Affordances, experimentation and actualization of FinTech: A blockchain implementation study

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Highlights

  • Actualizing three affordances of blockchain produces business outcomes.

  • Blockchain implementation includes affordance experimentation and actualization.

  • Affordance experimentation produces use cases for actualization.

Abstract

Blockchain, the technology underlying bitcoin, is an emerging financial technology (FinTech) that is poised to have strategic impacts on organizations. Because it is a new phenomenon, there are few studies on blockchain, and those studies have focused mainly on the technology’s potential impacts, whereas how to effectively implement it in an organization remains unknown. Our study intends to fill that gap. Using affordance-actualization (A-A) theory as the theoretical lens, we conducted a case study of blockchain implementation in an organization that has effectively implemented it. We identify three affordances of blockchain in the organization and a process model whereby these affordances are actualized. The process model extends A-A theory by adding an experimentation phase where blockchain’s use cases within the organization are identified, developed and tested through conceptual adaptation and constraint mitigation. Our study makes important theoretical contributions to the literature on A-A theory, blockchain, information technology (IT) implementation, and strategic information systems (SIS). Our study can also help IT practitioners to implement blockchain effectively and extract value from their investment.

Introduction

Blockchain is a financial technology (FinTech) first developed as the distributed ledgers for bitcoin. It used to be overshadowed by bitcoin, but in recent years it has started to attract attention in its own right and is becoming a core technology in the FinTech family (Economist, 2015b). Many practitioners and researchers have realized that the impacts of blockchain extend beyond bitcoin and even beyond the financial industry to drive change in many businesses (Economist, 2015a, Notheisen et al., 2017, Ølnes et al., 2017, Underwood, 2016). An article published in Harvard Business Review predicts that it is blockchain, and not big data, robotics, the social web, or even artificial intelligence, that is positioned to drive the most organizational changes in the coming decade (Tapscott and Tapscott, 2016).

The strategic information systems (SIS) literature has a tradition of publishing papers that examine emerging technologies with strategic impacts (Gable, 2010, Galliers et al., 2012). However, it has not yet examined the blockchain phenomenon. In general, information systems (IS) research on blockchain is limited, and scholars have mainly focused on its impacts (e.g., Kshetri, 2018, Notheisen et al., 2017, Ølnes et al., 2017, Underwood, 2016). Although these impact studies offer rich insights into how blockchain can change business operations, they offer limited insights into how blockchain should be implemented within organizations. A survey conducted by State Street shows that most of the senior managers interviewed saw blockchain as the technology of the future, but few knew how to implement it (State Street Corporation, 2016). Because effective implementation is a precondition for impacts, this gap in the literature will limit organizations’ ability to extract value from their investment.

Our study is intended to unpack the black box of blockchain implementation. To guide our exploration, we adopt affordance-actualization (A-A) theory as the theoretical lens (Strong et al., 2014). The concept of affordances was first introduced by Gibson (1986), an ecological psychologist, who used it to explain how animals perceive an object. According to Gibson, animals perceive an object in terms of the action potentials that it affords rather than its physical properties. Markus and Silver (2008) define affordances in the context of IS research as “the possibility for goal-oriented actions afforded to specific user groups by technical objects” (p. 622).

Affordances do not guarantee results, because they refer to action potentials rather than actual actions or final outcomes. To transform potentials into results, actors must take goal-oriented actions to use the technology to achieve an outcome, a process known as “affordance actualization” (Burton-Jones and Volkoff, 2017, Strong et al., 2014). An effective implementation of blockchain can thus be seen as a process in which its affordances are actualized. We thus derive two research questions: (1) what are the affordances of blockchain in an organization? (2) How does an organization actualize these affordances?

To answer these two questions, we conducted an in-depth case study of blockchain implementation in an organization that has effectively implemented the technology. Our case analysis reveals three affordances of blockchain in the organization and a process model in which these affordances are actualized. The process model extends A-A theory by adding an experimentation phase, whereby blockchain’s use cases within the organization are identified, developed, and tested through conceptual adaptation and constraint mitigation.

Our study makes four important theoretical contributions. First, it extends A-A theory by adding an experimentation phase and extends the applicability of A-A theory to blockchain. Second, our study contributes to the blockchain literature by offering insights into how the technology can be implemented effectively. Third, our study contributes to the IT implementation literature by deriving success factors that fit the specifics of blockchain and differ from prior findings. Fourth, our study contributes to the SIS literature by enriching studies on blockchain and extending A-A theory, which SIS scholars can use to examine how to implement emerging digital technologies to realize strategic impacts. From a practical perspective, our study can help IT practitioners effectively implement blockchain and extract value from their investment.

Section snippets

Existing perspective on blockchain

Blockchain is one of the most promising technologies in the upcoming FinTech revolution (Economist, 2015b). Although it was first developed to serve as distributed ledgers tracking bitcoin transactions, blockchain’s potential extends beyond bitcoin; it can change many business operations in both financial and commercial domains (Kshetri, 2018, Notheisen et al., 2017, Ølnes et al., 2017, Underwood, 2016).

A blockchain is a chain of data blocks each of which is created to record a transaction. A

Method

We chose a case study as our research method for two reasons. First, our research questions are exploratory in nature and are better answered through qualitative data (Walsham, 2006). Second, because organizational affordances and their actualization are reflected in the intricate actions of a group, a case study is needed to uncover embedded insights (e.g., Leonardi, 2013, Seidel et al., 2013, Strong et al., 2014). We used A-A theory to guide both data collection and analysis.

Findings

In this section, we present each set of findings in detail. For a summary of the findings, see Table 3. The table also demonstrates that we have carefully and intentionally separated affordances from both use and outcomes.

Theoretical and practical contributions

Our study makes four important theoretical contributions. First, our study extends A-A theory by adding an experimentation phase, where use cases of a technology within an organization are identified, developed and tested through conceptual adaptation and constraint mitigation (see Fig. 7). The original A-A theory does not include an experimentation phase. One explanation is that for EHR implementation, the use cases are already in place and how the technology should be used in an organization

Concluding remarks

Although the theoretical and practical contributions are rich, they must be considered in light of the limitations. First, a common criticism of single-case studies is the issue of generalizability (Walsham, 2006). Although our findings are generalizable at the analytical level because they are supported by the literature, generalization in a statistical sense is impossible with our research design (Lee and Baskerville, 2003). Future research could achieve statistical generalization by

Acknowledgment

Funding for this research was provided by China’s NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao (71529001), and the National Natural Science Foundation of China (71402187, 71402186).

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