Internet of Things Adoption in the Manufacturing Sector: A Conceptual Model from a Multi-Theoretical Perspective

Abstract

The manufacturing sector (MS) is considered one of the most important national economic sectors; therefore, global manufacturers strive to apply cutting-edge technologies to gain competitive advantages. The Internet of Things (IoT) has an inherent potential to enhance MS economic growth and maintain its dominance in global markets by using a vast network of smart sensors; nevertheless, IoT technology adoption in the MS remains in the early phase. This research aims to define the antecedents that affect IoT adoption in the MS and propose a conceptual model to explain the adoption intention. Based on an extensive literature review, the proposed model was constructed by three main antecedents: perceived value, perceived benefits, and perceived challenges, and 11 related variables. The model development used a multi-theoretical perspective by integrating three theories: the value-based adoption model, the diffusion of innovation theory, and the technology–organization–environment framework. This study provides decision-makers with valuable insight that promotes IoT adoption in MS and enriches the literature with a new perspective that encourages more studies on IoT adoption in organizations.

1. Introduction

Nowadays, businesses need to be innovative, flexible, lower-cost, and operate efficiently to ensure profitability, competitiveness, long-term survival, and overcome global economic challenges [1]. The MS faces several emerging challenges in different areas, such as customized products to meet customers’ demands, faster material flow in the supply chains, decreased product life cycle, sustainability, and intensified competition [2,3]. The unique potential of the IoT can overcome all these issues and provides new opportunities for MS to shift from traditional to smart manufacturing [4,5].

The IoT offers promising intelligent applications for the MS that can create additional value and enhance the organization’s competitiveness [6,7]. The IoT sensors allow manufactured machines to share, collect, and analyze real-time data with manufacturing facilities, supply chains, and operators and connect them to the network [5,8]. Decision-makers can gain new insights from IoT data, analyze market status, integrate the supply chains, enhance customer engagement, and implement more effective practices and policies [9]. In addition, IoT technology can increase machines’ automation and allow them to make decisions automatically and independently without human intervention, which contributes significantly to improving performance, reducing time and costs, and increasing productivity and profits [10,11].

The IoT adopters state that the IoT will be crucial to future organizational success, and companies that do not adopt the IoT will lag behind their rivals [3,12,13]; however, the IoT remains in an early-adoption phase in the MS [7,14,15,16,17,18,19]. Most IoT projects remain in the early stages of the planning phase, or they do not proceed beyond deployment at one or two sites [13,15,20]. McKinsey Global Institute partners Patel et al. [14] found that the IoT has a slower-than-expected growth rate within the industrial segment. This remains somewhat understandable given the novelty and sophistication of IoT technology, with few experts conducting successful experiments [18]; however, there is another reason IoT initiatives are affected and its spread reduced in the MS [18].

Verma and Bhattacharyya [21] suggested that the significant reason for the non-acceptance of new technology by the MS is that decision-makers do not realize its perceived value. While there have been many investigations of IoT technology adoption, none examined the function of perceived value and its effect on IoT adoption in the MS. A top executive from a leading pump manufacturing business stated that “the challenge for IoT adoption is not technical. Roughly speaking, anything can be achieved technically. It is more about finding the value [20]”; thus, decision-makers who realize the value of IoT technology adoption would be more liable to accept it and they would reduce IoT adoption barriers significantly [22].

Successful IoT implementation requires more than an in-depth understanding of the technology [20]. The MS decision-makers should possess a clear vision and strategy for IoT adoption with a strong focus on the perceived value that could positively affect the business [6,23]; however, the MS decision-makers are still unable to realize the expected IoT value due to the numerous surrounding challenges or to fully define the benefits of IoT adoption [24]. The IoT remains in the development phase and presents many challenges in different contexts, such as compatibility with legacy systems [8,16,25,26,27], high implementation cost [16,25,27,28,29,30,31,32,33], employees’ resistance [8,31], and lack of government regulation [16,25,26,34]. In addition, there is the lack of clarity in identifying the potential benefits [8,18]. The empirical evidence in IoT research lacks substantiation for such conceptual claims, specifically in organizations [6,7].

Therefore, this research aims to examine IoT adoption in the MS from the organizational perspective and develop a comprehensive model to reveal the antecedents that affect IoT adoption and investigate the impact of perceived value; thus, the following research questions (RQ) were formulated:

• RQ1: What are the antecedents that affect IoT adoption in the MS?
• RQ2: How does perceived value affect IoT adoption in the MS?

The rest of this paper is structured as follows: Section 2 presents the literature review of the research subject. Section 3 explains the research method used in the study, followed by the theoretical rationale in Section 4. Section 5 offers the conceptual model and associated research propositions. Section 6 presents the discussion and significance of the proposed research study. Section 7 concludes the study with observations about future work.

2. Literature Review

2.1. The IoT Technology

The term “IoT” was presented by Kevin Ashton in 1999 during a supply-chain management project presentation [35]; nonetheless, IoT technology was disseminated in 2011 after Germany launched the Industry 4.0 initiative [3,12,36], which marked the commencement of the fourth industrial revolution [1,11]. The IoT is described as a network of heterogeneous communicating items (“smart components”) that can sense, process, analyze, and transform data via the Internet [26]. IoT technology features several distinctive characteristics that can aid MS business improvement through instrumented ordinary objects, which are individually identified using information perception technologies, sensors, wireless sensor networks (WSNs), and radio-frequency identification (RFID) [2,37,38]; thus, all physical objects, such as manufacturing floors, production lines, repositories, trucks, and products, can be connected through an extensive network with unique identities [39]. The main purpose of IoT technology is to render all objects smart and connected and able to participate in data flow and decision-making through pervasive, ubiquitous computing [17,30]; therefore, the IoT plays a crucial role in data availability, analysis, and exchange, which enables the acquisition of many benefits [17,38,40].

2.2. IoT Benefits for the MS

The MS involves various departments with discrete system groups designed to support manufacturing operations, maintenance processes, involved businesses, and training functions. IoT technology is aimed at digitizing all manufacturing activities and connecting them into one platform, enabling interconnections among objects and exchanging real-time data with business analytics systems and machine learning [6,24]. The IoT-generated data are high-quality and high-accuracy, which renders them distinctive, valuable, and useful for providing context awareness and decision support, such as accurate market demand forecasts, resource management, and reduced inventory costs [1,39].

Furthermore, sensor and data storage integration with the business operation systems improves facilities and assets management [41], process automation [1,42], manufactory floor remote monitoring [17,38], enhances predictive and preventive production equipment maintenance [18,19,43], and reduces unplanned downtime and costs [3,4,27,37,44,45]. In addition, IoT sensors can monitor and detect equipment that does not have tasks and turn it off automatically for energy- and fuel-savings purposes [19,37,46,47].

IoT technology can help to improve workplace safety conditions by providing safe and practical solutions [28,37,48]. IoT capability enables the collaboration between human workers and robots, facilitates remote operations [4,37], provides wearable equipment to monitor employees’ health [39], and captures accurate data from low-safety environments where employees’ lives are in danger, such as from falls, collisions, or burns [49]; moreover, IoT technology can collect real-time data from supply-chain activities and integrate it with business actions. These seamless integration capabilities provide unparalleled visibility for tracking export and import activities, scheduling, and planning production activities [28,33,34], which bring transparency to the processes [9] and response to market demands [50]. The MS can gain the aforementioned benefits directly from IoT implementation and many other benefits can be observed over time [23].

Regarding the indirect benefits, the IoT can collect valued information during the product lifecycle, analyze customer behavior changes, and improve services and performance based on customers’ expectations and experiences [37]; thus, the IoT can produce higher customer satisfaction and loyalty levels with time [11,49,51]. Moreover, customer data collection and market status analysis enables the business to gain valuable insights to predict future products and services [4]. IoT prediction methods can help organizations become proactive by shifting from being “reactive-oriented” in response to events to being proactive in anticipation of them [52]; thus, recognizing and taking advantage of future opportunities [52]. This distinctiveness would give the organization a tactical benefit over competitors, creating new revenue opportunities and economic sustainability [28,53,54].

2.3. IoT Challenges in the MS

The IoT is associated with several aforementioned potential benefits; simultaneously, there are several adoption challenges surrounding it [55]. These challenges hinder wide IoT technology deployment, which reduces its delivered value [56]. The literature review found that IoT adoption challenges in MS were raised in three different contexts: te technological, organizational, and environmental contexts [27]. Technological challenges emerge from the technical characteristics of the technology itself. Security implications present a significant obstacle to wide IoT adoption in MS [18,38]. The potential for IoT security hazards is escalated due to the lack of collective IoT security standards and the development of numerous layers that require diverse security systems [19,24,57,58]; furthermore, the IoT requires the integration of numerous heterogeneous devices, which leads to increasing the complexity of devices and data management [32,57]. Given that the IoT is also a new technology, its compatibility with manufacturing legacy systems is highly challenging [4,44]; therefore, the IoT requires significant information technology (IT) infrastructure expansion [18,45] and a considerable initial investment in hardware, software, skilled staffing, training, operation, and maintenance [27].

Several organizational challenges can emerge during IoT implementation [12], mainly from internal organizational processes. Many decision-makers remain uninformed of the IoT and how it can positively change their businesses [18,32], or they are inclined towards closed thinking and are entrenched in traditional industry practices. Adopting IoT technology may require redefining the organizational business model to adapt to it and profit from its distinctive functions [2,12]. Most decision-makers are reluctant to take risks in modifying their business models, which leads to unanticipated interruptions and major alterations in the usual work practices and employees’ resistance [29]. Most employees are disinclined to acquire new technology skills or alter their routines [18,59] due to their feeling of ease and accord with the current technology and apprehension about their incapability to use the IoT [3].

External factors related to the surrounding environment can also be a substantial challenge to IoT adoption in the MS. To date, there are no clear IoT use policies and regulations, which result in the emergence of security, data-sharing, protection, and ownership challenges [37,53]. Governments should consider how new technologies, such as the IoT, can be supported and establish legislation and liability regulations to integrate such innovations into organizations to increase economic competitiveness systematically [9]; furthermore, market monopolies and competitive relations between IoT suppliers and vendors have hindered the adoption of the IoT [58]. IoT vendors use different architectures and protocols for their devices, making IoT solutions complex and impractical due to integration problems and difficulty of communication between devices [55].

2.4. Perceived Value of IoT Technology

Even though IoT technology is still nascent, it can largely contribute to driving greater business value by using new and advanced analytic tools to process big data [1,3,11,40,60]. The actual value of the IoT can be reached when connected sensors communicate with each other to exchange data and knowledge without challenges appearing on the technology side, then convert knowledge into tangible benefits on the business side to reach an advanced approach or create a new value [8,24,52,61]. The IoT perceived value represents an overall estimation of technology and how it might aid the MS in increasing its efficiency and achieving its goals.

The IoT would help the MS to move to the next stage of development by affecting value-creation activities, such as organization framework, operations administration, and supply chains [18]. IoT applications can manage manufacturing activities with less time and effort, optimize operations performance, and enhance efficiency through real-time monitoring of employees, production and distribution lines, increase control and production capacity, reduce human errors, remote diagnosis for machines, and increase security and safety [38,39,60,62], thus, contributing to adding significant functional value to the MS [1,11,50,60]. Furthermore, the IoT has a tremendous capability to link all products and customers with the organization, therefore understanding customers’ business processes better and involving them in the decision-making process [52]. The consultations and direct interactions with customers lead to long-term and strengthened customer relationships [62]. This method would enable the establishment of significant customer value [1,6,54,63] by providing swift updates on products and services, responding to customers’ demands rapidly, and enhancing the personalized experience [10,52,54]; thus, the IoT can access the primary customer values, which are reliability and trust [6,24].

Moreover, the IoT has much potential for altering prevailing business processes [64] by delivering highly customized products [44], shortening the innovation cycles, capturing internal and external inventory movements, aiding in achieving better purchasing decisions, and providing dynamic pricing for bills based on consumption [18,19,38,62], which means reducing overall costs and generating new revenue opportunities for the organization [28,52,53]. As a result, the IoT can contribute to adding significant financial value to the MS [6]; in addition, the IoT enables resource allocation in a safe and environmentally responsible manner based on real-time data [10,40,60]. These data yield thorough knowledge to construct a prospective organizational map about raw materials, inventory, water, and energy [37], which lead to waste reduction, green procedures [1], harmful gas emissions reduction [6,38,60], and energy conservation [47,49]; thus, these actions enable the achievement of sustainability targets and provide environmental value [6]. Figure 1 presents the summary of the IoT’s perceived value.

2.5. IoT Adoption in the MS

The IoT has the potential to perform processes in an easier-than-ever way and achieve numerous MS goals through increasing productivity, enhancing services efficiency, and improving products quality [5,16,17,19]. From the literature review, it can be observed that many studies have started to explore IoT adoption aspects in the MS from different points, such as enabling technologies for the IoT, potential application areas in the MS, and the key challenges [2,3,4,37,38,39]. Kiel et al. [10] investigated IoT benefits and challenges in the MS in terms of ecological, economic, and social sustainability and how IoT implementation can aid the achievement of sustainability goals. Similarly, Ihekoronye et al. [19] discussed IoT deployment benefits, challenges, and practical concerns for smart manufacturing.

Singh and Bhanot [64] studied barriers to IoT adoption in the MS by evaluating organizational priorities using maximum mean de-entropy (MMDE), interpretive structural modeling (ISM), and decision-making trial and evaluation laboratory (DEMATEL). Krommuang and Suwunnamek [1] examined the prioritization of the factors that affect decision-makers’ IoT application selections with a multiple-criteria decision-making (MCDM) method. Sumrit [17] proposed an evaluation framework for the degree of IoT adoption readiness using interval-valued Pythagorean fuzzy sets (IVPFS) and analytic hierarchy process (AHP). Mustapha et al. [18] developed a conceptual model to identify the determinant factors for IoT adoption in the MS based on the technology–organization–environment (TOE) framework.

Nevertheless, the IoT adoption models in the MS were examined in only a few studies [7,8,12,16,27,32,36,44]. Most of these studies used the TOE framework and the diffusion of innovation theory (DOI) to identify the elements that enable or inhibit the adoption of the IoT [7,8,12,16,27,32]. The TOE and DOI are not sufficient holistically to capture IoT adoption factors and might overlook some factors.

In IT literature, the perceived value was used as a critical indicator in technology-adoption decisions [21,65,66]; however, none of the previous studies explored the effect of perceived value on IoT adoption in the MS. The IoT is a new technology with unique capabilities and is expected to yield substantial value to organizations [23]. Krommuang and Suwunnamek [1] asserted that the value provided by IoT applications is an important factor for decision-makers when selecting MS applications. Rejeb et al. [67] stated that the prospect for the MS to leverage the IoT would become more evident by providing higher value, and a more complex model is required to enrich IoT adoption by examining the antecedents and provided value; thus, there is a need for a new model that clarifies the antecedents and the effect of perceived value in enhancing organizational IoT adoption.

3. Research Method

This study used the conceptual method to develop a comprehensive model capable of identifying the antecedents and impact of perceived value on organizational IoT adoption using theories and frameworks of information systems [68]. This method was chosen to investigate unexplored constructs in previous studies and introduce new constructs to bridge the gap in the existing literature. In contrast to theory studies, the conceptual study does not need to deliver a new theory but instead bridge existing theories and frameworks in exciting ways to create different insights, expand the field of exploration, and link findings across disciplines [68,69,70]. Jaakkola [70] identified four types of conceptual studies: theory adaptation, theory synthesis, model development, and typology. The model development type has been used in this study to construct a theoretical framework that predicts relationships between concepts [70]; thus, this conceptual paper proposes new relationships between constructs rather than testing them empirically and develops an integrated model that links these constructs to a number of propositions [69,70,71].

To conduct this conceptual study, a systematic literature review (SLR) was used following PRISMA guidelines [72]. Five academic databases have been identified to select articles: the IEEE, the Web of Science, Emerald, Science Direct, and Google Scholar, from the period 2015 to August 2022. The following search query was used to review and analyze literature: (“Internet of things” OR IoT) AND (adoption OR diffusion) AND (model OR framework) AND (“manufacturing sector”), to establish the foundation required to envision possible constructs that enable IoT adoption in the MS.

The articles selection procedure followed the screening and filtering iterations by reading each article’s title, abstract, and keywords. Then, the articles from the first filter were filtered further by carefully reading them in full text to select the ones related to the study only. In addition, the eligibility criteria were applied to all articles. The eligibility criteria included that the article should be in the English language, published in a journal or at a conference, dedicated to the organizational level, studied IoT technology, and not a technical article. Finally, a quality assessment was conducted to assess the risk of bias on the basis of 8 criteria [3] for all the included articles which are 42 articles to ensure the validity and reliability of the selection process. The quality assessment criteria and results can be found in Appendix A,

Table A1. Quality assessment criteria items.

Quality Assessment Criteria
1. Does the paper discuss IoT adoption factors/benefits/challenges?
2. Is the objective of the paper clearly stated?
3. Is the method of the paper clearly described?
4. Are the study settings and sample justified?
5. Is the data collection method(s) adequately described?
6. Is the data analysis adequately described?
7. Are the paper results and findings clearly stated?
8. Are the paper limitations presented?

and

Table A2. Quality assessment result.

Ref	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Total
[1]	1	1	1	1	1	1	1	1	8
[3]	1	1	1	1	1	1	1	0	7
[6]	1	1	1	0.5	0.5	1	1	0	6
[7]	1	1	1	1	1	0.5	0.5	1	7
[8]	1	1	1	1	0.5	1	1	1	7.5
[9]	1	1	1	1	1	1	1	1	8
[10]	1	1	1	1	1	1	1	1	8
[11]	1	1	1	1	1	1	1	1	8
[12]	1	1	1	1	1	1	1	1	8
[16]	1	1	1	1	1	1	1	0	7
[17]	1	1	1	1	1	1	1	0	7
[18]	1	1	1	0.5	0.5	0.5	1	1	6.5
[23]	1	1	1	1	1	1	1	1	8
[25]	1	1	1	0.5	1	0.5	1	0	6
[26]	1	1	1	1	1	1	1	1	8
[27]	1	1	1	1	1	1	1	1	8
[28]	1	1	1	1	1	1	1	1	8
[29]	1	1	1	1	1	1	1	1	8
[30]	1	1	1	1	1	1	1	1	8
[31]	1	1	1	0.5	0.5	0.5	1	0	5.5
[32]	1	1	1	0.5	0.5	1	1	0	6
[33]	1	1	1	1	1	1	1	1	8
[34]	1	1	1	0.5	1	1	1	0	6.5
[36]	1	1	1	0.5	0.5	1	1	0.5	6.5
[39]	1	1	1	0	0	0	1	1	5
[42]	1	1	1	1	0.5	1	1	0	6.5
[43]	1	1	1	1	1	1	1	1	8
[44]	1	1	1	0.5	0.5	1	1	1	7
[45]	1	1	1	0	0	1	1	0	5
[48]	1	1	1	1	1	1	1	1	8
[53]	1	1	1	1	1	1	1	0	7
[54]	1	1	1	1	1	1	1	1	8
[55]	1	1	1	1	1	0.5	1	0	6.5
[59]	1	1	1	1	1	1	1	1	8
[62]	1	1	1	1	1	1	1	1	8
[64]	1	1	1	1	1	1	1	1	8
[67]	1	1	1	1	1	1	1	1	8
[103]	1	1	1	0.5	0.5	0.5	1	0	5.5
[104]	1	1	1	1	1	1	1	1	8
[105]	1	1	1	1	1	1	1	0	7
[107]	1	1	1	0.5	0.5	0	1	0	5
[112]	1	1	1	1	1	1	1	1	8

Legend: 1 = the criterion is fully met; 0.5 = the criterion is partially met; and 0 = the criterion is not met. Each article needs to receive a total score of 5 or more to be included.

.

From the final set of selected articles, the relevant influential factors have been analyzed and incorporated into suitable IS theories to propose the conceptual model. The following section presents the theoretical rationale and constructs used in the study.

4. Theoretical Rationale

The IT adoption has been examined extensively in the literature to clarify the emerging technology adoption process. Information system research identified several theories and frameworks to study IT adoption. The value-based adoption model (VAM), DOI theory, and the TOE framework were used in this study to investigate the IoT adoption factors and are described in the following sections.

4.1. The VAM

The VAM is a variation of the perceived value concept and is defined as a general evaluation of the new technology utility based on the perception of what is achieved through the technology [73]. Since the late 2000s, the value perception linkage of IT technologies with adoption intention has been examined in several studies [22,74]. The significant function of the perceived value in new technology adoption was confirmed in previous studies, where higher value perception led to a more positive adoption decision [65,66,75,76,77]. The VAM has been used mainly to study the adoption intention at an individual level [78]. Nevertheless, the model has been examined at an organizational level in different studies [65,66]; thus, it has a significant level of external validity. Previous studies found VAM had great predictive power contrary to the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM), and the theory of planned behavior (TPB) [79,80]; thus, it is assumed that VAM has the ability to predict the value of IoT adoption in the MS [81]. The VAM model has two main constructs that influence the perceived value: perceived benefits with a positive impact and perceived sacrifices with a negative impact [78].

This study used perceived benefits to investigate the IoT adoption benefits and demonstrate its positive effect on perceived value. Many researchers have mentioned the potential benefits that the IoT may offer directly, such as increasing the operation process efficiency [17,19], improving decision-making [23], and improving supply chains and transportation [28,33], or indirectly, such as maintaining competitive advantage, innovating new products and services [4], and achieving sustainability [53,54]. However, IoT adoption literature in the MS lacks an examination of perceived direct and indirect benefits; therefore, perceived direct and indirect benefits constructs were added to the model as variables that could positively affect the IoT’s perceived value.

Perceived sacrifices are mostly related to individual intentions, whereas the aim of this research is to address organizational intentions. Individual and organizational intention beliefs differ in technology adoption decisions, as organizations focus on numerous factors, such as technological factors, organizational factors, and external threats [82]. The MS is relatively conservative when implementing new technologies, given the associated challenges of emerging technology which are subject to different contexts [21]; nonetheless, few studies examined contextual factors specifically, such as the organizational and environmental contexts and their effect on value perception in the industrial adoption setting [65]. Gao et al. [65] stated that more studies are needed to investigate the extent to which value serves to mediate the effect of technical characteristics, such as complexity and compatibility of IT adoption; thus, to achieve an adequate fit with the research problem, this study used perceived challenges rather than perceived sacrifices to examine the negative impact of perceived challenges from a comprehensive viewpoint in the organizational intention context. Given these VAM features and claims, this study investigated VAM at the organizational level, exploring the impact of the perceived value and perceived benefits on IoT adoption. In addition, the investigation was extended to include perceived challenges in order to understand the challenges related to technological, organizational, and environmental contexts in IoT adoption in the MS and their effects on the perceived value by using DOI theory and TOE framework.

4.2. The DOI Theory

Rogers [83] believed that IT innovation characteristics are the primary innovation diffusion determinants. The author stated that five innovation attributes explain a high percentage of technology adoption and decision-making processes: relative advantage, compatibility, complexity, trialability, and observability. The DOI theory has been extensively used to examine IT adoption at individual and organizational levels [30]. The DOI theory was selected for this study based on its explanatory power of technological innovation adoption and validity in supporting various organizational-level IT innovations.

However, two DOI theory attributes were adopted into the theoretical framework used in this research, namely, compatibility and complexity. Compatibility is a critical attribute in new technology adoption that represents the degree to which the technology can be consistent with the current infrastructure [83]; thus, the degree to which IoT technology is compatible with the existing systems and infrastructure in the MS will determine its adoption [8,16,18,31,43]. In terms of complexity, it represents the perceived difficulty of understanding and using the new technology [83]. IoT adoption in the MS requires integrating multiple heterogeneous devices with different standardizations and protocols, which is very challenging due to the absence of unified standards and IoT professionals; therefore, the complexity of IoT technology solutions is important for the MS to determine adoption decisions [16,25,31].

In IT adoption studies, the relative advantage was represented as the perceived technological benefits, which refers to the degree to which the technology is perceived as beneficial for the organization [84,85]. The relative advantage was excluded from this study due to the use of perceived benefits in the proposed model that have the same sense [86]. Furthermore, trialability and observability were also excluded from the model considering the nature of the technology studied. Given the complex systems and limitations in simulating realistic production environments, IoT technology remains new and in the early stages of acceptance and experimentation in the MS; therefore, it is difficult for MS decision-makers to observe the results of IoT technology [8].

Nevertheless, DOI theory is mainly focused on technology characteristics, and it could be a shortage when it comes to other contexts [7]; thus, the TOE framework was integrated with the DOI theory to discover the other related contexts of technology adoption.

4.3. The TOE Framework

The TOE framework considers technological, organizational, and environmental contexts that affect new technology adoption in organizations [87]. The technological context indicates the technical aspects of new technology that have an effect on the adoption of the innovation [87]. Contrastingly, the organizational context refers to the accessibility of the organizational characteristics and resources required to enhance technology adoption in organizations [87]. As for the environmental context, it represents the external factors surrounding the organization that could affect adoption decisions, such as government regulations and external pressure [87].

Contrary to the DOI theory, the TOE framework examines the internal-organizational environment and the external-environmental contexts, as they can affect technology adoption in organizations. The TOE framework demonstrated a high explanatory power of variance in adopting IT innovations [8]. Furthermore, the TOE framework has proved its ability to explain adoption behavior for innovations related to business administration and innovations integrated into core business procedures [82]. The TOE framework was used in this study to investigate IoT challenges in the MS in more detail in the three TOE contexts.

In the technological challenges, two constructs were considered to have an impact on the adoption decision: security and privacy concerns and implementation cost. IoT technology relies on data exchange via the Internet and the network, which may enhance the security and privacy risks and reduce the utility of IoT technology [11,16,18,27,31,34]. Besides the high initial investment costs, other costs related to implementation are considered a huge barrier to adoption [9,27,28,29,31,32].

In terms of the organizational challenges, three constructs were identified: employees’ resistance, organizational readiness, and top management support. Employees’ attitude is critical for technology adoption success. Employees often tend to refuse the new technology, fearing losing their jobs which the technology may replace [31,38]. Similarly, the lack of organizational readiness, such as financial, human, and technical resources, and unwillingness to take on the risk associated with IoT implementation, are considered disincentives to IoT adoption [16,26,27]. Top management support plays an important role in IoT adoption; therefore, the lack of technological awareness among top management and their perception of the IoT as not having strategic priority challenge the adoption of the IoT [18,42,53].

Regarding the environmental challenges, two constructs were considered as challenging IoT adoption: vendor issues and government regulation. The IoT is a new technology and needs continuous technical support from vendors; however, IoT vendors and suppliers lack good-quality technical support and training, which renders IoT solutions ineffective [27,31]. In contrast, the other challenge is the limitation of government regulation and policies that support the use of IoT technology, which raises concerns about data protection; thus, the lack of sufficient government regulations can discourage organizations from adopting IoT technology [16,25,26,34].

4.4. The Multi-Theoretical Perspective

In order to understand emerging technologies adoption, researchers have advocated integrating more than one theoretical perspective [88]. The multi-theoretical perspective overcomes the inherent limitations in each theory and enhances the understanding of technology adoption with high explanatory power [7]. Moreover, it can help to understand better the organizational decision-making about accepting the new technology, where variables are customized based on the technology under consideration through a comprehensive understanding of the research [88]. However, IoT adoption from a multi-theoretical perspective is still relatively rare [89].

Previous studies that examine IoT adoption in the MS at the organizational level concentrated on the TOE framework [12,16,18,27], or the integration between TOE and DOI [7,8]. The literature found that integrating the TOE framework and the DOI theory explained the technology adoption more effectively [88]. However, this study suggested integrating the VAM model with TOE and DOI, as it offers beneficial characteristics that are important in assessing technology adoption, such as perceived value. Furthermore, VAM can aid organizations in identifying the areas of their business that can benefit from new technology and any potential barriers to understanding the adoption process better [88]. The previous studies showed that the VAM had a high explanatory power when combined with other models to examine the newly emerging technology, such as the information systems success model [90], and the expectation–confirmation model [91].

Thus, the conceptual model was developed to explain IoT adoption in the MS by integrating VAM, DOI, and TOE framework to identify the antecedents that affect IoT adoption. This multi-theoretical perspective will provide an exhaustive understanding of IoT adoption by assessing different aspects of technology adoption, as shown in Figure 2.

Table 1. Mapping matrix of VAM, DOI, and TOE constructs used in this study.

Ref	Technology/Area	Theory	Constructs
			Perceived Value	Perceived Direct Benefits	Perceived Indirect Benefits	Security and Privacy	Compatibility	Complexity	Implementation Cost	Employees Resistance	Organizational Readiness	Top Management Support	Vendor Issues	Government Regulation
[7]	IoT adoption in manufacturing	DOI + TOE					X	X				X		X
[21]	Big Data analytics adoption in manufacturing	TOE	X				X	X	X			X
[25]	IoT adoption in the agricultural supply chain	TOE					X	X	X	X		X		X
[27]	IoT adoption in auto-component manufacturing SMEs	TOE				X	X		X		X	X	X
[31]	IoT adoption in agribusiness	TOE + HOT-fit				X		X	X	X		X	X
[66]	Tablet PCs adoption in firms	VAM	X			X	X		X
[75]	Enterprise 2.0 adoption in business	VAM	X			X
[84]	EDI adoption and impact on small organizations	TOE		X	X						X
[85]	EDI adoption in small businesses	TOE		X	X				X					X
[93]	Blockchain adoption in supply chains of SMEs	TAM + DOI + TOE				X	X	X	X			X	X	X
[94]	Cloud computing adoption in industries	DOI + TOE					X	X			X	X		X
[95]	Water supply systems adoption in the urban area	DOI					X	X
[96]	AI applications adoption in learning institutions	DOI					X	X
[97]	Cloud computing adoption in the higher education sector	DOI + TOE					X				X	X		X
[98]	Fintech adoption in small businesses	TOE		X	X				X					X

Legend: Ref = reference; SMEs = small and medium enterprises; HOT-fit = human-organization-technology; PC = personal computer; EDI = electronic data interchange; and AI = artificial intelligence.

illustrates the mapping matrix of constructs from VAM, DOI, and TOE based on the previous literature.

5. The Conceptual Model for IoT Adoption in the MS

The conceptual model was constructed based on the integration of VAM, DOI, and TOE framework. The model contains three main antecedents (perceived value, perceived benefits, and perceived challenges) and 11 related variables that could affect IoT adoption in the MS. The following sections present the propositions developed in this study.

5.1. Perceived Benefits

Perceived benefits are defined as the expected advantages of IoT technology for the MS [75,99,100]. Many researchers described the potential IoT benefits by analyzing the increased data created by sensors and process integration. Perceived benefits are a critical factor and might be a robust predictor of IoT adoption [12,16,25,26,30,33,51]. The IoT confers significant direct and indirect benefits to the MS [23,44,84,85,100,101]. Direct benefits are immediately perceived and frequently related to operational benefits [23,54], such as improving asset management and operational efficiency. While indirect benefits are related to organizational strategy development [23,54], such as sustainability and competitive advantage, and can be observed over time [84,101]. Studies in technology adoption showed that perceived benefits and perceived value were positively related [78,99]; thus, both direct and indirect IoT benefits [23,44,84,85,100,101] positively influence the perceived value. Based on this assumption, the following hypotheses (H) are formulated:

Hypothesis 1 (H1). 

Perceived direct benefits can have a positive impact on the IoT’s perceived value.

Hypothesis 2 (H2). 

Perceived indirect benefits can have a positive impact on the IoT’s perceived value.

5.2. Perceived Challenges

Perceived challenges are defined as the negative effects that disrupt or prevent successful IoT implementation [45,59]. These challenges add to the minimal IoT adoption rate and impede optimal technology value attainability [102]. Examining IoT challenges in the MS is essential as a part of the contributing factors affecting IoT adoption. Perceived challenges are frequently associated with a negative influence on technology adoption. Nevertheless, perceived challenges were included in the model as an important factor in explaining organizational adoption behavior. IoT adoption in organizations includes several challenges, which were categorized into three dimensions: technological [43], organizational [43,59], and environmental [59] in this study.

5.2.1. Technological Challenges

Security and Privacy

Security and privacy are the most significant challenges to IoT implementation [11,16,18,27,31,34,45,103]. IoT implementation is based on a multi-layered architecture that spans from the base data perception layer to the top application layer [44]. The IoT combines a vast network that contains many devices, sectors, and parties; thus, all data created from these IoT devices and network layers must be protected against leakage, illegal usage, or privacy violation, which could hinder data reliability [19,26,49]. Furthermore, IoT systems are highly dynamic [104], and there is no standard IoT security protocol to date [17], which leads to the emergence of periodic new cyber-attacks [6,38,53]. Implementing the IoT in organizations may enhance these risks through improper access by hackers; thus, these possibilities may reduce the value of the IoT in the MS, which may be identified as a significant adoption challenge [66]. Studies in IT found that security and privacy concerns and perceived value were negatively associated [66]. Accordingly, the following hypothesis is suggested:

Hypothesis 3 (H3). 

Security and privacy concerns can have a negative impact on the IoT’s perceived value.

Compatibility

Compatibility is defined as the new technology’s ability to work with or integrate into existing systems and practices [18,83]; thus, the IoT necessitates appropriateness with existing MS systems and other technological resources [16,92]. In implementing the IoT, there are millions of interconnected devices that need to integrate with MS legacy systems [25,26,27]. Nonetheless, manufacturing legacy systems were developed as data silos with limited external connectivity and difficult to develop and update. Legacy systems are considered to be a strong inhibitor to new technology adoption in the MS [37,92]. Furthermore, the MS encompasses different operating systems, limited infrastructure, and determining business practices, which lead to challenges when current information systems are used with new IoT devices and systems [8,31,43]. Previous studies found the compatibility construct as an important determinant in value perception and adoption decisions in organizations [21,66]. The IoT compatibility challenges might require additional effort, increased facility downtime, and IT infrastructure expansion [17,105], which may cause low-value perception [21,66]; therefore, the fourth hypothesis is:

Hypothesis 4 (H4). 

Compatibility challenges can have a negative impact on the IoT’s perceived value.

Complexity

Complexity is defined as the degree to which the technology is perceived as relatively difficult to utilize and understand [83]. According to Rogers [83], the more difficult technology is to use, the less likely it is to be adopted. The IoT is a complex technology that integrates various heterogeneous devices in a vast network [56,106]. The MS is still hesitant to adopt the IoT due to its complexity of use, lack of internationally acceptable standardizations and protocols, and heterogeneity of connected things [9,38,102]. The absence of standard protocols renders inter-IoT device communication challenging and inadequate access to real-time data, thus decreasing the chances of its utilization [16,25,31]. The complexity was found to have a negative association with organizational IT adoption [21]. In the same vein, Verma and Bhattacharyya [21] reported that complexity has a negative effect on perceived value in IT adoption in organizations; therefore, the H5 projected the following prediction:

Hypothesis 5 (H5). 

Complexity challenges can have a negative impact on the IoT’s perceived value.

Implementation Cost

Implementation cost is a crucial aspect that needs to be incurred in adopting new technology [84]. IoT technology requires large initial investment costs and indirect costs related to application development and maintenance costs, which are considered comparatively high [11,17,18,36,56,64,107]. The implementation cost is one of the most important factors that affect IoT adoption [3,16,25,29,30,33]. Implementation cost exerts a substantial negative effect on intentions to adopt the IoT, which indicates that a higher price is a barrier to adoption [9,27,28,29,31,32]. Accordingly, if the IoT implementation cost is unacceptable, the MS will then assess the IoT with low value [66]. In organizational IT adoption, the implementation cost was found to have a negative effect on perceived value [21,66]; therefore, the following hypothesis is offered:

Hypothesis 6 (H6). 

High implementation cost can have a negative impact on the IoT’s perceived value.

5.2.2. Organizational Challenges

Resistance from Employees

Employees’ resistance can be defined as employees’ behavior that tends to resist or disrupt organizational changes [108]. A significant reason for technology adoption failure in the MS is the attitude of the employees, who were inclined to refuse the technology as they feared job loss [31,38]. The IoT is expected to change the content of many jobs, where some traditional jobs will become obsolete while other jobs will require a new generation of digitally literate employees [3,4,17]. The employees commonly tend to possess low technical abilities and IT knowledge, which leads to challenges in using the IoT and its subsequent rejection [8,12,18,107]. Simultaneously, employees feel comfortable and familiar with existing technologies [105]. The employees’ resistance construct has been used as a crucial factor underlying the failure of organizational IT adoption [108,109]. Thus, employees’ resistance negatively affects technology efficiency [109] and its perceived value; therefore, the following hypothesis is proposed:

Hypothesis 7 (H7). 

Employees’ resistance can have a negative impact on the IoT’s perceived value.

Organizational Readiness

Organizational readiness indicates abundant organizational resources needed to ensure technology adoption success [16,26,27]. Resources represent the raw materials an organization can use to achieve its goals, including capabilities and tangible and intangible assets. The adoption of new technology in organizations is impacted by financial, human, and technical resources availability [92], such as necessary infrastructure availability, expertise, developing new business models [29,54,59,104], internal organizational improvement, enabling suppliers, and engaging customers [17,31]. Regardless, many organizations are unprepared for such radical changes and the expenses needed to update the resources [11,18,43,56,57]. The organizational readiness construct has been widely examined in technology and information systems studies and has strong empirical support as a significant predictor of technology adoption [110]. Thus, organizational readiness prepares a necessary condition for the successful adoption of the IoT and obtaining high value from the IoT. In contrast, the less the organization’s readiness to adopt the IoT, the lower its perceived value; therefore, the eighth hypothesis predicts:

Hypothesis 8 (H8). 

Lack of organizational readiness can have a negative impact on the IoT’s perceived value.

Top Management Support

Top management should support the development of plans and a clear vision about the importance of innovation and potential change [21]. The manner in which top management responds to new technology adoption and attitudes toward change is critical [12,16,107]. Top management support was identified as a significant factor in IoT adoption [7,25,26,27,31,34,103]. One challenge against IoT adoption is decision-makers’ lack of technology awareness [18,106]. Most of the organizational decision-makers stick to their standard routines and procedures, and they face difficulties in developing new strategies and plans to adapt to modern technology [53,92]. Singh et al. [42] reported that decision-makers lacked strategic plans to adopt IoT technology. The lack of top management support would render it challenging for organizations to overcome IoT adoption issues, which would negatively affect the perceived value [17,28,106]. It has been found that top management support directly impacts perceived value in organizational IT adoption [21]; therefore, the ninth hypothesis is:

Hypothesis 9 (H9). 

Lack of top management support can have a negative impact on the IoT’s perceived value.

5.2.3. Environmental Challenges

Vendor Issues

Technology vendor support is critical in IT adoption [27,31]. Organizations are still hesitant to adopt the new technology due to the prevalent vendor lock-in issues and associated challenges [111]. Vendor lock-in issues can appear in designing systems that are inconsistent with other vendors’ systems or using different standards for their devices that are difficult to integrate with other devices [18,111]; however, implementing IoT solutions in the MS requires the adoption of many smart devices and systems assembled into a single network [105]. A single vendor cannot fulfill all the organization’s requirements; therefore, IoT solutions must be acquired from various vendors [55,105]. This situation creates an additional challenge in devices integration and identifying suitable solutions [45], given the differing platform architectures [55] and the competitive relationship and market monopoly between technology suppliers and IoT vendors [58]. Vendor issues were identified as one of the most prevalent problems related to IT adoption [111], which would affect the technology’s perceived value [56]; thus, the following hypothesis is suggested:

Hypothesis 10 (H10). 

Vendor issues can have a negative impact on the IoT’s perceived value.

Government Regulation

Government regulation refers to the policies that support IT adoption and strategies that accommodate and protect data ownership and usage [37,53,59]. The lack of government regulations, policies, and directions is a major challenge in the adoption of the IoT [16,25,26,34]. Governments need to promote IoT deployment by designing effective policies and a regulatory environment while removing barriers [59]; nonetheless, these policies remain in the development phase and are highly limited [18,112]. The poor or absent coordination of IoT regulations and policies challenges and impedes IoT implementation and generates political risks [9,106]; thus, the MS is vulnerable to regulatory policies and legal challenges, such as cybersecurity, interoperability, and data ownership. The construct of government regulation has been used extensively in IT studies [110,113]. Prior research found a significant relationship between government regulation and IT adoption [110,113]. The lack of suitable legal and regulatory frameworks for IoT utilization leads to a negative effect on the adoption decision, hence, the expectation of a low value for IoT technology. Considering this negative perception, the following hypothesis is formulated:

Hypothesis 11 (H11). 

Lack of government regulation can have a negative impact on the IoT’s perceived value.

5.3. Perceived Value

The opportunities for the MS to leverage IoT technology became clearer with the significant potential presented by the information provided by connected sensors [6]. The IoT provides optimal solutions that not only save money and time but also resolve inefficient organization resource allocation, improve performance with less effort, and enhance sustainability and overall competitiveness [1,11]; thus, the IoT would help the organization to achieve significant value on the functional, financial, environmental, and customer levels [103]. Verma and Bhattacharyya [21] reported that the perceived value of the technology exerted the highest influence on IT adoption intention in organizations. A higher-value perception would encourage more positive adoption decisions in organizations, which were reported in IT adoption studies [65,66,75,114]. According to Lin et al. [75], perceived value has a direct positive influence on organizational adoption intention; thus, the following hypothesis is formulated:

Hypothesis 12 (H12). 

Perceived value can have a positive impact on IoT adoption intention.

Figure 3 present the conceptual model, in which perceived value is influenced by perceived benefits and perceived challenges, which exerts an effect on IoT adoption.

6. Discussion

IT adoption and diffusion have been extensively studied and continue to be a productive area of organizational-level investigation [21]. As the IoT remains an emerging technology, IoT adoption studies in organizations are largely in the preliminary stage. Remarkably few studies examined IoT adoption models in the MS and did not extend beyond the DOI and TOE framework to identify the determinant factors of IoT adoption [7,8,12,16,27,32]. However, IoT adoption factors cannot be captured holistically by TOE and DOI, and some may be overlooked; thus, there are still factors that might influence IoT adoption in organizations has not been investigated yet. Therefore, this study reviewed the literature and existing theories to propose a comprehensive model that can investigate the crucial factors that affect the adoption of the IoT.

This paper contributes to the existing IoT adoption literature from various points. Firstly, this study constructs a holistic conceptual model of the factors that could affect IoT adoption in the MS at the organizational level and explores the relationships between these factors. The conceptual model was developed from a multi-theoretical perspective that integrated VAM, DOI, and TOE framework with the constructs captured in 12 research propositions. Unlike previous models, the proposed model provides a theoretical insight into the effect of perceived value on IoT organizational adoption and contributes significantly to existing organizational theories. Examining perceived value’s influence on IoT adoption in the MS contributes to the academic literature and presents new possibilities for organizational success; furthermore, the developed model contributes to examining the effects of the direct and indirect perceived benefits of IoT adoption and investigates the perceived challenges that can affect IoT adoption in different contexts. The researchers could be used the model to study IoT adoption in other sectors or other technologies adoption in the MS.

Secondly, this study highlights the alternative way to expand IoT literature by conceptual study. Conceptual studies can open new horizons that help to understand the IoT adoption concept in organizations and suggest practical solutions, which could be a starting point for empirical studies.

Thirdly, decision-makers can use the proposed model as a strategy to promote wide-scale deployment of the IoT in organizations. The understanding of the factors that affect IoT adoption in the MS will aid informed decision-making about future implementation of the IoT, such as assessing IoT adoption benefits and challenges, and estimating the perceived value of IoT adoption. Finally, the model is considered beneficial to develop Industry 4.0 and promote smart manufacturing and a globally competitive market.

7. Conclusions

The IoT represents a new paradigm shift in industrial value creation that addresses emerging challenges facing the MS [12]. The unique IoT features enable the MS to overcome business and competition challenges by providing accurate real-time data. Despite these significant features, the derived value from IoT adoption in the MS remains unclear and requires further research. This study used the conceptual method to study the impact of perceived value on organizational IoT adoption and proposed a novel conceptual model to study the IoT’s perceived value in the MS. The conceptual model helps the decision-makers realize the delivered value from IoT adoption, which can provide significant value on a functional, financial, environmental, and customer level. The model provides a theoretical basis by integrating three theories, VAM, DOI, and TOE, to assess the antecedents that affect IoT adoption in the MS. The model included three main antecedents: perceived value, perceived benefits, perceived challenges, and many related variables that are gathered based on the literature review and IoT technology characteristics.

Considering Industry 4.0 initiatives, this is an original contribution exploring the influence of IoT adoption on value creation in the MS. The conclusions potentially significantly contribute to the existing literature and provide decision-makers with valuable insight and useful ideas to make IoT initiatives more effective in organizations; nevertheless, the main limitation was that the proposed model was not empirically validated; therefore, the possibility and process for implementing the proposed model should be explored in further qualitative and quantitative studies about the model and its relationships. The findings from future studies could provide new insights into the function of perceived value in IoT adoption by organizations in general and the MS in particular. Furthermore, this study focused on the conceptual method to build the conceptual model and identify the related factors for organizational IoT adoption; however, future studies can use other methods, such as meta-analysis or bibliometric analysis, to provide different visual representations and conclusions about research trends and the field as a whole.