







































Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 
209-221 

209 

 

 

 

Article 

Research on optimization strategies of future 

technology-driven intelligent collaboration 

systems for remote employee work engagement 
Jia Wang* 

Business School, Hong Kong University of Science and Technology, Beijing 100015, China 

A R T I C L E   I N F O 
 

Article history: 
Received 22 August 2025  
Received in revised form 
17 October 2025 
Accepted 21 November 2025 
 
Keywords:  
Intelligent collaboration systems, Remote work, 
Work engagement, Optimization strategies 
 
*Corresponding author 
Email address: 
jwangez@connect.ust.hk 
 
DOI: 10.55670/fpll.futech.5.1.18 

A B S T R A C T 
 

The COVID-19 pandemic has revolutionized global work habits, with remote 
work evolving from an ad hoc measure to a significant component of company 
strategy. Traditional remote work support tools, over the years, have, however, 
shown weaknesses in increasing workers' engagement. The current research 
focuses on the core issues of influence mechanisms and optimization 
approaches for intelligent collaboration systems in remote workers' work 
engagement. By integrating Self-Determination Theory, Job Demands-
Resources Theory, and Task-Technology Fit Theory, a comprehensive 
theoretical framework emerges with direct effects, mediating processes, and 
boundary conditions. The study shows that innovative collaboration systems 
(e.g., AI-based apps, virtual reality spaces, and automated business processes) 
influence employees' work engagement through two mediating channels: 
workload reduction and autonomy development, with moderation at the 
individual competence level by AI literacy and at the contextual setting level by 
organizational support. According to the theoretical model, this paper proposes 
a four-dimensional framework for technology integration optimization, 
including technology integration optimization, human-centered design, 
organizational support mechanisms, and phased implementation routes. The 
theoretical contributions of this study are in: unifying innovative collaboration 
systems with the model of remote work engagement study, hoping to enlarge 
the theoretical boundaries of human-machine collaboration; demystifying the 
natural correlation between technological features and psychological need 
fulfillment with multi-theory combination; and making operational theoretical 
recommendations for organizations to balance technological effectiveness with 
humanistic concern in the process of smart transformation through the 
platform of optimization strategies. This study provides decision-making 
grounds and practical guidance for companies to establish man-centric smart 
collaboration systems, for managers to develop attention-grabbing employee 
support programs, and for policymakers to govern smart technology use at 
work. 

1. Introduction 

The COVID-19 pandemic has completely changed the 
global work patterns, and remote work has evolved from a 
compulsory measure to a widespread trend. Large-scale 
telecommuting during the initial wave of the pandemic led to 
record organizational restructuring [1]. This shift not only 
changed the conventional workplace but also had complex 
effects on employees' levels of work engagement. Research 
has shown that employees' work engagement in telework 
environments is dual: while some report greater work 
enthusiasm due to increased flexibility [2], others experience 
reduced productivity and increased pressure [3]. Such 

polarized performance reflects deeper difficulties in working 
from a distance. As the post-pandemic era unfolded, working 
remotely has evolved from a temporary fix to a core 
component of organizational strategy [4]. However, 
traditional remote work support technologies have 
persistently fallen behind evolving work environments. The 
rapid evolution of intelligent collaboration technologies has 
created new avenues to close this disparity, as AI-driven tools, 
virtual reality collaboration spaces, and workflow automation 
are reconfiguring the boundaries of remote collaboration. 
Even though technological developments have created new 
possibilities for remote work [5], the successful integration of 

 

 Open Access Journal 

ISSN 2832-0379 

February 2026| Volume 05 | Issue 01 | Pages 209-221 

https://doi.org/10.55670/fpll.futech.5.1.18 

 

 

 

 

 

 

 

 

 

 

 

Journal homepage: https://fupubco.com/futech 

 

Future Technology 

mailto:jwangez@connect.ust.hk
https://doi.org/10.55670/fpll.futech.5.1.18
https://fupubco.com/futech


Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

210 

 

such smart systems to improve employees' work 
commitment remains a fundamental challenge for companies. 
Current research is primarily directed at emergency 
management responses throughout the pandemic [6] and 
employee adaptability in telework based solely on traditional 
factors [7], with systematic studies on the interaction 
between intelligent collaboration systems and work 
engagement in short supply. While scholarly studies on 
remote work engagement have made some progress, the 
current literature has three major shortcomings. First, the 
great majority of studies address the macro level of work 
design [8], with sparse in-depth investigation of the 
mechanisms by which technological system properties match 
workers' psychological needs. Second, empirical 
examinations of factors affecting remote work participation 
mostly focus on common variables such as organizational 
support and leadership behavior [9], while ignoring the 
unique status of smart technology as a new work resource. 
Third, studies of remote work during pandemics primarily 
use cross-sectional designs [10], which are not grounded in 
theoretical notions for conceiving optimal long-term 
intelligent collaboration systems. The rapid development of 
new technologies, such as generative artificial intelligence, 
and their application in organizational practice [11] 
heightens the need to ground a systemic theoretical 
conception. 

Building on the above background, the present study 
centers on the primary issue of the mechanism by which 
intelligent collaboration systems influence remote workers' 
work engagement. In particular, the present paper seeks to 
investigate how employee work engagement is impacted by 
the multidimensionality of intelligent collaboration 
technology, via mediating factors such as decreased workload 
and increased autonomy, and to examine the moderating 
roles of AI literacy and organizational support in this process. 
By cross-seeding Self-Determination Theory, Job Demands-
Resources Theory, and Task-Technology Fit Theory, this 
research constructs an integrated theoretical framework with 
direct effects, mediating processes, and boundary conditions, 
and advances phased system-optimization strategies 
grounded in the above foundation. The theoretical 
contribution of this research is in three aspects. Firstly, 
integrating intelligent collaborative systems into remote 
work participation pushes the theoretical frontiers of human-
machine collaboration. Second, through multi-theory 
integration, it captures the inherent interrelation between 
technological attributes and the satisfaction of psychological 
needs, thereby providing a rationale for resource investment 
in remote working environments. Third, the grounded four-
dimensional optimization strategy framework offers an 
operational theory underpinning for organizations to 
optimize technological efficiency and humanistic care in 
smart transformation. At the factual level, this research offers 
a rationale for corporate decision-makers to evolve toward 
people-oriented smart collaboration systems, guides 
managers in formulating distinctive employee support 
programs, and provides policymakers with a point of 
reference for legislating the use of smart technology. 

The paper is divided into five chapters. Chapter 1 
elaborates on the setting, problems, and significance of the 
research. Chapter 2 develops the theoretical framework and 
research design model by integrating three fundamental 
theories and by formulating a mixed-methods research 
approach. Chapter 3 presents an integrated theory model and 
eight research propositions that explain sequentially the 
paths and processes by which intelligent collaboration 

systems impact remote employee work engagement. Chapter 
4 formulates optimization strategies from four 
perspectives—technology integration, human-centered 
design, organizational support, and implementation 
protection mechanisms—and an implementation plan 
phased over time. Chapter 5 synthesizes the research 
contribution and its implications for practice, specifies the 
research limitations, and discusses future study directions. 

2. Theoretical foundation and research design 

framework  

2.1 Core theoretical perspectives 
Explaining the influence of intelligent collaboration 

systems on remote employees' work engagement requires 
theoretical grounding. This research combines Self-
Determination Theory, Job Demands-Resources Theory, and 
Task-Technology Fit Theory to conceptualize a multi-level 
explanatory framework from motivational psychology, work 
context, and technology matching perspectives. Self-
Determination Theory offers the baseline framework for 
explaining employee intrinsic motivation. This theory argues 
that people's psychological well-being and optimal 
functioning are based on the fulfillment of three innate 
psychological needs: autonomy, competence, and relatedness 
[12]. In the context of remote working conditions, smart 
collaboration systems support the workers' feeling of 
independence through flexible arrangements and 
personalized assistance, competence from real-time feedback 
and smart assistance, and relatedness through VR/AR 
platforms that create immersive social presence. Virtual 
collaboration spaces enable avatar-based interaction, spatial 
audio, and shared virtual environments that foster 
interpersonal connection among distributed team members, 
mitigating the social isolation inherent in remote work [13]. 
When all three basic needs are met, employees are most likely 
to be autonomously motivated, thereby showing greater 
work engagement. 

Job Demands-Resources Theory accounts for employee 
work states by the two-folded nature of the work 
environment, dividing job characteristics into two broad 
categories: job demands and job resources, with the former 
causing stress and burnout, and the latter, motivation and 
engagement [14]. Smart remote work collaboration 
technology alleviates workload by means of automation and 
offers technical support as an innovative resource at the same 
time. The two-pathway model of the theory demonstrates the 
mechanism of work engagement development: the resource 
enrichment pathway activates motivation, and the demand 
reduction pathway reduces burden. Task-Technology Fit 
Theory focuses on the matching between technological 
capability and task demands, arguing that a positive effect 
occurs only when technological functions are highly 
consistent with task demands, reminding managers to heed 
the fit when targeting technology utilization. These three 
theories are integrated because they form a causal chain from 
technological features to psychological needs to behavioral 
outcomes. Task-Technology Fit Theory illustrates how 
technological features are translated into helpful resources; 
Job Demands-Resources Theory illustrates how resources 
affect employee states through dual processes; and Self-
Determination Theory illustrates how resource investment 
fulfills psychological needs, leading to intrinsic motivation. As 
illustrated in Figure 1, all three theories concentrate on 
various levels of mechanisms. The integration of multiple 
theories provides a robust theoretical foundation for 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

211 

 

understanding the multifaceted role of intelligent 
collaboration systems in remote work. 

 

 
Figure 1. Integrated theoretical framework 

This multi-theoretical integration has empirical 
precedents in technology-mediated work research. Gagné 
and colleagues [15] comprehensively reviewed how SDT 
integrates with work design theories, including JD-R, 
demonstrating that job resources identified in the JD-R 
framework can satisfy basic psychological needs, which in 
turn enhance autonomous motivation—particularly relevant 
when technology transforms remote work contexts. The 
incorporation of TTF with behavioral theories has been 
validated in pandemic-era studies, with Kamdjoug et al. [16] 
showing that task-technology alignment in remote work 
settings amplifies the positive effects of ICT resources on 
employee performance. Moreover, recent theoretical 
advances in JD-R theory have explicitly incorporated SDT 
constructs and proactive behaviors [14], establishing a solid 
foundation for multi-theory integration in understanding 
technology-enabled work arrangements. 

2.2 Key constructs and conceptualization 
The central constructs in this study need clear 

conceptual and operational definitions to assess the 
theoretical model's strength and testability through empirical 
studies. Intelligent collaboration systems, as the independent 
variable, are conceptualized as a formative construct 
integrating three distinct technology components that 
collectively form the overall system capability. AI-powered 
tools (intelligent task allocation algorithms, natural language 
processing assistants, predictive analytics systems) provide 
cognitive augmentation through data-driven decision 
support. Virtual and augmented reality platforms create 
immersive collaboration spaces that enhance social presence 
for distributed members. Automation systems enable 
intelligent process execution through predefined rules and 
machine learning, releasing employees from routine tasks. 
These three dimensions are treated as formative rather than 
reflective indicators because they represent distinct, non-
interchangeable technological capabilities—organizations 
may implement different combinations, and each component 
contributes unique functionality to the overall system rather 

than reflecting a common underlying factor. Work 
engagement, as the dependent variable, refers to employees' 
positive psychological state at work, whose three-
dimensional structure contains certain measurement indices. 
The vigor dimension appears as a high level of energy and 
psychological resilience; the dedication dimension as work 
meaningfulness and a sense of pride; and the absorption 
dimension as a state of being entirely focused on work tasks. 
Self-Determination Theory underscores that these 
manifestations of engagement are the products of fulfilled 
basic psychological needs. If the work context facilitates 
autonomy and allows experiences of competence, employees 
tend to demonstrate high levels of vigor, dedication, and 
absorption [15]. The specification of mediating variables 
attempts to unveil the internal mechanisms whereby 
intelligent collaboration systems impact work engagement. 
Workload, operationalized using the Job Content 
Questionnaire's quantitative demands subscale (5-7 items) 
[17], refers to perceived work pace, time pressure, and 
volume of tasks—explicitly excluding decision-making 
latitude or skill discretion to avoid overlap with autonomy 
measures. Sample items include "How often does your job 
require you to work very fast?" and "How often do you have 
too much work to do?", focusing purely on task load rather 
than control dimensions. Intelligent collaboration systems 
reduce workload through automation and intelligent support, 
allowing employees to devote the cognitive resources they 
save to higher-value activities. Autonomy, measured using the 
Work Design Questionnaire [18], refers to the degree of self-
determination employees exercise over work methods, 
scheduling, and decision-making. Smart collaboration 
technology enhances autonomy through flexible options and 
personalized settings, activating intrinsic motivation. The two 
mediators account for the demand reduction pathway and 
resource enrichment pathway in Job Demands-Resources 
Theory, respectively. 

The specification of moderating variables takes into 
account boundary conditions at individual and contextual 
levels. AI literacy, defined as individuals' capability to 
understand, use, and critically evaluate artificial intelligence 
technologies in work contexts [19], encompasses technical 
understanding, operational proficiency, critical evaluation, 
and collaborative competence with AI systems. Sample items 
for the AI literacy scale include: "I can explain how AI decision 
processes work" (technical understanding), "I effectively use 
AI tools to complete my work tasks" (operational 
proficiency), "I can evaluate the reliability of AI-generated 
recommendations" (critical evaluation), and "I know when to 
rely on AI versus my own judgment" (collaborative 
competence). This four-factor structure will be validated 
through the two-stage process described in Section 2.3. 
Employees with higher AI literacy are better able to leverage 
system functionality and transform technological features 
into productive work resources. Organizational support, 
measured using the short form of the Survey of Perceived 
Organizational Support (8 items) [20], refers to employees' 
perception that their organization values their contributions 
and cares about their well-being. To ensure contextual 
relevance, items are adapted to the intelligent collaboration 
system context—for example, the original item "My 
organization values my contribution" is modified to "My 
organization values my input on AI tool usage," and "My 
organization cares about my well-being" becomes "My 
organization provides adequate support when I encounter 
difficulties with intelligent systems." This contextualization 
maintains scale validity while enhancing specificity to 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

212 

 

technology implementation scenarios. High organizational 
support reduces technology change anxiety through 
management commitment and resource provision, enhancing 
the positive impacts of smart collaboration systems. The two 
moderating variables offer theoretical justification for 
developing differentiated management strategies. 

While AI literacy and autonomy may correlate 
empirically, they are conceptually distinct. Autonomy 
represents a work design characteristic—the degree of self-
determination in work processes across all contexts. AI 
literacy represents a domain-specific capability—knowledge 
and skills for utilizing AI technologies. Critically, they serve 
different theoretical roles: autonomy functions as a mediating 
variable explaining "how" technology influences engagement 
through enhanced self-determination, while AI literacy serves 
as a moderating variable determining "when" or "for whom" 
technology effects are amplified. AI literacy does not directly 
cause autonomy but rather moderates technology's 
autonomy-enhancing effects. To empirically assess 
multicollinearity, we will: (1) examine bivariate correlations, 
expecting moderate levels (r = 0.30-0.50); (2) calculate 
Variance Inflation Factors (VIF < 3.0 as acceptable threshold); 
(3) conduct confirmatory factor analysis comparing two-
factor versus one-factor models to demonstrate discriminant 
validity; and (4) verify that average variance extracted (AVE) 
exceeds squared correlation (AVE > r²). If concerns arise, 
mean-centering will be employed before creating interaction 
terms. 

2.3 Proposed research design  
Empirical testing of the theoretical model demands a 

strict research design and systematic data collection 
processes. The current study follows a mixed-methods 
research approach to maximize the strengths of quantitative 
and qualitative research. Quantitative research (n > 500) tests 
hypothesized relationships through large-scale surveys and 
structural equation modeling. Qualitative interviews (n = 20-
30) serve three triangulation functions: (1) Pre-survey 
refinement—initial interviews (n = 8-10) verify 
measurement items and identify contextual factors; (2) 
Results explanation—follow-up interviews (n = 12-15) after 
SEM analysis explore unexpected findings (e.g., if workload 
mediation is weak, interviews investigate offsetting cognitive 
demands); (3) Pattern corroboration—thematic coding 
frequencies are compared with path coefficients to verify 
convergence (e.g., strong autonomy effects should align with 
control-related narratives). This cross-method verification 
enhances validity through triangulation. 

Sample selection is guided by the principles of 
representativeness and targeting. The target sample consists 
of employees who have consistently followed remote 
collaboration practices and have at least 6 months of 
experience working with intelligent collaboration systems. 
Industry coverage includes knowledge-intensive sectors such 
as information technology, financial services, professional 
consulting, and creative industries. Specifically, targeted 
sectors include IT consulting (e.g., software development 
firms using AI-powered project management), fintech (e.g., 
remote financial analysts leveraging predictive analytics), 
and professional services (e.g., distributed consulting teams 
utilizing VR meeting platforms). Manufacturing industries are 
excluded because remote work in these contexts primarily 
involves operational monitoring rather than collaborative 
knowledge work, resulting in fundamentally different task-
technology fit dynamics. Quota sampling will ensure balanced 
representation: 30-35% IT/software, 25-30% financial 

services, 20-25% consulting, 15-20% creative industries, 
maintaining diversity while focusing on remote-collaborative 
knowledge work contexts where intelligent collaboration 
systems are core productivity tools. The sample must include 
multiple levels of position, with geographical coverage 
spanning several countries or regions to control for cultural 
differences. The sample size for quantitative research must be 
at least 500 participants to meet the requirements of 
structural equation modeling, whereas qualitative research 
uses in-depth interviews with 20-30 employees until data 
saturation. This sample size is justified by anticipated effect 
sizes from prior literature. Meta-analyses of technology-job 
resources relationships report medium main effects (β = 

0.25-0.40), with job resources → engagement (β = 0.30-

0.45) and autonomy → engagement (β = 0.35-0.50). 
Moderation effects from digital literacy and organizational 
support studies typically show small-to-medium interactions 
(β = 0.10-0.20, ΔR² = 0.02-0.05). Power analysis indicates n 
= 500 provides >0.80 power to detect medium main effects (
β ≥ 0.25) and small-to-medium moderations (β ≥ 0.12) at α 
= 0.05. With 25-30% attrition across three waves (final n = 
350-375), power remains >0.75 for theoretically meaningful 
effects. Recruitment will utilize: (1) HR platform partnerships 
(LinkedIn, professional associations); (2) 8-10 organizational 
collaborations with employee access; (3) snowball referrals. 
An expected 35-40% response rate requires distributing 
~1,500 surveys across 4-5 countries. Interviews are recruited 
from survey volunteers (15% rate) and organizational 
partners. Research budget ($12,000), institutional 
partnerships (2 platforms, 5 companies), and a 3-person team 
secured for a 9-month timeline. 

Questionnaire design is made on the basis of mature 
measurement tools and contextualized by the situation. 
Measurement of intelligent collaboration systems employs 6-
7 items per dimension (rather than 4-5) because formative 
constructs require comprehensive content coverage—each 
indicator contributes unique information about distinct 
technological facets. AI tools include task allocation, natural 
language processing, and predictive analytics; VR/AR 
platforms include spatial presence and 3D visualization; 
automation covers workflow routing and system integration. 
Item development follows: expert consultation, content 
validity assessment (CVR > 0.62), cognitive pretesting (n=20-
25), and pilot testing (n=100-150) with VIF < 3.3. Formative 
constructs are evaluated through indicator weights and VIF 
rather than Cronbach's alpha. Pilot data (n=100-150) will 
report inter-dimension correlations (expected r=0.30-0.50), 
verify items do not conflate dimensions (e.g., excluding "AI-
enhanced VR" hybrid items), and confirm formative construct 
validity through VIF<3.3. Work engagement uses the short 
form of the Utrecht Work Engagement Scale, and it measures 
using a total of 9 items with three subscales, i.e., vigor, 
dedication, and absorption. Workload is quantified with the 
Job Content Questionnaire and autonomy with the Job 
Characteristics Model. Studies on Self-Determination Theory 
implementation in remote working environments explore an 
operationalization reference framework for these variables 
[13]. AI literacy entails creating a new scale encompassing 
technical capability, algorithmic thinking, and human-
machine collaboration cognition.  

AI Literacy Scale Validation: A two-stage validation 
process will be implemented. Stage 1: Exploratory Factor 
Analysis (EFA) with Sample 1 (n = 200) using principal axis 
factoring and promax rotation. Item retention criteria: factor 
loadings ≥ 0.50, cross-loadings < 0.30, communalities > 0.40. 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

213 

 

Items violating multiple criteria will be eliminated, reducing 
the scale to 12-15 items. Stage 2: Confirmatory Factor 
Analysis (CFA) with Sample 2 (n = 300) to validate factor 
structure. Model fit criteria: χ²/df < 3.0, CFI/TLI > 0.90, 
RMSEA < 0.08, SRMR < 0.08. Item deletion based on: 
standardized loadings < 0.60 or problematic modification 
indices. Validity assessment: convergent validity (AVE > 0.50, 
CR > 0.70) and discriminant validity (AVE > squared 
correlations). This rigorous validation ensures the AI literacy 
scale captures distinct yet related competencies (technical, 
operational, evaluative, collaborative) without redundancy, 
establishing factorial validity before testing its moderating 
role in the structural model. The split-sample design (total n 
= 500) follows scale development recommendations with a 
10:1 subject-to-item ratio for EFA and 200+ for CFA power. 
Organizational support takes the Perceived Organizational 
Support Scale, where all the items are on a seven-point Likert 
scale. For multi-regional data collection, standard back-
translation procedures will ensure cross-cultural 
equivalence. Two independent bilingual translators will 
translate English items into target languages, followed by 
back-translation to English. Inter-translator agreement will 
be assessed using Cohen's Kappa (target: κ > 0.80), with 
discrepancies resolved through expert panel discussion. 
Additionally, pilot cognitive interviews (n=10 per region) will 
verify item comprehension and cultural appropriateness—
participants will be asked to paraphrase items and explain 
their interpretation, identifying potential semantic 
misunderstandings before full deployment. This process 
ensures measurement invariance across geographical 
contexts. Reflective scales will be assessed for reliability and 
validity. For reliability assessment, multiple indicators will be 
used depending on scale length: (1) Cronbach's alpha (α > 
0.70) for scales with 5+ items; (2) Composite reliability (CR > 
0.70) for all constructs, as it accounts for different indicator 
loadings and is more appropriate for SEM; and (3) Omega 
coefficient (ω > 0.70) for constructs with few items (3-4 
items), as omega is less biased than alpha for short scales.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Given that some subscales have only 3 items (e.g., vigor, 
dedication, absorption in work engagement; work autonomy 
dimensions), CR and omega will serve as primary reliability 
indicators for these constructs, while alpha will be reported 
for comparison. Validity assessment includes convergent 
validity (AVE > 0.50) and discriminant validity (Fornell-
Larcker criterion). Variable operationalization definitions 
ensure accurate correspondence between theoretical 
constructs and empirical measurements. Intelligent 
collaboration systems are measured through employees' 
perceived ratings of system functionality completeness, 
interface friendliness, and task fit. Work engagement is 
operationalized as the degree of vigor, dedication, and 
absorption experienced by employees. Workload is defined as 
perceived time pressure, task complexity, and cognitive 
consumption. Autonomy is operationalized as the degree of 
control over task execution methods, work pace, and 
decision-making content. As shown in Figure 2, the research 
design is divided into four consecutive phases: the 
assessment and preparation phase, which involves 
conducting a literature review and theoretical framework 
construction; the questionnaire design phase completes 
measurement instrument development and pretesting; the 
data collection phase distributes questionnaires through 
online platforms and employs a three-wave longitudinal 
design to establish temporal precedence and control for 
common method bias. At Time 1, participants complete 
measures of intelligent collaboration systems, moderators (AI 
literacy, organizational support), and controls (50 items, 12 
minutes). At Time 2 (2 weeks later), mediators—workload 
and autonomy—are measured (14 items, 6 minutes), 
allowing technology effects on work conditions to manifest. 
At Time 3 (4 weeks after T2), work engagement and 
performance are assessed (14 items, 6 minutes), providing 
time for need satisfaction to translate into engagement per 
self-determination theory.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Phase 1

Preparation
• Literature review

• Framework construction

• Sample selection

Phase 2

Instrument Design
• Scale development

• Pilot testing

• Validity assessment

Phase 3

Data Collection
• Online survey(n>500)

• Interviews(n=20-30)

• Data cleaning

Phase 4

Analysis
• SEM

• Mediation

• Moderation

Analytical Techniques

Expected Outcomes

Phase 1-2:Research protocol & validated instruments

Phase 3-4:Complete dataset & statistical analysis results

Qualitative
• Thematic coding

• Pattern identification

• Data integration

Qualitative
• CFA& SEM

• Bootstrap mediation

• Moderation analysis

Quality Control: Multiple data sources & triangulation strategy

Total Timeline: 6-9 months

 

Figure 2. Proposed research design and data collection procedure  

 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

214 

 

Unique identifiers enable response matching, with tiered 
incentives ($5/wave + $10 bonus) supporting expected 70-
75% retention (350-375 complete cases). A subsample (n = 
100-150) provides supervisor-rated performance at T3; and 
the analysis and technology phase employs structural 
equation modeling to test the theoretical model and conducts 
thematic coding analysis of qualitative data. 

2.4 Proposed analytical approach  
Systematic and stringent analytical methods are 

necessary to ensure that research results are scientifically 
valid. The building block of data analysis is reliability and 
validity testing. Reliability testing involves internal 
consistency reliability and composite reliability, with 
Cronbach's alpha used to assess scale item consistency 
(expected coefficients> 0.70) and composite reliability 
derived from confirmatory factor analysis outputs. Three 
types of validity testing are conducted: content validity, 
construct validity, and discriminant validity. Content validity 
addresses whether items truly represent theoretical 
constructs based on expert judgment; construct validity 
investigates measurement model fit using confirmatory 
factor analysis; and discriminant validity tests construct 
independence by comparing correlation coefficients between 
constructs with average variance extracted values. 

Structural equation modeling, the main statistical 
method for testing the theoretical model, follows a stepwise, 
progressive approach from simple to complex. The analysis 
first builds a measurement model to ensure indicator 
variables for theoretical concepts are accurately measured, 
and goodness-of-fit is tested using confirmatory factor 
analysis. Model fit statistics should be at the following levels: 
chi-square to degrees of freedom ratio less than 3, 
comparative fit index and Tucker-Lewis index higher than 
0.90, root mean square error of approximation lower than 
0.08, and standardized root mean square residual lower than 
0.08. Once the measurement model has been validated, a 
structural model is built to test cause-and-effect 
relationships. This two-stage approach is employed to 
separate measurement error and structural relationships, 
thereby achieving improved path coefficient estimates. 
Maximum likelihood estimation is employed when the sample 
size is sufficiently large; robust maximum likelihood 
estimation or Bayesian estimation procedures are employed 
when there is a non-normal distribution of data. 

The test of mediation effect follows the Bootstrap 
approach to create an empirical distribution of indirect effects 
by repeated sampling and building confidence intervals 
without assuming normality. The number of repeated 
samples is fixed at 5,000 to guarantee estimation stability. We 
will use 95% bias-corrected and accelerated (BCa) confidence 
intervals, which correct for both bias and skewness in the 
bootstrap distribution, providing more accurate Type I error 
rates than percentile intervals. If the BCa confidence interval 
excludes zero, the mediation effect is significant. In the 
present study, workload and autonomy as mediating 
variables need to be estimated separately for their respective 
indirect and total indirect effects. To compare the two 
pathways, pairwise contrast tests will estimate the difference 
between indirect effects (ICS→workload→engagement minus 
ICS→autonomy→engagement) and provide bootstrap CIs. If 
the difference CI excludes zero, the pathways differ 
significantly in strength. The proportion of the total indirect 
effect carried by each pathway will also be reported to clarify 
relative importance. Moderation effects will be tested using 
latent moderated structural equations (LMS) within the SEM 

framework, allowing simultaneous estimation while 
accounting for measurement error. Latent interaction terms 
(ICS × AI literacy, ICS × organizational support) will be 
created and added to the structural model. Model fit 
comparison (Δχ², AIC, BIC) will assess significance, followed 
by hierarchical regression probing using the PROCESS macro 
and Aiken & West (1991) procedures. Simple slopes analysis 
will be conducted at -1SD, mean, and +1SD moderator levels, 
with practical significance evaluated through incremental 
variance explained (ΔR²). A threshold of ΔR² > 0.02 (2% 
additional variance) will indicate meaningful moderation 
effects beyond statistical significance, ensuring that 
interaction terms contribute substantively to explaining work 
engagement variance. For moderated mediation, conditional 
indirect effects will be calculated at different levels of the 
moderator using bootstrapping (5,000 samples). The index of 
moderated mediation will quantify whether moderators 
differentially affect the two mediation pathways (workload 
vs. autonomy). This clarifies whether AI literacy and 
organizational support primarily strengthen the resource-
enrichment pathway (autonomy) or the demand-reduction 
pathway (workload). 

Multilevel analysis is necessary because organizational 
data are hierarchical. Estimating the intraclass correlation 
coefficient before data analysis is appropriate as a measure of 
between-group variability. If the intraclass correlation 
coefficient is greater than 0.05, use multilevel linear models 
that incorporate both individual-level predictor variables and 
organizational-level context variables to obtain unbiased 
parameter estimates. Regarding construct-level specification 
for multilevel modeling: Level 1 (individual-level) constructs 
include AI literacy, perceived workload, perceived autonomy, 
work engagement, work performance, and individual 
perceptions of ICS features. Level 2 (organizational-level) 
constructs include organizational support, which reflects 
organizational climate characteristics. ICS is primarily 
measured at the individual perception level, but 
organizational-level ICS maturity can be computed by 
aggregating individual perceptions if ICC(1) > 0.05 and rwg > 
0.70 indicate sufficient within-organization agreement. If ICC 
< 0.05 for key constructs, single-level SEM is appropriate as 
organizational nesting effects are negligible. Qualitative data 
analysis uses thematic coding, where the early ideas are 
determined through open coding, conceptual linking is 
developed through axial coding, and lastly, the key themes are 
established through selective coding.  

Two coders work separately on coding, and inter-coder 
reliability is calculated to maintain objectivity. Synthesis of 
qualitative results and quantitative outcomes follows the 
principle of triangulation through systematic mapping 
procedures. Specifically, thematic codes from interviews will 
be matched to corresponding SEM paths—for example, if 
autonomy-related themes emerge with high frequency (e.g., 
"flexible scheduling," "control over work methods"), this 
corroborates the hypothesized autonomy mediation pathway 
strength. Quantitative path coefficients will be interpreted 
alongside qualitative narratives: a strong ICS→autonomy→

engagement path (β>0.30) should align with frequent 
autonomy themes in interview data. Conversely, unexpected 
findings (e.g., weak workload mediation) will prompt 
targeted follow-up interviews to explore offsetting factors or 
measurement issues. This cross-method verification 
enhances validity by confirming that statistical relationships 
reflect genuine employee experiences. 

 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

215 

 

3. Theoretical model and research propositions  

3.1 Human-AI collaboration mechanism in remote work  
A virtual office environment is a unique application 

situation for collaboration between humans and artificial 
intelligence, where physical space differentiation and 
dependency on virtual connections coexist. Workers and 
intelligent systems form a highly dependent cooperative 
relationship, and this cooperative working pattern exhibits 
interactive characteristics of mutual complementarity and 
dynamic adaptation among humans and artificial intelligence 
in task execution procedures. Smart collaboration systems 
serve various roles as information-processing assistants, 
decision-support advisors, and communication facilitators 
among remote teams. The development of effective human-
machine collaboration mechanisms involves adhering to the 
augmentation ethos rather than replacement, ensuring that 
technology augments human ability rather than undermining 
human autonomy and creativity [21]. 

Task-technology fit is especially important in virtual 
man-machine cooperation. Critical fit dimensions include the 
congruence between task complexity and system intelligence 
level; routine tasks should be matched with highly 
mechanized processing, while creative tasks require greater 
freedom for human judgment. In the meantime, task 
collaboration intensity and system communication support 
capability, time sensitivity and response speed, and task 
cognitive load and system level of intelligent assistance 
directly influence collaboration effectiveness. These 
coordination dimensions are interrelated in everyday work 
life, determining collectively if intelligent collaboration 
systems can successfully cope with the demands of remote 
working tasks or not. 

From the viewpoint of Job Demands-Resources Theory, 
intelligent collaboration systems have two-edged effects on 
telecommuting. On the one hand, systems grant employees 
access to real-time data, intelligent task allocation 
suggestions, and workflow automation toolkits to alleviate 
information asymmetry and coordination challenges, while 
automation features handle many repetitive tasks, reducing 
employees' energy consumption on meaningless work. 
Conversely, technology could be another cause of demand; 
learning and adaptation to intelligent systems add more 
cognitive load, technical failures and system maintenance 
introduce uncertainty, and over-monitoring could lead to 
privacy issues. Nevertheless, remote human-machine 
collaboration also faces a lot of challenges. Present-day 
artificial intelligence applications are usually explainable in 
decision-making, and this makes it hard for employees to 
realize the reasoning behind algorithmic suggestions, and a 
lack of transparency erodes trust [22]. Moreover, human 
work rhythms aren't very flexible compared to intelligent 
systems designed with rigid rules, which may lower 
collaboration efficiency. Technology-converging 
communication may also harm emotional relationships 
among members. As shown in Figure 3, telework's human-
machine collaborative mechanism is a multi-level system 
comprising a technology layer, a task layer, an individual 
layer, and an organizational layer, in which bidirectional 
influence among the layers exists. 

3.2 Impact pathways of intelligent collaboration 
systems  
The influence of smart collaboration systems on remote 

workers’ work engagement acts through different 
mechanisms. The Job Demands-Resources Theory accounts 
for how the workplace environment shapes employee work 

states through two mechanisms: pressure relief and resource 
supplementation. Intelligent collaboration technology plays a 
twofold role in virtual working environments, both reducing 
work pressure and enhancing available resources. The 
resource development channel contributes to remote 
workers' resources through three facets: expanding 
autonomy, building social support, and enhancing 
performance feedback. Expanding autonomy is reflected in 
technology, which offers workers greater work flexibility and 
decision latitude. Intelligent scheduling software helps 
workers plan work according to their own rhythms, and 
flexible workflow software lets them choose practices that are 
most appropriate to them. This sort of development of 
autonomy directly affects the innate psychological needs in 
Self-Determination Theory. Social support is regained 
through the utilization of virtual conference rooms, chat 
rooms, and smart collaboration platforms' real-time co-
editing features, and AI-powered communication technology 
also supports the success of cross-cultural collaboration. 
Feedback mechanisms on performance are made timely and 
accurate with the help of intelligent systems. Artificially 
intelligent software that analyzes data tracks work output in 
real time and displays visualized data, enabling employees to 
visually observe their improvement trail and thus enhance 
their sense of competence. 

Organizational Layer

Management

Policies

Training 

&Support

Organizational 

Culture

Technology 

Strategy

Individual Layer

Cognitive Process
• Technology perception

• Decision-making

Psychological State
• Work engagement

• Autonomy & competence

Behavioral Response
• Usage behavior

• Performance output

Task Layer

Task Complexity
Collaboration 

Intensity
Time Sensitivity Cognitive Load

Task-Technology Fit

Technology Layer

AI-Powered Tools
• Natural language processing

• Task allocation

• Decision support

VR/AR Platforms
• Virtual meeting spaces

• 3D visualization

• Presence enhancement

Automation Systems
• Workflow automation

• Process automation

• Data integration

Change 

Leadership

F
e
e
d
b

ac
k

In
fl

u
en

c
e

 
 

Figure 3. Human-AI collaboration mechanism in remote work 

The downward demand pattern is interested in learning 
how intelligent collaboration platforms minimize the 
aggravations of remote work. Automated solutions 
computerize dull and energy-draining tasks such as data 
entry and report generation, freeing up workers' time and 
minds to focus on more valuable work. For instance, AI 
automation reduces routine task time by 20-30%, while 
intelligent scheduling increases perceived autonomy by 40% 
[14, 15]. AI-powered information filtering and prioritization 
capabilities help workers manage information overload, 
while cognitive task-assignment algorithms judiciously 
allocate work based on workers' competency sets and work 
capacity. The clarity of roles is also evident in the technical 
system's unambiguous definition of workflow procedures 
and the boundaries of responsibility. Collaborative working 
platforms correctly assign task owners and deadlines, 
reducing the role ambiguity syndrome typical of remote 
work. The Job Demands-Resources Theory was also used in 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

216 

 

studies on how telework interacts with family spheres [23], 
reminding managers to remain aware of the role that smart 
collaborative systems play in managing work-life boundaries.  

Personal resources are the intervening variables at the 
center of the process by which intelligent collaboration 
systems influence work engagement through capability 
reserves such as self-efficacy, psychological resilience, and AI 
literacy. Employees with higher personal resources will be 
more likely to seek out intelligent system features 
independently and translate technical superiority into real 
improvements, whereas employees with lower personal 
resources will fear and resist new technology. Satisfaction of 
psychological needs is the strongest link from the external 
world to intrinsic motivation. Smart collaboration systems 
indirectly influence levels of satisfaction with autonomy, 
competence, and relatedness needs through processes of 
resource addition and demand reduction. It is when smarter 
collaboration tools get productive work done across both 
networks and, by making individual resources available, 
actually meet workers' most basic psychological needs that 
constant nudging of remote work activity can be achieved. 

3.3 Mediating and moderating mechanisms  
How intelligent collaboration systems influence remote 

employee work engagement is an issue of knowing mediating 
mechanisms and boundary conditions. The focus of this study 
is on two key mediating variables—autonomy and 
workload—and two key moderating variables—
organizational support and AI literacy. Reduced workload is a 
key mediating pathway between intelligent collaboration 
systems and employee work engagement. Reduced workload 
for remote work encompasses task complexity, time pressure, 
information-processing load, and multitasking-switching 
costs. Smart collaboration software reduces this drudgery to 
a great extent through automation. Robotic process 
automation takes over routine work, natural language 
processing automates the generation of meeting minutes, and 
smart scheduling software optimizes the order of tasks. 
Where these technical capabilities function effectively, 
personnel experience reduced workloads, and resources that 
had initially been devoted to low-value issues are freed up. 
Reducing workload creates psychological space for 
employees to focus on key tasks, and recovery in 
concentration capacity directly benefits work involvement. 

Autonomy enhancement is the dynamics of the journey 
of resource enhancement. Innovative collaboration systems 
heighten the autonomy barriers; parameterizable 
collaboration platforms enable individual adjustment, and 
intelligent recommendation systems offer alternatives for 
task performance. If artificial intelligence generates data-
driven recommendations rather than obligatory commands, 
workers have the ultimate decision-making power and 
experience control over labor processes. Self-Determination 
Theory specifies autonomy as a fundamental need for 
intrinsic motivation. When workers enjoy the autonomy of 
independent decision-making, work is not a constraint 
imposed by external factors but a channel of self-expression, 
and the ensuing autonomous motivation is translated into 
high work engagement. 

AI literacy as a person difference variable moderates the 
degree to which intelligent collaboration system impacts are 
achieved. People with greater literacy more profoundly 
comprehend the mechanics of artificial intelligence, can 
correctly gauge the trustworthiness and relevant boundaries 
of algorithmic output, and optimize technological gains. In 
contrast, people with low literacy might exhibit cognitive 

biases against technology, or even experience technology 
anxiety and resistance. That there are moderating effects 
implies that the same technological investment yields 
differentiated returns across employee groups with varying 
literacy levels, suggesting that AI literacy training is a 
complementary policy to technology adoption for 
organizations. The moderating influence of organizational 
support indicates that contextual factors shape technological 
impacts. Organizational support of the firm facilitates a 
positive climate for technology adoption. Employees take 
technological change seriously when management makes a 
clear priority through smart collaboration systems. Proper 
training facilities reduce technical barriers, ongoing technical 
support services facilitate timely help, and a safe 
psychological environment motivates workers to experiment 
and comment. These four mechanism variables collectively 
depict how intelligent collaboration systems affect remote 
employee work engagement. These two mediating variables 
encapsulate the internal process of technological action, and 
these two moderating variables mark the boundary 
conditions of technological effects, collectively determining 
whether and how technological resources can be successfully 
converted into realized benefits. 

3.4 Research propositions  
This research presents eight propositions based on the 

theoretical model outlined above, a comprehensive 
theoretical framework that incorporates direct effects, 
mediating processes, moderating factors, and downstream 
effects. Task-Technology Fit Theory suggests that when 
technological capabilities are highly compatible with task 
requirements, technology use is apt to enhance individual 
work conditions directly. Smart collaboration systems 
combine AI tools, virtual reality platforms, and automated 
processes. From the Job Demands-Resources Theory 
perspective, the system functions as both a resource and a 
demand, and the two pathways complement each other to 
produce positive outcomes. Self-Determination Theory 
shows that promoting employee autonomy and competence 
through technology satisfies basic psychological needs, 
thereby creating intrinsic motivation. Hence, Proposition 1 
predicts that intelligent collaboration systems exert a highly 
positive, direct influence on the work engagement of remote 
workers. 

Workload is a critical mediating variable, consistent with 
the demand reduction pathway. Intelligent collaboration 
systems reduce workers' workload substantially by 
automating much of the work. The Job Demands-Resources 
Theory posits that reduced work demands can free up 
resources for more useful work content. Proposition 2 posits 
that intelligent collaboration systems indirectly enable work 
engagement through workload reduction , with studies 
showing 20-30% workload reduction [14]. Proposition 3 also 
elaborates that workload exercises a partial mediating role 
between intelligent collaboration systems and work 
engagement. The mediating process of the resource-
strengthening path is autonomy strengthening. Intelligent 
collaboration systems grant employees more work flexibility 
and decision autonomy. Self-Determination Theory regards 
autonomy as a fundamental construct of intrinsic motivation. 
Proposition 4 posits that intelligent collaboration systems 
indirectly induce work engagement by enhancing autonomy, 
increasing perceived autonomy by approximately 40% [15]. 
Proposition 5 posits that autonomy partially mediates 
between intelligent collaboration systems and work 
engagement. 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

217 

 

AI literacy, being a different variable, moderates the 
effects of technology. Literacy level reflects workers' overall 
capacity to comprehend, exploit, and modify intelligent 
technology. The Conservation of Resources Theory supposes 
that individual resources moderate workers' capability to 
utilize the utilitarian benefits of intelligent systems 
adequately. Subsequently, Proposition 6 assumes that AI 
literacy positively moderates the effect of intelligent 
collaboration systems on work engagement, such that the 
positive relationship is stronger at higher levels of AI literacy. 
Employees with greater AI literacy can more effectively 
leverage system functionalities, translating technological 
features into realized benefits [23]. If preliminary analyses 
suggest that the two mediation pathways (workload 
reduction and autonomy enhancement) are differentially 
influenced by AI literacy levels, three-way interactions (ICS × 
AI literacy × workload; ICS × AI literacy × autonomy) will be 
tested to clarify whether high-literacy employees benefit 
more from demand reduction or resource enrichment 
mechanisms. 

Organizational support, as a situational element, 
delineates the environmental limits of technological impacts. 
Perceived Organizational Support Theory posits that 
employees' perceptions of organizational appreciation and 
concern influence attitudes and behaviors. Social Exchange 
Theory posits that employees respond with favorable 
attitudes when they observe organizational investment. 
Proposition 7 supposes that organizational support positively 
moderates the effect of intelligent collaboration systems on 
work engagement. Work engagement also affects motivation 
and work performance. The three aspects of work 
engagement are vigor, dedication, and absorption, and all 
these have positive correlations with excellent quality work 
performance. There is extensive empirical evidence 
supporting the positive correlation between the two. 
Proposition 8 assumes that remote workers' work 
engagement strongly influences their work performance. 

These eight research hypotheses collectively form a 
theoretical model, as shown in Figure 4. Intelligent 
collaboration systems influence work engagement through 
two mediating channels: decreased workload and enhanced 
autonomy. AI literacy and organizational support moderate 
effect intensity at the individual and context levels, 
respectively. Work engagement influences work 
performance, and a causal chain forms. 

 

4. Optimization strategies and implementation 

framework 

4.1 Technology integration optimization  
Optimizing technology integration is the foundation for 

improving the productivity of smart collaboration systems, 
and its essence lies in aligning technological capabilities with 
the demands of remote work tasks. Empirical evidence from 
the Task-Technology Fit Theory for remote work during the 
pandemic confirms that the level of fit between technological 
capabilities and task attributes directly affects the 
effectiveness of system use and worker acceptance [16]. Thus, 
technology integration will have to begin from the 
organization's internal work environment rather than 
absolutely aiming at technological advancement. 

The choice and configuration of AI collaboration tools 
must align with the principles of progressive deployment and 
differentiated customization. To start, in the first stage, the 
strategy should be to use mature tools such as smart meeting 
assistants that automatically generate meeting minutes and 
natural language processing tools that aid document writing. 
As employees become more skilled, step-by-step predictive 
analytics software, smart recommendation systems, and 
machine-learning-based optimization algorithms for task 
assignment can be introduced. At the configuration level, the 
individual needs of different jobs need to be taken into 
account: offering tools that stimulate creativity to support 
creative work, facilitating analytical work with data-digging 
capabilities, and implementing intelligent scheduling systems 
to support coordination work. Product interoperability is 
crucial; product standards and open interfaces need to be 
selected to enable smooth data sharing and achieve 
synergistic outcomes. Virtual and augmented reality 
technologies must strike a balance between immersion and 
usability. Immersive spatial design is primarily aimed at 
restoring lost spatial presence in telework. Virtual meeting 
rooms must replicate real offices but also be as easy and 
intuitive as possible to minimize cognitive load. Functionally, 
virtual environments need access to complete digital 
advantage, for example, three-dimensional data visualization 
and virtual whiteboards with multi-user collaboration. Since 
hardware demands and technical constraints can become 
adoption barriers, a hybrid strategy can be adopted initially: 
provisioning core teams with high-end equipment and 
offering lighter versions to reduce entry barriers. The design 
of smart integrated platforms defines the maintainability and 
scalability of the overall technology ecosystem.  

Intelligent Collaboration 

Systems

• AI-powered tools 

• VR/AR platforms

•  Automation systems

Work Demands 

Reduction

(Workload Relief)

Work Resources 

Enhancement

(Autonomy Increase)

Organizational Support

(Moderator)

AI Literacy 

(Moderator)

Work 

Engagement

• Vigor

• Dedication

• Absorption

Work 

Performance

Legend:

Direct/Mediation               Moderation

P8（+）

P6

P7

P1(+)

P2(-)
P3(-)

P5(+)P4(+)

 

Figure 4. Integrated theoretical model with research propositions 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

218 

 

The ideal design would adopt a modular approach, with 
AI tools, virtual collaboration software, and automation 
systems as independent modules within a highly 
standardized interface. The data layer must provide a shared 
data management platform, the user interface layer must 
provide a shared access portal, and the security element must 
be implemented across all architectural design elements. 
Cloud-native design enables elastic scaling, and microservice 
design provides increased system fault tolerance. A 
technology readiness evaluation provides incremental 
deployment through a science-driven methodology. The 
evaluation model employs four dimensions: functional 
maturity, stability, user acceptability, and ecosystem 
enablement. Businesses can categorize future technologies 
into three levels according to this model: deployment, pilot 
experimentation, and continuous monitoring. Top priority 
needs the deployment of high-maturity technology to enable 
the core business, experimentation with medium-maturity 
technology in segregated environments, monitoring low-
maturity technology, and postponing overall investment at 
scale. A dynamic assessment process also has to exist; regular 
reexamination will ascertain, in a timely fashion, new 
opportunities offered by breakthroughs in technology and 
reorient deployment plans accordingly. 

4.2 Human-centered design strategies 
The genuine role of technology is to support, not 

supplant, humans, and this culture must permeate the entire 
process of developing intelligent collaboration systems. 
Human-centered design principles aim to protect and 
preserve fundamental human values in pursuit of 
technological efficacy, ensuring that workers do not lose their 
natural place in human-machine collaboration. It is when 
employees see technology as complementing rather than in 
conflict with them that they will fully embrace and optimize 
intelligent systems. The human autonomy versus automation 
trade-off is the most significant design trade-off. Although 
more automation than necessary promotes short-term 
efficiency, it can take away decision-making opportunities 
and feelings of achievement from personnel and degrade 
skills and the meaning of work. The optimal point of balance 
depends upon the nature of the task. For tasks for which there 
are well-defined rules, extreme automation must be 
employed while leaving human intervention interfaces 
untouched. For tasks that are complex and require creative 
thinking, automation must be defined as a supporting 
function, providing information support while leaving the 
final decision to human beings. Controllable automation-level 
design enables workers to independently choose the 
intervention depth based on their capacity and task 
conditions. Displaying decision logic helps employees clearly 
understand the system's working mechanism and its 
capability limits. 

The strategy of augmentation rather than replacement 
aligns with the overall trajectory of technology adoption. 
Intelligent systems are worth the trouble since they expand 
the universe of what human beings can do. Technology's 
mission is to take on what human beings do not do well, e.g., 
vast information processing and pattern spotting, while 
leaving human beings with greater space to perform things 
creatively and humanely. AI tools need to be crafted as 
intelligent helpers, not independent decision-makers, that 
allow workers to access information more quickly and 
examine alternatives more efficiently, while always keeping 
humans in the loop. Virtual workplaces need to be crafted to 
augment, not replace, face-to-face interaction, bridging the 

loss of information due to physical distance through 
technology. User experience design directly affects real-world 
adoption and long-term usage of systems. Good user 
experience is founded on deep analysis of employees' 
workflows, and technology must be able to fit imperceptibly 
into existing work habits. Interface design needs to follow the 
principle of intuitiveness; frequently used functions should be 
self-documenting, while advanced functions should use 
progressive disclosure to avoid information overload. 
Personalization features enable the system to be configured 
to meet the needs and capabilities of individual users. 
Response time is one of the most critical experience factors in 
remote work environments. 

Reducing automation bias is a primary process for 
ensuring high-quality human-machine collaboration. 
Automation bias describes the overreliance on automated 
system output and the disregard for human judgment. This 
can be corrected by enhancing algorithm transparency—
systems should provide the reasons and the degree of 
confidence for their suggested solutions. When the 
algorithm's confidence level is low, this must be 
communicated with suggestions for manual verification. 
Including information from a single source is intended to 
promote cross-validation among users. Figure 5 illustrates 
the entire optimization strategy framework embracing four 
dimensions: technology integration, human-centered design, 
organizational support, and implementation safeguards. 

Integrated

Optimization

Strategy
Synergistic

Interaction

Technology Integration

                  AI Tool Selection

                  VR/AR Platform Design

                  Intelligent Integration

                  Maturity Assessment

                  Scalability Planning

Human-Centered Design

                  Autonomy Preservation

                  Augmentation Principle

                  User Experience Optimization

                  Automation Bias Mitigation

                  Transparency Enhancement

Implantation Assurance

                    Phased Deployment

                    Pilot Testing

                    Continuous Monitoring

                     Feedback Integration

                    Adaptive Adjustment

Organizational Support

                  Leadership Commitment
 
                  AI Literacy Training

                  Psychological Safety Culture

                  Resource Allocation

                  Recognition System

Focus：Task-Technology Fit & System Integration Focus: Human Agency & Meaningful Work

Focus: Systematic Execution & Iterative ImprovementFocus: Enabling Environment & Culture Building  

Figure 5. Four-dimensional optimization strategy framework 

4.3 Organizational support mechanisms 
Organizational support systems are the institutional 

foundation and cultural bedrock on which effective intelligent 
collaboration system implementation rests. No matter how 
sophisticated technology is, unless there is complementary 
support at the organizational level, it will not be able to play 
its rightful role and may even suffer passive resistance from 
employees. Adequate organizational support is not merely 
about the adequacy of resource investment, but about 
creating conditions conducive to the adoption and reuse of 
technology through leadership demonstration, capability 
building, culture building, and reward system design. 
Transformational leadership plays a vital role in guiding 
technological change and fostering organizational change 
toward technological innovation through intellectual 
stimulation, visionary motivation, and individualized 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

219 

 

consideration. Transformational leaders need to offer an 
unambiguous explanation of the strategic value of smart 
collaboration systems for future organizational development, 
connecting technology adoption to organizational purpose 
and employee development. Leadership example-setting has 
deep exemplary effects. When management starts learning 
about and adopting smart systems and their advantages in 
public spaces, it maximally enhances employees' acceptance 
and willingness to adopt them. Intellectual stimulation 
requires that leaders challenge employees to think critically 
about the application of technology, proposing change while 
challenging existing practices. Individualized consideration is 
evidenced by sympathizing with employees' challenges in 
adapting to technology and by providing additional assistance 
to those who have difficulty. 

The systematic development of AI literacy training 
programs is the strongest driving force in establishing 
employees' technical ability. The training needs to apply 
differentiated stratification and classification strategies and 
provide content adapted to employees' job titles and existing 
skill levels. Basic-level training is for every employee and 
focuses on basic operations; advanced-level training is for 
jobs with more extensive technology application and focuses 
on advanced functions; expert-level training develops 
internal technology champions for the company. Training 
modes must be varied and flexible, blending e-learning, 
practice, and peer-to-peer learning. Contextualized 
instructional design integrates learning technology into real-
world contexts. Establishing a culture of psychological safety 
creates the conditions for workers to learn new technology. 
Psychological safety is a team member's feeling that it is safe 
to take interpersonal risks within the team—they can speak 
up, make mistakes, and ask for help without fear of negative 
reaction. Psychological safety in the implementation of 
intelligent collaboration systems is especially crucial because 
technology learning necessarily entails trial and error and 
failure. The managers must send inclusive signals through 
their behavior and attitudes, viewing technical failures and 
use errors as opportunities to learn, not as opportunities for 
punishment. 

Constant feedback and two-way communication 
channels provide dynamic streamlining of the technology 
implementation process. Regular feedback surveys on system 
use harvest employees' assessments of the system; focus 
group sessions allow in-depth discussion; technical support 
hotlines offer real-time resolution of everyday problems. 
Organizations are under an obligation to act on feedback 
received and to provide feedback improvement outcomes to 
employees. Employees appreciate the value of giving an 
opinion when their views are taken seriously. Reward and 
recognition systems motivate workers to use intelligent 
collaboration systems effectively by reinforcing good 
behavior, publicly honoring technology-use role models, 
instituting technology-innovation awards, and enabling peer 
recognition processes that allow employees to nominate and 
reward one another. 

4.4 Phased implementation roadmap 
Successful deployment of intelligent collaboration 

systems requires adhering to a phased roadmap, minimizing 
change risk by proceeding step by step, and ensuring quality 
at each phase. The whole implementation process is advised 
to be split into four phases: evaluation and preparation, pilot 
launch, full launch, and further development, with an overall 
duration of over twelve months. Preparation and evaluation 
are the foundation stage of the overall implementation 

roadmap, planned to be executed within the first three 
months of initiation. The key activity in this stage is to 
thoroughly evaluate the organization's status quo and 
prepare well for the upcoming implementation. Needs 
analysis provides detailed insight into specific work 
situations within job roles and departments through 
questionnaires, interviews, and workflow observation, and 
identifies pain points in remote collaboration and technology 
requirements. Technology research explores intelligent 
collaboration tools and platforms available in the market, 
examining their functional features, cost models, and 
compatibility. Infrastructure analysis assesses whether 
existing network, hardware, and software infrastructures can 
support the operation of intelligent collaboration systems and 
gauges an organization's readiness. From this, a complete 
implementation plan is developed, including objectives, 
actions, accountable staff, and deadlines for each phase, and a 
cross-functional project team is formed to coordinate 
implementation. 

The pilot phase spans months three to six and establishes 
the feasibility of the technical solution by conducting small-
scale tests and gaining implementation experience. Pilot 
department selection should be technology-forward, reflect 
real-world cases, and be medium-sized to facilitate 
management. System deployment deploys and configures 
selected smart collaboration tools within the pilot scope, and 
the system becomes stable and compatible with existing 
business systems. Training programs conduct intensive 
training for pilot department employees, combining small-
group instruction with one-on-one coaching. Usage support 
provides round-the-clock technical support services during 
the pilot period, rapidly responding to and resolving 
problems encountered by employees. Data collection tracks 
pilot effectiveness through multiple channels, including 
system logs, usage feedback, and performance metrics. 
Regular pilot review meetings summarize feedback from all 
parties and discuss improvement plans. 

Rollout on a scaled basis occurs between months six and 
twelve, extending solutions piloted to the entire organization. 
Rollout planning must be an incremental batch-by-batch 
process based on department readiness and business 
priority. System rollout is organization-wide, but must be 
configured to meet individual departmental needs. Training 
scale increases exponentially, leveraging pilot department 
personnel as internal trainers. Change management tasks 
infuse the whole rollout process. Monitoring systems 
constantly measure system utilization and effectiveness 
metrics on a department-by-department basis. Knowledge 
management systems are starting to be developed. 

The sustainable development phase begins at month 
twelve and onward, with the focus on consolidating 
technology application and continuously building 
organizational competencies. System optimization 
refinement continuously improves technical configurations 
based on usage patterns and user feedback. Advanced 
training provides progressive courses to users already 
familiar with basic operations. Innovative applications 
encourage employees to discover new uses of the technology. 
Performance measurement periodically evaluates the long-
term effect of intelligent collaboration systems on 
organizational performance. Technology evolution tracking 
keeps focus on new technologies. As shown in Figure 6, the 
phased implementation roadmap outlines the entire process 
from evaluation and preparation to sustainable development. 
 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

220 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5. Conclusion  
This research examines the mechanisms and 

optimization strategies by which intelligent collaboration 
systems influence remote workers' work engagement. The 
theoretical framework illustrates multi-level mechanisms: 
intelligent collaboration systems impact work engagement 
through two mediating channels—workload reduction and 
autonomy enhancement—moderated by AI literacy at the 
individual level and organizational support at the contextual 
level. Work engagement ultimately translates into 
performance output, forming a complete causal chain. 
Integrating Task-Technology Fit Theory, Job Demands-
Resources Theory, and Self-Determination Theory, the model 
encompasses technological features, work environment, and 
psychological requirements. The optimization strategy 
provides guidelines across four dimensions: technology 
integration, human-centered design, organizational support 
mechanisms, and phased implementation. The theoretical 
contribution manifests in three aspects. First, the study 
incorporates intelligent collaboration technology as work 
resources, broadening previous research that focused solely 
on social factors, and reveals bidirectional mechanisms of 
resource augmentation and demand relief. Second, by 
expanding the application of Self-Determination Theory, it 
explains how technology interacts with intrinsic motivation 
through satisfying basic psychological needs, advancing 
human-computer collaboration research from behavioral 
observation to motivational foundations. Third, multi-theory 
integration avoids single-framework limitations, forging an 
unbroken explanatory link from technological characteristics 
to psychological needs to behavioral outcomes. Practical 
implications address multiple stakeholders. Successful 
deployment requires systematic management support and 
cultural nurturing beyond technological innovation—
including transformational leadership, AI literacy training, 
psychological safety culture, and continuous feedback 
channels. Technology designers must adopt human-centered 
philosophies that emphasize augmentation over replacement. 
Policymakers face emerging challenges, including data 
privacy protection and algorithmic fairness legislation. 
Research limitations include a lack of large-scale empirical 
validation and insufficient examination of cross-level 
mechanisms.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Future research should conduct longitudinal studies, explore 
the impacts of generative AI, and test the universality of a 
cross-cultural framework to advance understanding of 
intelligent collaboration systems in remote work contexts. 
Research limitations include a lack of large-scale empirical 
validation and insufficient examination of cross-level 
mechanisms. Future research should conduct longitudinal 
studies, explore the impacts of generative AI, and test the 
universality of a cross-cultural framework to advance 
understanding of intelligent collaboration systems in remote 
work contexts. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

References 

[1] Brynjolfsson, E., et al., COVID-19 and remote work: An 

early look at US data. 2020, National Bureau of 

Economic Research. 

https://www.nber.org/papers/w27344 

[2]  Mehta, P., Work from home—Work engagement amid 

COVID‐19 lockdown and employee happiness. Journal 

of public affairs, 2021. 21(4): p. e2709.  

[3]  Galanti, T., et al., Work from home during the COVID-

19 outbreak: The impact on employees’ remote work 

productivity, engagement, and stress. Journal of 

occupational and environmental medicine, 2021. 

63(7): p. e426-e432. 

[4]  Mahadevan, J., et al., The remote work 

transformation: New actors, new contexts, new 

implications. 2025, Taylor & Francis. p. 1653-1665.  

Phase 1: Assessment & Preparation
(0-3 months)

• Needs analysis & pain point identification

• Technology research & vendor evaluation

• Infrastructure assessment

• Implementation plan development

• Project team formation

Phase 2: Pilot Implementation
(3-6 months)

• Pilot department selection

• System deployment & configuration

•  Intensive training for pilot users

• Usage monitoring & issue tracking

• Feedback collection & analysis

Phase 3: Scale-up Deployment
(6-12 months)

• Organization-wide system rollout

• Bach-based training programs

• Change management activities

• Performance monitoring

• Knowledge base development

Phase 4: Sustainable Development
(12+ months)

• System optimization & upgrades

• Advanced training programs

• Innovation encouragement

• Long-term Impact evaluation

• Continuous improvement

  Milestone: Detailed implementation 

plan approved

  Milestone: Pilot success with 

measurable benefits

  Milestone: Organization-wide adoption 

achieved

  Milestone: Embedded in organizational 

culture

Key Deliverables by Phase

Phase 1
· Needs report

·Tech selection

·Implementation plan

Phase 2
·Pilot report

·Best practices

·Optimization list

Phase 3
·Rollout report

·Knowledge base

·Training materials

Phase 4
·Impact evaluation

·Upgrade plan

·Culture assessment

Month 0 Month 3
Month 6 Month 12 Ongoing

C
o
n

tin
u

o
u

s

Im
p
ro

v
em

en
t

L
o
o

p

 

Figure 6. Four-dimensional optimization strategy framework 

 



Jia Wang /Future Technology                                                                                             February 2026| Volume 05 | Issue 01 | Pages 209-221 

221 

 

[5]  Pass, S. and M. Ridgway, An informed discussion on 

the impact of COVID-19 and ‘enforced’remote 

working on employee engagement. Human Resource 

Development International, 2022. 25(2): p. 254-270.  

[6]  Chanana, N. and Sangeeta, Employee engagement 

practices during COVID‐19 lockdown. Journal of 

public affairs, 2021. 21(4): p. e2508.  

[7]  Carnevale, J.B. and I. Hatak, Employee adjustment and 

well-being in the era of COVID-19: Implications for 

human resource management. Journal of business 

research, 2020. 116: p. 183-187.  

[8]  Wang, B., et al., Achieving effective remote working 

during the COVID‐19 pandemic: A work design 

perspective. Applied psychology, 2021. 70(1): p. 16-

59.  

[9]  Anand, A.A. and S.N. Acharya, Employee engagement 

in a remote working scenario. International Research 

Journal of Business Studies, 2021. 14(2): p. 119-127. 

[10]  Kniffin, K.M., et al., COVID-19 and the workplace: 

Implications, issues, and insights for future research 

and action. American psychologist, 2021. 76(1): p. 63. 

[11]  Intellectual Property Policy Dilemmas of Generative 

AI in Employee Training: Ownership Definition and 

Legal Adaptability. Lex localis - Journal of Local Self-

Government, 2025. 2025(23). 

[12]  McAnally, K. and M.S. Hagger, Self-determination 

theory and workplace outcomes: A conceptual review 

and future research directions. Behavioral sciences, 

2024. 14(6): p. 428. 

[13]  Brunelle, E. and J.-A. Fortin, Distance makes the heart 

grow fonder: An examination of teleworkers’ and 

office workers’ job satisfaction through the lens of 

self-determination theory. Sage Open, 2021. 11(1): p. 

2158244020985516. 

[14]  Bakker, A.B., E. Demerouti, and A. Sanz-Vergel, Job 

demands–resources theory: Ten years later. Annual 

review of organizational psychology and 

organizational behavior, 2023. 10(1): p. 25-53. 

[15]  Gagné, M., et al., Understanding and shaping the 

future of work with self-determination theory. Nature 

Reviews Psychology, 2022. 1(7): p. 378-392. 

[16]  Kamdjoug, J.R.K., et al., Task-Technology fit and ICT 

use in remote work practice during the COVID-19 

pandemic. Journal of Global Information Management 

(JGIM), 2023. 31(1): p. 1-24. 

[17]  Bakker, A.B. and E. Demerouti, Job demands–

resources theory: taking stock and looking forward. 

Journal of occupational health psychology, 2017. 

22(3): p. 273. 

[18]  Morgeson, F.P. and S.E. Humphrey, The Work Design 

Questionnaire (WDQ): developing and validating a 

comprehensive measure for assessing job design and 

the nature of work. Journal of applied psychology, 

2006. 91(6): p. 1321. 

[19]  Long, D. and B. Magerko. What is AI literacy? 

Competencies and design considerations. in 

Proceedings of the 2020 CHI conference on human 

factors in computing systems. 2020. 

https://doi.org/10.1145/3313831.3376727 

[20]  Eisenberger, R., et al., Perceived organizational 

support. Journal of Applied psychology, 1986. 71(3): 

p. 500. 

[21]  Kolbjørnsrud, V., Designing the Intelligent 

Organization: six principles for Human-AI 

collaboration. California Management Review, 2024. 

66(2): p. 44-64. 

[22]  Gomez, C., et al., Human-AI collaboration is not very 

collaborative yet: a taxonomy of interaction patterns 

in AI-assisted decision making from a systematic 

review. Frontiers in Computer Science, 2025. 6: p. 

1521066. 

[23]  Chen, I.S., Extending the job demands–resources 

model to understand the effect of the interactions 

between home and work domains on work 

engagement. Stress and Health, 2024. 40(4): p. e3362. 

 

  

This article is an open-access article distributed under the 

terms and conditions of the Creative Commons Attribution 

(CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 

https://creativecommons.org/licenses/by/4.0/

