Markus & Robey: Imperatives in Information Technology (IT) and Organizational Change

1. Technological Imperative
   • Core Idea: Technology is a powerful, deterministic force driving organizational change, making it the independent variable in organizational outcomes. In this view, technology is seen as an exogenous factor that forces organizations to adapt in specific ways, often without much flexibility or control by users.
   • Mechanism: Technology, once introduced, constrains and directs organizational behavior. Here, IT dictates change by enabling certain structures and interactions while limiting others.
   • Example: The prediction from the 1960s and 70s that technology, like email, would flatten organizational hierarchies, making organizations less dependent on middle management layers. The assumption was that technology’s inherent capabilities (such as direct communication with higher management) would naturally lead to structural changes in organizations.
   • Modern Context: In today’s context, AI might represent a technological imperative if AI-driven automation forces organizations to restructure around its capabilities, limiting user control and reinforcing a top-down effect where the technology, not human choice, primarily drives change.
2. Organizational Imperative
   • Core Idea: Technology’s role is shaped by conscious organizational decisions and strategic objectives, making technology a dependent variable that aligns with the organization’s goals and needs. Here, the organization is the causal agent, with managers and designers using IT intentionally to achieve certain objectives.
   • Mechanism: The organizational imperative assumes almost unlimited choice over how technology is applied. It reflects an understanding that technology can be tailored, designed, and implemented to fit organizational needs, often reflecting specific requirements dictated by the organization itself.
   • Example: Information Processing Theory proposes that organizations design their structures, including IT systems, to match their information processing needs. When faced with uncertain environments, organizations may adopt IT systems that help manage and reduce uncertainty, aligning with organizational objectives.
   • Related Model: The Technology Acceptance Model (TAM) is aligned with this imperative, as it examines how perceptions of usefulness and ease of use influence technology adoption within organizational contexts. TAM suggests that adoption depends on the organizational context and user perception rather than the inherent characteristics of the technology.
3. Emergent Perspective
   • Core Idea: Outcomes of technology use emerge from interactions between technology, users, and their context. This perspective sees IT’s impact as neither fully driven by the technology (as in the technological imperative) nor solely by the organization’s intentions (as in the organizational imperative). Instead, it emerges dynamically from complex, context-dependent interactions.
   • Mechanism: IT use and its outcomes are contextually constructed, varying significantly based on the setting, user actions, and the organizational environment. This model suggests that the same technology can have different effects in different contexts, influenced by organizational culture, user adaptability, and local factors.
   • Example: An ERP system might lead to improved efficiency in one hospital due to positive user engagement and supportive leadership, but face resistance and underutilization in another due to differences in organizational culture and staff dynamics.
   • Use in Research: This perspective aligns well with interpretive studies that focus on understanding user experiences within a specific context. The emergent perspective allows for the recognition that technology outcomes are not universal but shaped by situational factors.

Process vs. Variance Theories

1. Process Theory
   • Core Idea: Process theories emphasize sequences and stages where one stage enables the next but does not guarantee it. Each step is necessary for the following one but not sufficient to ensure its occurrence.
   • Mechanism: In process theories, causality is understood as necessary conditions, where progression follows a path or “recipe” of stages. Each stage is required to reach the next, though some organizations may halt progress partway.
   • Example: Nolan’s Stages of Growth, a six-stage model (initiation, contagion, control, integration, data administration, maturity), describes necessary steps in organizational IT maturity. Each stage is required for the next, but advancement is not guaranteed—an organization may stop at the “control” stage without moving on to “integration.”
   • Illustrative Analogy: Installing a fan by following a step-by-step guide represents a process theory. Each step is needed to move forward, but taking one step does not ensure the next will be completed.
2. Variance Theory
   • Core Idea: Variance theories focus on necessary and sufficient relationships, where the presence of a certain variable (X) reliably leads to an outcome (Y).
   • Mechanism: Variance theories operate on a static or cross-sectional basis, predicting outcomes through direct, consistent causal relationships. If X exists, Y will occur, as this theory assumes a dependable connection between cause and effect.
   • Example: Transaction Cost Theory posits that asset specificity (X) leads to increased transaction costs (Y). Here, asset specificity is a necessary and sufficient condition for higher transaction costs.
   • Comparison with Process Theory: Unlike process theories, variance theories assume that the presence of X will always result in Y, without the need for staged progression or temporal factors.

Integration of Process and Variance Theories

• Complementary Insights: Integrating variance and process models provides a fuller understanding by combining outcome predictability with insights into causal mechanisms. For example, while a variance model may show a strong relationship between asset specificity and transaction costs, a process model could explore the stages or conditions that influence this relationship.
• Challenges: Combining these models is complex and resource-intensive, often requiring both qualitative (process analysis) and quantitative (variance analysis) methods.

Levels of Analysis: Macro, Micro, and Mixed-Level Approaches

1. Macro-Level Analysis: Focuses on broad impacts, often at the organizational or societal level. For instance, studies of IT investment creating organizational value operate at this level, examining large-scale effects of technology across entire organizations or industries.
2. Micro-Level Analysis: Looks at individual or small group behaviors, such as using TAM to study individual user acceptance of new technology.
3. Mixed-Level Analysis: Combines both levels, exploring how individual behaviors (micro) aggregate to influence broader organizational outcomes (macro).
   • Example: IT investments contribute to organizational value (macro level), but the adoption and effective use by individuals (micro level) mediate this effect. A mixed-level analysis reveals the interplay where individual usage influences overall value creation, linking micro-level actions with macro-level outcomes.

Applying the Imperatives to Modern Technology, e.g., AI

• Framing AI with the Technological Imperative: If AI is viewed as an inflexible force driving inevitable changes, it aligns with the technological imperative. Organizations must adapt to AI’s capabilities, with AI largely dictating new structures and workflows.
• Framing AI with the Organizational Imperative: Organizations that deliberately design AI applications to meet specific goals follow an organizational imperative, shaping AI to fit into pre-existing objectives rather than allowing it to dictate change.
• Framing AI with the Emergent Perspective: Here, AI’s impact is contextually dependent. Different organizations experience AI’s effects differently based on internal culture, staff adaptability, and user attitudes, resulting in varied outcomes across similar AI implementations.

Summary and Key Takeaways

• Causal Agency: Differentiating between technological, organizational, and emergent perspectives helps clarify the role of IT in shaping organizational change.
• Process vs. Variance Theories: Recognizing when conditions are necessary but not sufficient (process) vs. necessary and sufficient (variance) is critical for research design.
• Levels of Analysis: Mixed-level analysis is particularly useful in capturing the interactions between individual actions and broader organizational impacts, providing a more complete view of technology’s role.
• Comprehensive Approaches: Although variance models dominate in research, incorporating process theories offers deeper insights into the dynamics of change, enhancing understanding of both “what” and “why” in organizational IT studies.

This version maintains your original notes’ structure while supplementing with key details and examples from the paper to build a more complete picture of each imperative and theory.