Antecedents and outcomes of collaborative innovation capabilities on the platform collaboration environment

https://doi.org/10.1016/j.ijinfomgt.2018.08.007Get rights and content

Highlights

  • This paper examines the drivers of collaborative innovation capability, and its effects on the digital collaboration capability.

  • The fit of platform technology and collaborative business need is the critical driver of collaborative innovation capability.

  • The collaborative innovation capability positively impacts on digital collaboration capability.

  • The impact of the fit on digital collaboration capability is completely mediated by collaborative innovation capability.

Abstract

The application of emerging collaborative digital technologies enhanced firms’ collaborative innovation and firm performance. This paper examines the drivers of collaborative innovation capability, and its effects on the digital collaboration capability. Based on the technology-push and need-pull perspective as well as operational capability hierarchy view, we proposed the model of antecedents and outcomes of higher-level operational capability, such as collaborative innovation capability. We tested the research hypotheses using a field survey of 184 Chinese corporations. The research model was validated using hierarchical regression analysis with the data collected from the survey data. The results provide strong support for the proposed research model. In particular, we found that as hypothesized, the fit of platform technology and collaborative business need is the critical driver of collaborative innovation capability. Furthermore, the higher-level operational capability (i.e., collaborative innovation capability) impacts on the lower-level operational capability (i.e., digital collaboration capability). Especially, the impact of the fit on digital collaboration capability is completely mediated by collaborative innovation capability. By integrating the technology-push and need-pull perspective as well as operational capability hierarchy literature, this paper provides significant implications for antecedents and outcomes of collaborative innovation capabilities under the platform collaboration environment.

Introduction

In recent decades, new collaborative digital technologies (such as digital devices, digital platforms, social media, and cloud technology) and their widespread applications encouraged organizations to innovate their products, services, and processes (Ali, Warren, & Mathiassen, 2017; Karakaya & Demirkan, 2015; Ngai, Tao, & Moon, 2015; Sedera et al., 2016; Wu, Wu, & Si, 2016). Firms start looking at digital technologies (e.g., social media platform) as effective mechanisms to interact more with their customers (Alalwan, Rana, Dwivedi, & Algharabat, 2017; Misirlis & Vlachopoulou, 2018). Especially, IT-based networks and real-time data flows enable external innovation collaboration1 (Alegre et al., 2014; Thomke, 2006). For instance, Tianhong, an asset management company in China, through cooperating with Alipay.com (similar to Paypal.com in the United States), generates the digital innovation product, Yuebao (Internet financial product). This digital collaborative innovation has accelerated the innovation of Internet finance2 in China. Therefore, understanding how to utilize digital technologies (de Reuver, Sørensen, & Basole, 2017) to achieve collaborative innovation capabilities and enhance performance has become a concern for practitioners.

Prior information systems research mainly focused on internal firm innovation (Constantinides, Henfridsson, & Parker, 2018; Parker, Van Alstyne, & Choudary, 2016), where companies internally manage all of the process involved in the innovation life cycle (Chesbrough, 2003). For example, Kleis et al. (2012) found that, combined with IT, internal research and development (R&D) has a strong and positive relationship with innovation production. Similarly, Tarafdar and Gordon (2007) used the resource-based view of firms to explore how information system competencies affect process innovation in an organization. Recently, with the development of new digital technologies (such as digital platforms, Industry 4.0), the traditional internal innovation paradigm has been challenged (Han et al., 2012; Sedera et al., 2016; Gerke, Dickson, Desbordes, & Gates, 2017; Constantinides et al., 2018; Parker et al., 2016). Therefore, in order to respond to the fast-changing environment and rapidly evolving consumers’ needs, firms need to develop the collaborative innovation capability with the help of digital technologies (de Reuver et al., 2017; Han et al., 2012; Lee et al., 2012). Baldwin and von Hippel (2011) further argued that both innovation by individual users and open collaborative innovation increasingly compete with and may displace producer innovation in many parts of the economy. Also, Han et al. (2012) investigated the economic and strategic value of open innovation alliances, by which participating firms cocreate economic value through the joint development and comarketing of IT innovations.

Some studies have examined the role of IT in firm innovation and begun to study collaborative innovation in the platform environment (Han et al., 2012; Sedera et al., 2016; Yoo et al., 2012; Yoo, 2013). There are still some limitations in past studies. First, although prior scholars studied the effects of IT on the products, services or processes innovation, relatively little research attention has been paid to other non-IT drivers of collaborative innovation (Ali, Kan, & Sarstedt, 2016). Second, previous literature has explored the relationship between innovation and performance (Slater, Mohr, & Sengupta, 2014), but the value generation process of collaborative innovation is still unclear (Wang et al., 2016). For example, some firms have a high collaborative innovation capability and still have difficulties in achieving firm performance.

To fill the gap in the extant literature, this paper firstly adds non-IT drivers of collaborative innovation capability from the technology-push and need-pull perspective of innovation literature (Di Stefano, Gambardella, & Verona, 2012; Schon, 1967; Voss, 1984). Both platform technologies and collaborative business need can explain the generation of collaborative innovation capability. Platform technology is the critical infrastructure necessary to guarantee the activities of collaborative innovation, and the collaborative business need reflects the innovation motivation of the focal firm with channel partners, according to the market environment and customers’ needs. Based on the technology-push and need-pull perspective, both platform technology and collaborative business need is associated with the level of collaborative innovation capability. Therefore, we study the interaction effect of technology and need to explore the generation process of collaborative innovation capability.

Furthermore, based on the perspective of operational capability hierarchies3 (Mishra, Devaraj, & Vaidyanathan, 2013), collaborative innovation capability is a kind of higher-level operational capability which can be applied at the functional or operational level. It can affect the lower-level operational capabilities, which can be applied at individual task- or process- levels, such as digital collaboration capabilities with distributors. Therefore, our two research questions are: (1) How is collaborative innovation capability generated by the fit of platform technologies and collaborative business need? (2) How does collaborative innovation capability affect the lower-level operational capabilities, such as digital collaboration capabilities?

This study contributes to the evolving literature on collaborative innovation capability in three respects. First, based on the technology-push and need-pull perspective, this paper introduces a new non-IT driver of collaborative innovation capabilities, namely collaborative business need. We further find that the fit of platform technology and collaborative business need is a significant predictor of collaborative innovation capability (Chuang & Lin, 2013). Second, prior studies mainly explored the relationship between dynamic and operational capabilities, neglecting the hierarchical structure in the dynamic or functional capabilities (Mishra et al., 2013; Peng, Quan, Zhang, & Dubinsky, 2016). Based on the operational capability hierarchies view, this paper finds that, in the platform collaboration environment, as a higher-level operational capability, collaborative innovation capability also affects the lower-level operational capability such as digital collaboration capability with distributors. Third, we find that collaborative innovation capability fully mediates the effect of the fit of platform technology and collaborative business need on digital collaboration capability. Thus, firms should create and nurture collaborative innovation capability, which serves as a precondition to the creation of lower-level operational capabilities. This finding contributes to platform innovation literature by exploring the key role of collaborative innovation capability in effecting information technology capability on the platform collaboration environment (Constantinides et al., 2018; de Reuver et al., 2017; Yoo, 2013).

Section snippets

Technology-push and need-pull perspective

For many years, researchers investigated the critical driving forces of innovation, usually from two alternative perspectives (Voss, 1984; Kim & Lee, 2009; Di Stefano et al., 2012; Dutta & Hora, 2017). The first perspective is technology-push, which focused on the key role of science and technology in developing innovations. The other perspective is demand-pull, which identified the market features (such as the needs of customers) that affects the performance of innovation.

Based on the

Research model and hypotheses

Built on the technology-push and need-pull perspectives and existing research linking higher-level and lower-level operational/functional capabilities, we provide a theoretical model studying the relationship among the fit of platform technologies and collaborative business needs, collaborative innovation capability, and digital collaboration capabilities. To formulate our arguments, we build on studies that suggest both technologies and market or user needs contribute to organizational

Research method

Data for the study were gathered through a cross-sectional survey. The population frame of the survey was firms located in China. Survey data was collected from firms that interact with distributors using platform technologies4. These firms demonstrated a large variance in terms of their digital platform collaboration. Hence, we believe that they are appropriate for testing our two hypotheses.

Results

The proposed hypotheses in this paper included an interaction effect and mediation effect. The interaction effect is the fit between platform technology and collaborative business need (H1), while a mediation effect is the impact of fit on digital collaboration capabilities through digital collaborative innovation (H2). To test these interaction and mediation hypotheses, ordinary least squares (OLS) analysis was conducted (Muller, Judd, & Yzerbyt, 2005) using SPSS version 18.

For the OLS

Main findings

Our goal in this paper was to understand the antecedents and outcomes of collaborative innovation capabilities on the platform collaboration environment. We assess the antecedents and outcomes by exploiting a field survey based on the technology-push and need-pull perspective as well as operational capability hierarchy view.

Our results yield three major findings. First, the finding shows that fit of platform technology and collaborative business need is a significant predictor of collaborative

Conclusion

With the development of emerging platform technologies, many firms obtain better firm performance through nurturing collaborative innovation capability. However, there is little research concerned with the generation and effect mechanisms of collaborative innovation capability. In this paper, based on the technology-push and need-pull perspective, and operational capability hierarchy literature, we proposed that the fit of platform technology and collaborative business need drives the

Funding

This research has been supported by grants from the National Natural Science Foundation of China under Grant 71801104, 71372174, 71271099 and 71471074, supported by grants from the National Social Science Foundation of China under Grant 15CTQ007, and supported by the Fundamental Research Funds for the Central UniversitiesCCNU18QN041.

Maomao Chi is an assistant professor of Information Systems in the School of Information Management at Central China Normal University, Wuhan, China. He was a joint training PhD student (the China Scholarship Council Program) in College of Business at Iowa State University. His research interests include e-business value creation, and e-business platform. His research papers have appeared in International Journal of Information Management, Industrial Management & Data Systems, International

References (77)

  • P. Kivimaa et al.

    Creative destruction or mere niche support? Innovation policy mixes for sustainability transitions

    Research Policy

    (2016)
  • J. Lindič et al.

    Deploying information technologies for organizational innovation: Lessons from case studies

    International Journal of Information Management

    (2011)
  • Abhay Nath Mishra et al.

    Capability hierarchy in electronic procurement and procurement process performance: An empirical analysis

    Journal of Operations Management

    (2013)
  • N. Misirlis et al.

    Social media metrics and analytics in marketing – S3M: A mapping literature review

    International Journal of Information Management

    (2018)
  • Sriram Narayanan et al.

    Assessing the contingent effects of collaboration on agility performance in buyer–supplier relationships

    Journal of Operations Management

    (2015)
  • Gregory F. Nemet

    Demand-pull, technology-push, and government-led incentives for non-incremental technical change

    Research Policy

    (2009)
  • E.W.T. Ngai et al.

    Social media research: Theories, constructs, and conceptual frameworks

    International Journal of Information Management

    (2015)
  • Photis M. Panayides et al.

    The impact of trust on innovativeness and supply chain performance

    International Journal of Production Economics

    (2009)
  • J. Peng et al.

    Mediation effect of business process and supply chain management capabilities on the impact of IT on firm performance: Evidence from Chinese firms

    International Journal of Information Management

    (2016)
  • Eve D. Rosenzweig

    A contingent view of e-collaboration and performance in manufacturing

    Journal of Operations Management

    (2009)
  • Darshana Sedera

    Innovating with Enterprise Systems and Digital Platforms: A Contingent Resource-Based Theory View

    Information & Management

    (2016)
  • G.N. Stock et al.

    The joint influence of technology uncertainty and interorganizational interaction on external technology integration success

    Journal of Operations Management

    (2008)
  • Xinlin Tang et al.

    The moderating effects of supplier portfolio characteristics on the competitive performance impacts of supplier-facing process capabilities

    Journal of Operations Management

    (2012)
  • Monideepa Tarafdar et al.

    Understanding the influence of information systems competencies on process innovation: A resource-based view

    The Journal of Strategic Information Systems

    (2007)
  • J. Wu et al.

    The influences of internet-based collaboration and intimate interactions in buyer–supplier relationship on product innovation

    Journal of Business Research

    (2016)
  • Z. Zhu et al.

    Leveraging e-business process for business value: A layered structure perspective

    Information & Management

    (2015)
  • L.S. Aiken et al.

    Multiple regression: Testing and interpreting interaction

    (1991)
  • Joaquin Alegre

    IT competency and the commercial success of innovation

    Industrial Management & Data Systems

    (2014)
  • Carliss Baldwin et al.

    Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation

    Organization Science

    (2011)
  • R.M. Baron et al.

    The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations

    Journal of Personality and Social Psychology

    (1986)
  • Sundar Bharadwaj et al.

    The Performance Effects of Complementarities Between Information Systems, Marketing, Manufacturing, and Supply Chain Processes

    Information Systems Research

    (2007)
  • Michael W. Browne et al.

    Alternative ways of assessing model fit

    Sociological Methods & Research

    (1992)
  • H. Chesbrough

    Open innovation: The new imperative for creating and profiting from technology

    (2003)
  • Maomao Chi et al.

    Mediation and time-lag analyses of e-alignment and e-collaboration capabilities

    Industrial Management & Data Systems

    (2015)
  • W.W. Chin

    Issues and opinion on structure equation modeling

    MIS Quarterly

    (1998)
  • P. Constantinides et al.

    Introduction—Platforms and infrastructures in the digital age

    Information Systems Research

    (2018)
  • Fariborz Damanpour

    Organizational Innovation: A Meta-Analysis of Effects of Determinants and Moderators

    The Academy of Management Journal

    (1991)
  • M. de Reuver et al.

    The digital platform: A research agenda

    Journal of Information Technology

    (2017)
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      Hence, additional research on digital collaboration capability is required. Moreover, the extant work holds mixed views on the relationship between digital collaboration capability and innovation (Chi et al., 2015; Chi et al., 2018; Cohen, 2018; Queiroz et al., 2018). Although several studies ascribe such inconsistencies to information overload and a lack of analytics capability (Haefner et al., 2021; Lauritzen and Karafyllia, 2018; Lee et al., 2018; Mikalef et al., 2020), the majority of studies are theoretical in nature, with just a few studies providing actual data to back up their claims.

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    Maomao Chi is an assistant professor of Information Systems in the School of Information Management at Central China Normal University, Wuhan, China. He was a joint training PhD student (the China Scholarship Council Program) in College of Business at Iowa State University. His research interests include e-business value creation, and e-business platform. His research papers have appeared in International Journal of Information Management, Industrial Management & Data Systems, International Journal Networking and Virtual Organisations, International Journal of information Systems and Change Management, China Journal of Information Systems and in several proceedings of international conferences such as IEEE International Conference on Computer Science and Information Technology and Wuhan International Conference on e-Business.

    Weijun Wang is a professor and deputy director of the Key Laboratory of Adolescent Cyberpsychology and Behavior (Ministry of Education), Central China Normal University, China. His research interests include data analysis and user behavior, IT service and E-commerce. In the past five years, he has published more than 30 papers in the major refereed journals and international conferences. He has served several program committees of the major international conferences in the field of information management and knowledge management, and also serves as a fellow in the WG6.11of the IFIP and members of the AIS, ASIS&T.

    Xinyuan Lu is a Professor of information management at the Central China Normal University and is currently the Vice President of School of Information Management. He received his Bachelors in 1997, a Master’s in Engineering in February 2003, a Doctorate in Management in March 2006 and a one-year Visiting Scholar in the Georgia State University in 2011. His research area includes knowledge management, risk management, evaluation theory and methods and has hosted two National Natural Science Foundation projects and has published more than 30 refereed journals and conferences in related fields.

    Joey F. George is a professor of Information Systems and the John D. DeVries Endowed Chair in Business in the College of Business at Iowa State University. He received his doctorate in management from the University of California at Irvine in 1986 and his bachelor’s degree in English from Stanford University in 1979. His research interests focus on the use of information systems in the workplace, including deceptive computer-mediated communication, computer-based monitoring, and group support systems. He was the editor-in-chief of Communications of the Association for Information Systems from 2006–2009, and he currently serves as a senior editor for Information Systems Research. He served as conference co-chair for the 2001 International Conference on Information Systems (ICIS) in New Orleans, LA, and as the conference chair for the 2012 ICIS in Orlando, FL. In 2008, he was selected as a Fellow of the Association for Information Systems, of which he is a past president. He has chaired 25 doctoral committees, and his work has been funded by the National Science Foundation and the U.S. Air Force Office of Scientific Research.

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