The Multiplier Effect: AI, Power, & Politics

Part 12 of 14 in our series on generative AI and organizational dynamics

Generate an image that solely features iconic symbols associated with politics and governance, without any textual elements. Focus on universally recognized symbols such as the scales of justice, a gavel, the Capitol building, or an olive branch, to represent the essence of political power, law, and peace. This composition should visually convey the fundamental aspects of politics and governance through these symbols alone, highlighting their significance in shaping societies and influencing human interactions.

FuturePoint Digital is a research-based consultancy positioned at the intersection of artificial intelligence and humanity. We employ a human-centric, interdisciplinary, and outcomes-based approach to augmenting human and machine capabilities to create super intelligence. Our evidenced-based white papers can be found on FuturePoint White Papers, while FuturePoint Conversations aims to raise awareness of fast-breaking topics in AI in a less formal format. Follow us at: www.futurepointdigital.com.

Given the title of this white paper it’s difficult, especially in a U.S. presidential election year, to prevent our minds from instantly turning to national politics. The focus of our 14-part series has been primarily on the interaction of emerging artificial intelligence (AI) capabilities and organizational dynamics, and not the broader societal domain. (FuturePoint Digital has another upcoming series that explores AI multiplier effects on societal dynamics). Nevertheless, there are some obvious and, dare we say, unavoidable overlaps between organizational and societal politics, so we have elected to explore both in this paper. Where necessary, we will distinguish between the two with respect to how AI plays (or may play) an impactful or critical role vis-a-vis power and politics in organizations and society.

This distinction notwithstanding, a key point is that the rise of AI has transcended technological boundaries, embedding itself within the fabric of societal governance and political discourse. As AI-based technologies become more sophisticated, their impact on power structures and political processes becomes increasingly profound, offering new tools for policy-making, governance, and civic engagement while also raising critical questions about privacy, surveillance, and the digital divide. Therefore, we must carefully understand the full ramifications of AI with respects to power and politics, and ensure that these new and emerging capabilities serve to advance humankind both optimally and ethically.

Traditional Models of Power & Politics

Mention the words power and politics and many, if not most of us have an an immediate negative reaction. This reaction may stem from the association of power and politics with a form of intractable political ideology and dogma, which can often lead to a polarization of views and a reluctance to engage in open, constructive dialogue. This unfortunate and, frankly, pernicious form of power and politics can often impede our ability engage in rational thought, to employ critical thinking skills, and even to comport ourselves with basic human decency.

However, power and politics, when employed constructively, can serve as indispensable tools for societal progress and organizational success. When approached with the intent to foster collaboration, mutual understanding, and respect, these elements can help facilitate the exchange of ideas, the negotiation of differences, and the achievement of common goals. By redefining our relationship with power and politics, emphasizing transparency, accountability, and inclusivity, we can transform these concepts from sources of division to catalysts for innovation and change. This positive reorientation allows individuals, communities, and organizations alike to harness these dynamics in a way that promotes constructive debate, drives effective decision-making, and nurtures a culture of empathy and cooperation. Ultimately, embracing a more enlightened view of power and politics can empower us to overcome barriers, address challenges, and work together towards a more sustainable and prosperous future.

Organizational Political Frameworks

Organizational politics involves the actions and behaviors that individuals and groups engage in to influence organizational decision-making and resource allocation. Understanding and navigating organizational politics requires insight into various frameworks or elements that highlight power dynamics, conflict, and informal networks within organizations. Here are some organizational frameworks or elements related to organizational politics:

  • Power and Dependence Framework: This framework, inspired by the work of French and Raven among others, focuses on the bases of power within organizations (such as legitimate power, reward power, coercive power, expert power, and referent power) and how individuals or groups become dependent on others for resources, information, or support. It explores how power is acquired, maintained, and used in organizational settings (French & Raven, 1959).

  • Conflict Theory in Organizations: Conflict theory examines the sources and types of conflict within organizations, whether they are task, relationship, or process conflicts. It considers how conflict can stem from differing interests, goals, and power imbalances and how it can be managed or resolved (Freeman, 1984).

  • Resource Dependency Theory: This theory posits that organizational behavior and power dynamics are heavily influenced by external resources that the organization depends on. Organizations must therefore engage in strategies to manage these dependencies and power relations with external entities, which often leads to political behavior both within and outside the organization (Pfeffer & Salancik 1978).

  • Stakeholder Theory: Stakeholder theory identifies and models the groups or individuals that have a stake in the organization (such as employees, customers, suppliers, shareholders, and the community). It explores how these stakeholders influence organizational decisions and strategies and how organizations manage these influences, often leading to political negotiations and compromises (Freeman, 1984).

  • Organizational Culture and Politics: Organizational culture refers to the shared values, beliefs, and norms that influence how members of the organization behave. Politics often emerges from the cultural context, where certain behaviors, power structures, and informal networks are more accepted or encouraged than others (Schein, 2010).

  • Social Network Analysis in Organizations: This approach examines the informal networks within organizations, focusing on how relationships and social structures influence political behavior, information flow, and power dynamics. Understanding these networks is crucial for identifying key influencers and power brokers within the organization (Cross & Parker, 2004).

  • Mintzberg’s Political Games: Henry Mintzberg identified various "games" that individuals play in organizations to gain power and influence decisions. These include games like resistance, counter-resistance, sponsorship, alliance-building, and empire-building, each highlighting different tactics and strategies used in organizational politics (Mintzberg, 1985).

  • The Pfeffer Model of Organizational Power: Jeffrey Pfeffer’s work emphasizes the importance of power in organizations and offers strategies for acquiring and using power effectively. He identifies key sources of power, such as controlling scarce resources, occupying central positions, and being able to cope with uncertainty, and discusses how individuals can leverage these sources in political activities (Pfeffer, 1992).

These frameworks and elements provide insights into the complex and often subtle dynamics of organizational politics. Understanding these aspects can help individuals and leaders navigate political landscapes more effectively, influence decisions, and achieve organizational goals while maintaining positive work environments.

Societal Political Frameworks

Understanding societal politics involves several traditional models and frameworks that scholars have developed over the years to explain how societies organize, govern, and make decisions. These models provide insights into the dynamics of power, governance, and the interaction between different actors within the political landscape. Here are some of the foundational frameworks:

  • Pluralism: Pluralism posits that power is distributed among many groups in society, including interest groups, lobbying groups, and coalitions. These groups compete in the political arena to influence policy and decision-making. According to this model, no single group consistently dominates politics, and public policy results from the competition and bargaining among these diverse interests (Dahl, 1961).

  • Elitism: In contrast to pluralism, elitism argues that a small, unified elite class holds the most power in society and that this elite essentially makes or influences all significant political decisions. According to this model, the elite may consist of individuals from top economic, political, or military positions, and they operate to protect and advance their own interests (Mills, 1956).

  • Marxism: Based on the ideas of Karl Marx, this framework views societal politics through the lens of class struggle and economic power. Marxism posits that society is divided into classes based on their relationship to the means of production, and political power is used to maintain the dominance of the ruling class (bourgeoisie) over the working class (proletariat). Marxism anticipates the eventual overthrow of capitalist systems through a workers' revolution (Marx & Engels, 1848).

  • Institutionalism: Institutionalism emphasizes the role of formal institutions (such as the constitution, legal systems, and government bodies) and informal rules and norms in shaping political behavior and outcomes. It looks at how these institutions evolve over time and how they influence, and are influenced by, political actors (March & Olsen, 1989).

  • Political Economy: This model looks at the relationship between politics and the economy, examining how public policy and economic systems influence each other. It considers how economic interests and structures affect political structures and vice versa, exploring issues like regulation, taxation, and government spending (Smith, 1776; Oatley 2019).

  • Social Constructivism: Social constructivism focuses on the role of ideas, beliefs, and identities in shaping political outcomes. It argues that many fundamental aspects of the political world, including the concepts of sovereignty, nationality, and power, are constructed through social interactions and shared understandings rather than being inherent or natural (Wendt, 1992).

  • Systems Theory: Systems theory views politics as a complex system of interactions between various components of society, including government institutions, political parties, and the electorate. It analyzes how these components work together to maintain the stability and functionality of the political system as a whole (Easton, 1957).

Each of these models offers a different perspective on how societies organize politically and how power and resources are distributed and contested among various actors. They provide useful frameworks for analyzing political processes, understanding the dynamics of power, and examining the impact of policies on different groups within society.

Types of Organizational Power

Understanding organizational power is crucial for navigating and influencing the workplace dynamics effectively. Organizational power refers to the ability of individuals or groups to influence decisions, control resources, and direct the behavior of others within an organization. Here are the main types of power identified in organizational contexts:

  • Legitimate Power: Also known as positional power, it stems from the official position or title one holds within the organization. Individuals with legitimate power have the authority to make decisions, allocate resources, and direct others based on their formal role (French & Raven, 1959).

  • Reward Power: This type of power comes from the ability to confer valued rewards on others. Rewards can include promotions, salary increases, bonus payments, or any other benefits that can be given or withheld by the power holder (Raven, 1992).

  • Coercive Power: Opposite to reward power, coercive power is based on the ability to administer punishments or remove rewards from others. This can include demotions, disciplinary actions, or withholding of benefits, used to enforce compliance or change behavior (Raven, 1992).

  • Expert Power: This power derives from possessing specialized knowledge, skills, or expertise that is valued within the organization. Individuals with expert power are influential because others depend on their expertise to accomplish tasks or solve problems (Yuki, 2012).

  • Referent Power: This type of power is based on the personal traits and relationships of an individual. Referent power exists when others identify with, like, respect, or admire the person. It often comes from personal characteristics, charisma, or the ability to build strong relationships (Conger & Kanungo, 1988).

  • Informational Power: Stemming from the control over access to important information or the possession of knowledge that others need or want. Informational power can influence decision-making and negotiation processes within the organization (Tushman & Scanlan, 1981).

  • Connection Power: This power is derived from the individual's network or relationships with influential people inside or outside the organization. Having connections with powerful individuals can increase one's ability to influence outcomes and access resources (Burt, 1992).

These types of power are not mutually exclusive and can overlap; individuals in organizations often possess and use a combination of these power sources to achieve their goals and influence others. Understanding these types of power can help individuals navigate organizational politics, build effective influence strategies, and contribute to positive organizational outcomes.

Types of Societal Power

Societal power refers to the capacity to influence, control, or direct the behaviors, actions, and thoughts of individuals and groups within a society. Unlike organizational power, which operates within the confines of a specific institution, societal power is broader, encompassing the various mechanisms through which control and influence are exerted over social norms, values, and structures. Here's an overview of the types of societal power:

  • Political Power: This type of power is wielded by individuals or groups within governmental or political structures. It involves the ability to create laws, policies, and regulations that govern the behavior of individuals and institutions within the society. Political power is often manifested through elected officials, governmental agencies, and political parties (Lasswell, 1936).

  • Economic Power: Economic power lies in the control of wealth, resources, and the means of production. It includes not just financial capital but also the ability to influence economic policies, labor markets, and the distribution of resources and wealth within a society. Corporations, business leaders, and financial institutions typically wield significant economic power (Piketty, 2014).

  • Cultural Power: This refers to the ability to shape societal norms, values, beliefs, and practices. Cultural power is often held by media, educational institutions, religious organizations, and the entertainment industry. It influences what is considered acceptable, desirable, or normal within a society (Bourdieu, 1984).

  • Social Power: Social power is derived from the capacity to influence others based on status, reputation, or position within social networks. It can be seen in the influence of social leaders, influencers, and prominent figures within communities who shape social attitudes and behaviors (Webber, 1947).

  • Ideological Power: Ideological power comes from the ability to control and disseminate dominant ideas and beliefs. It is often intertwined with cultural power and can be used to justify and maintain other forms of power by shaping the worldview and consciousness of society members (Althusser, 1971).

  • Legal Power: This type of power is based on the ability to influence, create, or enforce laws and regulations. While closely related to political power, legal power specifically pertains to the judiciary, law enforcement, and the legal system's role in maintaining social order and resolving conflicts (Fuller, 1969).

  • Technological Power: With the advent of the digital age, technological power has become increasingly significant. It encompasses the ability to influence society through the creation, dissemination, and control of technology and information. This power is held by tech companies, innovators, and those who control information flows and data (Castells, 1996).

  • Military Power: Military power refers to the capacity to use force or the threat of force to influence domestic or international affairs. This form of power is typically wielded by the state but can also be held by non-state actors with significant military or paramilitary capabilities (Clausewitz, 1984).

Understanding the dynamics of societal power is crucial for analyzing social change, conflicts, and the ways in which societies organize themselves. It highlights the complex interplay between different power structures and the impact they have on individuals' lives and societal development.

AI's Influence on Societal and Organizational Power & Politics Dynamics

The capabilities of AI in data analysis, surveillance, and predictive modeling have the potential to significantly alter power dynamics, both within organizations and among states (Tegmark, 2017). By enabling a deeper understanding of massive datasets and predicting future trends with greater accuracy, AI technologies provide governments and powerful organizations new and dynamic insights into public behavior, economic shifts, and potential security threats. This enhanced capability can lead to more informed decision-making processes and, ideally, more effective governance (Mayer-Schönberger & Cukier, 2013). However, it also introduces the risk of centralizing power in the hands of those who control these advanced AI systems (O’Neil, 2016).

First, within organizations, AI's analytical capabilities enable a more nuanced understanding of internal risks and external competitive landscapes, fostering both protective measures and strategic planning (Brynjolfsson & McAfee, 2014). Across organizations, the adoption of AI in security protocols sets new standards for corporate espionage and data protection, pushing industries towards a preemptive stance on threats. In both the political and corporate realms, AI's role in forecasting and mitigating risks heralds a paradigm shift towards anticipatory governance and management, reshaping power dynamics by privileging entities that adeptly wield these advanced technological tools (Tufekci, 2015).

Similarly, at the broader political state level, the integration of AI into national security frameworks significantly enhances surveillance capabilities, transforming both the internal oversight within states and their international relations (Lyon, D. (2001). Internally, AI-driven surveillance strengthens a state's capacity to detect and monitor threats, improving the precision and efficiency of its security operations. Externally, the expanded surveillance reach facilitated by AI not only deepens a state's intelligence gathering beyond its borders but also influences global power dynamics by altering the balance of geopolitical knowledge and capabilities (Walsh, 2018).

Second, AI's role in economic forecasting and management is reshaping global economic power dynamics (Agrawal, et al, 2018). Countries and corporations that harness AI for economic analysis and forecasting can anticipate market changes, optimize resource allocation, and secure a competitive advantage. This can potentially widen the gap between the economically powerful and the less economically developed states or organizations, reinforcing existing inequalities (Lee, 2018).

Third, AI technologies exert a profound influence on both societal structures and organizational behaviors through their pivotal role in shaping political discourse, public opinion, and internal corporate culture (Tufekci, 2015). On the societal front, AI-driven social media algorithms, aimed at boosting user engagement, can inadvertently prioritize and amplify divisive content, significantly impacting electoral outcomes and public sentiment. This, with the strategic use of AI in disseminating disinformation campaigns, poses challenges to the integrity of democratic processes and risks deepening societal divides (Woolley & Howard, 2019).

Similarly, within organizations, AI's capacity for data analysis and pattern recognition is increasingly used to tailor internal communications, manage brand reputation, and influence stakeholder perceptions (Kaplan & Haenlein, 2019). This dual capacity can enhance organizational effectiveness but also necessitates careful management to avoid fostering internal divisions or misrepresenting organizational values to the public. The overarching impact of AI, therefore, spans the spectrum from enhancing connectivity and understanding to potentially undermining democratic values and organizational integrity, highlighting the need for vigilant oversight and ethical AI deployment in both public and private sectors (Diakopoulos, 2016; Mittelstadt, et al, 2016; Davenport & Ronanki, 2018).

Furthermore, the application of AI across both public policy-making and organizational strategy opens novel pathways for civic and stakeholder engagement, alongside participatory governance models. In the public sector, AI's analytical capabilities enable a more precise identification of societal needs, allowing for the simulation of policy impacts prior to their enactment and thereby enhancing the transparency and accountability of the governmental decision-making process (Eggers, et al, 2017).

Concurrently, within organizations, AI tools can streamline operations, tailor services to consumer demands more effectively, and foster a culture of inclusivity through data-driven insights into employee and customer experiences (Davenport & Ronanki, 2018). However, this growing dependence on AI underscores the critical need for rigorous frameworks that promote the ethical use of AI, safeguard against inherent biases in algorithms, and protect the privacy rights of both citizens and consumers. Ensuring these safeguards are in place is paramount to maintaining trust and integrity in AI's role within society and the business ecosystem alike (Mittelstadt, et al, 2016).

Lastly, the digital divide — the stark disparity in access to digital technologies — is magnified by the unequal distribution of AI's advantages, affecting both societal inclusivity and organizational equity (Norris, 2001). AI's potential to improve the quality of life and spur economic growth is substantial, yet its benefits are not evenly distributed across populations or within industries. This imbalance presents a formidable obstacle to achieving fair governance and fostering a cohesive society, while also challenging businesses to address internal disparities in technology access and literacy among their workforce and customer base (Eubanks, 2018). The situation underscores the urgent necessity for collaborative international efforts and comprehensive policy frameworks that not only bridge the technological divide but also ensure the equitable dissemination of AI's rewards, both within nations and among the global business community (Cath, et al, 2018).

Imagine a modern, brightly lit office environment where the TechForward Inc. team is engaging with a futuristic AI-driven platform named PowerShare. This platform is depicted as a large, interactive digital screen with an intuitive, user-friendly interface. On the screen, dynamic flowcharts and data visualizations represent the allocation of leadership roles and decision-making responsibilities, shifting fluidly based on project needs and team members' expertise. Around the screen, diverse employees collaborate, with some discussing projects in small groups while others engage with the platform, making suggestions or receiving guidance. The atmosphere is one of enthusiasm and mutual respect, highlighting a culture of collaboration. Visual cues such as icons or avatars on the screen indicate leadership roles, tasks, and team interactions, showcasing the system's ability to identify and adjust power dynamics and ensure a balanced distribution of responsibilities. The office space reflects an open and flexible work environment, with no rigid hierarchies or secluded offices, embodying the fluid and equitable distribution of power within the organization.

The Multiplier Effect in Action: AI, Power, & Politics

The integration of AI into power structures and political processes creates a multiplier effect, amplifying the reach and impact of political actions and decisions. The deployment of AI in analyzing vast datasets, enhancing surveillance capabilities, and forecasting future trends offers unprecedented opportunities for advancing governance and decision-making processes. However, this technological leap also necessitates a careful consideration of the ethical implications, as the centralization of power among those who control AI systems poses significant risks to democratic principles and equality. The balance between harnessing AI's potential for societal benefit while mitigating its risks requires vigilant oversight and a commitment to ethical deployment, highlighting the complex interplay between technology, power, and politics. Below is a case study that explores the power of AI to enhance power and politics to achieve positive outcomes for a fictional organization.

(The following is a fictional company and scenario).

In an innovative approach to organizational management, TechForward Inc., a burgeoning software development company, adopted a revolutionary AI-driven platform designed to distribute power and politics within its corporate structure based on practical, changing needs. Recognizing the challenges inherent in traditional hierarchical models—where power often becomes concentrated, leading to inefficiencies and discontent—TechForward sought to create a more fluid and equitable environment.

The AI platform, named PowerShare, was developed to allocate leadership roles and decision-making responsibilities dynamically, based on the specific needs of projects and the expertise of team members. Unlike conventional structures, PowerShare assesses ongoing projects and tasks, assigning leadership and decision-making power to those with the most relevant skills and experience for the task at hand. This system ensures that power and leadership is not static but flows to where it’s most needed, fostering a culture of collaboration and mutual respect.

PowerShare also features advanced monitoring capabilities to analyze power dynamics within the organization continually. By tracking decision-making patterns, leadership roles, and team interactions, the platform identifies instances where power becomes overly concentrated or when certain team members are underutilized. These insights enable proactive adjustments, ensuring that power imbalancesare addressed promptly, and all team members feel valued and engaged.

Moreover, PowerShare includes a coaching and recommendation system, offering personalized advice to employees on how to develop their leadership skills or effectively contribute to different projects. For those in temporary leadership positions, it suggests strategies to foster buy-in and support from their teams, ensuring that transitions in leadership are smooth and that every project leader has the backing they need to succeed.

For team members, PowerShare provids guidance on how to assert their ideas and expertise positively, helping them gain visibility and influence in project decisions. This aspect of the platform is particularly beneficial in nurturing a sense of agency among employees, encouraging them to take active roles in project leadership and decision-making.

TechForward's implementation of PowerShare revolutionizes how power and politics operate within the company. By ensuring that power is task-specific, dynamically allocated, and monitored for balance, the organization has seen significant improvements in productivity, employee satisfaction, and innovation. PowerShare exemplifies how AI can be used to foster positive outcomes in organizational power dynamics, creating a more adaptive, inclusive, and effective corporate culture (Based partially on Conversations with OpenAI’s ChatGPT, March, 2024).

AI's role in transforming power dynamics and political processes represents a critical area of exploration for policymakers, technologists, and citizens alike. By harnessing the multiplier effect of AI, political systems can achieve greater efficiency, inclusivity, and responsiveness. However, realizing these benefits requires a commitment to ethical principles, transparency, and public engagement to ensure AI technologies enhance rather than undermine democratic values and human rights.

How might Future Point Digital help your organization reimagine the art of the possible with respect to new ways of working, doing, thinking, and communicating via emerging technology? Follow us at: www.futurepointdigital.com, or contact us at info@futurepointdigital.com.

About the Author: David Ragland is a former senior technology executive and an adjunct professor of management. He serves as a partner at FuturePoint Digital, a research-based technology consultancy specializing in strategy, advisory, and educational services for global clients. David earned his Doctorate in Business Administration from IE University in Madrid, Spain, and a Master of Science in Information and Telecommunications Systems from Johns Hopkins University. He also holds an undergraduate degree in Psychology from James Madison University and completed a certificate in Artificial Intelligence and Business Strategy at MIT. His research focuses on the intersection of emerging technology with organizational and societal dynamics.

References

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.

Althusser, L. (1971). Ideology and ideological state apparatuses. In L. Althusser (Ed.), Lenin and philosophy and other essays (pp. 127-186). Monthly Review Press.

Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Harvard University Press.

Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, 47(6), 795-817.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.

Burt, R. S. (1992). Structural holes: The social structure of competition. Harvard University Press.

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence and the ‘Good Society’: The US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505-528.

Castells, M. (1996). The rise of the network society. Blackwell Publishers.Conger, J. A., & Kanungo, R. N. (1988). The empowerment process: Integrating theory and practice. Academy of Management Review, 13(3), 471-482.

Clausewitz, C. von. (1984). On war (M. Howard & P. Paret, Trans.). Princeton University Press. (Original work published 1832)

Cross, R., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Harvard Business School Press.

Dahl, R. A. (1961). Who governs? Democracy and power in an American city. Yale University Press.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62.

Easton, D. (1957). An approach to the analysis of political systems. World Politics, 9(3), 383-400.

Eggers, W. D., Schatsky, D., & Viechnicki, P. (2017). AI-Augmented government: Using cognitive technologies to redesign public sector work. Deloitte Insights.

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.

French, J. R. P., & Raven, B. (1959). The bases of social power. In D. Cartwright (Ed.), Studies in social power (pp. 150-167). Institute for Social Research.

Fuller, L. L. (1969). The morality of law (Revised ed.). Yale University Press.March, J. G., & Olsen, J. P. (1989). Rediscovering institutions: The organizational basis of politics. The Free Press.

Lee, K.-F. (2018). AI Superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin Harcourt.

Marx, K., & Engels, F. (1848). The Communist Manifesto.

Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. John Murray.

Mills, C. W. (1956). The Power Elite. Oxford University Press.Mintzberg, H. (1985). The organization as political arena. Journal of Management Studies, 22(2), 133-154.

Lasswell, H. D. (1936). Politics: Who gets what, when, how. Whittlesey House.

Lyon, D. (2001). Surveillance society: Monitoring everyday life. Open University Press.

Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21.

Oatley, T. (2019). International political economy (6th ed.). Routledge.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

Piketty, T. (2014). Capital in the twenty-first century. Harvard University Press.

Pfeffer, J. (1981). Power in organizations. Pitman.

Pfeffer, J. (1992). Managing with power: Politics and influence in organizations. Harvard Business School Press.

Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row.

Raven, B. H. (1992). A power/interaction model of interpersonal influence: French and Raven thirty years later. Journal of Social Behavior and Personality, 7(2), 217-244.

Robbins, S. P., & Judge, T. A. (2019). Organizational behavior (18th ed.). Pearson.

Schein, E. H. (2010). Organizational culture and leadership (4th ed.). Jossey-Bass.

Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations.

Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.

Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13(2), 203-218.

Tushman, M. L., & Scanlan, T. J. (1981). Boundary spanning individuals: Their role in information transfer and their antecedents. Academy of Management Journal, 24(2), 289-305.

Walsh, J. I. (2018). The eye of the beholder: The impact of counterterrorism on the interpretation of surveillance. Intelligence and National Security, 33(4), 582-597.

Weber, M. (1947). The theory of social and economic organization. (A. M. Henderson & T. Parsons, Trans.). Oxford University Press. (Original work published 1922). Wendt, A. (1992). Anarchy is what states make of it: The social construction of power politics. International Organization, 46(2), 391-425.

Woolley, S. C., & Howard, P. N. (Eds.). (2019). Computational propaganda: Political parties, politicians, and political manipulation on social media. Oxford University Press.

Yukl, G. (2012). Leadership in organizations (8th ed.). Pearson Education.