Building an Implementation Model of IoT and Big Data and Its Improvement
Published at : 09 Dec 2021
Volume : IJtech
Vol 12, No 5 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i5.5178
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 |
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
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.
Acar, A.Z., Acar, P.,
2012. The Effects of Organizational Culture and Innovativeness on Business Performance in Healthcare Industry. Procedia – Social and Behavioral
Sciences, Volume 58, pp. 683–692
Agrawal, A., Schaefer, S.,
Funke, T., 2018. Incorporating Industry 4.0 in Corporate Strategy. In: Analyzing
the Impacts of Industry 4.0 in Modern Business Environments,
Brunet-Thornton, R., Martinez, F. (eds.), IGI Global, Hershey, Pennsylvania,
USA, pp. 161–176
Ahmed, E., Yaqoob, I.,
Hashem, I.A.T., Khan, I., Ahmed, A.I.A., Imran, M., Vasilakos, A.V., 2017. The
Role of Big Data Analytics in Internet of Things. Computer Networks,
Volume 129(Part 2), pp. 459–471
Akhlaghi, H., Asadi, H.,
2002. Essentials of Telemedicine and Telecare. Wiley, Chichester, UK.
Ardi, R., Iqbal, B.M.,
Sesarea, S., Komarudin, K., 2020. What Drives Individuals to Dispose of Waste
Mobile Phones? A Case Study in Indonesia. International Journal of
Technology, Volume 11(3), pp. 631–641
Berawi, M.A., 2018.
Utilizing Big Data in Industry 4.0: Managing Competitive Advantages and
Business Ethics. International Journal of Technology, Volume 9(3), pp.
430–433
Bijmolt, T.H.A.,
Broekhuis, M., de Leeuw, S., Hirche, C., Rooderkerk, R.P., Sousa, R., Zhu,
S.X., 2021. Challenges at the Marketing–Operations Interface in Omni–Channel
Retail Environments. Journal of Business Research, Volume 122, pp. 864–874
Boyes, H., Hallaq, B.,
Cunningham, J., Watson, T., 2018. The Industrial Internet of Things (IIoT): An Analysis
Framework. Computers in Industry, Volume 101, pp. 1–12
Chui, M., Loffler, M.,
Roberts, R., 2010, The Internet of Things. McKinsey Quarterly Available Online
at https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-internet-of-things
Cullen, K.L., Edwards,
B.D., Casper, W.C., Gue, K.R., 2014. Employees’ Adaptability and Perceptions of
Change–Related Uncertainty: Implications for Perceived Organizational Support,
Job Satisfaction, and Performance. Journal of Business and Psychology,
Volume 29, pp. 269–280
Davenport, T.H., 2014. How
Strategists Use “Big Data” to Support Internal Business Decisions, Discovery
and Production. Strategy & Leadership. Volume 42(4), pp. 45–50
De Mauro, A., Greco, M.,
Grimaldi, M., 2015. What is Big Data? A Consensual Definition and a Review of
Key Research Topics. In: AIP Conference Proceedings, Volume 1644
De Mauro, A., Greco, M.,
Grimaldi, M., Ritala, P., 2018. Human resources for Big Data Professions: A
Systematic Classification of Job Roles and Required Skill Sets. Information
Processing & Management, Volume 54(5), pp. 807–817
De Mauro, A., Greco, M.,
Grimaldi, M., 2019. Understanding Big Data Through a Systematic Literature
Review: The ITMI Model. International Journal of Information Technology and
Decision Making, Volume 18(04), pp. 1433–1461
Del Giudice, M., 2016.
Discovering the Internet of Things (IoT) Within the Business Process Management:
A Literature Review on Technological Revitalization. Business Process
Management Journal, Volume 22(2), pp. 263–270
Donalek, C., Djorgovski,
S.G., Cioc, A., Wang, A., Zhang, J., Lawler, E., Yeh, S., Mahabal, A., Graham, M.,
Drake, A., 2014. Immersive and Collaborative Data Visualization using Virtual
Reality Platforms. In: Proceedings – IEEE International Conference on
Big Data. IEEE Big Data, Washington, USA
Dubey, R., Gunasekaran,
A., Childe, S.J., Fosso Wamba, S., Roubaud, D., Foropon, C., 2021. Empirical
Investigation of Data Analytics Capability and Organizational Flexibility as
Complements to Supply Chain Resilience. International Journal of Production
Research, Volume 59(1), pp. 110–128
Dwivedi, Y.K., Hughes, L.,
Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards,
J., Eirug, A., Galanos, V., Ilavarasan, P.V., Janssen, M., Jones, P., Kar,
A.K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R.,
Meunier-FitzHugh, K.L., Le Meunier-FitzHugh, L.C., Misra, S., Mogaji, E.,
Sharma, S.K., Singh, J.B., Raghavan, V., Raman, R., Rana, N.P., Samothrakis,
S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., Williams, M.D., 2019.
Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging
Challenges, Opportunities, and Agenda for Research, Practice and Policy. International
Journal of Information Management, Volume 57, https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ebner, K., Bühnen, T.,
Urbach, N., 2014. Think Big with Big Data: Identifying Suitable Big Data
Strategies in Corporate Environments. In: Proceedings of the 47th
Annual Hawaii International Conference on System Sciences, Hilton Waikoloa, Big
Island
Economist, 2010, Data, Everywhere: A Special Report on Managing Information. The Economist
Erevelles, S., Fukawa, N.,
Swayne, L., 2016. Big Data Consumer Analytics and the Transformation of
Marketing. Journal of Business Research, Volume 69(2), pp. 897–904
Gandomi, A., Haider, M.,
2015. Beyond the Hype: Big Data Concepts, Methods, and Analytics. International
Journal of Information Management, Volume 35(2), pp. 137–177
Guo, B., Zhang, D., Wang,
Z., Yu, Z., Zhou, X., 2013. Opportunistic IoT: Exploring the Harmonious
Interaction between Human and the Internet of Things. Journal of Network and
Computer Applications, Volume 36(6), pp. 1531–1539
Gupta, A., Tsai, T., Rueb,
D., Yamaji, M., Middleton, P., 2017. Forecast: Internet of Things — Endpoints
and Associated Services, Worldwide, 2017. Gartner. Available Online at https://www.gartner.com/en/documents/3840665/forecast-internet-of-things-endpoints-and-associated-ser,
Accessed on May 16, 2020
Gupta, R., Mejia, C.,
Kajikawa, Y., 2019. Business, Innovation and Digital Ecosystems Landscape
Survey and Knowledge Cross Sharing. Technological Forecasting and Social
Change, Volume 147, pp. 100–109
Gutierrez–Gutierrez, L.J.,
Barrales–Molina, V., Kaynak, H., 2018. The Role of Human Resource–Related
Quality Management Practices in New Product Development: A Dynamic Capability
Perspective. International Journal of Operations & Production Management,
Volume 38(1), pp. 43–66
Gutierrez, A., O’Leary,
S., Rana, N.P., Dwivedi, Y.K., Calle, T., 2019. Using Privacy Calculus Theory
to Explore Entrepreneurial Directions in Mobile Location-Based Advertising:
Identifying Intrusiveness as the Critical Risk Factor. Computers in Human Behavior, Volume 95, pp.
295–306
Jonny., Kriswanto., 2021.
Modelling the Use of Social Network Marketing in Indonesia. In: IOP
Conference Series: Earth and Environmental Science
Kudryavtseva, T.,
Skhvediani, A., 2020. Effectiveness Assessment of Investments in Robotic
Biological Plant Protection. International Journal of Technology. Volume
11(8), pp. 1589–1597
Sestino, A., Prete, M.I.,
Piper, L., Guido, G., 2020. Internet of Things and Big Data as Enablers for
Business Digitalization Strategies. Technovation, Volume 98, doi:
10.1016/j.technovation.2020.102173
Tenenhaus,
M., Amato, S., Vinzi, V.E., 2004. A Global Goodness-of-Fit Index for PLS
Structural Equation Modeling. In: Proceedings of the XLII SIS Scientific
Meeting, pp. 739–742