Servitization for the Environment? The Impact of Data-Centric Product-Service Models

ABSTRACT

Recent developments in data-centric technologies (e.g., big data, Internet of Things, cloud computing) have given rise to the data-centric models, such as servitization. Servitization here refers to firms selling a product as a service instead of selling product ownership, which has been viewed as a green business model that can benefit the environment. Despite the potential environmental superiority of servitization, previous studies and empirical observations have shown that the servitization model may generate lower profits than the conventional product sales model, which poses challenges for firms to choose their business models. However, the existing literature has not considered the role of data-centric technologies that are increasingly embedded in the servitization model, in which firms can co-create value with consumers by leveraging product usage data to improve service offerings. In this study, we build an analytical model to scrutinize the economic and environmental performance of the data-centric servitization model compared to the product model. We find that the data-centric servitization model is more profitable than the product model only when a firm’s service improvement capability is relatively high. Unfortunately, a high service improvement capability may exacerbate the negative environmental impact, resulting in the servitization model being less environmentally friendly than the product model. We discuss the circumstances under which using the servitization model can yield win-win outcomes in terms of profitability and environmental impact. The findings can help managers and policymakers reconcile the tension between firm profitability and environmental damage and make judicious decisions on business model choices and the application of emerging data-centric technologies.

KEYWORDS:

• Service-improvement capability
• business models
• environmental impact
• network effects
• servitization
• data-centric models

Acknowledgments

The authors would like to thank the Editor-in-Chief, Vladimir Zwass, and three anonymous reviewers for their constructive comments and suggestions during the review process, which helped to improve this paper significantly.

Supplementary information

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2022.2127454

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

¹ For instance, General Motors launched a carsharing service called Maven in 2016. Also, Mercedes Benz launched a carsharing service called Car2Go, and BMW launched a carsharing service called DriveNow. In 2019, Mercedes Benz and BMW merged Car2Go and DriveNow to form a joint carsharing venture called ShareNow. Note that car manufacturers (e.g., GM) may establish a subsidiary (e.g., Maven) to run the carsharing service. In our context, we do not consider them (e.g., GM and Maven) as different entities. Instead, we assume that they belong to the same conglomerate, and that only one (vertically integrated) firm decides the service model.

² The ticking meter effect stems from constant visual or mental reminders of the usage expenses as consumption continues, which could dampen consumption enthusiasm, thereby leading to lower usage [58].

³ Since we do not consider a firm’s subsidiary as an independent entity, one firm fully owns the consumers’ usage data.

⁴ https://www.intellias.com/data-personalization-in-car-sharing-who-wins/

⁵ Prior literature has assumed similar utility functional properties with a concave relationship, for example, [5, 59].

⁶ We collectively refer to all forms of data-enabled service as service improvements.

⁷ We focus on the interesting region in which both models are feasible for the firm.

⁸ In Figure 5, we consider the scenario where the product exhibits identical impacts in the product and use phases, that is, $\alpha =\beta =1$. When we consider different $\alpha$ and $\beta$, the result remains qualitatively the same.

⁹ We also considered other forms of $u_s\left(N_s\right)$, such as $u_s\left(N_s\right)=\lambda \sqrt{N_s}d$. Our analysis showed that the key results still hold.

¹⁰ Since our focus is to compare the product model and service model for a focal firm, we assume the service improvement capabilities in the two business models are the same.

¹¹ For example, the General Data Protection Regulation law in Europe sets many restrictions on data exploitation which directly restrict a firm’s ability to use data-centric technologies. See details at https://ec.europa.eu/info/law/law-topic/data-protection_en

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (Nos. 72271225, 71921001, 72091215/72091210); the Anhui Provincial Natural Science Foundation (No. 2208085J06); and the USTC Research Funds of the Double First-Class Initiative (No. YD2040002017).

Notes on contributors

Xin Zhang

Xin Zhang ([email protected]) is a Ph.D. candidate at the School of Management, University of Science and Technology of China. He is in a joint doctoral program with City University of Hong Kong. His research interests lie in the economics of information systems, digital markets, platform economy, consumer privacy and data analytics. His research has been published in Information Systems Frontiers, Information Technology & People, and in the proceedings of International Conference on Information Systems, Pacific Asia Conference on Information Systems, and others.

Xiaolong Guo

Xiaolong Guo ([email protected]) is an Associate Professor at the School of Management, University of Science and Technology of China. Dr. Guo’s research interests include service operations management, supply chain management, and emerging business models (e.g., crowdfunding, sharing economy, platform economy). His work has been published in Manufacturing & Service Operations Management, Production and Operations Management, Service Science, European Journal of Operational Research, and other journals.

Wei Thoo Yue

Wei Thoo Yue ([email protected]) is a Professor of Management Information Systems in the Department of Information Systems at City University of Hong Kong. He received his Ph.D. in Management Information Systems from Purdue University. Prior to joining City University of Hong Kong in 2009, he was on the faculty of University of Texas, Dallas. Dr. Yue’s research interests focus on the economic and operational aspects of information systems. His work has appeared in Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Decision Support Systems, and other journals.

Yugang Yu

Yugang Yu ([email protected]) is a Distinguished Professor of Management Science, and executive dean of School of Management, University of Science and Technology of China (USTC). Before joining USTC in 2012, he was on the faculty of Rotterdam School of Management, Erasmus University. His research focuses on data-driven supply chain management, Internet of Things, smart logistics, and online platforms. Dr. Yu has published numerous papers in a variety of journals, including Manufacturing & Service Operations Management, Production and Operations Management, Information Systems Research, Transportation Science, IISE Transactions, European Journal of Operational Research, and other journals.