Analysing the Influence of Augmented Reality on Organization Performance via Supply and Logistics Value Chain Functions: A Hybrid ANN-PLS Model Assessment in the Gulf Cooperation Council Region

Abstract

Background: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited. This article aims to investigate the potential effects of AR technology on organizational performance through the mediation role of SCM and logistics value chain functions to address the existing knowledge gap. Methods: This research employed a cross-sectional design and an explanatory survey as a deductive approach for hypothesis development. The primary data collection method involved the self-administration of a questionnaire to furniture suppliers located in the Gulf Cooperation Council (GCC), including six countries. Of the 656 questionnaires submitted to suppliers, 483 were considered usable, yielding a response rate of 73.6%. The research utilized partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques to evaluate the gathered data. Results: The current paper’s statistical evidence demonstrates that AR implementation has a positive impact on the supply and logistics value chain activities and organizational performance of furniture suppliers in the GCC region. Moreover, it illustrates that the design and planning variable of supply chain value dominates as the primary predictor of organization performance. The results indicated that the ANN strategy provided a more comprehensive explanation of internally generated constructs compared to the PLS-SEM technique. Conclusions: This study demonstrates its usefulness by advising furniture industry decision-makers on what to avoid and what aspects to consider when creating plans and regulations. The report also suggests operations managers apply machine learning (ANN) for prediction and decision-making in supply and operations value chains. This essay looks at how the AR and resource-based supply value chain view may affect company performance across countries, firm sizes, and ages.

1. Introduction

Augmented reality (AR) is a revolutionary technology that can profoundly influence various business domains, including operations management, supply chain management (SCM), and marketing management [1,2]. Indeed, it ranks among the most rapidly advancing technologies and will integrate into our daily existence. This technology is blurring the distinctions between the digital and physical realms [3,4]. It superimposes multiple layers of digital information onto the surrounding environment, rendering it rich, significant, and interactive [5,6]. Consumers are enthusiastic in utilizing AR technology for shopping [2]. Retailers are employing AR technology to deliver an exceptional and engaging shopping experience for their clientele [4]. The Amazon AR application enables customers to perceive a virtual representation of several real-world products and assess their appearance inside their home environment. Ikea Place, an application by Ikea, enables users to visualize Ikea furniture in their homes [3,6].

AR’s ability to present data on three-dimensional screens allows for intricate and captivating data visualization techniques [4]. Utilizing virtualized platforms, individuals may see, evaluate, and collaborate on their data within their designated virtual environment [5]. AR can aid production personnel by delivering immediate information regarding equipment failures, maintenance concerns, and safety alerts. AR enables supply chain enterprises to improve digital experiences for both employees and customers [2,4,7]. These encompass improved repair and maintenance functionalities in manufacturing, storage, and logistics [2]. These immersive technologies will improve product visualization for consumers, as well as store layout and planning for businesses [8]. Businesses can utilize several technologies, like artificial intelligence (AI), virtual reality (VR), and AR, to acquire insights on their customers’ interests and activities. Companies can utilize this data for supply chain demand predictions, targeted marketing, and customized product recommendations [8,9]. As a result of the swift progression of information technology, AR has gained significant strategic relevance and has emerged as a crucial asset for numerous organizations [4,5,8,9].

For supply chain systems to stay competitive in a constantly shifting environment and volatile markets, they must include developing technology and transform into sustainable operations. Businesses suffer severe consequences if they are unable to adapt to this fast-paced environment and fierce competition [10]. In summary, digital technology has changed how businesses operate and transformed many industries. This new trend opens up new opportunities for businesses and has a big impact on SCM [2,11]. Companies must restructure and modify their supply chain strategy in order to fully realise their digitalisation potential. SCM is essential across all enterprises, applications, and sectors because it facilitates the procurement of accurate product and service data. This, consequently, enhances the delivery of superior services and communication protocols for users [12,13]. SCM efficiently manages an increased number of data points, therefore reducing the likelihood of undesirable problems and failures in service delivery to users. The implementation of digital techniques such as AI, AR, and VR enhances the ability to store, access, process, and manage data in SCM, leading to improved efficiency for applications and organizations to bolster the firm’s competitive advantage [1,4,7,14,15]. Numerous studies [5,9,16,17,18,19] indicate that the application of big data methodologies improves the ability to store, access, analyse, and manage data in supply SCM.

In SCM, the increase in energy costs and the continued use of outdated manufacturing methods have significantly raised operational costs. Consequently, corporations found themselves motivated to reduce production costs while upholding their quality standards within a specific range. AR is an emerging technology that provides cost-effective solutions to the rising operational expenses of enterprises. This technology assists various participants in supply chains, including “truck drivers, warehouse personnel, supervisors, and managers, by overlaying digital information onto the physical environment”. This computer-generated information aids players in monitoring the movement of items from one location to another throughout a supply chain. The use of AR in SCM is progressively transforming traditional, sluggish, and paper-dependent logistics and supply chain operations into a rapid and technology-oriented sector [5,15].

Prior studies investigated the application of AR in SCM and logistics, concentrating on assessing the efficacy of AR solutions in particular and constrained process stages via a systematic literature review [5,15,20]. Still, there is not a full study of how it can be used and how it affects different supply chain management tasks, based on real-world data from the manufacturing industry around the world, especially in the Gulf Cooperation Council (GCC) region. Despite the rapid advancement and increasing popularity of AR across various human activities [6,21,22], a thorough literature review did not exist. We executed a comprehensive initiative in the GCC region to address this research deficit and ascertain new research domains and applications. We conducted a systematic literature review and survey-based research that encompassed not only definite procedure phases in SCM and logistics, such as order picking, but also all SCM functions and processes, including “warehousing, manufacturing, outdoor logistics, planning and design, and personnel training”. The aim of this study was to evaluate the maturity level of AR technology utilized by suppliers in the furniture sector. Furthermore, it sought to assess the impact of this technology on suppliers’ organizational performance, which is mediated by the prospective effects of SCM functions.

The main purpose of this preliminary section is to provide the reader with an overview of the research that is presented through the body of this work. It commences with a brief discussion of the background and the significance of the study, followed by an exploration of the literature review, research methodology, data analysis, and a discussion of the findings.

2. Literature Review

AR in SCM

Digitization refers to the conversion of operations, functions, models, processes, or activities through the utilization of digital technologies. Digitization facilitates new business models and serves as a significant catalyst for innovation, possessing the capacity to instigate the forthcoming wave of innovation [10,23]. The era of digital transformation has introduced numerous novel technologies that significantly affect supply chains [5]. People call technology that merges the real and virtual worlds ‘AR’ [24]. It describes a real-world environment augmented by visualization and specialized hardware and software [25]. AR is an emerging technology that offers cost-effective solutions to enterprises’ rising operational expenses. This technology assists various participants in supply chains, including truck drivers, warehouse personnel, supervisors, and managers, by overlaying digital information onto the physical environment [15]. In fact, these computer-generated data aid players in monitoring the movement of commodities along a supply chain. AR applications in enterprises are progressively revolutionizing conventional, sluggish, and paper-dependent logistics and supply chain operations into a rapid and technology-oriented sector. Pick-and-pack services, collaborative logistics, maintenance services, procurement, and last-mile delivery are sectors presently employing AR technology [26,27,28].

Supply chain systems must integrate emerging technology and evolve into sustainable operations to remain competitive in a continuously changing environment and turbulent markets. The inability to adjust to this rapid climate and intense competition leads to dire repercussions for businesses. In essence, digital technology has revolutionized numerous industries and altered company operations. This emerging tendency generates new chances for enterprises, significantly affecting supply chain management. Businesses cannot completely achieve their digitization potential without reorganizing and adapting their supply chain strategy. This reimagined supply chain needs to be more interconnected, scalable, intelligent, and expedited compared to conventional supply chains [15,26]. Over 1 billion AR-enabled smartphones and tablets are in use, according to Koul [28]. Thus, supply and logistics organizations can benefit from AR technology without waiting for low-cost glasses [4]. AR technologies utilized in logistics and SCM vary. These include marker-based, marker-less, projection-based, and overlaid AR.

AR can be utilized in contemporary SCM, which contains numerous layers of sourcing, planning, multi-modality, and information exchanges at every step. The emergence of new business models involves mass personalization. Production and operational processes must evolve to enable specialization and customization. This evolution will involve the dynamic reconfiguration and adjustment of machine and replenishment stock locations and production line architecture to provide a flexible workplace and ecology [15]. AR can improve supply chain and logistics efficiency [29], adding value and improving user experiences. AR-based industrial systems should enable storage, maintenance, and assembly. This paper systematically reviews AR technologies’ potential to digitize supply chains and enable smart industries [30].

A previous study by Stoltz et al. [27] indicated that the rapid rise in e-commerce transactions, the need to reduce inventory, and increased customer responsiveness have made storage strategy increasingly important for businesses. For instance, a company’s warehouse operation strategy is vital to supply chain performance. Computer technologies and ICT have solved warehouse management problems for decades. New technology like AR will change warehouse management. AR can be used in warehouse operations for receiving, storage, order picking, and shipping. Koul [28] found that sensors in packing materials allow companies to assess their products’ market success. Based on sensor data in packaging materials, a manufacturer can change commodity production in real time, according to the author. Data scientists and logistics specialists in the supply chain network can gain new information from things anywhere. If sales match estimates, producers can decide whether to reduce production and focus distribution on high-demand locations to reduce losses.

Attaran [5] reviewed the literature to determine how digital technologies might enable, improve, and streamline digital supply chain (DSC) performance. That study found that AR and VR can present data in 3D, enabling detailed and compelling data visualization. Virtualized platforms let users see, analyse, and collaborate on their data. Manufacturing workers can use AR to obtain real-time machinery malfunction, maintenance, and safety warnings. Supply chain companies may improve staff and consumer digital experiences with AR and VR. These include manufacturing, storage, and logistics repair and maintenance improvements. Immersive technologies will improve customer product visualization and retailer store layout and planning. Saunders [8] further supported this finding by demonstrating how AR/VR can enhance supply chains. Managers can monitor manufacturing in real time. They can constantly check facilities, distribution centres, and warehouses to maintain process efficiency; speed up and reduce errors in order picking; and provide the finest transportation routes to optimize delivery efficiency and on-time delivery.

Accordingly, the subsequent statement concisely outlines the principal contributions of the present work. First, this study examined the maturity level of AR technology used by furniture industry suppliers. Employing a creative and comprehensive study methodology will improve our understanding of the elements affecting AR tool application. We developed a theoretical model utilizing the technology readiness level model (TRLM) to underscore the significance and predictability of the results. This study employed all SCM functions as mediators to investigate the impact of AR adoption on organizational performance. To our knowledge, no current research has examined how various aspects facilitate AR deployment and how this implementation may affect SCM functions’ ability to achieve long-lasting performance in the furniture sector. As a result, there is a compelling need to increase research on AR deployment and its impact on organizational performance. Second, based on the primary goal of the current work, this subject holds considerable importance for both furniture suppliers and customers. Within the furniture industry, both vendors and clients are increasingly motivated to enhance their benefits and embrace a wider range of recommendations. Both suppliers and customers recognize the impact of AR’s acceptability and adoptability on their technology usage, which extends beyond their decision-making processes. Third, this study uniquely used the TRLM to examine the importance of AR in the furniture field, particularly in SCM. This study differentiates itself from prior research by Rejeb et al. [2], Attaran [5], Cao et al. [20], and Demir et al. [15], which focused on availability and mobility, by assessing its influence on suppliers’ organization performance through the TRL model. This study fundamentally examined AR’s effectiveness from a strictly supplier-centric perspective. The National Aeronautics and Space Administration (NASA) has developed a well-established model that this work applied, setting it apart from previous studies and encouraging the adoption of such technologies. In fact, this is the first study that applied such a model in the SCM context. Finally, this study utilized an innovative and versatile method of data analysis by employing a machine learning (ML) analytical tool. Almarzouqi et al. [31] widely acknowledge ML as the most efficient instrument for predicting different dependent variables in various business contexts.

3. Theoretical Framework Development

3.1. AR Applications in SCM

This study employed a conceptual framework to systematically categorize the potential of AR applications in SCM functions and then moved on to examine its effect on organization performance. AR technology’s adaptability transcends a singular organizational role, facilitating many activities across the value chain. Consequently, we ground our approach in Porter’s [32] value chain model. A value chain model serves as a fundamental tool, breaking down a business into strategically relevant and value-enhancing activities. “It links an organization’s supply side (which includes raw materials, inbound logistics, and production processes) with its demand side (outbound logistics, marketing, and sales), which includes supporting activities (firm infrastructure, human resource management, technology development, and procurement)” [2]. We modified the value chain model in our research to show the progression of activities from planning and design to sales and external logistics. In essence, several scholars, such as Rejeb et al. [2], adopted the value chain model in the current AR-SCM literature and classified AR applications in SCM as warehousing, manufacturing, sales and outdoor logistics, planning and design, and human resource management.

Advanced production computer and communication tools are transforming organizations. These developments greatly affect product design, factory automation, cost and quality control, planning, processing, workforce skills, and output management [11,33,34]. According to Petruse et al. [35], AR-aided manufacturing involves more workers in production. AR can aid in product conceptualization and design by enabling staff to view computer-generated content in a factory setting; help companies move to sustainable manufacturing; improve product development efficiency; reduce faults, waste, and resource consumption; and speed up manufacturing and delivery. In addition, several authors stressed the importance of AR-assisted assembly instruction and supervision [36]. Real-time AR with tracking technologies and a display in the operator’s field of view improves assembly design and planning, simulates product assembly and disassembly before manufacturing, and provides virtual instructions for monitoring and guidance [37]. AR can help maintenance personnel by enabling visual interactions and superimposing virtual production equipment instructions. AR technology can increase remote maintenance in severe environments and workflow efficiency compared to paper instructions [2,38,39].

The widespread existence of multi-product and multi-brand enterprises and manufacturing fragmentation drive product visualization and identification. Visualizing or digitizing a product, also called a digital twin, can reduce production errors and non-conformance costs [28,34,40]. AR solutions replicate reality and enable data-driven visualization. AR settings can also give operators 3D images of the target object (e.g., a warehouse shelving system) and relate it to reality, such as a warehouse facility. An AR solution can reduce errors and damage and allow visual management and monitoring of warehouse products moving to the assembly bay [11,28]. AR apps also provide mobility, position, pervasiveness, and context awareness data to help logistics track smart things. For example, an AR-based smart palletization solution improves warehouse visibility and navigation. The solution reduces palletization errors, increases productivity, and improves product identification and visibility [34,41]. In SCM, order picking is one of the most labour-intensive warehouse tasks because of the time required to handle each item and travel between sites. According to Chen et al. [42], order picking systems generally suffer from crowded temporary storage, long wait times, inefficient operations, and poor communication. AR enhances order picking efficiency by lowering item retrieval times and increasing output. Compared to pick-by-voice, AR’s navigation capability reduces product location search times and mispacks [2].

Outside logistics includes all operations needed to deliver products to customers in the outdoors. Transportation relies on digital data and planning tools. AR can improve logistics, package identification, truck sequencing, loading, and routing, according to Ginters and Gutierrez [40]. Indeed, sensing devices and AR systems work together to improve outdoor logistics processes, ensuring security, product identification, and item presentation. These capabilities can boost international trade because merchandise shipments must meet industry standards and government laws and trade operations’ paperwork can be more reliable. In addition, AR technology enables creative business models like value co-creation with customers, helping businesses stand out, customize their offerings, and gain a competitive edge. Ro et al. [43] showed that AR-enabled wearable gadgets boost brand value by facilitating consumer engagement. Therefore, AR technologies improve product presentation and enable unique and realistic situations to boost marketing and sales.

Facility layout is crucial to long-term operational efficiency. A practical layout can help smart production and digital manufacturing succeed [44], increasing flexibility, efficiency, and optimization. AR technologies are transforming companies into flexible, reconfigurable manufacturing systems [44,45] using AR technology’s immersive nature. Using AR methods, AR helps companies swiftly examine their storage space. This lets them overlay the layout plan and superimpose virtual elements on the real-world layout facilities for an optimal arrangement. A library of robots, machines, and racks can be used to blend their functionality and location with real equipment and things. This method optimizes layout design and reduces costs from inefficient routings, machine installations, and manufacturing and storage space usage. Thus, AR technology can simplify layout planning and design by delivering a clear, precise image. This helps make decisions and trade-offs when multiple designs conflict [44,45]. Additionally, AR tools help clients visualize product design and adjustments before buying. Thus, AR can improve users’ product design comprehension and engage them in a new value co-creation paradigm. Thus, AR tools attempt to involve clients in mass customization and integrate industrial design. They also help design and create items [2,44]. Rohacz and Strassburger [24] demonstrated a Daimler AG application that enables final assembly logistics planning using AR. Mobile intralogistics planning helps systems using AR technology like smartphones and tablets that are convenient and effective.

AR tools also help organizations consider modern technological innovations to maintain workforce training and expand employee knowledge, skills, and competency to contribute to value-generating activities. Recent research by Gangabissoon et al. [46] emphasized using AR to improve participation in training. They emphasized the use of technology in learning and development, indicating a growing trend of integrating creative methods into training approaches. AR’s unique qualities can improve training, according to Gangabissoon et al. [46]. AR allows students to interact with real items under virtual instructions in mobile just-in-time learning. Using an AR system for training in real time can save time and money, improve training processes, and provide interactive feedback [47,48]. Businesses can streamline training with AR-based solutions, providing real-time, location-independent capabilities to inexperienced personnel during seminars and workshops [47]. To accelerate learning, add AR to supply chain and logistics training. Research by Hořejší [49] suggested that AR architecture can accelerate learning for organizations with significant worker turnover, resulting in faster learning for more employees.

The link between AR and SCM functions has been extensively studied in various organizations and sectors. The benefits of AR implementation will drive suppliers to maintain their technology, impacting their operations. This suggests the following hypothesis.

H1: 

Implementing AR in SCM has a positive impact on supply value chain activities.

3.2. SCM and Organization Performance

Organizational performance refers to a company’s capacity to achieve its financial and strategic goals in relation to its competitors [50]. It refers to the manner in which a firm evolves in terms of the quality and quantity of its organizational activities, as well as how these advancements have favourable direct or indirect financial impacts on the firm. The literature presents numerous metrics to evaluate corporate success. Researchers have analysed it from multiple perspectives. Financial performance is a common term for evaluating corporate performance and is assessed by a variety of indicators, including return on investment and return on assets. Furthermore, scholars have examined non-financial success through many variables, including customer satisfaction, employee engagement, and social responsibility [44,50,51]. The alternative viewpoint, termed the resource-based view, asserts that a company’s resources and capabilities influence its performance. Resources and capabilities encompass technology, corporate repute, and highly skilled personnel [52]. The final method is dynamic capabilities, which denote a firm’s capacity to adjust to its business environment in order to attain greater performance [53]. Cao and Zhang [54] proposed assessing business performance by sales growth, profit margin on sales, return on investment, operating revenue, and market share.

Previous research examined a positive correlation between SCM functions and organizational achievement [16,55,56,57,58]. People recognize supply chain digital capabilities for their significant impact on enhancing corporate performance. By investing in and developing supply chain analytics skills, a company acquires the capacity to derive important insights from the extensive data produced throughout the supply chain. Currently, nearly every sector generates substantial amounts of data on a continuous basis. Companies can pinpoint their bottlenecks and modify their plans in response to market fluctuations using innovative technologies. This will lead to increased customer satisfaction and improved profitability. The adoption of digital supply chains entails more than just new technology. It goes further. Several scholars advise the corporation to link its digital initiatives with its supply chain goals. Every company recognizes the potential of the most recent digital supply chain technology. It can boost company performance and set the stage for outperforming competitors [56]. To achieve exceptional organizational performance, the corporation must implement digital supply chain techniques. Heizer et al. [50] and Alicke and Forsting [59] found that many organizations want to improve their supply chains but are only using a few digital technologies. Most enterprises believe that deploying digital SCM could increase earnings before interest and taxes and raise their yearly revenue. Thus, the subsequent hypotheses were proposed.

H2: 

Digitalized value chain activities in SCM have a positive impact on organization performance.

H3: 

Digitalized value chain activities in SCM significantly moderate the relationship between AR and organization performance.

3.3. Model Formulation

Figure 1 illustrates the development of a research theoretical framework. Based on the above three hypotheses, a theoretical model of supply chain value functions that mediates the relationship between AR and organizational performance was developed and is displayed in Figure 1. The links among AR, supply chain value functions, and organizational performance are incorporated in one single model. In these three hypotheses, AR is an independent variable and supply chain value functions are a mediating variable, while organizational performance is a dependent variable. In the three hypotheses, the relationships among the independent variable (AR), the mediating variable (supply chain value functions), and the dependent variable (organizational performance) are examined.

4. Research Methodology

4.1. Data Collection

The need for furniture in the GCC is increasing significantly. Mordor Intelligence [60] predicts that the GCC furniture industry will grow by 7.54% from 2022 to 2029, reaching USD 20.88 billion. GCC countries have historically led in adopting new technology, especially in the furniture industry. Furniture products are in demand due to rising purchasing power and lifestyle trends. As more people use technologies like AI, AR, VR, data analysis, the internet of things (IoT), and more, the furniture industry is expanding. For example, furniture can be virtually designed using 3D modelling before it is physically built. This method enables the designers to preview the finished product in advance. When designing furniture, designers can test, experiment, and modify the design to meet the unique needs of the customers using a virtual prototype. It is possible to experience the texture, colour, location, load-bearing capacity, and aesthetics in advance. Overall, the GCC has 536 furniture and supply manufacturers (FSMs) [60,61].

The study employed a cross-sectional design with an explanatory survey to deductively formulate hypotheses, enhancing data variability and generalizability [62]. We employed quantitative research to evaluate models and hypotheses for conclusions and generalizations [63]. The primary data-gathering method for GCC FSMs was self-administered Google Form questionnaires. This research included 355 of the 536 registered FSMs, yielding a response rate of 66%, adequate for any SEM and ANN-ML investigation. In this investigation, we found that Hair et al. [64] employed a probability sampling technique utilizing a basic random systematic approach. The particular policies of the selected FSMs affected the choice of sampling methodology, as simple random sampling is an effective approach for time, cost, and resources for acquiring large samples [63]. The survey was disseminated to all SCM managers, directors, and senior management professionals who were formally engaged in SCM operations and possessed a comprehensive awareness of the financial and marketing dimensions of each participating organization. Consequently, we employed “the company” as the analytical unit. These experts represent a substantial repository of expertise on AR-SCM processes inside any supplier organization.

4.2. Questionnaire Design

The principal data-gathering tool utilized in this investigation was a structured questionnaire. This study’s use of the NASA-developed TRL model underscores the necessity of AR in the furniture industry, particularly in SCM. NASA developed the TRL scale, a recognized metric that delineates the degree of technical progress [65,66]. This study employed the NASA TRL scale to integrate TRL criteria for AR systems intended for practical application. Additionally, the NASA TRL offers a spreadsheet application that allows the user to answer a series of questions pertaining to the AR technology program. The spreadsheet computes and displays the attained TRL. The calculator calculates the weighted geometric mean for all question categories possessing both a non-zero category TRL and a non-zero category weight as follows:

$${TRL}_{wgtd}=\sum _{i=0}^1\sqrt[w_i]{{\mathsf{\Pi }}_{L_i}^{w_i}}$$

where TRL_wgtd is the weighted geometric mean; w_i is the assigned category weights; with n being the number of categories included in the calculation; and L_i is the category TRLs.

The existing TRL scale comprises three primary components, namely, research, simulation, and operational implementation, which collectively encompass nine levels. Each level represents the degree of advancement in the development of a technology, starting from the first concept (level 1) and extending to its complete implementation in the market (level 9) [67]. We assessed the aspects described above using a 5-point Likert-type scale, where 1 signifies “strongly disagree” and 5 indicates “strongly agree”. Practically speaking, this work introduces a new technique based on examples to classify and evaluate AR technology by mapping them onto the TRL, which indicates their level of maturity and availability.

Finally, research conducted by Blankemeyer et al. [7], Berkemeier et al. [14], Hammerschmid [39], Ustundag et al. [47], Schweigkofler et al. [68], Elbert and Sarnow [69], Hannola et al. [70], and Kokkas and Vosniakos [71] laid the foundation for the SCM and logistics value chain measure, which comprises 27 elements for six main factors: warehousing, manufacturing, outdoor logistics, sales, design and planning, and human resource activities. Akgün et al. [72] and Ellinger et al. [73] derived the organization performance (OP) scale, which consists of seven items. We assessed each of the SCM and logistics value chain and OP scores using a 5-point Likert-type scale, where 1 signifies “strongly disagree” and 5 indicates “strongly agree”.

5. Data Analysis

5.1. FSMs’ Demographic Information

The data shown in

Table 1. FSMs’ characteristics information.

Criterion	Factor	Frequency	Percentage (%)
Country	Saudi Arabia	135	38.0
	UAE	178	50.0
	Kuwait	13	3.7
	Oman	9	2.5
	Qatar	16	4.5
	Bahrain	4	1.3
Company size	Small	72	20.3
	Medium	269	75.8
	Large	14	3.9
Respondent’s position	Logistics Manager	66	18.6
	Procurement Manager	43	12.0
	SCM Manager	178	50.0
	MRP/ERP Manager	9	2.5
	CEO	4	1.3
	Other	55	15.6
AR system utilization	Less Than 1 year	155	43.7
	1 To 5 Years	185	52.0
	More Than 5 Years	15	4.3
Total		483	100%

indicate that the majority of the companies included in this study were situated in the United Arab Emirates (50.0%). Moreover, Table 1 shows that the FSMs exhibited a very uniform distribution of sizes and enjoyed a rather extensive use of AR systems. Approximately 75.8% of them are classified as medium in size; out of them, 65% have been utilizing an AR system for a duration exceeding 1 year. Regarding the respondents’ job types, Table 1 shows that 50.0% of them are employed as SCM managers.

5.2. Measurement Model Analysis

When examining the measurement model, it was crucial to determine the construct validity, including convergent validity, and the construct reliability, inclusive of Cronbach’s alpha. The following table (

Table 2. Internal consistency, FA, CR, and AVR tests.

Factor	Item	FA	CR	AVE	Cronbach’s Alpha
AR-SCM	AR 1	0.734	0.871	0.764	0.878
	AR 2	0.687
	AR 3	0.602
	AR4	0.718
	AR5	0.611
	AR6	0.509
	AR7	0.692
	AR8	0.640
	AR 9	0.799
Warehousing	WR1	0.738	0.798	0.732	0.801
	WR2	0.682
	WR3	0.705
	WR4	0.599
	WR5	0.831
Manufacturing	MA1	0.764	0.846	0.722	0.811
	MA2	0.719
	MA3	0.626
	MA4	0.683
	MA5	0.729
	MA6	0.724
Logistics	LOG1	0.637	0.728	0.751	0.716
	LOG2	0.633
	LOG3	0.715
Sales	SL1	0.599	0.701	0.772	0.724
	SL2	0.602
	SL3	0.728
Design and Planning	DP1	0.816	0.774	0.794	0.786
	DP2	0.727
	DP3	0.826
	DP4	0.773
	DP5	0.783
Human Resource	HR1	0.557	0.785	0.755	0.805
	HR2	0.637
	HR3	0.828
	HR4	0.789
	HR5	0.783
Organizational Performance	OP1	0.759	0.806	0.788	0.844
	OP2	0.738
	OP3	0.824
	OP4	0.790
	OP5	0.834
	OP6	0.748
	OP7	0.689

) displays Cronbach’s alpha values ranging from 0.716 to 0.878, which, according to Nunnally and Bernstein [74], appear to exceed the established threshold of 0.7 for determining construct dependability. However, the results in the table confirmed the construct’s robustness, establishing it as a construct devoid of errors. Based on the table, it is evident that the composite reality (CR) value exceeded the recommended threshold of 0.7 as proposed by Kline [75], as it fell within the range of 0.701 to 0.871. An essential aspect of assessing convergent validity is determining the average variance extracted (AVE) and component loading. The table shows that the factor loading values were higher than the usual 0.5 level and the AVR was between 0.722 and 0.788, which was also above the usual 0.7 level. This confirmed the construct validity and generally met the requirements for convergent validity.

5.3. PLS-SEM Model Fit Indicators

We assessed the PLS-SEM goodness-of-fit for the study (

Table 3. Model fit indicators.

Complete Model
Indicator	Saturated Model	Estimated Model
SRMR	0.033	0.034
d_ULS	0.783	1.424
d_G	0.579	0.582
Chi-Square	441.337	446.793
NFI	0.848	0.852
RMS	0.068

). For the table below, the saturated model evaluated each construct’s association with the others. The model structure was taken into consideration in the calculated model, which was based on a total impact scheme. The study’s RMS theta value of 0.068 confirmed the validity of the PLS model. According to Hair et al. [64], the PLS-SEM model had a satisfactory fit when the RMS theta value fell between 0.00 and 0.12.

5.4. Structural Model and Hypothesis Testing

5.4.1. Hypotheses Testing Using PLS-SEM

This study offered an auxiliary way for hypothesis testing. Specifically, we used ANN techniques and PLS-SEM. This paper presents a methodology to assess the impact of AR-SCM on operational supply value chain (SVC), which in turn will affect OP. Based on well-known theory, Calcagno et al. [76] wrote that the PLS-SEM model should be used to test conceptual models and make predictions about dependent variables. The ANN algorithm efficiently identifies a dependent variable based on independent factors.

The route analysis, shown in

Table 4. Structural standardized path coefficients.

Model Relations	Std. Path Coefficient	t-Value	p-Value	Hs. Decision
AR SVC	0.654	7.313 **	0.002	Accepted (+)
SVC OP	0.472	4.262 **	0.006	Accepted (+)

** p < 0.01. Note: + (positive).

, utilizing p-values, t-values, and path coefficients, empirically corroborated the study’s hypotheses. Chin [77] categorized route coefficient values over 0.67 as strong, those ranging from 0.33 to 0.67 as moderate, and those between 0.19 and 0.33 as weak, while deeming any value below 0.19 as unsuitable for further investigation. According to the table results, AR in SCM strongly influenced SVC, which in turn positively affected OP. Consequently, we endorsed H1 and H2. Additionally,

Table 5. Mediation analysis.

Mediation Relations	Test Statistic	St. Error	p-Value	Hs. Decision
AR > SVC > OP	3.280 **	0.094	0.001	Accepted (+)

** p < 0.01.

corroborates the mediating function of the SVC between AR and OP. Consequently, we endorsed H3. Moreover,

Table 6. R square of the endogenous latent variables.

Model Constructs	R Square	Predictive Power
AR on SVC	0.765	High
SVC on OP	0.734	High
AR and SVC on OP	0.682	High

below illustrates that the employed model possessed significantly high predictive capability for the variation in forecasting AR in SCM.

5.4.2. Artificial Neural Network (ANN) Model Analysis

This study used the ANN model because it estimates accurately, making it preferable to PLS-SEM and regression modelling. ANN tools excel at modelling complicated connections with variable non-linear response values, leading to better predictions than traditional methods [78]. According to Lie’bana-Cabanillas et al. [79], ANN modelling finds latent non-linear practical ties in statistics and lets the modelling tool utilize the correlations on a new set of data. The ANN analysis was carried out operating SPSS. In the ANN analysis, only the relevant predictors derived from the PLS-SEM results were utilized. The ANN analysis considered the latent variables, as depicted in Figure 2 (network diagrams 1–3). Diagram 1 comprises one input neurone (AR) and one output neurone (SVC). Diagram 2 comprises one input neurone (SVC) and one output neurone (OP). Diagram 3 comprises two input neurones (AR and SVC “6 constructs”) and one output neurone (OP). The results indicated that the predictive capability of the ANN analysis (R² = 83%) significantly surpassed that of PLS-SEM (R² = 68.2%). The results indicated that the ANN technique more effectively elucidated endogenous constructs compared to the PLS-SEM approach.

To evaluate normalized significance, the average of each predictor was correlated with the highest mean value, represented as a fraction.

Table 7. Independent variable importance of OP.

Construct	Importance	Normalized Importance
AR-SCM	0.079	17.6%
Warehousing	0.009	2.0%
Manufacturing	0.391	87.3%
Outdoor Logistics	0.043	9.6%
Sales	0.016	3.6%
Design and Planning	0.448	100.0%
Human Resource	0.014	3.2%

and Figure 3 display the average relevance and normalized significance of each predictor in the ANN models. Table 7’s “sensitivity analysis” demonstrates that design and planning serve as the most significant predictors of OP.

6. Discussion

This study investigated the effects of AR on organizational performance in SCM furniture organizations as well as the role of supply and logistics value chain as a mediator. We used two well-known analysis models, PLS-SEM and ANN, as theoretical lenses. We found that the AR implementation process significantly improved SVC and OP among furniture suppliers in the GCC countries.

In fact, the study found that SVC was significantly affected by the adoption and the implementation of AR. Our findings are consistent with previous studies that showed that the integration of virtual elements into a real-world environment can help to enhance supply and logistics value chain activities [1,2,28,35,38,39].

Indeed, AR has several supply chain applications. AR enables novel value-added solutions for SCM and logistical processes. AR technologies can improve value chain efficiency and help firms overcome issues including poor planning and scheduling, process integration, and resource waste. The potentials we expect are that AR will enhance operations and reduce losses. The possibilities are based on primary and support tasks within the value chain, which include planning and designing the firm’s infrastructure and supporting human resource management, inbound logistics, operations, marketing, and sales. These functions are projected to improve significantly with AR deployment, necessitating additional training. This study shows that AR’s greater visualization, navigation, and immersive engagement can bridge the virtual and real worlds, simplify processes, and aid decision-making. AR pick-by-vision guiding often improves task performance and reduces workload. The organization may improve operating efficiency, cut expenses, and adapt to changes via AR-enabled visualization. AR helps logistics workers control industrial processes and gain situational awareness. This is possible with real-time data generation throughout the supply chain. We create a logical framework for AR implementation in a firm, bringing significant commercial value.

In detail, the results of the study specifically supported the assertion that organizations are changing as a result of sophisticated production computers and communication technologies. In essence, AR can aid in product conceptualization and design by enabling staff to view computer-generated content in a factory setting; help companies move to sustainable manufacturing; improve product development efficiency; reduce faults, waste, and resource consumption; and speed up manufacturing and delivery. In addition, several authors stressed the importance of AR-assisted assembly instruction and supervision [36]. Real-time AR with tracking technologies and a display in the operator’s field of view improves assembly design and planning, simulates product assembly and disassembly before manufacturing, and provides virtual instructions for monitoring and guidance [37]. AR can help maintenance personnel by enabling visual interactions and superimposing virtual production equipment instructions. AR technology can increase remote maintenance in severe environments and workflow efficiency compared to paper instructions [1,38,39]. Furthermore, the study results proved that AR solutions replicate reality and enable data-driven visualization. AR settings can also give operators 3D images of the target object (e.g., a warehouse shelving system) and relate it to reality, such as a warehouse facility. An AR solution can reduce errors and damage and allow visual the management and monitoring of warehouse products moving to the assembly bay [11,28]. AR apps also provide mobility, position, pervasiveness, and context awareness data to help logistics track smart things. For example, an AR-based smart palletization solution improves warehouse visibility and navigation. The solution reduces palletization errors, increases productivity, and improves product identification and visibility [34,41].

The study’s results further indicated that AR systems work together to improve outdoor logistics processes, ensuring security, product identification, and item presentation. These capabilities can boost international trade because merchandise shipments must meet industry standards and government laws and trade operations’ paperwork can be more reliable. In addition, AR technology enables creative business models like value co-creation with customers, helping businesses stand out, customize their offerings, and gain a competitive edge. However, these results were consistent with Ro et al. [43]. They showed that AR-enabled wearable gadgets boost brand value by facilitating consumer engagement. Therefore, AR technologies improve product presentation and enable unique and realistic situations to boost marketing and sales. In addition, the study’s findings confirmed that AR helps companies swiftly examine their storage space. This lets them overlay the layout plan and superimpose virtual elements on the real-world layout facilities for an optimal arrangement. A library of robots, machines, and racks can be used to blend their functionality and location with real equipment and things. This method optimizes layout design and reduces costs from inefficient routings, machine installations, and manufacturing and storage space usage. Thus, AR technology can simplify layout planning and design by delivering a clear, precise image. This helps make decisions and trade-offs when multiple designs conflict [44,45]. These findings are consistent with recent research. Rohacz and Strassburger [24] provide an example of a Daimler AG application that uses augmented reality to facilitate logistics planning for final assembly. AR-enabled mobile intralogistics planning apps for tablets and smartphones are practical and efficient. AR tools are also beneficial.

This study, moreover, indicated that organizations have had to consider modern technological innovations to maintain workforce training and expand employee knowledge, skills, and competency to contribute to value-generating activities. This argument mirrors recent research by Gangabissoon et al. [46], which emphasized using AR to improve participation in training. Furthermore, Al Harthy [80] emphasised the use of technology in learning and development, indicating a growing trend of integrating creative methods into training approaches. AR’s unique qualities can improve training, according to Gangabissoon et al. [46]. AR allows employees to interact with real items under virtual instructions in mobile just-in-time learning. Using an AR system for training in real time can save time and money, improve training processes, and provide interactive feedback [47,48]. Therefore, businesses can streamline training with AR-based solutions, providing real-time, location-independent capabilities to inexperienced personnel during seminars and workshops [47]. To accelerate learning, add AR to supply chain and logistics training. Research by Hořejší [49] suggested that AR architecture can accelerate learning for organizations with significant worker turnover, resulting in faster learning for more employees.

This study also showed that AI-SVC integration improves OP. Previous research found a positive association between SCM functions and organizational success. People recognize the potential of supply chain digital capabilities to improve corporate performance. By investing in and developing supply chain analytics expertise, a company can gain valuable insights from significant supply chain data. Nearly every industry continuously creates large amounts of data. Innovative technology helps companies identify obstacles and adapt to market changes. This improves consumer satisfaction and revenue. Adopting digital supply chains requires more than new technology. It goes further. Several scholars recommend that the company align its digital and supply chain goals. All companies value innovative digital supply chain technology. Improved company performance can pave the way for outperforming the competition [56]. A company needs digital supply chain techniques to excel. Heizer et al. [50] and Alicke and Forsting [59] revealed that many companies aspire to strengthen their supply networks but use little digital technology. Most companies believe digital SCM can boost earnings before interest, taxes, and annual sales.

6.1. Theoretical and Practical Contributions

This work provides empirical proof to both academic and management knowledge in many ways. This study systematically assessed the impact of AR installation on operational performance for GCC furniture suppliers, rendering it distinctive. This research enhances theories on AR implementation within the GCC FSM provider industry. Consequently, the study broadens the scope of AR-SVC in SCM research. This research analysed AI-SVC dimensions and operational performance in the furniture sector in a developing economy, drawing upon prior discoveries.

This study is the first to examine AR elements and organizational performance measures in emerging nations’ furniture systems. Several manufacturing industries, particularly in underdeveloped nations, have focused more on AR than furniture. Using the established model, this study validates the AR model in FSMs. In addition to the commonly held idea that AR-SVC can improve corporate performance, this study provides a crucial fresh perspective on SVC activities. As a result, future researchers will have a more complete and broad understanding of these factors, which may help them develop more effective and empirically validated AR-SVC models. SVC moderates the association between corporate performance and AR implementation, as this study shows. The literature under-represents this topic. This study is the first to examine mediation in the relationship between OP and SVC activities at GCC furniture suppliers. Additionally, the study’s paradigm provides a foundation for future research in this sector. We could replicate this study in the fields of healthcare, finance, education, and hospitality.

To improve AR-SCM research methods, this study evaluated parameter data fluctuations using PLS-ANN. An ANN analytical instrument, an innovative and comprehensive data analysis tool, is best for furniture technology adoption forecasts. SCM leaders and managers should prioritize an AR-SVC work environment that builds strong relationships with key clients to optimize furniture industry supply and on-time delivery. AR can increase value chain efficiency and help organizations overcome poor planning, scheduling, and process integration and resource waste. The potential AR should improve operations and cut losses. The possibilities depend on the primary and support functions of the value chain, such as infrastructure planning and design, human resource management, inbound logistics, operations, marketing, and sales. Collaboration amongst FSMs should create structures and procedures. Finally, this study enhances furniture suppliers’ managers’ awareness of AR-SVC, which may help them comprehend its benefits and optimum implementation strategies. This study’s model prepares GCC suppliers for AR. The developed model could highlight AR-SVC aspects that enhance OP.

6.2. Limtations and Future Resaerch

The fact that the study could only analyse the association in a cross-sectional timeframe is one of its limitations. Identification of the evolving business environment is necessary. In order to determine whether or not the association between the variables taken into consideration in the current study has altered, future research must use longitudinal design flow. Apart from temporal and financial constraints, the data used in this study were exclusively collected from the furniture industry, which reflects a remarkable service culture. Furthermore, the proposed model in this study is quite straightforward because it looks at how AR completely affects OP through the SVC functions. Thus, more advanced supplier orientation models should be developed in future research on this subject. Understanding how each SVC function affects OP, for example, will be fascinating. In addition, the survey questionnaire was the sole method used to collect the necessary data from the employees for this study. For supply managers and customers to have a comprehensive understanding of the AR applications in SCM, it is advised that various data-gathering methods or data-triangulation techniques, such as observations and interviews, be used.

7. Conclusions

This work employed the PLS-ANN hybrid model to illustrate the influence of AR in SCM on OP via SVC functions. The proposed model exhibited strong internal consistency, reliability, and predictive validity. The utilization of AR in business has risen owing to its capacity to discern the elements affecting the efficacy of AR-SVC in SCM firms, therefore bolstering the communication and resource-based perspective. This study effectively extracted AR results, highlighting the significance of supplier data assimilation for enhanced performance and competitive advantage. This study aimed to furnish senior management with insights for securing a competitive advantage via effective organizational performance.