1 Introduction

In the current technological landscape, remote collaboration has become indispensable across various sectors (e.g., industrial, medical, and educational domains, among others). The ability to collaborate effectively with team members who are geographically dispersed is crucial for harnessing diverse expertise to tackle increasingly diverse and complex problems [2, 38, 9]. Collaboration implies joint/interdependent activities between team-members to achieve shared interests [43, 15, 10, 24]. In order for remote collaborators to establish a common ground during shared tasks, it is necessary that they align and integrate their activities in a seamless manner. This implies the mastery of multiple domains of knowledge, and also a strong level of proficiency in each in order for context-related information to be exchanged, providing support to communication, discussion, learning, and knowledge sharing [44, 14]. In this regard, solutions that may improve how the team can perceive and manage a common context are the focus of strong research and notably include eXtended Reality (XR).

eXtended reality for remote scenarios

To address the fact that team members do not share a common space, in remote collaborative scenarios, there has been an increasing interest in using XR technologies, i.e., combining real and virtual environments. This umbrella term for a variety of distinct concepts include representative forms such as Virtual, Augmented and Mixed Reality technologies (VR/AR/MR) and the areas interpolated among them [42, 5, 13]. To elaborate, scenarios of remote collaboration supported by XR can combine the advantages of virtual environments and the possibility of seamless interaction with real-world objects and other collaborators [3, 6]. Given XR ability to enhance awareness, and situation understanding, it is possible to create a shared space for situation mapping and analysis of issues. In turn, this will generate knowledge by sharing real-time spatial information for addressing the tasks at hand [11, 20, 34].

Following up on this trending interest, the field has primarily been focusing on advancing the supporting hardware, with most of the research efforts devoted to experimenting with the technology and proposing methods to address challenges in a wide range of multidisciplinary scenarios. This has been leading to the design and development of novel prototypes [7, 25] exploring approaches based on live video streams, standard or 360\(^{\underline{\textrm{o}}}\) images, virtual replicas/digital twins or 3D reconstructions of physical spaces, and researching a plethora of interaction methods covering, e.g., speech, pointers, controllers, hand or head gestures, gaze, and others [1, 17, 12]. However, while much has been explored regarding interaction, other dimensions of these systems, also central to the collaborative effort, have received less systematic attention. That is the case for how content is created and visualized across the collaboration process.

Challenge and opportunity

In XR remote collaboration, the few evaluation studies that have been performed place a strong emphasis on designing evaluations to support the comparison among distinct interaction possibilities [47, 48, 26, 26] with content creation methods for remote collaborators e.g., creation of augmented annotations facilitated by distinct devices and display apparatus for on-site team members (e.g., visualization of augmented annotations through different devices) kept as single approaches and a fixed part of the setup throughout the evaluation.

Moreover, using a single scenario remains the predominant task considered for such studies. This means that most prototypes are only validated for a specific purpose. By doing so, the research community is not able to understand how they would perform when facing tasks with different characteristics. This also emphasizes that few studies have explored comparison among different creation and display methods according to the collaborator role. Possibly, because most authors are focused on validating their features for a single device. Thus, they are not interested in having multi-platform solutions that explore various methods at the same time.

Also important, the impact of display type on user performance has deserved attention for several application contexts. The literature shows [4] that characteristics such as display fidelity may impact overall performance for goals such as visual search, data visualization, and spatial judgment, with other characteristics such as head tracking, field of view [40], monocular/binocular views [16], or display curvature [37] potentially having an impact depending on the nature of the task [19]. And the characteristics of the display type often mediate the possibilities left open for interaction which can also impact performance, particularly during content creation. In this regard, as the field of remote collaborative XR matures and with the growing number of prototypes, the path to achieve impactful solutions must entail an explicit understanding regarding: which method leads to better results according to different physical tasks scenarios and collaborator role (e.g., on-site or remote)? Overall, gaining insight on how different display types mediate user performance demands explicit action towards a more systematic study of its impact and can foster an increased understanding to inform aspects such as worker safety and health [16].

Contribution

Following on the identified gap, in this paper, we sought to investigate the efficacy of eXtended Reality (XR) technologies in facilitating seamless collaboration between distributed team members. By evaluating the impact of different content creation and display methods on user performance across various tasks, this work aims to identify optimal approaches for improving remote collaborative practices. To this end, two user studies with different samples of 30 participants each, considering different physical tasks are described: Lego pieces assembly; Tangram puzzle assembly; Maintenance procedures; Resources management in a map; Training on a laboratory classroom. The goal was to understand which method stands out according to the characteristics of the tasks. The first study focuses on different content creation methods for remote collaborators: Laptop Computer; Video Wall & Keyboard; Interactive Projector, while the second study centers on the comparison of distinct display methods for on-site collaborators: Hand-Held Device (HHD); HHD & Articulated Support; Head-Mounted Display (HMD). Then, the results of both studies are discussed to emphasize which method has been perceived better suited to support each task characteristics.

Paper structure

The remnant of this paper is structured as follows. Section 2 describes a user study focusing on content creation methods for remote collaborators during distinct task scenarios. The results obtained are described through a data analysis and critically discussed in light of the dimensions of collaboration selected. Then, Section 3 describes the second study, focusing on display methods for on-site collaborators. Again, the results are presented and discussed accordingly. Afterward, Section 4 presents final remarks, connecting the results of each study in regard to the tasks considered. Last, conclusions and future directions are drawn in Section 5.

2 User study - remote content creation methods

A user study was conducted to investigate the influence of physical tasks with distinct characteristics, all requiring remote guidance, on the effectiveness and efficiency of different content creation methods. In particular, this study was done having the remote part of the collaborative process in mind. Later on, Section 3 will focus on display methods for on-site collaborators.

2.1 Experimental setup

As mentioned, three methods were considered for remote participants to establish a common ground and provide guidance during the selected tasks: C1 - Laptop Computer; C2 - Video Wall & Keyboard; and C3 - Interactive Projector (see Fig. 1). Although different conditions were considered, the same XR-tool based on 2D annotations was used for all of them. Remote collaborators may use audio communication and annotation features like drawing, placing pre-defined shapes or notes, as well as sorting annotations, to enhance shared images. This allows to express difficulties, highlight areas of interest, or specify questions (Fig. 2). This can be repeated iteratively until the task is successfully accomplished [22, 26, 28, 27, 32].

As for development, the Unity 3D game engine was used, based on C# scripts. Plus, a dedicated server responsible for all communication was considered, using WebRTC calls over Wi-Fi through a WRT 1200 AC Dual-Band Router - 5GHz. Regarding the hardware, the computer used was a MacBook Pro 13-inch, 2017 (2,3 GHz Dual-Core Intel Core i5, 8 GB 2133 MHz LPDDR3, Intel Iris Plus Graphics 640 1536 MB with a 1080p camera) running macOS Monterey 12.3.1. As for the video wall, an equipment composed by 9 screens (NEC UN552V, 55”) with the dimensions: 3,60x2,04m and 8K resolution was used, which was connected to the previous computer. The interactive projector was a Sony Xperia Interactive Projector running the Android operating system. This device is capable of transforming a surface into an interactive touch-screen through a combination of infrared sensors and a 60 frames per second camera.

Fig. 1
figure 1

Conditions considered for the remote part of the collaborative process. Example considering a maintenance guidance task on a boiler: 1 - Laptop Computer; 2- Video Wall & Keyboard; 3- Interactive Projector. At the bottom, is an illustration of the remote expert generating situated instructions that will be shared with the on-site counterpart to assist in fulfilling the intended tasks, in this case, how to properly remove the fan of the boiler

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Fig. 2
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Illustration of how the distinct conditions shared information through the XR-tool, in order to establish a common ground between the distributed team members and fulfill the intended tasks

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2.2 Experimental design

A randomized within-subjects experimental design was used. The null hypothesis (H0) was considered, i.e., all experimental conditions are equally usable and acceptable. The independent variable was the creation method used during the collaborative process for remote participants. Three levels were considered, corresponding to the experimental conditions: C1 - Laptop Computer; C2 - Video Wall & Keyboard; and C3 - Interactive Projector. The dependent variables were participants’ opinions regarding a set of dimensions of collaboration deemed relevant to assess the intended goals. As secondary variables, participants’ demographic data, previous experience with XR and with collaborative tools were considered.

2.3 Tasks

Regarding the tasks, five assignments were selected after analyzing the literature. Also, taking into consideration the author’s own experience running multiple user studies on collaborative scenarios over the years. The chosen tasks encompass a range of scenarios commonly encountered in collaborative settings. These scenarios include traditional toy problems, a staple in the validation of initial prototypes by many research groups. Additionally, educational and training activities were incorporated due to their alignment with collaborative scenarios, where one individual instructs another on task execution. Maintenance procedures were also included, reflecting the prominent role of industrial scenarios in remote tasks, characterized by their complexity and practical relevance. Moreover, the task selection extended to include scenarios akin to resource management activities, acknowledging the need for effective remote collaboration in such situations [44, 8, 18, 41, 35, 25, 33].

Overall, the tasks considered by the research team took into consideration aspects such as the learning curve (i.e., time and effort required to learn how to perform the task effectively), number of steps (i.e., extent to which the task depends on the completion of other sub-tasks), objects size (i.e., physical dimensions of objects involved in the task), interactions with collaborative counterparts (i.e., need for clear and effective communication between collaborators), cognitive load (i.e., mental effort required to understand and execute the task), and tools needed (i.e., physical instruments required to perform the task).

Fig. 3
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Illustration of the tasks considered: A - Lego pieces assembly; B - Tangram puzzle assembly; C - Maintenance procedure on a boiler; D - Resources management in a map; E - Training activity on a laboratory

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To further elaborate, the tasks considered included distinct scopes and levels of complexity (Fig. 3), namely: Task A - Lego pieces assembly, i.e., the participant needed to explain how to assemble a given structure (e.g., car, robot, house, etc.). This task tested the clarity and effectiveness of content creation methods in conveying detailed procedures; Task B - Tangram puzzle assembly, i.e., the participant needed to teach how to build a specific tangram puzzle (e.g., house, boat, rocket, etc.). This task evaluated the suitability of different creation methods for conveying visual instructions and facilitating spatial understanding; Task C - Maintenance procedure on a boiler, i.e., the participant needed to describe how to conduct a set of procedures on a specific equipment, i.e., illustrating how to remove the fan of a boiler. This task highlighted the clarity and precision of content creation methods in communicating complex technical information; Task D - Resources management in a map - the participant needed to simulate an emergency response procedure, explain where to place a group of vehicles and units in specific areas of interest, and how to connect them all through the map. This task emphasized the role of content creation methods in visualizing spatial relationships and coordinating resource allocation; Task E - Training activity in a laboratory, i.e., the participant needed to explain how to perform various procedures in a science lab to finish a kaolin grinding. This task highlighted the effectiveness of content creation methods in facilitating practical skill development and procedural understanding. For all tasks, participants needed to use the conditions at their disposal, allowing them to share their thoughts and ideas.

2.4 Measurements

All data was collected through the CAPTURE toolkit [21, 23]. This Unity Package was used due to its capacity to support contextual data collection and analysis for scenarios of remote collaboration. This way, it is possible to obtain an additional perspective on selected aspects of the collaboration. In this vein, the following dimensions were considered: D1 - Effectiveness in expressing ideas properly; D2 - Level of attentional allocation; D3 - Effectiveness in perceived information sharing; D4 - Level of mental effort. These were considered given their predominant role in existing research, as relevant dimensions to help illustrate how the collaborative process occurred, in particular, when analyzed as a whole [25, 31]. Participants’ preferences and opinions were also registered.

2.5 Procedure

Participants were introduced to the study goals, the tasks, as well as the experimental setup and design. After giving their informed consent, they were introduced to the conditions considered. Then, an adaptation period was provided to explore the condition features, with the goal of minimizing novelty and/or learning bias.

Participants acted as remote instructors using different conditions. To minimize bias, i.e., learning effects, different orders were considered, following a Latin Square design methodology (as illustrated in Table 1). The creation process of this approach began by establishing base sequences for both conditions and tasks. For conditions, the base sequence was C1, C2, and C3. Each subsequent row involved a cyclic permutation. For instance, the second row shifted the sequence to C2, C3, C1. This rotation continued until each condition appeared in each position an equal number of times across different rows. Similarly, the tasks were distributed in a cyclic manner. Starting with a base sequence of A, B, C, D, E. Each subsequent row shifted this sequence to ensure a balanced task presentation. This pattern continued, ensuring that each task appeared in each position equally over the different rows. By combining these cyclically permuted sequences of conditions and tasks, every participant encounters a unique sequence.

In addition, the on-site counterpart was ensured by a researcher, who followed the instructions provided by the participants. This approach is in line with the current state-of-the art for evaluating these systems (e.g., [39, 50, 45, 36, 25, 31]). It was also adopted to ease how the collaborative process occurs, given that having other participants acting as the on-site member could generate additional complexity, which was unnecessary for the intended goal of the study.

Participants started by filling out a demographic questionnaire. Next, the selected tasks were completed with all conditions, while observed by a researcher who assisted them if necessary and registered any relevant event. Afterward, participants answered a post-study questionnaire associated with the collaborative process and their preferences towards the conditions used. Last, a semi-structured interview was conducted (Fig. 4).

Table 1 Latin Square design methodology used to minimize learning effects, i.e., learning effects
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Fig. 4
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Schematic representation detailing the experimental procedure used in the study. The figure illustrates the sequence of steps involved. Each component is depicted to provide a clear overview of the experimental process and its various stages

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2.6 Participants

In total, 30 participants were recruited (11 female - 36.7%), whose ages ranged from 20 to 63 years old (M = 29.87, Q1=24, Q3=32). Participants had various professions, e.g., Faculty members, Researchers, as well as Master and PhD students from different fields. Also, Front-End Developers, Software Engineers, a Housewife, an Assembly Line Operator, and an Industry 4.0 Engineer. All participants had previous know-how of XR and tools for remote collaboration. This choice aimed to ensure that participants could focus on the goals of the tasks without being hindered by the learning curve associated with new technology. To elaborate, we were concerned that newcomers to XR might be influenced by the novelty factor, potentially affecting their performance and the reliability of the results. By selecting participants with prior experience, we aimed to mitigate this WOW effect and obtain more focused and meaningful data.

2.7 Data analysis methodology

The obtained results, for the user study, were analyzed using SPSS and Statistica S/W. Exploratory, descriptive, and inferential (non-parametric tests, due to the ordinal data of the assessed dimensions and lack of normality (as verified by Shapiro-Wilk tests), as well as the existence of outliers of time data) techniques were used. As referred in the experimental design section, a within-subjects experimental design was used. As a first step the null hypothesis (H0) tested with the Friedman test (ANOVA matched non-parametric). If H0 is rejected, then as a second step, multiple pairwise comparison tests, with Bonferroni corrections, were used to identify the pairs responsible for the rejection.

To elaborate, for the data related to the assessment of the conditions, through the four dimensions, as we used an ordinal scale (qualitative) and we had matched samples, the Friedman test (the ANOVA matched non-parametric test) was also used, since for ordinal data the median should be used as measure of central tendency, and the Friedman test compares, simultaneously, the equality of all medians of the matched samples. Moreover, as the Friedman test is an omnibus test, i.e., it rejects the null hypothesis if, at least, one median is different but does not pinpoint which (one or more) is/are different, we used post-hoc (multiple pairwise comparison) tests to identify the pairs responsible for the rejection. To counteract the increased risk of a type I error (rejecting the null hypothesis that is actually true) due to multiple pairs being tested, the Bonferroni correction to the p-values was used.

2.8 Results and discussion

Figures 5 and 6 display an overview of participants’ evaluation for all dimensions of collaboration considered for the various tasks, while using the three conditions, rated using a seven-point Likert-type scale (1- Low to 7- High). The decision of the Friedman test and the multiple pairwise comparison tests (with the respective p-values) are displayed in Table 2.

2.8.1 Task A - overview of the selected dimensions

For task A - Lego pieces assembly, conditions C1 - Laptop computer - and C2 - Video Wall & Keyboard - were considered equivalent for all dimensions, and both better than condition C3 - Interactive projector - for dimensions: level of attentional allocation (D2), level of effectiveness in perceived information understanding (D3) and level of mental effort.

2.8.2 Task B - overview of the selected dimensions

This task B - tangram puzzle assembly, conditions C1 - Laptop computer - and C2 - Video Wall & Keyboard had similar results; also, for dimensions effectiveness in expressing ideas properly (D1) and level of mental effort (D4), condition C1 matched C3 - Interactive projector. For this task, condition C2 was statistically better than C3, for all four dimensions, and C1 better than C3 for the level of attentional allocation (D2) and the level of effectiveness in perceived information understanding (D3).

2.8.3 Task C - overview of the selected dimensions

This task C - Maintenance procedure on a boiler, discriminated the conditions used more than the previous two. There is only equality between C1 - Laptop computer and C3 - interactive projector for the level of mental effort (D4). For the other 3 dimensions, C1 has been perceived better than C3; for all the dimensions C2 - Video Wall & Keyboard - has been perceived better than C3; as for the comparison between C1 and C2, C2 is also better than C1 for all four dimensions.

Fig. 5
figure 5

Collaborative process of tasks: A - Lego pieces assembly; B - Tangram puzzle assembly; C - Maintenance procedure on a boiler. Dimensions considered: D1 - Effectiveness in expressing ideas properly; D2 - Level of attentional allocation; D3 - Effectiveness in perceived information sharing; D4 - Level of mental effort. Conditions considered: C1 - Laptop Computer (in Grey); C2 - Video Wall & Keyboard (in Yellow); and C3 - Interactive Projector (in Blue). Data displayed using a Likert-type scale: 1 - Low; 7 - High

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Fig. 6
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Collaborative process of tasks: D - Resources management in a map; E - Training activity on a laboratory. Dimensions considered: D1 - Effectiveness in expressing ideas properly; D2 - Level of attentional allocation; D3 - Effectiveness in perceived information sharing; D4 - Level of mental effort. Conditions considered: C1 - Laptop Computer (in Grey); C2 - Video Wall & Keyboard (in Yellow); and C3 - Interactive Projector (in Blue). Data displayed using a Likert-type scale: 1 - Low; 7 - High

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2.8.4 Task D - overview of the selected dimensions

This task D - Resources management in a map, discriminated in a complementary way to task A and task B, the conditions used, namely conditions C1 and C2 (considered equivalent for all dimensions in those tasks and different (better C2) for all dimensions in this task D). As for tasks B and C, once again C2 has been perceived better than C3 for all the dimensions. For this task D, conditions C1 and C3 are equally assessed for all four dimensions.

Table 2 Summary table - remote study
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2.8.5 Task E - overview of the selected dimensions

Task E - Training activity on a laboratory, discriminated the conditions used in a similar manner to task C but allowed more equalities. For most of the tasks, C2 - Video Wall & Keyboard has been perceived better, for all dimensions, than C3 - Interactive Projector. Concerning C3 and C1 - Laptop computer - condition C1 has been perceived better than C3 (except for the D1 dimension, where they were considered equal; interestingly, in task C they were considered different for this dimension, but equal for dimension D4, which in this case considers C1 better than C3). Comparing C1 and C2, we can say that C2 has been perceived better than C1 for dimensions D2 and D3 and considered equal for dimensions D1 and D4.

2.8.6 Final remark

Table 3 illustrates a dominance matrix, in which Ci represents the lines and Cj the columns. Yellow represents Ci=Cj and green Ci>Cj. That being said, having a higher amount of green cells in the same line illustrates that a condition is preferred to the other alternatives, This representation helps to confirm the aforementioned results. For example, considering D2 and C2, it is possible to verify that it obtains eight green cells. In turn, C3 is never better than the other conditions, which is corroborated by the lack of green cells in the same line.

Table 3 Visual representation of the dominance matrix - remote study
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2.8.7 Participants preferences and opinion

Next, participants’ preferences towards the conditions used, not focusing on any of the tasks considered, but on the overall experience are presented, although all were able to fulfill the intended tasks. Results suggest that condition C2 - Video Wall was rated higher, followed by condition C1 - Laptop Computer, and last, condition C3 - Interactive Projector. This was confirmed by the Friedman test (p-value < 0-000), rejecting the equality of distributions/medians of the three assessed conditions, and the increasing preferences, with a pairwise comparison test, namely: C3 (median=5; sum=143) to C1 (median=6; sum=169) - p-value=0.014); C1 to C2 (median=7; sum=191) - p-value=0.043).

When categorizing the overall preference by gender, there was no significant difference between males and females when applying the non-parametric Mann-Whitney test (two independent samples and ordinal data). To elaborate, for the remote role, considering the overall preference, the obtained test results were, p-value=0.966, p-value=0.767, p-value=0.800, for the laptop computer, video-wall and keyboard, and interactive projector, respectively. Regarding technological expertise with XR, no analysis was conducted since all participants had previous experience using collaborative tools and XR applications [30].

The majority of participants (26 out of 30) emphasized that the use of condition C2 - Video Wall & keyboard provided a much larger viewing area, allowing one to view shared content with greater clarity. This increased visibility made it easier to collaborate and engage with the material being shared, leading to improved comprehension, in particular, when addressing more complex tasks, having more components, or areas of interest (e.g., maintenance procedure on a boiler, resources management in a map, training activity on a laboratory). Also relevant, this condition created a more immersive experience due to the viewing area, more easily capturing participant’s attention and minimizing distractions, causing a reduced level of mental effort overall.

As for condition C1 - Laptop Computer alternative, most participants (18 out of 30) stated that this device provides a more flexible and familiar workspace since they are used to using it on a daily basis. Besides, these devices can be carried out easily from one location to another, enabling remote collaborators to work from different environments (e.g., home, office, or while traveling). Thus, staying connected and collaborating with their team members regardless of their physical location.

When considering condition C3 - Interactive Projector, none of the participants had used such a device before. Some even comment that it looked like ’a large tablet placed on a table’. Even though almost all quickly learn to use it, it is clear from the results that it had a higher level of mental effort. Despite this, most emphasize that continuous finger contact with a surface can lead to fatigue over time, especially in prolonged collaboration sessions. Also relevant, the level of precision and accuracy depends on the surface used, as well as participants’ finger width, which can affect content creation and ultimately participants’ capacity to express themselves.

Fig. 7
figure 7

Conditions considered for the on-site part of the collaborative process: 1 - HHD; 2- HHD & Articulated Support; 3 - HMD. Examples of tasks include a Lego pieces assembly (top), training in a laboratory (middle), and a maintenance procedure on a boiler (bottom), where the interface of the XR-tool is also illustrated for all conditions, illustrating how a user would visualize superimposed instruction from the remote expert on top of the real-world environment

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When asked their preference for multi-user scenarios, i.e., having various co-located experts assisting an on-site member [29, 27], most (19 out of 30) participants selected condition C2 - Video Wall & Keyboard due to its capacity to facilitate brainstorming sessions, design/review sessions, and content authoring, without compromising visibility or interaction.

Last, some participants (12 out of 30) suggested the use of a Virtual Reality (VR) headset and controllers to be fully immersed during the collaborative experience, a concept that starts to emerge and can be quite relevant for 3D scenarios [46, 29].

3 User study - on-site display methods

A second user study was conducted to explore how the use of different display methods affected effectiveness and efficiency of receiving remote guidance, when faced with physical tasks with distinct characteristics. This time, it was intended to focus on the on-site part of the collaborative process.

3.1 Experimental setup

Three distinct methods were considered for on-site participants to establish a common ground, i.e., receive XR-based instructions and visualize them them to help perform the intended tasks: C1 - HHD; C2 - HHD & Articulated Support; and C3 - HMD (see Fig. 7). The selected display methods were chosen based on their potential to enhance spatial perception, interaction, and overall task performance, while also considering their ability to superimpose augmented content onto the real-world environment, which aligns with recent approaches reported in the literature, enabling on-site members to receive situated instructions. Moreover, the selection of display methods prioritized ease of deployment and flexibility while still ensuring effective support for remote collaboration tasks. To elaborate, the HHD condition represents one of the most commonly used approaches according to the literature, offering portability and ease of deployment in various scenarios. Its lightweight design allow users to carry and place it on any surface, making it suitable for on-the-go tasks and dynamic environments. Moreover, the HHD & Articulated Support provides a mobile and adjustable viewing experience, allowing users to interact with digital content without having to hold the device at all times, while also maintaining physical mobility. This flexibility could be advantageous for tasks that require dynamic movement or varied viewing angles. Likewise, this configuration enabled users to adjust the display position according to their preferences, potentially enhancing comfort and usability during prolonged task duration’s. Last, the HMD offers an immersive viewing experience, enveloping the user’s field of vision with digital content. This immersive nature was expected to enhance spatial perception and precision, particularly in tasks involving physical manipulation.

Although different conditions were considered, the same XR-tool based on 2D annotations was used for all of them, just like in Section 2. On-site collaborators could use audio communication, as well as receive enhanced images, i.e., XR-based annotations shared by the remote counterpart. This would help understand where to act, and what to do by augmenting the instructions on top of the real-world environment. This could be repeated iteratively until the task was accomplished [22, 26, 28]. Regarding the development, the same approach as the previous study was used. As for the hardware used, the HHD was a Lenovo Phab 2 Pro smartphone (Qual-comm Snapdragon 652 MSM8976, 4 GB RAM with a 16MP rear camera and 8MP front camera) running Android operating system. In respect to the HMD, a Microsoft HoloLens 2 was considered, which used multiple sensors, advanced optics, and holographic processing for seamlessly combining augmented data with the environment.

3.2 Experimental design

A within-group experimental design was once again used. The null hypothesis (H0) was considered, i.e., all experimental conditions are equally usable and suitable. The independent variable was the display method used during the collaborative process for on-site participants, with three levels corresponding to the experimental conditions: C1 - HHD; C2 - HHD & Articulated Support; and C3 - HMD. The dependent variables were participants’ opinions regarding a set of dimensions of collaboration deemed relevant to assess the intended goals. As secondary variables, participants’ demographic data, previous experience with XR and with collaborative tools were considered.

3.3 Tasks

As for the tasks, the same contexts used in Section 2 were considered (Fig. 3), although, this time considering conducting the tasks from the on-site participant’s point of view: Task A - Lego pieces assembly, i.e., the participant needed to conduct the assembly of the structure suggested by the remote member; Task B - Tangram puzzle assembly, i.e., the participant needed to build the tangram puzzle illustrated by the remote member; Task C - Maintenance procedure on a boiler, i.e., the participant needed to remove the fan from a boiler equipment following the instructions of the remote members; Task D - Resources management on a map - the participant needed to place mini figures illustrating first responders vehicles and units on a map, according to the remote member suggestions; Task E - Training activity on a laboratory, i.e., the participant needed to conduct various steps using distinct utensils to finish a kaolin grinding. For all tasks, participants needed to use the conditions at their disposal, allowing them to receive guidance from the remote experts. Regarding the responsibilities of the remote counterpart, detailed explanations can be found in Section 2.2, which focused particularly in creating augmented annotations to guide the on-site participants according to the context of each task, as well as provide any further assistance on how to accomplish the intended goals through the XR platform. As for the task complexity, the same aspects mentioned in Section 3.3 were considered by the research team.

3.4 Measurements

As in the study of Section 2, all data was collected through the CAPTURE toolkit [21, 23]. As before the following dimensions were considered: D1 - Level of attentional allocation; D2 - Effectiveness in perceived information sharing; D3 - Level of mental effort. Participants’ preferences and opinions were also registered.

Fig. 8
figure 8

Collaborative process of tasks: A - Lego pieces assembly; B - Tangram puzzle assembly; C - Maintenance procedure on a boiler. Dimensions considered: D1 - Level of attentional allocation; D2 - Effectiveness in perceived information sharing; D3 - Level of mental effort. Conditions considered: C1 - Hand-Held Device (in Grey); C2 - Hand-Held Device & Articulated Support (in Yellow); and C3 - Head-Mounted Display (in Blue). Data displayed using a Likert-type scale: 1 - Low; 7 - High

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Fig. 9
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Collaborative process of tasks: D - Resources management in a map; E - Training activity on a laboratory. Dimensions considered: D1 - Level of attentional allocation; D2 - Effectiveness in perceived information sharing; D3 - Level of mental effort. Conditions considered: C1 - Hand-Held Device (in Grey); C2 - Hand-Held Device & Articulated Support (in Yellow); and C3 - Head-Mounted Display (in Blue). Data displayed using a Likert-type scale: 1 - Low; 7 - High

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3.5 Procedure

A similar procedure to Section 2 was used. Participants were introduced to the goals of the study, the tasks, as well as the experimental setup and design. After giving their informed consent, they were introduced to the conditions considered, and an adaptation period was provided to explore the condition features, with the goal of minimizing novelty and/or learning bias. Participants acted as the on-site counterpart, using the instructions to complete the tasks with all conditions. To minimize bias, i.e., learning effects, different orders were considered, following a Latin Square design methodology similar to the one presented in the previous user study. In turn, the remote counterpart was ensured by a researcher who created instructions and provided guidance on what to do following a script, following a similar approach as the one described in the previous study. Participants started by filling a demographic questionnaire. Next, the selected tasks were completed with all conditions. fterward, participants answered a post-study questionnaire associated with the collaborative process, and their preferences towards the conditions used were registered. Last, a semi-structures Interview was conducted.

3.6 Participants

In total, 30 participants were recruited (14 female - 46.7%), whose ages ranged from 19 to 64 years old (M = 29.67, Q1 = 22; Q3 = 32). To clarify, none of the participants in the current study had taken part in the previous study, ensuring that their responses and behaviors were not influenced by prior exposure to the research procedures or objectives. Participants had various professions, e.g., Faculty members, Researchers, Master’s and PhD students from different fields. Also, Software Engineers, a Banker, a Tourist Manager, and a Manufacturing Worker. All participants had previous know-how of XR and remote tools. As mentioned in the previous section, this choice aimed to ensure that participants could focus on the goals of the tasks without being hindered by the learning curve associated with XR technology.

3.7 Results and discussion

Next, the results from the second user study are described and discussed, following a similar data analysis methodology as the one described in Section 2.7. Figures 8 and 9 display an overview of participants’ evaluation of all the dimensions of collaboration considered for the various tasks, while using the three conditions, rated using a 7 point Likert-type scale (1 - Low to 7 - High). The decision of the Friedman test and of the multiple pairwise comparison tests (with the respective p-values) are displayed in Table 4.

3.7.1 Task A - overview of the selected dimensions

For task A - Lego pieces assembly, condition C3 - HMD has been perceived better than C1 - HHD for all dimensions and better than C2 - HHD & articulated support in dimension D1 - level of attentional allocation. For the other two dimensions, C3 and C2 are considered equivalent. Condition C2 has been perceived better than C1, except for dimension D1, where they matched.

Table 4 Summary table - onsite study
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3.7.2 Task B - overview of the selected dimensions

In this task B - tangram puzzle assembly, condition C3 - HMD is again better than C1 - HHD for all dimensions and now also than C2 - HHD & articulated support for all dimensions. Conditions C1 and C2 are considered equivalent for dimensions D1 - level of attentional allocation and D2 - effectiveness in perceived information sharing and C2 has been perceived better than C1 concerning the level of mental effort.

3.7.3 Task C - overview of the selected dimensions

This task C - Maintenance procedure on a boiler, was the one that discriminated more the conditions, just one equality between C2 - HHD & articulated support and C3 - HMD for dimensions D3 - level of mental effort. For the other two dimensions, C3 has been perceived better than C2. Once again, C3 has been perceived as better than C1 - HHD for all dimensions, as well as C2, which is also better than C1 for all dimensions.

3.7.4 Task D - overview of the selected dimensions

In this task D - Resources management in a map, condition C2 - HHD & articulated support and C3 - HMD were considered equal for all the dimensions. Moreover, as in the previous tasks, C3 was better than C1 - HHD for all the dimensions. Also, as in task C, C2 has been perceived better than C1 for all the dimensions.

3.7.5 Task E - overview of the selected dimensions

This task E - Training activity on a laboratory, presents some complementary assessments related to task D - Resources management in a map. Now, the equality of conditions was between C1 and C3 (in task C was between C2 and C3). Moreover, in task C, condition C2 was better than condition C1 for all the dimensions, and now, in this task, condition C1 has been perceived as better than C2 for all dimensions. Once again, condition C3 has been perceived better than condition C2 for all dimensions.

3.7.6 Final remark

Table 5 illustrates a dominance matrix, in which Ci represents the lines and Cj the columns. Yellow represents Ci=Cj and green Ci>Cj. That being said, having a higher amount of green cells in the same line illustrates that a condition is preferred to the other alternatives, This representation helps to confirm the aforementioned results.

3.7.7 Participants preferences and opinion

Next, participants’ preferences towards the conditions used, not focusing on any of the tasks considered, but on the overall experience are presented, although all participants were able to use the proposed conditions and fulfill the intended tasks. All in all, it becomes clear that condition C3 - HMD was rated higher, followed by condition C2 - HHD & Articulated Support, and last, condition C1 - HHD. This was confirmed by the Friedman test (p-value<0.005), rejecting the equality of distributions/medians of the three assessed conditions. C2 (median=6; sum=164) is considered equal to C3 (median=6; sum=177) and both are preferred to C1 (median=5; sum=144) - p-value=0.030).

When categorizing the overall preference by gender, there was no significant difference between males and females when applying the non-parametric Mann-Whitney test (two independent samples and ordinal data). To elaborate, for the on-site role, considering the overall preference the obtained test results were, p-value=0.294, p-value=0.918, p-value=0.759, for the HHD & Articulated Support, HHD & Articulated Support, and HMD, respectively; As before, participants’ technological expertise with XR was not evaluated as they all had previous experience with XR and remote collaboration tools [30].

Most participants (21 out of 30) highlighted condition C3 - HMD for providing situated content on top of the real-world environment while supporting a hands-free setting. This allowed accomplishing physical tasks while visualizing additional information in their surroundings. Overall, this was considered most useful for more demanding tasks like the maintenance procedure on a boiler or the learning activity in the laboratory. Despite this, some (9 out of 30) showed concern regarding the effects of using such devices over longer periods, both for ergonomic reasons, as well as battery life and how this could affect the collaborative process.

Table 5 Visual representation of the dominance matrix - onsite study
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Regarding condition C2 - HHD & Articulated Support, some positive reactions also occurred. In fact, 14 out of 30 suggested that this approach could be quite useful for addressing tasks with a reduced area of interest (e.g., Lego pieces assembly; Tangram puzzle assembly, Resources management in a map), given that it could also provide a hands-free setting. This would ensure it remains in a fixed position while participants interacted with the HHD. Plus, this also allowed them to focus on the content and their interactions without worrying about holding the device steady, which may be disruptive to the remote session. Additionally, participants reported that by offering adjustable positioning and tilting options, they were able to find the optimal viewing angle for capturing the real-world environment and viewing the content shared by their counterparts despite their pose (e.g., standing up, sitting on a chair, etc.).

Concerning condition C1 - HHD, all participants emphasized their familiarity with such approach, given their extended use of mobile devices. Most (22 out of 30) reported that it provides mobility and flexibility for remote scenarios, allowing pointing the device camera at specific areas of interest more easily than with the HMD. As such, it seems more adjusted to specific use cases, as is the case of the training activity on a laboratory, where participants needed to move through the environment. Regardless, they also emphasized that it requires at least one hand for holding the device, which may affect accomplishing some tasks, forcing the device to be positioned in the surrounding environment.

To finish, a small number of participants (4 out of 30) were curious about using Spatial AR solutions, commenting they had seen similar approaches in educational and cultural exhibits and if such methods could be relevant for the on-site collaborator, at least, for addressing some tasks.

4 Discussion

Altogether, going back to the question initially raised: which method leads to better results according to different physical task scenarios and collaborator role (e.g., on-site or remote)?, the results obtained from the two studies and the data analysis clearly show that the choice of content creation and display methods has an impact on participants’ collaborative process across distinct tasks. The characteristics of such tasks determine the level of detail, complexity, and communication involved in the collaborative process, e.g., access to immediate feedback, detailed information, or precise interaction to establish a common ground.

All things considered, in the remote study, the use of a Video Wall & Keyboard combination was found to be particularly suited for content creation during maintenance procedures, learning activities, and training activities. This indicates that the expansive display area offered by the Video Wall, along with the precise control facilitated by the Keyboard, contributed to enhanced performance in tasks demanding intricate visualization, information processing, and interactive engagement. The combination of these features allowed participants to effectively manipulate and interpret visual data, process information with accuracy, and engage interactively with the content presented. This suggests that the utilization of such technology fosters an environment conducive to effective collaboration, particularly in scenarios where detailed visualization and precise interaction are paramount. In contrast, when considering the remaining task scenarios, specifically those involving Lego and Tangram assembly, no significant difference in performance was observed between the Laptop Computer and Interactive Projector methods. This suggests that the display and interaction functionalities offered by these approaches adequately met the requirements of these specific physical tasks. The absence of a significant difference in performance implies that both methods were equally effective in facilitating collaboration during the assembly of Lego structures and Tangram puzzles. This underscores the notion that the capabilities provided by the Laptop Computer and Interactive Projector were well-suited to support the collaborative process in tasks characterized by assembly and construction.

Complementary to this, in the on-site study, the choice of display method also played a crucial role in determining how the collaborative process occurred across various tasks. In tasks such as Lego and Tangram assembly, the HMD emerged as the preferred option. Its immersive characteristics likely contributed to an enhanced sense of spatial perception and improved precision in manipulating physical objects. Additionally, the HHD & Articulated Support demonstrated satisfactory performance, indicating that a mobile display with adjustable positioning can offer advantages for these assembly tasks. This underscores the versatility of hardware setups in accommodating diverse task requirements and user preferences. Likewise, in the context of maintenance procedures, the HMD was also identified as the superior choice, closely followed by the HHD % Articulated Support. These results suggest that immersive displays and adaptable hardware configurations can effectively support tasks involving spatial manipulation and detailed inspection. The comparable satisfaction levels observed between the HHD & Articulated Support and the HMD in the management of resources on a map further reinforce the notion that versatile display options can offer viable solutions for collaborative tasks across different domains. Lastly, in the training activity conducted in a laboratory setting, both the HHD and HMD conditions were deemed suitable. This indicates that either option can adequately support hands-on training experiences that necessitate user movement within the task environment.

4.1 Alignment with the broader context of XR-based remote collaboration

The research presented in this paper directly addresses several key challenges within the field of remote collaboration supported by XR. One notable aspect is the consideration of physical tasks with distinct characteristics. Unlike many previous studies that focus on simplistic activities, this work delves into real-world scenarios, providing a more comprehensive understanding of how XR technologies perform across different collaborative contexts. Moreover, this research stands out by exploring multiple methods for content creation and visualization. While most studies tend to assess a single solution, this work systematically evaluates different approaches for content creation and visualization. This multifaceted approach allows for a nuanced comparison of the effectiveness of various XR methods in supporting remote collaboration, offering valuable insights for both researchers and practitioners.

Furthermore, this study contributes to advancing the evaluation efforts in the field through its comprehensive data analysis and multidisciplinary sample. By conducting rigorous assessments with a diverse group of participants from various backgrounds, including academia, industry, and beyond, this research expands upon the traditional scope of evaluation studies. This inclusive approach not only enhances the validity and reliability of the findings but also addresses one of the most pressing challenges identified in recent surveys, i.e., the need for more robust and interdisciplinary evaluation efforts in XR research. By doing so, this work contributes to advancing the understanding and application of XR technologies for remote collaboration in diverse real-world scenarios.

This study offers another significant contribution to the field by evaluating different XR methods from the perspectives of both on-site and remote team members, the research provides valuable insights into the preferences and effectiveness of various methods for each role. This nuanced understanding enables researchers and practitioners to tailor XR solutions to the specific needs and preferences of different collaborators, optimizing collaboration outcomes. Additionally, the results of this study offer practical guidance for future research and development efforts in the field. By identifying the preferred methods for content creation and visualization for each role in remote collaboration scenarios, the research presented provides a roadmap for selecting the most appropriate XR tools and technologies in future studies. This helps to streamline the decision-making process for researchers and practitioners, saving time and resources while ensuring optimal collaboration experiences.

4.2 Generalization to broader applications

In brief, the results obtained demonstrate that the choice of content creation and display methods significantly impacts the collaborative process across distinct tasks. While the study provides valuable insights into the effectiveness of various XR methods for remote collaboration across specific tasks, it’s essential to consider the generalization of these findings to broader task types. However, it’s also crucial to acknowledge that the tasks evaluated in the study represent a specific subset of activities, each with unique characteristics such as complexity, interactivity, and spatial requirements. Likewise, the collaborative process is influenced not only by the nature of the tasks but also by the roles of the collaborators, whether on-site or remote. While certain XR methods may prove effective for specific tasks, their suitability may vary depending on the role of the participants. Therefore, extrapolating these findings to diverse task domains requires caution and further investigation.

Conversely, there are some potential use cases in various domains where tasks share similar characteristics to those evaluated in the study, where the methods considered may prove beneficial. These include:

  • Healthcare Training or Simulation - similar to the training activity in a laboratory, medical training often involves hands-on experiences where learners must interact with equipment and perform procedures. Both the HHD and HMD conditions could be valuable in medical training simulations, allowing trainees to immerse themselves in realistic scenarios and manipulate virtual objects with precision. For instance, surgeons practicing surgical procedures or medical students could benefit from immersive displays and interactive feedback for enhanced learning experiences;

  • Manufacturing or Product Design - tasks involving assembly and prototyping, such as Lego and Tangram assembly evaluated in the study, are common in manufacturing and product design industries. Immersive displays like HMDs could facilitate precision assembly tasks by providing users with enhanced spatial perception and feedback. Additionally, mobile displays with adjustable positioning, such as HHD & Articulated Support, could be advantageous in manufacturing environments where workers need flexibility in viewing assembly instructions or schematics while manipulating physical components;

  • Geospatial Analysis or Urban Planning - resource management in a map, as evaluated in the study, shares similarities with tasks in which users need to manipulate and analyze spatial data. Both HHD & Articulated Support and HMD conditions could be suitable for these tasks, providing users with immersive experiences and versatile display options for navigating complex datasets and making informed decisions. For example, urban planners analyzing demographic trends or environmental factors could benefit from immersive displays for visualizing and interacting with spatial data in real-time;

  • Architectural Design and Visualization - in architectural design, tasks often require detailed visualization and spatial understanding, akin to maintenance procedures evaluated in the study. The use of a Video Wall & Keyboard combination could be beneficial for collaborative design sessions, where architects and stakeholders can explore complex 3D models with detailed information sharing and interactive input. Furthermore, the use of HMDs could enhance architects’ ability to perceive spatial relationships and evaluate designs in immersive environments, particularly during on-site visits or virtual walkthroughs of proposed structures.

All things considered, the findings described emphasize the importance of considering specific task requirements when selecting the appropriate technology. Different tasks demand different levels of visualization, interaction, and spatial perception, and the choice of creation and display must align with these requirements. By carefully matching the technological capabilities with the task demands, researchers and practitioners can optimize user performance and enhance the overall experience of distributed team members for various scenarios of collaboration. These factors contribute to informing the research community, allowing to make informed decisions when selecting the appropriate methods and technologies for distributed teams. This knowledge can facilitate effective information sharing, interaction, and spatial understanding among remote individuals, contributing to optimizing collaboration, communication, and overall performance in various task scenarios.

4.3 Limitations

Despite the positive outcomes, it is also essential to critically examine the limitations of the study. While emphasizing positive outcomes is crucial for showcasing the potential contributions of this work, a thorough examination of limitations is equally essential. Reflecting on this not only demonstrates transparency and integrity but also serves as a means to enhance the credibility and reliability of the research findings. Moreover, it provides valuable insights for readers and the community, informing future research and guiding the development of more robust methodologies. Next, some limitations are identified and carefully examined, offering meaningful reflections that can inform and shape future research endeavors in the field:

  • Homogeneous sample - The participant sample may lack more diversity. Particularly in terms of age, with an average age of 30 years old. Also, it’s important to acknowledge a certain tendency towards highly educated individuals. This could potentially affect the generalization of the findings to broader populations with different age and education distributions;

  • Controlled environment - The study took place within controlled settings. While this allows for rigorous experimental control, it may not fully capture the complexities and nuances of real-world remote collaboration scenarios (e.g., number of persons in the environment, background noise, etc.), potentially affecting ecological validity;

  • Absence of longitudinal analysis - The study primarily focused on evaluating immediate outcomes. As such, there is a lack of a longitudinal analysis to track potential changes or improvements in participants’ performance and behaviour over time. Consequently, the study does not capture the nature of skill development and adaptation in remote collaboration contexts, providing only a snapshot of participants’ performance;

  • Limited scope of content creation - The study exclusively relied on 2D instructions for content creation, avoiding the complexities introduced by 3D alternatives. This may overlook critical differences in the content creation process between 2D and 3D environments, limiting the generalization of the findings to scenarios involving more intricate content creation processes, such as those requiring manipulation of 3D objects.

These limitations underscore the need for cautious interpretation of the study findings and suggest avenues for future research to address these gaps and enhance the validity and applicability of findings in real-world contexts.

5 Conclusion and future work

Despite the efforts in developing novel methods for creation and display during scenarios of remote collaboration supported by XR, few works comparing various methods among each other still exist, in particular, when considering the extent to which such methods can assist the different parties involved in the collaborative process (remote and on-site) when facing physical tasks with distinct characteristics.

This paper describes two user studies with 30 unique participants each focused on understanding which method stands out according to the characteristics of distinct physical tasks (Lego pieces assembly; Tangram Puzzle assembly; Maintenance procedures; Resources management in a map; Training activity on a laboratory) and the participant’s role during collaborative scenarios. First, different content creation methods for remote members were compared (Laptop Computer; Video Wall & Keyboard; Interactive Projector). Then, distinct display methods for on-site members were analyzed (HHD; HHD & Articulated Support; HMD).

The studies illustrate that as for the remote part of the collaborative process, using a Video Wall & Keyboard appears to be more suited for tasks associated with maintenance procedures, learning, and training activities, while it does not appear to be a significant difference among methods for the remaining task scenarios. Regarding the on-site part, using HMD was considered the better option for Lego and Tangram assembly, followed by HHD & Articulated support and HMD respectively. As to the maintenance procedures, HMD, closely followed by HHD & Articulated was selected as the better option. Concerning the learning Resources management in a map, HHD & Articulated and HMD conditions can be considered equivalently satisfactory. Last, the training on a laboratory was considered suitable for HHD and HMD conditions.

When comparing the results of the two studies, it becomes evident that the choice of creation and display methods significantly influenced participants across different tasks. It is important to note that the suitability of existing methods may vary depending on specific factors, such as the complexity of the task, the level of precision required, or the need for hands-free operation, among others. The selection of the most appropriate condition should consider these factors in conjunction with the preferences and comfort of the on-site team member. This study has shown that it is essential to consider the specific requirements and constraints of each task and adapt the technology accordingly to ensure optimal results for both remote and on-site members.

Overall, this study offers unique contributions to the field of XR by providing empirical evidence on the efficacy of different methods in supporting remote collaboration, paving the way for enhanced XR applications in diverse domains. To elaborate, this study makes a unique contribution to the field of XR research by addressing a critical gap in existing literature. While previous studies have predominantly focused on either content creation methods or visualization techniques in the context of remote collaboration, this work comprehensively analyzes both aspects. This way, a more holistic understanding of their effectiveness in supporting collaborative tasks is offered. This approach provides valuable insights into how different technologies can be leveraged to enhance remote collaboration experiences for both on-site and remote participants.

Another distinguishing factor is the inclusion of multiple tasks with distinct complexities. Unlike many existing works that often rely on simplified or toy problems, this research incorporates a diverse range of real-world tasks. This comprehensive task selection allows for a more robust evaluation of the effectiveness of different methods across a variety of scenarios, offering practical insights that can be directly applied to real-world collaborative environments. Moreover, this study stands out due to the unique composition of our participant sample. Unlike the majority of works in this domain that often feature homogeneous samples with limited diversity, our study includes participants with varied backgrounds and expertise. This diverse participant pool, comprising individuals from academia, industry, and different professional fields, enriches the generalization of our findings and provides a more representative perspective on the effectiveness of different XR methods in supporting collaborative tasks.

Last, this study distinguishes itself by conducting a comprehensive analysis of the data obtained. Rather than merely presenting isolated findings, we systematically examine the relationships between different variables and explore the nuanced interactions between creation and display methods, task characteristics, and participant roles. This rigorous analytical approach not only enhances the validity of our conclusions but also contributes to a deeper understanding of the collaborative process in XR environments. All in all, this work makes significant contributions to the advancement of XR research by offering practical insights, methodological innovations, and theoretical implications that have the potential to shape future developments and applications in various fields.

Later, we intend to integrate shared 3D virtual models into the XR-based collaborative tool considered. To do so, we anticipate some challenges may arise, namely related to data compatibility, synchronization, and user interface design. Afterward, it is important to reproduce the user studies with newer tasks that require the use of such type of content, to comprehend if the outcomes of this study are similar when 3D scenarios are used to obtain a common ground, or if other insights are attained. This may also pose challenges, namely in task design, to incorporate the necessary characteristics of 3D procedures supported by XR. Following, we plan to explore the use of a VR headset and controllers for remote experts, an approach that allows taking advantage of virtual replicas, expanding the range of scenarios supported by the collaborative tool, e.g., addressing industry scenarios where digital twins are becoming extremely important. To handle this, it may be relevant to handle hardware compatibility, user comfort, and interaction design. Also relevant, considering a more in-depth analysis of how task complexity influences the effectiveness of various methods. Subsequently, propose a set of guidelines for the research community, to inform which creation and display methods are better according to the collaborator role and task characteristics, so that it may help the community systematize existing knowledge and better understand how to tackle similar scenarios moving forward.