• International Journal of Technology (IJTech)
  • Vol 12, No 5 (2021)

Building an Implementation Model of IoT and Big Data and Its Improvement

Building an Implementation Model of IoT and Big Data and Its Improvement

Title: Building an Implementation Model of IoT and Big Data and Its Improvement
Jonny, Kriswanto, Matsumura Toshio

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Cite this article as:
Jonny, Kriswanto, Toshio, M., 2021. Building Implementation Model of IoT and Big Data and Its Improvement. International Journal of Technology. Volume 12(5), pp. 1000-1008

Jonny Department of Industrial Engineering, Faculty of Engineering, Bina Nusantara University, Jl. KH Syahdan No. 9, Palmerah, Jakarta 11410, Indonesia
Kriswanto Department of Accounting, Faculty of Economics and Communication, Bina Nusantara University, Jl. KH Syahdan No. 9, Palmerah, Jakarta 11410, Indonesia
Matsumura Toshio Graduate School of Language and Culture, Studies in Language and Society, Osaka University, 8-1-1, Aomatani, Minoh City, Osaka, Japan
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Building an Implementation Model of IoT and Big Data and Its Improvement

Emerging technologies had marked the emergence of the Industry 4.0 era. Despite the problems experienced with COVID-19, many experts believe that Industry 4.0 is an inevitable reality that many businesses must face in the future. One of those technologies is the Internet of Things (IoT), which may generate so-called “big data” that will be useful for business insight. However, after performing a rigorous literature review, articles related to the impact of the IoT and big data implementations for business performance in the form of a model were rarely found. Among the available literature, some elements that may be considered are: (1) business process improvement; (2) marketing strategies; (3) business management innovation; and (4) business performance. Therefore, this paper proposes an implementation model of IoT and big data to pursue business performance. Thus, a survey was conducted with managerial respondents from the manufacturing industry. For analysis purposes, a partial least squares structural equation modeling (PLS-SEM) methodology was implemented to examine the fitness of the model. The analysis was conducted in Smart PLS 3.0, and the goodness of fit (GoF) calculated for this model was 0.63, larger than the required 0.38 for a robust and accurate result. This model was implemented in a sample manufacturing company to seek improvement. Regarding this effort, several improvements were added as input by the manufacturing players to enhance the model.

Big Data; IoT; Smart PLS 3.0


Long before the COVID-19 pandemic spread around the world, business faced the advent of emerging technologies that marked the start of the Industry 4.0 era. The adoption of these technologies for business purposes has been accelerating since the pandemic. This includes the adoption of the Internet of Things (IoT), which is enabled by the advancement of internet technology and other devices. By adopting this platform, multiple devices, such as sensors, cameras, and other communication/computing hardware, can be interconnected. The IoT capability enables them to communicate with each other to run any instructions given to them in providing desired functionality. In general, there are three categories of applications that have been proposed for the use of IoT platforms: lifestyle improvement for an individual, business process improvement for a business, and city improvement for government as a form of Society 5.0 development. For individuals, the improvement may be in the form of wearable devices and smart home appliances. This adoption can enhance the quality of life for those individuals who use those applications. For businesses, it can be in the form of healthcare systems or industrial automation that improves the business process of a company so that it may more effectively and efficiently serve its customers, resulting in an improved competitive advantage (Kudryavtseva and Skhvediani, 2020). For governments, this adoption can be in the form of smart cities, which may also accelerate Society 5.0 development (Acar and Acar, 2012; Agrawal et al., 2018). These possibilities have encouraged many entities to apply this technology to enhance the quality of lives of the individuals they serve. Thus, it is understandable that IoT applications have grown rapidly to reach about 43 billion units in 2023 (Ahmed et al., 2017).  The main reason for those entities in adopting this technology is due to its ability in computing and communicating so that real-time information can be solicited as support for management to make better decisions regarding their activities.

At the business level, many companies that have adopted the IoT have gained a competitive advantage in the form of improved products and services. This has allowed them to obtain more business opportunities in the digital era. This is because the IoT possesses many advantages in learning customer behaviors, attitudes, and choices, which are useful for marketing studies. First, digitalization can be supported by using the IoT so that processing and routine activities can be enhanced (Akhlaghi and Asadi, 2002). Second, the use of the internet and information sharing can also be enhanced with the IoT so that a company can improve its relationship with its customers (Boyes et al., 2018; Bijmolt et al., 2021). The use of an IoT platform can generate a large volume of information, which is one form of what has become popularly known as “big data” (Berawi, 2018). Most of the data are in an unstructured form because they are from a variety of sources and are of different types, such as images, video, and audio. These unstructured data will not mean anything unless they can be carefully structured and analyzed using big data analytics; companies can then use the information to gain insight for better decision-making (Chui et al., 2010; De Mauro et al., 2015). This is especially important because this platform’s devices and services are varied and may include smartphones, online transactions, social networks, electronics, and machining communications (Economist, 2010). Customer behavior that is captured through a collection of sensors of an IoT platform is often highlighted in descriptions of possible systems. However, from a case study of a typical industrial application, it was reported that the utilization of data from about 30,000 sensors was only about 1% of the data. This may change as systems become more integrated.

While there are many advantages in harnessing the use of IoT and big data, previous studies have shown that this field of research is still in the early stages of knowledge development. Some of them discuss the concept of IoT without further discussion on how it may impact business performance. Thus, within the focus on the adoption and utilization of this platform, a gap of knowledge still exists. This gap is especially interesting for study because of its relation to big data utilization for better business performance. In order to address this gap of knowledge in the field, this paper proposes a model as guidance for harnessing IoT and big data for better business performance, especially with regard to the manufacturing industry. The research objectives were: (1) develop a model for implementing IoT and big data; (2) investigate what elements should be considered when ensuring the benefits of IoT and big data adoption for better business performance; and (3) form guidance for businesses that are interested in implementing IoT and big data for gaining a competitive advantage.


    This paper has resulted in a preliminary model of IoT and big data implementation to enhance business performance. The model covers marketing strategies, business management innovation, business process improvement, and business performance. Among the five hypotheses, two hypotheses were statistically significant with strong relationships, two were significant but weak, and one was insignificant. With this dataset, business process improvement was shown to significantly impact business model innovation and marketing strategies. Thus, practice in a typical manufacturing company is strengthened to ensure its relationship. Although this model is still preliminary, it can still provide guidance for companies when they want to implement and ensure that adoption is beneficial for business performance. However, this research still has limitations in the form of the sample size, the respondents’ profiles, and the need to explore the model further with additional data. Therefore, for future research, it is recommended that researchers take these matters into consideration.


    This work is supported by the Research and Technology Transfer Office, Bina Nusantara University as a part of Bina Nusantara University’s International Research Grant entitled “Developing Internet of Things (IoT) dan Big Data Implementation Model” with contract number: 017/VR.RTT/III/2021 and contract date: March 22, 2021.


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