Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights

Abstract

The Internet of Things (IoT) holds immense potential for the social and economic development of developing countries, as recognized by academia and professionals. However, there is a notable lack of theoretical research on IoT adoption within agricultural settings. To address this gap, this study introduces a model focusing on nine critical “Technology-Organization-Environment” (TOE) factors. Empirical validation was conducted using data from 179 managers in diverse agricultural organizations. The research model was evaluated by using “Partial Least Squares Structural Equation Modeling” (PLS-SEM). The results underscored the significance of governmental support and technological compatibility in driving IoT adoption. Moreover, financial considerations within organizations and the evolving digital landscape were identified as key influencers of smart farm adoption. This study offers valuable insights with significant implications for sustainable IoT adoption in research and practical applications.

1. Introduction

All Malaysian businesses and industries have been affected by technology, and agriculture is an obvious example in this regard. IoT is among the novel technological achievements that imply the objectification of three Web4.0 features, including ubiquity, identity, and connectivity [1]. The “Internet of Things” (IoT), which is sometimes known as the “Internet of Everything” or “Industrial Internet”, can be regarded as a novel and possibly disruptive computation paradigm that will potentially transform business procedures, methods, and competencies in different industrial fields [2]. A device to sense and another device to route and communicate along with a cloud-based application make up the IoT. IoT includes different applications of monitoring as well as controlling according to a network of devices that sense and actuate and have the ability of self-configuration with remote control using the Internet [3].

Revolutions in all industrial fields have been observed due to the IoT technology which contributes significantly to the renovation of the agricultural industries [4]. This technology is among the most recent digital transformations, representing a paradigm change in different business operations [5]. The fast growth of the global population along with the restricted available resources necessitates the improvement of crop production processes to meet the needs of the ever-increasing population. In this regard, IoT is one of the technological innovations that can be used to deal with this serious challenge. IoT can be considered an effective tool in the agricultural industries as it facilitates automation and the ability to monitor from every location across the world through human replacement [6,7]. IoT has a variety of applications in agricultural fields, such as smart farming or precision agriculture, environment, monitoring of irrigation, cattle animals, and other areas [8]. Various IoT technologies, including “radio frequency identification” (RFID), “wireless sensor networks” (WSN), sensors, and “global positioning systems” (GPS) have different applications in agriculture. Moreover, evaluation of the field variables, including soil status, atmospheric conditions, and animals or plants’ biomass can be facilitated by IoT. This technology is also employed in the assessment and control of variables, including temperature, moisture, vibrations, or shocks in the course of product transportation [4]. Reports predict an increase in the number of connected agricultural tools from 13 million in 2014 to 225 million in 2024, while there is also an increase in IoT device installations with a CAG rate of 20% across the world [9,10]. According to Kite-Powell [11], improvement of productivity as well as profitability is possible along with significant transformations in the management of farms and usage of sustainable agricultural methods through IoT. Application of the most current IoT technologies compared to conventional methods can bring about fundamental changes in different dimensions and lead to new agricultural patterns associated with precision agriculture [12]. Thus, agricultural industries will gain more importance shortly.

Fast economic developments took place in the Malaysian context after independence, particularly following the emergence of heavy industries in the 1980s and 1990s. Malaysia, which is dependent on agricultural and fishery activities, is now under the influence of services that assign approximately 55% of the country’s GDP to themselves [13]. Farming industries require IoT to provide food for a population of 9.6 billion people worldwide by 2050. Currently, 39% of the global palm oil production and 44% of exports are allocated to Malaysia [14]. Consequently, agriculture requires large-scale structural transformations using the smart farms revolution or smart agriculture in Malaysia. Even though considerable benefits are suggested by IoT, its adoption has just started in the agricultural context. Based on Dlodlo et al. [15], research in this area is still in its initial phases and there are a few studies accessible in this regard. Moreover, the effects of IoT adoption in the agricultural sector on their sustainability in the field of Information Systems (IS) have also not been examined in the recent relevant studies. As a result, the present paper aims to investigate the “technological, organizational, and environmental” (TOE) factors that affect the adoption of IoT in the agricultural sector of Malaysia.

2. Theoretical Background and Related Works

2.1. Concept of the IoT in Agriculture

Agricultural industries are of considerable importance in Malaysia, playing a vital role in the economy of this country for a long time through agricultural products for domestic usage as well as export. This industry is also important in the country’s “Gross Domestic Product” (GDP). Over 1.6 million people were employed by the agricultural industry in 2013, accounting for 10.9% of the overall employment and contributing to over 23% of the export income, and adding approximately 7.2% to the country’s Gross Domestic Products [16]. Development of IoT can be broadly observed in agricultural production, processes, sales, and circulation, bringing about different benefits regarding convenience and automation in maintaining and monitoring the products, while improvement of productivity and speed of operations in the management processes has become also possible [7]. For the successful construction of smart agricultural environments, it is necessary to develop the required IoT technology with the desired optimizations for agricultural industries, including hardware, middleware, routing protocols, and application services for agricultural contexts [7]. IoT is supposed to contribute primarily to different applications in agricultural industries [17] due to its potential such as fundamental communication infrastructures along with the scope of services, including local or distant data acquisition, users’ interface, decision-making, and cloud-based information analysis, as well as automation of agricultural operations. These potentials bring about considerable revolutions in the agricultural industries that are currently suffering inefficiency in contribution to the economic value chain [18].

IoT has been used in agriculture and smart farming in relevant literature [4,19,20,21]. Moreover, Ruan and Shi [20] proposed an IoT framework for the assessment of fruit freshness in the area of e-commerce as a non-conventional retail delivery which had faced specific problems regarding transport, because the products are perishable and require expensive logistics. On the other hand, Muangprathub et al. [4] designed and developed a control system through node sensors in the crop fields using data management with smartphones along with a web application. In another study, Luan et al. [21] conducted the development of a synthetic system for the integration of drought monitoring and prediction along with forecasts on the amount of irrigation into an IoT-based platform with consideration of hybrid programming and parallel computations. Nevertheless, although IoT has considerable opportunities and advantages for users and organizations, its adoption has not had the expected speed [22].

2.2. IoT Adoption Studies

Studies considering technologies’ adoption, diffusion, and acceptance are seen to have reached their maturity in the IS-relevant literature [23,24]. Thus, a considerable number of studies have taken IT innovations’ adoption and diffusion into account. On the other hand, some theoretical and experimental research works have considered these innovations’ adoption along with testing a variety of theories. De Boer et al. [1] used the “Technology Acceptance Model” to explore the acceptance and behavioral intention to adopt IoT technologies while investigating the scope to which the IoT competencies may be affected by Internet capabilities. Based on their conclusions, how individuals assess their IoT competencies and the practical issues are of importance in the acceptance of IoT, while appropriate internet capabilities are required to have enough skills in the use of IoT. An investigation of the contribution of IT/OT convergence to IoT adoption in manufacturing firms was carried out by Ehie and Chilton [5]. Based on the authors’ reports, IT infrastructures, and governance along with interoperable systems were associated with IT/OT convergence directly and positively. IT/OT convergence signifies the strategic integration of digital technologies, such as computer networks and software, with the physical systems and processes governing agricultural operations. This convergence fosters seamless communication and data exchange among various components of agricultural systems, ultimately enabling analysis, real-time monitoring, and data-driven decision-making. In addition, Madushanki et al. [25] investigated IoT adoption in the agricultural field and smart farming aimed at urban greening. According to their findings, water management was introduced as the highest-regarded IoT sub-vertical, after which smart farming, livestock management, crop management, and irrigation management came with similar percentages.

Lin et al. [26] constructed a framework called TOE standing for “Technology-Organization-Environment” to examine the effective factors of IoT adoption in Chinese agricultural supply chains. Based on their findings, employees’ resistance and uncertainty were not of importance regarding the effects of the adoption of IoT. Yoon et al. [27] studied the predicting factors on smart farms’ adoption in the Korean context, after which they analyzed the determining factors, challenges, and gaps, as well as future IoT achievements. Their findings indicated the effects of technological compatibility, organizational financial costs, and changes in the digital context on smart farms’ adoption.

Gomes and Osman [28] examined the available research on IoT and integrated the TOE framework with DOI theory, according to which they found comprehensive discussions and analysis of various methods in the relevant studies. An overall view is represented in the present paper considering the applied solutions in the IoT market through investigation of the available interpretations in relevant studies and using them to obtain experimental findings. Hsu and Yeh [29] used a hybrid model of TOE together with DEMATEL for the evaluation of the complicated factors that influence IOT adoption in Taiwanese logistics industries. The researchers concluded that environmental, organizational, and security aspects have the highest priorities for development. Arnold and Voigt [30] applied the TOE framework in manufacturing corporations. Based on their findings, factors from all three mentioned dimensions affected IoT adoption significantly. Shi and Yan [31] employed the TOE framework and conducted an exploratory examination of the affecting factors of the RFID technique’s adoption in the product distribution of Chinese agricultural industries. Experimental analyses showed that technological environments including technological complexity, technological compatibility, perceived usefulness, and costs, along with organizational dimensions including the size of the organization, top management supports, trust between business entities, and technical knowledge, as well as environmental aspects including competitive pressures and support from the Chinese government, affected RFID adoption significantly in the agricultural product distribution industries of China [31]. Examination of the relevant literature showed that the majority of research on the adoption of IoT has been carried out in developed contexts including Germany [30], the Netherlands [1], the United States [5], and China [20]. Consequently, more studies are needed in developing nations, involving the Malaysian context.

2.3. Technological, Environmental, and Organizational (TOE) Framework

Tornatzky et al. [32] suggested the TOE framework to investigate technological innovations’ adoption. Based on their arguments, adoption decisions of technological innovations are in accordance with organizational as well as environmental factors together with technological features. This framework can provide acceptable theoretical backgrounds to analyze the adoption of recent technologies at the organizational level [29,33]. The framework has been regularly used in research associated with technology adoption [34]. Moreover, a variety of disciplines and contexts have investigated it to show its theoretical robustness, experimental support, and effectiveness in the investigation of preparedness, adoption, and application of different types of innovation [33]. Different research works on the adoption of IT have been successful in its application, among which RFID adoption [35,36], cloud computing adoption [37,38], e-business [39], and IoT adoption [30] can be mentioned. A deliberate selection of the consideration factors for the decision framework was performed for the construction of the TOE framework on solid theoretical grounds in the present paper. Given the existing studies, the main determinants of the adoption of IoT receiving the highest attention in previous literature can be observed in

Table 1. Factors affecting IoT adoption in the literature.

Dimensions	Factors	References
Technological	Relative Advantage	[27,30,36,40]
	Complexity	[26,27,31,36,40,41]
	Compatibility	[26,27,30,31,36,40,41]
	Technology competence	[42]
	Cost	[26,27,31,41]
Organizational	Technical Knowledge	[26,31]
	Top Management support	[29,30,31,36,40]
	Organizational Readiness	[29,40]
	Organizational Size	[30,31,36,40]
Environmental	Competitive Pressure	[27,29,31,36,40]
	Government support	[26,27,31,41]
	Information Intensity	[36,40]

.

3. Research Model and Hypothesis Development

According to the theoretical background and the findings in the previous studies, the current paper proposes a three-dimensional model, incorporating technological, organizational, and environmental variables to understand the decision toward the adoption of IoT in the agricultural industries. A broad scope of factors could be found in prior studies. A limited number of factors believed to be of importance in figuring out and justifying the adoption of IoT were selected to avoid repetition. A discussion of the hypotheses and development of the research model is brought in this section. The predicted research model is indicated in Figure 1.

3.1. Technological Dimension

The Diffusion of Innovation (DOI) Theory, developed by Rogers [43], provides us with a robust theoretical framework to analyze the technological factors influencing the uptake of IoT technologies by agricultural organizations. Key factors within this framework include the relative advantages provided by these technologies, compatibility with established practices, and complexity. By systematically applying the DOI theory, we can isolate the most influential technological determinants of IoT adoption. This understanding paves the way for tailored strategies to accelerate its acceptance among agricultural stakeholders, thereby driving the sustainable transformation of agricultural practices.

As Rogers [43] has stated, relative advantages refer to the degree of perceiving technology as something that provides more benefits for organizations. It seems reasonable if the organization takes the benefits of innovations’ adoption into account. Different research works have referred to the positive association of this variable with new IT adoption. Perception of new IT as something that offers relative advantages compared to the existing organizational methods leads to a higher probability of its adoption [44]. As Yoon et al. [27] put it, productivity and quality of products are increased in smart farms, while less labor, energy, and fertilizer are required compared to the past. As a result, smart farms are supposed to result in higher organizational competitiveness. In summary, higher perceptions of the relative advantages of IoT adoption will lead to a higher possibility of its adoption by organizations. Therefore, we propose the following hypothesis:

Hypothesis 1. 

Relative advantages affect the adoption of IoT positively.

Complexity refers to the scope based on which the innovations are regarded as correspondingly challenging to understand and use [45]. Given that the complexity of innovations may inhibit successful execution, it seems to have a negative association with adoption [36,46]. Even though IoT brings about different organizational advantages, the perceived technological features are still sophisticated. When the application of a special form of technology is challenging for organizations, top management teams decide whether to ignore it or propose it later [31]. Therefore, it is primarily hypothesized that the complexity of IoT has negative effects on its adoption. Consequently, we propose the following hypothesis:

Hypothesis 2. 

Complexity affects the adoption of IoT negatively.

Compatibility refers to the degree of perceiving innovations as in agreement with the requirements of the current methods employed by possible adopters [47]. This factor is one of the main determining factors in the adoption of innovations since considerable transformations can be made in the current work processes through innovations [48]. Higher levels of compatibility seem to facilitate the adoption of innovations [49]. Compatibility of smart agricultural technologies in line with the farm’s existing procedures would result in a higher possibility of their adoption by the agricultural organization. As a result, the following hypothesis is raised:

Hypothesis 3. 

Compatibility has a positive relationship with IoT adoption.

Technological competencies (TC) are associated with the technological resources that are accessible in the organization, including IT infrastructures, incorporating the installed technologies, systems, and applications [50]. It is possible to achieve higher levels of performance with the use of competencies [51]. Ritter and Gemünden [52] referred to the technological competencies as organizational enablers that assist in internally understanding, using, and exploiting technologies. Indeed, technological competencies are a way to support in preparation for technological infrastructures such as the adoption of a fundamental level of knowledge associated with the existing technology [53]. Thus, organizations that have higher levels of technological competencies and knowledge of marketing may have better conditions to evaluate the advantages of IoT. Therefore, we propose the following hypothesis:

Hypothesis 4. 

Technological competencies have positive effects on the adoption of IoT.

Based on Tornatzky and Klein [47], costs prevent the of new technologies. The perception of excessive costs associated with adopting innovations can reduce the intention to adopt, even in the presence of potential benefits. Therefore, for a decision to adopt innovation to be made, the benefits must outweigh the adoption costs [48]. Because of the variety and large-scale characteristics of the costs, the costs related to these types of projects will be found considerable. Accordingly, costs will contribute critically to the decision to adopt [38]. In the present study, costs range from hardware facility expenditures (such as network, RFID, sensors, middleware, cloud-based or data center infrastructures) to expenses associated with implementing, integrating, operating, and maintaining the system. Therefore, we propose the following hypothesis:

Hypothesis 5. 

Higher costs have negative effects on the intention to adopt IoT.

3.2. Organizational Dimension

Technical knowledge indicates the professional knowledge of IT that the organizations possess within themselves. After the related knowledge and skills associated with a new form of technology are obtained, organizations can efficiently evaluate the influencing factors on the adoption of the novel technology, involving benefits, drawbacks, costs, and so on [31]. Based on Rezvani and Khosravi [54], technical knowledge makes the application of accurate working strategies possible to thoroughly deal with machinery and equipment. Thus, Hypothesis 6 is raised as follows:

Hypothesis 6. 

Technical knowledge affects the adoption of IoT positively.

Young and Jordan [55] define management support as the commitment of senior management, including project sponsors, champions, CEOs, or other senior managers, to invest time in evaluating plans, forecasting outcomes, and addressing management challenges. Management support is widely recognized as a critical factor in the successful adoption of new technologies, and it is strongly believed to be positively correlated with the adoption process [36,46]. As Shi and Yan [31] put it, top management’s support seems essential for new technologies’ adoption in organizations. Reyes et al. [56], similar to Sila [57], have argued that RFID adoption needs considerable investments, which in turn requires engagement and support from the top management to implement and perform successfully. Managers should support the business associates’ inappropriate information as well as communication technology implementation so that better communications and smoother workflows are obtained. Managers must support, motivate, and admit the recent technologies in the construction industry to increase organizational efficiency. Accordingly, we propose the following hypothesis:

Hypothesis 7. 

Top management support has a positive relationship with the adoption of IoT.

According to Tan et al. [58], organizational preparedness is defined as what the managers perceive and evaluate of the levels of organizational awareness, resources, commitments, and governance toward IoT adoption. This concept aims at measuring whether the organizations are capable of adopting the innovations [59]. Based on prior studies on the adoption of electronic data interchange, this factor considers whether the firms have enough ICT complexity and financial resources or not [60]. Ramdani et al. [61] found that organizational preparedness is a key factor that determines the adoption of business applications through SMEs. It is argued here that organizations with more efficient infrastructures, expert staff, and financial support will have higher levels of awareness regarding technology adoption. Therefore, we propose the following hypothesis:

Hypothesis 8. 

Organizational preparedness affects the adoption of IoT positively.

Company size can also affect novel technologies’ adoption, as indicated in prior literature [30]. Large organizations can often have more resources available to evaluate the novel technological forms, helping them to decide on their adoption. Meanwhile, achievement of the scale economies will be more possible for these organizations, while the risks related to the adoption of novel technologies will be also reduced [31]. Raguseo [62] investigated French firms and stated that the size of organizations affects RFID adoption significantly. According to Low et al. [63], organizational size is a determining factor that affects the adoption of cloud computing in high-tech industries. Matta et al. [64] concluded that organizational size had an important contribution when innovations were experimented and implemented. Positive effects of a firm’s size can be because large firms can access more resources as well as qualifications. Therefore, the following hypothesis is proposed:

Hypothesis 9. 

Organizational size has positive effects on the adoption of IoT.

3.3. Environmental Dimension

Competitive pressures indicate the amount of pressure felt by organizations from competitors in the same industries [63]. Competitive pressures are regarded as considerably significant determining factors in the adoption of IT [36,65]. Competitive pressures put by competitors and other entities in supporting industries can mainly direct the organizations toward innovation [29]. With the increase in market competition, organizations may be required to look for competitive advantages with the use of innovations. According to Oliveira and Martins [66], the use of innovations can reinforce organizational competitive capabilities and create novel strategies to perform better than competitors. The first-mover organizations in the application of novel technologies tend to obtain the highest degree of benefits. Lin and Lin [67] indicated that competitive pressures are critical factors that impact e-business diffusion. Adoption of novel technologies is usually a strategic requirement for competition in the marketplace. For example, through the adoption of cloud computing, organizations can take advantage of more operational productivity, better market visibility, and higher correct availability of real-time data [68]. Accordingly, we propose the following hypothesis:

Hypothesis 10. 

Competitive pressures have positive effects on the adoption of IoT.

Governmental support refers to the initiatives, policies, legal domains, and agencies set up by the government to support novel technologies or innovations adoption [27,69]. Prior literature shows that governments can mainly affect the adoption of special information technologies in small and medium-sized business entities [70]. Thus, policies and regulations recommended by the government of Malaysia, along with the financial support provided can contribute significantly to the promotion of recent technologies’ adoption. Consequently, we propose the following hypothesis:

Hypothesis 11. 

Governmental support has a positive relationship with IoT adoption.

Information is essential and critical in every entity that exists in a dynamic business context. Information intensity is associated with the degree of information presence in the products or services [71]. Organizations that are in environments with higher information intensity will experience a higher possibility of new IT adoption compared to those that are in contexts with lower intensities of information [36]. Thus, the information intensity of the products in businesses can affect the innovations’ adoption [72]. Wang et al. [36] mentioned that information intensity would affect RFID adoption positively. Consequently, businesses selling information-intensive products or services may have a higher potential toward adoption of IoT technology. Thus, we propose the following hypothesis:

Hypothesis 12. 

Information intensity has positive effects on IoT adoption.

4. Research Methodology

4.1. Construct Measures

The fundamental construct measures were chosen according to the available instruments. Modification of the items was performed for their fitness to the IoT context. Items for the relative advantages were taken from [27], while the items considering complexity were obtained from Wang et al. [36] as well as Savoury [73]. Five items associated with the compatibility construct were adapted from Arnold and Voigt [30] as well as Oettmeier and Hofmann [74]. Items for the support by top management were obtained from Sila [57] and Matta et al. [64]. The items associated with competitive pressures were acquired from Wang et al. [36], Bhattacharya and Wamba [48,75]. Three items of governmental support were taken from Yoon et al. [27]. The items regarding technological competencies were taken from San-Martín et al. [53]. Items associated with the size of organizations were adapted from Wang et al. [36]. Four items influencing the costs were obtained from Yoon et al. [27] and Shi and Yan [31]. Items related to the technical knowledge as well as information intensity were taken from Shi and Yan [31] and Wang et al. [36]. Five items associated with the preparedness of the organizations were adapted from [76]. A five-point Likert scale with a range from 1 indicating “complete disagreement” to 5 indicating “complete agreement” was employed for all items. A summary of the measurement items of the independent variables can be observed in

Table A1. Measurement items of the independent constructs.

Constructs	Measurement Items
Relative Advantage	RA1. Smart agriculture allows you to manage business operations efficiently.
	RA2. The use of smart agriculture services improves the quality of operations.
	RA3. Using smart agriculture allows you to increase business productivity.
Complexity	CP1. My company believes that IoT is complex to use.
	CP2. My company believes that IoT development is a complex process.
	CP3. The skills needed to adopt IoT are too complex for employees of the firm.
Compatibility	COM1.The implementation of IoT technologies would require few firm-specific adaptations.
	COM2. The physical integration of IoT technologies into our company would be unproblematic.
	COM3. We could integrate the software necessary for IoT with little effort into our existing IT landscape.
	COM4. IoT fits our company well.
	COM5. The use of smart agriculture will be compatible with existing hardware and software in the company.
Top Management Support	TMS1.Our top management is likely to invest funds in IoT.
	TMS2.Our top management is willing to take risks involved in the adoption of the IoT.
	TMS3.Our top management is likely to be interested in adopting the IoT to gain a competitive advantage.
	TMS4.Top management actively encourages employees to use IoT technology in their daily tasks.
Competitive Pressure	COMPET1.It is a strategic necessity to use IoT to compete in the marketplace.
	COMPET2.My company experienced competitive pressure to implement IoT.
	COMPET3.My company would have experienced a competitive disadvantage if IoT had not been adopted.
	COMPET4.We are aware of IoT implementation in our competitor organizations.
	COMPET5. We understand the competitive advantages offered by IoT in our industry.
Government Support	GS1.The government provides various forms of support for agriculture organizations to introduce smart agriculture.
	GS2. The government encourages smart agriculture by promoting successful case studies and technical training.
	GS3. The government supports various agricultural informatization projects for agriculture organizations.
Technological Competence	TC1.The company’s infrastructure is available to support the activity.
	TC2.The company is committed to ensuring that employees are familiar with the recent activity.
	TC3.The company has a high level of knowledge about the IoT business.
	TC4.The technology infrastructure of my company is available for supporting RFID-related applications.
Organizational Size	OS1.The capital of my organization is high compared to the industry.
	OS2. The revenue of my organization is high compared to the industry.
	OS3. The number of employees at my organization is high compared to the industry.
	OS4. The annual business volume of my organization is high compared to the industry.
Cost	CS1.Adopting IoT technologies will increase hardware facility costs.
	CS2.Adopting IoT technologies will increase operations and maintenance costs.
	CS3.The cost of investing in smart agriculture is a big burden for our farming organization.
	CS4. Our agriculture organization will have financial difficulty if smart agriculture is introduced.
Technical Knowledge	TK1.Enterprises in the agricultural sector have relevant technical knowledge of IoT.
	TK2. Enterprises in the agricultural sector have professional staff trained in IoT use.
	TK3. Our company has the technical knowledge and skills to adopt IoT.
	TK4. I have extensive technical knowledge about technologies like IoT.
Information Intensity	II1. The product/service in my industry requires a lot of information to sell.
	II2. The product/service in my industry is complicated or complex to understand or use.
	II3. The ordering of products in my industry by customers is a complex process.
Organizational Readiness	OR1. Top management support is important for IoT operations.
	OR2. Chain scale related to the slack in resources is important for implementing IoT.
	OR3. IT expertise related to the ability to operate IoT is important.
	OR4. Chain culture relevant to the attitude of the company toward a recent technology is important for IoT implementation
	OR5. Championing image related to the ambition of the chain to enhance business Image status is important for IoT implementation.

.

4.2. Data Collection and Sample

Agriculture contributes significantly to the Malaysian economic conditions. The agricultural industries have remained critical for the Malaysian government for distinct reasons, especially as a source of export income and provision of raw materials for industries and to ensure a degree of food security, while increasing the levels of income in rural regions, providing employment opportunities, and creating new tourist attractions. Qualified respondents, including senior managers and decision-makers in Malaysian agricultural organizations, received online questionnaires. Most of the organizations belonged to Malaysian agricultural supplies, services, products, and farming companies. A random sampling method was employed in this paper, through which 630 invitations consisting of the information on the aim and scope of the study and direct links to the questionnaires were distributed to the respondents by email. Following a two-month interval, 202 completed questionnaires were sent back. Detailed checking of the total items of the questionnaires led to the removal of some of them, after which 179 questionnaires were regarded to have acceptable validity to be used and analyzed for the study purpose. Details of the descriptive statistics of the study sample can be observed in

Table 2. Respondents’ characteristics.

Characteristics	Number of Respondents	Percentage (%)
Gender
Male	162	90.50
Female	17	9.50
Company Age
<10	56	31.28
10–25	82	45.81
25–40	38	21.23
>40	3	1.68
Positions
Senior Manager	21	11.73
Manager	67	37.43
Product Manager	33	18.44
Director Manager	9	5.03
Other Managers	49	27.37
Organization Category
Agricultural supplies	47	26.26
Agricultural services	56	31.28
Agriculture products	43	24.02
Farming	33	18.44

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5. Data Analysis and Results

A two-step procedure was considered for data analysis [77]. In this regard, an examination of the fitness and construct validity considering the suggested model was conducted through the assessment of reliability and discriminant and convergent validity. In the next step, the structural model was investigated in terms of the direction and strength of associations of the theoretical constructs.

5.1. Measurement Model

The assessment of measurement item reliability in this study utilized Cronbach’s alpha coefficient, composite reliability, and convergent validity for the constructs. As shown in

Table 3. Reliability and convergent validity.

Constructs	Indicator	Outer Loading	Cronbach’s α	Composite Reliability	AVE
Compatibility	COM1	0.815	0.851	0.894	0.628
	COM2	0.848
	COM3	0.827
	COM4	0.754
	COM5	0.712
Competitive Pressure	COMPET1	0.774	0.824	0.876	0.585
	COMPET2	0.788
	COMPET3	0.735
	COMPET4	0.762
	COMPET5	0.764
Complexity	CP1	0.867	0.755	0.86	0.672
	CP2	0.777
	CP3	0.813
Cost	CS1	0.83	0.843	0.894	0.679
	CS2	0.853
	CS3	0.802
	CS4	0.812
Government Support	GS1	0.845	0.755	0.86	0.672
	GS2	0.866
	GS3	0.743
Information Intensity	II1	0.764	0.708	0.835	0.628
	II2	0.81
	II3	0.803
Internet of Things Adoption	IOTA1	0.825	0.868	0.905	0.657
	IOTA2	0.854
	IOTA3	0.855
	IOTA4	0.8
	IOTA5	0.711
Organizational Readiness	OR1	0.842	0.824	0.876	0.587
	OR2	0.746
	OR3	0.742
	OR4	0.762
	OR5	0.733
Organizational Size	OS1	0.848	0.817	0.879	0.645
	OS2	0.803
	OS3	0.782
	OS4	0.777
Relative Advantage	RA1	0.88	0.771	0.869	0.689
	RA2	0.87
	RA3	0.732
Technology Competence	TC1	0.712	0.763	0.846	0.58
	TC2	0.79
	TIC3	0.781
	TC4	0.76
Technical Knowledge	TK1	0.759	0.784	0.861	0.608
	TK2	0.748
	TK3	0.837
	TK4	0.771
Top Management Support	TMS1	0.895	0.83	0.888	0.665
	TMS2	0.764
	TMS3	0.776
	TMS4	0.82

, Cronbach’s α coefficients for all constructs were above 0.70, indicating a strong internal consistency. Additionally, the composite reliability values surpassed the recommended threshold of 0.70 [77]. This further affirms the constructs’ reliability. Based on these results, it is reasonable to assume a satisfactory level of reliability for the measurement items [78], This suggests strong convergent validity for all measurement items associated with the constructs.

According to Fornell and Larcker [78], discriminant validity can be observed when the loads of the items on their respective constructs are higher than the loads on other constructs, and when the square root of AVE values is higher than the inter-construct correlations. In

Table 4. Fornell–Larcker criterion.

	COM	COMPET	CP	CS	GS	II	IOTA	OR	OS	RA	TC	TK	TMS
COM	0.793
COMPET	0.673	0.765
CP	0.25	0.241	0.82
CS	0.673	0.534	0.241	0.824
GS	0.762	0.662	0.246	0.681	0.82
II	0.469	0.423	0.383	0.438	0.556	0.793
IOTA	0.791	0.741	0.217	0.573	0.793	0.511	0.811
OR	0.683	0.531	0.198	0.609	0.69	0.366	0.706	0.875
OS	0.2	0.22	0.106	0.246	0.216	0.275	0.162	0.025	0.803
RA	0.68	0.637	0.195	0.558	0.69	0.397	0.732	0.658	0.214	0.83
TC	0.702	0.738	0.204	0.612	0.662	0.49	0.758	0.591	0.219	0.653	0.762
TK	0.686	0.729	0.27	0.572	0.659	0.451	0.741	0.604	0.227	0.628	0.692	0.78
TMS	0.592	0.451	0.246	0.567	0.59	0.315	0.562	0.766	0.015	0.568	0.459	0.531	0.815

Note: (COM = compatibility; COMPET = competitive pressure; CP = complexity; CS = cost; GS = government support; II = information intensity; IOTA = Internet of Things adoption; OR = organizational readiness; OS = organizational Size; TC = technology competence; RA = relative advantage; TK = technical knowledge; TMS = top management support).

, it is evident that the square roots of AVE values shown on diagonals exceed the inter-construct correlations, indicating acceptable discriminant validity. To further evaluate discriminant validity, the loadings of the indicators and their cross-loadings were examined [79]. The ranges of cross-loadings and factor loadings for the constructs are displayed in

Table 5. Cross-loading and factor-loading.

	COM	COMPET	CP	CS	GS	II	IOTA	OR	OS	RA	TC	TK	TMS
COM1	0.815	0.576	0.176	0.531	0.692	0.419	0.672	0.597	0.109	0.556	0.6	0.634	0.519
COM2	0.848	0.536	0.169	0.561	0.687	0.44	0.653	0.542	0.146	0.559	0.586	0.615	0.459
COM3	0.827	0.567	0.263	0.571	0.616	0.368	0.69	0.553	0.128	0.563	0.56	0.572	0.48
COM4	0.754	0.488	0.168	0.555	0.521	0.285	0.493	0.494	0.158	0.5	0.481	0.398	0.446
COM5	0.712	0.489	0.205	0.454	0.477	0.326	0.596	0.51	0.265	0.512	0.544	0.462	0.436
COMPET1	0.494	0.774	0.175	0.384	0.525	0.357	0.525	0.394	0.206	0.493	0.577	0.534	0.342
COMPET2	0.552	0.788	0.209	0.422	0.567	0.326	0.628	0.476	0.137	0.538	0.575	0.687	0.44
COMPET3	0.519	0.735	0.193	0.467	0.532	0.308	0.647	0.428	0.2	0.519	0.633	0.534	0.312
COMPET4	0.443	0.762	0.217	0.331	0.407	0.319	0.455	0.291	0.145	0.39	0.47	0.483	0.267
COMPET5	0.545	0.764	0.127	0.409	0.467	0.309	0.53	0.405	0.147	0.462	0.532	0.519	0.343
CP1	0.231	0.199	0.867	0.279	0.2	0.379	0.189	0.158	0.152	0.18	0.225	0.272	0.251
CP2	0.204	0.221	0.777	0.152	0.22	0.326	0.184	0.13	0.05	0.137	0.145	0.195	0.157
CP3	0.173	0.17	0.813	0.153	0.183	0.221	0.156	0.204	0.052	0.163	0.124	0.192	0.196
CS1	0.567	0.444	0.157	0.83	0.558	0.33	0.477	0.48	0.183	0.542	0.528	0.508	0.425
CS2	0.578	0.467	0.105	0.853	0.549	0.403	0.479	0.463	0.247	0.408	0.553	0.468	0.39
CS3	0.483	0.372	0.252	0.802	0.481	0.313	0.389	0.476	0.17	0.366	0.402	0.387	0.471
CS4	0.577	0.463	0.283	0.812	0.635	0.387	0.524	0.579	0.206	0.505	0.516	0.504	0.577
GS1	0.63	0.529	0.192	0.584	0.845	0.498	0.601	0.561	0.178	0.552	0.54	0.506	0.484
GS2	0.669	0.59	0.237	0.522	0.866	0.46	0.75	0.609	0.149	0.63	0.589	0.605	0.52
GS3	0.569	0.502	0.169	0.584	0.743	0.412	0.58	0.521	0.215	0.504	0.491	0.498	0.443
II1	0.303	0.283	0.342	0.361	0.475	0.764	0.327	0.251	0.216	0.254	0.35	0.342	0.234
II2	0.388	0.378	0.301	0.327	0.414	0.81	0.457	0.307	0.227	0.359	0.459	0.432	0.257
II3	0.412	0.333	0.279	0.361	0.447	0.803	0.413	0.305	0.211	0.316	0.345	0.291	0.257
IOTA1	0.625	0.618	0.216	0.393	0.663	0.428	0.825	0.579	0.094	0.609	0.576	0.623	0.511
IOTA2	0.647	0.688	0.222	0.467	0.7	0.444	0.854	0.57	0.169	0.608	0.641	0.688	0.488
IOTA3	0.645	0.644	0.145	0.464	0.673	0.37	0.855	0.568	0.128	0.598	0.706	0.596	0.405
IOTA4	0.666	0.571	0.164	0.495	0.628	0.421	0.8	0.595	0.13	0.64	0.604	0.573	0.453
IOTA5	0.63	0.465	0.125	0.515	0.54	0.412	0.711	0.556	0.137	0.508	0.537	0.514	0.422
OR1	0.563	0.372	0.174	0.496	0.588	0.324	0.54	0.842	0.041	0.557	0.456	0.479	0.814
OR2	0.563	0.468	0.265	0.487	0.563	0.32	0.562	0.746	0.101	0.521	0.452	0.507	0.684
OR3	0.509	0.445	0.11	0.4	0.503	0.255	0.594	0.742	−0.009	0.467	0.506	0.456	0.485
OR4	0.492	0.376	0.055	0.444	0.515	0.264	0.535	0.762	−0.008	0.511	0.422	0.415	0.67
OR5	0.477	0.358	0.151	0.519	0.463	0.231	0.452	0.733	−0.038	0.456	0.414	0.454	0.725
OS1	0.166	0.181	0.088	0.231	0.204	0.259	0.143	0.027	0.848	0.146	0.192	0.219	0.003
OS2	0.151	0.154	0.087	0.169	0.178	0.232	0.142	−0.011	0.803	0.194	0.203	0.168	−0.025
OS3	0.181	0.202	0.098	0.23	0.157	0.202	0.113	0.042	0.782	0.176	0.148	0.199	0.033
OS4	0.146	0.175	0.069	0.163	0.149	0.181	0.118	0.028	0.777	0.175	0.152	0.141	0.049
RA1	0.565	0.496	0.11	0.448	0.568	0.32	0.591	0.566	0.158	0.88	0.535	0.47	0.478
RA2	0.575	0.559	0.157	0.486	0.616	0.354	0.663	0.533	0.195	0.87	0.553	0.5	0.442
RA3	0.553	0.528	0.223	0.454	0.529	0.309	0.563	0.54	0.178	0.732	0.538	0.6	0.501
TC1	0.525	0.55	0.193	0.473	0.455	0.388	0.426	0.398	0.198	0.461	0.712	0.545	0.355
TC2	0.543	0.582	0.178	0.396	0.5	0.387	0.575	0.44	0.135	0.472	0.79	0.499	0.346
TC3	0.561	0.629	0.105	0.482	0.549	0.321	0.709	0.513	0.163	0.581	0.781	0.555	0.34
TC4	0.511	0.473	0.173	0.524	0.498	0.425	0.538	0.428	0.184	0.455	0.76	0.518	0.37
TK1	0.63	0.545	0.177	0.47	0.615	0.336	0.624	0.509	0.236	0.546	0.602	0.759	0.455
TK2	0.469	0.559	0.192	0.333	0.491	0.283	0.559	0.402	0.238	0.49	0.462	0.748	0.336
TK3	0.531	0.59	0.279	0.5	0.511	0.401	0.55	0.505	0.116	0.454	0.542	0.837	0.451
TK4	0.496	0.58	0.198	0.475	0.423	0.387	0.568	0.461	0.11	0.457	0.542	0.771	0.406
TMS1	0.505	0.341	0.217	0.466	0.511	0.287	0.446	0.772	0.031	0.497	0.376	0.449	0.895
TMS2	0.505	0.438	0.311	0.457	0.495	0.279	0.468	0.673	0.088	0.458	0.365	0.474	0.764
TMS3	0.454	0.345	0.102	0.411	0.456	0.207	0.461	0.693	−0.012	0.463	0.355	0.381	0.776
TMS4	0.459	0.341	0.169	0.512	0.459	0.253	0.451	0.711	−0.058	0.43	0.398	0.421	0.82

, demonstrating that the loads on their respective constructs are higher than the cross-loads on other constructs. In summary, these analyses illustrate the satisfactory fit of the proposed model to the data, confirming the good validity and reliability of the measurement model. This lends support to the application of the constructs for evaluating the structural model.

5.2. Structural Model

The evaluation of path coefficients using Partial Least Squares (PLS) results, along with p-values and t-statistics obtained from bootstrapping, was conducted as presented in

Table 6. Hypothesized relationships.

Hypothesis		Sign	Path Coefficient	t-Value	p-Values	Result
H1	Relative Advantage → IoT Adoption	(+)	0.124	2.212	0.014	Supported
H2	Complexity → IoT Adoption	(-)	−0.024	0.687	0.246	Not Supported
H3	Compatibility → IoT Adoption	(+)	0.219	2.793	0.003	Supported
H4	Technology competence → IoT Adoption	(+)	0.15	2.368	0.009	Supported
H5	Cost → IoT Adoption	(-)	−0.14	2.148	0.016	Supported
H6	Technical Knowledge → IoT Adoption	(+)	0.141	2.153	0.016	Supported
H7	Top Management Support → IoT Adoption	(+)	0.145	1.654	0.049	Supported
H8	Organizational readiness → IoT Adoption	(+)	0.259	2.178	0.015	Supported
H9	Organizational Size → IoT Adoption	(+)	−0.039	0.967	0.167	Not Supported
H10	Competitive Pressure → IoT Adoption	(+)	0.13	1.788	0.037	Supported
H11	Government Support → IoT Adoption	(+)	0.244	3.347	0	Supported
H12	Information Intensity → IoT Adoption	(+)	0.063	1.354	0.088	Not Supported

to assess the research hypotheses. The structural model was evaluated to scrutinize the hypotheses. Out of the twelve effects assessed, nine exhibited statistical significance. Specifically, relative advantages, compatibility, technological competencies, costs, technical knowledge, top management’s support, organizational preparedness, competitive pressures, and governmental support significantly and positively influenced the adoption of IoT.

Additionally, based on the acquired results, factors like complexity, organization size, and information intensity did not show significant contributions to the adoption of IoT. It is important to note that a substantial portion of the variance in adoption intention, 81.4% (R² = 0.814), can be explained by the independent variables. Figure 2 provides a visual representation of the paths among the constructs and their respective coefficients in the structural model.

6. Discussion and Conclusions

6.1. Key Findings

Analysis of the research hypotheses indicated that IoT adoption in agricultural industries was affected by relative advantages, compatibility, technological competencies, and costs from the technological dimension, technical knowledge, top management support, organizational preparedness from the organizational dimension, and competitive pressures and governmental support from the environmental dimension. Nevertheless, the size of the organization, complexity, and information intensity had no impact on IoT adoption. The following points can be discussed according to the research results. First, the adoption of IoT is positively affected by relative advantages, as shown in a considerable number of prior studies [30,68,75]. Accordingly, IoT advantages contribute significantly to deciding on the adoption of this novel production approach. Considering IoT, innovation is seen as bringing relative advantages from the perspective of automation, effectiveness, immediate data availability, and saving costs [80].

Furthermore, the present study showed significant effects of compatibility on the adoption of IoT, which is consistent with the findings obtained in the relevant literature, confirming that perceived compatibility of innovations affects their adoption positively [36,81]. Thus, IoT is regarded in line with the current values, demands, and previous experiences of the probable adopters. Overall, the application of a form of IT is usually more convenient if higher degrees of technological compatibility are offered. As the empirical results show, technological competencies affected the adoption of IoT positively. Technological resources have been systematically regarded as a significant contributing factor in the success of adopting information systems [82]. As Jaafar et al. [83] have stated, higher rates of managerial as well as technical skills will lead to higher effectiveness in technological preparedness and support. Meantime, Lin and Lee [84] have pointed out that organizations having staff with the required skills and technical knowledge will potentially have more successful development of e-business applications. Moreover, [85] indicated that technological competencies have significant contributions in e-business applications. Consequently, the implementation of IoT applications needs novel IT skills and components, along with adaptation of the available information systems. Therefore, it is argued that higher levels of technological competencies lead to more favorable conditions for the adoption of IoT, while a lack of these competencies is supposed to prevent IoT adoption.

According to the results, costs had negative effects on the adoption of IoT in the agricultural sector. Perceived costs refer to all kinds of expenses that organizations need to pay to adopt novel technologies, including hardware, software, and system integration [86]. Prior research has considered this factor and concluded that it is one of the primary drivers that influence organizational willingness toward IoT adoption [26,87,88].

Based on the findings, technological knowledge affected the adoption of IoT significantly. This finding is consistent with the results of Shi and Yan [31] according to whom technical knowledge affects the adoption of RFID positively in the distribution industries related to agricultural products. As Cicibas and Yildirim [89] have stated, technical knowledge is important for the decision-makers in the adoption processes. Kleinveld and Janssen [90] also indicated that technical knowledge relevant to business fields and performance contributes significantly to the IT departments and is considered one of the central capabilities in the coordination of sourced IT functions. In addition, Priyadarshinee et al. [91] mentioned that organizations that have been previously successful in the implementation of information technologies will benefit from better technical knowledge, fostering skills to implement novel IT solutions and developing a more in-depth perception of the economy as well as organizational effects of the novel IT. Consequently, the present paper assumes that technical knowledge planning is necessary for the adoption of IoT so that the risks of adopting innovations are decreased and special production systems can be operated at competitive levels, which will in turn lead to the improvement of operational capabilities.

This paper indicated that top management support and adoption of IoT were positively associated. This result is consistent with [73,81]. In addition, Reyes et al. [56] also referred to the fact that higher levels of managerial support result in higher levels of RFID adoption. Thus, this factor contributed critically to the adoption of IoT because it can affect the integration of services, resource sharing, and re-engineering procedures [92]. Besides, as Wang and Wang [93] stated, with no support and assistance from the executive managers, the organization would show more resistance against the adoption of IoT. In this regard, top management support can ensure the appropriate allocation of the necessary financial resources to implement the recent technologies successfully across organizations.

Organizational preparedness affected the adoption of IoT significantly. This is in line with previous studies on the implementation of IT innovations [44,94,95]. Accordingly, the establishment of appropriate organizational preparedness seems necessary in the process of recent programs, methods, or policy implementation across the organization. Moreover, Iacovou et al. [96] found that organizations that are more ready to adopt electronic data interchange face a higher possibility of adoption and would benefit more than those that are less ready. As a result, with no appropriate technological as well as financial resources, organizations are not capable of adopting IoT.

Competitive pressures affected the adoption of IoT positively and significantly. This is in line with the findings of previous literature relevant to innovative technologies’ adoption [31,73,93]. According to the results obtained in this paper, competitive pressures imposed by competitors and other industries result in organizational innovation. As was found in the study, intense competitive conditions lead organizations toward technology adoption to remain competitive, since they think that IoT adoption helps in these competitions.

The present paper also indicated that governmental support and adoption of IoT were positively associated. This is both reasonable and in line with the previous studies [97,98]. The government contributes significantly to the support for organizations to adopt new technologies regarding regulations as well as initiatives in technologically developing countries [97]. Besides, the government’s understanding and planning may have direct or indirect impacts on the expansion of IT/IS and accelerate technology adoption [99].

Surprisingly, the technological dimension’s complexity did not have significant effects on the adoption of IoT in an agricultural context. The complexity of IoT adoption is associated with immature IoT technologies, lack of common standards, and difficult integration of this technology with the current organizational information systems as well as business processes. Based on Zhong et al. [100], the broad scope of IoT devices makes the technology more complex across the selection of the product and planning. Thus, complex IoT implementation contributes as a significant inhibitor of its adoption.

As indicated, information intensity was not a significant predicting factor in the adoption of IoT. This finding is consistent with the findings of Wang et al. [36], according to whom information intensity inhibited the adoption of RFID. Yap [101] stated that organizations with higher levels of information intensity will face a higher possibility of new IT adoption compared to organizations with lower levels of information intensity. Based on Lim Junn [102], businesses in different areas typically indicate a variety of information processing requirements, while higher levels of information intensity result in a higher possibility of IoT adoption compared to businesses with lower levels of information intensity. In other words, agricultural organizations in Malaysia need higher levels of information to adopt IoT and enhance their information processing capabilities, supporting the information processing perspective.

Eventually, it was found that the size of the organization does not affect its decision toward the adoption of IoT significantly or directly. This is in line with the results of Arnold and Voigt [30], who investigated the determining factors of IoT adoption in Germany’s manufacturing industries and concluded that organizational size was not a significant predicting element in the adoption of IoT. However, it is interesting to note that in prior research on technology adoption, this factor had been regarded as a significant determinant [63,103].

6.2. Theoretical Implications

IoT represents an era of disruptive technologies that offer both advantages and disadvantages to organizations. However, its adoption has not been widespread across organizations, necessitating a deeper understanding, particularly in the agricultural sector [23]. Hence, comprehending the determining factors of IoT adoption in these industries is essential. The “Technology-Organization-Environment” (TOE) framework has been utilized in previous research to identify technology adoption in various contexts. However, IoT adoption in the agricultural industries of Malaysia has not been explored using this framework. Thus, the present study undertook the development and validation of a research model, applying the TOE framework, to investigate the impacts of nine contextual constructs on IoT adoption in agricultural industries. This research contributes to the relevant literature in the following ways:

This paper presents significant results and insights into the factors affecting IoT adoption in an agricultural context. Technological, organizational, and environmental conditions determine whether organizations employ IoT in the agricultural field. Nine factors, namely relative advantages, compatibility, technological competencies, costs, technical knowledge, top management support, organizational preparedness, competitive pressures, and governmental support, were found to be significant in influencing IoT adoption. Governmental support emerged as the most significant factor in the adoption of IoT technology in Malaysian agricultural industries, followed by compatibility as the second influencing factor.

Moreover, this paper provides empirical validation affirming the feasibility and effectiveness of TOE frameworks in understanding IoT adoption within an organizational context. IoT adoption decisions are influenced by technological dimensions on the one hand and organizational and environmental factors on the other. The paper also investigates two significant predictive factors, governmental support, and compatibility, which have rarely been examined in prior research on IoT adoption.

6.3. Practical Implications

The integration of Internet of Things (IoT) technology in agriculture holds immense potential to transform farming practices, promoting sustainability, diversification, and high yields while minimizing environmental impact. IoT sensors and devices enable precision agriculture, allowing farmers to monitor crop health and conditions in real time. This data-driven approach optimizes resource use, such as water or fertilizer, minimizing waste and reducing costs. By tailoring practices to specific crop varieties, climates, and soil conditions, IoT fosters diversification, promoting biodiversity and making agricultural systems more resilient. Furthermore, IoT-enabled supply chains increase efficiency, match diverse crop distribution with consumer demand, and reduce food waste. This combination of data-driven decision-making, resource efficiency, and optimized production ensures high agricultural yields without compromising long-term sustainability, making IoT a key tool for resilient and adaptable farming in the face of global challenges.

This paper offers vendors and managers a reference framework to analyze organizational conditions before embarking on IoT adoption, considering key factors affecting adoption and assimilation. The suggested model can be adopted in developing strategies aimed at improving an organization’s readiness to use innovations, with a focus on overall organizational outcomes. Strategies can be devised to support the successful adoption of innovations in agricultural organizations concerning IoT.

6.4. Limitations and Future Directions

Although this study holds promising implications, it is limited in several aspects. The sample comprised agricultural organizations in Malaysia. Given that IoT can be applied in various organizational settings across different countries, these diverse organizations should be considered in future research. Additionally, the determinants investigated here only represent a limited number of probable adoption factors. Therefore, future research should concentrate on other factors that have been deemed important in previous works, such as technological infrastructures, trading partner pressures, and other areas relevant to IoT adoption. Future research should also explore alternative methods of data collection, such as grounded theory or case studies, to gain a deeper understanding of the influencing factors on IoT adoption in new contexts.