there is a growing body of research that confirms the effectiveness of the UTAUT model for guiding future research technology acceptance-related analyses (Venkatesh et al., 2003), including the acceptance levels for the adoption of the IoT.
The use of the UTAUT model is also highly appropriate for the purposes of this study because individual acceptance levels of information technology (IT) have been a key focus of many of the studies concerning new technology implementations to date (Venkatesh, Thong & Xu, 2016). Furthermore, the UTAUT draws on a growing body of scholarship that supports the logical notion that in order to enhance acceptance levels of new technologies, individuals must first have the opportunity to gain hands-on experience with them (Venkatesh et al., 2016). In this regard, Venkatesh et al. (2016) emphasize that, “There is also research on technology adoption by groups and organizations that holds the premise that one must first use a technology before one can achieve desired outcomes, such as improvement in employee productivity and task/job performance in organizations” (p. 328).
Four key factors comprise the UTAUT model as follows: (1) performance expectancy, (2) effort expectancy, (3) social influence, and (4) facilitating conditions as well as four moderators (1) age, (2) gender, (3) experience, and (4) voluntariness that can facilitate the analysis of behavioral intention levels to actually use a new technology, generally in organizational contexts (Venkatesh et al., 2016). An analysis of the accuracy of the four key factors of the UTAUT model in predicting new technology acceptance levels by Venkatesh et al. (2016) showed that performance expectancy, effort expectancy, and social influence influenced behavioral intention to use a technology were all predicted accurately. Likewise, various facilitating conditions and behavioral intention influenced actual technology usage (Venkatesh et al., 2016).
In addition, other researchers have also used the UTAUT model to examine the effects of the above-stated four moderators on acceptance of new technology levels and found that the model explained more than three-quarters (77%) of variance levels in respondents’ behavioral intention to use a technology and more than half (52%) of their variance in actual technology usage (Venkatesh et al., 2016). In sum, the UTAUT model provides a useful conceptual framework in which to develop timely and informed answers to the following guiding research questions and subquestions as stated in the introductory chapter:
RQ1. Why do SMEs view implementing and using IoT a risk?
RQ1a. What are the barriers (real or perceived) to implementation of IoT?
RQ2. How are IoT security risks and business risks related?
RQ2a. Under what conditions would SMEs adopt IoT implementation?
RQ3. Why is the IoT applicable and beneficial in an SME context?
RQ3a. What evidence is available to indicate that IoT will benefit SMEs?
Overview of the Internet of Things
The game-changing nature of the Internet is well documented and countless studies have examined the ways in which these technologies have changed how people learn, live, work, recreate and shop (Bok, 2014). Indeed, tens of thousands of new applications are introduced to the marketplace each year, and current signs indicate that these trends will continue well into the foreseeable future (Haghshenas & Richards, 2016). In other words, the Internet is here to stay – at least until the “next big thing” comes along to replace it – and software engineers and IT professionals are hard at work to exploit these technologies even further, including using cloud-based technologies and sensor-equipped devices to collect, aggregate and analyze a wide range of data in real time.
Although there are different IT platforms that are currently being used for these computing purposes, one of the main contenders for many businesses at present is the Internet of Things or IoT. For example, according to An and Wang (2018), “After the Internet and mobile communication network, the Internet of Things has become an important trend in the development of information technology, greatly promoting the reform of the information industry in the world” (p. 2386). The IoT can be conceptualized graphically as depicted in Figure 1 below.
Figure 1. Representative Internet of Things Configuration
Source: Cass, 2019
As can be easily discerned from Figure 1 above, the IoT can be applied to virtually anything with the right type of sensor (An & Wang, 2018), and the quality and extensiveness of the data that is collected in only dependent on the respective processing speeds of the host computer network and the sensors that are used in a given configuration (Davis, 2017). These attributes mean that the IoT can be applied by small businesses in innovative ways to achieve and sustain a competitive advantage, especially in an increasingly globalized marketplace (Schaffhauser, 2018).
Some of the most important features of IoT applications include the fact that conventional mobile devices such as smartphones, various types of personal computers, video systems and other handheld devices as well as objects that have traditionally not been viewed as being amenable can be incorporation into computer-based networks in meaningful ways. As Kaushik (2019) points out, “In the context of internet of things (IoT), the things may be anything like cell phones, iPods, mobile devices, resources, services, education, people, animal, any physical objects etc.” (p. 2).
Moreover, the IoT has made it possible to create and even remotely control so-called “smart buildings” as well as a myriad of other everyday objects with a dizzying array of IoT applications using radio frequency identification (RFID) tags and other sophisticated sensor devices. In this regard, Cubo, Nieto and Pimentel (2014) report that, “The Internet of Things is a technology based on the inter-connection of everyday life objects with each other, applications and database data” (p. 14071). In fact, the combinations of devices that are combined in a given IoT network appear to be infinite, and the utility of these technologies is limited only by the imagination of end users (Tucker & Bulim, 2018).
It is also important to point out that the IoT is not an abstract concept existing only on a software engineer’s drawing board, but is rather transforming the manner in which companies of all sizes and types are managing their information technology resources. As Cubo and his colleagues conclude, “This new Internet has led the evolution of the Ubiquitous Web 2.0, in integrating physical world entities into virtual world things, as some initiatives are already addressing” (p. 14072). Although precise figures are unavailable, it is reasonable to posit that the number of IoT deployments has increased exponentially in line with the proliferation of mobile devices together with powerful sensing devices that can be fitted to virtually any type of consumer product or artifact (Banham, 2016).
Many of the same attributes that make the IoT a valuable resource for businesses of all sizes and types, though, are the same attributes that create security risks for its implementation and administration (Banham, 2016). For example, a study by Celik, Fernandes, Tan and McDaniel (2019) noted that recent innovations in IoT technologies have made it possible to interconnect billions of devices in ways that not only facilitate the aggregation of data but enhance the ability of organizations to respond to changes in consumer preferences and demand. Further, new applications for IoT technologies continue to be developed on a near-daily basis, and most authorities agree that the IoT represents the “next big thing” in information technology for the foreseeable future (Cassidy & Nandaraj, 2019).
Notwithstanding these significant benefits, however, Celik and his associates (2019) also emphasize that, “IoT is now pervasive—new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces” (p. 1). Some of the digitally augmented spaces in IoT implementations that are most vulnerable to security risks include those depicted in Figure 2 below.
Figure 2. Smart objects that pose the greatest security risks
Source: Khvoynitskaya, 2019
Certainly, the objects depicted in Figure 2 above are only representative of the vast array of “things” that can be incorporated into IoT networks, but the respective percentages cited for the security risks for these objects do underscore the challenges that are involved in protecting these networks from unauthorized intrusions, including those security risks that are depicted in Figure 3 below.
Figure 3. IoT components vulnerable to hacking
Source: Khvoynitskaya, 2019
In fact, even the U.S. Department of Defense has taken notice of these trends and has stressed the need for additional security measures for potentially vulnerable IoT networks, most especially those that support the nation’s energy grids. For instance, according to Cassidy and Galio (2019), “The internet of things is the network of web-enabled objects and devices in society that are able to collect, transmit and exchange data. In recent years, the Defense Department repeatedly has emphasized the need to bolster cybersecurity standards and policies for these systems” (p. 63).
In their haste to jump on the IoT bandwagon, however, some small businesses may fail to take into account the numerous challenges, obstacles and risks that are involved in implementing and managing these technologies in real-world settings, most especially in view of recent and current trends in the use of mobile and other devices that comprise an IoT network as discussed below.
Current Trends
Around mid-2015 (see Figure 3 below), the global population of human beings was outpaced by the number of active cellphone subscriptions based on data from the World Bank and the United Nations’ International Telecommunications Union (Murphy, 2019).
Figure 3. Number of cell phones versus global population: 2005 – 2018
Source: Quartz (2019) at https://qz.com/1608103/there-are-now-more-cellphones-than-people-in-the-world/
As can be seen from the trends depicted in Figure 3 above, there were more than eight billion active cellphone subscriptions as of end of year 2018 versus a global population of 7.7 billion (World population clock,2019), Of these subscriptions, the International Telecommunications Union estimates that approximately 5.3 billion are mobile broadband subscriptions (Murphy, 2019). Although these figures do not mean that every human on earth has at least one active cellphone subscription (many consumers in affluent nations have more than one cellphone subscription), they do underscore the enormity of the current trends that are fueling interest in IoT implementations.
While the proliferation of active cellphone subscriptions has slowed somewhat in recent months, the growth of active subscription rate continues to outpace the growth of the global population so the trends depicted in Figure 2 above will likely remain consistent for the foreseeable future (Murphy, 2019). For instance, Figure 4 below depicts the forecasted number of mobile devices globally during the period from 2019 to 2023.
Figure 4. Forecast number of mobile devices worldwide from 2019 to 2023 (in billions)
Source: Mobile devices, Statisa (2019) at https://www.statista.com/statistics/245501/multiple-mobile-device-ownership-worldwide/
As can be seen from the trend depicted in Figure 3 above, the number of mobile devices is projected to increase to a staggering 16.8 billion in 2023. Given the complexity of many IoT networks and the importance of their contributions to organizational success, it is apparent that small businesses must take the risks that are involved in implementing these technologies into account from the outset and these issues are discussed further below.
Risks Associated with the Implementation of IoT
The computing environment in which IoT is implemented is highly dynamic, and adequate security protocols that are in place today may be woefully outdated within a short period of time. Furthermore, the risks that are currently associated with IoT implementations continue to evolve and expand in ways that demand ongoing security oversight. For example, according to Banham (2016), “Soon virtually everything will be IoT-enabled. As this occurs, new risks are emerging for homeowners and businesses alike, and traditional insurance policies may not cover losses that result” (p. 40)..
Indeed, not only are the risks that are associated with IoT implementations expanding and growing in sophistication, there are already numerous types of risks that have been identified that can derail even the most well-designed and thoughtful IoT deployment. Although the following list is not exhaustive, the risks described in Table 1 below represent the tip of the security iceberg at present when it comes to IoT implementations.
Table 1
Current risks associated with IoT implementations
Risk type
Description
Device updates
This is one of the biggest IoT risks is the use of insecure or outdated software and firmware. As part of the device discovery or adoption process, be sure to enter each device into your asset register. Also, be sure to include which versions of software and hardware the devices run, and keep track of when updates are available and track when devices must be retired. Updating and patching devices is a critical component of any security strategy; however, an IoT environment presents a number of potential patching challenges. First, some devices are inaccessible. Second, not all devices can be taken offline for long periods of time such as critical manufacturing equipment that can cost an industrial organization millions if it is offline for an hour or smart grids that have millions of people depending on them for heat or electricity. Some IoT devices are simple sensors, with no user interface or screen -- or that some cannot accept updates.
Device discovery
In a network computing environment, it is axiomatic that, “You can't protect what you can't see.” To help protect from these types of risks, port scanning, protocol analysis and other detection techniques can help enterprises determine which devices connect to the corporate network.
Authentication and authorization
IoT devices, by definition, have a unique identifier that can help with the tasks of authentication and authorization. Once users have discovered what IoT devices are on their networks, it is time to decide what they can access and what they talk to; however, with hundreds or thousands of unique IDs to deal with, the task can seem daunting. Generally speaking, hardware-based roots of trust are considered the strongest IoT security option -- these are built directly into the hardware and embedded on the device. Digital certificates issued from a trusted public key infrastructure (PKI) can also be used, although some devices do not have the ability to process these, so other lightweight cryptographic algorithms may be used.
Disruption, DDoS attacks and IoT botnets
This is also a major risk because it is nearly impossible to prevent a DDoS attack. An organization, however, can take steps to prevent an attack from succeeding, For this purpose, uses should employ intrusion prevention and detection systems (IDS and IPS) with DDoS features, or partner with an internet service provider that can detect and filter DDoS packets before they reach a network. Users should also follow other basic security hygiene, such as changing default passwords
IoT passwords
Enterprises and end users should be diligent in updating passwords and using strong passwords or passphrases -- a choice they do not have if passwords are hardcoded. For this risk, manufacturers must accept their share of the blame. Simply put, hardcoded passwords are passé and should never be part of a device's design process. Manufacturers can also remedy the default password security issue if they require default credentials be reset by users prior to the device functioning.
Encryption
Many connected devices such as the small sensors collecting temperature, humidity or moisture data can cause the greatest IoT risks, as they do not have the power, processing or memory resources required to run traditional encryption algorithms. These devices must use an algorithm with high security, but low computation -- one that considers the size, power consumption and processing capabilities of resource-constrained devices.
Securing the network
One of the major challenges and risks of IoT is the operational technology networks connecting to IT networks generally were never considered a threat. They didn't connect to the internet, and while sometimes subjected to hacks, they did not pose an imminent risk to IT networks. Plus, these legacy systems -- some decades old -- often run their own proprietary systems, meaning common security mechanisms may overlook their issues during routine checks. Because the devices and machines cannot be easily or cost-effectively replaced, organizations must properly update, patch and secure them
Source: Adapted from Shea, 2019
Other researchers have focused on the type of so-called “cyber physical systems” in which IoT technologies are implemented to categorize the types of risks that are involved. For example, a study by Ahamed and Rajan (2016) noted that, “IoT can be implemented as smart cities, smart homes, traffic management systems etc. Research has proven that, any system with the characteristics of remote management, dynamic topology, resource constraints and wireless communication medium are always susceptible to security issues” (p. 1). Some of the typical risks that are associated with each of these types of categorized implementations are set forth in Table 2 below.
Table 2
IoT security risks by category of implementation
IoT implementation
Associated risks
Smart buildings
· Limited authentication, authorization and accounting services;
· Security in Web-based interfaces; and,
· Lack of effective cryptographic support.
Smart cities
· Limited privacy;
· Insecure cloud connectivity;
· Insecure device connectivity; and,
· Lack of effective cryptographic support.
Smart health
· Limited privacy;
· Insecure device connectivity;
· Insecure cloud connectivity;
· Insecure mobile connectivity;
· Security in Web-based interfaces; and,
· Limited availability.
Source: Adapted from Ahamed and Rajan, 2016, p. 3
Some of the respective vulnerabilities and impacts of these types of categorized security risks are depicted in Figure 5 below.
Figure 4. Mapping of IoT application, vulnerabilities and their impacts
Source: Ahamed and Rajan, 2016, p. 4
Taken together, it is clear that there are a number of risks that are associated with the implementation of IoT. While some of these risks are standalone, others are interrelated and concern many of the constraints that are involved in any interconnected computing environment. For example, according to Bhardwaj, Subramanyam, Avasthi and Sastry (2016), “There is an urgent need of solutions for end user data protection and privacy during the times when migrating from one cloud service provider to another” (p. 2277).
In fact, even the word, “urgent,” fails to adequately characterize the predicament of many small business owners that are searching for IoT solutions to help them grow their companies, and the risks that are involved may dissuade some small businesses from even making the effort. The types of security risks that are associated with IoT deployments also include the following:
· Identify and implement strategies to protect usage data collected from IoT-connected computer systems;
· Develop protections for the sensitive information from health and bank accounts;
· Provide adequate protections for personally identifiable information; and,
· Protect the device identity information that is uniquely traceable such as IP addresses and unique hardware identities (Bhardwaj et al., 2016).
Like turning a rock in a healthy garden over and finding a worm every time, it seems that there are additional security risks that are associated with IoT implementations everywhere security managers look. For example, a study by Shon, Cho, Han and Choi (2014) identified five salient security risks that are associated with information management platforms such as IoT that rely on a cloud-based platform:
1. Smartphone data slinging. Cloud environments enable more users to access large amounts of data on the devices of their choice with many unaddressed security issues. These include unsecured cloud-based backup and highly confidential data on mobile devices.
2. Requirement for better access control and identity management. The cloud is highly virtualized and highly federated and needs an approach to establish control and manage identities across the cloud and other clouds.
3. Ongoing compliance concerns. Organizations still must consider the completely different processes that exist for managing data and apps in the cloud.
4. Risk of multiple cloud tenants. Because most cloud services actually adopt virtualization technology, the associated risk for multiple organizations’ data exist, which is based on a single physical hypervisor platform, and will keep up to unless specific segmentation measures are enacted.
5. Emergence of cloud standards and certifications. Because security can be evaluated by choosing cloud services, standards and certifications are getting more important to help customers measure how their data will be kept in secure (Shon et al., 2014, pp. 406-407).
The foregoing security risks, of course, continue to evolve in unforeseen ways, thereby making the implementation of any IoT initiative time- and situation-dependent. While there are strategies that are available to address these risks, the research is consistent in emphasizing that these risks are dynamic in nature and current strategies may become inadequate or completely obsolete virtually overnight. Moreover, compounding the challenges for organizations seeking to leverage IoT technologies to achieve and sustain a competitive advantage even further is the fact that there are a number of other barriers to implementing IoT as discussed below.
Other Barriers that Prevent Implementation of IoT by Small Businesses
Some of the barriers to the implementation of the IoT technologies are the same as with any organizational change. Many people detest changes and will go out of their way to prevent change from disrupting their comfortable daily routines unless they are convinced that the change initiative can help them perform their jobs better. In addition, and potentially more impactful, is the reluctance of some key individuals in organizations to support IoT implementations since they may fear the loss of “turf.” In this regard, Ullah, Sepasgozar & Wang (2018) emphasize that one of the major barriers to the implementation of IoT technologies is “the traditional rigid mindset of managers, agents and service providers, whereby information is withheld, is a key factor” (p. 34). A number of human resource management theories are available that can be used to help overcome these barriers, but the bottom line for small business owners is the ongoing need for due diligence to ensure that IoT implementations achieve their intended outcomes and their constituent components are protected to the maximum extent possible.
Summary
This chapter provided a description of the study’s guiding Unified Theory of Acceptance and Use of Technology theoretical/conceptual framework together with an overview of the Internet of Things. In addition, chapter 2 also presented an analysis of the current trends which are affecting the proliferation of IoT networks and sensor-enabled devices. The research was consistent in showing that virtually all of the nodes that are interconnected in an IoT deployment are vulnerable to intrusion by unauthorized actors, and the potential for intrusions to disrupt critical organizational functions remains high at present. In addition, an analysis of the risks that are associated with IoT implementations was followed by a discussion concerning some of the other barriers that currently hinder IoT implementations by small businesses. These barriers were shown to include many of the same constraints that can adversely affect any organizational change, but there were also some unique issues involved that small business owners must take into account when implementing IoT solutions. As discussed further in chapter 3 that follows below, a qualitative method was deemed best suited for developing informed and timely answers to the study’s above-stated research questions and subquestions based on the supporting rationale described in chapter 3below.
References
Ahamed, J., & Rajan, A.V. (2016). Internet of Things (IoT): Application systems and security vulnerabilities. 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), 1-5.
Aishah, S., Sahandi, R., Prakoonwit, S., & Khan, W. (2019). Big data and IoT opportunities for small and medium-sized enterprises (SMEs). In Handbook of Research on Big data and the IoT. doi: 10.4018/978-1-5225-7432-3.ch005
An, N. & Wang, J. (2018, October). Research and practice on innovative methods of ideological and political education for college student. Educational Sciences Theory and Practice, 18(5), 2386-2391.
Banham, R. (2016, July-August). IoT complexity: As the Internet of Things expands, the risks-and questions of liability-become more complicated. Risk Management, 63(6), 39-44.
Bhardwaj, A., Subramanyam, G. V., Avasthi, V. & Sastry, H. (2016). Review of solutions for securing end user data over cloud applications. International Journal of Advanced Computer Research, 6(27), 2277-2291.
Bok, H. (2014, September). An empirical study on factors influencing Internet advertising effects. Advances in Management, 7(9), 6-9.
Business.com. (2018). Disaster preparedness for small businesses. Business.com, September 26. Retrieved from https://www.business.com/articles/business-disaster-prep/.
Cass, J. (2019). Internet of Things: What it is, how it works, examples and more. Just Creative. Retrieved from https://justcreative.com/2018/11/19/internet-of-things-explained/.
Cassidy, S. & Nandaraj, M. (2019, May). Keep an eye on Internet of Things legislation. National Defense, 103(786), 63.
Celik, Z. B., Fernandes, E., Tang, G. & McDaniel, P. (2019, August). Program analysis of commodity IoT applications for security and privacy: Challenges and opportunities. ACM Computing Surveys, 52(4), 1-30.
Chahal, H., & Gulia, P. (2016). Big data analytics. Research Journal of Computer and Information Technology Sciences, 4(2), 1-4.
Chinn, D. (2019). The purpose of contingency planning. Chron, January 11. Retrieved from https://smallbusiness.chron.com/purpose-contingency-planning-24864.html
Cubo, J., Nieto, A. & Pimentel, E. (2014). A cloud-based internet of Things platform for ambient assisted living. Sensors, 14, 14070-14105.
Davis, T. (2017, Summer). The Internet of Things for kids: In the "Talking Window Garden" project, students create "smart" plant pots that use sensors to collect and analyze data on the health of their plants. Science and Children, 54(9), 84-88.
Fisher, R., Norman, M., & Klett, M. (2017). Enhancing infrastructure resilience through business continuity planning. Journal of Business Continuity & Emergency Planning, 11(2), 163–173. Retrieved from https://search-ebscohost-com.proxy1.ncu.edu/login.aspx?direct= true&db=tsh&AN=126935165&site=eds-live.
Goebel, R., Norman, A., & Karanasios, S. (2015). Exploring the Value of Business Analytics Solutions for SMEs. UK Academy for Information Systems Conference Proceedings 2015, 22. http://aisel.aisnet.org/ukais2015/22.
Haghshenas, H. & Richards, C. (2016, January 1). The adoption of mobile game applications as sustainable social and behavioral change. Competition Forum, 14(1), 108-111.
Kaushik, A. (2019, January 1). Perceptions of library and information science professionals towards Internet of Things (IoT). Library Philosophy and Practice, 1-3.
Khvoynitskaya, S. (2019). Triple A approach to IoT security: How it works. iTransition. Retrieved from https://www.itransition.com/blog/triple-a-approach-to-iot-security-how-it-works.
Liew, X., Bin, J. & Liu, Z. (2019). Software as a service: the future of NDI data analysis in the cloud. Insight, 61(6), 341-346.
Minerva, R., Biru, A., & Rotondi, D. (2015). Towards a definition of the Internet of Things (IoT). IEEE Internet Initiative. Retrieved from https://www.researchgate.net/publication/317588072_Towards_a_definition_of_the_Internet_of_Things_IoT
Murphy, M. (2019, April 29). Cellphones now outnumber the world’s population. Quartz. Retrieved from https://qz.com/1608103/there-are-now-more-cellphones-than-people-in-the-world/.
Ni, Z., Rong, L., Wang, N., & Cao, S. (2019). Knowledge model for emergency response based on contingency planning system of China. International Journal of Information Management, 46, 10–22. https://doi-org.proxy1.ncu.edu/10.1016/j.ijinfomgt.2018.10.021
OECD. (2005). Glossary of statistical terms: Small and medium-sized enterprises (SMEs). Retrieved from https://stats.oecd.org/glossary/detail.asp?ID=3123#targetText=Definition:,This number varies across countries.&targetText=Small firms are generally those,in some cases 5, workers.
Preimesberger, C. (2018). Key considerations for financing enterprise disaster recovery. EWeek, 1. Retrieved from https://search-ebscohost-com.proxy1.ncu.edu/login.aspx?direct=true&db=a9h&AN=132117851&site=eds-live
Safar, L., Sopko, J., Bednar, S., & Poklemba, R. (2018). Concept of SME business model for Industry 4.0 environment. TEM Journal, 7(3), 626–637. https://doi-org.proxy1.ncu.edu/10.18421/TEM73-20
Schaffhauser, D. (2018, March-April). Iot has arrived (just not in the way you expected). T H E Journal (Technological Horizons In Education), 45(2), 6-9.
Sevinç, A., Gür, S., & Eren, T. (2018). Analysis of the difficulties of SMEs in Industry 4.0 applications by analytical hierarchy process and analytical network process. Processes, 6, 264-280. doi: 10.3390/pr6120264
Shea, S. (2019, January 31). The top seven enterprise IoT risks to consider. IoT Agenda. Retrieved from https://internetofthingsagenda.techtarget.com/tip/Internet-of-Things-IOT-Seven-enterprise-risks-to-consider.
Shin, D.-I. (2017). An exploratory study of innovation strategies of the internet of things SMEs in South Korea. Asia Pacific Journal of Innovation and Entrepreneurship, 11(2), 171-189. doi: 10.1108/APJIE-08-2017-025
Shon, T., Cho, J., Han, K. & Choi, H. (2914). Toward advanced mobile cloud computing for the Internet of Things: Current issues and future direction. Mobile Network Applications, 19, 404-413.
Tati?, K., Džafi?, Z., Hara?i?, M., & Hara?i?, M. (2018). The use of business intelligence (BI) in small and medium-sized enterprises (SMEs) in Bosnia and Herzegovina. Economic Review: Journal of Economics & Business/Ekonomska Revija: Casopis Za Ekonomiju i Biznis, 16(2), 23–37. Retrieved from https://search-ebscohost-com.proxy1.ncu.edu/login.aspx?direct=true&db=bth&AN=136410108&site=eds-live
Tucker, K. & Bulim, J. (2018, April 1). Internet industry: A perspective review through Internet of Things and Internet of Everything. International Management Review, 14(2), 26-31.
Ullah, F., Sepasgozar, S. M. & Wang, C. (2018). A systematic review of smart real estate technology: Drivers of, and barriers to, the use of digital disruptive technologies and online platforms. Sustainability, 10, 1-44.
Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y. & Xu, X. (2016, May). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
World population clock. (2019). WorldoMetersInfo. Retrieved from https://www.worldometers. info › world-population.