Big Data Analytics to Open Innovation Strategies

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This entry is adapted from the peer-reviewed paper 10.3390/risks11060106

Dynamic business environment has pushed service-oriented firms such as banks to collaborate with external partners through open innovation (OI) to address issues of service differentiation, optimize customer experience, and create effective open innovation strategies (OIS).

open innovation big data analytics strategic resources

1. Introduction

Innovation is a key to organizational success, growth, and the acquisition of strategic resources (Alassaf et al. 2020). Particularly, open innovation (OI) is essential to resolve complex organizational issues by suggesting the most relevant ideas, solutions, and people entirely from outside the organization (Chesbrough 2004). Through OI, organizations successfully acquire breakthrough ideas by connecting with a global pool of talented people, which allows them to develop innovative products and services, respond to dynamic workforce requirements, and find a solution to unresolved issues (Bigliardi et al. 2021). Progressive organizations have started to transcend their boundaries to improve their innovative activities through the conscious inflow and outflow of knowledge, which is helping them to embrace OI and optimize innovation performance (Chesbrough 2003; Naseer et al. 2021). Alternatively, acquiring revolutionary ideas and knowledge through external sources help organizations in reducing costs and investment in R&D as well as sharing risk with external partners (Elia et al. 2020; Leckel et al. 2020).

The concept of OI has received considerable attention from scholars and practitioners over the past two decades; however, a large cluster of studies have focused on investigating OI from a firm-level perspective (Antons et al. 2016; Bertello et al. 2022; Greco et al. 2021; Nestle et al. 2019; Radziwon and Bogers 2019; Sengupta and Sena 2020; Shaikh and Randhawa 2022; Teplov et al. 2018; West and Bogers 2017). The findings of these studies have established that the management of OI hinges on firms’ openness (Bogers et al. 2018a; Laursen and Salter 2006), the selection of external partners (Sofka and Grimpe 2010), OI methods (Veugelers and Cassiman 1999), formalizing collaboration processes (Vlaar et al. 2007), and internal practices (Lakemond et al. 2016). A few studies have also highlighted that firms in the past have used multiple OI models to successfully develop innovation strategies and accelerate innovation-based business activities which require external support, critical knowledge, and identifying innovative methods for acquiring and creating knowledge (Bogers et al. 2017; Gatzweiler et al. 2017; West and Bogers 2017; Zhu et al. 2019). This represents a disagreement in the literature about the barriers and drivers of OI which can be classified into cultural, legal, financial, and economic (Greco et al. 2019; Vanhaverbeke et al. 2017). The admitted complexities of the OI process and organizations’ surge for innovative strategies demand logical, analytical, and technology-based solutions for the management of the OI process.

2. Background of OI

The invention of OI can be linked to the unconventional practices of large innovative firms deviating from traditional innovation methods (Chesbrough 2003, 2006). The pioneering study on OI defined it as “the flow of inbound or outbound ideas towards the organization and transferred to the market from inside or outside the organization” (Chesbrough 2003). The current definition of OI has been significantly modified by innovation scholars and Chesbrough to emphasize entities’ surge for inflow and outflow of knowledge. Chesbrough (2006) recoined OI and asserted that organizations purposely use knowledge inflows and outflows for accelerating their internal innovation process and market expansion. Recent modifications in OI are associated with different business models practiced by progressive organizations and it can be defined as “purposive management of the inflows and outflows of knowledge across organizational boundaries to create a distributive innovation process using financial and non-financial methods in a way that it diverges with organizations existing business models” (Chesbrough and Bogers 2014). Precisely, OI can be described as a distributed innovation process resulting due to the deliberate flow of information across entire organizational hierarchies (Naseer et al. 2021).

A number of studies have explored multiple aspects of OI ranging from underlying issues to the requirement of experts and the nature of the project (Ahn et al. 2017; Du et al. 2014; Kim et al. 2015). A few studies have also focused on investigating the significance of organizational platforms, business ecosystems, and social issues in publicly administrated organizations (Ahn et al. 2019; Schmidthuber et al. 2019). Scholars argued that the successful capitalization of OI relies on establishing a flexible culture essential to restructuring current business models in a way that fosters OIS (Bogers et al. 2019). This highlights entities’ need to integrate strategic and smart assets coupled with technologically driven internal and external sources aligning with their business models to power OI. The current era characterized by unprecedented changes demands organizations to resolve issues by extracting value from existing knowledge through modern architectures and systems instead of developing an entirely new piece of knowledge (Naqshbandi and Jasimuddin 2022). Despite the significance of integrating systems and architectures to gain real value from knowledge (Chesbrough 2006), there is no evidence in the extant literature about the strategic assets required to manage OI in organizations.

3. Operationalizing BDA in Banks

Data scientists defined BDA as “a unified approach rendered for the management, processing, and analysis of unstructured data to extract a meaningful insight for creating sustained value, optimizing performance, and achieving competitive advantage” (Wamba et al. 2017). Earlier studies on BDA described it as a 3Vs (volume, velocity, and variety) concept (Duan and Xiong 2015); later on, Wamba et al. (2015) recoined the term and characterized it as a 5Vs (volume, velocity, verity, veracity, and value) phenomenon. A categorical interpretation of the 5Vs highlighted that volume represents the daily creation of voluminous data from multiple sources at an exponential rate, velocity determines the prompt response to capture BD, variety represents multiple data sources (including new ones), veracity determines the reliability of data, and value means the extraction of economic benefits from available BD. Recently, a few studies (Mishra et al. 2017; Seddon and Currie 2017; Wamba and Mishra 2017) extended BD’s dimensions and established that it should be described as a 7Vs (volume, velocity, verity, veracity, value, variability, and visualization) concept due to variations in the flow and sources of data (variability) and the importance of visualizing data by experts to prepare it for analysis (visualization).

BDA has become a top trend in academia and research in recent years and its analytical capabilities have convinced academicians and practitioners to position it at the forefront of future research agendas in the fields of business management and information systems (De Mauro et al. 2016; Gandomi and Haider 2015; Del Vecchio et al. 2018). The research in academia on BDA gained significant momentum after the hallmark study of McAfee et al. (2012), who regarded it as a major frontier of science, innovation, and the industrial revolution of the new millennium. BDA is categorized as large datasets originating from multiple sources at a high speed. BDA trends, applications, and growth started to take off in 2015, and a number of studies were conducted to analyze its impact on business, organizations, and many other domains of life (Del Vecchio et al. 2018).

A few recent studies have described BDA as a strategic component used for managing customer relations, operational risks, and overall operations of firms to maximize their financial performance (Bresciani et al. 2018; Germann et al. 2014; Kiron et al. 2013; Mikalef et al. 2019; Wamba et al. 2017). From a managerial perspective, BDA offers infinite data to streamline business processes, supply chains, and workforce performance, as well as to improve organizations’ internal collaboration and analyze consumers’ behavioral patterns (Bresciani et al. 2018; Dubey et al. 2019). Additionally, reports have argued that BDA helps in gaining a deeper insight into customers’ preferences extending beyond the traditional methods of information collection, especially related to the latent needs of customers (Mora Cortez and Johnston 2017; Watson et al. 2018). Furthermore, organizations in the past have successfully implemented complex and voluminous data for strategic decision making, as well as scientifically supported and logically explained actions (Bertello et al. 2021; George et al. 2016; Mazzei and Noble 2017). Nonetheless, organizations concerned with developing new customer management strategies; creating innovative products, services, and business models; and enhancing customer experience, satisfaction, and loyalty are required to carefully manage millions of data sources (Levine et al. 2017; Mahmoud et al. 2018; Shipilov et al. 2017).

BDA and its significance in the financial industry are also widely debated in the literature as it is a frontier of future innovations (Hasan et al. 2020). Innovative financial services create large datasets daily through online peer-to-peer lending, crowdfunding, SME financing, assets, wealth, and trading, as well as mobile-payment-managing platforms, cryptocurrencies, and remittance administration channels. These datasets are used by financial analysts for strategic investment decisions to investigate consumers’ spending behaviors for products and service customization (Hale and Lopez 2019). BDA has also contributed to improving different stakeholders’ understanding of financial market trends, strategic decision making to enhance the quality and security of services, transparency, risk analysis, algorithm trading, and transformational culture (Ali et al. 2021; Diebold et al. 2019; Shen and Chen 2018).

4. Operationalizing BDA in Banks

Almost all the features of BDA (7Vs) exceptionally fit into the fundamental requirements of OI outlined by Chesbrough (2003). Particularly, OI assumptions such as the wide distribution of innovative ideas, the lack of monopolistic ideas, the lack of timely discovery of innovative ideas to gain a competitive advantage, the selection of relevant business models based on their technological performance, and the perishability of intellectual property and services in the context of banks can be supported and discussed through the lens of BDA. The multi-dimensional context of BDA, with its ability to integrate into various organizational perspectives (external R&D, range of methods to create and advance intellectual properties, etc.), can explain diverging forms of OI (Barlatier et al. 2020).

Past research on firms practicing OI using BDA has highlighted that proactive participation in OI powered by BDA allows firms to interact with various organizations and professionals (Sun et al. 2020). From a banking perspective, this will help banks to create and monetize digital services, reduce costs, improve user experience, enhance business value, accelerate digital transformation, and place banks in an innovation position (Bogers et al. 2018b). Particularly, banks may follow the mechanisms of large firms to use BDA as an innovation fuel for strategic decision making for creating Fintech(ization), establishing state-of-the-art technology, and strengthening their mutual networks (Barlatier et al. 2020). There are several data-driven organizations such as Tesla, AT&T, Cisco, and Linux that have benefited in the past from BDA to foster OI for building new products and services and offering a unique customer experience (Bogers et al. 2019). Contextualizing this to the banks (primarily described as data-driven entities) should consider leveraging BDA to accelerate OI for forecasting sales, planning and designing data-driven operations, creating new and innovative business models, and rectifying governance issues.

The integration of BDA into the OI process renders several benefits and challenges to organizations as well as banks. Generally, banks are criticized for their lack of transparency, which can be resolved by their participation in the OI process through BDA, ensuring that information is available across multiple platforms accessible to different stakeholders (Manyika et al. 2011). During the preliminary stages of OI, banks may benefit from BDA to pretest products’ efficiency, which will help in overcoming performance issues (Yang et al. 2017). Moreover, market segmentation is a complex issue as it is directly related to customer satisfaction and profitability (Eriksson and Mattsson 1996), which can be addressed by implementing automated algorithms while banks embark on the OI process for customizing their products to fulfill the varying needs of different segments (Barlatier et al. 2020).

5. Theoretical Background and Hypotheses Development

This employed a dynamic capability view (DCV) to empirically investigate the influence of BDA on creating and managing OIS in banks. DCV is an extension of Barney’s (1991) resource-based view (RBV) theory, which is operationalized to explain the strategic progress of banks while contemplating a competitive advantage in a dynamic environment (Hitt et al. 2016; Teece et al. 1997). Based on the underpinning of DCV, the acquisition of strategic resources and the management of these resources are critical for the banks concerned to gain a competitive advantage while operating in a frequently changing environment. Past studies have used the fundamentals of DCV to outline BDA as a source of competitive advantage for organizations operating in a dynamic environment and requiring transparency (Akter et al. 2016; Dubey et al. 2019).

Subsequently, DCV logic is used to conceptualize BDA as a reflective construct and establish that the acquisition of strategic resources is essential in developing strategies pertaining to OI in banks (Barney 1991). Earlier studies have confirmed that firms concerned with designing OIS are required to manage their dynamic resources such as openness (Bogers et al. 2018a; Laursen and Salter 2006), selection of external partners (Sofka and Grimpe 2010), OI methods (Veugelers and Cassiman 1999), formalizing collaboration processes (Vlaar et al. 2007), and internal practices (Lakemond et al. 2016). Following this argument, BDA is used as a higher-order construct for linking the creation of OIS in the banks by managing these underlying factors and investigating their role in supporting the OI process.

Despite an established fact about the opportunities created by BDA in managing OI firms (Del Vecchio et al. 2018), the extant literature lacks empirical evidence about the potential and methods of harnessing BDA to promote OIS in the banks (Barlatier et al. 2020; Fasnacht 2018). A few recent studies in the banking sector have focused on highlighting the management of information and digital technologies at the organizational level to manage OI in banks and improve financial performance (Gianiodis et al. 2014; Martovoy et al. 2015; Naseer et al. 2021). Factors such as banks’ openness (BOP), selection of external partners (SEP), open innovation methods (OIM), formalizing collaboration processes (FCP), and banks’ internal practices (BIP) need to be investigated prior to discussing the actual impact of information and digital technologies acquired by the banks as a source of competitive advantage to support OIS.

The studies on banks’ openness (BOP) have linked it with the stability of the banking sector, financial development, reduction in bank risk, and stimulation of domestic competition allowing banks to produce a range of products and services (Bayraktar and Wang 2006; Bekaert et al. 2011; Luo et al. 2016; Ma and Yao 2022). Following the argument of banks’ surge for the creation of openness, banks may strategically enhance their openness through OIS, as suggested by Bogers et al. (2018a) and Laursen and Salter (2006). However, enhancing BOP through OI requires information sharing among collaborating partners of banks’ internal data, such as financial portfolios, products and services, and customers. Simultaneously, BDA, through its dynamic capabilities, may allow banks to enhance BOP, resulting in better banking stability, deep financial development, lower risk, and better domestic competition (Ali et al. 2021; Diebold et al. 2019; Shen and Chen 2018).

While creating OIS, 21st-century organizations actively collaborate with numerous partners to build cooperative relationships with research and development (R&D) and their potential stakeholders, including customers, suppliers, competitors, and public institutions (Enkel et al. 2009). The selection of external partners (SEP) has been recognized as the most crucial aspect of creating OIS due to the complexities of knowledge required by the organizations and the fact that SEP significantly influences OIS capabilities and overall innovation (Lassen and Laugen 2017; Marina and Gulbrandsen 2013). Banks are known as multidisciplinary technology-implementing service entities producing a range of products and services having a short life cycle to fulfill dynamic market needs. Currently, banks do not possess the required capabilities to produce sophisticated products and services, which forces them to collaborate with external partners by creating effective OIS. However, the underlying complexities in the process of SEP based on the existing capabilities require leveraging smart and strategic resources enabled by BDA, as it allows the pretesting of products and overcoming performance issues, which may positively influence the creation of OIS (Yoon and Song 2014; Yang et al. 2017). Past studies have rendered different methods and approaches to SEP, such as morphology analysis (MA), generative topology maps (GTMs), effectuation, and causation, for creating effective OIS (Marina and Gulbrandsen 2013; Yoon and Song 2014).

Open innovation methods (OIMs) are considered another key area to creating effective OIS and rely on organizations’ internal capabilities and access to external sources and knowledge (Yildirim et al. 2022). Generally, organizations use three widely practiced OIMs (inbound, outbound, and a combination of both) to create OIS, and selecting an accurate OIM is critical due to the benefits associated with the identification of required capabilities, time, and overall innovation performance (Chesbrough and Bogers 2014; West and Bogers 2017). Past studies are yet to fully categorize the essential factors of an accurate OIM relevant to creating OIS. A few studies have attempted to highlight the issue by proposing organizational awareness and the nature of the project as the main criteria to consider during the selection of an OIM (Oztaysi et al. 2017; Yildirim et al. 2022). Practically, it is difficult to generalize and export these criteria to banks due to the differences in access and availability of the resources essential to select relevant OIM for authenticating OIS (León et al. 2020). Following the dynamic capabilities of BDA and banks’ broad access to voluminous data from multiple platforms, it is predicted that BDA may act as a substantial tool to identify, select, test, evaluate, and correct relevant OIMs for creating effective OIS.

Formalizing the collaboration process (FCP) is central to OIS as it ensures the success of OI processes and facilitates organizations in designing the right innovation structure (Bagherzadeh et al. 2021; Obradović et al. 2021). Recent studies have associated the identification of collaborating partners, shortage of experts, ambiguity of goals, organizational decision making, and governance structure with FCP (Brown et al. 2021). Alternatively, these factors can be described as organizational, legal, and regulatory barriers to FCP and can be managed by organizations’ strategic resources such as information and knowledge. Banks are the most regulated entities as they follow various stringent internal and external formal regulations. Therefore, FCP in banks for creating effective OIS demand extra attention to overcome the heterogeneity issues, ensure effective portfolio and innovation management, and resolve governance issues. BDA offers a strategic solution to the organizations in the form of the availability of information and knowledge to be used as a tool for formalizing FCP for the identification of collaborating partners, people, goals, decision-making process, and governance structure, which will contribute to creating effective OIS.

The permeability of organizational boundaries has pushed organizations to consider multiple operational approaches to achieve a competitive advantage (Lu and Chesbrough 2022). This has resulted in a variety of business practices and models to manage the multidimensional operations of organizations. Particularly, service-oriented organizations (banks) operating in a dynamic business environment often face the critical issue of customer satisfaction, which drives them to design various OIS (Bogers et al. 2018b). In this regard, banks’ internal practices (BIP) such as strategic resources, internal knowledge and skill, internal processes related to operations and governance, and manpower development to enhance skill may positively contribute to creating effective OIS (Barlatier et al. 2020). Additionally, banks operating in saturated markets are expected to park support for Fintech by integrating resources in a way that conforms to the emerging requirements of global competitive markets (Barlatier et al. 2020). Banks may achieve these goals by integrating the strategic resources available in the form of BDA to streamline BIP for creating effective OIS, which will help them to fulfill customers and market needs (Ali et al. 2021).

References

Alassaf, Deemah, Marina Dabić, Dara Shifrer, and Tugrul Daim. 2020. The Impact of Open-Border Organization Culture and Employees’ Knowledge, Attitudes, and Rewards with Regards to Open Innovation: An Empirical Study. Journal of Knowledge Management 24: 2273–97.
Chesbrough, Henry. 2004. Managing Open Innovation. Research-Technology Management 47: 23–26.
Bigliardi, Barbara, Giovanna Ferraro, Serena Filippelli, and Francesco Galati. 2021. The Past, Present and Future of Open Innovation. European Journal of Innovation Management 24: 1130–61.
Chesbrough, Henry. 2003. Open Innovation: The New Imperative for Creating and Profiting from Technology. Brighton: Harvard Bus. Press.
Naseer, Saima, Kausar Fiaz Khawaja, Shadab Qazi, Fauzia Syed, and Fatima Shamim. 2021. How and When Information Proactiveness Leads to Operational Firm Performance in the Banking Sector of Pakistan? The Roles of Open Innovation, Creative Cognitive Style, and Climate for Innovation. International Journal of Information Management 56: 102260.
Elia, Gianluca, Antonio Messeni Petruzzelli, and Andrea Urbinati. 2020. Implementing Open Innovation through Virtual Brand Communities: A Case Study Analysis in the Semiconductor Industry. Technological Forecasting and Social Change 155: 119994.
Leckel, Anja, Sophie Veilleux, and Leo Paul Dana. 2020. Local Open Innovation: A Means for Public Policy to Increase Collaboration for Innovation in SMEs. Technological Forecasting and Social Change 153: 119891.
Antons, David, Robin Kleer, and Torsten Oliver Salge. 2016. Mapping the Topic Landscape of JPIM, 1984–2013: In Search of Hidden Structures and Development Trajectories. Journal of Product Innovation Management 33: 726–49.
Bertello, Alberto, Paola De Bernardi, Alberto Ferraris, and Stefano Bresciani. 2022. Shedding Lights on Organizational Decoupling in Publicly Funded R&D Consortia: An Institutional Perspective on Open Innovation. Technological Forecasting and Social Change 176: 121433.
Greco, Marco, Michele Grimaldi, Giorgio Locatelli, and Mattia Serafini. 2021. How Does Open Innovation Enhance Productivity? An Exploration in the Construction Ecosystem. Technological Forecasting and Social Change 168: 120740.
Nestle, Volker, Florian A. Täube, Sven Heidenreich, and Marcel Bogers. 2019. Establishing Open Innovation Culture in Cluster Initiatives: The Role of Trust and Information Asymmetry. Technological Forecasting and Social Change 146: 563–72.
Radziwon, Agnieszka, and Marcel Bogers. 2019. Open Innovation in SMEs: Exploring Inter-Organizational Relationships in an Ecosystem. Technological Forecasting and Social Change 146: 573–87.
Sengupta, Abhijit, and Vania Sena. 2020. Impact of Open Innovation on Industries and Firms—A Dynamic Complex Systems View. Technological Forecasting and Social Change 159: 120199.
Shaikh, Ibrahim, and Krithika Randhawa. 2022. Managing the Risks and Motivations of Technology Managers in Open Innovation: Bringing Stakeholder-Centric Corporate Governance into Focus. Technovation 114: 102437.
Teplov, Roman, Ekaterina Albats, and Daria Podmetina. 2018. What does open innovation mean? Business versus academic perceptions. International Journal of Innovation Management 23: 1950002.
West, Joel, and Marcel Bogers. 2017. Open Innovation: Current Status and Research Opportunities. Innovation 19: 43–50.
Bogers, Marcel, Henry Chesbrough, and Carlos Moedas. 2018a. Open Innovation: Research, Practices, and Policies. California Management Review 60: 5–16.
Laursen, Keld, and Ammon Salter. 2006. Open for Innovation: The Role of Openness in Explaining Innovation Performance among U.K. Manufacturing Firms. Strategic Management Journal 27: 131–50.
Sofka, Wolfgang, and Christoph Grimpe. 2010. Specialized Search and Innovation Performance–Evidence across Europe. R&D Management 40: 310–23.
Veugelers, Reinhilde, and Bruno Cassiman. 1999. Make and Buy in Innovation Strategies: Evidence from Belgian Manufacturing Firms. Research Policy 28: 63–80.
Vlaar, Paul W. L., Frans A. J. Van Den Bosch, and Henk W. Volberda. 2007. Towards a Dialectic Perspective on Formalization in Interorganizational Relationships: How Alliance Managers Capitalize on the Duality Inherent in Contracts, Rules and Procedures. Organization Studies 28: 437–66.
Lakemond, Nicolette, Lars Bengtsson, Keld Laursen, and Fredrik Tell. 2016. Match and Manage: The Use of Knowledge Matching and Project Management to Integrate Knowledge in Collaborative Inbound Open Innovation. Industrial and Corporate Change 25: 333–52.
Bogers, Marcel, Ann-Kristin Zobel, Allan Afuah, Esteve Almirall, Sabine Brunswicker, Linus Dahlander, Lars Frederiksen, Annabelle Gawer, Marc Gruber, Stefan Haefliger, and et al. 2017. The Open Innovation Research Landscape: Established Perspectives and Emerging Themes across Different Levels of Analysis. Industry and Innovation 24: 8–40.
Gatzweiler, Alexandra, Vera Blazevic, and Frank Thomas Piller. 2017. Dark Side or Bright Light: Destructive and Constructive Deviant Content in Consumer Ideation Contests. Journal of Product Innovation Management 34: 772–89.
Zhu, Xiaoxuan, Zhenxin Xiao, Maggie Chuoyan Dong, and Jibao Gu. 2019. The Fit between Firms’ Open Innovation and Business Model for New Product Development Speed: A Contingent Perspective. Technovation 86–87: 75–85.
Greco, Marco, Michele Grimaldi, and Livio Cricelli. 2019. Benefits and Costs of Open Innovation: The BeCO Framework. Technology Analysis & Strategic Management 31: 53–66.
Vanhaverbeke, Wim, Nadine Roijakkers, Annika Lorenz, and Henry Chesbrough. 2017. The Importance of Connecting Open Innovation to Strategy. In Strategy and Communication for Innovation. Edited by N. Pfeffermann and J. Gould. Cham: Springer.
Chesbrough, Henry. 2006. New puzzles and new findings. In Open Innovation: Researching a New Paradigm. Edited by Chesbrough Henry, Wim Vanhaverbeke and West Joel. Oxford: Oxford University Press, pp. 1–12.
Chesbrough, Henry, and Marcel Bogers. 2014. Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. In New Frontiers in Open Innovation. Edited by Henry Chesbrough, Wim Vanhaverbeke and West Joel. Oxford: Oxford University Press, pp. 3–28.
Ahn, Joon Mo, Tim Minshall, and Letizia Mortara. 2017. Understanding the Human Side of Openness: The Fit between Open Innovation Modes and CEO Characteristics. R&D Management 47: 727–40.
Du, Jingshu, Bart Leten, and Wim Vanhaverbeke. 2014. Managing Open Innovation Projects with Science-Based and Market-Based Partners. Research Policy 43: 828–40.
Kim, Namkuk, Dong-Jae Kim, and Sungjoo Lee. 2015. Antecedents of Open Innovation at the Project Level: Empirical Analysis of Korean Firms. R&D Management 45: 411–39.
Ahn, Joon Mo, Nadine Roijakkers, Riccardo Fini, and Letizia Mortara. 2019. Leveraging Open Innovation to Improve Society: Past Achievements and Future Trajectories. R&D Management 49: 267–78.
Schmidthuber, Lisa, Frank Piller, Marcel Bogers, and Dennis Hilgers. 2019. Citizen Participation in Public Administration: Investigating Open Government for Social Innovation. R&D Management 49: 343–55.
Bogers, Marcel, Henry Chesbrough, Sohvi Heaton, and David J. Teece. 2019. Strategic Management of Open Innovation: A Dynamic Capabilities Perspective. California Management Review 62: 77–94.
Naqshbandi, M. Muzamil, and Sajjad M. Jasimuddin. 2022. The Linkage between Open Innovation, Absorptive Capacity and Managerial Ties: A Cross-Country Perspective. Journal of Innovation & Knowledge 7: 100167.
Wamba, Samuel F., Angappa Gunasekaran, Shahriar Akter, Steven Ji-fan Ren, Rameshwar Dubey, and Stephen J. Childe. 2017. Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. Journal of Business Research 70: 356–65.
Duan, Lian, and Ye Xiong. 2015. Big data analytics and business analytics. Journal of Management Analytics 2: 1–21.
Wamba, Samuel F., Shahriar Akter, Andrew Edwards, Geoffrey Chopin, and Denis Gnanzou. 2015. How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study. International Journal of Production Economics 165: 234–46.
Mishra, Deepa, Zongwei Luo, Shan Jiang, Thanos Papadopoulos, and Rameshwar Dubey. 2017. A Bibliographic Study on Big Data: Concepts, Trends and Challenges. Business Process Management Journal 23: 555–73.
Seddon, Jonathan J. J. M., and Wendy L. Currie. 2017. A Model for Unpacking Big Data Analytics in High-Frequency Trading. Journal of Business Research 70: 300–307.
Wamba, Samuel F., and Deepa Mishra. 2017. Big Data Integration with Business Processes: A Literature Review. Business Process Management Journal 23: 477–92.
De Mauro, Andrea, Marco Greco, and Michele Grimaldi. 2016. A Formal Definition of Big Data Based on Its Essential Features. Library Review 65: 122–35.
Gandomi, Amir, and Murtaza Haider. 2015. Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management 35: 137–44.
Del Vecchio, Pasquale, Alberto Di Minin, Antonio Messeni Petruzzelli, Umberto Panniello, and Salvatore Pirri. 2018. Big Data for Open Innovation in SMEs and Large Corporations: Trends, Opportunities, and Challenges. Creativity and Innovation Management 27: 6–22.
McAfee, Andrew, Erik Brynjolfsson, Thomas Davenport, D. J. Patil, and Barton Dominic. 2012. Big Data: The management revolution. Harvard Business Review 90: 61–68.
Bresciani, Stefano, Alberto Ferraris, and Manlio Del Giudice. 2018. The Management of Organizational Ambidexterity through Alliances in a New Context of Analysis: Internet of Things (IoT) Smart City Projects. Technological Forecasting and Social Change 136: 331–38.
Germann, Frank, Gary L. Lilien, Lars Fiedler, and Matthias Kraus. 2014. Do Retailers Benefit from Deploying Customer Analytics? Journal of Retailing 90: 587–93.
Kiron, David, Renee Boucher Ferguson, and Pamela Kirk Prentice. 2013. From value to vision: Reimagining the possible with data analytics. MIT Sloan Management Review 54: 1.
Mikalef, Patrick, Maria Boura, George Lekakos, and John Krogstie. 2019. Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment. British Journal of Management 30: 272–98.
Dubey, Rameshwar, Angappa Gunasekaran, and Stephen J. Childe. 2019. Big Data Analytics Capability in Supply Chain Agility. Management Decision 57: 2092–112.
Mora Cortez, Roberto, and Wesley J. Johnston. 2017. The Future of B2B Marketing Theory: A Historical and Prospective Analysis. Industrial Marketing Management 66: 90–102.
Watson, George F., Scott Weaven, Helen Perkins, Deepak Sardana, and Robert W. Palmatier. 2018. International Market Entry Strategies: Relational, Digital, and Hybrid Approaches. Journal of International Marketing 26: 30–60.
Bertello, Alberto, Alberto Ferraris, Stefano Bresciani, and Paola De Bernardi. 2021. Big Data Analytics (BDA) and Degree of Internationalization: The Interplay between Governance of BDA Infrastructure and BDA Capabilities. Journal of Management and Governance 25: 1035–55.
George, Gerad, Ernst C. Osinga, Dovev Lavie, and Brent A. Scott. 2016. Big data and data science methods for management research. Academy of Management Journal 59: 1493–507.
Mazzei, Matthew J., and David Noble. 2017. Big Data Dreams: A Framework for Corporate Strategy. Business Horizons 60: 405–14.
Levine, Sheen S., Mark Bernard, and Rosemarie Nagel. 2017. Strategic Intelligence: The Cognitive Capability to Anticipate Competitor Behavior. Strategic Management Journal 38: 2390–423.
Mahmoud, Mahmoud Abdulai, Robert E. Hinson, and Patrick Amfo Anim. 2018. Service Innovation and Customer Satisfaction: The Role of Customer Value Creation. European Journal of Innovation Management 21: 402–22.
Shipilov, Andrew, Frédéric C. Godart, and Julien Clement. 2017. Which Boundaries? How Mobility Networks across Countries and Status Groups Affect the Creative Performance of Organizations. Strategic Management Journal 38: 1232–52.
Hasan, Md. Morshadul, József Popp, and Judit Oláh. 2020. Current Landscape and Influence of Big Data on Finance. Journal of Big Data 7: 21.
Hale, Galina, and Jose Angel Lopez. 2019. Monitoring Banking System Connectedness with Big Data. Journal of Econometrics 212: 203–20.
Ali, Qaisar, Hakimah Yaacob, Shazia Parveen, and Zaki Zaini. 2021. Big Data and Predictive Analytics to Optimise Social and Environmental Performance of Islamic Banks. Environment Systems and Decisions 41: 616–32.
Diebold, Francis X., Ghysels Eric, Mykland Per, and Zhang Lan. 2019. Big data in dynamic predictive econometric modeling. Journal of Econometrics 212: 1–3.
Shen, Dehua, and Shu-heng Chen. 2018. Big Data Finance and Financial Markets. In Big Data in Computational Social Science and Humanities. Computational Social Sciences. Cham: Springer, pp. 235–48.
Barlatier, Pierre-Jean, Anne-Laure Mention, and Avni Misra. 2020. The Interplay of Digital Technologies and the Open Innovation Process: Benefits and Challenges. In Open Innovation: Bridging Theory and Practice—Managing Digital Open Innovation. Singapore: World Scientific Publishing Co Pte Ltd., pp. 1–34.
Sun, Yongbo, Jingyan Liu, and Yixin Ding. 2020. Analysis of the Relationship between Open Innovation, Knowledge Management Capability and Dual Innovation. Technology Analysis & Strategic Management 32: 15–28.
Bogers, Marcel, Nicolai J. Foss, and Jacob Lyngsie. 2018b. The ‘Human Side’ of Open Innovation: The Role of Employee Diversity in Firm-Level Openness. Research Policy 47: 218–31.
Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. 2011. Big Data: The Next Frontier for Innovation, Competition, and Productivity. San Francisco: McKinsey Global Institute.
Yang, Chaowei, Qunying Huang, Zhenlong Li, Kai Liu, and Fei Hu. 2017. Big Data and Cloud Computing: Innovation Opportunities and Challenges. International Journal of Digital Earth 10: 13–53.
Eriksson, Kent, and Jan Mattsson. 1996. Organising for Market Segmentation in Banking: The Impact from Production Technology and Coherent Bank Norms. The Service Industries Journal 16: 35–45.
Barney, Jay. 1991. Firm resources and sustained competitive advantage. Journal of Management 17: 99–120.
Hitt, Michael A., Kai Xu, and Christina Matz Carnes. 2016. Resource Based Theory in Operations Management Research. Journal of Operations Management 41: 77–94.
Teece, David J., Gary Pisano, and Amy Shuen. 1997. Dynamic capabilities and strategic management. Strategic Management Journal 18: 509–33.
Akter, Shahriar, Samuel Fosso Wamba, Angappa Gunasekaran, Rameshwar Dubey, and Stephen J. Childe. 2016. How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment? International Journal of Production Economics 182: 113–31.
Fasnacht, Daniel. 2018. Open Innovation in the Financial Services—the Magic Bullet. In Open Innovation Ecosystems: Creating New Value Constellations in the Financial Services. Edited by Daniel Fasnacht. Cham: Springer Management for Professionals.
Gianiodis, Peter T., John E. Ettlie, and Jose J. Urbina. 2014. Open Service Innovation in the Global Banking Industry: Inside-Out Versus Outside-In Strategies. Academy of Management Perspectives 28: 76–91.
Martovoy, Andrey, Anne-Laure Mention, and Marko Torkkeli. 2015. Inbound Open Innovation in Financial Services. Journal of Technology Management and Innovation 10: 117–31.
Bayraktar, Nihal, and Yan Wang. 2006. Banking Sector Openness and Economic Growth. Policy Research Working Paper No. 4019. Washington, DC: World Bank. Available online: https://openknowledge.worldbank.org/handle/10986/9273 (accessed on 13 March 2022).
Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad. 2011. Financial Openness and Productivity. World Development 39: 1–19.
Luo, Yun, Sailesh Tanna, and Glauco De Vita. 2016. Financial Openness, Risk and Bank Efficiency: Cross-Country Evidence. Journal of Financial Stability 24: 132–48.
Ma, Yong, and Chi Yao. 2022. Openness, Financial Structure, and Bank Risk: International Evidence. International Review of Financial Analysis 81: 102065.
Enkel, Ellen, Gassmann Oliver, and Henry Chesbrough. 2009. Open R&D and open innovation: Exploring the phenomenon. R&D Management 39: 311–16.
Lassen, Astrid Heidemann, and Bjørge Timenes Laugen. 2017. Open Innovation: On the Influence of Internal and External Collaboration on Degree of Newness. Business Process Management Journal 23: 1129–43.
Marina, Solesvik, and Magnus Gulbrandsen. 2013. Partner Selection for Open Innovation. Technology Innovation Management Review 8: 11–16.
Yoon, Byungun, and Bomi Song. 2014. A Systematic Approach of Partner Selection for Open Innovation. Industrial Management & Data Systems 114: 1068–93.
Yildirim, Ercan, Ilker Murat AR, Marina Dabić, Birdogan Baki, and Iskender Peker. 2022. A Multi-Stage Decision Making Model for Determining a Suitable Innovation Structure Using an Open Innovation Approach. Journal of Business Research 147: 379–91.
Oztaysi, Basar, Sezi Cevik Onar, and Cengiz Kahraman. 2017. Selection among innovative project proposals using a hesitant fuzzy multiple criteria decision making method. Journal of Economics Finance and Accounting 4: 194–202.
León, G., A. Tejero, and José N. Franco-Riquelme. 2020. New Methodology for Profiling and Comparison of Open Innovation Models to Conduct R&D Activities. IEEE Access 8: 48491–502.
Bagherzadeh, Mehdi, Stefan Markovic, and Marcel Bogers. 2021. Managing Open Innovation: A Project-Level Perspective. IEEE Transactions on Engineering Management 68: 301–16.
Obradović, Tena, Božidar Vlačić, and Marina Dabić. 2021. Open Innovation in the Manufacturing Industry: A Review and Research Agenda. Technovation 102: 102221.
Brown, P., C. Von Daniels, N. M. P. Bocken, and A. R. Balkenende. 2021. A Process Model for Collaboration in Circular Oriented Innovation. Journal of Cleaner Production 286: 125499.
Lu, Qinli, and Henry Chesbrough. 2022. Measuring Open Innovation Practices through Topic Modelling: Revisiting Their Impact on Firm Financial Performance. Technovation 114: 102434.

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