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Article

Investigating Effects of Digital Innovations on Sustainable Operations of Logistics: An Empirical Study

by
Zulkaif Ahmed Saqib
1,2,* and
Luo Qin
1,2,*
1
College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China
2
Shenzhen University, Shenzhen 518118, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5518; https://doi.org/10.3390/su16135518
Submission received: 30 May 2024 / Revised: 20 June 2024 / Accepted: 24 June 2024 / Published: 28 June 2024
(This article belongs to the Special Issue Digital Innovation and Technology Transfer in Emerging Markets and/or from Emerging Markets)
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Abstract

:
The current investigation seeks to investigate the relationship between digital innovations and the sustainability perspectives of logistics operations. Despite the advancement of technologies for sustainable goals, it is evident that the importance of digital innovation is increasing as a means of enhancing sustainable operations. Current research is examining the determinants of digital innovation in small logistics firms. A new theoretical framework is developed based on technology adoption, technology integration, and sustainable operations by following the diffusion of innovation (DOI) theory. For this study, data were collected from 540 small logistical firms in China. The covariance-based structural equation modeling technique was used to test the proposed hypothesis. The results from the analysis indicate that technology adoption positively affects the environmental perspectives with coefficients = 0.293, social perspectives with coefficients = 0.461, and economical perspectives with coefficients = 0.461 of sustainable operations. Similarly, technology integration positively affects the environmental perspectives with coefficients = 0.512, social perspectives with coefficients = 0.347, and economical perspectives with coefficients = 0.415 of sustainable operations. In conclusion, the implementation of digital technology for innovation can help firms improve their logistical operations in terms of sustainability, which will deliver a robust business improvement according to this research.

1. Introduction

In today’s rapidly evolving business scenery, sustainability has emerged as a critical concern for firms across various industries [1,2,3,4,5]. As corporations strive to fulfill the increasing expectations of stakeholders for environmentally and socially responsible operations [3,6,7]. Sustainable operations are typically analyzed through three key dimensions, often referred to as the triple bottom line: environmental, social, and economic perspectives [8,9]. Tran et al. [10] argued that the operations of a firm may be more sustainably run if they adhere to the principles outlined across all of the following aspects. Specifically, the environmental dimension focuses on minimizing the negative impact of a firm’s operations on the natural environment, such as reducing waste, reducing emissions, offering environmentally friendly services/products, conserving resources, and adopting ISO 14001 standards [4,11,12,13,14]. The social dimension emphasizes the impact of a firm’s operations on its employees and stakeholders, which includes employee well-being, human rights, and social responsibility [15,16,17]. Third, the economic dimension focuses on the financial performance of a firm and its ability to create value over the long term [18,19]. Gelashvili et al. [20] documented that the economic perspective deals with financial accomplishment, cost efficiency, innovation growth, and value creation. Sustainable operations require an integrated approach [21] where the environmental, social, and economic dimensions are not treated in isolation but are considered holistically. This integration ensures that initiatives aimed at improving one dimension do not negatively impact the others. For example, adopting energy-efficient technologies (environmental) can lead to cost savings (economic) and enhance the company’s reputation, thereby improving employee morale and community relations (social).
Currently, digital innovation is not just a competitive advantage but a necessity for survival and growth [22,23]. For instance, Paridaens et al. [24] conclude that Maersk’s use of IoT sensors and blockchain ensures optimal shipping conditions and transparent tracking, reducing waste and emissions for the enhancement of sustainability in logistics. The second example of robotics and AI technology implementations is Amazon’s developments in warehouses that provide employees safety and efficiency by optimizing the delivery routes [25]. Briefly, Hopkins et al. [26] explained that DHL’s big data analytics optimizes delivery routes and inventory management, leading to cost savings and economic efficiency. These advancements collectively demonstrate how digital technologies contribute to more sustainable logistics practices. However, as companies face increasing pressures from global challenges such as climate change, resource scarcity, and heightened social expectations [27], the role of digital technologies in promoting sustainable operations becomes crucial [28]. Digital innovation for small businesses involves adopting new technologies and processes to improve efficiency [27,29], enhance employee experiences, drive growth, and provide a competitive edge [30]. Efficiency, profitability, and competitive advantage are essential for a firm and depend on business operations [31,32]. In the logistics industry, which is characterized by intense competition and high operational demands, digital technology has emerged as a cornerstone for achieving efficiency and sustainability [22,33]. The integration of advanced technologies such as artificial intelligence, blockchain, and the Internet of Things (IoT) is reshaping how logistics companies manage their complex networks and vast resources [34,35]. Specifically, digitalization in logistics constitutes mainly software-based digital technologies that modify business operations, processes, functions, and even entire business models by converting analog information into digital formats, automating processes, and integrating digital technologies into all business operations [36]. For example, dating back to the 1970s and 1980s, the electronic data interchange was attempted for technology adoption into logistics operations for the exchange of documents electronically, speeding up processes that were previously paper-based and time consuming [37,38]. Currently, the logistics sector has advanced technologies with extensive data and enhanced computing power that enable AI systems to perform complex analytics for predictive maintenance and route optimization, blockchain technology to enhance transparency across the supply chain, and IoTs to improve connectivity options and sophisticated data management systems. Each of these technologies not only solves existing challenges but also drives the industry towards new levels of efficiency and transparency in operations. Together, AI, blockchain, and IoT are not just addressing current challenges but are also driving digital innovation in the logistics industry by opening new avenues for development and application. Xu et al. [39] verified that digitalization allows logistics firms to transform traditional models into more dynamic, efficient, and customer-focused operations. At this juncture, digital innovation refers to the application of digital technology to existing business problems and practices to create new or significantly improved products, processes, or services that depend on technology adoption and integration. This study investigates the extent to which technology adoption and integration within logistics operations can impact sustainable operational practices across three critical dimensions: environmental, social, and economic. This research aims to fill a significant gap in the existing literature by specifically examining how technology adoption and integration as digital innovation in logistics operations can contribute to sustainable operations. While previous studies have addressed the broader impacts of technology on business performance, there is a need for a focused exploration of how the digital innovation of these technologies influences sustainability outcomes within the logistics sector. Based on the above discussion, we posed two research questions for this study that are given below.
RQ1: 
How do technology adoption and integration in logistics affect the sustainable operations of a firm?
RQ2: 
What role do technology adoption and integration play in meeting the sustainable goals of logistics operations?
The remaining structure of this article includes several steps: first, design a research model based on the diffusion of innovation (DOI) theory; second, a well-design questionnaire designed for data collection; third, the survey was conducted to collect the data; fourth, the collected data were analyzed through CFA and the SEM method; five, the results are interpreted; and finally, discussion and conclusions are presented with implications and limitations.

2. Literature Review

2.1. Theoretical Framing

By following the diffusion of innovation (DOI) theory, we developed the main research framework (see Figure 1). DOI was developed by Everett Rogers under an explanation of how, why, and at what rate new technology should be spread/adopted within a culture [40,41]. The theory identifies several key elements that influence the adoption of an innovation: the innovation itself, communication channels, time, and the social system [42,43]. Innovation refers to the idea, practice, or object perceived as new that is adopted by any individual/entity [44]. In this research, technological adoption and integration for sustainable operations are under investigation; however, DOI theory is highly suitable for different points. First, DOI theory posits that innovations perceived as having a relative advantage over existing solutions are more likely to be adopted [40,44]. On the other hand, we assume technological adoption and integration as digital innovation that enhances the sustainable aspect of business operations, indicating a clear relevance and importance. Second, innovations that are compatible with existing values, past experiences, and the needs of potential adopters are more readily adopted [45]. Digital innovation (technologies) is often aligned with a firm’s sustainability goals and stakeholder expectations [46,47,48]. Third, DOI theory emphasizes the importance of communication channels in spreading new technologies [49]. In the context of our research framework, effective communication about the benefits of technological adoption and innovation can enhance their diffusion across the organization and industry. Last, the rate of adoption time can vary considering DOI [42]; thus, understanding the time dynamics in the research framework can help predict how quickly sustainable technologies will be implemented and their effects realized. In summary, DOI theory is well suited to our research framework and proposed hypothesis and provides robust links for understanding how and why firms adopt and innovate with new technologies [42,43,45].

2.2. Hypothetical Framing

Recent developments in green and innovative technologies have occasioned in several innovations in business operations to accelerate sustainability performance [50,51,52]. Digital innovations have a direct relationship with sustainability, offering tools to enhance resource efficiency and reduce waste and support renewable energy and other sustainable activities/issues [53]. Technologies like IoT, AI, and big data analytics optimize resource use in sectors like agriculture and energy, reducing environmental footprints. Sustainable supply chains benefit from blockchain and IoT for better transparency and efficiency. Even, users’ behavior shifts towards sustainability through apps and platforms that raise awareness and incentivize eco-friendly practices. Through digitalization, digital innovation has contributed to the management literature [54]. Sustainability in the logistics industry is a crucial topic and has been investigated in literary works. Although the literature provides different views on environmental sustainability, there is no clear consensus that the logistics industry has long-term explanations for sustainability. Therefore, this research will investigate the impact of digital innovation on sustainable operations for sustainability objectives such as environmental sustainability, social sustainability, and economic sustainability.
Impact of digital innovation on environmental sustainability in operations: environmental sustainability in digitalization has been studied in literary works for a triple-line model that includes the challenges and opportunities linked with advanced technologies that have been investigated; investigators have concluded that there is a positive impact between technological applications and environmental benefits [55]. The firm integrates several advanced technologies to reduce its environmental impact and improve operational efficiency [46,56]. Shahzad et al. [50] investigated how firms implement advanced telematics systems to optimize routes and reduce idle times, further enhancing fuel efficiency. In warehouses, firms adopt automated technologies, including robotics and AI-driven inventory management systems that increase operational efficiency and reduce energy consumption [57,58,59]. The installation of solar panels on warehouse rooftops and at distribution centers ensures that a significant portion of a firm’s energy needs is met through renewable sources, reducing reliance on non-renewable energy and cutting down on carbon emissions [60]. The company’s commitment to sustainability is demonstrated by implementing an environmental management system (EMS) in line with ISO 14001 standards [46,61,62], ensuring continuous improvement in environmental performance and compliance with environmental regulations [63]. Industry 4.0 has a beneficial influence on environmental sustainability since it fully digitizes the process, resulting in more precise, high-quality management and real-time event management for the external environment [54,64]. Samuel et al. [64] verified that AI and other digital technologies have significant environmental impacts that include heavy carbon dioxide emissions associated with energy consumption.
Impact of digital innovation on social sustainability in operations: The sustainability concept in the literature is one of the hottest topics currently because of humanity issues [65]. Fortunately, the inequality in socioeconomic factors is arising as a wave for humans and the interest in socialism is not limited to researchers without creating social opportunities for the coming generations because consumers are shifting their demand towards sustainable products/services [66]. Shaw et al. [67] argued that social sustainability is an essential component of sustainable development that focuses on social values, equity, and justice. It addresses basic needs, safety, and health while promoting a sense of community, social capital, and diversity. It also upholds human rights, with the ultimate goal of reducing poverty, improving quality of life, and enhancing living standards [68]. The relations of some aspects of digital technology and social sustainability are still under consideration for research. For example, Rodrigues et al. [69] analyzed the working condition enhancements for communities or individuals as a result of the adoption or integration of digital technologies. Another investigation argued that digital technologies have an impact on the unemployment of youth by the creation of job opportunities for communities [70].
Impact of digital innovation on economic sustainability in operations: Technology innovation has long been recognized as a crucial factor in driving economic growth, as evidenced by early studies on innovation [71]. The relationship between technology and growth was characterized in terms of quantitative expansion, with a specific emphasis on the size of the firm [72]. During the latter stages of the 1990s, the connection between digitalization and economic issues continued to grow as knowledge flowed and innovation networks formed, shifting towards a focus on interactions between technology and parts of society [73,74,75]. Digital tools can streamline operations, reducing waste and operational costs. For example, predictive analytics can optimize supply chain management, reducing inventory costs. Today, a firm develops and uses sustainable packaging materials [76,77], such as biodegradable and recyclable materials [78], and implements a packaging return and reuse practice in operations [79], reducing the environmental impact of packaging waste [56,80]. By adopting a digital logistics platform that utilizes AI and big data analytics [51,58], firms enhance supply chain transparency and efficiency [81,82], enabling real-time tracking of shipments and data-driven decision making [83,84]. Ceynowa et al. [58] argued that over three years, these technological adoptions led to a steady decrease in fuel consumption, CO2 emissions, and waste to landfills, while the share of renewable energy and sustainable packaging usage increased. Based on the above scenario, this study proposes that the positive impact of technology adoption and integration towards the sustainable operations of a firm will create a strong potential for logistics companies to improve their sustainability performance through technological investments.
H1. 
A higher level of technology adoption in logistics leads to an improved environmental perspective in sustainable operations.
H2. 
A higher level of technology adoption in logistics leads to an improved social perspective in sustainable operations.
H3. 
A higher level of technology adoption in logistics leads to an improved economic perspective in sustainable operations.
H4. 
A higher level of technology integration in logistics leads to an improved environmental perspective in sustainable operations.
H5. 
A higher level of technology integration in logistics leads to an improved social perspective in sustainable operations.
H6. 
A higher level of technology integration in logistics leads to an improved economic perspective in sustainable operations.

3. Methodology

Based on the diffusion of innovation theory, this study aims to examine whether digital innovation factors such as technology adoption and technology integration in operations impact the sustainable operations of a firm. The proposed research model involves controlling for firm-related factors such as firm age, employee training, and IT staffing to better capture the comprehensive influence of digital innovation. To achieve the study aim, a survey questionnaire was designed, the data were collected through a survey, the quality of the data was analyzed using confirmatory factor analysis, and the proposed hypothesis was tested through the structural equation modeling (SEM) method.

3.1. Survey Questionnaire

We design a comprehensive questionnaire (data tool) with 26 scale items and 5 firm-specific questions. In final questionnaire, the three explanatory constructs are the environmental perspective (EnvPr) of sustainable operations, social perspective (SciPr) of sustainable operations, and economic perspective (EcnPr) of sustainable operations, comprising fifteen items in the questionnaire. Specific questions linked with EnvPr are “EnvPr1: our firm has significantly reduced waste production through the use of digital technologies”, EnvPr2: our firm has significantly reduced carbon emissions through the use of digital technologies”, EnvPr3: our firm has significantly optimized material resources through the use of digital technologies”, “EnvPr4: digital technologies are utilized to improve transparency and accountability”, and “EnvPr5: advanced technologies have contributed to the enhancement of our environmental reporting capabilities”. For SciPr, the six items are “digital technologies have improved the safety conditions of our workplace (SciPr1)”, “our firm actively engages with community stakeholders on sustainability issues (SciPr2)”, “our digital initiatives have improved employee satisfaction and engagement (SciPr3)”, “digital technologies have been instrumental in promoting diversity and inclusion within our workforce (SciPr4)”, “our firm uses digital technologies to ensure compliance with labor laws and ethical standards (SciPr5)”, and “digital technology adoption has enhanced the firm ability to respond to social concerns (SciPr6)”. Four items for EcnPr are “digital technologies have improved the operational cost efficiency of our firm (EcnPr1)”, “our firm has increased its revenue from sustainable operations/services (EcnPr2)”, “our firm has a cost-benefit analysis from sustainable operation (EcnPr3)”, and “our firm has reduced packing material costs due to digital technologies (EcnPr4)”.
Similarly, the response variables of digital innovation are technology adoption (TechA) and technology integration (TechI) comprising eleven items. Five items associated with TechI are “digital technologies are fully integrated into the firm’s daily operations (TechI1)”, “employees across all levels are trained to utilize digital technologies effectively (TechI2)”, “our firm uses digital technologies to monitor and manage environmental impact continuously (TechI3)”, “digital technologies are utilized to improve transparency and accountability (TechI4)”, “our firm has a high level of collaboration between IT and other departments to ensure effective use of digital technologies (TechI5)”, and “feedback from digital technology usage is regularly analyzed and used for continuous improvement (TechI6)”. Included items for TechA are (TechA1:our firm regularly invests in digital technology for operational efficiency), (TechA2: our firm adopted multiple digital technologies such as AI, IoT, and blockchain), (TechA3: the adoption of digital technologies is a priority of our firm’s strategic planning), (TechA4: our firm actively explores emerging digital technologies that could impact our services), and (TechA5: our firm evaluates the potential of digital technologies primarily based on the ability to improve sustainability in operations). The corresponding options for responses were based on a 5-point Likert scale (1 strongly disagree to 5 strongly agree). The scale items were adapted to measure all five constructs included in Figure 1. A team of experts provided their review to finalize the questionnaire. Further, the indicators of the sustainability perspective (EnvPr, EcnPr, and SciPr) were adapted from Ethos Institute (a non-governmental firm in Brazil founded in 1998), which has an objective of supporting small firms to resolve sustainability issues [85,86]. The indicators of digital innovation (TechI and TechA) were selected to prepare the final instrument [30,87].

3.2. Data Collection

After finalizing the questionnaire, qualified key informants for this study were identified. The recruitment of one key informant from one company for participation in the survey was aimed for. This study established a collaborative relationship with a prominent research firm to provide expert members for the research panel. Regarding this relationship, the research firm facilitates the collection and organization of the data from target informants. Moreover, through this channel, the integrity and accuracy of the collected data were ensured. The partner research firm surveyed all eligible participants by distributing the survey electronically. Before finalizing the questionnaire, we ran a pilot examination on a survey tool with 30 sample sizes from reputable informants. The survey was moved forward after conducting a pilot examination and ensuring reliability and validity. Data were gathered from informants within January and February of 2024 in two phases. In the first phase, the data were collected from 200+ respondents as complete responses, and 300+ complete responses were gathered in the second phase. The complete sample size was 540 responses for data analysis. In the first step after data collection, the distribution of the data (skewness and kurtosis values), variability of the data (variance, standard deviation), and central tendency of the data were examined through descriptive statistics (see Table 1).
In empirical research, common method bias (CMB) is a potential issue for data analyses that do not have a potential source of method biases [88]. CMB can inflate the correlations between constructs while leading to the validity of proposed relations [89]. To handle CMB, data were collected through different sources in different waves, such as from the dyads of firm leaders/managers and team members. In the first waves, we targeted managers for the data survey by following their psychological power and personality characteristics. The second wave of the data survey was directed at the remaining employees. The collected data are visualized for each item and response (see Figure 2).

3.3. Confirmatory Factor Analysis

The confirmatory factor analysis was employed to verify the accurate and meaningful foundation in the structure of the observed constructs (TecI, TecA, EnvPr, SciPr, and EcnPr). CFA allows the researcher to verify the structural model for hypothesis testing, model validation, model fitness, and dimensionality assessment by following the collected data [90]. We measure the model assessment through model estimation (factor loadings, variances, and covariances), model fit indices (chi-square = 354.732; number of model parameters = 62; number of observations = 541; degrees of freedom = 289; chi-square/degrees of freedom = 1.227; RMSEA = 0.021; GFI = 0.952; AGFI = 0.942; SRMR = 0.031; NFI = 0.931; TLI = 0.985; CFI = 0.986), model reliability and validity (discriminant and convergent validity), and multicollinearity (VIF values). For CFA assessment, we create a structural model based on CB-SEM in SmartPLS 4.0 (see Figure 3 and Table 2).
In CFA, factor loadings were examined to show how the observed variables are correlated with the underlying latent factor and the magnitude of loadings indicates the strength of the relationship. The examined factor loadings were above 0.50 (see Table 2); the threshold “>0.50 to 1.00” for factor loadings is typically considered acceptable [90]. Next, correlation values were inspected, which is a statistical method to measure and describe the strength and direction of the association between two variables. The outcome of this analysis is typically expressed as a correlation coefficient, which ranges from −1 to +1. There is a certain correlation between the dimensions of sustainable operations and digital innovation. Specifically, technology adoption has significant correlations with the environmental perspective of sustainable operations (r = 0.510), the social perspective of sustainable operations (r = 0.587), and the economic perspective of sustainable operations (r = 0.513). Technology integration has significant correlations with the environmental perspective of sustainable operations (r = 0.510), the social perspective of sustainable operations (r = 0.587), the economic perspective of sustainable operations (r = 0.513), and technology integration (r = 0.661). Technology integration shows substantial correlations with the environmental perspective of sustainable operations (r = 0.606), the social perspective of sustainable operations (r = 0.531), and the economic perspective of sustainable operations (r = 0.538). The correlations among environmental perspective and economic perspective, environmental perspective and social perspective, as well as economic perspective and social perspective are r = 0.528, r = 0.522, and r = 0.497, respectively. In conclusion, the proposed hypothesis holds here. In conclusion, the correlation analysis shows a clear connection between the digital innovation and sustainable operation dimensions.
The reliability and validity were measured through convergent validity (CV) and discriminant validity (DV) methods, which are essential components of construct validity in psychometric analysis (see Table 2). These methods ensure that the measurements used in a study accurately reflect the theoretical constructs they are intended to measure. The average variance extracted (AVE) values were considered to examine the CV; the acceptable threshold is >0.50. The results show that the data has established CV effectively for each construct. Next, the Heterotrait–Monotrait Ratio (HTMT) method was used to measure the DV for each construct; this is the most appropriate and modern technique to assess validity and involves comparing the loadings and the ratio of between-trait correlations to within-trait correlations. A threshold for an HTMT value of < 0.90 (or sometimes 0.85) is advised to confirm DV. The outcomes show that all HTMT values were below 0.662. In conclusion, the CFA results show that the measurement of the model is acceptable to test the proposed hypothesis.
To isolate the effects of digital innovation on sustainable operations, controls were essential. Firm age, employee training time, and IT-related staffing were considered as controls. Firm age was essential because older firms have established practices and are slower in the adoption of new technologies, whereas new companies are possibly more agile but less experienced. Similarly, the number of IT professionals or employees in IT-related roles can influence technology integration and adoption. Lastly, employee training time might have an isolated effect, especially when using new technologies; we consider employee training in hours on a yearly basis.

3.4. Empirical Engineering

To develop mathematical equations for the structural model of this research, three dimensions of sustainable operation (EcnPr, SciPr, and EnvPr) were included as explanatory constructs and two constructs (TecA and TecI) were included as core explanatory constructs.
E n v P r = a 0 + a 1 ∗ T e c I + a 1 ∗ c o n t r o l s + I T   s t a f f i n g + F r i m   A g e + E m p l o y e e   T r a i n i n g + e
S c i P r = b 0 + b 1 ∗ T e c I + b 1 ∗ c o n t r o l s + I T   s t a f f i n g + F r i m   A g e + E m p l o y e e   T r a i n i n g + e
E c n P r = c 0 + c 1 ∗ T e c I + c 1 ∗ c o n t r o l s + I T   s t a f f i n g + F r i m   A g e + E m p l o y e e   T r a i n i n g + e
E n v P r = d 0 + d 1 ∗ T e c A + d 1 ∗ c o n t r o l s + I T   s t a f f i n g + F r i m   A g e + E m p l o y e e   T r a i n i n g + e
S c i P r = e 0 + e 1 ∗ T e c A + e 1 ∗ c o n t r o l s + I T   s t a f f i n g + F r i m   A g e + E m p l o y e e   T r a i n i n g + e
E c n P r = f 0 + f 1 ∗ T e c A + f 1 ∗ c o n t r o l s + I T   s t a f f i n g + F r i m   A g e + E m p l o y e e   T r a i n i n g + e
where, Equations (1)–(3) represent empirical path engineering that classifies the effect of technology integration on EnvPr, SciPr, and EcnPr. The effects of TecA on EnvPr, SciPr, and EcnPr are represented in Equations (4)–(6). In addition, the controls are IT staffing, firm age, and employee training.

4. Results and Discussions

4.1. Structural Equation Modeling

This research proposed a theoretical model with multiple hypothetical relationships among the observed and latent variables. In the literature, structural equation modeling (SEM) is a powerful statistical method that allows the researcher to examine complex models [91]. By using SmartPLS 4.0, Covariance-Based Structural Equation Modeling (CB-SEM), a form of SEM that focuses on the observed covariance matrix, has been employed in this work. In the first stage, the model fit indices such as the chi-square test, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) were examined carefully (See Table 3). The results for model fitness are chi-square/degrees of freedom = 1.984, RMSEA = 0.043, GFI = 0.927, AGFI = 0.913, SRMR = 0.121, NFI = 0.886, TLI = 0.933, and CFI = 0.940, which shows the good fit of the structural model.
CB-SEM was analyzed on 540 data observations and the probability of observation was followed as <0.05, <0.01, and <0.001. The output statistics suggest that digital innovation has a significant positive effect on sustainable operations. Specifically, the SEM results indicate that for every single unit increase in technology adoption for innovation, the environmental perspectives of sustainable operations are expected to increase by 0.293 units, which supports proposed hypothesis 1. Statistically, a t-value of 4.872 suggests the strong influence of TecA on EnvPr, which is highly significant. In summary, increases in technology adoption are linked with improvements in how a firm manages its environmental impact. These results highlight the beneficial role of technology in enhancing environmental performance, which can guide strategic decision making for both businesses and policymakers. However, further research could enrich these findings, providing more nuanced insights into how specific technologies and different contexts affect environmental outcomes. The result for H2 from the SEM analysis provides an interesting set of data, indicating a strong positive effect for technology adoption on the social perspectives of sustainable operations with path coefficients (0.461), T-value (7.364), and p ≤ 0.001. This suggests a significant impact, where the adoption of technology is strongly linked with improvements in the social aspects of sustainable operations, highlighting the transformative potential of digital technologies in enhancing social sustainability. Next, the results from the analysis related linked with hypothesis 3 indicate that “technology adoption” positively and significantly influences the “economic perspectives of sustainable operations”, with path coefficients (0.346), T-value (5.539), and p ≤ 0.001, which shows technology adoption for innovation is a key driver of economic sustainability in firms. By following the results for TechA, this study provides a strategic alignment for firms: they should ensure that their technological adoption plans are in line with sustainable goals.
Further, the SEM results for H4 indicate a positive and statistically significant relationship between technology integration for innovation and the economic perspectives of sustainable operations, which shows a substantial positive impact with path coefficients (0.415), T-value (6.461), and p ≤ 0.001, showing that TecI can lead to greater operational efficiencies by reducing costs and enhancing productivity. This underscores the need for strategic planning in technology investments and integration to maximize economic returns while promoting sustainability. Further, the SEM outcome for H5 demonstrates a strong and statistically significant effect for TecI on social perspectives (H5, coefficient: 0.347), which suggests that the greater integration of technology in innovative ways significantly enhances social sustainability aspects such as employee well-being and community relations. Last, for H6, TecI has a stronger impact on the environmental perspective of sustainable operations (H6, coefficient: 0.512), including reduced emissions and resource management. Overall, the findings underline the importance of not just adopting technology but thoroughly integrating it into organizational practices in innovative ways to maximize sustainable goals. The results for SEM are represented in Table 4 and Figure 4.
The SEM results verified the positive impact of digital innovation on the economic sustainability of operations, which shows that this positive impact helps logistics companies in terms of cost savings, increased productivity, better customer retention, scalability in operations, and sustainability. Specifically, logistics companies can significantly lower their operational costs by optimizing routes, reducing fuel consumption, and improving efficiency. For productivity, automation and advanced technologies streamline operations, allowing companies to handle higher volumes with the same or fewer resources. For consumer retention, enhanced customer experience through real-time updates and personalized services leads to higher customer satisfaction and loyalty. The current results show that digital innovations enable logistics companies to scale their operations efficiently, supporting growth without proportional increases in costs. For the sustainability of operations, a positive impact allows firms to adopt eco-friendly practices that not only help the environment but also meet the increasing regulatory requirements and customer expectations for sustainability. Further, for environmental sustainability, the current results show the positive impacts of digital innovations that help logistical firms to obtain a market advantage, mitigate environmental risk, comply with environmental regulations, and attract environmentally conscious consumers. In summary, digital innovation enhances the environmental sustainability in logistics by reducing emissions, optimizing resource use, and promoting sustainable practices. For example, by considering these results, logistics firms can use electric robots in warehouses to reduce energy, packaging, and inventory management. For social sustainability, the results show that digital innovation positively impacts social sustainability in logistics operations, verifying that technological innovation can enhance worker safety, improve working conditions, ensure ethical practices, and foster community engagements. Logistical firms can grow productivity by improving working conditions and safety, as a good working environment can help to attract and retain top talent.
In this study, we investigate two research questions, the results consistently demonstrate that both technology adoption and integration are critical to enhancing sustainable operations across all three dimensions. To answer RQ1, the results identify that the standardized path coefficients show a robust positive relationship in every case, which is supported by highly significant T values and p values of 0.000, confirming the strong influence of technological innovation on sustainability outcomes. For RQ2, the result explained that technology adoption and technology integration play pivotal roles in meeting the sustainable goals of logistics operations by driving efficiencies and innovations that align with environmental, social, and economic objectives. From an environmental perspective, the results validate that technologies like electric vehicles, alternative fuels, and advanced fleet management systems can dramatically reduce the carbon footprint of transportation activities. Moreover, integrating real-time data and predictive technologies to create optimal delivery routes can minimize travel distances and reduce fuel consumption. From a social perspective, digital technologies can be innovative to increase the ability to enhance safety, create opportunities to enhance the employee’s skills, and upgrade engagement for high-value jobs. Last, from an economic perspective, the findings prove that digital innovation plays a positive role in lowering operational costs by improving inventory systems such as warehouse management costs, faster delivery times and tracking systems, and less wastage of material.

4.2. Research Implications

Our research offers substantial implications for researchers, employees, and policymakers in the field of technology and management. Firstly, these results encourage the expansion of existing theoretical frameworks, such as the diffusion of innovation (DOI) theory framework, to incorporate a deeper understanding of how, why, and what rate new technological advancements influences sustainable perspectives. DOI theory can enhance the understanding of the mechanisms by which technological innovations are adopted and integrated within firms and how these processes influence sustainability. For researchers, this study offers an opportunity to develop new models by applying DOI, which could lead to developing refined research frameworks that describe the adoption curves and the factors influencing the uptake of sustainable technologies in different industries. This approach can help in identifying the characteristics that distinguish early adopters from late adopters or laggards within the context of sustainable technology. Methodologically, the value of longitudinal studies to track the long-term effects of technology on sustainability and cross-sectional studies to understand contextual variations across industries or regions is indispensable. Moreover, mixing DOI theory helps in crafting training and promotional programs that effectively address barriers to technology adoption and integration, thereby enhancing the overall sustainability of operations across environmental, social, and economic spheres. These specific approaches ensure that innovations are implemented throughout all sectors of a firm and are efficiently benefited to obtain the greatest possible sustainable advantages.
The practical implications of this work highlight the transformative potential of technology in achieving comprehensive sustainability goals in operations. Stakeholders across the spectrum, from business executives to policymakers, can leverage these insights to drive meaningful progress through strategic technology adoption and integration. This research suggests that firms should encourage employees to adopt and integrate advanced technologies in practice to actively monitor systems to continuously assess their effects. Next, to maximize the benefits of technology adoption and integration, business leaders are advised to ensure that they are capable of addressing the specific sustainability challenges faced by industries while providing services. For sustainable goals, policymakers should engage in partnerships with businesses and governments to pilot and scale technologies because collaborative efforts could help in understanding the requirements and refining technology offerings accordingly.

5. Conclusions

This research concluded that technology adoption for innovation is linked with a notable improvement in environmental sustainability, which is even more pronounced when technology is fully integrated into operational practices. These findings indicate that proactive engagement with technology not only supports basic compliance with environmental standards but also enhances overall environmental performance. For example, Ant Forest (a Chinese firm that uses digital technologies), an initiative by Ant Financial, encourages users to adopt low-carbon activities through a mobile app, which translates into planting real trees, reducing the carbon footprint, and promoting environmental sustainability. Another conclusion from the social perspective is that the positive effect of technology adoption on social sustainability perspectives is confirmed, showing a significant enhancement in areas such as workforce well-being and community relations. Similarly, technology integration also positively influences these aspects, suggesting that deeper technology integration can further advance social responsibility goals. Technology adoption contributes to economic aspects by improving efficiency and potentially increasing profitability. The impact of technology integration on economic performance is even stronger, highlighting that the thorough integration of innovative technologies is key to unlocking greater economic benefits from sustainability efforts. Overall, this study underlines that the effective use of technology in terms of adoption and integration is crucial for enhancing sustainability in modern firms.
This work has limitations that need to be highlighted. By considering these limitations and focusing on the provided gaps for further improvements, scholars can provide insightful, more accurate, and actionable contributions on the impact of advanced technology on sustainability aspects. First, the findings may not be generalizable across all industries, regions, or scales of business because of the restriction of the data sample. For further research, the impact of technology on sustainability could vary significantly depending on the industry’s specific characteristics, regulatory environment, and cultural aspects. Next, the source of data from logistical firms could be highly variable, which poses a significant limitation for current work. In the future, the data source could be one of the other sectors, such as manufacturing or focal firms that have installed advanced technologies for their operations. Next, this study has limited factors in the proposed model; external factors such as economic fluctuations, policy changes, or technological advancements that occurred concurrently might have influenced the results but were not accounted for. In detail, digital innovations involve the collection, analysis, and storage of large volumes of sensitive data, including operational metrics and proprietary algorithms. So, if cybersecurity measures such as technological advancements are inadequate, the information becomes vulnerable to breach. Future research can be directed by considering cybersecurity factors as moderators rather than direct effects. Lastly, the current study may not account for the diversity in technologies and their distinct impacts on sustainable aspects.

Author Contributions

Conceptualization, Z.A.S. and L.Q.; methodology, Z.A.S.; software, Z.A.S.; validation, Z.A.S. and L.Q.; formal analysis, Z.A.S.; investigation, Z.A.S.; resources, Z.A.S.; data curation, Z.A.S. and L.Q.; writing—original draft preparation, Z.A.S.; writing—review and editing, Z.A.S. and L.Q.; visualization, Z.A.S.; supervision, L.Q.; project administration, Z.A.S. and L.Q.; funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by a grant from the Department of Education of Guangdong Province (No. 2022KCXTD027); Guangdong Key Construction Discipline Research Ability Enhancement Project (No. 2021ZDJS108), and Shenzhen UAV Test Public Service Platform and Low-altitude Economic Integration and Innovation Research Center (No. 29853MKCJ202300205).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data set available on request.

Acknowledgments

I extend my sincere gratitude to Shenzhen Technology University and Shenzhen University, whose support was invaluable in the completion of this project. The resources provided by the University, along with the encouragement from the faculty and staff, have been instrumental in my academic journey. I am particularly thankful for the access to Shenzhen Technology University Library, which significantly enhanced my research capabilities. Additionally, I would like to express my appreciation to Professors and Teachers for their expert guidance and unwavering support throughout this project. Their insights and feedback were critical in shaping the outcome of this work. This project would not have been possible without the supportive and enriching environment provided by Shenzhen Technology University, and I am profoundly grateful for the opportunity to pursue my studies with such excellent resources and support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Data visualization for 26 scale items of constructs, where, SA = strongly agree, A = Agree, N = neutral, D = disagree, SD = strongly disagree, TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, and EcnPr = economic perspective.
Figure 2. Data visualization for 26 scale items of constructs, where, SA = strongly agree, A = Agree, N = neutral, D = disagree, SD = strongly disagree, TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, and EcnPr = economic perspective.
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Figure 3. CFA analysis, where, TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, and EcnPr = economic perspective.
Figure 3. CFA analysis, where, TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, and EcnPr = economic perspective.
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Figure 4. Final results of CB-SEM. Note: TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, EcnPr = economic perspective.
Figure 4. Final results of CB-SEM. Note: TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, EcnPr = economic perspective.
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Table 1. Descriptive Stats.
Table 1. Descriptive Stats.
FactorsMeanStandard DeviationExcess KurtosisSkewness
Gender1.4720.507−1.7500.197
Firm Size1.8700.5492.9090.544
Employee Training2.9831.524−1.429−0.022
IT Staffing2.0940.6362.7941.220
TecA14.2430.9672.420−1.585
TecA24.0481.0600.803−1.172
TecA34.1281.0561.205−1.336
TecA44.0351.0240.994−1.149
TecA54.0801.0570.799−1.178
TecI14.3220.8922.924−1.635
TecI24.2220.9691.840−1.411
TecI34.2260.9271.846−1.371
TecI44.1850.9581.385−1.265
TecI54.1311.0221.385−1.310
TecI64.1591.0191.587−1.367
EnvPr14.2870.8972.702−1.569
EnvPr24.2130.9471.296−1.276
EnvPr34.1910.9301.978−1.370
EnvPr44.1240.9840.949−1.152
EnvPr54.1670.9901.450−1.316
SciPr14.3570.8672.930−1.649
SciPr24.2590.9332.088−1.458
SciPr34.2090.9671.824−1.414
SciPr44.2001.0001.934−1.478
SciPr54.2220.9732.653−1.594
SciPr64.2040.9772.020−1.456
EcnPr14.2330.9201.135−1.210
EcnPr24.1170.9921.274−1.228
EcnPr34.1690.9700.875−1.162
EcnPr44.0651.0600.606−1.112
Note: TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, EcnPr = economic perspective.
Table 2. CFA analysis.
Table 2. CFA analysis.
Factor Loadings for EcnPr, EnvPr, SciPr, TechA, and TecI
ItemsEcnPrEnvPrSciPrTecATecI
EcnPr10.752
EcnPr20.680
EcnPr30.581
EcnPr40.663
EnvPr1 0.689
EnvPr2 0.652
EnvPr3 0.654
EnvPr4 0.685
EnvPr5 0.694
SciPr1 0.716
SciPr2 0.642
SciPr3 0.692
SciPr4 0.669
SciPr5 0.671
SciPr6 0.653
TecA1 0.757
TecA2 0.647
TecA3 0.689
TecA4 0.701
TecA5 0.674
TecI1 0.739
TecI2 0.634
TecI3 0.641
TecI4 0.695
TecI5 0.597
TecI6 0.587
Construct-based estimated correlations
EcnPr1.0000.5280.4970.5130.538
EnvPr0.5281.0000.5220.5100.606
SciPr0.4970.5221.0000.5870.531
TecA0.5130.5100.5871.0000.661
TecI0.5380.6060.5310.6611.000
Established reliability and validity (convergent validity)
Cronbach’s alpha0.7720.8360.8560.8030.831
Composite reliability (rho_c)0.8710.9160.9280.8950.908
Average variance extracted (AVE)0.6290.6870.6820.6320.623
Discriminant validity—Heterotrait–Monotrait Ratio (HTMT)
EcnPr1.000
EnvPr0.5281.000
SciPr0.5080.5271.000
TecA0.5200.5120.5881.000
TecI0.5440.6130.5310.6621.000
Note: TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, EcnPr = economic perspective.
Table 3. Model fitness.
Table 3. Model fitness.
Fit IndicesEstimated Model
Chi-square581.274
Degrees of freedom293.000
p value0.000
ChiSqr/df1.984
RMSEA0.043
GFI0.927
AGFI0.913
PGFI0.774
SRMR0.121
NFI0.886
TLI0.933
CFI0.940
Table 4. Final results of CB-SEM.
Table 4. Final results of CB-SEM.
Paths Path Coefficients (Standardized)Standard ErrorsT Valuesp ValuesDecision
H1 = TecA -> EnvPr0.2930.0484.8720.000Supported
H2 = TecA -> SciPr0.4610.0507.3640.000Supported
H3 = TecA -> EcnPr0.3460.0565.5390.000Supported
H4 = TecI -> EcnPr0.4150.0636.4610.000Supported
H5 = TecI -> SciPr0.3470.0525.8720.000Supported
H6 = TecI -> EnvPr0.5120.0587.8190.000Supported
Note: TecA = technology adoption, TecI = technology integration, EnvPr = environmental perspective, SciPr = social perspective, EcnPr= economic perspective.
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Saqib, Z.A.; Qin, L. Investigating Effects of Digital Innovations on Sustainable Operations of Logistics: An Empirical Study. Sustainability 2024, 16, 5518. https://doi.org/10.3390/su16135518

AMA Style

Saqib ZA, Qin L. Investigating Effects of Digital Innovations on Sustainable Operations of Logistics: An Empirical Study. Sustainability. 2024; 16(13):5518. https://doi.org/10.3390/su16135518

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Saqib, Zulkaif Ahmed, and Luo Qin. 2024. "Investigating Effects of Digital Innovations on Sustainable Operations of Logistics: An Empirical Study" Sustainability 16, no. 13: 5518. https://doi.org/10.3390/su16135518

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