Leadership Under Multimodal Pressure: How Organizational Decisions Shape Human Interaction with Immersive Technologies

Abstract

The increasing integration of multimodal technologies, including augmented and virtual reality and interactive digital systems, has shifted the focus of innovation from technological capability to user experience and interaction. In organizational settings, leadership structures play a key role in shaping how these technologies are designed and experienced. This study examines how leadership control orientation influences innovation outcomes through multimodal experience design and cognitive burden. Using structural equation modeling on a sample of 3017 employees who actively use multimodal systems, the study develops a process-based model linking leadership, multimodal experience design, cognitive burden, and innovation. The findings suggest that control-oriented leadership is negatively associated with multimodal experience design and positively associated with cognitive burden, whereas well-structured multimodal systems are associated with lower levels of cognitive burden. Multimodal design emerges as a central driver of perceived innovation, whereas cognitive overload negatively affects innovation outcomes. The results further reveal a sequential mediation process involving multimodal experience design and cognitive burden. Multi-group analysis confirms that these relationships remain stable across different levels of environmental control. The study contributes by integrating leadership, human–technology interaction, and experience design into a unified framework, offering a process-oriented explanation of innovation in multimodal environments.

1. Introduction

The rapid diffusion of multimodal technologies, including augmented and virtual reality, interactive interfaces, and sensor-based systems, has fundamentally changed how people engage with digital environments [1,2,3]. These technologies are increasingly embedded inside organizational contexts, where they function not only as tools for task execution but also as platforms through which users experience, interpret, and evaluate innovation [4,5,6]. Contemporary research on smart hospitality further confirms that AI-enabled and next-generation communication infrastructures are becoming embedded in service environments and, as a result, reshaping organizational processes, user interactions, and innovation capacity [7]. As a result, innovation is no longer determined solely by technology-based capabilities, but by the quality of human–technology interaction, encompassing perceptual, cognitive, and experiential dimensions [8].

Despite this shift, existing research remains fragmented. Studies in innovation and organizational behavior have traditionally emphasized leadership as a driver of performance and creativity [9,10], while research in human–computer interaction and user experience has focused on system design and usability [11]. These streams have largely evolved in parallel, with limited integration. Consequently, there is insufficient understanding of how organizational decisions, particularly leadership structures, shape the way users engage with multimodal systems and, ultimately, how innovation is perceived in such environments. This represents a critical gap, as multimodal technologies operate at the intersection of organizational control, system design, and cognitive burden, requiring a more integrated analytical perspective.

A further limitation of the existing literature lies in its reliance on contingency-based explanations, which assume that organizational conditions, such as levels of control, fundamentally alter behavioral relationships [12,13,14]. While this perspective has generated valuable insights, it often overlooks the possibility that underlying mechanisms may remain stable across contexts [15]. In the case of multimodal systems, where interaction processes are structured through design and cognitive processing, it remains unclear whether environmental control modifies the structure of relationships or simply influences their intensity [16,17,18]. Addressing this issue is essential for advancing both theoretical and practical understanding of innovation in complex digital environments.

Despite growing interest in leadership, user experience, and technology-mediated innovation, three important gaps remain. First, existing studies typically examine leadership and human–technology interaction as separate domains rather than as interconnected processes. Second, limited attention has been devoted to understanding how leadership influences innovation indirectly through multimodal experience design and cognitive burden. Third, little evidence exists regarding whether these mechanisms remain stable across organizational environments characterized by different levels of control. Against this background, the present study investigates how leadership control orientation is associated with innovation outcomes through its relationship with multimodal experience design and cognitive burden. Rather than examining leadership as a direct determinant of innovation, the study conceptualizes it as an organizational factor associated with interaction conditions, which are subsequently linked to innovation experiences and evaluations. This approach combines insights from organizational theory [19,20], experience design [21,22], and cognitive processing [23,24] to explain how innovation emerges within technology-mediated environments.

The study addresses the following research question: How are organizational leadership structures associated with user interaction in multimodal technologies, and through which mechanisms are these interactions linked to perceived innovation outcomes? To address this question, a structural model is developed and empirically tested in which leadership control orientation is associated with innovation outcomes both directly and indirectly through multimodal experience design and cognitive burden. In addition, a multi-group SEM analysis is employed to examine whether these relationships differ across low- and high-control environments.

The contribution is threefold. First, a process-based model of innovation is advanced, integrating leadership, multimodal experience design, cognitive burden, and innovation outcomes, thereby bridging macro-level organizational dynamics and micro-level user experience. Second, the mediating roles of multimodal experience design and cognitive burden are demonstrated, providing a deeper understanding of how leadership effects are transmitted within technology-mediated environments. Third, dominant contingency assumptions are challenged by empirically confirming the structural invariance of relationships across different control conditions, offering evidence for the stability of underlying interaction mechanisms.

Overall, the study contributes to organizational research, human–computer interaction, and multimodal systems by providing empirical evidence on the mechanisms linking leadership structures and innovation perceptions in technology-mediated environments.

2. Literature Review and Hypothesis Development

Contemporary digital environments are increasingly shaped by multimodal technologies that integrate visual, auditory, and interactive elements into unified user experiences [25,26]. In such contexts, innovation is no longer defined solely by technological advancement, but by how users perceive, process, and engage with complex systems [27,28]. This shift places human–technology interaction at the center of innovation research [29], requiring a closer examination of how organizational conditions are associated with experiential and cognitive processes [30,31]. Rather than asking whether technology improves innovation, the more relevant question concerns how organizational decisions, especially leadership structures, are associated with the way users experience and interpret multimodal systems [32,33,34]. Leadership has long been recognized as a central driver of organizational outcomes [35,36], yet its role in technology-mediated environments extends beyond strategic direction into the structuring of interaction itself.

Multimodal technologies are designed to combine multiple communication and interaction channels, including visual, auditory, tactile, and interactive inputs, into a coherent user experience [2,16]. Unlike traditional digital systems that rely predominantly on a single mode of interaction, multimodal environments enable users to process information through complementary sensory pathways, thereby enhancing engagement, immersion, and interpretive richness [25,26]. Contemporary multimodal frameworks emphasize the integration and orchestration of different modalities into coherent interaction processes that support information acquisition, navigation, decision-making, and experiential evaluation [17,32]. Research in immersive virtual environments further suggests that multimodal interaction is associated with higher levels of user presence, participation, and involvement by facilitating more natural and adaptive forms of human–technology communication [1,29]. These characteristics make multimodal environments particularly suitable for examining the relationships among organizational conditions, user experiences, cognitive burden, and perceptions of innovation.

Within these frameworks, experience design represents a critical mechanism through which multimodal technologies influence user perceptions and behavioral outcomes. Experience design extends beyond interface functionality to encompass sensory integration, usability, perceived control, engagement, and the overall quality of interaction experiences. Well-designed multimodal environments facilitate intuitive interaction pathways, reduce cognitive effort, and encourage active user involvement in the interaction process [8,29,31]. User participation is particularly important because innovation perceptions increasingly emerge from experiential engagement and interaction quality rather than from technological features alone [27,28]. Consequently, the effectiveness of multimodal systems depends not only on technological sophistication but also on the quality of interaction experiences they create, making multimodal experience design and cognitive burden central mechanisms through which innovation outcomes are formed [8].

Leadership has been examined through multiple theoretical perspectives, including transformational, transactional, servant, participative, and digital leadership approaches, each emphasizing different mechanisms through which leaders influence organizational outcomes [37,38,39]. While these perspectives have substantially contributed to understanding employee motivation, creativity, and innovation, they primarily focus on vision articulation, empowerment, relational support, or strategic guidance. However, the present study is situated within multimodal technological environments characterized by increasing levels of digital coordination, algorithmic monitoring, procedural standardization, and technology-mediated governance. In such settings, leadership influences innovation not only through inspiration or motivation but also through the regulation of interaction conditions under which users engage with technological systems. Recent research on algorithmic management and AI-enabled organizational governance suggests that contemporary digital environments increasingly rely on formalized control structures embedded within technological infrastructures, making issues of monitoring, coordination and governance central to organizational functioning [40,41].

Consequently, this study focuses on Leadership Control Orientation because it captures the organizational mechanisms most directly associated with the regulation of human–technology interaction. Unlike broader leadership constructs, Leadership Control Orientation reflects the extent to which organizational environments emphasize standardization, supervision, performance monitoring, and behavioral regulation. These dimensions are particularly relevant in multimodal systems, where user experiences are increasingly shaped by digitally mediated control structures and algorithmic decision processes. Prior studies indicate that algorithmic management systems significantly influence user autonomy, cognitive demands, and interaction experiences, while AI governance frameworks highlight the growing importance of organizational control mechanisms in shaping technology adoption and evaluation [42,43]. Accordingly, Leadership Control Orientation provides the most theoretically appropriate lens for examining how organizational authority becomes translated into multimodal experience design, cognitive burden, and perceived innovation outcomes.

Control-oriented leadership, characterized by standardization, monitoring, and performance pressure, tends to prioritize efficiency and predictability over flexibility and exploration [44,45]. In multimodal environments, such orientations can restrict the design space within which interactive systems are developed, limiting opportunities for sensory richness, user agency, and narrative immersion. From a human–computer interaction perspective, these constraints reduce the adaptive and experiential qualities of systems, thereby shaping how users engage with them [46,47]. Accordingly, leadership is not external to the technological experience but embedded within it, influencing the structure and quality of interaction [48]. This perspective suggests that leadership control orientation negatively affects multimodal experience design by constraining its flexibility and exploratory potential.

H1. 

Leadership Control Orientation negatively influences Multimodal Experience Design.

Beyond its association with system design, leadership may also be related to the cognitive conditions under which interaction occurs [49,50]. Cognitive load theory posits that environments characterized by complexity, pressure, and limited autonomy increase mental effort and reduce clarity [51]. In controlled organizational settings, users interacting with multimodal systems are likely to experience heightened cognitive strain due to rigid procedures and reduced interpretive freedom [52,53]. Research in human–technology interaction further indicates that poorly aligned systems amplify cognitive burden, particularly when users must navigate complex or constrained interfaces [46]. This suggests that leadership control orientation is associated with increased cognitive load and reduced clarity during interaction.

H2. 

Leadership Control Orientation positively influences Cognitive Burden.

At the same time, the design of multimodal systems plays a critical role in shaping cognitive outcomes [54,55]. Well-integrated multimodal environments organize sensory input, support intuitive navigation, and provide coherent interaction pathways, thereby reducing cognitive friction [56]. Experience design research emphasizes that immersive and narrative-rich systems enhance user understanding and perceived control, facilitating smoother cognitive processing [47,57]. In this sense, design operates as a regulatory mechanism that can either amplify or mitigate cognitive demands.

H3. 

Multimodal Experience Design negatively influences Cognitive Burden.

The experiential quality of multimodal systems is also closely tied to perceptions of innovation [58]. Innovation is increasingly evaluated through user experience, where novelty, engagement, and immersion serve as key indicators of value [59,60]. Systems that successfully integrate multiple sensory channels and interactive elements tend to be perceived as more innovative because they provide richer and more engaging experiences [57]. This aligns with the broader shift toward experience-based innovation, in which the perception of innovation emerges from interaction rather than purely technical features.

H4. 

Multimodal Experience Design positively influences Innovation Outcome.

User cognition further mediates the relationship between system interaction and innovation perception. Cognitive overload, excessive mental effort, and difficulties in information processing reduce usability and undermine perceived innovation [47,61]. Accordingly, cognitive burden plays a central role in translating system interaction into innovation outcomes.

H5. 

Cognitive Burden negatively influences Innovation Outcome.

Taken together, these relationships suggest a process in which leadership influences innovation indirectly through its effects on system design and cognitive processing. Rather than acting as a direct determinant of innovation [62], leadership may be associated with the interaction conditions under which users engage with technology, which are subsequently linked to perceptions of innovation. This aligns with mediation frameworks in organizational research that emphasize the importance of intermediate mechanisms linking antecedents and outcomes [63,64]. In the context of multimodal systems, this process can be conceptualized as a sequential pathway in which leadership is associated with multimodal design, multimodal design is related to cognitive burden, and cognitive burden is linked to innovation outcomes.

H6. 

Multimodal Experience Design and Cognitive Burden sequentially mediate the relationship between Leadership Control Orientation and Innovation Outcome.

In addition to these indirect effects, leadership may also exert a direct influence on innovation perception [65,66]. Control-oriented environments can suppress autonomy and limit experiential variability, which may reduce perceived innovation even in the absence of design or cognitive constraints. This suggests the presence of a direct negative pathway from leadership to innovation outcomes.

H7. 

Leadership Control Orientation negatively influences Innovation Outcome.

Finally, the role of environmental control as a potential moderator warrants examination. Traditional contingency perspectives would suggest that the strength of relationships within the model varies across different control conditions [67]. However, emerging process-oriented approaches argue that underlying mechanisms may remain stable across contexts, even when conditions differ [68]. Recent research in the context of leadership also indicates that leadership competencies can be assessed through structured multi-criteria methods, which supports the broader movement toward more systematic and detailed leadership measurement in complex environments, such as the contemporary business environment [69]. In the context of multimodal systems, this raises the question of whether control intensity alters the structure of relationships or merely affects their contextual expression.

H8. 

The structural relationships within the model are invariant across different levels of environmental control.

These hypotheses form a unified framework that integrates leadership, multimodal design, and cognitive processing into a coherent explanation of innovation in technology-mediated environments. The proposed framework is grounded in a process-based human–technology interaction perspective, which conceptualizes innovation as an emergent outcome of sequential interactions between organizational structures, technological environments, and user cognition. Within this perspective, leadership is not expected to be associated with innovation primarily through direct intervention, but through its association with the conditions under which users interact with multimodal systems. These interaction conditions are reflected in the quality of multimodal experience design, which is subsequently associated with cognitive burden during technology use. The level of cognitive burden is subsequently associated with how users interpret, evaluate, and perceive innovation outcomes. Consequently, the proposed sequence from Leadership Control Orientation to Multimodal Experience Design, Cognitive Burden, and Innovation Outcome reflects a theoretically informed process linking organizational conditions with user-level innovation perceptions.

Based on the theoretical arguments and hypotheses developed above, Figure 1 presents the proposed conceptual framework and the hypothesized relationships among the study constructs.

3. Materials and Methods

The data collection process was conducted over a twelve-month period, from March 2025 to March 2026, using the Prolific academic platform [70,71]. The extended data collection period was intentionally adopted to capture respondents from a wide range of industries and organizational settings in which multimodal technologies are implemented at different rates and levels of maturity. Because the study focuses on relatively specialized technological environments, a longer collection window was necessary to obtain a sufficiently large and heterogeneous sample. Moreover, the investigated constructs represent relatively stable perceptions of organizational control, multimodal experience design, and innovation outcomes, as well as relatively stable levels of cognitive burden, rather than short-term reactions to specific events. Consequently, the length of the data collection period was not expected to introduce systematic temporal bias into the results.

Participants were recruited through the Prolific academic research platform, which provides access to a large pool of pre-registered respondents. Participation was restricted to unique Prolific accounts, ensuring that each respondent could complete the survey only once through their individual Prolific ID. Respondents received monetary compensation in accordance with Prolific’s recommended payment guidelines. Eligibility was determined through a combination of platform-based recruitment and additional screening questions embedded within the survey instrument. Only respondents who satisfied all inclusion criteria and successfully completed the quality-control procedures were retained in the final dataset.

To assess potential non-response bias, a late-response bias analysis was conducted by comparing the first and last 25% of valid responses. As shown in

Table 1. Late-response bias assessment.

Construct	Early Respondents (n = 754)	Late Respondents (n = 754)	p-Value
Leadership Control Orientation	3.007	2.996	0.731
Multimodal Experience Design	3.010	2.993	0.610
Cognitive Burden	3.020	3.027	0.845
Innovation Outcome	2.971	2.993	0.584

Note: Independent-samples t-tests revealed no statistically significant differences between early and late respondents across any focal construct (all p > 0.05), suggesting that late-response bias was not a concern.

, no statistically significant differences were observed between early and late respondents across any focal construct (all p > 0.05), indicating that late-response bias is unlikely to have affected the findings. Additionally, 180 responses (5.6% of all collected cases) were excluded during data screening due to incompleteness (n = 100) or failure of embedded attention-check procedures (n = 80). Because these cases did not provide complete and valid measurements across the focal constructs, formal comparisons with the retained sample were not feasible. Nevertheless, the relatively small proportion of excluded cases reduces the likelihood that their exclusion introduced substantial bias into the final dataset.

The target population consisted of adult employees working in organizations where digital and multimodal technologies form part of routine work processes. To ensure that respondents possessed sufficient experience to evaluate both organizational and technological dimensions of the study, several eligibility criteria were applied. Participants were required to (1) be at least 18 years old, (2) be currently employed within an organization, (3) regularly use digital technologies as part of their work activities, and (4) have prior experience interacting with systems involving multiple communication or interaction modalities (e.g., virtual reality, augmented reality, simulation systems, interactive dashboards, or comparable digital environments). Only respondents who satisfied all screening requirements, completed the questionnaire, and successfully passed embedded attention checks were retained for analysis.

This approach enabled access to a large and diverse pool of respondents while allowing for precise screening based on predefined eligibility criteria, ensuring that only participants embedded in relevant organizational and technological contexts were retained. The sample is structurally defined by a strict screening procedure that retains only respondents embedded in organizational contexts and actively engaged with digital and multimodal systems. All 3017 participants confirmed employment within an organization, routine use of digital technologies, and prior interaction with systems involving multiple input/output modalities. The fact that all retained respondents satisfied the screening requirements indicates that the dataset does not include peripheral or inexperienced users, but rather reflects a population for whom interaction with complex digital environments constitutes a normal component of work practice. Such a design ensures that subsequent analyses capture situated human–technology interaction rather than abstract attitudes, thereby strengthening the empirical grounding of the study. The distribution of exposure to immersive and interactive systems further supports this positioning. A substantial proportion of respondents report frequent (40.1%) or occasional (39.6%) use, while a smaller segment indicates rare engagement (20.2%). This configuration suggests a balanced sample in which both experienced and moderately exposed users are represented, without over-reliance on expert users. The absence of respondents with no exposure confirms that all participants meet a minimum experiential threshold, allowing the analysis to focus on variations in intensity rather than presence versus absence of interaction. This gradient is particularly relevant for capturing differences in cognitive processing, interface adaptation, and experiential interpretation.

Organizational structuring of system use appears as a differentiated but clearly present condition. Over seventy percent of respondents report the existence of either clearly defined or partially defined guidelines governing the use of digital and immersive systems, indicating that such technologies are not used in an ad hoc manner but are embedded within formal or semi-formal organizational frameworks. At the same time, nearly one fifth of respondents operate without formal guidelines, and an additional segment remains uncertain about their existence. This dispersion reflects uneven institutionalization of multimodal technologies across organizations and introduces meaningful variability in procedural clarity and normative expectations. Such variability is analytically valuable, as it provides a contextual layer through which leadership orientation and control can be interpreted.

Perceived organizational control over interaction with these systems reveals a polarized structure. Respondents are distributed across both low-control conditions (22.8% reporting no control and 22.7% low control) and high-control conditions (20.5% high control and 20.7% very high control), while a smaller proportion occupies a moderate position (13.4%). This near-symmetric distribution between low and high control environments indicates the coexistence of contrasting organizational logics, ranging from autonomy-oriented settings to highly regulated and monitored contexts. The relatively limited presence of moderate control reinforces the distinction between these two poles and supports their analytical separation. In practical terms, this enables the construction of clearly differentiated groups that reflect fundamentally different approaches to managing human–technology interaction.

The technological landscape represented in the sample is both diverse and evenly distributed. Respondents report primary use across augmented reality, virtual reality, simulation systems, multi-screen environments, interactive dashboards, and other advanced interfaces, with no single category dominating the dataset. This balance reduces the risk of technology-specific bias and allows the analysis to operate at the level of multimodal interaction rather than being driven by particular devices or applications. The inclusion of multiple system types reflects the broader transformation of work environments toward integrated, sensor-based, and interactive systems, where users navigate complex streams of information across modalities.

The frequency of system use suggests that interaction with such technologies is deeply embedded in everyday work processes. A majority of respondents engage with these systems on a daily or weekly basis, suggesting sustained exposure rather than occasional or experimental use. This intensity is critical for capturing stable perceptions of usability, cognitive load, and control, as repeated interaction allows users to develop expectations, routines, and evaluative frameworks. The presence of less frequent users introduces additional variability, enabling the analysis to account for differences in familiarity and adaptation without compromising the overall experiential depth of the sample.

The sectoral composition aligns with domains in which multimodal and immersive systems are functionally integrated into operational processes. Tourism and hospitality together form the largest segment, reflecting environments where digital interfaces, augmented experiences, and interactive service systems are increasingly central to user engagement. Logistics and warehousing contribute contexts characterized by real-time navigation, system-assisted decision-making, and operational precision. Education and training capture structured environments in which simulation and virtual reality are used for skill development, while industry and manufacturing represent settings involving digital twins, simulations, and sensor-driven systems. This cross-sectoral distribution ensures that the analysis captures variation in organizational structure, task complexity, and control intensity, while maintaining a consistent underlying condition of technologically mediated work. Overall, the dataset reflects a population situated at the intersection of organizational structures, leadership practices, and advanced technological environments. The combination of strict screening, variability in exposure, differentiated control conditions, and sectoral diversity provides a robust empirical basis for examining the associations between organizational control and user experience within multimodal systems.

The measurement instrument consisted of 32 statements designed to capture leadership structures, multimodal interaction characteristics, cognitive processing, and perceived innovation within organizational technology environments. The questionnaire items were developed through a theory-driven scale construction process. Because the proposed model integrates organizational control, multimodal interaction, cognitive processing, and innovation perception within a single framework, no previously validated instrument existed that fully captured all study dimensions simultaneously. Therefore, the items were adapted and synthesized from established theoretical foundations in organizational control, leadership research, human–computer interaction, experience design, cognitive load theory, and innovation studies [44,45,46,47,48,49,50,51,52,53,54,55,56,57]. Prior to the main survey, the questionnaire was reviewed by three academic researchers with expertise in organizational behavior, digital technologies, and quantitative research methods to assess clarity, content validity, and conceptual consistency. Minor wording adjustments were implemented based on their feedback before the instrument was administered to the full sample. The item pool was developed to operationalize the four core constructs of the study: Leadership Control Orientation, Multimodal Experience Design, Cognitive Burden, and Innovation Outcome. A summary of the constructs, measurement dimensions, and their theoretical foundations is presented in

Table 2. Summary of Constructs and Measurement Dimensions.

Construct	Description	Initial Items	Final Items	Theoretical Basis
Leadership Control Orientation	Organizational monitoring, standardization, supervision, and behavioral regulation	8	5	[40,44,45]
Multimodal Experience Design	Sensory integration, usability, immersion, engagement, and interaction quality	8	6	[46,47]
Cognitive Burden	Cognitive load, mental effort, information-processing demands, and reduced clarity	8	6	[51,53]
Innovation Outcome	Perceived novelty, value creation, innovation effectiveness, and innovation evaluation	8	6	[57,58,59,60]

. All items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Detailed information regarding the complete item pool, retained indicators, eliminated items, and the final factor structure is provided in Appendix A.

Following exploratory factor analysis, the measurement model was refined to improve construct validity, parsimony, and interpretability. Items with low factor loadings, cross-loadings, or conceptual redundancy were systematically removed. Specifically, nine items were removed during the purification process. Item elimination was guided by a combination of statistical and theoretical criteria, including low factor loadings, substantial cross-loadings, and conceptual redundancy. The objective was to retain indicators that demonstrated strong factorial performance while preserving the theoretical integrity of each construct. Detailed information regarding the complete item pool, retained indicators, eliminated items, and the final factor structure is provided in Appendix A.

The retention criterion was based on strong primary loadings, minimal cross-loadings, and theoretical consistency with the emerging factor structure. This process resulted in a reduced set of 23 items that exhibited clear factorial separation and high internal coherence. The reduction from 32 to 23 items reflects a data-driven purification of the measurement model, ensuring that each retained indicator contributes uniquely to its underlying construct without introducing measurement noise or conceptual overlap. This step enhances both convergent and discriminant validity, which is particularly critical in models integrating organizational, technological, and cognitive dimensions.

The final instrument therefore represents a parsimonious and empirically robust operationalization of the constructs, suitable for confirmatory factor analysis and structural modeling. To enhance the rigor of construct validation and address potential concerns regarding the use of the same dataset for both exploratory and confirmatory procedures, the full sample (N = 3017) was randomly divided into two independent subsamples of approximately equal size. The first subsample was used for exploratory factor analysis (EFA) and scale purification, whereas the second subsample was reserved for confirmatory factor analysis (CFA) and structural equation modeling (SEM). This split-sample validation procedure reduces the likelihood of overfitting and provides a more stringent assessment of the measurement and structural models.

The analytical procedure followed a multi-stage approach designed to ensure the robustness, validity, and structural consistency of the proposed model. In the first stage, exploratory factor analysis (EFA) was conducted on the exploratory subsample using maximum likelihood extraction to identify the underlying latent structure of the measurement instrument and to refine the scale by retaining only statistically and theoretically consistent indicators. Given the expected intercorrelation among constructs, an oblique rotation method was applied to allow for correlated factors and to improve interpretability of the factor solution. Following factor extraction and item purification, confirmatory factor analysis (CFA) was performed on the independent validation subsample to assess the adequacy of the measurement model and confirm the factor structure identified during the exploratory stage. The CFA model was estimated using the Maximum Likelihood (ML) method based on the covariance matrix of the validation subsample. The measurement model was specified a priori according to the factor structure identified in the exploratory stage, and all indicators were restricted to load only on their theoretically specified latent constructs. No correlated error terms were introduced, and no post hoc model modifications based on modification indices were applied. Model fit was assessed using a combination of absolute, incremental, and residual-based fit indices, in line with recommended SEM reporting standards [72].

Reliability and convergent validity were evaluated using composite reliability (CR) and average variance extracted (AVE), while discriminant validity was assessed through both the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio. To assess the potential influence of common method bias, both procedural and statistical remedies were applied. Procedurally, respondent anonymity was guaranteed, participation was voluntary, attention-check questions were included, and respondents were informed that there were no correct or incorrect answers. Statistically, Harman’s single-factor test was conducted using all retained measurement items.

In the next stage, structural equation modeling (SEM) was applied using maximum likelihood estimation to test the hypothesized relationships among latent constructs. Direct, indirect, and total effects were estimated to capture both primary relationships and underlying mediation mechanisms. Mediation effects were further examined using bias-corrected bootstrap procedures, allowing for more robust inference regarding indirect pathways [64]. To assess the stability of the model across different organizational conditions, a multi-group SEM analysis was conducted.

The selection of environmental control level as the grouping criterion was theoretically grounded in contingency and organizational control perspectives, which suggest that broader organizational contexts may influence the expression of leadership practices and technology-mediated interactions. While Leadership Control Orientation represents a latent construct capturing respondents’ perceptions of leadership behaviors related to monitoring, standardization, supervision, and performance regulation, environmental control level reflects the overall organizational context within which technology use takes place. Thus, the grouping variable was conceptualized as a contextual characteristic rather than a leadership characteristic. This distinction enabled the examination of whether the structural relationships identified in the model remain stable across organizational environments characterized by different degrees of control.

A grouping variable was constructed based on respondents’ assessments of the overall level of organizational control governing interaction with digital and multimodal systems. Participants who reported no control or low control were classified into the low-control group, whereas those reporting high or very high control were classified into the high-control group. Respondents indicating moderate control were excluded from the group comparison procedure to maximize the distinction between contrasting organizational environments. The analysis proceeded by comparing an unconstrained model with a constrained model in which structural paths were fixed to be equal across groups. Differences between groups were evaluated using both critical ratio (z-test) comparisons of path coefficients and chi-square difference testing, following established procedures for invariance assessment [73]. All analyses were conducted using IBM SPSS Statistics (Version 28) for preliminary analysis and AMOS for CFA and SEM estimation.

4. Results

Prior to factor extraction, the adequacy of the exploratory subsample (n = 1508), obtained through a random split of the full sample (N = 3017), was assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity. The KMO value of 0.973 indicates excellent sampling adequacy, substantially exceeding recommended thresholds and confirming that the correlation structure is highly appropriate for factor analysis. This level reflects strong shared variance among variables and supports the extraction of stable and clearly defined latent constructs. Bartlett’s test of sphericity was statistically significant (χ² = 35,504.138; df = 496; p < 0.001), rejecting the null hypothesis of an identity matrix and confirming that inter-item correlations were sufficiently strong for factor analysis. These results demonstrate that the exploratory subsample satisfies all assumptions required for exploratory factor analysis and provides a robust basis for identifying the latent structure of the measurement instrument.

The results presented in

Table 3. Total Variance Explained and Factor Extraction Results (Maximum Likelihood, Exploratory Subsample n = 1508).

Factor	Initial Eigenvalues (Total)	% of Variance	Cumulative %	Extraction SS Loadings (Total)	% of Variance	Cumulative %	Rotation SS Loadings (Total)
1	12.293	38.416	38.416	11.882	37.133	37.133	9.164
2	3.642	11.381	49.797	2.962	9.255	46.388	8.191
3	3.412	10.663	60.460	3.233	10.104	56.492	7.002
4	2.768	8.651	69.112	2.627	8.210	64.702	7.909

Note: Extraction method: Maximum Likelihood. Only factors with eigenvalues greater than 1 are reported. Rotation sums of squared loadings are not additive because an oblique rotation method was applied and the factors were allowed to correlate.

indicate a clear four-factor solution within the exploratory subsample, supported by eigenvalues greater than 1. The first factor accounts for 38.416% of the total variance, followed by three additional factors contributing 11.381%, 10.663%, and 8.651%, respectively. After extraction, the four-factor structure explains 64.702% of the total variance, exceeding commonly accepted thresholds for social science research and indicating substantial explanatory power. The dominance of the first factor suggests a strong underlying dimension within the dataset, while the remaining factors contribute meaningful and conceptually distinct variance components. Following rotation, variance is more evenly distributed across factors, indicating improved interpretability and a clearer factor structure. The stability of the four-factor solution within the exploratory subsample provides initial support for the proposed measurement framework and justifies subsequent validation through confirmatory factor analysis using the independent validation subsample.

The results presented in

Table 4. Pattern Matrix.

	Factor
	Innovation Outcome	Cognitive Burden	Leadership Control Orientation	Multimodal Experience Design
Process Standardization	−0.013	0.013	0.760	0.024
Rule Compliance	−0.005	−0.001	0.763	0.007
Consistency Focus	0.022	0.003	0.783	−0.009
Process Monitoring	0.030	0.013	0.786	−0.015
Performance Pressure	−0.030	0.027	0.758	0.026
Interactive Design	−0.009	−0.010	0.004	0.788
User Influence	−0.005	−0.021	0.046	0.793
Sensory Engagement	−0.026	−0.024	0.006	0.798
Modal Integration	−0.004	0.017	−0.011	0.781
Immersive Experience	0.023	0.011	−0.011	0.765
Narrative Enhancement	0.003	0.010	−0.018	0.786
Mental Effort	−0.001	0.842	−0.001	0.040
Sensory Demand	0.028	0.820	0.010	−0.025
Feedback Clarity	0.016	0.818	−0.005	−0.030
Process Clarity	−0.029	0.804	0.007	0.022
User Control	−0.014	0.815	0.004	0.007
Action Influence	0.020	0.820	−0.023	−0.037
Perceived Novelty	0.853	−0.011	0.006	−0.025
Interaction Novelty	0.852	−0.008	0.005	−0.029
Feature Innovation	0.827	−0.020	−0.029	0.007
Attention Retention	0.869	0.006	0.010	−0.018
Reuse Intention	0.831	0.006	0.003	0.037
Recommendation Intention	0.848	0.024	−0.002	0.005

indicate a clear and theoretically coherent four-factor structure within the exploratory subsample. Items associated with Innovation Outcome exhibit consistently high loadings (0.827–0.869) on the first factor, with negligible cross-loadings, confirming strong convergent validity and the stability of this outcome construct. Similarly, the indicators of Cognitive Burden load highly (0.804–0.842) on the second factor, capturing a well-defined cognitive dimension without substantial overlap with other latent variables. The third factor, Leadership Control Orientation, is characterized by stable and substantial loadings (0.758–0.786), suggesting a clearly delineated organizational control dimension. Finally, items representing Multimodal Experience Design demonstrate strong loadings (0.765–0.798) on the fourth factor, confirming that multimodal design features form a coherent and independent construct. The absence of meaningful cross-loadings further supports discriminant validity, as each indicator loads predominantly on its intended factor. Overall, the pattern matrix provides robust empirical support for the proposed conceptual structure and justifies subsequent validation through confirmatory factor analysis using the independent validation subsample.

To evaluate the potential influence of common method bias, Harman’s single-factor test was performed using all retained measurement items. The results showed that the first unrotated factor accounted for 39.102% of the total variance, which is below the commonly accepted threshold of 50%. Therefore, common method bias is unlikely to represent a substantial threat to the validity of the findings.

The factor structure identified in the exploratory subsample was subsequently evaluated using CFA on the independent validation subsample (n = 1509). The measurement model demonstrated excellent fit to the data. The chi-square statistic was non-significant (χ² = 185.909, df = 224, p = 0.970), while the χ²/df ratio was 0.830, indicating minimal discrepancy between the observed and estimated covariance matrices. Absolute fit indices were high (GFI = 0.989; AGFI = 0.987) and residual error remained low (RMR = 0.010). Incremental fit indices also indicated excellent model fit (NFI = 0.992; IFI = 1.002; TLI = 1.002; CFI = 1.000), while RMSEA = 0.000 (PCLOSE = 1.000) suggested negligible approximation error. Taken together, these results provide strong support for the adequacy and stability of the measurement model within an independent validation sample. The model was estimated using the Maximum Likelihood method based on the covariance matrix, and no correlated error terms or post hoc model modifications were introduced during the CFA procedure.

As shown in

Table 5. Construct Reliability and Convergent Validity.

Construct	Items	CR	AVE
Innovation Outcome	6	0.936	0.709
Cognitive Burden	6	0.920	0.658
Leadership Control Orientation	5	0.879	0.593
Multimodal Experience Design	6	0.910	0.626

, all constructs demonstrate satisfactory internal consistency, with Composite Reliability (CR) values substantially exceeding the recommended threshold of 0.70. Convergent validity is also supported, as all Average Variance Extracted (AVE) values are above the recommended threshold of 0.50, indicating that the retained indicators adequately capture their respective latent constructs. These results provide additional evidence for the reliability and convergent validity of the measurement model within the independent validation subsample.

As shown in

Table 6. HTMT Ratios.

Construct	F1	F2	F3	F4
Innovation Outcome (F1)	—	0.524	0.369	0.514
Cognitive Burden (F2)	0.524	—	0.371	0.409
Leadership Control Orientation (F3)	0.369	0.371	—	0.318
Multimodal Experience Design (F4)	0.514	0.409	0.318	—

and

Table 7. Fornell–Larcker Criterion.

Construct	F1	F2	F3	F4
Innovation Outcome (F1)	0.842	−0.523	−0.369	0.514
Cognitive Burden (F2)	−0.523	0.811	0.371	−0.409
Leadership Control Orientation (F3)	−0.369	0.371	0.770	−0.319
Multimodal Experience Design (F4)	0.514	−0.409	−0.319	0.791

, discriminant validity is strongly supported. All HTMT values range from 0.318 to 0.524, remaining well below the conservative threshold of 0.85, indicating that the constructs are empirically distinct and do not exhibit problematic conceptual overlap. The highest HTMT value is observed between Innovation Outcome and Cognitive Burden (0.524), while the remaining values are substantially lower, further supporting construct differentiation. Additional evidence is provided by the Fornell–Larcker criterion. The square roots of the AVE values (0.770–0.842) exceed all corresponding inter-construct correlations, confirming that each construct shares more variance with its own indicators than with other latent variables in the model. Although moderate correlations are observed between certain constructs, particularly between Innovation Outcome and Cognitive Burden (r = −0.523) and between Innovation Outcome and Multimodal Experience Design (r = 0.514), these relationships remain well below the square roots of the respective AVE values. Taken together, the HTMT and Fornell–Larcker results provide robust evidence of discriminant validity and support the distinctiveness of Innovation Outcome, Cognitive Burden, Leadership Control Orientation, and Multimodal Experience Design within the independent validation subsample.

As illustrated in Figure 2, the structural model demonstrates strong and consistent relationships among the latent constructs, with all standardized path coefficients aligning with the hypothesized directions and confirming the overall robustness of the model.

Table 8. Structural Model Results.

Hypothesis	Path	β (Standardized)	SE	C.R.	p-Value	Hypothesis Supported
H1	Leadership Control Orientation → Multimodal Experience Design	−0.319	0.031	−10.939	<0.001	Yes
H2	Leadership Control Orientation → Cognitive Burden	0.268	0.032	9.394	<0.001	Yes
H3	Multimodal Experience Design → Cognitive Burden	−0.324	0.030	−11.395	<0.001	Yes
H4	Multimodal Experience Design → Innovation Outcome	0.333	0.031	12.276	<0.001	Yes
H5	Cognitive Burden → Innovation Outcome	−0.336	0.030	−12.200	<0.001	Yes
H7	Leadership Control Orientation → Innovation Outcome	−0.139	0.032	−5.345	<0.001	Yes

presents the results of the structural model. All path coefficients are statistically significant at p < 0.001, providing support for all hypothesized relationships. Leadership Control Orientation is negatively associated with Multimodal Experience Design (β = −0.319), supporting H1. Leadership Control Orientation is positively associated with Cognitive Burden (β = 0.268), supporting H2. Multimodal Experience Design is negatively associated with Cognitive Burden (β = −0.324), supporting H3. Furthermore, Multimodal Experience Design is positively associated with Innovation Outcome (β = 0.333), supporting H4, whereas Cognitive Burden is negatively associated with Innovation Outcome (β = −0.336), supporting H5. Among the structural relationships, the strongest standardized effect is observed between Cognitive Burden and Innovation Outcome. Leadership Control Orientation also demonstrates a significant negative direct effect on Innovation Outcome (β = −0.139), supporting H7. The results indicate that Leadership Control Orientation is associated with both Multimodal Experience Design and Cognitive Burden, while Multimodal Experience Design and Cognitive Burden are significantly related to Innovation Outcome. The significance and direction of all estimated paths are consistent with the proposed conceptual model.

The mediation analysis results are presented in

Table 9. Mediation Effects (Standardized).

Hypothesis	Path	Direct Effect	Indirect Effect	95% BC CI Lower	95% BC CI Upper	Mediation Type	Hypothesis Supported
H6	Leadership Control Orientation → Multimodal Experience Design → Cognitive Burden → Innovation Outcome	−0.139	−0.231	−0.261	−0.192	Partial mediation	Yes

. The indirect effect of Leadership Control Orientation on Innovation Outcome through Multimodal Experience Design and Cognitive Burden is statistically significant (β = −0.231), with a 95% bias-corrected confidence interval ranging from −0.261 to −0.192. Because the confidence interval does not include zero, the indirect effect is supported. At the same time, the direct effect of Leadership Control Orientation on Innovation Outcome remains statistically significant (β = −0.139), indicating partial mediation. Therefore, H6 is supported.

The explained variance results presented in

Table 10. Explained Variance (R²).

Endogenous Construct	R2	Interpretation
Multimodal Experience Design	0.102	Weak
Cognitive Burden	0.175	Weak to Moderate
Innovation Outcome	0.260	Moderate

indicate that the model achieves varying levels of predictive power across endogenous constructs. Multimodal Experience Design exhibits a weak level of explained variance (R² = 0.102), indicating that Leadership Control Orientation accounts for a relatively limited proportion of its variability. Cognitive Burden demonstrates a weak-to-moderate level of explained variance (R² = 0.175), suggesting that Leadership Control Orientation and Multimodal Experience Design jointly contribute to its prediction. Innovation Outcome exhibits the highest explanatory power in the model (R² = 0.260), representing a moderate level of explained variance. This finding indicates that Leadership Control Orientation, Multimodal Experience Design, and Cognitive Burden collectively account for a meaningful proportion of variance in perceived innovation outcomes.

Multi-Group SEM Analysis

Prior to comparing structural relationships across groups, measurement invariance was assessed within the multi-group SEM framework. First, a configural invariance model was estimated across low-control and high-control environments, demonstrating satisfactory model fit (χ² = 413.638, df = 453, CFI = 1.000, RMSEA = 0.000). Subsequently, metric invariance was tested by constraining factor loadings to equality across groups. The constrained model also demonstrated excellent fit (χ² = 430.581, df = 472, CFI = 1.000, RMSEA = 0.000), with no meaningful deterioration relative to the configural model (ΔCFI = 0.000; ΔRMSEA = 0.000). These results support measurement equivalence across groups and provide an appropriate basis for subsequent comparisons of structural relationships. As shown in

Table 11. Measurement Invariance Assessment.

Model	χ2	df	CFI	RMSEA	ΔCFI	ΔRMSEA
Configural Invariance	413.638	453	1.000	0.000	–	–
Metric Invariance	430.581	472	1.000	0.000	0.000	0.000

Note: Measurement invariance was evaluated using changes in CFI and RMSEA. Following established recommendations, ΔCFI ≤ 0.010 and ΔRMSEA ≤ 0.015 indicate support for invariance.

, the metric invariance model demonstrated no meaningful deterioration in fit relative to the configural model (ΔCFI = 0.000; ΔRMSEA = 0.000), supporting measurement equivalence across groups.

To examine whether the structural relationships differ across environmental conditions, a multi-group SEM analysis was conducted comparing low- and high-control environments. As presented in

Table 12. Results of Multi-Group SEM Path Comparison Between Low- and High-Control Environments.

Path	Low Control β	High Control β	Δz (z-Value)	p-Value
Leadership Control Orientation → Multimodal Experience Design	−0.310	−0.338	0.86	0.390 ns
Leadership Control Orientation → Cognitive Burden	0.257	0.276	−0.56	0.575 ns
Multimodal Experience Design → Cognitive Burden	−0.334	−0.303	−0.71	0.477 ns
Multimodal Experience Design → Innovation Outcome	0.332	0.308	0.75	0.452 ns
Cognitive Burden → Innovation Outcome	−0.325	−0.307	−0.55	0.582 ns
Leadership Control Orientation → Innovation Outcome	−0.184	−0.186	0.06	0.952 ns

, the comparison of standardized path coefficients reveals no statistically significant differences between groups. All Δz values remain below the critical threshold of ±1.96, with corresponding p-values exceeding 0.05, indicating that the strength and direction of relationships are consistent across conditions. The effect of Leadership Control Orientation on Multimodal Experience Design remains stable (β = −0.310 vs. β = −0.338; Δz = 0.86, p = 0.390), as does its influence on Cognitive Burden (β = 0.257 vs. β = 0.276; Δz = −0.56, p = 0.575). The relationship between Multimodal Experience Design and Cognitive Burden is similarly invariant (β = −0.334 vs. β = −0.303; Δz = −0.71, p = 0.477), while its effect on Innovation Outcome shows no significant variation (β = 0.332 vs. β = 0.308; Δz = 0.75, p = 0.452). Likewise, the negative effect of Cognitive Burden on Innovation Outcome remains consistent across groups (β = −0.325 vs. β = −0.307; Δz = −0.55, p = 0.582). The direct effect of Leadership Control Orientation on Innovation Outcome is also statistically equivalent (β = −0.184 vs. β = −0.186; Δz = 0.06, p = 0.952).

These results are corroborated by the chi-square difference test reported in

Table 13. Results of Structural Path Invariance Testing Across Low- and High-Control Environments.

Model	χ2	df	Δχ2	Δdf	p
Unconstrained	409.831	448	–	–	–
Constrained (structural paths equal)	413.638	453	3.807	5	0.578 ns

. The unconstrained model (χ² = 409.831, df = 448) does not differ significantly from the constrained model in which structural paths are held equal across groups (χ² = 413.638, df = 453), as indicated by a non-significant chi-square difference (Δχ² = 3.807, Δdf = 5, p = 0.578). This confirms that imposing equality constraints does not reduce model fit, providing strong evidence of structural invariance. Accordingly, H8 is supported, confirming that environmental control level does not moderate the structural relationships within the model. The findings demonstrate that the relationships among Leadership Control Orientation, Multimodal Experience Design, Cognitive Burden, and Innovation Outcome operate consistently regardless of the level of environmental control.

5. Discussion

5.1. Innovation as a Process Rather than a Direct Leadership Outcome

The findings of this study directly address the fragmentation identified in prior research by integrating leadership, system design, and user cognition into a single process-based mechanism. Rather than supporting conventional explanations in which leadership exerts a predominantly direct influence on innovation, the results suggest that innovation perceptions emerge through a sequential process linking organizational control structures, multimodal experience architecture, and user cognition. Leadership appears to shape the conditions under which innovation is experienced by influencing both system design and cognitive processing, thereby operating as a system-level structuring force rather than as an isolated predictor of innovation outcomes.

This perspective extends process-based approaches in organizational and innovation research, which emphasize that outcomes arise from interconnected systems of interaction rather than from independent causal drivers [63,64]. The findings indicate that control-oriented organizational environments influence how users engage with digital systems, while multimodal experience design functions as a critical transmission mechanism connecting organizational conditions to user-level outcomes. In this context, innovation is not merely a consequence of technological novelty or managerial action; it emerges through the interaction between organizational structures, experience design, and cognitive responses.

The identification of a significant sequential mediation mechanism provides additional support for this interpretation. It suggests that organizational conditions are translated into innovation evaluations through intermediate design and cognitive processes. This extends traditional mediation perspectives [63] by demonstrating how macro-level organizational structures become linked to user-level innovation perceptions through multimodal experience design and cognitive burden. At the same time, the persistence of a direct relationship suggests that leadership remains an important influence within the innovation process, although its effects are only partially explained by the proposed mediating mechanisms.

5.2. Theoretical Significance of Multimodal Experience Design

A central contribution of this study lies in the conceptual repositioning of multimodal experience design within the innovation process. Existing research frequently treats multimodal features as interface characteristics that influence usability, engagement, user experience, and interaction quality [11,29]. The present findings suggest a broader role. Rather than functioning merely as a technological layer through which users access information, multimodal experience design appears to operate as a system-level mechanism that shapes how innovation is perceived, interpreted, and evaluated.

This perspective extends prevailing understandings of digital innovation by demonstrating that innovation outcomes are influenced not only by technological capabilities and organizational decisions but also by the quality of the experiential architecture through which users interact with those capabilities [27,28]. Multimodal experience design therefore represents more than a design feature; it constitutes an organizing structure that translates organizational intentions into user experiences and ultimately into innovation perceptions. This interpretation is particularly relevant in contemporary digital environments, where user evaluations increasingly depend on experiential quality rather than on technological functionality alone.

The findings further suggest that multimodal experience design serves as a critical link between organizational and individual levels of analysis. At the organizational level, leadership structures influence the conditions under which digital systems are developed and implemented [23,50]. At the user level, individuals evaluate those systems through cognitive and experiential processes [11,52]. Multimodal experience design occupies the space between these levels by transforming organizational conditions into concrete interaction experiences that shape cognitive responses and innovation evaluations. In this sense, it functions as a bridging construct capable of connecting macro-level organizational processes with micro-level user experiences within a single explanatory framework.

From a broader theoretical perspective, these findings support emerging arguments that innovation should be understood not only as the introduction of new technologies but also as the creation of meaningful, immersive, and cognitively manageable experiences [2,57]. As digital environments become increasingly interactive, multisensory, and AI-driven, the quality of multimodal experience design may become a defining factor in determining whether technological advancements are ultimately perceived as innovative by users. This positions multimodal experience design as a central component of contemporary innovation processes rather than as a secondary characteristic of digital interfaces.

5.3. Structural Invariance and the Limits of Contingency Explanations

A particularly important contribution of this study emerges from the observed structural invariance across environmental conditions. Contrary to expectations derived from contingency theory, the proposed mechanism remained stable across low- and high-control environments, suggesting that environmental control does not fundamentally alter the way innovation perceptions emerge. Rather than reshaping the relationships among leadership, multimodal experience design, cognitive burden, and innovation outcomes, control appears to influence the context within which these relationships operate.

This finding challenges the traditional assumption that organizational outcomes are primarily determined by contextual variation [66]. While contingency perspectives generally propose that structural relationships change across environmental conditions, the present findings suggest that the underlying process linking organizational control, experience design, cognition, and innovation may be more stable than previously assumed. Innovation perceptions therefore appear to emerge through a relatively consistent mechanism that persists across different levels of environmental control.

From a theoretical perspective, this result supports a shift from context-dependent explanations toward process-oriented accounts of innovation. Rather than viewing environmental control as a moderator that reconfigures organizational dynamics, the findings suggest that control functions as a contextual condition that affects the intensity of experiences without altering the fundamental structure through which innovation perceptions are formed. This interpretation is consistent with growing calls for research that focuses on identifying underlying generative mechanisms rather than exclusively examining contextual contingencies [65,68].

The broader implication is that innovation may be governed by relatively stable interaction processes that transcend specific organizational settings. If confirmed by future research, this perspective would support the development of more generalizable theories capable of explaining innovation perceptions across a wide range of technological and organizational environments. Within this framework, leadership, multimodal experience design, and cognition should be understood not as isolated factors whose effects vary unpredictably across contexts, but as interconnected components of a robust process through which innovation is experienced and evaluated.

5.4. Practical Implications

The findings offer several practical implications for managers, designers, and organizations implementing digital and AI-enabled systems. The results indicate that improving innovation perceptions cannot be achieved solely through technological investments or organizational control mechanisms. Instead, organizations should prioritize the design of multimodal environments that are intuitive, integrated, and cognitively manageable for users. The central role of multimodal experience design suggests that user interaction quality should be treated as a strategic consideration during system development and implementation. Organizations should invest in interaction architectures that support clarity, engagement, and ease of use while minimizing unnecessary cognitive demands. Such investments may help maintain positive innovation perceptions even within highly structured organizational environments.

The findings also caution against excessive reliance on control-oriented leadership approaches. Although control mechanisms may support coordination and operational consistency, they may simultaneously restrict design flexibility and contribute to less favorable user experiences. Managers should therefore seek an appropriate balance between organizational control and adaptive, user-centered design practices. Encouraging collaboration among leadership teams, technology developers, and user-experience specialists may help organizations create environments that support both operational effectiveness and innovation outcomes.

More specifically, the findings provide actionable guidance for organizational stakeholders involved in digital transformation initiatives. Senior managers should monitor not only technology adoption rates but also indicators of user cognitive burden, as excessive cognitive demands were associated with less favorable innovation evaluations. UX designers and system developers should prioritize interface simplification, consistency across communication modalities, and reduction of unnecessary information complexity in order to minimize cognitive burden and strengthen innovation perceptions. Human resource managers may use these findings to develop training programs that improve users’ ability to navigate multimodal environments while reducing cognitive overload. In addition, organizations implementing AI-enabled systems should establish cross-functional teams involving leadership, technology specialists, and user-experience professionals to ensure that organizational control requirements do not unintentionally undermine interaction quality. The results suggest that innovation outcomes can be improved not only through technological investments but also through managerial decisions that support cognitively efficient and user-centered system design.

5.5. Limitations and Future Research

The findings should be interpreted in light of several limitations. The cross-sectional nature of the study restricts causal inference and does not allow observation of how the relationships among leadership, multimodal experience design, cognitive burden, and innovation perceptions evolve over time. Although the proposed model captures a coherent process, longitudinal designs would provide a more robust understanding of its temporal dynamics and potential feedback effects. The reliance on self-reported measures introduces the possibility of perceptual and common-method biases. Future investigations could strengthen the robustness of the findings by combining perceptual assessments with behavioral indicators, system-generated interaction data, or objective performance measures. Such an approach would offer a more comprehensive perspective on how organizational structures and user experiences jointly shape innovation evaluations. Although the study employed a split-sample validation procedure to strengthen measurement robustness, additional validation using independent samples from different organizational and technological contexts would further enhance the generalizability of the findings.

Another limitation concerns the operationalization of environmental control as a dichotomous construct. While this approach was appropriate for testing the proposed model, it may not fully capture the complexity of contemporary organizational environments, where control mechanisms often coexist in multiple and overlapping forms. Future research could examine more nuanced conceptualizations of control and explore whether different dimensions of organizational control influence innovation processes in distinct ways. The generalizability of the findings would also benefit from validation across different industries, technological settings, and cultural contexts. As digital environments become increasingly adaptive and AI-driven, further research should investigate how personalized interaction systems reshape the relationships among organizational structures, cognitive responses, and innovation perceptions. Such work would help clarify the boundary conditions of the proposed model and extend understanding of innovation processes in emerging digital ecosystems.

6. Conclusions

This study demonstrates that innovation perceptions in multimodal environments are shaped by an interconnected process linking leadership control orientation, multimodal experience design, and user cognition. The findings suggest that leadership influences innovation not only directly but also indirectly through its effects on experience design and cognitive burden. In this framework, innovation emerges as a product of organizational structures, interaction experiences, and cognitive responses rather than as a consequence of technological novelty alone. The study contributes to the literature by positioning multimodal experience design as a central mechanism connecting organizational conditions with innovation evaluations and by demonstrating the structural stability of this mechanism across different environmental control conditions. Taken together, the findings support a process-oriented understanding of innovation and highlight the growing importance of experience-centered approaches for understanding how innovation is perceived and evaluated in increasingly digital and AI-enabled environments.