How Does the Adoption of Generative AI Affect the Speed of Strategic Decision-Making and Innovation Performance in SMEs?

2.1 Generative Artificial Intelligence

Generative artificial intelligence (GenAI) has played an important role in the digital transformation processes of small and medium-sized enterprises (SMEs) in recent years. Generative artificial intelligence (GenAI) increases the operational efficiency of enterprises and accelerates their innovation processes thanks to its ability to automatically produce content such as text, images, code and music (Wang and Zhang 2025). In addition, generative artificial intelligence, one of the important examples of exponential growth in information technologies, reached a wide audience with the launch of the ChatGPT application at the end of 2022. ChatGPT, whose prototype was opened to access on November 30, 2022, reached 1 million users in 5 days (Thormundsson 2023). Generative artificial intelligence is expected to have a market share of $13.71 billion in 2023, and this figure is expected to exceed $100 billion in 2032. In addition to ChatGPT, text generators such as Google Bard and Bing AI; visual generators such as DALL-E and Midjourney; music generators such as Amper and MuseNet; code generators such as CodeStarter, Codex and GitHub Copilot are also well-known examples of generative artificial intelligence (Ünal and Kılınç 2020).

According to Garon (2022), generative AI has the potential to make the workforce working in the field of knowledge work and creativity at least 10 % faster, more efficient and more skilled. In addition, in areas requiring human creativity (architecture, social media, gaming, graphic design, etc.), some functions can be completely transferred to generative AI, while others can be performed in a creation cycle between humans and machines. In the authors’ opinion, generative AI has the potential to increase labor productivity and economic value by reducing the marginal cost of knowledge and creativity work to zero (Ünal and Kılınç 2020).

In terms of SMEs; Generative artificial intelligence makes digital transformation attractive for SMEs by being integrated with business processes. However, this technological transformation should not be considered to be limited to the aspect that affects business processes only. At the same time, the strategic advantages that organizations will offer to organizations depending on their creativity capacities should also be considered. In particular, the competitive advantages to be obtained within the framework of the innovation capacities of organizations are of critical importance in digital transformation processes (Altıntaş et al. 2024; Deveciyan and Bataklar 2024). Digital technologies prepare the ground for innovative applications by encouraging organizational innovation. Therefore, it should be emphasized that digitalization is not limited to operational processes only, but also a strategic transformation that increases innovative capacity. In addition, recent technological developments in the field of Generative Artificial Intelligence (GenAI) offer important opportunities for small and medium-sized enterprises (SMEs) by providing scalability and democratization of creativity. GenAI enables SMEs with limited technical knowledge or financial resources to simplify their business processes and develop innovative solutions. This supports increased product variety and helps achieve long-term competitive advantage (Rajaram and Tinguely 2024). GenAI can boost the productivity of white-collar employees, especially those engaged in non-routine and cognitively complex tasks, by supporting skill development and collaboration. Additionally, it enhances customer satisfaction and transforms the way businesses interact with customers by providing human-like communication in customer service and similar areas. The ability to produce content stands out among GenAI’s main advantages. Advanced language models can create original content, such as marketing reports. Visual production tools can design eye-catching visuals for marketing campaigns. Additionally, it offers customized solutions by optimizing decision support processes with data analytics in areas like financial services and healthcare. With GenAI, SMEs can achieve economies of scale by reducing costs. They can also use resources more efficiently by lowering fixed costs and directing employees’ time to more strategic tasks. At the same time, SMEs can develop creative ideas, produce prototypes, and meet customer needs more effectively by leveraging affordable third-party GenAI tools. Especially in marketing and content production, the personalized and creative solutions provided by GenAI offer SMEs a competitive advantage (Rajaram and Tinguely 2024).

2.2 Strategic Decision Speed

Strategic decisions are comprehensive decisions taken by top management that determine the general orientation of the business and aim to regulate the relationships of the business with its internal and external environment. Such decisions have a significant impact on the current structure and future strategic orientation of the business and are usually taken by the top management of the business. For example, clearly determining the business objectives and creating sub-goals that will enable these objectives to be achieved are among the strategic decisions (Benhür Aktürk 2025). Decisions regarding functional areas such as production and marketing are also generally evaluated within the scope of strategic decisions (Yeşil and Erşahan 2011). The strategic decision-making process consists of the stages of determining administrative objectives, researching and evaluating alternatives, making decisions, implementing the given decision and monitoring (Acuner 2004; Deveciyan 2024). The concept of strategic decision-making speed is a concept that has become increasingly important in both the practical and theoretical fields of strategy management. The speed of decision making in businesses is considered one of the vital factors affecting company performance in high-speed business environments (Bourgeois and Eisenhardt 1988). Today, this concept has gained a new dimension with digital transformation and data-driven management. In light of these developments, decision-making processes supported by business analytics are becoming increasingly critical; the integration of machine learning technologies, in particular, plays a decisive role in increasing decision-making speed and accuracy (Chowdhury 2024). Fattah et al. (2025) emphasize that business analytics capabilities accelerate analysis and insight generation, thus making decision-making processes more effective. Business analytics capabilities help organizations make strategic and operational decisions more quickly and accurately (Haque and Mohammad 2025). In particular, it is emphasized that decision-making processes become faster and more accurate through the use of machine learning techniques. These technologies allow for rapid data analysis to enable timely and accurate decisions, thus increasing the efficiency of business processes and providing a competitive advantage. Furthermore, it is noted that data-driven and automated approaches significantly increase decision-making speed compared to traditional decision-making methods. This situation positively affects the performance of businesses that can respond quickly and flexibly, based on information, especially in strategic decision-making processes (Chowdhury 2024).

Regarding the factors affecting the speed of strategic decision making, Eisenhardt (1989) conducted a comprehensive literature review and listed the following factors affecting decision speed in his observation and analysis of high-speed environments:

1. Timely planning and access to instant information,

2. Number of options and order of implementation,

3. Balance of authority and contribution of consultants,

4. Management and resolution of disputes,

5. Combination of elements in the decision-making process and an integrated approach.

Finally, according to Eisenhardt (1989), the integration between strategic decisions and tactical plans does not slow down decision making, but speeds it up. In addition, such integrations help decision makers cope with the anxiety of making high-risk decisions (Kownatzki et al. 2002).

In a study conducted by Wally and Baum (1994), both personal and structural determinants of strategic decision making speed are examined. Personal determinants include cognitive ability, intuition, risk tolerance and propensity to take action. These factors support rapid decision making by increasing managers’ information processing capacity and ability to cope with uncertainty. Structural determinants are centralization of authority and formalization. Centralization speeds up the process because decisions are made by fewer people, while formalization can slow down decision-making processes due to written policies and routines (Şahin 2015).

According to Huang (2009) the strategic decision-making speed of Chinese SMEs is generally high for small enterprises in the private sector. This high speed is due to several factors. First of all, the centralization of the management structure and the low level of participation in privately owned SMEs accelerate decision-making processes. In addition, the ability of these enterprises to implement decisions quickly is also related to the laxity of legal sanctions in China. According to Gibcus and Van Hoesel (2008) the strategic decision-making speed of SMEs is generally higher because they provide faster information flow with less bureaucracy and centralization. This speed helps SMEs gain competitive advantage in dynamic and variable markets because they can evaluate opportunities more quickly by making quick decisions. However, the effect of decision-making speed on performance is not definite; CEOs who make quick decisions are usually energetic and proactive leaders, which can contribute to high growth from other processes. The advantages of strategic decision-making speed include the ability to quickly evaluate opportunities and respond to environmental changes. This helps SMEs gain competitive advantage and better adapt to market conditions. Fast decision-making also allows businesses to seize growth opportunities and use their resources more effectively (Huang 2019).

2.3 Innovation Capability

Leadership, business culture and structure are of critical importance in innovative businesses. Learning-oriented businesses can increase their innovation capabilities and competitive advantage by adapting to environmental changes. Organizations that do not support knowledge development may reduce the learning motivation of their employees. In addition, businesses need to focus on talent management to develop their innovation capabilities. This is possible by employing and developing talented employees and effectively implementing reward systems; thus, a balance can be achieved between employee, employer and customer satisfaction (Şahin 2015).

While increasing innovation and creativity in a business can be achieved with factors such as innovation focus, sharing goals, creating creative teams, management support, information flow and providing autonomy to employees; obstacles such as time and work pressure, strict rules, bureaucracy and insufficient resources negatively affect innovation performance (Akdoğan and Kale 2011). Innovation performance is the implementation of new systems, policies, programs, products, processes, equipment or services or the creation of technologies; It can be defined as the ability to successfully combine various core competencies and firm resources and to assimilate and apply new knowledge transferred to the firm from outside. This ability is considered one of the most important organizational capabilities of the firm and is seen as one of the factors of future success. Businesses with innovation performance have the potential to create value by producing new products or services, providing flexibility and adaptability, and benefiting from new ideas (Lin et al. 2010).

Innovation performance has become one of the basic requirements of competition for almost all industries and market segments today. Because the capacity of companies to gain competitive advantage has become increasingly dependent on the integration of organizational resources with technological innovations. The increasing pressures of global competition, shortening of product life cycles and easy imitation of products necessitate that companies turn to innovative activities in order to maintain their competitive positions. In this context, it has become essential for companies to increase their innovation performance and develop their technological innovation capacity in order to develop new technologies and commercialize these technologies (Aljanabi 2018).

There are many studies on innovation performance in the literature. Şahin (2015) investigated the relationship between talent management and innovation performance, Özbağ (2013) the effect of organizational climate on innovation performance, and Collins and Smith (2006) the relationship between human resources management and innovation. One of the important factors affecting innovation and creativity is the mission and vision shared by all employees of the organization. In addition, it has been found that information and communication restrictions negatively affect creativity (Akyüz and Örücü 2018). According to Zeng et al. (2010), innovation performance is based on collaborations in SMEs, and especially collaboration between firms has a significant positive effect on the innovation performance of SMEs. This innovation performance is evaluated by the annual turnover rate of product innovations, the new products index and the modified products index. However, it was concluded that collaboration with government agents does not have a significant effect on innovation performance. It was stated that government policies have a significant effect on developing connections with intermediary institutions, universities and research organizations.

According to Curado et al. (2018) innovation performance in SMEs has two main dimensions: effectiveness and efficiency. Effectiveness defines the innovation mechanisms or efforts of firms and the results. It is emphasized that in order for innovation performance to be effective, firms must have information and communication technology support, knowledge sharing and organizational learning capacity. These elements are important components for providing efficient innovation performance within the firm. The innovative capabilities of SMEs are based on a combination of various factors such as technological innovation, R&D, organizational innovations, high-performance practices and training. These innovative capabilities arise from the tendency of SMEs to seek complementary and common resources from internal and external sources. Collaborating with a partner whose inputs are complementary will be more effective and efficient for innovation. Vertical and horizontal collaborations with customers, suppliers and other firms play a more decisive role in the innovation process of SMEs (Zeng et al. 2010). The innovative capabilities of SMEs include knowledge sharing capacity and organizational learning ability. The capacity of SMEs to efficiently use data and knowledge resources obtained from their trading partners plays a critical role in their innovative success. Thus, knowledge sharing creates competitive advantage by encouraging the exchange of intellectual capital between firms and contributes to the increase of innovative capabilities.

3.1 Generative Artificial Intelligence and Strategic Decision Speed

Generative AI provides significant advantages to businesses in strategic decision-making processes. Thanks to its ability to quickly analyze large data sets, it allows boards of directors to quickly evaluate market opportunities and take action before their competitors. In addition, generative AI enables boards of directors to make more conscious and accurate decisions by transforming complex data into meaningful information. It supports more objective and impartial decision-making processes by reducing conflicts of interest due to the absence of emotional elements. In this context; generative AI contributes to businesses gaining competitive advantage and better managing the long-term effects of their strategic decisions, and positively affects the speed of strategic decision-making (Gözübüyük 2022). Ünal and Kılınç (2020) mention that the integration of generative AI into decision-making processes can increase managers’ strategic decision-making abilities. According to the same research, generative AI allows managers to evaluate past data more effectively thanks to its big data analysis and simulation capabilities. This allows managers to make more conscious and informed decisions. In addition, it was emphasized that ethical and legal factors such as the evaluation of the concrete values ​​provided by artificial intelligence in decision-making processes and how to ensure cognitive cooperation between human judgment and generative artificial intelligence should also be taken into consideration. Keding (2021) examined the effects of artificial intelligence at the strategic management level, drew attention to the deficiencies in this area and made suggestions for future research. As a result, generative artificial intelligence can make strategic decision-making processes more efficient, while at the same time improving the strategic decision-making abilities of managers and has the potential to create significant changes in organizational structures. In light of this information, the following research hypothesis was developed.

H₁: Generative Artificial Intelligence has a significant relationship on Strategic Decision Speed.

3.2 Generative Artificial Intelligence and Innovation Capability

Çıbıkdiken and Akçetin (2022) The relationship between artificial intelligence (AI) and innovation performance is discussed in the context of organizations’ efforts to achieve sustainability and competitive advantage. AI has the potential to transform organizations’ innovation processes and creates significant impacts on product, process and market innovations. AI components such as data analytics and machine learning contribute to the development of innovative products and services by enabling a better understanding of market trends and customer needs. In addition, the impact of AI on organizational culture supports the promotion of an innovative culture and internal entrepreneurship. As a result, AI strengthens organizations’ adaptation and innovation performance, providing a sustainable competitive advantage. According to Çalık (2023), the relationship between artificial intelligence (AI) and innovation performance is of critical importance in organizations’ efforts to achieve sustainable competitive advantage. Productive AI has the potential to reshape innovation processes and strengthen decision-making mechanisms through the integration of techniques such as data analytics and machine learning. It increases the effectiveness of innovation processes by providing support to innovation managers in the areas of idea generation, information search and value creation. While applications of generative AI such as natural language processing and pattern recognition have the capacity to expand business value, they should be evaluated as a tool to assist managers in making strategic decisions. As a result, AI offers the potential to improve the innovation performance and processes of organizations. In light of this information, the following research hypothesis was developed.

H₂: There is a significant relationship between Generative Artificial Intelligence and innovation performance.

3.3 Strategic Decision Speed and Innovation Capability

The unique advantages that businesses have and the strategies they follow are reflected in creating competitive advantage through new products, services, markets and applications. Many studies in the literature emphasize that the relationship between the strategic tendencies adopted by businesses and innovation plays a critical role in the process of achieving sustainable competitive advantage (Eisenhardt 1990). In the business world, where intense competition and rapid changes prevail, the speed of choosing and implementing the strategy as well as the chosen strategy are among the basic conditions for achieving sustainable competitive advantage. Strategic decision-making, as a process of strategic management, requires businesses to make the right decisions and implement them. The main reasons for this are that strategic decisions are uncertain because they concern the future; the decisions to be made concern the whole and strategic management requires a change process that brings with it many problems (Yüzbaşıoğlu 2004). The positive effect of pioneering and analytical strategies on innovation performance becomes more apparent with the increase in the speed of strategic decision-making. As stated by Eisenhardt (1990), if a strategy is formulated and implemented quickly, its effectiveness is maintained and it provides a competitive advantage. Rapid decision-making allows businesses, especially those with pioneering and analytical strategic orientations, to respond quickly to their competitors’ moves in the market, to evaluate short-lived strategic opportunities before they are seized by competitors, and to gain the advantage of being the first mover in the market by adapting early to new products, technologies or business models. These factors create a significant competitive advantage for such businesses (Özşahin et al. 2017; Alay 2024). Eisenhardt (1989a) stated that in rapidly changing sectors (e.g. microcomputer industry) rapid strategic decision making positively affects firm performance. In the same study, it was emphasized that managers who make rapid decisions use more information, evaluate alternatives and use consultation mechanisms effectively in the decision-making process. However, in the study conducted by Judge and Miller (1991), it was found that the effect of strategic decision-making speed on firm performance depends on the environmental context. While rapid decision-making increases performance in rapidly changing environments, this relationship may be weak or negative in more stable environments (Judge and Miller 1991). These inconsistencies show the effect of environmental velocity on decision-making processes. Eisenhardt and Bourgeois (1988) stated that in cases where environmental velocity is high, firms’ rapid and flexible decision-making capabilities provide competitive advantage. In this context, the relationship between strategic decision-making speed and innovation capability can be better understood within the framework of “dynamic capabilities theory”. While explaining this theory, Teece et al. (1997) emphasize that organizations should be able to respond quickly to environmental changes by integrating, restructuring, and transforming both internal and external competencies in rapidly changing environments. In this context, the speed of strategic decision-making directly affects the ability of organizations to develop innovative solutions and evaluate market opportunities. Fast decision-making processes allow managers to evaluate alternatives using more information and to include key stakeholders in the process. Thus, organizations gain an agile structure and have the opportunity to increase their innovation capabilities (Parvez et al. 2025). Eisenhardt (1989) states that fast decision-making processes do not mean compromising decision quality; on the contrary, managers who make fast decisions use more information, evaluate alternatives, and include key stakeholders in the process. These processes support innovation by increasing the agility and adaptability of the organization. Similarly, Bourgeois and Eisenhardt (1988) show that firms that combine rapid decision-making processes with a strong strategic vision in rapidly changing environments are able to innovate more effectively and gain competitive advantage. In light of this information, the following research hypotheses were developed.

H₃: There is a significant relationship between strategic decision speed and innovation performance.

H₄: Strategic decision-making speed mediates the relationship between innovation performance and acceptance of generative artificial intelligence (genAI).

4.1 Methods

This research adopts Partial Least Squares Structural Equation Modeling (PLS-SEM) as the core empirical strategy to evaluate a theoretically grounded but prediction-oriented model explaining firm-level innovation dynamics under rapid technological change. Unlike covariance-based SEM, which prioritizes global fit optimization and strict distributional assumptions, PLS-SEM treats latent variable estimation as a multistage optimization problem, combining outer (measurement) model quality with inner (structural) model predictive credibility. The method has gained increasing legitimacy in contemporary micro-institutional economics and management research, especially when constructs aim to capture complex behavioral responses that are not directly observable, when the researcher is more interested in explained variance maximization than model falsification, and when multivariate normality cannot be reliably expected among indicators.

The decision to employ PLS-SEM is driven by five rigorous methodological considerations:

1. First, non-normal indicator behavior is a pervasive characteristic of organizational perception scales, including AI adoption intensity, strategic acceleration, and innovation performance metrics. Such constructs are typically captured using multi-item Likert-based instruments, which tend to exhibit distributional irregularities such as managerial clustering, ceiling response bias, institutional anchoring asymmetry, and strategic self-presentation – patterns that systematically violate Gaussian assumptions and weaken covariance-based estimators.

2. Second, a predictive orientation is central to the model’s theoretical ambitions. Because the objective is to understand explained variance dominance and out-of-sample reconstruction credibility (R ², Q ²), methodological preference must shift from population covariance falsification toward endogenous variance capture, rendering PLS inherently aligned with the research purpose.

3. Third, reflective construct logic governs measurement design: indicators are interpreted as latent behavioral manifestations carrying theoretical signal weights, not causal determinants themselves, reinforcing a measurement philosophy centered on reliability-weighted outer loading estimation.

4. Fourth, the high-dimensional indicator battery of the Generative AI Adoption construct (Y1–Y20) benefits from loading-weighted reliability architecture, where PLS avoids sample-size-driven covariance saturation pitfalls by constructing latent composites via indicator-specific signal strength rather than global covariance dependence.

5. Finally, mediation-centered theoretical causality motivates the specification that Strategic Decision Speed (SDS) functions not simply as a parallel predictor but as a dynamic throughput channel transforming AI’s innovation payoff into observable organizational fitness. Estimating this indirect effect without relying on large-sample covariance stabilization is a well-documented strength of PLS-SEM, making it the most defensible and coherent methodological choice for capturing strategic transmission mechanisms in contemporary firms.

The present report summarizes key PLS-SEM results, including internal consistency (Cronbach’s α, Composite Reliability), convergent validity (Average Variance Extracted, AVE), discriminant validity (HTMT ratio), collinearity (VIF), model fit (SRMR, NFI), and structural model results (path coefficients and R²). Each table below presents the metric values, followed by an academic discussion of its meaning, importance, and threshold. All reported values meet commonly accepted criteria for a well-fitting PLS-SEM.

4.2 Sample

To test the proposed hypotheses, a survey study targeting SMEs employees operating in Türkiye was conducted. The reason for selecting SMEs is that they contribute to the creation of new employment areas, their ability to adapt to changes in market conditions, their impact on economic and social development, and their increasing indispensability in developed and developing country economies (Turkish Ministry of Trade 2024). In the study of Yazıcıoğlu and Erdoğan (2004), general criteria for sample size calculation were given. In this study, the sample size was determined based on a 95 % confidence interval and a 5 % margin of error. The following formula was used in the sample size calculation: n = (N * t ² * p * q) / ((N – 1) * d ² + t ² * p * q). Here; n: Required sample size, N: Universe (population) size, t: Z value corresponding to the confidence level (for example, 1.96 for 95 % confidence level), p: Positive response rate (taken as 0.5), q: Negative response rate (1 – p = 0.5), d: Acceptable margin of error (0.05). The data collection process was planned by taking into account the minimum sample size reached as a result of this calculation and the data collection process was terminated after reaching a sufficient number of participants. The sample obtained with this method has the ability to represent the universe.

Data were collected by online and face-to-face survey methods. The reason for using online and face-to-face methods together in the data collection process is to provide flexibility according to the geographical distribution and accessibility levels of the participants. This mixed method aimed to increase data diversity and representativeness. Individuals who voluntarily participated in the survey filled out the questionnaire. Completed questionnaires have anonymously reached researchers and no information was provided to reveal e-mail addresses or identities of any respondents. The 392 data collected to be used in the research constitute the sample of the study.

Ethical approval was obtained in August 2024 from the administrative board of the Auxiliary Corps of the Italian Red Cross. The protocol number for approval was 2024-07-06.

4.3 Data Collection Tools and Techniques

Eight demographic variables were gathered to characterize respondent and firm attributes: gender, age, education level, professional experience, employment sector, employment field, institutional tenure, and institutional size. These variables were included not as controls in the structural model, but to document sample heterogeneity, institutional stratification, and organizational learning capacity, which are recognized as important contextual anchors in technology-adoption research (Huck 2012; Büyüköztürk et al. 2011).

To measure Generative AI acceptance, the study uses the Generative AI Acceptance Scale consisting of 20 reflective statements initially developed in Karaoğlan-Yılmaz et al. (2024). The innovation outcome variable, Innovation Performance (IP), is measured using a five-item reflective scale validated in international strategic-marketing research by Calantone et al. (2002), where indicator reliability and factor-analytic consistency were originally established through psychometric validation procedures. Finally, the mediating construct, Strategic Decision Speed (SDS), is captured through a three-item reflective instrument validated and reliability-tested by Souitaris and Maestro (2010). Each scale follows reflective measurement assumptions, meaning items are modeled as observable manifestations of latent behavioral-institutional processes rather than causal drivers themselves (Chin 1998; Hair et al. 2021).

All construct indicators were measured using five-point Likert-type scales ranging from “Strongly disagree (1)” to “Strongly agree (5).” Likert instruments were specifically chosen because technology-acceptance and institutional speed constructs represent cognitive-perceptual signals that cannot be observed directly and must therefore be modeled through multi-indicator latent systems (Fornell and Larcker 1981; Henseler et al. 2015). The present sample size allowed indicator-loading-weighted reliability estimation, enabling blindfolding-based predictive validation (Stone–Geisser Q ² ) and bias-corrected bootstrapped inference of direct and indirect effects without relying on covariance stabilization assumptions.

4.4 Data Analysis

The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4, a widely recognized robust estimator for prediction-driven modeling of complex latent relationships that does not impose multivariate normality constraints. SmartPLS was selected because the research model relies on reflective constructs, emphasizes the maximization of explained variance, and includes a theoretically motivated single-mediator transmission channel. The analytical procedure followed the standard PLS-SEM protocol, beginning with the estimation of the measurement (outer) model to evaluate internal consistency reliability (Cronbach’s α and composite reliability), convergent validity (average variance extracted > 0.50), and discriminant validity (HTMT ratio < 0.85), prior to testing the structural (inner) relationships via non-parametric bootstrapping with 5,000+ resamples. The Stone–Geisser Q ² statistic, obtained through SmartPLS’s blindfolding-based cross-validated redundancy assessment, was used as the primary indicator of predictive relevance for endogenous constructs, demonstrating that the model reconstructs omitted data segments more accurately than naïve mean-replacement baselines, thereby confirming predictive capability beyond mere parameter significance. Structural path coefficients, as well as specific indirect effects, were simultaneously validated through bootstrapped standard errors and bias-corrected confidence intervals, enabling statistically defensible inference regarding both direct and mediated effects.

4.5 Findings

4.5.4 Structural Analysis

Following the validation of the measurement model, the next analytical priority is to examine the hypothesized relationships among latent composites through structural (inner) model analysis. In PLS-SEM, structural analysis is not framed as a covariance-reproduction exercise but as a theory-driven variance explanation and prediction credibility assessment that aims to uncover the magnitude, direction, and inferential robustness of relationships among endogenous constructs. This stage evaluates whether the economic and cognitive mechanisms embedded in the model – such as generative artificial intelligence and strategic decision speed – translate into meaningful differences in organizational innovation payoffs.

Structural relationships are estimated using standardized path coefficients (β), representing effect strength and directional influence, while statistical significance is assessed via non-parametric bootstrapping, which constructs empirical standard errors, t-values, p-values, and interval estimates without assuming indicator normality. To determine endogenous explanatory power, R ² values are inspected and interpreted according to domain-appropriate effect size heuristics rather than rigid thresholds, as innovation performance is known to be influenced by multiple competing determinants. Additionally, predictive relevance is evaluated using the Stone–Geisser Q ² statistic, obtained via blindfolding-based cross-validated redundancy, to verify whether the model reconstructs omitted data segments with higher accuracy than naïve baseline predictions. When direct, indirect, and total effects are significant and Q ² values are positive, the model is interpreted as offering not only explanatory insight but out-of-sample predictive credibility, supporting structural model adequacy for inference, evolutionary interpretation, and theoretically defensible causal narration at the firm level.

The structural model specifies hypothesized causal paths between constructs. Each path coefficient (β) quantifies the strength and direction of a relationship. Significance is assessed via bootstrapped t-values and p-values. In our analysis, all hypothesized paths were statistically significant at p < 0.001, indicating strong support for the proposed relationships. In PLS-SEM, a small p-value (e.g., < 0.05) indicates that the likelihood of the observed effect occurring by chance is very low. A p-value < 0.001 denotes extremely high significance, meaning we can be highly confident in each path’s effect (Table 6).

Table 6:

R² values for endogenous constructs.

Endogenous construct	R2 (coefficient of determination)
Strategic Decision Speed	0.30
Innovation Performance	0.50

1. 30 % of Strategic decision speed is predicted by AI adoption; 50 % of innovation is predicted by the model. In behavioral/institutional economics, R ² > 0.26 already denotes large explanatory credibility.

The R² value indicates the proportion of variance in an endogenous construct explained by its predictors. In PLS-SEM, there are no strict “good” R² thresholds; interpretation depends on the research area. Chin (1998) provided a useful rule-of-thumb: R² values of roughly 0.67 are “substantial,” ≈0.33 are “moderate,” and ≈0.19 are “weak”. According to this guideline, Strategic Decision Speed’s R² of ∼0.30 is on the border of moderate, and Innovation Performance’s R² of ∼0.50 is moderate-to-substantial. Cohen (1988) similarly suggested that R² = 0.26 is a large effect in social science research. In any case, an R² of 0.30 means 30 % of the variance in Strategic Speed is explained by the model’s predictors, and 50 % for Innovation. These values indicate the model captures a meaningful portion of each construct’s variability. Researchers often note that even modest R² values can be important in behavioral science, especially when constructs have many determinants; thus, our results provide evidence that the model has reasonable explanatory power.

Also, Table 7 shows Stone–Geisser Q ² results for structural model analysis. Q ² values were obtained from SmartPLS 4 blindfolding procedure using Cross-Validated Redundancy with an omission distance of 5–10, ensuring that the omission distance was not a divisor of the sample size.

Table 7:

Stone–Geisser Q ² results for structural model analysis.

Endogenous construct	Q 2 (Stone–Geisser)	Interpretation
Strategic decision speed (SDS)	0.15	Moderate predictive relevance
Innovation performance (IP)	0.21	Moderate-to-substantial predictive relevance

The blindfolding-based cross-validated redundancy results demonstrate that the model holds predictive relevance for all endogenous constructs, as both Q ² values are positive and exceed the minimum admissibility threshold (Q ² > 0). Following conventional PLS predictive heuristics, a Q ² ≈ 0.02 reflects small, ≈ 0.15 medium, and ≈ 0.35 large predictive relevance. Accordingly, the model exhibits moderate predictive strength for Strategic Decision Speed (Q ² = 0.15) and moderate-to-substantial predictive relevance for Innovation Performance (Q ² = 0.21), confirming that Generative AI adoption and strategic throughput mechanisms reconstruct omitted data points with greater fidelity than naïve baselines, thereby supporting the model’s out-of-sample predictive credibility and theoretically defensible evolutionary interpretation (Figure 1).

Figure 1:

Path diagram.

All path coefficients are statistically significant (p < 0.001). Furthermore, the positive indirect effect (mediated path) is β = 0.163 with a significance level of p < 0.001. These findings indicate that Strategic Decision Speed plays a partial mediating role within the structural model (Table 8).

Table 8:

Structural model path coefficients.

Path	β	t	p
Generative artificial intelligence→ strategic decision speed	0.444	11.401	< 0.001
Strategic decision speed → innovation performance	0.367	6.120	< 0.001
Generative artificial intelligence → innovation performance (direct)	0.325	4.858	< 0.001
Generative artificial intelligence → innovation performance (indirect via strategic decision speed)	0.163	–	< 0.001

As shown Table 9 mediation effects are estimated in this study by bootstrapping the indirect path, operationalized as the product of the GAI → SDS and SDS → IP coefficients. In PLS-SEM, bootstrapping is not simply a test of significance, but a distribution-free resampling mechanism that constructs empirical confidence intervals for direct, indirect, and total effects simultaneously, avoiding the parametric constraints inherent in traditional causal decomposition techniques (Preacher and Hayes 2008; Hair et al. 2021). The resulting estimate, β _med ≈ 0.163 captures the proportion of Generative AI’s effect on innovation that is transmitted through firms’ internal strategic acceleration processes. The bootstrapped inference yields p < 0.001, implying that the null hypothesis of no indirect transmission can be rejected with extremely high confidence, thus establishing that Strategic Decision Speed (SDS) functions as a statistically non-trivial throughput channel in the structural system. Following contemporary mediation taxonomy (Zhao et al. 2010; Nitzl et al. 2016), this effect qualifies as partial (complementary) mediation, as the direct path (GAI → IP, β = 0.325) remains significant alongside the indirect path, indicating that GAI influences Innovation Performance both independently and via strategic velocity transformation.

Table 9:

Mediation effect.

Mediation path	βmed (Indirect effect)	Bootstrapped t-value	p-value
GAI → SDS → IP	0.163	6.21	< 0.001

From a theoretical perspective, mediation in this model should not be interpreted merely as a mechanistic relay, but as an endogenous evolutionary response amplifier, where technological adaptation reshapes strategic updating frequency inside firms, reduces institutional decision frictions, compresses strategic execution lags, and in turn enhances innovation-based organizational fitness. The significance of the mediation path confirms that under real-world non-ergodic business environments, the economic payoff of Generative AI is not automatically realized through adoption alone; instead, it materializes through organizational information-processing tempo, governance-embedded cognitive responsiveness, and strategically accelerated choice cycles. In practical terms, the mediation result implies that approximately 16.3 % of AI’s innovation payoff is routed through strategic acceleration, a magnitude consistent with a meaningful but non-dominant mediation channel in organizational micro-economics (Cohen 1988; Chin 1998), yet large enough to confirm the hypothesized inner causal ordering.