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Article

Contingent Affordance Actualization: Nexus of Digital Technology Adoption and Sustainable Performance with the Roles of Supply Chain Innovation and Environmental Munificence

by
Zifeng Li
1,†,
Jinliang Chen
1,† and
Yu Wang
2,*
1
Business School, Central University of Finance and Economics, Beijing 100081, China
2
Business School, Beijing Technology and Business University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 297; https://doi.org/10.3390/jtaer20040297
Submission received: 16 August 2025 / Revised: 5 October 2025 / Accepted: 10 October 2025 / Published: 1 November 2025
(This article belongs to the Special Issue Digitalization and Sustainable Supply Chain)
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Abstract

Digital technology adoption can be beneficial for sustainable development of firms. This study seeks to illuminate how it works, based on the contingent affordance actualization theory that emphasizes both action potential and achievement context of technology. Specifically, it considers supply chain innovation as the underexplored mechanism and environmental munificence as the context factor. With the matched multisource data of 157 human resources service firms in China, the empirical findings show that supply chain innovation mediates the relationship between digital technology adoption and sustainable performance. Additionally, environmental munificence, the extent of the resources available in an environment, weakens this indirect relationship. By demonstrating these relations, this study provides firms with insights that allow them to utilize both the functional and coordinated action potentials of digital technology to conduct supply chain innovation, which in turn enhances sustainable performance. During this process, firms are further advised to be watchful to cope with organizational inertia when environmental munificence is high.

1. Introduction

With the digital technology revolution in full swing, digital technology adoption is fundamentally reshaping business practices such as innovation and supply chain management [1,2,3]. This has a major impact on firm performance, not only in financial aspects but also in environmental aspects, operational aspects, etc. [2,4,5]. For one thing, digital technology adoption is conducive to economic performance through improved operational efficiency, decision-making accuracy, and dynamic capability [6,7,8]. For another, its adoption is conducive to social responsibility performance through upgrading green technology and product design, optimizing resource allocation and production processes, and providing inclusive digital solutions and remote work opportunities [9,10]. As such, the alignment of economic performance and social responsibility enables firms to achieve sustainable development from digital technology adoption. Put another way, digital technology adoption can be introduced as a primary motivator for sustainable performance [3,11].
Most existing researches are based on resource-based views and dynamic capabilities theory. They argue that the positive performance of digital technology adoption comes from making the required resources and capabilities accessible [5,12], which can be mediated by internal capabilities [6,7], resources [12], and processes [13]. However, other studies draw different conclusions, such as the insignificant and inverted U-shape nexus of digital technology adoption and performance [14,15,16]. These mixed conclusions make it necessary to seek a new perspective to better understand how digital technology adoption influences performance, especially for sustainable performance. Compared with the deterministic resources and capabilities, affordance theory, which emphasizes the action potentials of technology and the possible achievement [17,18,19,20], is an appropriate lens. From this lens, digital technology adoption acts as an action potential, which could be possibly transformed as achieved actions before influencing sustainable performance. Therefore, this study builds on affordance theory to investigate the impact of digital technology adoption on sustainable performance.
With this in mind, two research gaps need to be filled. Under the dual thrust of globalization and unhooking, supply chain innovation, which refers to incremental and radical changes related to all functions in the supply chain and the entire supply chain model, increasingly grows [21,22,23,24,25]. Being an integrative change that solves innovation problem across all functions and members in supply chain [23,26], supply chain innovation is associated with inter-firm alignment, integration, and adaptation to each other [27,28]. This not only leaves a broad application space for digital technology [24], but is also considered as a fundamental enabler of sustainable performance [24,29,30,31]. For example, an emerging circular economy mode of supply chain innovation conducted by Apple, Foxconn, and JD, jointly named as the “reuse and recycling program”, recycles the unwanted cellphones from users with digital technology adoption, contributing to the firm’s sustainable performance in entire supply chain [26]. While the practical sector has concentrated on the performance effects of digital technology adoption and acknowledged the importance of supply chain innovation, it remains theoretically underexplored. In other words, how supply chain innovation mediates the relationship between digital technology adoption and sustainable performance is under-researched in literature [6,32].
Additionally, scholars and practitioners are increasingly interested in the external environments in supply chain setting, especially in the post-COVID-19 era [24,30,33,34]. As a core characteristic of external environment, environmental munificence refers to the resources available in an environment [35,36]. Higher environmental munificence with abundant resources would imply lower uncertainty and dynamics of the environment [36]. In light of this, examining the contextual role of environmental munificence is beneficial for understanding the mechanism of supply chain innovation between digital technology adoption and sustainable performance. However, while external environments are considered as essential contextual factors influencing the innovative application of supply chain management [2,37,38], the specific role of environmental munificence in the relationship between digital technology adoption and sustainable performance through supply chain innovation remains understudied.
To fill the aforementioned research gaps, this study posits the following two research questions:
RQ 1:
How does digital technology adoption affect the sustainable performance indirectly through supply chain innovation?
RQ 2:
Does environmental munificence play a moderating role in the indirect relationship between digital technology adoption and sustainable performance via supply chain innovation?
To answer these questions, this study, based on the contingent affordance theory, develops a theoretical model to reveal the mechanisms underlying the relationship between digital technology adoption and sustainable performance, as well as the contingent role of external environment. In particular, by analyzing the matched multisource data of 157 Chinese human resources service firms, we investigate the mediating role of supply chain innovation in the relationship between digital technology adoption and sustainable performance, as well as the moderating role of environmental munificence in this indirect relationship. Furthermore, we confirmed the robustness of our results in three ways.
Accordingly, we make three major contributions. First, we uncover the action potentials of digital technology and the action actualization role of supply chain innovation to transform digital technology adoption into sustainable performance according to the affordance actualization (A-A) theory. This action potential is different from the deterministic resources and capabilities acquired by digital technology adoption according to resource-based view and dynamic capability frameworks. Second, we offer a more nuanced understanding of the digital technology adoption–sustainable performance links by considering environmental munificence as an affordance actualization context, which constrains this indirect actualization process. In this vein, environmental munificence provides a boundary condition to influence the transformation of supply chain innovation from digital technology adoption to sustainable performance. Third, we extend affordance theory by considering both the A-A pathway and the A-A context. The extended affordance theory is named as the contingent affordance actualization in this study.

2. Literature Review and Theoretical Background

2.1. The Performance Implications of Digital Technology Adoption

Digital technology is an umbrella term for various cutting-edge technologies in information, computing, communication, and connectivity, such as cloud computing, big data analytics, and blockchain [21,39,40,41]. It is increasingly adopted by firms across various sectors to gain competitive advantages and sustainable development. For example, Geely, a leading Chinese firm in the automobile manufacturing industry, utilizes digital technology to share resources and technology with Renault, gaining competitive advantages by reducing supply chain disruption risk [42]. Consequently, the sustainable performance outcomes of digital technology adoption have emerged as a growing concern (see Table 1).
Among the literatures in Table 1, scholars have confirmed the role of digital technology adoption in related dimensions of sustainable performance, including isolated dimension (e.g., [45]) and multidimension (e.g., [2,11]), as well as direct relationship investigations (e.g., [45]) and indirect process analyses (e.g., [3]). However, there are still three research concerns that need to be addressed: First, investigation is lacking on sustainable performance that integrates economic profits and social responsibility rather than any single component. Second, it is neglected that supply chain innovation can be used to bridge digital technology adoption and sustainable performance, since existing studies mainly takes the perspectives of resource or capability based on resource-based views and dynamic capabilities theory (e.g., [45]). Third, examinations in the service industry context need further research, since existing research mostly depends on the manufacturing sectors. In response to these concerns, it is necessary to develop a theoretical model that uncovers the mechanism role of supply chain innovation in the relationship between digital technology adoption and sustainable performance in the service industry context, as well as identifies a contingency role of environmental munificence.

2.2. Supply Chain Innovation in the Less Munificent Environment

Supply chain innovation refers to “an integrated change from incremental to radical changes in product, process, marketing, technology, resource, and/or organization, which are associated with all related parties, covering all related functions in the supply chain and creating value for all stakeholders” [26]. Accordingly, it is a multifaceted process [23,24] and is practically used to respond to rapid changes in the environments [24,28,48].
In promoting supply chain innovation, digital technology adoption has attained substantial attention. For example, Hopkins [49], utilizing a descriptive survey in Australia, investigated which digital technologies are anticipated to have the greatest impact and which specific innovations in supply chain are expected to drive based on these technologies. In essence, a body of studies showed that digital technology and its related capabilities have positive impact on supply chain innovation in the less munificent environment fraught with climate change, political conflict, and epidemic outbreaks [24,27,50,51]. In addition, the role of supply chain innovation in obtaining sustainable development in such an environment has attracted substantial attention. For example, Wang and Gong [29] found that first-tier suppliers’ digital-enabled supply chain innovation can reduce CO2 emissions, which is distinctly moderated by structural holes in their upstream and downstream network. Other literature empirically demonstrates that the result of supply chain innovation can be sustainability to withstand low environmental munificence [31,52].
Collectively, supply chain innovation can be a valid machine between digital technology adoption and sustainable performance improvement. Some studies have explored the fundamental role of supply chain innovation in using novel digital technology for superior performance; however, they primarily focused on specific digital technology, such as big data analytics and generative artificial intelligence, and domain performance, such as export and innovation performance [25,53,54,55], rather than the adoption of multiple digital technologies as a whole and the sustainable performance as a performance synthesis. As a result, supply chain innovation is a seriously underexplored mechanism in the relationship between digital technology adoption and sustainable performance, especially when considering the munificence level of external environment.

2.3. Affordance Theory in the Digital Context

The concept of affordance originated from ecological psychology, which is defined as the possibilities that the environment offers to organisms, such as the air that allows animals to breathe [56]. It is gradually introduced into technology context, in which technology affordance refers to “an action potential, that is, to what an individual or organization with a particular purpose can do with a technology” [17]. From this definition, it can be seen that technology affordance is neither the technology itself nor the actor, but the potential for action that emerges from the interaction and dynamic relationship between the two [18,57]. Numerous previous studies applying this theory have analyzed the components and outcomes of technology-related affordances at the organizational level. For example, Yang et al. [58] found that IT affordances have a positive effect on business-to-business performance in multi-channel network businesses context. With the evolution of IT to digital technology, the discussion around technology affordance is evolving from IT affordance to digital technology affordance (e.g., [19,59,60]). More recent contributions shed light on the impact of digital technology and its affordance on organizational performance [18,59,60].
Given the essence of affordance as action potentials, some scholars stressed that it is necessary to clearly distinguish between an affordance and its actualization [19,20,61]. The former is interpreted as the existence of affordances and a precondition for the latter, while the latter relates to the details of specific actions taken by actors through technology usage [19,20]. In other words, the effective achievement of affordance requires not only the technology itself but also the actor to leverage it with specific actions and goals. It is also worth noting that affordances change dynamically with the environment [18,62]. Accordingly, in addition to organizational actions as actualization pathways, the environment, as the affordance actualization context, is equally decisive, which provides the necessity to extend the A-A perspective to the contingent A-A perspective, which is named as the contingent affordance theory.
Therefore, the affordance theory, particularly the contingent A-A perspective, fits well with this study. Specifically, digital technology, as the technological object with different action potentials, can drive significant improvements in sustainable performance when combined with effective supply chain innovation as a specific actualization pathway and appropriate environmental munificence as an important actualization contingency.

3. Hypotheses Development

3.1. The Direct and Indirect Impact of Digital Technology Adoption

From the contingent A-A perspective, it is the actions of an actor that make the affordances actualization possible to achieve a goal [18]. In an organizational context, as businesses have been driven to involve innovation into their supply chain, we build upon the contingent affordance theory to argue that supply chain innovation could function as a specific organizational action to actualize the affordances of digital technology in two ways.
On the one hand, firms adopt digital technology to enhance supply chain innovation by technological affordances, namely, the functional action potentials that digital technology offers for an organization aimed at optimizing supply chain management. Digital technology provides new momentum, such as a continuous learning culture [63], essential resources such as data [50], and accurate technological techniques (e.g., artificial intelligence-based supply chain analytics) [24], for a superior and proactive supply chain management. All of these open new conduits in supply chain innovation that can enhance knowledge, optimize resource allocation, change organizational model, and reengineer business process, which further lead to increased innovation of products, services, technology, process, and marketing within the supply chain [24,52,54]. For example, firms utilize innovative logistics equipment, such as Internet of Things (IoT), to track and monitor the flow of goods in real time. It is not only a supply chain technology innovation related to efficiency improvements, but also a supply chain process innovation related to various functions (purchasing, inventory management, and sales) within the chain [52]. According to the importance of shared affordances, it is more likely to occur that network changes at the supply chain level when all related parties simultaneously converge on the same subset of digital technology’s functional affordances, which could lead to the most profound implications at the supply chain level [64].
On the other hand, firms adopt digital technology to enhance supply chain innovation by organizational affordances, namely, the coordinated action potentials that digital technology offers for an organization faced with relational and inter-organizational interactions. Digital technology adoption, such as blockchain functioned as collaborative physical space, is helpful for extended connectivity, open knowledge exchange, rapid information flows, and transparent information sharing among internal departments and external supply chain actors. Then, all of these further facilitate communication, strengthen partner trust, and reduce costs in information search, negotiation, and supervision [22,54]. On this basis, firms are more inclined to shape common values, form relationship commitments, and share risks and rewards, thus effectively promoting collaboration and coordination of upstream and downstream to drive integrative supply chain mechanisms and overall innovation in the chain [31,52,65]. These benefits are especially pronounced when facing risks such as supply chain disruptions.
As a result of the above discussions, the following hypothesis is formed.
H1. 
Digital technology adoption has a positive effect on supply chain innovation.
In turn, the enhanced supply chain innovation is expected to be leveraged to improve sustainable performance in a synergistic way. Specifically, supply chain innovation aligns with the broader goals of sustainable development, including economic prosperity and social responsibility, thus positively impacting sustainable performance from two aspects.
In terms of the economic aspect, supply chain innovation implies increased integration of the entire chain through radical and incremental solutions, contributing to improving processes efficiency such as reduced lead times, controlling costs such as reduced inventory, and creating new added value for customers such as personalized services [25,28,54]. In terms of the social responsibility, supply chain innovation, such as remanufacture mode, sustainable sourcing methods, and green technology adoption, allows waste reduction in the use of resources, provision of consistent quality, prosperity of the regional economy, as well as meeting of environmental standards in social concerns [26,31,66]. Furthermore, innovation in supply chain governance can serve as the strategy of managing corporate social responsibility [67].
Taken together, supply chain innovation is likely to serve as a crucial mediating mechanism through which digital technology adoption influences sustainable performance. Put differently, digital technology adoption inspires the innovations occurring in the supply chain with their functional and coordinated action potentials, which, in turn, improves the sustainable performance of firms. As a result of the above discussions, the following hypothesis is formed.
H2. 
Supply chain innovation mediates the relationship between digital technology adoption and a firm’s sustainable performance.

3.2. The Moderating Role of Environmental Munificence

From the contingent A-A perspective, the value realization of digital technology depends not only on supply chain innovation as a specific actualization pathway, but also on the environment as a specific actualization context [62]. Considering that the effects of supply chain innovation is usually embedded in environments [34], we propose that environmental munificence affects the indirect affordance actualization process from digital technology to supply chain innovation and sustainable performance. Unlike the intuitive perception that high munificence is beneficial, environmental munificence is a double-edged sword in different situations [68,69]. Based on this established foundation, we expect the positive effect of digital technology adoption on sustainable performance via supply chain innovation to be weaker for firms in highly munificent environments due to organizational inertia [15,70].
Specifically, high environmental munificence signals the relatively sufficient resources, continuous growth opportunities, and less competitive pressure [68,71], which implies the risk of falling into an “inertia trap” that tends to maintain the status quo. In this situation, it is more likely to induce risk aversion and a sense of security, thus lacking change and innovation propensity. This phenomenon may be more pronounced in supply chain innovation, which introduces a high-risk perception due to its association with highly challenging internal and external changes [68]. In contrast, an environment lacking in munificence is characterized by shortage of resources, limited growth opportunities, and intensive competition [72]. In such an environment, firms realize that greater efforts need to be taken for maintaining sustainability. Meanwhile, this unfavorable environment enables firms to emphasize deep penetration of existing markets and reliable interactions [71], thus highly motivating to engage in supply chain innovation. Clearly, resorting to supply chain innovation is a more viable way for firms to respond to the scarcity of resources. As a result of the above discussions, the following hypothesis is formed. Figure 1 summarizes the conceptual model.
H3. 
The environmental munificence weakens the indirect impact of digital technology adoption on a firm’s sustainable performance through supply chain innovation.

4. Methods

4.1. Sample and Data Collection

This study used matched multisource data, including survey data from dyadic senior managers and objective data from a public website. Due to the COVID-19 pandemic, we collected online survey data in Shandong Province of China from June to August 2021. On the one hand, the choice of this province was strategically suitable due to its crucial role in the Bohai Rim, one of China’s three primary economic belts. Located in China’s eastern coastal areas at the forefront of digital economy development, this province is known for its dynamic economic activity, significant Gross Domestic Product (GDP) contribution, and role as a hub of traditional Chinese culture, making it representative of typical Chinese business practices. On the other hand, the human resources service industry is appropriate to offer a particularly novel testing ground for the theorized relationships. The changes of blurred industry boundaries and online trends are prompting the human resources service industry to adopt digital technologies, such as smart robots, artificial intelligence, and big data analytics, especially during and after COVID-19. In addition, supply chain innovation through digital technologies, such as pursuing a cutting-edge system, updating of various equipment, innovating supply chain processes, adopting agile and responsive processes, and adjusting supply chain structure, has attracted increasingly attentions in the human resources service industry to purse sustainable performance. A trained survey team representing us sent emails including a two-part questionnaire to the senior managers (Source 1 for entrepreneurs and Source 2 for digital department managers) of 1846 human resources service firms with the support of the Human Resources and Social Security Work of Shandong Province. We assured each participant of strict anonymity and confidentiality, acquiring 494 responses from entrepreneurs in relation to supply chain innovation and sustainable performance, as well as 174 responses from digital department managers in relation to digital technology adoption. After matching the two source responses of this channel, we received 163 valid responses. Second, according to the names of 163 firms, we collected objective data from Chinese official website of the National Bureau of Statistics to measure environmental munificence. Finally, after removing firms that have been deregistered and cannot be queried for the specific deregistration time, 157 valid samples were included for analysis in this study.
Table 2 shows the characteristics of the 157 firms. Among them, most firms are private firms (77.71%) and the number of employees in a firm is generally less than 1000 (98.09%). Firm age and digital transformation modes are relatively balanced. In terms of firm age, less than 3 years, 3–10 years, and more than 10 years respectively account for 49.68%, 29.30% and 21.02%. As for digital transformation modes, 43.31% are business-pull mode, and 56.69% are technology-push mode.

4.2. Measures

The structured survey instrument in this study was designed in advance based on well-validated scales in previous literature, using the 7-point Likert scale with different interpretations. For example, depending on the measurement needs, 1 indicated “totally not adopted” and 7 indicated “totally adopted” when measuring digital technology adoption, while 1 indicated “totally disagree” and 7 indicated “totally agree” when measuring supply chain innovation. We also employed a forward–backward translation method to ensure conceptual equivalence.
Digital technology adoption. We measured this by using eight items derived from Frank et al. [73] and Li et al. [2], with a Cronbach’s α (CA) value of 0.973. Specifically, the digital department manager was asked to explain the extent to which their firms have implemented cloud computing, big data and analytics, network security technology, (intelligent) robots, blockchain, Cyber Physical System, embedded technology, and IoT in their operations. We developed an average composite measure based on eight items to capture the overall level of firms’ digital technology adoption.
Supply chain innovation. We measured this by using six items derived from Afraz et al. [48], with a CA value of 0.990. Specifically, six items are “Our organization pursues a cutting-edge (leading technology) system to integrate information on the supply chain,” “Our organization focuses on continuous updating of various equipment (including vehicles, packages or other physical assets) to foster supply chain innovation,” “Our organization pursues continuous innovation in core global supply chain processes,” “Our organization pursues agile and responsive processes against changes,” “Organizational changes within our organization are taken into account,” and “The adjustment and change of supply chain structure are taken into account.”
Sustainable performance. We measured this by using fifteen items adapted from Li and Atuahene-Gima [74] and Oberseder et al. [75] from both economic and social responsibility perspectives, with a CA of 0.935. In the economic performance respect, entrepreneurs as respondents subjectively rated their perceptual performance in terms of return on investment, return on sales, return on assets, profit growth, and cash flow from market operations. Among other items measuring social responsibility performance, “Our organization contributes to the economic development of the region,” “Our organization preserves jobs in the region,” and “Our organization respects regional values, customs, and culture” were used from the community domain. Three items were used from the shareholder domain, including “Our organization invests capital of shareholders correctly,” “Our organization communicates openly and honestly with shareholders,” and “Our organization provides sustainable growth and long-term success.” The last four items were used from the societal domain, including “Our organization employs people with disabilities,” “Our organization makes donations to social facilities,” “Our organization supports employees who are involved in social projects,” and “Our organization contributes to solving societal problems”. It is worth addressing that according to the triple bottom line principle, sustainable performance includes three dimensions: economic, social, and environmental. The reason why this study excludes the environmental dimension is due to industry factors; that is, the human resources service industry does not involve the emission of three wastes (waste gas, waste water, and waste residue).
Environmental munificence. This construct was calculated using the growth of industry sales in previous studies [35,36]. Considering that our samples come from the same industry but are located in different regions, we calculated the munificence based on regional GDP to reflect the extent to which environmental resources support the sustained growth for the firms in the region. First, we collected the GDP of each city from 2017 to 2021 through Chinese official website of the National Bureau of Statistics. Second, using annual data from the city where the firm is located, we used the ordinary least squares (OLS) regression to predict regional GDP, in which time served as an independent variable. Third, we adopted the ratio of the slope in the regression to the regional GDP’s mean as the measurement of environmental munificence.
Control Variables. Given the impact of other organizational factors, we included four firm-level control variables: firm age, firm size, firm ownership, and digital transformation modes. Firm age was measured as the difference between the year 2021 and the firm’s founding year. Firm size was included after logarithmic transformation of total employee numbers. Firm ownership (0 for non-state-owned firms and 1 for state-owned firms) and digital transformation modes (0 for technology-push mode and 1 for business-pull mode) were measured both as binary variables.

4.3. Reliability and Validity

To ensure the content validity of the questionnaire, we invited two innovation research experts to assess and modify the statements. Additionally, 50 Chinese firms were selected for a pilot study to further modify the item descriptions. Table 3 presents the reliability and validity of the measures. First, the CA and composite reliability (CR) values were all greater than the cutoff value of 0.7, indicating acceptable reliability of all constructs. Second, the average variance extracted (AVE) values were 0.615, 0.813, and 0.937 for sustainable performance, digital technology adoption, and supply chain innovation, respectively, which were all larger than the 0.50 cutoff [76]. These results indicate acceptable convergent validity. Third, all square roots of AVE (along the diagonal in Table 3) are greater than all correlations, indicating acceptable discriminant validity [77].

4.4. Common Method Bias

The potential issue of common method bias (CMB) was controlled and assessed according to Podsakoff et al. [78]. Procedurally, preventive measures were implemented, including protecting respondents’ anonymity and collecting combined data [79]. Statistically, Harman’s single-factor test shows that there are three factors with eigenvalues greater than one extracted from the data by unrotated factor analysis. The first factor accounted for 46.42% of the variance, which was less than the 50% cutoff [79,80]. Therefore, CMB was not a significant concern.

5. Results

5.1. Descriptive Analysis

Table 3 also shows the mean values, standard deviations, and correlation matrix of all variables. All correlation coefficients are below the threshold of 0.70 [81], showing that the correlation coefficients are appropriate. The variance inflation factor (VIF) values for the regression models are far lower than the threshold of 10 [76,82], indicating few concerns about multicollinearity.

5.2. Hypotheses Test

This study used Model 4 of the PROCESS version 3.3 utility for SPSS 26.0 to test the direct and indirect impact of digital technology adoption, with results shown in Table 4. The regression results show that digital technology adoption (β = 0.256, p < 0.001) is significantly and positively related to supply chain innovation, thus supporting H1. As for the mediation effect, the indirect effect of digital technology adoption on sustainable performance via supply chain innovation is significantly positive (indirect effect = 0.143, 95% CI = [0.090, 0.198]), thereby supporting H2.
This study then utilized Model 14 of the PROCESS version 3.3 utility to test the proposed moderation [83]. The results are presented in Table 5. The product of supply chain innovation and environmental munificence is negatively and significantly related to sustainable performance (β = −3.853, p < 0.001, CI95% = [−5.860, −1.846]). Furthermore, environmental munificence (index = −0.988, 95% CI = [−1.676, −0.421]) negatively moderated the mediating role of supply chain innovation, thus supporting H3.
Using the pick-a-point approach outlined by Aiken and West [84], we plotted the simple slope plots in terms of environmental munificence on the high and low levels (1 SD above and below the mean) in Figure 2. Figure 2 shows that the higher the values for environmental munificence, the lower the slopes of the lines. This reveals that with the same increase in supply chain innovation, the increase in sustainable performance at high environmental munificence is less than that at low environmental munificence. Moreover, the interaction plot based on the Johnson–Neyman technique that calculates the moderator values to define significant regions is presented in Figure 3 [83]. Figure 3 shows that the positive impact of supply chain innovation on sustainable performance decreases with environmental munificence and the Johnson–Neyman value for environmental munificence is 0.090. Specifically, when environmental munificence is less than 0.090, the impact of supply chain innovation on sustainable performance is significantly positive. Otherwise, supply chain innovation cannot significantly predict sustainable performance. These findings from Figure 2 and Figure 3 further support H3.

5.3. Robustness Analysis

We conducted the robustness check in three ways. First, we retested the mediating role of supply chain innovation by dividing sustainable performance into four different dimensions (economic, communal, shareholder, and societal). The results are presented in Table 6. As shown in Table 6, the indirect effect of digital technology adoption on economic (indirect effect = 0.111, 95% CI = [0.059, 0.172]), communal (indirect effect = 0.157, 95% CI = [0.097, 0.219]), shareholder (indirect effect = 0.170, 95% CI = [0.103, 0.237]), and societal performance (indirect effect = 0.151, 95% CI = [0.089, 0.215]) via supply chain innovation are significantly positive. Thus, the same signs and significance of coefficients in four dimensions show the robustness of mediation results.
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Figure 2. Interaction plot for moderating role of environmental munificence based on pick-a-point approach. Notes: Low supply chain innovation = 1 SD below mean, High supply chain innovation = 1 SD above mean.
Figure 2. Interaction plot for moderating role of environmental munificence based on pick-a-point approach. Notes: Low supply chain innovation = 1 SD below mean, High supply chain innovation = 1 SD above mean.
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Figure 3. Interaction plot for moderation of environmental munificence based on Johnson–Neyman technique.
Figure 3. Interaction plot for moderation of environmental munificence based on Johnson–Neyman technique.
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Table 6. Robustness test results of mediation of supply chain innovation in relationship between digital technology adoption and different sustainable performance dimensions.
Table 6. Robustness test results of mediation of supply chain innovation in relationship between digital technology adoption and different sustainable performance dimensions.
DVPredictorEstimatesSEp-Value95%CI
Supply chain innovationConstant4.652 ***0.3150.000[4.030, 5.275]
Firm Age0.0070.0150.636[−0.022, 0.036]
Firm Size−0.0600.0560.290[−0.171, 0.052]
Ownership−0.1270.1910.507[−0.505, 0.251]
Digital transformation modes0.0420.1590.793[−0.272, 0.355]
Digital technology adoption0.256 ***0.0450.000[0.167, 0.346]
R20.186
F Value6.909 ***
Economic performanceConstant1.090 *0.5140.035[0.075, 2.105]
Firm Age0.0080.0150.616[−0.022, 0.037]
Firm Size0.229 ***0.0590.000[0.112, 0.345]
Ownership0.2530.2000.207[−0.142, 0.648]
Digital transformation modes0.2510.1660.131[−0.076, 0.578]
Digital technology adoption0.0370.0520.478[−0.066, 0.140]
Supply chain innovation0.434 ***0.0850.000[0.266, 0.602]
R20.254
F Value8.521 ***
Communal performanceConstant2.195 ***0.3850.000[1.433, 2.956]
Firm Age0.0180.0110.122[−0.005, 0.040]
Firm Size0.090 *0.0440.044[0.002, 0.177]
Ownership−0.0780.1500.605[−0.374, 0.219]
Digital transformation modes−0.0830.1240.506[−0.328, 0.163]
Digital technology adoption−0.0290.0390.466[−0.106, 0.049]
Supply chain innovation0.614 ***0.0640.000[0.288, 0.739]
R20.431
F Value18.929 ***
Shareholder performanceConstant2.458 ***0.3910.000[1.686, 3.230]
Firm Age−0.0040.0120.715[−0.027, 0.019]
Firm Size0.0600.0450.184[−0.029, 0.149]
Ownership−0.0080.1520.960[−0.308, 0.293]
Digital transformation modes−0.0510.1260.686[−0.300, 0.198]
Digital technology adoption−0.0680.0400.090[−0.146, 0.011]
Supply chain innovation0.661 ***0.0650.000[0.533, 0.788]
R20.430
F Value18.877 ***
Societal performanceConstant2.172 ***0.4360.000[1.311, 3.033]
Firm Age0.0050.0130.724[−0.021, 0.030]
Firm Size0.0840.0500.096[−0.015, 0.183]
Ownership−0.1490.1700.380[−0.484, 0.186]
Digital transformation modes0.0740.1400.600[−0.204, 0.351]
Digital technology adoption−0.0050.0440.912[−0.092, 0.082]
Supply chain innovation0.590 ***0.0720.000[0.448, 0.732]
R20.361
F Value14.143 ***
The indirect impactsEffectBoot SEBootLL95%CIBootUL95%CI
Digital technology adoption → Supply chain innovation → Economic performance0.1110.0290.0590.172
Digital technology adoption → Supply chain innovation → Communal performance0.1570.0310.0970.219
Digital technology adoption → Supply chain innovation → Shareholder performance0.1700.0340.1030.237
Digital technology adoption → Supply chain innovation → Societal performance0.1510.0320.0890.215
Notes: Number of samples is 157. * p < 0.05; *** p < 0.001.
Second, we retested the moderating role of environmental munificence by using an alternative measurement referring to Keats and Hitt [85]. We treated the natural logarithms (a linear transformation) of regional GDP. Then, we also used the OLS regression to predict regional GDP. Third, we adopted the anti-logarithm of the standard error of the slope in the regression as the alternative measurement. The results are presented in Table 7. Environmental munificence significantly weakens the impact of supply chain innovation (β = −3.157, CI95% = [−5.296, −0.919]) on sustainable performance. Furthermore, the moderated mediation role of environmental munificence is negative and significant (index = −0.81, 95% CI = [−1.528, −0.214]). The results confirm that the moderated mediation results are robust.
Third, we used partial least squares–structural equation modeling (PLS-SEM) technique with Smart PLS 4.1 to test the entire mediation–moderation model, with the results detailed in Table 8 and Figure 4. The empirical results show that explained variance R2 values are greater than 0.15 and predictive relevance Q2 values are greater than 0 (supply chain innovation: R2 = 0.179, Q2 = 0.168; sustainable performance: R2 = 0.583; Q2 = 0.306). They indicate that this model has strong in-sample explanatory power and predictive accuracy [86]. Moreover, the f 2 values, a measure accessing the substantive impacts of an exogenous factor on the endogenous construct, are all above 0.15. Corresponding to the small, medium, and large f 2 effect sizes with the guideline of 0.02, 0.15, and 0.35, it could be said that the quality of this model is good and the variables discussed in this article have a substantial impact [86].
In terms of hypotheses test results, as shown in Table 8, digital technology adoption positively impacts supply chain innovation (β = 0.423, p < 0.001), supporting H1. This study further used bootstrapping analysis (n = 5000) to investigate the mediating role of supply chain innovation. The indirect effect of digital technology adoption on sustainable performance through supply chain innovation is 0.300, with a 95% confidence interval of [0.197, 0.407]. This confirms the mediating role of supply chain innovation again. The results also indicate that environmental munificence weakens the impact of digital technology adoption on sustainable performance via supply chain innovation (β = −0.207, p < 0.001). Thereby, all prior results are robust.

6. Discussion and Implications

6.1. Research Findings

In the current transformative era, although digital technology adoption is becoming a trend in sustainable management practice, its performance outcomes are uncertain [4]. Thus, it is necessary to rethink how to realize the potential of digital technology in sustainability. This study empirically investigates how digital technology adoption affects supply chain innovation in pursuit of sustainable performance, particularly at different levels of environmental munificence. Extending to the contingent affordance theory, we theorize that supply chain innovation is an affordance actualization pathway linking digital technology adoption and sustainable performance, while environmental munificence is an affordance actualization context of this indirect process.
Using the matched multisource data of 157 human resources service firms in China, our proposed model was subjected to empirical testing and all hypotheses are supported. First, the results highlight that digital technology adoption facilitates supply chain innovation, consistent with the findings of Chung et al. [51]. This substantial impact is above the moderate level according to its f 2 effect sizes. Furthermore, the results highlight that supply chain innovation mediates the relationship between digital technology adoption and sustainable performance. This aligns with previous studies that emphasize the indirect impacts of digital technology adoption on performance [1,2]. Third, our results highlight that environmental munificence significantly weakens the indirect positive impact of digital technology adoption on sustainable performance via supply chain innovation. This is consistent with the idea of previous studies that innovative supply chain management initiatives are more essential for improving performance in environments with low munificence [71,87].

6.2. Theoretical Implications

First, this study incorporates supply chain innovation to bring out the affordance actualization process occurring between digital technology adoption and sustainable performance. For one thing, instead of focusing on a specific digital technology such as big data analysis and the domain performance such as operational performance in previous literature [7,54,88], we treat digital technology as a holistic concept and sustainable performance as a performance synthesis to investigate the relationship between them, advancing a more comprehensive understanding on digital technology performance benefits. For another, previous studies focus primarily on the indirect impact of digital technology adoption on performance with the mechanisms of internal capabilities [6,7] and resources [12], leaving indirect links in the supply chain level under-researched. Supplementally, the current study pays the attention to supply chain innovation as an intermediary. It argues that digital technology adoption inspires the innovative activities occurring in the supply chain with their functional and coordinated action potentials. In turn, supply chain innovation aligns with the broader goals of sustainable development, including economic prosperity and social responsibility, thus contributing to sustainable performance. The results confirm that supply chain innovation mediates the relationship between digital technology adoption and sustainable performance. In this respect, this study delves into the specific operational pathways through which digital technology can be transformed into an improvement in sustainable performance. As such, we contribute to the related literature not only by moving beyond broad discussions about digital technology performance implications, but also by constructing a more complete picture of its performance improvement pathways. This study also extends the scope of supply chain innovation’s mediation role by unveiling its potential to transform digital technology adoption into sustainable performance, which is different from the deterministic resources and capabilities according to resource-based views and dynamic capability frameworks.
Second, this study offers a more nuanced understanding of the boundary conditions for the affordance actualization process from digital technology adoption to supply chain innovation and sustainable performance, by considering environmental munificence as an affordance actualization context. Extant quantitative research related to supply chain management highlights the vital role of environmental factors [2,37,38]. In the supply chain innovation settings that particularly stresses a quick response to changes of environment [24,28,48], it is necessary to investigate their boundary conditions. The contingent role of environmental munificence in supply chain innovation is unclear, especially when supply chain innovation functions as an intermediary. This study investigates the moderated mediating role of environmental munificence. As expected, environmental munificence is found to weaken the indirect impact of digital technology adoption on sustainable performance via supply chain innovation. This finding also contributes to more complete and refined understanding of the contingency impacts of environmental munificence in digital and innovative context.
Third, this study contributes to the affordance theory by extending the A-A perspective to the contingent A-A perspective. While existing research applying A-A perspective has made some contributions, such as Trocin et al. [19] and Li et al. [59], they primarily drawn on process framework investigate the link and mechanism between affordances and outcomes through case studies, with less investigation of boundary conditions. Even though only a few exceptions were examined, they often combine with other established theoretical consensus rather than being grounded in affordance theory [18]. Notably, affordance theory clearly posits that affordances change among different contexts or goals and are not actualized in a vacuum [18,61,62]. However, although prior research has identified contingency theory as an important one in digital technology (e.g., [4]) and supply chain research (e.g., [48]), contingent perspectives on the affordance theory are underdeveloped in the literature. This study puts forward the contingent A-A by considering both the A-A pathway and the A-A context, which is also quantitively examined by grounding the moderated mediation model.

6.3. Practical Implications

From a practical standpoint, this study provides more effective strategies and actionable insights for managers seeking to leverage digital technology for sustainable performance in uncertain environments.
First, managers should invest in digital technology as a crucial infrastructure for sustainable development. Specifically, adopting digital technology is helpful for conducting supply chain innovation to enhance sustainable performance. Managers should utilize both the functional action potentials such as smart contracts of blockchain and the coordinated action potentials such as digital platforms to conduct supply chain innovation. For example, human resources service firms could utilize the powerful natural language processing and non-verbal behavior analysis function of AI for initial recruitment interviews, enhancing their service speed. Moreover, the form, scope, and degree of supply chain innovation should not be limited, including the innovativeness in any supply chain functions and aspects (product, technology, process, or both) in an incremental or radical way. For example, human resources service firms could develop online learning and personnel management platforms to improve their training and outsourcing services. In turn, both economic and social responsibility performance improvements are achieved.
Second, during the process of supply chain innovation using digital technology, managers should watch out the level of environmental munificence. This study finds that environmental munificence weakens the indirect impact of digital technology adoption on sustainable performance via supply chain innovation. In this case, managers are advised to be watchful and cope with organizational inertia when environmental munificence is high, thus effectively harnessing digital technology to drive supply chain innovation and gain high sustainable performance. For example, being in the film industry with relatively sufficient resources, continuous growth opportunities, and less competitive pressure, that is, a munificent environment, led the former industry giant—Kodak—to fall into the organizational inertia of relying on its dominant film division and fail to effectively leverage digital technology for supply chain innovation, thus ultimately declining at a fast pace.

6.4. Limitations and Future Research

There are still some research limitations that need to be improved in future research. First, the cross-sectional design is a major limitation. While this study combines subjective dyadic survey data and objective data to avoid the potential problems that may exist from a single data source, it is still cross-sectional data and cannot confirm the causal relationship. Future research should consider potential remedies such as longitudinal designs to conquer this limitation. Second, the measurements of environmental munificence and sustainable performance are a concerned limitation. We operationalize environmental munificence using regional GDP growth and capture sustainable performance by simultaneously considering economic and social responsibility dimensions excluding the environmental dimension. Future studies could consider alternative indicators of these two variables: combining both subjective evaluations and objective aspects to measure environmental munificence, and using more widely recognized “Triple Bottom Line” consisting of economic, environmental, and social performance to measure sustainable performance. Third, the adoption of digital technology as a “holistic construct” might obscure nuanced effects of different technologies. This study views digital technology adoption as a holistic construct. A more nuanced understanding of affordances with different digital technology adoption strategies and technolohgy types should be studied in the future. Indeed, this concept can be more nuancedly identified, such as information and communication digital enabling technologies (IDETs) and advanced robotics and integration digital enabling technologies (ADETs) in terms of different technology types [89], as well as breadth and depth in terms of different adoption strategies [1,8]. With reference to these, future studies could enrich and refine the understanding of digital technology affordances and the relationships in our theoretical model.

Author Contributions

Conceptualization, Z.L., J.C. and Y.W.; investigation, Z.L. and J.C.; methodology, Z.L. and Y.W.; writing—original draft, Z.L.; writing—review and editing, J.C. and Y.W.; funding acquisition, J.C. and Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China under Grant Nos 72302010 and 71302128, Beijing Social Science under Grant No. 22GLC044, the National Key R&D Program of China under Grant No. 2021YFF0901303, the 2021 Undergraduate Teaching Reform Innovation Project of Higher Education in Beijing, and the Program for Innovation Research in Central University of Finance and Economics.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Academic Committee of business school in Central University of Finance and Economics (protocol code HC2025082201, 22 August 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The authors will make the raw data available upon request.

Acknowledgments

The authors appreciate the valuable comments of the editor and anonymous reviewers.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
A-AAffordance actualization
GDPGross Domestic Product
IoTInternet of Things
IDETsInformation and communication digital enabling technologies
ADETsAdvanced robotics and integration digital enabling technologies

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 4. Results of PLS-SEM model. Notes: ** p < 0.01; *** p < 0.001, and ns p > 0.05.
Figure 4. Results of PLS-SEM model. Notes: ** p < 0.01; *** p < 0.001, and ns p > 0.05.
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Table 1. Most relevant literatures on sustainable performance outcomes of digital technology adoption.
Table 1. Most relevant literatures on sustainable performance outcomes of digital technology adoption.
StudyPerformanceMediatorModeratorIndustry
[1]Competitive advantageProduct and service innovation/M
[2]Economic and environmentalDigital supply chain platformsEnvironmental dynamismM
[3]SustainabilityStakeholder collaboration/M
[9]Corporate social responsibilityEnvironment protection management; Internal control quality//
[11]Economic and environmentalDigital technology capabilityDigital strategyM
[43]Sustainable performance /Corporate social responsibilityM
[44]Financial and marketDigital transformation strategy;
Organizational innovation		/S
[45]Economic/Environmental assetsM
[46]ESGDigital supplier relationship management capability; Coopetition within supply networks/M&S&O
[47]Financial and non-financial//M
This studySustainable performanceSupply chain innovationEnvironmental munificenceS
Notes: We focused on types of performance, mediator, moderator, and sample industry. M = Manufacturing industry. S = Service industry. O = Other industry.
Table 2. Profile of sample.
Table 2. Profile of sample.
Count% Count%
Firm age Firm size (employee number)
X < 37849.68%Y < 10014592.36%
3 ≤ X < 104629.30%100 ≤ Y < 100095.73%
X ≥ 103321.02%Y ≥ 100031.91%
Ownership Digital transformation modes
Private firms12277.71%Business-pull mode6843.31%
Non-private firms3522.29%Technology-push mode8956.69%
Notes: Number of samples is 157.
Table 3. Descriptive statistics, correlations, and validity.
Table 3. Descriptive statistics, correlations, and validity.
12345678
1. Sustainable performance(0.784)
2. Digital technology adoption0.270 **(0.902)
3. Supply chain innovation0.684 **0.420 **(0.968)
4. Environmental munificence−0.0340.05−0.04——
5. Firm age0.093−0.0420.0120.011——
6. Firm size (employee number) 10.159 *−0.049−0.0920.070.197 *——
7. Firm ownership 2−0.051−0.003−0.0560.01−0.178 *−0.055——
8. Digital transformation model 20.0650.0490.052−0.1460.001−0.069−0.119——
Mean5.3064.0945.523−0.0195.9802.3220.7800.433
SD0.8341.7241.0620.0455.5831.4160.4180.497
CA0.9350.9730.990——————————
CR0.9440.9720.989——————————
AVE0.6150.8130.937——————————
Notes: Number of samples is 157; SD, standard deviation; CA = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted; bold numbers on the diagonal are the square root of the AVE; off-diagonal elements are correlations between each pair of constructs; 1 Natural logarithm of variable; 2 Dummy variable; * p < 0.05, ** p < 0.01, two-tailed tests.
Table 4. Mediation of supply chain innovation in relationship between digital technology adoption and sustainable performance.
Table 4. Mediation of supply chain innovation in relationship between digital technology adoption and sustainable performance.
DVPredictorEstimatesSEp-Value95%CI
Supply chain innovationConstant4.652 ***0.3150.000[4.030, 5.275]
Firm Age0.0070.0150.636[−0.022, 0.036]
Firm Size−0.0600.0560.290[−0.171, 0.052]
Ownership−0.1270.1910.507[−0.505, 0.251]
Digital transformation modes0.0420.1590.793[−0.272, 0.355]
Digital technology adoption0.256 ***0.0450.000[0.167, 0.346]
R20.186
F Value6.909 ***
Sustainable performanceConstant1.873 ***0.2980.000[1.285, 2.461]
Firm Age0.0060.0090.463[−0.011, 0.024]
Firm Size0.129 ***0.0340.000[0.061, 0.196]
Ownership0.0280.1160.812[−0.201, 0.256]
Digital transformation modes0.0770.0960.426[−0.113, 0.266]
Digital technology adoption−0.0080.0300.786[−0.068, 0.051]
Supply chain innovation0.557 ***0.0490.000[0.460, 0.654]
R20.521
F Value27.241 ***
EffectBoot SEBootLL95%CIBootUL95%CI
Direct effect of digital technology adoption on sustainable performance−0.0080.030−0.0680.051
Digital technology adoption → Supply chain innovation → Sustainable performance0.1430.0280.0900.198
Notes: Number of samples is 157. DV = dependent variable; *** p < 0.001.
Table 5. Moderated mediation effects of environmental munificence on relationship between digital technology adoption and sustainable performance.
Table 5. Moderated mediation effects of environmental munificence on relationship between digital technology adoption and sustainable performance.
DVPredictorEstimatesSEp-Value95%CI
Supply chain innovationConstant−0.871 **0.3150.006[4.030, 5.275]
Firm Age0.0070.0150.636[−0.022, 0.036]
Firm Size−0.0600.0560.290[−0.171, 0.052]
Ownership−0.1270.1910.507[−0.505, 0.251]
Digital transformation modes0.0420.1590.793[−0.272, 0.355]
Digital technology adoption0.256 ***0.0450.000[0.167, 0.346]
R20.186
F Value6.909 ***
Sustainable performanceConstant4.976 ***0.1880.000[4.605, 5.346]
Firm Age0.0110.0090.182[−0.005, 0.028]
Firm Size0.121 ***0.0330.000[0.056, 0.187]
Ownership0.0320.1110.774[−0.188, 0.252]
Digital transformation modes0.0730.0930.434[−0.111, 0.257]
Digital technology adoption−0.0200.0290.489[−0.078, 0.038]
Supply chain innovation0.553 ***0.0470.000[0.460, 0.647]
Environmental uncertainty0.0351.0320.973[−2.004, 2.074]
Supply chain innovation * Environmental munificence−3.853 ***1.0160.000[−5.860, −1.846]
R20.564
F Value23.934 ***
Index of moderated mediationEffectBoot SEBootLL95%CIBootUL95%CI
Environmental munificence−0.9880.325−1.676−0.421
Notes: Number of samples is 157. DV = dependent variable; * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 7. Robustness test results of moderated mediation effects of environmental uncertainty by alternative measurement.
Table 7. Robustness test results of moderated mediation effects of environmental uncertainty by alternative measurement.
DVPredictorEstimatesSEp-Value95%CI
Supply chain innovationConstant−0.871 **0.3150.006[4.030, 5.275]
Firm Age0.0070.0150.636[−0.022, 0.036]
Firm Size−0.0600.0560.290[−0.171, 0.052]
Ownership−0.1270.1910.507[−0.505, 0.251]
Digital transformation modes0.0420.1590.793[−0.272, 0.355]
Digital technology adoption0.256 ***0.0450.000[0.167, 0.346]
R20.186
F Value6.909 ***
Sustainable performanceConstant4.939 ***0.1920.000[4.561, 5.318]
Firm Age0.0100.0090.250[−0.007, 0.027]
Firm Size0.129 ***0.0340.000[0.062, 0.195]
Ownership0.0430.1140.704[−0.182, 0.268]
Digital transformation modes0.0910.0960.342[−0.098, 0.280]
Digital technology adoption−0.0170.0300.567[−0.076, 0.042]
Supply chain innovation0.552 ***0.0480.000[0.457, 0.648]
Environmental uncertainty0.3881.1780.743[−1.941, 2.716]
Supply chain innovation * Environmental munificence−3.157 **1.1330.006[−5.296, −0.919]
R20.545
F Value22.187 ***
Index of moderated mediationEffectBoot SEBootLL95%CIBootUL95%CI
Environmental munificence−0.8100.334−1.528−0.214
Notes: Number of samples is 157. DV = Dependent variable; * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 8. Robustness test results of PLS-SEM assessment.
Table 8. Robustness test results of PLS-SEM assessment.
Pathβ Valuep-ValueResults
Firm age → Sustainable performance0.0740.152-
Firm size → Sustainable performance0.183 **0.002-
Firm ownership → Sustainable performance0.0120.917-
Digital transformation modes → Sustainable performance0.0640.576-
Digital technology adoption → Supply chain innovation0.423 ***0.000Support H1
Supply chain innovation → Sustainable performance0.711 ***0.000-
Environmental munificence → Sustainable performance0.0240.647-
Supply chain innovation * Environmental munificence → Sustainable performance−0.207 ***0.000Support H3
PathSample means95% CIIndirect impactResults
Digital technology adoption → Supply chain innovation → Sustainable performance0.303[0.197, 0.407]0.300Support H2
VariablesR2Q2f2
Supply chain innovation0.1790.168Digital technology adoption → Supply chain innovation: 0.218
Sustainable performance0.5830.306Supply chain innovation → Sustainable performance: 1.187
Notes: Number of samples is 157. CI = confidence interval. * p < 0.05; ** p < 0.01; *** p < 0.001.
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MDPI and ACS Style

Li, Z.; Chen, J.; Wang, Y. Contingent Affordance Actualization: Nexus of Digital Technology Adoption and Sustainable Performance with the Roles of Supply Chain Innovation and Environmental Munificence. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 297. https://doi.org/10.3390/jtaer20040297

AMA Style

Li Z, Chen J, Wang Y. Contingent Affordance Actualization: Nexus of Digital Technology Adoption and Sustainable Performance with the Roles of Supply Chain Innovation and Environmental Munificence. Journal of Theoretical and Applied Electronic Commerce Research. 2025; 20(4):297. https://doi.org/10.3390/jtaer20040297

Chicago/Turabian Style

Li, Zifeng, Jinliang Chen, and Yu Wang. 2025. "Contingent Affordance Actualization: Nexus of Digital Technology Adoption and Sustainable Performance with the Roles of Supply Chain Innovation and Environmental Munificence" Journal of Theoretical and Applied Electronic Commerce Research 20, no. 4: 297. https://doi.org/10.3390/jtaer20040297

APA Style

Li, Z., Chen, J., & Wang, Y. (2025). Contingent Affordance Actualization: Nexus of Digital Technology Adoption and Sustainable Performance with the Roles of Supply Chain Innovation and Environmental Munificence. Journal of Theoretical and Applied Electronic Commerce Research, 20(4), 297. https://doi.org/10.3390/jtaer20040297

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