Digital Governance PDF

Stuvia - Koop en Verkoop de Beste Samenvattingen W4: Digital Governance Hanisch, M., & Goldsby, C. M. 2022. The boon and bane of blockchain: Getting the governance right. California Management Review, 64(3): 141–168. Blockchains: decentralized databases (transparency) where new data can only be a...

Stuvia - Koop en Verkoop de Beste Samenvattingen W4: Digital Governance Hanisch, M., & Goldsby, C. M. 2022. The boon and bane of blockchain: Getting the governance right. California Management Review, 64(3): 141–168. Blockchains: decentralized databases (transparency) where new data can only be appended if there is consensus on the new record among participants (validation), and where no data can be deleted once it is registered in the database (immutability). Blockchains are particularly effective for securing information flows where network participants may not fully trust each other, and the risk of fraud or opportunistic behavior is high. Blockchain technologies offer an exciting opportunity to digitally manage large intra- and inter-organizational networks. As companies operate in increasingly large and interconnected networks of customers, partners, subsidiaries, suppliers, and regulators, the need to manage these transactions efficiently and securely has also increased. The blockchain trajectory: Initiation → Execution → Adaption The three stages in the governance of enterprise blockchains are: 1. Analysis: Managers need to carefully analyze the network in which blockchain will be deployed, taking into account the needs and concerns of participants, to understand existing network interdependencies and competitive tensions. This stage is crucial for identifying the most suitable governance form for the blockchain initiative. 2. Adoption: Managers should seek to understand for whom and for what purpose the blockchain is being used to tailor the coordination and control mechanisms to the network structure and the needs of the participants. This ensures that the blockchain governance is appropriately configured for the specific context in which it will operate. 3. Adjustment: Blockchain governance is not static but dynamic. Managers must remain aware of and responsive to governance dynamics within the network, which may necessitate continuous adjustments to the blockchain governance. This flexibility allows for the network's growth and changing needs over time, ensuring the blockchain initiative remains effective and relevant. Challenges in blockchain networks: Coordination: the management of interdependencies within and across organizations. This involves organizing and aligning various parties involved in the blockchain, considering the high organizational interdependencies inherent in such environments. Control: the allocation of decision-making authority and the resolution of disputes within blockchain networks. Governance structures may either center control in one organization or share it among multiple entities. Control dictates the network's future direction, funding, permissioning rules, data visibility, and how disputes are resolved. Control is distinct from coordination as it specifically relates to decision-making authority and enforcement. Governance modes: Blockchains do not represent a self- sufficient governance mode but need to be complemented by a well-balanced governance structure. 4 generic blockchain governance modes: Chief: The blockchain is orchestrated and directed by the same instance, meaning coordination and control reside “in-house.” This mode is typical for large corporations controlling blockchain initiatives for internal processes. It involves internal coordination activities like stakeholder alignment, budget allocation, and project communication. Control is centralized with strict internal participation rules and decision-making authority. This mode benefits holding companies with many business units requiring transparency. Downsides include conflicts in intra-organizational settings due to insufficient consideration of subsidiary needs. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Clan: Similar to the chief mode, clan mode coordinates activities within an organization but allows shared control over the blockchain network. It is used for optimizing operations and sharing control over information, funding, decisions, and disputes among departments or subsidiaries. Coordination activities are internal, but control is shared among participants. This mode fosters a supportive culture and drives solutions designed for all entities but can be inconvenient when expanded to more organizations. Custodian: Applies when coordination occurs across organizational boundaries, and control of the network is centralized in a single actor. Coordination involves synchronizing inter- organizational relationships around a common blockchain purpose, but control remains hierarchical. The custodian mode is advantageous for fast decision-making and execution but can become estranged from critical network organizations. Consortium: Organizations collaboratively control activities for a blockchain network across organizational boundaries. It enables coordination and shared control among organizations, fostering consensus-based decisions on information sharing, funding, and strategic decisions. While inclusive of key parties, this mode often faces bureaucratic challenges and requires robust agreements to ensure data and intellectual property security. There are four strategic moves for blockchain governance: Connecting: The blockchain is extended to include and coordinate external organizations. Technically, this means setting up new nodes in the network. It is beneficial as a gateway for network effects, allowing quick integration of other organizations through open standards. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Isolating: This move reduces dependencies on external organizations, focusing future design and development on internal coordinative actions. It allows organizations to concentrate on their operational needs and test blockchain adoption internally before involving others. This strategy, however, may lead to market fragmentation with many small networks having proprietary standards instead of a single interoperable network. Loosening: This involves releasing control to other organizations in the governance structure. It facilitates more open mutual decisions around participation, information sharing, funding, decision authority, and dispute resolution. Loosening is beneficial for giving critical network organizations a seat at the table and ensuring consensus in decisions. Tightening: This strategic move concentrates control over blockchain activities in one or a few network organizations. It is useful when organizations wish to regain control over design, implementation, operation, or customization decisions, particularly when developments take unfavorable turns. Tightening can lead to higher implementation speed due to centralized decision-making but may also estrange network participants and slow network growth due to emerging frictions. The blockchain governance journey: Managers must analyze network interdependencies and transaction alignment, adopt a suitable governance mode, and be agile in adjusting governance mechanisms — connecting, isolating, loosening, tightening — to reflect network changes and ensure growth. Conclusion: Effective governance that is attuned to participant needs and early conflict identification enhances network value and economic efficiency. The paper suggests that careful consideration of governance modes — chief, clan, custodian, or consortium — and the readiness to adapt through one of the strategic moves (connecting, isolating, loosening, or tightening) is crucial as network compositions change. The four governance modes are presented not just as static models but as strategic tools that managers can use to address coordination and control challenges. Blockchain governance is a dynamic process requiring continuous adjustments and alignment with network evolution. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Hanisch, M., Goldsby, C. M., Fabian, N. E., & Oehmichen, J. 2023. Digital governance: A conceptual framework and research agenda. Journal of Business Research, 162: 1–13. The conceptual framework of digital governance distinguishes between: Analog: Involves centralized control, with decision-making concentrated at the top, and relies on bilateral coordination between parties. Incentives are bureaucratic, based on organizational hierarchies, and trust is actor-based, dependent on human relationships and reputations. Augmented: Features distributed control, allowing decision authority across various levels. Coordination is multilateral, encompassing several parties. Incentives are programmatic, structured through predefined programs, and trust is actorithmic (= a blend of human and algorithmic trust). Automated: Employs decentralized control, with decision-making widely distributed, and omnilateral coordination, engaging numerous parties simultaneously. Cybernetic incentives are driven by data and algorithms, and trust is primarily algorithmic, based on system reliability and predictability. The authors focus on ‘augmented governance’, which blends both other domains. They predict automated governance becomes more cost-efficient than augmented governance and, ultimately, analog governance as transactivity (number of contributors, connections, and level of exchange consistency) increases. 3 important theoretical contributions: 1. definitional clarity regarding the concept of digital governance, a distinct form of governance that has spawned a new field of research requiring a conceptual foundation. 2. strategic decision-making parameters and tradeoffs associated with digital governance, and we define relevant governance mechanisms associated with digital exchange that are critical in discussions of advanced system designs such as AI and blockchains. 3. wider discussion on digital transformation that has gained prominence in management and organizational research by shifting the focus from organizational processes and business models to how digital technology impacts governance. Governance literature: The governance challenge involves creating mechanisms that help integrate, direct, and monitor the distributed efforts in productive exchange relationships. To meet this challenge, exchange partners must find ways to control relevant exchange processes (e.g., allocation of resources and tasks), outcomes (e.g., generation and distribution of financial, environmental, and social value), and relationships (e.g., opportunistic behaviors). The design of control mechanisms can be complemented and substituted by appropriate coordination, incentives, and trust mechanisms to achieve desired governance benefits. Hence, governance broadly concerns the establishment of rules that help verify inputs and outputs (i.e., control mechanisms), divide and allocate tasks (i.e., coordination mechanisms), align competing interests (i.e., incen- tive mechanisms), and attenuate relational vulnerabilities (i.e., trust mechanisms). If we map this: Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen The authors’ conceptual framework advances the debate on digital governance by: 1) automated control no longer relies on hierarchical control but on decentralized checks-and-balances protocols; 2) automated coordination transforms bilateral agreements into omni-lateral arrangements; 3) incentives transition from bureaucratic rules to cybernetic protocols that update autonomously via dynamic inputs; and 4) trust can be algorithmically enhanced by shifting from individual actors to a complete system. The new challenges of the digital age are examined in terms of: 1. Establishing & Building Digital Relationships: o Governance in digital networks is key for performance enhancement and as a strategic differentiator. Companies use governance as a competitive advantage, like Apple’s privacy policy versus Google. New collaborative trends include transparency movements and open-source algorithmic protocols. 2. Maintaining & Adapting Digital Relationships: o Digital governance moves decision-making from exchange participants to digital tool developers. Aligning exchange participants with governance setters is crucial to prevent tensions, as seen in disputes like Epic vs. Apple. This shift has sparked new collaborative forms, including solidarity among affected content creators. 3. Restoring & Terminating Digital Relationships: o Governance mechanism designs significantly impact the termination of digital relationships. Effective governance can lead to platform disintermediation as users bypass platform constraints. Poor governance decisions can result in mass platform exits, demonstrated by incidents like Facebook's Cambridge Analytica scandal. Digital governance, enabled by technologies like blockchains and AI, shifts towards automated governance, differing significantly from analog methods. It ranges from augmenting human efforts to full automation, enhancing efficiency and transparency in exchanges. However, it often requires analog governance to balance technological capabilities and mitigate limitations like rigidity and compliance issues. Automated governance is not standalone but interrelated with and complemented by analog governance, particularly in areas where digital approaches are insufficient. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Governance modes: Control: The shift from analog to automated governance transforms from centralized to decentralized systems. Analog governance relies on contracts and hierarchical authority to enforce outcomes and behaviors. In contrast, automated governance employs decentralized controls through autonomous algorithms, enhancing outcome certainty and behavioral control. Digital tools facilitate distributed control, offering lateral authority and partial automation. These changes allow for flexible and efficient management of control mechanisms in governance settings. Coordination Coordination evolves from bilateral in analog governance to omni-lateral in automated governance. Analog methods involve task division between parties, often routinized for reliability. Automated coordination uses systems for autonomous task assignment, as seen in platforms like Uber. An intermediate, augmented form combines digital tools with direct actor involvement, allowing for adaptive task assignment and responding to routine changes. This spectrum enables more dynamic and responsive coordination methods. Incentives: Incentives in governance transition from bureaucratic forms in analog settings to cybernetic systems in automated governance. Analog incentives are contract-based and subject to renegotiation, focusing on aligning partner objectives. Automated governance introduces cybernetic incentives, where self-adapting algorithms in feedback loops continuously adjust rewards, exemplified by cryptocurrency staking. Additionally, programmatic incentive structures emerge, combining predefined, automated rules with manual adjustments, offering a nuanced approach to incentivization. Trust: Trust shifts from actor-based in analog governance to algorithmic in automated contexts. Analog trust is built on repeated interactions and responsible behavior between partners. Automated governance places trust in systems like blockchain, ensuring transaction validation without identity revelation. Augmented governance introduces actorithmic trust (mix of actor- based and algorithmic) evident in platforms like eBay and Airbnb, where trust is built through both human interactions and digital technologies. This evolution reflects a more complex and multifaceted approach to establishing trust in governance structures. Combining governance forms: In combining these governance modes, organizations have the flexibility to tailor their governance structures to suit specific needs and contexts. Control, coordination, incentives, and trust can be mixed and matched from analog, augmented, and automated forms to create hybrid governance models. This approach allows for gradual implementation and adaptation to changing conditions. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen The ‘governance choice framework’ for the digital age is guided by "transactivity,": the number of contributors, connections, and consistency in exchanges. Costs for analog governance rise exponentially with transactivity, while automated governance, despite high initial costs, scales efficiently at low marginal costs, favoring high transactivity settings. Augmented governance, combining both, is cost-effective at medium transactivity. Governance designers must balance these costs against the benefits, considering stakeholder perspectives, organizational characteristics, and regulatory pressures. 7. Developing a research agenda on digital governance : Research Agenda Proposal: The article proposes a research agenda aimed at deepening and broadening the understanding of digital governance, with two distinct avenues. Two Key Avenues: Avenue 1: Governance by Algorithms: o Discusses the shift towards digital technologies for automated control and coordination. o Highlights the need for insights into cognitive, emotional, and organizational processes in digital governance. o Raises concerns about the impact of digital governance on human emotions and perceptions. Avenue 2: Governance of Algorithms: o Focuses on the responsibility and accountability in digital governance. o Addresses legal considerations and the role of policymakers. o Emphasizes the need for cybersecurity measures against cyberthreats. o Questions the balance between automation and vulnerability to attacks. o Highlights the growing strategic importance of cybersecurity in organizations. Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Lecture 4: Big data and Business Analytics Big data Big data = data that contains greater variety, arriving in increasing volumes and with more velocity (also known as the 3 V’s). Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before. Big data management = the organization, administration and governance of large volumes of both structured and unstructured data. The goal of big data management is to ensure a high level of data quality and accessibility for business intelligence and big data analytics applications. Implications of big data management: 1. Privacy: need to ethically deal with privacy concerns and regulations - Privacy dilemma: in order to avoid privacy issues it is necessary to distort the data, which lowers the accuracy of data due to anonymization - More privacy is lower data quality 2. Bias: most data sources are biased; discriminatory data is everywhere - Representativeness problem: almost all big data from customers are self-selected leading to a biased picture 11 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen 3. Security: most data sources are unsafe and public data can be easily manipulated - Openness vs. security: you must make the sources open to allow user-generated data, but open data sources lack protection 4. High costs: creating and maintaining big data infrastructure can be costly (apps, servers, cloud, data scientists) Business analytics Business analytics = techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions. Strategic business decisions = managerial choices that are important in terms of the resources committed, the actions taken and the precedents set. They define the direction of the organization and have long-term effects on the firm’s administration, structure and performance. Stages of analytics and key characteristics: Types of analytics: descriptive, diagnostic, predictive and prescriptive 12 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Decision-making and analytics - 3 phases of decision making in Mintzberg et al. (1976): 1. Managers recognize a performance-objective gap in the data and thoroughly define the strategic problem - Suitable type of data analytics: descriptive, diagnostic 2. Managers identify and design alternative actions to solve the problem - Suitable type of data analytics: descriptive, diagnostic, predictive 3. Managers select the most feasible solutions and evaluate them in relation to organizational goals to arrive at the final choice - Suitable type of data analytics: prescriptive - Analytic 3.0 Analytics competitors = compete in Analytics 3.0. Generate analytics in a coordinated way, as part of an overarching strategy championed by top leadership and pushed down to decision makers at every level. A fundamental aspect of the internet era is that firms generate big data. Firms can and must compete in analytics because if they don’t, rivals will. Characteristics of analytics competitors: - Widespread use of modeling and optimization: goes beyond basic statistics to generate predictive and prescriptive tasks - Enterprise approach: multiple applications supporting many parts of the business (supply chain, sales, consume research and marketing) - Senior executive advocates: chief executives who drive the shift to analytics at their companies. Chief analytics officers. - Culture: companywide respect for measuring, testing, and evaluating quantitative evidence (from what we do think to what do we know) - Right people: data analysts with the necessary skills Sources of competitive advantage through the lens of management theory Competitive advantage = an advantage a firm has when it earns a higher rate of economic profit than the average rate of economic profit of other firms competing within the same market. How can a firm attain a competitive advantage using big data analytics? → 4 different management theories 1. Resource-based view RBV theory states that organizational resources and capabilities vary across firms and differentiate firms’ performance and competitive advantage. Resources and capabilities constitute the main components of RBV. In order to achieve a competitive advantage, organizations need to acquire and develop resources that are valuable, rare, imperfectly imitable, and non-substitutable. 13 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen Related applications: - RBV approaches big data as a resource/capability which is able to directly or indirectly (through enhancing other organizational capabilities or resources) promote better performance and innovation - Big data analytics as a firm-level innovation enables firms to achieve heterogeneity and hence affords higher value and awareness in securing sustainable advantages - Big data analytics is seen as a distinctive capability and a high-performance business process to support business needs - Does the theory’s assumptions of valuable, rare, inimitable and non-substitutable resources hold with big data? → big data does not always meet these requirements. 2. Dynamic capabilities view Dynamic capability has emerged as an extension of RBV. The theory states that dynamic capabilities enable firms to modify their resources to adapt rapidly to changing conditions, helping them to maintain their competitive advantage. Related applications: - Big data analytics is considered a capability that can provide competitive advantage to organizations in highly dynamic and uncertain environments - Big data initiatives help organizations respond to external and internal changes - Big data analytics applications allow for the creation or enhancement of dynamic capabilities such as organizational agility by means of effective internal and external knowledge management - Big data analytics for information processing reduces uncertainty 3. Knowledge-based view The knowledge-based view considers that knowledge and related intangibles (from individual to collective knowledge) are important to competitive advantage since they are considered to be valuable, rare, inimitable, and non-substitutable resources. Accordingly, firms need to leverage these resources into productive outcomes to achieve competitiveness Related applications: - The theory can stimulate discussions about the knowledge necessary to manipulate a massive quantity of data (filtering, analysis and other elaborate actions) and how to create this knowledge, thus creating value and competitive advantage for the organizations - The knowledge-based view is used to support the importance of data quality for predictive big data analytics in supply chain management - In conjunction, the knowledge-based view and dynamic capabilities are used to understand the role of big data analytics in the creation of agility. 14 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?  Stuvia - Koop en Verkoop de Beste Samenvattingen 4. Transaction cost theory Transaction cost theory considers the transaction as the most basic unit of management, and focuses on how much effort, resources, or cost is necessary for two parties to complete an exchange. Related applications: - TCT is useful in studying big data analytics in e-commerce transactions. Big data analytics can benefit online firms by improving market transaction cost efficiency, managerial transaction cost efficiency and time cost efficiency. (e.g. platforms that act as match-makers) - Transaction cost theory also presents a rational view for evaluating ‘make versus buy’ decisions related to big data initiatives 15 Gedownload door: matsmolenberg | [email protected] ¤ 912 per jaar Dit document is auteursrechtelijk beschermd, het verspreiden van dit document is strafbaar. extra verdienen?