updates

Whetten (1989): “What Constitutes a Theoretical Contribution?”

Whetten provides a framework for identifying strong theories. A good theory answers four questions:

1. What: What are the main ideas or concepts?
2. How: How are these concepts connected?
3. Why: Why do these connections exist? What logical reasoning supports them?
4. Who, Where, and When: What are the boundaries of the theory? Under what circumstances does it apply?

Whetten stresses that a theory must define its constructs and relationships clearly and include assumptions about where it applies. A good theory advances understanding by filling gaps in knowledge, improving existing ideas, or challenging current assumptions. For example, a theory of leadership should specify if it applies only to large organizations or all types of workplaces.

The “why” aspect is particularly important. It explains the logic behind the relationships, ensuring the theory is not just descriptive but also explanatory and predictive. Whetten emphasizes the importance of boundaries—without them, the theory may lack focus or applicability.

For exams, you should critique theories for clarity, completeness, and contribution. Check if they adequately define constructs, explain relationships, and identify assumptions and limits.

2. Bacharach (1989): “Organizational Theories: Some Criteria for Evaluation”

Bacharach argues that a theory must meet two key criteria:

1. Falsifiability: The theory must be testable. Can it be proven right or wrong?
2. Utility: The theory must explain or predict something meaningful.

Theories are built on:

• Constructs: The main ideas or concepts (e.g., “motivation”).
• Relationships: How these constructs are connected (e.g., “motivation leads to higher productivity”).

Bacharach stresses that theories must also define boundaries. For example, a theory about teamwork may apply to office settings but not remote work. This clarity ensures theories are relevant and applicable.

Bacharach also distinguishes theories from models. While a model illustrates relationships, a theory explains why those relationships exist. For instance, a model might show that motivation affects performance, but the theory must explain how and why this happens.

For exams, focus on critiquing theories using Bacharach’s criteria. Ensure they are testable, useful, and clearly defined in scope.

3. Sutton & Staw (1995): “What Theory is Not”

Sutton and Staw clear up common misconceptions about what constitutes a theory. A theory is not:

• Data: While important, data is evidence, not a theory.
• Variables: Listing variables doesn’t explain anything on its own.
• Hypotheses: These are derived from theories but don’t substitute for them.
• References: Citing prior work supports a theory but doesn’t create one.
• Diagrams: Visual aids help illustrate ideas but don’t explain why relationships exist.

A true theory provides an explanation of why and how something happens. For example, data may show that teamwork leads to better performance, but a theory must explain why—perhaps because teamwork improves communication or creates a shared sense of purpose.

The authors emphasize that theory must integrate constructs and relationships to explain causal mechanisms. For exams, you should identify when a paper lacks a true theory and focus on its explanatory depth.

4. Weick (1995): “What Theory is Not, Theorizing Is”

Weick argues that theorizing is a continuous process of developing and refining ideas. He emphasizes that theories don’t emerge fully formed but evolve through observation, experimentation, and reflection. Theorizing begins with identifying patterns in the real world, proposing explanations, and then testing and revising those explanations.

The process involves abduction (generating hypotheses), sensemaking (interpreting patterns), and iteration (improving theories over time). For example, early ideas about leadership might focus on communication but later expand to include trust and cultural differences.

Weick also highlights the creative side of theorizing. It involves taking risks, combining ideas, and embracing uncertainty. He contrasts this with static views of theory, which focus only on the final product.

For exams, discuss why theorizing is an ongoing process and how iteration leads to better theories. Highlight the importance of creativity and refinement in theory development.

5. Suddaby (2010): “Construct Clarity in Theories of Management and Organization”

Suddaby emphasizes that constructs are the building blocks of theories, and they must be clear. A construct like “leadership” or “culture” must:

1. Be well-defined: Everyone should understand what the construct means.
2. Have boundaries: Specify what it includes and excludes.
3. Be measurable: It should be possible to observe and measure the construct in practice.

The paper critiques the use of vague constructs that are trendy but lack clear definitions. For example, “innovation” must be defined (e.g., new products, ideas, or processes) to avoid confusion. Constructs with unclear boundaries are difficult to test or apply in real-world contexts.

For exams, be prepared to critique constructs in theories. Identify unclear ideas and suggest ways to make them more precise and useful.

6. Healy (2015): “F$#! Nuance”

Healy argues that theories should focus on clarity and general applicability rather than excessive nuance. While nuance can add depth, overloading a theory with too many exceptions or details can make it less useful. A simple, clear explanation is often more effective.

For example, “trust improves teamwork” is a straightforward and actionable theory. Adding too many conditions or exceptions, like “trust only works in small teams under certain conditions,” can dilute its impact. Healy advocates for prioritizing clarity over unnecessary complexity.

For exams, explain how simplicity enhances a theory’s usefulness and why over-complication can undermine its value.

7. Feldman (1994): “The Decision to Retire Early”

Feldman identifies three key factors influencing early retirement:

1. Financial: Can the person afford to retire?
2. Organizational: Are they satisfied at work, or do policies encourage early retirement?
3. Personal: Do family, health, or other personal factors play a role?

The paper also discusses bridge jobs, part-time roles that help individuals transition into full retirement. These jobs allow retirees to stay active and maintain a sense of purpose while reducing work demands.

For exams, know these factors and how organizations can design retirement policies that consider employee needs and preferences.

8. Grover & Malhotra (2003): “Transaction Cost Framework in Operations and Supply Chain Management”

This paper applies Transaction Cost Theory (TCT) to explain outsourcing decisions. TCT suggests that companies outsource tasks when the cost of doing them internally is higher than outsourcing. Key factors include:

• Coordination costs: Managing the task internally.
• Control costs: Ensuring quality and performance.

For instance, a company might outsource IT services to reduce costs and focus on core activities. TCT also helps explain supply chain decisions, such as whether to produce goods in-house or source them from external suppliers.

For exams, discuss how TCT applies to outsourcing and supply chain strategies, using real-world examples.

9. Orlikowski & Baroudi (1991): “Studying IT in Organizations”

This paper outlines three research approaches for studying IT:

1. Positivist: Focused on testing hypotheses and finding patterns in data.
2. Interpretive: Understanding how people interact with IT systems in their unique contexts.
3. Critical: Examining how IT systems influence power dynamics and inequalities.

Each approach offers different insights. For example, positivist research might show that IT improves efficiency, while interpretive research explores how employees adapt to new systems.

For exams, compare these approaches and discuss when each is most useful.

10. Lee (1991): “Integrating Positivist and Interpretive Approaches”

Lee proposes combining positivist and interpretive methods to create more robust research. He outlines three levels:

1. Subjective understanding: What individuals think or feel.
2. Interpretive analysis: How researchers analyze those thoughts and feelings.
3. Positivist testing: Validating findings with empirical data.

For example, a study might use interviews (interpretive) to understand employee attitudes and surveys (positivist) to confirm these findings. For exams, explain how this integration improves research quality.

Russell L. Ackoff: “Management Misinformation Systems” (1967)

Russell L. Ackoff challenges the effectiveness of traditional Management Information Systems (MIS), arguing that they often misdirect managerial decision-making by providing too much irrelevant data. He critiques five prevalent assumptions in MIS design: (1) managers always need more information, (2) they know what information they need, (3) MIS can automatically provide this information, (4) better communication improves decision-making, and (5) all managerial problems can be solved quantitatively.

Ackoff introduces the concept of misinformation systems, where systems fail by overwhelming managers with unfiltered or irrelevant information, leading to confusion rather than clarity. He argues that information should be actionable, focusing on quality rather than quantity. MIS should aim to eliminate noise and provide decision-makers with data tailored to their specific needs.

His critique underlines the importance of aligning MIS outputs with the cognitive and operational needs of managers. He calls for systems that facilitate decision support, emphasizing timeliness, relevance, and simplicity. Ackoff’s insights remain applicable in modern contexts, particularly in addressing challenges like information overload and ensuring MIS designs prioritize user-centricity.

This paper has had a lasting impact on how MIS is conceptualized, steering it away from merely automating information delivery toward enhancing decision-making processes. Ackoff’s arguments have also influenced the development of Decision Support Systems (DSS), which aim to synthesize complex data into actionable recommendations.

2. Jay R. Galbraith: Organization Design (1977) & “Organization Design: An Information Processing View” (1974)

Jay R. Galbraith’s work provides a comprehensive framework for understanding how organizations process information to manage uncertainty and complexity. In his 1974 paper and 1977 book, he argues that organizations are fundamentally information-processing systems, where the design choices impact how well they adapt to environmental demands.

Galbraith introduces the Information Processing View, positing that organizations face two main challenges: (1) managing uncertainty and (2) achieving coordination. Effective design involves balancing information demand and processing capacity. He identifies several mechanisms to handle complexity, such as task specialization, hierarchies, rules, decentralization, and lateral relations like cross-functional teams.

Central to Galbraith’s theory is the idea that as uncertainty increases, so does the need for greater information processing. Organizations must adapt by either reducing their information needs (e.g., creating clear rules) or increasing their processing capacity (e.g., through IT systems or team-based communication).

This work is influential in linking organizational structure to MIS. It highlights how information systems can enhance an organization’s capacity to handle complexity. For example, IT systems can facilitate real-time communication or provide data analytics to reduce uncertainty in decision-making.

Galbraith’s contributions provide foundational insights for MIS researchers, emphasizing the role of information flows in organizational design. His framework remains relevant for designing information systems that align with organizational goals, particularly in dynamic environments.

3. Peter G. W. Keen: “MIS Research: Reference Disciplines and a Cumulative Tradition” (1980)

Peter Keen’s work addresses the academic foundations of MIS, advocating for its establishment as a distinct and cumulative discipline. He argues that MIS should integrate insights from multiple reference disciplines—including economics, psychology, computer science, and organizational theory—while developing its own unique body of knowledge.

Keen critiques the fragmented nature of MIS research in its early years, emphasizing the importance of building a cumulative tradition. A cumulative approach requires researchers to build on prior work, creating theoretical frameworks that can guide future studies. This approach helps avoid redundancy and provides a clearer path for advancing the field.

He also highlights the interdisciplinary nature of MIS, arguing that its strength lies in its ability to bridge technical, organizational, and behavioral aspects of information systems. Keen emphasizes the need for practical relevance in MIS research, ensuring that theoretical advancements address real-world organizational challenges.

This paper provides a roadmap for the maturation of MIS as an academic field. Keen’s insights have influenced how MIS research is conducted, particularly in fostering collaboration across disciplines and ensuring the development of robust, theoretically grounded frameworks.

4. William R. King: “Strategic Planning for Management Information Systems” (1978)

William R. King’s paper underscores the importance of aligning MIS with an organization’s strategic goals. He introduces a strategic planning framework for MIS, emphasizing the need for proactive design and management of information systems to support long-term organizational objectives.

King outlines a step-by-step process for strategic MIS planning, which involves (1) understanding the organization’s strategy, (2) assessing current and future information needs, (3) aligning IT resources with strategic priorities, and (4) evaluating the effectiveness of MIS in achieving organizational goals. He stresses the importance of involving senior management in the planning process to ensure alignment between MIS and business strategy.

A key contribution of this paper is its focus on the role of MIS in creating a competitive advantage. King argues that well-designed MIS can enhance decision-making, improve efficiency, and enable innovation. He also highlights the need for flexibility in MIS design to adapt to changing organizational and environmental conditions.

This paper laid the foundation for the concept of strategic alignment, a central theme in modern IT and MIS research. King’s insights continue to guide organizations in leveraging information systems for strategic success.

5. Richard L. Nolan: “Managing the Crises in Data Processing” (1979)

Nolan’s seminal work introduces the stages-of-growth model for data processing, describing how organizations evolve in their use of IT. He identifies six stages: Initiation, Contagion, Control, Integration, Data Administration, and Maturity. Each stage represents increasing sophistication and challenges in managing IT resources.

In the early stages, organizations face rapid adoption of IT (contagion) without adequate controls, leading to inefficiencies. As they progress, they implement formal governance structures (control) and integrate IT systems into core business processes. The final stages focus on optimizing IT’s strategic value through advanced data management and integration.

Nolan’s model highlights the crises organizations face at each stage, such as resource misallocation, resistance to change, and escalating costs. He provides practical recommendations for IT leaders to navigate these challenges, emphasizing the need for planning, control mechanisms, and alignment with organizational goals.

This framework has become a cornerstone in IT management, offering insights into how organizations can manage the growth and complexity of their IT systems effectively.

6. George P. Huber: “Cognitive Style as a Basis for MIS and DSS Designs: Much Ado about Nothing?” (1983)

Huber critically examines the emphasis on cognitive styles in MIS and DSS design, arguing that it is an unreliable and limited foundation. He identifies several flaws in cognitive style research, including measurement challenges, inconsistency in definitions, and weak empirical evidence linking cognitive styles to system effectiveness.

Huber contends that focusing on cognitive styles constrains system design, making it overly rigid and less adaptable to diverse user needs. Instead, he advocates for flexible systems that can adapt to various cognitive styles, enabling broader usability and effectiveness.

This critique shifted the focus of MIS and DSS research toward developing user-centric and adaptive systems, emphasizing functionality and flexibility over tailoring systems to specific cognitive profiles. Huber’s insights remain influential in guiding MIS design principles, particularly in today’s diverse and dynamic user environments.