Workforce adaptability and social influences on robotic process automation in sustainable transport – Nature

Executive Summary: Robotic Process Automation and Sustainable Development in the Transport Sector

This report details an investigation into the adoption of Robotic Process Automation (RPA) within the transportation industry, utilizing an expanded technology acceptance framework that incorporates compliance and safety. The study, based on a survey of 190 transportation professionals, examines how RPA implementation aligns with key Sustainable Development Goals (SDGs). The findings reveal that trust in technology is a significant driver of adoption, while perceived risk acts as a deterrent. Crucially, the research establishes a strong positive correlation between the actual use of RPA and enhanced transport safety and compliance. This directly supports SDG 3 (Good Health and Well-being) by reducing accidents and SDG 11 (Sustainable Cities and Communities) by creating safer transport systems. The statistical model demonstrated high efficacy in predicting behavioral intention, actual use, and safety outcomes. The report concludes that successful RPA integration, vital for achieving SDG 9 (Industry, Innovation, and Infrastructure), depends on addressing human and organizational factors, particularly building trust and managing risk perceptions. These insights are critical for organizations seeking to leverage RPA to advance operational safety and contribute to global sustainability targets.

Introduction: Aligning RPA with Sustainable Development Goals

The integration of Robotic Process Automation (RPA) into the transportation sector represents a significant step towards achieving sustainable operational dynamics. This technological shift is not merely about efficiency but is deeply intertwined with the human element and its contribution to broader sustainability objectives. As organizations deploy RPA to enhance operational performance, they are also contributing to the advancement of several Sustainable Development Goals.

• SDG 9 (Industry, Innovation, and Infrastructure): RPA is a key innovation that helps build resilient and sustainable transport infrastructure by automating processes, improving reliability, and optimizing resource use.
• SDG 11 (Sustainable Cities and Communities): The effective use of RPA can lead to more efficient and safer transportation systems, which are fundamental components of sustainable cities.
• SDG 8 (Decent Work and Economic Growth): The transition to automated systems necessitates a focus on workforce adaptation, reskilling, and ensuring that technological advancements support, rather than disrupt, decent work conditions.

The challenge for transportation companies lies in balancing the benefits of automation with the concerns of the workforce. A successful RPA implementation enhances existing social and professional structures, fostering a culture of continuous learning and adaptability. This holistic approach, which considers both technological and human factors, is essential for realizing the full potential of RPA in promoting sustainable transportation and achieving long-term sustainability targets.

The Imperative of Workforce Adaptability for Sustainable Innovation

Workforce adaptability and supportive social dynamics are critical determinants for the successful adoption of RPA and the achievement of sustainable transportation goals. Organizations that prioritize these human factors are significantly more likely to meet their implementation objectives while maintaining operational excellence. This focus directly supports SDG 8 (Decent Work and Economic Growth) by ensuring that technological transitions are managed in a way that empowers employees and enhances their skills.

Key Dimensions of Adaptability and Social Dynamics

• Operational Excellence and Service Quality: An adaptable workforce can integrate seamlessly with RPA technologies, leading to fewer disruptions, improved service reliability, and higher customer satisfaction. This contributes to the economic sustainability of the transport sector.
• Innovation and Process Improvement: Employees who are adaptable are more likely to identify opportunities for process enhancements that align with sustainability objectives, such as optimizing routes to reduce emissions or improving logistics to minimize waste, thereby supporting SDG 9.
• Organizational Resilience and Risk Management: Strong social dynamics and effective communication channels build resilience, enabling organizations to navigate the challenges of technological transformation while maintaining business continuity.
• Integration of Sustainability Principles: An adaptable workforce is better equipped to incorporate environmental considerations into automated processes, facilitating the achievement of sustainability goals through RPA. This is crucial for advancing SDG 11 and other environmental targets.
• Employee Well-being and Skill Development: Fostering a supportive environment during RPA adoption leads to higher job satisfaction, lower turnover rates, and effective knowledge transfer, ensuring the long-term human capital needed for a sustainable and innovative industry as envisioned by SDG 8.

Research Objectives and Hypotheses in the Context of SDGs

This study aims to investigate the factors influencing RPA adoption and its subsequent impact on sustainable outcomes. The research questions are designed to explore the interplay between technology acceptance, human factors, and the achievement of safety and compliance, which are integral to sustainable transport systems.

1. How do perceived ease of use and usefulness of RPA influence the intention to use these systems, thereby affecting the potential for innovation under SDG 9?
2. What role do social influence and facilitating conditions play in shaping behavioral intention towards RPA, reflecting the importance of a supportive work environment as per SDG 8?
3. How do trust in technology and perceived risk impact the adoption of RPA, and what are the implications for ensuring safe and reliable infrastructure (SDG 9 and SDG 11)?
4. To what extent does behavioral intention predict the actual use of RPA systems in transport operations?
5. How does the actual use of RPA contribute to transport safety and compliance, directly supporting SDG 3 and SDG 11?

Methodology: A Framework for Assessing Sustainable Technology Adoption

A mixed-method approach was employed to investigate RPA adoption patterns among 190 personnel in the transportation sector. The sample size was determined using G*Power estimation to ensure statistical adequacy for detecting meaningful relationships. This robust methodology allows for a comprehensive assessment of how technology adoption can contribute to sustainable development outcomes.

Sample Information

The study population included a diverse range of demographics, ensuring the findings are representative of various income levels, professional experience, and age groups within the transport industry.

Instruments and Data Collection

A structured questionnaire based on a 5-point Likert scale was used for quantitative data collection. A pilot study confirmed the instrument’s reliability, with a Cronbach’s alpha exceeding 0.80. All procedures adhered to ethical guidelines, with informed consent obtained from all participants and ethical approval granted by Manipal University Jaipur, India. Qualitative data was gathered through semi-structured interviews with 25 respondents to provide deeper context.

Data Analysis and Results

Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted using SmartPLS 4.0 to analyze the complex relationships between demographic variables and RPA adoption factors. The analysis was structured to first assess the measurement model’s validity and then evaluate the structural model to test the study’s hypotheses.

Measurement Model Assessment

The measurement model demonstrated excellent psychometric properties. All constructs showed strong internal consistency, with Composite Reliability (CR) values ranging from 0.895 to 0.928. Convergent validity was confirmed, with Average Variance Extracted (AVE) values (0.642 to 0.698) exceeding the required threshold. Discriminant validity was also established, confirming that the constructs are distinct. These results indicate that the measures used in the study are both reliable and valid for assessing RPA adoption in the context of sustainable transport.

Structural Model Assessment and Hypothesis Testing

The path coefficient analysis revealed several significant relationships that underscore the drivers of RPA adoption and its contribution to sustainable outcomes:

• Perceived Ease of Use had a strong positive effect on Perceived Usefulness (β = 0.458), highlighting that user-friendly technology is more likely to be seen as beneficial for innovation (SDG 9).
• Trust in Technology (β = 0.345) and Perceived Usefulness (β = 0.385) were significant predictors of Behavioral Intention.
• Perceived Risk had a significant negative effect on Behavioral Intention (β = -0.298), indicating that safety concerns must be addressed to encourage adoption.
• Behavioral Intention was a strong predictor of Actual Use (β = 0.512).
• Most importantly, Actual Use of RPA had a strong positive effect on Transport Safety and Compliance (β = 0.485). This provides direct evidence that RPA adoption contributes to safer transport systems, aligning with SDG 3 and SDG 11.

All nine hypotheses were supported, providing robust empirical evidence for the proposed framework.

Model Fitness and Predictive Relevance

The overall model demonstrated an excellent fit, with a Standardized Root Mean Square Residual (SRMR) of 0.048 and a Normed Fit Index (NFI) of 0.935. The model explained a substantial portion of the variance in the endogenous constructs, particularly for Behavioral Intention (R² = 0.568). The model’s strong predictive relevance was confirmed by Q² values, indicating its utility in forecasting RPA adoption and its impact on safety outcomes.

Discussion: RPA’s Role in Advancing Sustainable Transport and Decent Work

The findings provide strong empirical support for the extended technology acceptance framework and highlight the critical role of RPA in advancing sustainable transportation. The study confirms that successful RPA implementation is a multifaceted process influenced by technological, organizational, and human factors.

The robust link between RPA use and improved safety and compliance (β = 0.485) is a key finding with significant implications for sustainable development. By automating routine tasks and enhancing monitoring capabilities, RPA can reduce human error, leading to fewer accidents and a safer environment for both workers and the public. This directly contributes to:

• SDG 3 (Good Health and Well-being): By reducing traffic-related injuries and fatalities.
• SDG 11 (Sustainable Cities and Communities): By making transport systems safer and more reliable.
• SDG 8 (Decent Work and Economic Growth): By creating safer working conditions for transport sector employees.

The study also underscores the importance of building trust and mitigating perceived risks to foster technology adoption. This requires transparent communication, comprehensive training, and a supportive organizational culture that encourages workforce adaptability. By investing in these areas, organizations can ensure that the transition to automation supports decent work and promotes inclusive and sustainable economic growth, in line with SDG 8 and SDG 9.

Implications for Sustainable Development Policy and Management

This research offers critical insights for managers and policymakers aiming to leverage technology for sustainable development. The findings suggest that RPA should not be viewed merely as a tool for efficiency but as a strategic asset for enhancing safety, ensuring compliance, and contributing to broader sustainability goals.

Managerial Implications

1. Adopt a Holistic Strategy: Implementation strategies must balance technical deployment with a focus on human factors. This includes investing in training and development to enhance workforce adaptability (SDG 8).
2. Build Trust and Manage Risk: Organizations must proactively address employee concerns about RPA by demonstrating its reliability and safety benefits.
3. Promote a Culture of Safety and Sustainability: Frame RPA adoption as part of a broader commitment to improving safety and achieving sustainability targets, thereby aligning organizational goals with SDG 3 and SDG 11.

Limitations and Future Research Directions for Sustainable Transport

While this study provides valuable insights, it has certain limitations. The cross-sectional design captures a single point in time, and the sample was drawn from a specific geographical region. Future research could employ longitudinal designs to track adoption patterns over time and include cross-cultural samples to enhance generalizability. Further studies could also explore the moderating effects of organizational culture and digital literacy on RPA adoption and its impact on the SDGs. Investigating other transport technologies and their specific contributions to safety and sustainability would also be a valuable avenue for future research, further strengthening the evidence base for technology-driven progress towards the Sustainable Development Goals.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on Robotic Process Automation (RPA) in sustainable transportation addresses several Sustainable Development Goals (SDGs) by focusing on the intersection of technology, workforce, safety, and environmental sustainability.

1. SDG 3: Good Health and Well-being

   The article strongly emphasizes “transport safety,” “operational safety,” and “compliance.” The study’s abstract and main body highlight that a primary benefit of RPA adoption is the improvement of safety outcomes. This directly connects to ensuring well-being by reducing the risks associated with transportation operations.

2. SDG 8: Decent Work and Economic Growth

   The article extensively discusses the “human element” of technological transition, including “workforce adaptability,” concerns about “job stability,” the need for “comprehensive training programs,” and the impact on “employee well-being” and “job satisfaction.” It explores how to manage the adoption of automation to enhance, rather than disrupt, working relationships and promote a supportive work environment, which is central to the principles of decent work.

3. SDG 9: Industry, Innovation, and Infrastructure

   This is a core SDG for the article, which is centered on the adoption of an innovative technology (RPA) to upgrade the transportation industry’s infrastructure. The text discusses “digital transformation,” “technological transitions,” and using automation to improve “operational efficiency” and “service quality,” all of which are key components of building resilient infrastructure and fostering innovation.

4. SDG 11: Sustainable Cities and Communities

   The article is framed within the context of “sustainable transportation.” By investigating how automation can make transport systems more efficient, safer, and aligned with “environmental objectives,” it directly addresses the goal of creating sustainable transport systems, which are essential for the development of sustainable cities and communities.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s discussion, several specific SDG targets can be identified:

• Under SDG 3: Good Health and Well-being

  • Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidents. Although the 2020 deadline has passed, the principle of reducing transport-related harm is central. The article’s finding that “Actual Use of RPA systems have a positive effect on Transport Safety and Compliance” directly supports this target by highlighting a technological pathway to improve safety in the transport sector.

• Under SDG 8: Decent Work and Economic Growth

  • Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation. The article discusses how RPA implementation improves “operational efficiency,” reduces “interruptions,” and enhances “service quality,” which are all measures of increased productivity driven by technological innovation.
  • Target 8.5: By 2030, achieve full and productive employment and decent work for all… The article’s focus on managing the “human element,” ensuring “job stability,” and improving “job satisfaction” during the RPA transition aligns with the goal of ensuring that technological progress supports decent work.

• Under SDG 9: Industry, Innovation, and Infrastructure

  • Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure… The study explores how RPA can enhance the reliability and sustainability of transportation systems, contributing to the development of more advanced and resilient infrastructure.
  • Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes. The article presents RPA as a technology that helps transportation companies achieve “sustainability objectives” and “more efficient usage of resources,” directly aligning with this target.

• Under SDG 11: Sustainable Cities and Communities

  • Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all… The entire study is framed around making transportation more sustainable and safer through technology. The focus on “sustainable transportation frameworks” and improving “safety outcomes” directly contributes to this target.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions several quantitative and qualitative indicators that can be used to measure progress toward the identified targets.

• For SDG 3 (Target 3.6):

  • Indicator: The “Transport Safety & Compliance” outcome variable in the study’s model. Progress can be measured by tracking a reduction in safety incidents, accidents, or compliance breaches after RPA implementation. The study provides a direct measure of this relationship (β = 0.485).

• For SDG 8 (Targets 8.2 and 8.5):

  • Indicator: Success rates in RPA adoption. The article states that organizations with comprehensive training achieved “40% greater success rates.”
  • Indicator: Operational efficiency. The article notes that adaptable workforces encounter “40% less interruptions during RPA adoption.”
  • Indicator: Cost-effectiveness. It is mentioned that businesses with adaptable staff “save an average of thirty percent on implementation expenses.”
  • Indicator: Employee well-being metrics. The article cites that companies with robust social support have “staff turnover rates that are at least fifty percent lower and job satisfaction scores that are forty percent higher.”

• For SDG 9 (Targets 9.1 and 9.4):

  • Indicator: Rate of technology adoption. The “Actual Use” of RPA systems is a key variable in the study, serving as a direct indicator of technology uptake.
  • Indicator: Innovation capacity. The article suggests that adaptable workforces have a “60% higher likelihood of recognizing and executing process enhancements.”

• For SDG 11 (Target 11.2):

  • Indicator: Achievement of sustainability goals. The article states that adaptable workforces have a “55% greater chance of successfully meeting their sustainability goals through the use of RPA.” This can be measured by tracking performance against predefined environmental and sustainability targets.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com