Special Issue on Data-Driven Innovation: Unraveling the Dynamics of Organizational Transformation'

Guest Editors

Ke Rong,¹ Jiang Wei,^2,3 Hai Che,⁴ Theodoros Evgeniou,⁵ and Helen Bao⁶

¹Tsinghua University, China, ²Zhejiang University of Finance & Economics, China, ³Zhejiang University, China, ⁴University of California Riverside, US, ⁵INSEAD, France, and ⁶University of Cambridge, UK

Proposal submission deadline: March 31, 2025

Manuscript submission deadline: December 31, 2025

Special Issue Theme Background

As the world becomes increasingly interconnected and digitalized, data has emerged as a new factor of production and a critical driver of economic growth (Cong, Xie, & Zhang, 2021; Jones & Tonetti, 2020). As reported by Statista, the aggregate volume of data generated, acquired, replicated, and utilized globally has undergone a dramatic surge, from 2 zettabytes in 2010 to 64.2 zettabytes in 2020. It is expected to continue the growth path, with an estimated 180 zettabytes by 2025^[1]. The future of data in China is exceptionally promising. China’s mass adoption of digital technologies has made data integral to every aspect of society. Advancements in Artificial Intelligence (AI), cloud computing, and data analytics create unprecedented opportunities. The strategic significance of data has been underscored by the Chinese government's recognition of it as a fundamental factor of production in 2019, accompanied by a series of supportive policies. The availability of vast amounts of data, coupled with advancements in new intelligent technologies such as AI and cloud computing, presents unprecedented opportunities for organizations to leverage insights, transform their operations, and facilitate innovation. In the rapidly evolving landscape of business and management, the effective utilization of data has become a strategic imperative for Chinese firms aiming to thrive in today's competitive environment.

Data holds immense potential to facilitate innovation within organizations. From customer preferences and market trends to internal processes and employee behaviors, data provides a wealth of information that can fuel the creation and implementation of innovative strategies (Visconti, Larocca, & Marconi, 2017). By harnessing the power of data analytics, firms can uncover valuable insights, make informed decisions, and generate novel ideas (Brynjolfsson & McElheran, 2016). Data-driven insights can help firms optimize resource allocation, enhance productivity, and increase customer commitment. The cross-organizational flow and interplay of data also facilitate effective collaboration and knowledge sharing, promoting open innovation and community building.

Understanding the pivotal role of data in driving firm innovation is of paramount importance for scholars, researchers, and practitioners in the field of management and organization. As such, Management and Organization Review (MOR) invites submissions that explore and advance our knowledge of how data facilitates firm innovation. We seek research that delves into data-driven innovation, referring to the use of data analysis and interpretation by firms to generate new insights, solutions, or improvements. Although closely related to the literature on digitization, artificial intelligence, digital platforms, and ecosystems, this special issue looks to make a difference, and we specifically seek research on how firms innovate through the leverage of data. We welcome research investigating the mechanisms, processes, and strategies through which data can empower firms to achieve breakthroughs and maintain a competitive edge.

^[1] Source: https://www.statista.com/statistics/871513/worldwide-data-created/

Aim and Scope of the Special Research Forum

This call for proposals aims to shed light on the critical questions surrounding the interplay between data and innovation within organizational contexts.

We welcome submissions from diverse disciplinary perspectives, methodological approaches, and empirical contexts, with a particular interest in studies that examine the role of data in facilitating innovation within various industries, organizational sizes, and geographical regions. We encourage researchers to delve into the fascinating realm of data-driven innovation and contribute to advancing our understanding of how data facilitates firm innovation in the global landscape.

Sample Research Questions

Types of Innovation

The multiplier effect of data on building competitive advantages and enhancing production efficiency is becoming increasingly prominent, making it the most characteristic production factor of this era. The full utilization of data can help enterprises improve decision-making efficiency and predictive accuracy, which in turn bolsters both corporate productivity and profitability (Brynjolfsson, Jin, & McElheran, 2021; Farboodi, Mihet, Philippon, & Veldkamp, 2019; Müller, Fay, & Vom Brocke, 2018). Furthermore, data is exerting an increasingly significant influence on the innovation activities of enterprises. The role of data as a new type of resource may reshape novel ways for firms to manage resources and innovations. In addition to the production-accompanying nature of data, the network effect, another significant characteristic of the digital economy, also profoundly influences corporate innovation decisions. Due to the zero-cost replication and distribution inherent to data as a production factor, a company's data can be employed not only for its own innovation but also to assist in the innovation of other businesses through data-sharing platforms and innovative writing platforms, thus facilitating industry-wide collaborative development and mutual progress. According to the breadth of impact, degree of novelty, and whether the innovation occurs at the product, process, or market level, innovation can be categorized into different types, such as radical innovation (Forés & Camisón, 2016; Tiberius, Schwarzer, & Roig-Dobón, 2021) and incremental innovation (Chen, Chang, & Lin, 2014; Forés & Camisón, 2016), exploratory innovation (Phelps, 2010) and exploitative innovation (Jansen, Van Den Bosch, & Volberda, 2006; Raisch, Birkinshaw, Probst, & Tushman, 2009), disruptive innovation (King & Baatartogtokh, 2015; Markides, 2006), etc. Literature has suggested that data-driven insights can support certain types of innovations such as incremental process improvements (Wu, Hitt, & Lou, 2020). We seek studies that explore the following questions.

• Are the selection of a firm's innovation strategy and the performance of various types of innovation influenced by data factors?
• How does the leverage of data resources affect firms' innovation and competitive advantages?
• Are the patterns of innovation in data-intensive industries different from those in traditional industries? What are the mechanisms behind this and could they challenge existing theories related to corporate innovation?
• What role does the unique Chinese context play in determining these patterns?

 

Data and Product or Service Innovation

During the process of firms’ digital transformation, data has emerged as a vital resource, primarily due to its significant influence on product or service innovation (Ma, Mao, & An, 2022; Rong, Zhou, Shi, & Huang, 2022; Zhan, Tan, Ji, Chung,  & Tseng, 2017). Product or service innovation usually involves creating and implementing novel ideas, technologies, or approaches to meet the preferences of customers, or to create and capture new market opportunities (Damanpour & Aravind 2012). As the inherent value of data continues to be recognized, it is being increasingly utilized for product or service innovation in many ways. A typical example lies in platforms that collect and leverage user behavioral data to analyze user preferences, enabling them to deliver enhanced services and provide more precise ad recommendations (Lee, Hosanagar, & Nair, 2018, Rong et al., 2022). Data enables firms to obtain a more precise understanding of the characteristics of new products and facilitates a more effective assessment of the market impact these new products can generate (Amado, Cortez, Rita, & Moro, 2018; Tan & Zhan, 2017). Data also helps firms develop new business models to provide new services or combine different business models (Sorescu, 2017; Trabucchi & Buganza, 2019). Companies are increasingly utilizing data-driven approaches to experiment with new products (Camuffo, Cordova, Gambardella, & Spina, 2020; Deniz, 2020; Felin, Gambardella, Stern, & Zenger, 2020; Koning, Hasan, & Chatterji, 2022). Recent literature also suggests that the effect of data-driven new product innovations may vary across different organizations (Allen & McDonald, 2023). While existing research has made efforts to explore the role of data in product or service innovation, it is evident that there is still substantial work to be undertaken in this field. We enthusiastically welcome further exploration and investigation in the following areas of research:

• How does data facilitate new product/service development?
• How does data influence R&D?
• How does data foster business model innovation?
• How do the contextual factors in China affect the process

 

Data and Process Innovation

Process innovation is the implementation of new or improved methods, techniques, or systems to enhance operational efficiency, reduce costs, improve productivity, and optimize business processes within an organization (Damanpour & Aravind, 2012; Ettlie & Reza, 1992). Unlike product or service innovation, process innovation focuses more on improving and optimizing firm operations within the organization. The combination of data with other technologies, particularly AI algorithms, offers significant potential for driving innovation and improving the efficiency of individual processes (Park & Bae, 2022; Rialti, Marzi, Silic, & Ciappei, 2018). By leveraging data-driven insights and advanced algorithms, organizations can introduce innovative solutions that streamline and enhance specific processes. For example, by collecting and analyzing large volumes of data throughout the production process, companies can establish patterns and correlations that enable the identification of potential quality issues, thus using an innovative method to efficiently conduct the quality check process. Additionally, data plays a crucial role in facilitating the integration of different processes within an organization. By effectively utilizing data, organizations can break down silos and create a connected ecosystem where processes seamlessly interact and share information (Park & Bae, 2022). Such data-driven innovation is also a crucial foundation for many organizations to achieve process optimization during the Industry 4.0 era. Therefore, we seek studies that explore how data contribute to innovation in firms’ production process.

• How can novel data-driven methods be applied to processes such as manufacturing, service delivery, after-sale services, etc.?
• How does data enable the coordination across different processes within or even across organizations?

 

Data and Marketing Innovation

Data also bring new opportunities in revolutionizing marketing innovation, which is defined as the new marketing practices in the design, distribution, promotion, or pricing of a product or service. (Gupta, Malhotra, Czinkota, & Foroudi, 2016; Purchase & Volery, 2020). Existing research has identified the role of digital technology in increasing customer commitment (Reydet & Carsana, 2017). With the abundance of data and advanced analytics, digital platforms become new distribution channels that improve distribution efficiency, and expand market reach for organizations. Data also has the potential to redefine novel promotion strategies, by uncovering customer preferences and emerging trends, to deliver personalized experiences across marketing channels. In addition, pricing strategies are also undergoing significant transformations due to data availability. Organizations can leverage data from multiple sources such as customers, complementors, and competitors to enable more dynamic and personalized pricing. Therefore, we welcome research proposals looking at the new implications brought by data to marketing innovation, including but not limited to questions such as:

• What innovative marketing strategies and tactics are brought by data and data technologies?
• How do digital firms and traditional enterprises transform in China?
• How does data change online and offline marketing channels in China?
• Does data-driven marketing bring any side effects, and how to govern issues such as user privacy protection?
• What is the influence of relevant policies?

 

Data and Organizational Innovation

The advent of data-driven approaches brings implications to organizational innovation, including changes in organizational structure and management that facilitate organizational change and growth (Damanpour & Aravind, 2012). Organizations can level data to reshape organizational procedures, such as enhancing operational efficiency and improving resource allocation. Data can also provide valuable insights into employee engagement, knowledge sharing, and collaboration networks, creating a more flexible organizational structure and innovative organizational culture. Another opportunity lies in investigating how data drive the boundary-spanning activities of organizations. By utilizing data analytics, organizations connect with more decentralized partners, and build communities and ecosystems (Wu, Lou, & Hitt, 2019). This enables firms to constantly break organizational boundaries, collaborate in open innovation initiatives, and co-create value through external relationships. In addition, it is crucial to examine the potential challenges and ethical considerations associated with the organizational changes brought by data, such as the governance of data sharing, privacy protection, and trust-building mechanisms in the context of external collaborations.  To better understand this theme, we welcome research including but not limited to the following questions:

• What organizational structures, cultures, and capabilities are essential for leveraging data-driven innovation?
• How does data change firms' organizational operations in China?
• How does data influence firms' external boundary-spanning activities?
• How to ensure responsible data utilization in communities and ecosystems?
• What are the influence of ethical issues such as governance of data sharing, privacy protection, and trust-building mechanisms, and what are current dynamics in China?

 

Facilitating Data-driven Innovation in the International Context

In the rapidly evolving global landscape, the cross-border data flow is hindered by diverse and conflicting data regulations imposed by different countries. This surge in data protectionism and data localization policies poses significant legal compliance challenges for multinational enterprises (MNEs), leading to escalated costs. Consequently, there is a pressing need to explore how firms can overcome these obstacles and foster data-driven global innovation. While existing literature has contributed to the development of international business theory frameworks in the digital economy (Luo, 2022; Banalieva & Dhanaraj, 2019), the specific role of data remains underdeveloped. Therefore, we strongly encourage further theoretical and empirical research that explores the relationship between data and global innovation, ultimately leading to the advancement of temporal international business theories. Potential avenues for exploration include but are not limited to, the following questions:

• Data legal compliance and the liability of foreignness: How do data regulation restrictions introduce new uncertainties and hazards to multinational enterprises (MNEs), particularly Chinese MNEs? What strategies can be explored to overcome these emerging liabilities?
• Internationalization process of big data enterprises or data-driven firms: What is the internationalization process like for big data enterprises or data-driven firms? How do they adapt their strategies when expanding into global markets?
• Data and dynamic capabilities in global competition: What specific capabilities are required for firms to effectively leverage data-driven innovation in a rapidly changing global environment? How does data contribute to dynamic capabilities in the context of global competition?
• Global data cooperation and innovation: How can strategies be developed to establish a robust global data value chain or global data network that promotes collaboration and innovation on an international scale? What is the role of China?

 

Schedule and Timeline

1. Proposal submission (deadline March 31, 2025): Please submit proposals to Ke Rong (r@tsinghua.edu.cn) under the following subject line: 'Data-Driven Innovation'. Please ensure that your proposal includes the following components: motivation, research questions, theoretical foundation, methodology, expected outputs, and implications. The proposal should not exceed five pages when single-spaced. The decision-making process will be ongoing, allowing authors to submit their proposals before the deadline.
2. Paper Development Workshop (June 15, 2025 in Xi'an IACMR conference): Accepted research proposals will be invited to participate in a workshop to refine the research. Selected proposals will receive invitations to submit a full paper.
3. Full paper submission (deadline December 31, 2025)
4. Publication of the Special Issue (TBD)

 

Special Issue Guest Editors

Ke Rong (r@tsinghua.edu.cn) is Professor and Director of the Institute of Economics, School of Social Science at Tsinghua University in China. His research focuses on business and innovation ecosystems, the digital economy, and data ecosystems. He has published more than 70 papers in esteemed journals such as the Journal of International Business Studies, Production and Operations Management, and Management and Organization Review. Additionally, he has authored two books titled The Theory of Data (in Chinese) and Business Ecosystems. In recognition of his academic achievements, he has been honored with several awards, including the Youth Changjiang Scholar of the Ministry of Education, Chief Expert of the National Social Science Major Project, Elsevier Highly Cited Scholar in China, and the 2022 Innovation Research Award of the China Information Economics Society. He serves as the founding chief editor of the Journal of Digital Economy and as a board member of JIBS, JIBP, MOR, and Technovation. He is also a member of the Expert Network of the World Economic Forum.

Jiang Wei (weijiang@zju.edu.cn), the vice-principal of Zhejiang University of Finance & Economics, is a Changjiang Chair Professor and a professor of innovation and strategy management at Zhejiang University. Dr. Wei actively engages in multiple academic roles, which include being a member of both the National Assessment Committee of Business Administration Discipline at the Degree Committee of the State Council and the National Teaching Guidance Committee of Business Administration Discipline at the Ministry of Education, as well as serving on the Management Department within the Science and Technology Committee of the Ministry of Education. Dr. Wei’s expertise lies in strategic management and innovation management. He has chaired 6 international research projects, 17 NSFC/NSSFC projects, and more than 60 other national and provincial research projects. He has authored 27 academic monographs and 10 textbooks, and published over 400 papers across various Chinese and international academic journals, including JIBS, ERD, MOR, Technovation, R&D Management, AOM, Management World (Guanli Shijie), among others. The citation of his research has been at the forefront of domestic management academia for more than a decade.

Hai Che (hai.che@ucr.edu) has been an associate professor in Marketing at the University of California Riverside since January 2017. Before that, he was a tenured associate professor at Indiana University in Bloomington between 2012 and 2016. He has also held tenure-track positions at the University of California Berkeley and the University of Southern California. Hai's research work is mainly in the area of Data-Driven Marketing Strategies based on Consumer Purchases and Social Activities. He has published in top marketing journals such as Marketing Science, Journal of Marketing Research, and Quantitative Marketing and Economics. Hai was named a Marketing Science Young Scholar in 2009 and has received research funding from the Marketing Science Institute. He currently serves as an area editor at the Journal of the Academy of Marketing Science and is on the review board of Customer Needs and Solutions. He has also won the best paper award from the 2015 Asia Marketing Association Conference and received a university-wide teaching award from the University of California in 2018. Hai has been consulting with companies in the digital marketing, pharmaceutical, consumer packaged goods, banking, and automobile industries in both the US and Asian countries.

Theodoros Evgeniou (theodoros.evgeniou@insead.edu) is a professor of Decision Sciences and Technology Management at INSEAD and director of the INSEAD Executive Education programme, Transforming your Business with AI. He has been working on Machine Learning and AI for almost 30 years, on areas ranging from AI innovations for business process optimization and improving decisions, to AI risks and regulations, as well as on new Machine Learning methods. His research has appeared in leading journals, such as in Science Magazine, Nature Machine Intelligence, Machine Learning, Lancet Digital Health, Journal of Machine Learning Research, Management Science, Marketing Science, Harvard Business Review magazine, and others. Professor Evgeniou has been a member of the OECD Network of Experts on AI, an advisor for the BCG Henderson Institute, a World Economic Forum Academic Partner for AI, and together with three INSEAD alums also a co-founder of Tremau, a B2B SaaS company whose mission is to build a digital world that is safe and beneficial for all. He gives talks and consults for a number of organizations in his areas of expertise. He has received four degrees from MIT, two BSc degrees simultaneously, one in Computer Science and one in Mathematics, as well as an MS and PhD in Computer Science.

Helen Bao (Hxb20@cam.ac.uk) is a Professor of Land Economy at the Department of Land Economy, University of Cambridge. Helen's research focuses on government policy and interventions that facilitate market operations and mitigate market failures in urban settings, such as sustainable urbanisation and housing affordability. On the technical front, she specialises in the application of behavioural insights and hedonic price modelling in land and housing markets. Helen's book, Behavioural Science and Housing Decision Making: A Case Study Approach, pushes the teaching and research frontier of behavioural urban studies. Helen has published extensively on the application of behavioural insights in real estate markets in Urban Studies, Regional Sciences, Land Use Policy, Housing Studies, Journal of Real Estate Finance and Economics, Cities, Transport and Research Part F - Traffic Psychology and Behaviour, among others. Helen’s research has received grants from the Economic and Social Research Council (UK) and the National Natural Science Foundation of China. She is an Associate Editor of Cities and an editorial board member of key journals in the area of urban studies, such as Land Use Policy.

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