Investigating the Factors Affecting the Readiness Level of IoT Technology Acceptance (Case Study: Financial Activists, Stock Exchange, and Financial Institutions)
Subject Areas : Software Engineering and Information Systems
Amir Abbas Farahmand
1
,
Reza Radfar
2
,
Alireza Poorebrahimi
3
,
Mani Sharifi
4
1 - Ph.D Candidate, Department of Technology Management, UAE Branch, Islamic Azad University, Dubai, United Arab Emirates
2 - Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Assistant Professor, Department of Industrial Management, Alborz Branch, Islamic Azad University, Karaj, Iran
4 - Associate Professor, Department of Industrial Engineering and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Keywords: Technology Acceptance, IoT, Ecommerce,
Abstract :
IoT, a state-of-the-art technology, faces many challenges in its growth and development. One of the main concerns is the potential threats posed by the spread of such technology in the world. The widespread adoption and spread of such a technology can threaten us much more seriously than the Internet currently available. The challenges we face in adopting such technology will include both the social and the technical aspects. Technical limitations include security considerations, privacy, as well as the resource, energy, and capacity issues for such a large amount of data and processing. Besides, socially, cultural infrastructure must first be provided for the diffusion of such technologies among the community. This study aimed to investigate the factors affecting the readiness level of the acceptance of IoT technologies. The relationships are examined as six main categories identified, namely the social aspect, the cultural aspect, the human aspect, the technological aspect, the financial aspect, the management aspect, government laws, and regulations. The opinions of senior ICT executives nationwide were collected. The statistical population of this study consists of experts and users of the financial sector, stock exchange, and financial institutions. Since the statistical population is infinite, 384 randomly available individuals are selected. SMART.PLS was used to validate the model and test the relationships between variables. The results indicate the impact of the identified categories on IoT adoption readiness.
2
Journal of Advances in Computer Engineering and Technology
Investigating the Factors Affecting the Readiness Level of IoT Technology Acceptance
(Case Study: Financial Activists, Stock Exchange, and Financial Institutions)
Received (Day Month Year)
Revised (Day Month Year)
Accepted (Day Month Year)
Abstract— IoT, a state-of-the-art technology, faces many challenges in its growth and development. One of the main concerns is the potential threats posed by the spread of such technology in the world. The widespread adoption and spread of such a technology can threaten us much more seriously than the Internet currently available. The challenges we face in adopting such technology will include both the social and the technical aspects. Technical limitations include security considerations, privacy, as well as the resource, energy, and capacity issues for such a large amount of data and processing. Besides, socially, cultural infrastructure must first be provided for the diffusion of such technologies among the community. This study aimed to investigate the factors affecting the readiness level of the acceptance of IoT technologies. The relationships are examined as six main categories identified, namely the social aspect, the cultural aspect, the human aspect, the technological aspect, the financial aspect, the management aspect, government laws, and regulations. The opinions of senior ICT executives nationwide were collected. The statistical population of this study consists of experts and users of the financial sector, stock exchange, and financial institutions. Since the statistical population is infinite, 384 randomly available individuals are selected. SMART.PLS was used to validate the model and test the relationships between variables. The results indicate the impact of the identified categories on IoT adoption readiness.
Keywords: Ecommerce, IoT, Technology Acceptance
I. INTRODUCTION
I
n the IoT World, many objects around us, such as sensors and actuators, will be connected to global networks based on standard communication protocols and will share the data received between different platforms to a single target. The main strengths of the IoT idea are its significant impacts on different aspects of daily life such as smart homes, smart transport (a.k.a. intelligent transportation), smart cities, and electronic health [1]. IoT, a state-of-the-art technology, faces many challenges in its growth and development. One of the main concerns is the potential threats posed by the spread of such technology in the world. The widespread adoption and spread of such a technology can threaten us much more seriously than the Internet currently available. The challenges we face in adopting such technology will include both the social and the technical aspects. Technical limitations include security considerations, privacy, as well as the resource, energy, and capacity issues for such a large amount of data and processing. Besides, socially, cultural infrastructure must first be provided for the diffusion of such technologies among the community [2]. The unique ability to identify objects is critical to the successful use of IoT technology. This not only allows users to individually identify goods (commodities) in business processes but also enables remote control of equipment over the Internet. Among the most critical features to create a unique address are its uniqueness, reliability, consistency and persistence, and scalability. Any commodity that is already connected to, or intends to be connected to, the supply chain information must be identified according to a unique identifier, location, and characteristics [3].
According to IoT studies, it can be seen that various factors can influence the extent to which organizational users accept (or adopt) this technology. In this regard, Miltgen et al. (2013) investigated the acceptance (or adoption) of IoT-based health services based on the TAM (Technology Acceptance Model) and UTAUT (Unified Theory of Acceptance and Use of Technology) models [4]. The results showed that the characteristics perceived by users influence the degree to which this technology is accepted. According to technology acceptance model studies, factors such as trust, social influence, perceived behavioral control, and structural factors influence the level of IoT users' willingness [5].
Wojcik (2016) suggested the potential of using IoT in libraries. According to them, new-age technologies such as Augmented Reality, 3D printing, and wearable technologies can help consumers of the current age by creating new services based on the growing demands. While new technologies such as IoT have their benefits, they also pose infrastructure security challenges. Now it's time to look at the factors that drive consumers to use IoT as well as the factors that discourage them from using IoT.
We are living in an age of disruption. Economic shocks permanently descend on organizations, and they must constantly adapt to these changes. Competition conditions (requirements) are also changing. In the global economic system, competitors from across the globe face each other. An increase in competition requires organizations to fully equip themselves against competitors that produce new products, as well as innovative organizations that pursue creativity, initiative, or entrepreneurship. Those organizations that can respond appropriately to changes in the competition occurring rapidly will be successful.
IoT adoption is limited to just a few applications. In developing countries, the benefits of IoT adoption as a key factor in a country's socio-economic development have also been recognized by academics and practitioners. Currently, few studies have discovered IoT adoption from multiple theoretical perspectives, namely Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and Technology Acceptance Model (TAM) [5].
The Unified Theory of Acceptance and Use of Technology (UTAUT) provides appropriate fundamental information needed to study the adoption of electronic services. Venkatesh et al. [6] empirically tested the UTAUT model with eight technological models for the adoption of electronic services. These models include TRA, TPB, TAM, Motivational Model (MM), TAM-TBP, Model of PC Utilization (MPCU), Innovation Diffusion Model (IDM), and Social Cognitive Theory (SCT). Experimental studies focused on the adoption of electronic services [7].
IoT is advancing rapidly. Various statistics suggest that a network of 25 billion devices will emerge by 2020, bringing in nearly $ 2 trillion in economic profits. Although the enormous impact of this Third Industrial Revolution on the retail, manufacturing, transportation, and energy industries is well seen, these impacts are not yet sufficiently visible in the financial services sector. Since the core value of IoT is data transfer and the financial sector and Fintech (Financial Technology) is heavily dependent on data transfer and analysis, the evolution of the IoT financial services industry is not impossible. Financial institutions, especially retail banks, have invested a great deal of money in developing internal infrastructure and customer-related technological capabilities. IDC Financial Advisory Institute firm has predicted that retail banks have invested $ 16 billion in digital transformation technologies and this investment will grow. According to the sixth annual PwC financial services digital survey, IoT is ranked as one of the ten leading industries in investing in sensors for potential innovations. We live in an age where change and keeping up with change and speed are essential, so paying attention to electronic communications can help governments succeed and failing to pay attention can make them fail. Iran is no exception. Identifying the barriers to the implementation of this system by academics can help implement and advance IoT goals. Today's organizations have been able to create unique value for their customers by utilizing ICT features and capabilities.
One of the major and vital developments is “IT” as well as its importance and role in organizations and communities. One consequence of the emergence of ICT in communities is the emergence of the concept of "e-government," which is said to have been the most controversial issue since the Industrial Revolution. Nowadays, communities and organizations are facing very rapid, comprehensive, and radical changes. In this age, successful organizations are making the necessary changes on their own, while being aware of the contexts of these developments and adapted to them. Accentuating (attaching importance to) and accelerating IoT has recently been the focus of academics and policymakers. The advent of comprehensive sensors, smart devices, and wide Internet bandwidth (broadband Internet) has synchronized the integration of networks to collect and process information, ultimately facilitating quick decision-making and physical responses. To this end, it is necessary to identify the factors influencing IoT adoption readiness. The remainder of the article provides an overview of the research literature. The next section deals with research and information gathering. After interpreting the research findings, the conclusions are drawn.
II. Literature Review
The emergence and use of Internet-connected objects in various sectors and have improved our quality of life have experienced rapid growth in recent years, and these objects are present in all aspects of our lives. IoT has practically allowed objects around us to think and make decisions for us and to work together to improve the quality of human life by making it possible to communicate between objects and share information between them. IoT has been able to break away from the traditional notion of objects and transform them into smart objects through sophisticated computing technologies, technologies embedded in objects, communications technologies, as well as sensor networks and Internet protocols. Over time, with the ever-expanding IoT concept, standardization of its architecture has become an integral and fundamental principle in IoT and has created a competitive environment for different companies to present their products [8].
New business opportunities will be created with the help of IoT. Since the development of business applications and models is facilitated by IoT smart objects [9], successful business models always need sufficient information. The information collected automatically helps exchange information between devices to solve problems, embed new services, and develop a new revenue model [10].
The rapid advances in IT and its widespread use in all walks of life have led today's society to face unprecedented challenges. The everyday experiences and routine schedules of humans have changed so much that they cannot be found at any point in history. These changes have been linked to human life as one would expect stability in the present situation to be far-fetched. In the meantime, organizations have not been immune to the changes and transformations caused by IT, and these impacts have changed the foundation of organizations. Accordingly, the use of IT in organizations begins to reduce costs and speed up daily and repetitive work, and continues to fundamentally transform organizations. To improve their status and position by ICT, organizations need to plan rapidly to expand their capabilities and potential in various organizational, structural, and technical aspects, and to expand existing capabilities. This capacity is measured by the e-readiness level of the organization. It should be noted that the diverse applications of ICTs are slowly expanding and penetrating into most countries despite the investment in ICT. The main cause of this problem is the low e-readiness of the community to adopt and deploy ICT within and between communities and organizations. Therefore, it is essential to have a proper understanding of the extent of this readiness to enter the ICT realm as well as to keep pace with the developments of today's developed societies [11].
Despite the many potential benefits of IT in an organization, one of the problems and challenges facing government agencies is the adoption and application of new IT. In this respect, a key question is "how to ensure that users will adopt and use new IT in business processes [12]. Researchers have presented various concepts for technology acceptance and IT so far. Although, according to Fotino (2011), there is no clear definition of IT acceptance in previous studies. According to Karahana et al. (2002), there are differences between employees' perceptions of preliminary acceptance and final acceptance. According to information systems researchers, the adoption (acceptance) of IT and technological innovation virtually belong to the same category. Hence, it can be stated that IT acceptance is a multi-dimensional topic that requires a comprehensive definition specifically in organizations [13].
Parasuraman (2000) stated that each person's mental perceptions of technology are different emotions and attitudes that occur. Technology can manifest itself in the form of feelings of anxiety, amusement, or optimism that reflect the behavior and thoughts of each person. Moreover, negative or positive feelings about technology are different for each individual [14].
The challenge today is to understand to what extent the main theories explain the use, acceptance (adoption), and behavior. Three theories, namely TAM, TRA, and TPB, are compared within the IoT framework. Attitude structure is introduced as a reference to Behavior Intention (BI) and PEOU and PU are introduced as predecessors compared to using TAM 2, TAM 3, and UTAUT [15, 16].
According to TRA, the purpose of applying BI of a product or system is dependent on one's attitude toward the behavior and subjective norms of behavior. BI mainly predicts actual behavior. The Attitude toward the Behavior (ATB) is an individual assessment of a particular behavior as measured by behavioral beliefs about outcomes and characteristics [17]. The Subjective Norm (SN) is an area influenced by the beliefs and practices of parents, spouses, friends, teachers, and other influential individuals [17]. BI is one's readiness to perform an action and an introduction to real behavior [17]. TRA has been used to study user participation and involvement in a variety of areas such as consumer behavior, work behavior, and sociological behavior [18, 19].
Kardas et al. (2019) showed that the use of Internet of Things sensors helps employees to be able to perform executable processes automatically[38]. Prabu et al. (2021) proposed system provides security to the ATM as well as customers from the physical attacks and it is designed on the idea of to catch the thief inside the ATM centre only. The system is integrated with microcontroller unit, pressure sensor, and temperature sensor. Whenever any irrelevant things happened to the ATM unit from the burglars then immediately the sensors send signal to the microcontroller unit. Once the micro controller receives the signal from the sensors it do simultaneous things like closing the door, leakage of gas using solenoid valve, sending alert message to the higher authorities and police station by using GSM and GPS technology, activating the buzzer and led section for visual warnings. The closed door will not open until unless the correct password is given to the system with the help of keypad. One emergency button is fixed on the top of ATM to protect the user from the blackmailers[39].
In Ismael & Maolood (2021) paper, a medical platform has architecture that depends on middleware and database supports people with Coronavirus, and this platform mainly relies on three users. The scheme is designed to promote and provide access to care facilities for both patients and physicians, and it complies with General Data Protection Regulation (GDPR)[40].
Bendavid et al. (2009) evaluated the impact of RFID technology on a five-tier supply chain in the service sector. According to their studies, RFID creates integration and collaboration in the supply chain, thereby increasing the efficiency of the evolution process. IoT opens the way for connectivity between humans, machines, and operations through a global network of small objects. While there are applications such as smart homes, smartwatches, and client-side smart refrigerators, Business Process Optimization (BPO) using smart tags and objects is a topic that seems to drive IoT adoption, smart tracking guidance, and system monitoring toward supply [20]. IoT manages market competition using a combination of smart equipment, expert systems, and communications technology [3]. It is assumed that RFID in consumer environments leads to privacy breaches; however, consumers will accept it if they feel that the value provided is far greater than the risks perceived by them [21]. For example, the core proposition of the Uber core depends on the real-time geographical location of drivers and passengers who create the value of the new service on the supply side as well as the consumer side. Currently, RFID technology is used in many areas, including healthcare, supply chain management, smart homes, urban planning, retail management, logistics, inventory management, transportation, and warehouse management. RFID technology provides efficiency in many industries and at the same time brings enormous benefits to the consumer [22].
Davis's TAM (1989) has examined the impact of perceived usefulness (PU) and ease of use on technology acceptance [23]. The acceptance of many technological innovations by TAM has been studied [6, 24]. Its application in psychological and behavioral fields has been studied in the context of TRA [24]. Application in marketing, advertising, and public relations (PR) has been examined mainly in the context of TPB [25-27]. It has also been used to study professional social behaviors, applied nutrition (al) interventions, and environmental psychology [28-30]. Nevertheless, very few studies have addressed the issue of exploring the adoption of IoT from the perspective of multiple theories, namely, TRA, TPB, and TAM. Our research is one of these efforts to investigate the adoption of IoT in India. This research is a step towards expanding research into a wide range of IoT applications such as healthcare, elderly wellbeing and support, smart cities, and smart supply chains, etc.
In this regard, Francisco et al. (2016) examined the collaborative structure of IoT-based behavioral models. This paper considered the requirements to perform a wide range of computational tasks on a set of devices in a user interface with limited computational resources [31]. This approach has considered the structure of the social aspect of the IoT using client-side computational resources without damaging the performance of other embedded IoT devices. The framework mainly consists of a computational load model, a scheduling mechanism, and an add-on approach to transfer between devices. Experiments show the feasibility of the approach and compare different implementation options. Park et al. (2017) examined holistic approaches to IoT adoption by users in a smart home environment and found that key determinants of users' technology acceptance behavior consisted of three positive drivers, namely adaptation, communication, and control, and a negative barrier, namely the cost [32]. This study can be used as a basis for future research to improve IoT technologies in a smart home environment, taking into account user experiences. Mital et al. (2017) investigated the use of IoT in India by testing competing models using a structural equation modeling (SEM) approach [33]. The use of IoT has been reviewed from the perspective of multiple theories, namely TRA, TPB, and TAM. According to the results, IoT has created the necessary application areas for IoT, health, elderly well-being and support, and the urban supply chain. Karahoca et al. (2017) examined the tendency for IoT acceptance in healthcare and showed that perceived benefits, perceived structural ease (facility), and perceived image plays an important role in the intention to adopt IoT technology [5]. Banafa (2017) examined three major challenges facing IoT and found that IoT acceptance faces three major challenges: customer, business, and industry [34]. Lu et al. (2018) systematically reviewed IoT literature from the perspective of users and organizations and showed that privacy and asset security play an important role in the exploitation of IoT [35]. They also showed that facilitating technology infrastructures, and supporting and equipping technology, can increase the efficiency and effectiveness of IoT and enhance its acceptance and use in organizations. Ammirato et al. (2019) investigated a method to support the acceptance of IoT innovation and its application in the security of Italian bank branches. Business Process Reengineering (BPR) steps were considered to create an appropriate organizational framework for IoT adoption [36]. The results of above researches showed that the working environment and the amount of work support affect the development and adoption of IoT.
III. Methodology
After analyzing the content of the collected studies, dimensions and variables were extracted thematically.
So finally identified six main categories, namely social aspect, cultural aspect, human aspect, technological aspect, financial aspect, management aspect, government laws, and regulations, as factors affecting IoT adoption, using qualitative research results at the level of expertise of senior ICT executives nationwide, directly through technology and IoT. This study investigated the extent of the impact of variables on the paradigm model. The statistical population of this study consists of experts and users of the financial section, stock exchange, and financial institutions. Since the statistical population is infinite, 384 randomly available individuals are selected. SMART.PLS was used to validate the model and test the relationships between variables.
3.1. Research Variables and Model
3.1.1. Validity and Reliability of Research Model Variables
Since a standard questionnaire was used to measure the variables, the indices were translated first, and then necessary adjustments were made by referring to the elite. The strength of the relationship between the factor (ie, latent variable) and the observed variable is shown by the factor loading, a value between 0 and 1. If the factor loading is less than 0.3, the relationship is considered weak and thus ignored. A factor loading between 0.3 and 0.6 is acceptable and is highly desirable if it is greater than 0.6. According to Table 1, it can be seen that the value of all factor loadings of the variables is greater than 0.5, which confirms that the reliability of the measurement model is acceptable.
TABLE I FACTOR LOADINGS AND RESEARCH VARIABLES | ||||||
Direction | Factor Loading | t-Statistic | Direction | Factor Loading | t-Statistic | |
q01 ← Cultural Aspect | 0.797 | 22.801 | q32 ← Financial Aspect | 0.745 | 20.969 | |
q02 ← Cultural Aspect | 0.746 | 16.472 | q33 ← Financial Aspect | 0.834 | 34.569 | |
q03 ← Cultural Aspect | 0.702 | 13.369 | q34 ← Financial Aspect | 0.788 | 22.793 | |
q04 ← Cultural Aspect | 0.645 | 10.473 | q35 ← Financial Aspect | 0.797 | 24.676 | |
q05 ← Cultural Aspect | 0.664 | 11.048 | q36 ← Financial Aspect | 0.753 | 22.757 | |
q06 ← Cultural Aspect | 0.65 | 11.428 | q37 ← Management aspect | 0.667 | 12.371 | |
q07 ← Cultural Aspect | 0.712 | 14.496 | q38 ← Management aspect | 0.78 | 22.668 | |
q08 ← Cultural Aspect | 0.73 | 14.502 | q39 ← Management aspect | 0.681 | 15.126 | |
q09 ← Human Aspect | 0.827 | 27.557 | q40 ← Management aspect | 0.624 | 12.097 | |
q10 ← Human Aspect | 0.866 | 38.83 | q41 ← Management aspect | 0.796 | 22.574 | |
q11 ← Human Aspect | 0.823 | 27.513 | q42 ← Management aspect | 0.584 | 7.316 | |
q12 ← Human Aspect | 0.851 | 33.175 | q43 ← Management aspect | 0.676 | 15.482 | |
q13 ← Technological Aspect | 0.689 | 16.755 | q44 ← Management aspect | 0.805 | 23.172 | |
q14 ← Technological Aspect | 0.681 | 11.252 | q45 ← Management aspect | 0.777 | 25.468 | |
q15 ← Technological Aspect | 0.809 | 28.763 | q46 ← Management aspect | 0.738 | 19.29 | |
q16 ← Technological Aspect | 0.602 | 10.626 | q47 ← Government Aspect | 0.819 | 26.778 | |
q17 ← Technological Aspect | 0.647 | 10.547 | q48 ← Government Aspect | 0.701 | 15.65 | |
q18 ← Technological Aspect | 0.583 | 10.296 | q49 ← Government Aspect | 0.679 | 13.284 | |
q19 ← Technological Aspect | 0.853 | 32.213 | q50 ← Government Aspect | 0.708 | 15.8 | |
q20 ← Technological Aspect | 0.802 | 19.259 | q51 ← Government Aspect | 0.627 | 11.602 | |
q21 ← Technological Aspect | 0.713 | 15.867 | q52 ← Government Aspect | 0.609 | 9.826 | |
q22 ← Technological Aspect | 0.605 | 9.416 | q53 ← Government Aspect | 0.727 | 15.667 | |
q23 ← Technological Aspect | 0.697 | 13.754 | q54 ← Government Aspect | 0.674 | 14.364 | |
q24 ← Technological Aspect | 0.782 | 27.579 | q55 ← Government Aspect | 0.814 | 26.376 | |
q25 ← Technological Aspect | 0.833 | 35.359 | q56 ← Government Aspect | 0.75 | 19.384 | |
q26 ← Financial Aspect | 0.682 | 12.468 | q57 ← Government Aspect | 0.708 | 15.8 | |
q27 ← Financial Aspect | 0.796 | 27.471 | q58 ← Government Aspect | 0.737 | 21.876 | |
q28 ← Financial Aspect | 0.724 | 20.493 | q59 ← Government Aspect | 0.737 | 17.232 | |
q29 ← Financial Aspect | 0.766 | 23.635 | q60 ← Government Aspect | 0.746 | 20.407 | |
q30 ← Financial Aspect | 0.638 | 12.661 | q61 ← Government Aspect | 0.698 | 18.633 | |
q31 ← Financial Aspect | 0.729 | 16.21 | q62 ← Government Aspect | 0.698 | 16.194 | |
|
|
| q63 ← Government Aspect | 0.621 | 12.304 | |
Then, the reliability of the research variables was assessed by Cronbach's alpha indices with a standard value above 0.7 (Cronbach, 1951), Composite Reliability (CR) with a standard value above 0.7, and Average Variance Extracted (AVE) with a standard value above 0.5 (Fornell and Larcker, 1981) using Smart-PLS. According to Table 2, it can be seen that the research variables have convergent reliability and validity.
TABLE II
CONVERGENT RELIABILITY AND VALIDITY OF RESEARCH MODEL VARIABLES
Variables | Cronbach's alpha | Composite Reliability (CR) | AVE |
|---|---|---|---|
Financial Aspect | 0.923 | 0.934 | 0.566 |
Government Aspect | 0.938 | 0.945 | 0.506 |
Management aspect | 0.893 | 0.913 | 0.513 |
Cultural Aspect | 0.856 | 0.888 | 0.500 |
Technological Aspect | 0.921 | 0.933 | 0.519 |
Human Aspect | 0.863 | 0.907 | 0.709 |
Cronbach's alpha for all variables is greater than 0.7; therefore, all variables are confirmed in terms of reliability. The value of AVE is always greater than 0.5; thus, convergent validity is also confirmed.
3.2. Divergent Validity (Fornell and Larcker Method):
In the divergent validity section, the differences between the indices of a construct are compared with those of the other constructs in the model. This value is calculated by comparing the square root of AVE of each construct and the values of the correlation coefficients between the constructs. To do so, a matrix must be formed whose main diagonal values are the square root of the AVE coefficients of each construct, and the values of below and above its main diagonal are the correlation coefficients between each construct with the other constructs. This matrix is shown in Table 3:
TABLE III
COMPARISON MATRIX OF SQUARE ROOT OF AVE AND CONSTRUCT CORRELATION COEFFICIENTS
| Financial Aspect | Government Aspect | Management aspect | Cultural Aspect | Technological Aspect | Human Aspect |
Financial Aspect | 0.752 |
|
|
|
|
|
Government Aspect | 0.381 | 0.711 |
|
|
|
|
Management aspect | 0.504 | 0.662 | 0.717 |
|
|
|
Cultural Aspect | 0.181 | 0.114 | 0.537 | 0.707 |
|
|
Technological Aspect | 0.425 | 0.159 | 0.541 | 0.140 | 0.721 |
|
Human Aspect | 0.334 | 0.112 | 0.382 | 0.179 | 0.502 | 0.842 |
According to the matrix above, the square root of AVE of each construct is higher than the correlation coefficients of those constructs with other constructs, indicating the acceptable divergent validity of the constructs.
3.3. General Structural Model Quality Test
Tennenhouse et al. (2005) introduced the Goodness of Fit (GoF) to investigate the model fit. The general criterion of fit can be obtained by calculating the geometric mean of the average commonality and the coefficient of determination (R2). For this index, values of 0.01, 0.25, and 0.36 are described as weak, medium, and strong, respectively.
TABLE IV
COMMONALITY AND R2
[1] Variable | [2] Commonality | [3] R2 |
[4] Financial Aspect | [5] 0.566 | [6] 0.254 |
[7] Government Aspect | [8] 0.506 | [9] ---- |
[10] Management aspect | [11] 0.513 | [12] 0.804 |
[13] Cultural Aspect | [14] 0.500 | [15] ---- |
[16] Technological Aspect | [17] 0.519 | [18] 0.252 |
[19] Human Aspect | [20] 0.709 | [21] ---- |
According to the table above, only endogenous variables have value. After the calculation, the value of the GoF index is 0.491, which is a strong index indicating the overall high quality of the model.
IV. Findings
The relationship between the investigated variables in each of the research hypotheses has been tested based on a causal structure using the Partial Least Squares (PLS) technique. In the general research model depicted in Fig. 1, the measurement model (ie, the relationship between each of the observed variables and the latent variable) and the path model (ie, the relationships of the latent variables to each other) are calculated. Furthermore, t-statistic has been calculated to evaluate the significance of the relationships using the bootstrapping technique presented in Figure 2.
Fig. 1. Partial Least Squares Technique for General Research Model
Fig. 2. The t-Statistic of the General Research Model Using the Bootstrapping Technique
4.1. Investigating the Impact of Causal Conditions (i.e., Human Aspect) on the Core Category of Technology:
The severity of the impact of causal conditions (ie, human aspect) on the core category of technology is calculated to be 0.502 and the test probability statistic is obtained to be 7.757, which is greater than the critical value of t at a 5% error level, ie 1.96, indicating a significant observed effect. Thus, causal conditions (ie, human aspect) have a significant impact on the core category of technology, at a 95% confidence level.
4.2. Investigating the Impact of the Core Category of Technology on management strategy:
The severity of the impact of the core category of technology on management strategy is calculated to be 0.395 and the test probability statistic is obtained to be 10.891, which is greater than the critical value of t at the 5% error level, ie 1.96, indicating a significant observed effect. Therefore, the core category of technology has a significant impact on management strategy, at a 95% confidence level.
4.3. Investigating the Impact of Contextual Conditions (i.e., State Laws and Regulations) on Management Strategy:
The severity of the impact of the contextual conditions (ie, government laws and regulations) on management strategy is calculated to be 0.552 and the test probability statistic is obtained to be 12.857, which is greater than the critical value of t at a 5% error level, ie 1.96, indicating a significant observed effect. Therefore, contextual conditions (ie, state laws and regulations) have a significant impact on management strategy at a 95% confidence level.
4.4. Investigating the Impact of Intervening Conditions (ie, Social, Cultural Aspects) on Management Strategy:
The severity of the impact of intervening conditions (ie, social, cultural aspects) on management strategy is calculated to be 0.419 and the probability statistic is obtained to be 11.464, which is greater than the critical value of t at a 5% error level, ie 1.96, indicating a significant observed effect. Therefore, intervening conditions (ie, social, cultural aspects) have a significant impact on management strategy, at a 95% confidence level.
4.5. Investigating the Impact of Management Strategy (ie, Financial Aspect) on Outcomes:
The severity of the impact of management strategy (ie, financial aspect) on outcomes is calculated to be 0.504 and the test probability statistic is obtained to be 7.483, which is greater than the critical value of t at a 5% error level, ie 1.96, indicating a significant observed effect. Therefore, management strategy (ie, financial aspect) has a significant impact on outcomes, at a 95% confidence level.
4.6. Investigating the Indirect Impact of Causal Conditions (ie, Human Aspect) on Management Strategy:
The severity of the impact of causal conditions (ie, human aspect) on management strategy is calculated to be 0.198 and the test probability statistic is obtained to be 5.680, which is greater than the critical value of t at a 5% error level, 1.96, indicating a significant observed impact. Thus, causal conditions (ie, human aspect) have a significant impact on management strategy, at a 95% confidence level.
V. Conclusion
Virtual communication between humans and objects has created good management efficiency and convenience for network operators, end-users, and other activists, leading to the adoption of IoT-based operating systems in production and consumption. Now, IoT is out of its infancy and has provided a variety of innovative applications and services for businesses, individuals, and governments. Hence, researchers and international research organizations have introduced it as the next revolution in ICT. Following an increase in the computational power of IoT devices, business processes can be used to provide real-world information as well as reduce part of the business process, resulting in a decrease in the amount of information exchanged and central processing. Since IoT is an emerging phenomenon, there is no comprehensive information about it and this can provide business opportunities for existing entrepreneurs and companies. IoT is introduced as a broad social and technical phenomenon, covering various technical, physical, social, and economic elements. IoT provides exciting opportunities for consumers and businesses; however, it comes with a whole host of new security challenges.
This study considered human and management aspects as causal factors, governmental laws and regulations as contextual factors, technological aspects as a strategic factor, socio-cultural aspect as an intervening factor, and financial aspect as results and outcomes. Validation, including "CV-Commonality" and "CV-Redundancy", was used to check the quality or validity of the model. The results showed that the indices of independent and dependent variables were positive and greater than 0. Therefore, it can be said that the model has acceptable quality and reliability. The Goodness of Fit (GoF) index was used to evaluate the fit of the general model. The value of GoF was 0.491, which is a strong index indicating the high quality of the general model. All the variables identified in the model, as well as the relationships between the endogenous and exogenous variables of the model, were significant. The results of the model showed that the categories defined in the paradigm model derived from the qualitative part were significant and the variables considered had a significant impact on each part. In this regard, Gao et al. (2014) have shown that perceived usefulness, perceived ease, social impact, perceived enjoyment, perceived behavioral control, and trust plays an important role in IoT adoption. Ensa et al. (2015) found that internal and external organizational factors, in particular preparedness (readiness), strategies, managers' perceptions, and external pressures by partners, influence e-commerce acceptance [37]. Fathi et al. (2016) showed that appropriate knowledge, competitiveness, innovation fitness, organizational top management support, information security, IT skills, IT infrastructure, and return on investment (ROI) play an important role. Karahoca et al. (2017) showed that perceived benefits, perceived structural ease, and perceived image plays an important role in the intention to use IoT technology [5]. Lu et al. (2011) showed that privacy protection and asset security play an important role in deploying IoT. Technology infrastructure facilitation and technology support and equipment can also increase IoT efficiency and effectiveness and enhance the extent to which they are accepted (adopted) and used in organizations [35]. Metalo et al. (2018) showed that the main difference in the processes of creating business models depends on the different innovations and capabilities of the organizations. In conclusion, this study provides a theoretical understanding of the critical factors in the process of value creation in IoT industrial organizations as well as interesting implications for the theory of management and action. Naha et al. (2019) showed that IoT offers highly competitive advantages for local companies. Offering vertical integration into joint ventures provides a market entry strategy for foreign investors. Based on the results of the research findings, the following recommendations are made:
It is recommended that corporate executives facilitate appropriate procedures for IoT implementation and use flexible organizational structures.
For IoT adoption, it is recommended to employ sufficiently skilled IT professionals in the fields of IT to easily implement IoT.
Extra-organizational factors have a great effect on the lack of deployment. To this end, it is advisable to provide sufficient support by the executing companies as well as the procedures and policies needed to implement IoT.
The company must provide the infrastructure needed for IoT adoption. Technology and Informatics units should be deployed in the company and the necessary tools should be made available to the company management.
As executives play an important role in IoT adoption, they need to be trained. Moreover, the benefits of using IoT should be explained to managers so that no stance or resistance can be made against these organizational changes.
Corporate executives should develop policies for implementing IoT in the company. These policies should be well explained to employees.
Employees play a major role in implementing organizational change. As such, it is important to pay close attention to employees as executors of new policies and provide them with the necessary training.
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Amir abbas Farahmand: Ph.D. Candidate of Technology Management, Research & Development, Islamic Azad University,U.A.E Branch.
Master of Management Information System, Ferdowsi University of Mashhad.
Bachelor of Industrial Management, Faran University.
Lecturer in the management schools of Islamic Azad University, in Central of Tehran and South of Tehran Branches for 5 years.
Having several scientific research articles and published articles & attending in many domestic and foreign conferences.
R&D Manager of Hico software Company for 5 years.
Sales Manager of Sepehr abi software Company for 4 years.
IT Manager of Nourabad Sugar Company for 5 years.
IT Manager of Lorestan Sugar Company for 4 Years.
IT Manager of Sivand shahr Company for 5 years.
Reza Radfar: Professor in system Management, Science and Research Branch, IAU, and advisor in some national industrial projects. He graduated in System Management in 2003 and ever since he was pursuing his doctoral Research, he has undertaken work for some national industrial companies in fields of energy, still, auto Industry, etc.
He is the founder of Technology Management Department in the Management faculty and has worked as a professional academic in Iranian higher education over the last 18 years. He was in Bristol Business School, UWE,UK, as a Senior Researcher working in the area of Decision Making Modelling and Information Systems, 2010-12.
He supervised more than 100 ph.D and master dissertations, and has published more than 50 refereed papers on various themes.
Alireza Poorebrahimi: He is an Assistant Professor and Researcher at the Department of Industrial Engineering of the Islamic Azad University, Alborz Branch. He teaches researches and consults, MIS,DSS, IT security, machine learning , date mining & IOT.He published many papers in the fields.
Some of his Administrative positions areas follow:
Executive Director of the Second and Third Conference of the Industry 4 of Iran.
IT Consultant of Iran Insurance Company.
IT Consultant Asia Insurance Company.
President of Islamic Azad University E-Campus.
Mani Sharifi: He is in Mechanical and Industrial Engineering Department, Ryerson University, Toronto, Ontario, M5B 2K3, Canada.
The Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab), 232A Eric Plain Hall (EPH), 87 Gerrard Street East Toronto, Ontario M5B 1G6, Canada.manisharifi@ryerson.ca
He is a post-doctoral research fellow at Ryerson University, Mechanical and Industrial Engineering Department, and the Reliability, Risk and Maintenance Mani Research Laboratory (RRMR Lab).He holds a B.Sc degree for Qazvin Islamic Azad University, an M.Sc degree from the south of Tehran Branch of Islamic Azad University, and a Ph.D. degree from Tehran Research and science Islamic Azad University in Industrial Engineering.
He was Managerial editor of the Journal of Optimization in industrial Engineering.
He joined Ryerson University’s Department of Mechanical & Industrial Engineering (MIE) in November 2018.His area of interest includes reliability engineering, combinatorial optimization, statistical optimization as well as production scheduling.