Abstract
Small- and medium-sized enterprises (SMEs) play a critical role as innovation intermediaries (IIs) in supply chains (SCs) by adopting emerging technologies, such as artificial intelligence (AI), which drives smart data-driven decision making. However, there is a paucity of empirical evidence on the role of intangible organisational capabilities to drive AI adoption within SMEs that will lead to SC productivity, low carbon management, and resilience. To bridge this gap in the literature, our research employs perceived organisational support (POS) as the theoretical lens to develop a theoretical model that is tested by surveying 375 Vietnamese managers of manufacturing SMEs. Our findings from structural equation modelling analysis demonstrate that organisational change capacity will facilitate AI adoption, which will lead to SC productivity, resilience, and low carbon management because of SMEs’ ability to leverage AI for data-driven decision making. Based on POS theory, our research highlights the role of intangible SME resources in implementing sustainable digital SCs’ transformation, an essential strategy for acting as IIs in business ecosystems. Our findings will help SMEs to develop strategies that will enhance skills, competencies, expertise, and organisational creativity conducive to the needs of the human workforce. This will enhance the capacity and capability of SMEs to innovate, manage, and efficiently adapt to change in a technologically turbulent, dynamic, uncertain, and volatile business environment.
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The research reported in this manuscript is funded by “British Council Environmental Links grant—528201836” for the project, ‘Circular Economy Knowledge Hub: Promoting Multi-Disciplinary Research, Capacity Building and Leadership’.
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Author 1 declares no conflicts of interest. Author 2 has received the funding from the British Council Environmental Links grant – 528201836. Author 3 has received the funding from the British Council Environmental Links grant – 528201836. Author 4 declares no conflicts of interest. Author 5 declares no conflicts of interest. Author 6 declares no conflicts of interest.
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Appendix
Survey instrument
Construct | Proxies measuring the construct | Adapted from |
|---|---|---|
Risk Proclivity | In my organization we take bold and wide-ranging acts to achieve firm objectives We typically adopt a bold aggressive posture in order to maximize the probability of exploiting potential opportunities In general, my firm has a strong proclivity for high-risk projects (with chances of very return) In dealing with competitors, my firm adopts a competitive, "undo-the-competitors" posture—i.e., we have to be the best and outperform others My organisation adopts a more risk-oriented approach to new ventures such as using emerging data-driven technologies (AI and analytics) My organisation has a strategic orientation towards becoming agile, experimental, and adaptable | Mikalef and Gupta, (2021); Hanelt et al., (2021); Ransbotham et al., (2018); Avlonitis and Salavou, (2007) |
Organisational Creativity | My organization has produced many novel and useful ideas (services/products) My organization fosters an environment that is conductive to our own ability to produce novel and useful ideas (services/products) My organization spends much time for producing novel and useful ideas (services/products) My organization considers producing novel and useful ideas (services/products) as important activities My organization actively produces novel and useful ideas (services/products) My organisation encourages new ideas and developing new ways of operating My organisation promotes ideas of employees My organisation encourages fresh approach to solving problems | |
Skills and competencies | My organisation provides AI related training to our employees My organisation recruits new employees who have good exposure to AI and digital innovation Senior management in my organisation have strong understanding of the capabilities of AI Employees in my organisation are able to coordinate effectively with all intra departments, suppliers and customers in the context of implementing and adopting AI strategy Employees in the organisation have relevant recognised certification demonstrating knowledge of AI and its application Staff in my organisation has the right multi-disciplinary skills to adopt and implement AI in decision-making Employees have competencies to understand how AI systems will execute Employees have skills to interpret the AI outputs Employees have skills to provide inputs to AI system Employees have skills to make decisions from AI outputs Employees have good sense of where to apply AI Senior management are able to understand business problems and to direct AI initiatives to solve them Employees are capable of coordinating AI-related activities in ways that support the organization, suppliers and customers in the supply chain | Chowdhury et al., (2022a, 2022b); Mikalef and Gupta, (2021); Spector and Ma, (2019); Davenport and Wilson et al., (2017) |
Organisational change capability | We are able to anticipate and plan for the organizational resistance to change We recognize the need for managing change We are able to make the necessary changes in human resource policies for process re-engineering Senior management commits to new values We are capable of communicating the reasons for change to the members of our organization We are able to reconfigure and optimise operational processes to cope up with uncertainties We are capable of making agile decisions to withstand complex market and trade dynamics We have adequate resources to adhere to our flexible plans—business operations and services We have extended our knowledge base to employ lean management in uncertain volatile market conditions | Chowdhury et al., (2022a); Mikalef and Gupta, (2021); Chui and Malhotra, (2018); Ransbotham et al., (2018); Davenport and Ronanki, (2018); Besson and Row, (2012); Orlikowski, (1996) |
ADSS adoption | Organisation has employed AI-based decision support systems to | Dubey et al., (2021); Bag et al., (2021); Gupta et al., (2021); Tseng et al., (2022) |
monitor and track products in the value chain optimize resource utilization, e.g., using waste as a resource, optimal energy consumption making decisions to support reuse and recycling practices make green low carbon decisions monitoring the environmental information (such as toxicity, energy used water used, air pollution) attract new customers and understand their evolving needs Making decisions related to business process reconfiguration (logistics/production Understanding uncertainty in the dynamic market environment Predict and manage risks | ||
Performance | Compared to our competitors, | Chowdhury et al., (2022a, 2022c); Mikalef and Gupta, (2021); Dey et al., (2022) |
We have reduced our manufacturing costs in recent years We have reduced inventory carrying costs We have reduced business waste across our processes We have achieved energy efficiency across our processes Our organisation is more successful Our organization has a greater market share Our organisation is growing aster Our organization is more profitable Our organization is more innovative We have achieved resource efficiency across our processes We have created more jobs to support the community and thus contributed to nation’s entrepreneurial growth Employee turnover of our organisation is optimum Our level of customer loyalty has increased in recent years | ||
Low carbon management | We apply environmental criteria in the selection of suppliers along with quality, cost and time Our product designs aim to extend the product life and promote extended use/reuse of materials and products We have practices for efficient handling of raw materials, work-in-progress and finished products in the production processes We have practices for reducing the consumption of energy in the production processes We have practices to reduce impact on environment (water, air and noise pollution) We work with our suppliers to find ways to reintroduce end-of-life items into our supply chain or someone else’s supply chain We have processes (reverse logistics) to collect from customer and recycle We employ just in time for eco-friendly forward and reverse logistics practices | Dey et al., (2022); Dey et al., (2019); Mishra et al., (2018); Samuel et al., (2020) |
Supply chain resilience | We are able to cope with changes brought by the supply chain disruption We are able to adapt to the supply chain disruption easily We are able to provide a quick response to the supply chain disruption We are able to maintain high situational awareness at all times My organisation's business practices have led to process reliability Business practices have led to decrease in hazardous incidents My organisation's business practices have led to service reliability We are able to reconfigure business processes considering environmental issues to remain competitive in the market We are able to trace and track the activities in our supply chain |
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Roux, M., Chowdhury, S., Kumar Dey, P. et al. Small and medium-sized enterprises as technology innovation intermediaries in sustainable business ecosystem: interplay between AI adoption, low carbon management and resilience. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05760-1
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DOI: https://doi.org/10.1007/s10479-023-05760-1