The field of international business is focused on understanding how things change and develop over time, especially through ongoing actions and events. One of the main topics studied is how companies expand internationally, which involves understanding the steps they take and how their strategies evolve. However, some researchers have criticized the way these processes are studied, arguing that the methods used don’t always fully capture the changes over time or use long-term data.
Another area of interest is how relationships between company headquarters and their foreign branches evolve. This includes studying tensions, like when headquarters want control but local branches want independence, or when branches need to balance the demands of both their headquarters and local markets.
A third focus is how multinational companies (MNEs) deal with political and social pressures in different countries. This is especially important as these companies face growing concerns about their impact on society and global issues like climate change.
Researchers are calling for more studies that focus on these processes, but studying them can be tricky because they involve big-picture, multi-level complexities. This paper aims to provide tools and methods to help researchers study these processes effectively, while also discussing their pros and cons.
The paper starts by explaining what process research means, highlights the challenges in international business, and provides examples of good process studies. It then identifies the key elements of high-quality process research.
Variance Theorizing: An In-Depth Explanation
You can think of variance theorizing as the “normal” or traditional approach to theorizing in many fields, especially in social sciences and management. It’s the most common method researchers use when they want to explain cause-and-effect relationships by focusing on measurable variables and testing hypotheses.
For example:
- If you want to study how training (X) affects employee performance (Y), you’ll use variance theorizing.
- If you’re examining how customer satisfaction (X) impacts loyalty (Y), it’s variance theorizing.
Variance theorizing is a widely used approach in social science research, particularly in fields like management, psychology, and economics. It explains phenomena by identifying relationships between causes (independent variables) and effects (dependent variables). This approach assumes that phenomena can be understood through statistical associations or causal links, often expressed in “if X, then Y” terms. Below, we break down its principles, methods, strengths, and limitations.
Core Principles of Variance Theorizing
- Variables as the Foundation:
- Complex phenomena are simplified into measurable variables (e.g., “trust,” “absorptive capacity,” “institutional distance”).
- These variables are treated as stable and discrete constructs.
- Focus on Static Relationships:
- Variance theorizing captures relationships at a specific point in time or as aggregated snapshots, often overlooking how things unfold over time.
- Generalizability:
- The goal is to identify universal patterns or laws that can apply across contexts (e.g., “Trust increases knowledge transfer”).
- Predictive Emphasis:
- It focuses on predicting outcomes (dependent variables) based on inputs (independent variables).
- Quantitative Orientation:
- Statistical methods, such as regression or structural equation modeling, are central to variance studies.
An Example in Practice
Let’s consider practice transfer in multinational enterprises (MNEs). A variance study might:
- Identify predictors (independent variables):
- Factors like trust, absorptive capacity, or institutional distance.
- Define outcomes (dependent variable):
- The success of the transfer, measured by adoption rate or performance improvement.
- Examine moderators:
- For example, the type of subsidiary (greenfield vs. acquired) could influence how predictors affect outcomes.
The study might conclude that trust and absorptive capacity positively impact transfer success, but institutional distance weakens this effect.
Key Methodology in Variance Studies
- Hypothesis Testing:
- Researchers develop hypotheses (e.g., “Higher trust improves transfer success”).
- Variable Measurement:
- Variables are operationalized through surveys, archival data, or other sources.
- Statistical Analysis:
- Techniques like regression or ANOVA quantify relationships between variables.
- Control Variables:
- Extraneous factors (e.g., firm size, industry) are controlled for more accurate causal testing.
Strengths of Variance Theorizing
- Precision:
- The use of quantifiable relationships ensures clear, testable hypotheses.
- Generalizability:
- Large datasets enable findings to apply across multiple contexts.
- Predictive Utility:
- Useful for making actionable predictions (e.g., “Improving trust leads to better outcomes”).
Limitations of Variance Theorizing
- Lack of Temporal Insight:
- It ignores how phenomena develop over time (e.g., the process of building trust).
- Reductionism:
- Simplifies complex phenomena into variables, losing contextual richness.
- Assumption of Stability:
- Treats variables as static, overlooking their evolution or interaction.
- Neglect of Context:
- Universal patterns may fail to capture situational nuances (e.g., cultural differences).
Variance vs. Process Theorizing
| Aspect | Variance Theorizing | Process Theorizing |
| Focus | Relationships between variables | Sequences of events or activities over time |
| Unit of Analysis | Variables (e.g., X and Y) | Events, interactions, and temporal pathways |
| Time | Static or aggregated | Dynamic and unfolding |
| Goal | Prediction and generalization | Explanation of how and why phenomena occur |
| Methods | Quantitative (e.g., surveys, experiments) | Qualitative (e.g., case studies, ethnography) |
When Is Variance Theorizing Appropriate?
Variance theorizing works best when:
- Phenomena are stable and predictable: For example, routine processes or behaviors.
- Causal claims need testing: Such as identifying the effect of a specific variable.
- Generalization is key: When broad patterns matter more than specific contexts.
In contrast, process theorizing is more suitable for dynamic or complex phenomena like organizational change or innovation, where understanding temporal and contextual nuances is critical.
Critiques in Practice
In their review of MNE research, Fortwengel et al. (2023) noted that while practice transfer is inherently about processes (e.g., involving negotiation, conflict, and adaptation), most studies relied on variance models. This oversimplifies a dynamic phenomenon into static relationships, failing to capture the sequence of interactions and events that drive real-world outcomes.
Variance theorizing is a powerful approach for identifying patterns and testing hypotheses. However, it struggles with capturing temporal and contextual complexity. To address this, researchers are increasingly combining variance and process approaches—such as using longitudinal or mixed-method designs—to achieve both predictive accuracy and rich, nuanced explanations.
Sure! Here’s an expanded explanation:
Process theorizing is about understanding the journey—it looks at how things happen over time, rather than just the end result or static relationships between variables. Instead of merely identifying that “trust leads to adoption” (a cause-and-effect relationship), process theorizing digs into the details, asking questions like:
- How does trust develop?
- What actions, decisions, or events build trust?
- At what point does trust start influencing adoption?
- What factors or conditions accelerate or hinder this process?
It breaks the phenomenon into a timeline of events, showing how each step connects to the next and contributes to the overall outcome.
Example: Trust and Technology Adoption
Imagine a company is introducing new software for employees. Instead of just studying whether trust in management (the cause) leads to successful adoption (the effect), process theorizing would look at the sequence of events that shape this outcome. It might involve:
- Initial Hesitation: Employees are skeptical about the new software and don’t immediately adopt it.
- Management’s Efforts: Leaders hold meetings to explain the benefits of the software and address concerns.
- Early Adoption: A few employees try the software and report positive experiences.
- Peer Influence: Those early adopters share their success, encouraging others to try it.
- Widespread Adoption: Trust in management grows as employees see the promised benefits, leading to full adoption.
Each of these steps shows how trust doesn’t just magically lead to adoption but is built over time through communication, small wins, and peer influence. Process theorizing captures this timeline and explains the “how” and “why” behind the change.
Key Characteristics of Process Theorizing
- Focus on Dynamics Over Time:
- Instead of treating trust and adoption as fixed variables, it studies how they interact and evolve through sequences of actions.
- Interdependence:
- It recognizes that events and decisions are connected. For example, how management communicates might directly impact how quickly trust develops and influences adoption.
- Contextual Sensitivity:
- Process theorizing pays attention to the specific environment or conditions (e.g., company culture, team dynamics, or external pressures) that shape how things unfold.
- Explains Change and Development:
- While variance theorizing might say, “If there’s high trust, adoption will happen,” process theorizing asks, “What happens between the start (low trust) and the end (full adoption)?”
When Process Theorizing is Crucial
Process theorizing is particularly valuable in situations where:
- Time is a Key Factor: Understanding how things evolve step-by-step is critical (e.g., the stages of a company’s international expansion).
- Change is Complex: When the outcome depends on interactions between multiple actors or events (e.g., cultural integration during mergers).
- Context Matters: When external factors or unique circumstances influence the process (e.g., how trust-building differs across cultures).
How It Differs From Variance Theorizing
| Aspect | Variance Theorizing | Process Theorizing |
| Focus | Relationships between variables (X → Y) | Sequences of actions/events over time |
| Question | “Does trust lead to adoption?” | “How is trust built, and how does it lead to adoption?” |
| Approach | Statistical analysis of cause-effect relationships | Narrative or timeline-based explanation |
| Goal | Prediction and generalization | Explanation of dynamics and changes |
Conclusion
Process theorizing gives us a deeper, more detailed understanding of how things happen. It’s especially useful for studying complex, dynamic situations where the steps and interactions matter. By focusing on the sequence of events, it not only explains the outcome but also helps us learn how to replicate or influence the process in the future.
What Variance Theorizing Does Well
Imagine you’re trying to figure out why something happens, like why some people succeed at work while others don’t. Variance theorizing looks at specific factors (causes) that might explain this success and connects them to the outcome (effect) in a straightforward way. It’s like saying:
- Example: “People who get more training (cause) perform better at work (effect).”
To study this, you’d measure:
- Training (how many hours or sessions someone attended).
- Performance (like their sales numbers or job ratings).
You’d then use statistics to show that more training is linked to better performance. That’s variance theorizing—it focuses on identifying these static cause-and-effect relationships.
Why Variance Theorizing Isn’t Always Enough
Now, think about something more complex, like how trust develops in a team over time. Trust isn’t something that happens instantly—it’s built through a series of actions, events, and experiences, like:
- Step 1: A manager gives team members more responsibility.
- Step 2: Team members complete tasks successfully.
- Step 3: The manager praises them, and trust grows.
Variance theorizing doesn’t explain these steps or the sequence of events. It would simply say, “Higher trust leads to better teamwork,” without digging into how trust actually develops or unfolds over time.
What Process Theorizing Does Differently
Process theorizing is like telling a story. Instead of focusing only on the “cause and effect,” it explains the journey—the steps, activities, or events that happen along the way. For example:
- Variance theorizing might say: “High trust improves teamwork.”
- Process theorizing would ask: “How is trust built in the first place? What actions, choices, or events lead to trust over time?”
It looks at:
- The sequence of events (e.g., first the manager assigns responsibility, then trust builds gradually).
- The context in which things happen (e.g., cultural differences, organizational rules).
When Variance Theorizing Falls Short
Variance theorizing works great when you’re studying simple, predictable relationships, like:
- Does advertising increase sales?
- Does job satisfaction reduce employee turnover?
But it struggles with complex or dynamic phenomena, like:
- How does an organization adapt to major changes over time?
- How do conflicts within a multinational team evolve and get resolved?
These situations involve lots of moving parts, interactions, and events that unfold gradually. Variance theorizing can’t fully capture these complex processes because it looks at snapshots rather than the entire timeline or story.
Why Process Theorizing Complements Variance Theorizing
Think of it like this:
- Variance theorizing is like taking a photo: It gives you a clear picture of the relationship between two things at a specific moment in time.
- Process theorizing is like recording a video: It captures how things evolve and change over time.
For example, if you’re studying trust in teams:
- Variance theorizing might say: “Teams with high trust perform better.”
- Process theorizing would explain: “Trust starts with small tasks, grows as team members support each other, and is reinforced when leaders acknowledge success.”
By combining both approaches, you can understand both the big picture patterns (variance) and the step-by-step journey (process).
- Variance theorizing: Best for studying clear, static relationships (e.g., “If X happens, Y will result”).
- Process theorizing: Best for understanding dynamic, evolving phenomena (e.g., “How and why does X happen over time?”).
- Together, they give a more complete understanding of both what happens and how it happens.
Most research in management tends to rely heavily on variance theorizing, even when the phenomena being studied are better suited to process theorizing. For example, the topic of practice transfer within multinational companies (MNEs) inherently involves dynamic and ongoing processes—things that unfold over time through a series of interactions, adjustments, and negotiations. Yet, much of the existing research on this topic simplifies the complexity by focusing on static cause-and-effect relationships between variables.
A recent review highlighted this issue. It examined decades of research on practice transfer within MNEs and revealed that only 16% of the studies adopted a process perspective. In other words, the vast majority of research focused on identifying predictors, outcomes, and moderators (e.g., “How does trust affect transfer success?”), but it overlooked the actual events and sequences of activities involved in the transfer. This means most studies have not addressed how practices are transferred step-by-step, what obstacles arise during the transfer, or how actors adapt and respond over time.
For instance:
- A variance study might conclude that trust between headquarters and subsidiaries is positively linked to the success of practice transfer.
- However, a process study would examine the steps through which trust is built during the transfer, such as initial meetings to establish alignment, follow-up actions to demonstrate commitment, and the resolution of misunderstandings over time.
The absence of process-based studies leaves a significant gap in understanding the “how” and “why” behind these phenomena. This is problematic because many management topics—especially those involving human behavior, interactions, and organizational change—cannot be fully understood by reducing them to a set of variables. They require a more nuanced examination of the underlying dynamics, sequences, and context.
Why This Gap Exists
There are several reasons why variance theorizing has dominated research in this area:
- Ease of Measurement: It is simpler to measure variables (e.g., trust levels, institutional distance) and test their relationships using statistical models than to track and analyze a sequence of events.
- Established Research Norms: Quantitative studies based on variance theorizing are more common and often seen as more “rigorous” or “scientific,” making them the default choice in many fields.
- Complexity of Processes: Process studies require detailed, time-intensive data collection (e.g., longitudinal case studies, ethnographies) and often involve analyzing multi-level interactions across time and context, which can be methodologically challenging.
- Pressure for Generalizability: Variance studies, which often rely on large samples and statistical methods, are better suited for generating broadly applicable conclusions, while process studies tend to focus on rich, detailed insights from specific cases.
Implications of This Gap
The lack of process-based studies in topics like practice transfer has real consequences for both theory and practice:
- Theoretical Incompleteness: Without understanding how practices are actually transferred, theories remain shallow, addressing only surface-level relationships instead of the underlying mechanisms.
- Practical Limitations: Practitioners (e.g., managers in MNEs) need actionable insights about what to do at each stage of practice transfer, but variance studies offer little guidance on handling the complexities of real-world processes.
- Missed Opportunities: Important phenomena like conflicts, adaptations, and feedback loops that emerge during the transfer process are ignored, leading to an oversimplified view of organizational dynamics.
Opportunities for Improvement
This review highlights the need for more process-based research to complement variance studies and provide a fuller picture of complex phenomena like practice transfer. Process theorizing can:
- Illuminate the Sequence of Events: By studying the steps and interactions that occur during practice transfer, researchers can uncover critical turning points, such as moments where trust is built or conflicts arise.
- Capture Contextual Nuances: Process studies can reveal how factors like cultural differences or industry norms shape the transfer process.
- Provide Actionable Insights: Understanding the “how” and “why” behind successful transfers can help managers design better strategies for navigating similar processes.
While variance theorizing remains valuable for identifying broad patterns and testing cause-and-effect relationships, it falls short in capturing the dynamics and complexities of phenomena like practice transfer. Topics like these are inherently processual, involving events and activities that unfold over time. The dominance of variance approaches has left a significant gap in understanding, with only a small fraction of studies adopting a process perspective. Addressing this gap through process-oriented research offers the potential for richer insights, more practical relevance, and a deeper understanding of the mechanisms driving organizational phenomena.
1. Synoptic View of Process: A Stable Organization That Changes
The synoptic view looks at an organization as something that exists over time and undergoes changes, but its core identity remains the same. Think of it like this:
- Example: Imagine a tree. Over the years, it grows taller, its branches spread out, and it sheds leaves in the fall and grows new ones in the spring. But no matter how much it changes, you still think of it as the same tree. It has a stable identity even though it’s evolving.
In the synoptic view, an organization (like a company or a school) is treated as a “thing”—something that exists independently. Processes like growth, restructuring, or adopting new technology are seen as events that happen to the organization over time, but the organization itself remains a fixed entity.
For example:
- A company might adopt new technologies or change its leadership team, but people still think of it as the same company—its name, brand, or mission give it a stable identity.
In research, this view assumes that we can study organizations as “objects” and observe how they change while remaining fundamentally the same.
2. Performative View of Process: Organizations as Dynamic and Ever-Changing
The performative view, on the other hand, sees an organization not as a stable, unchanging “thing” but as something that is constantly being created and recreated through ongoing actions, interactions, and decisions. It’s like saying:
- “An organization isn’t a ‘thing.’ It’s what people are doing every day that makes it exist.”
Here’s an analogy:
- Example: Think of a bonfire. It’s not a fixed “object”—it’s a process. The fire only exists because someone is adding logs, fanning the flames, and maintaining it. If the actions stop (no one adds logs or stirs the fire), the bonfire disappears.
In the performative view, organizations are like the bonfire. They only “exist” because people are constantly making decisions, interacting, and taking actions. These actions create and recreate the organization every single day.
For example:
- A company is only “alive” because employees show up, make decisions, interact with customers, and produce goods or services. Without those activities, there’s no company—it’s just an empty office.
Key Differences Between Synoptic and Performative Views
| Aspect | Synoptic View | Performative View |
| How it sees organizations | As stable entities that evolve over time. | As ongoing processes created through actions. |
| Focus | What happens to the organization over time (e.g., changes in structure or strategy). | What people do to make the organization exist daily (e.g., decisions, routines, activities). |
| Example | A company that expands into new markets but remains the same company. | A company that is constantly being “rebuilt” through employees’ daily work and decisions. |
| Time | Looks at snapshots of changes over time. | Focuses on the continuous flow of activities. |
How These Views Apply in Research
- Synoptic View in Research:
- Researchers using this view study organizations as fixed objects that change over time.
- Example: They might examine how a company’s leadership structure evolves after a merger or how adopting new technology impacts productivity.
- It’s like taking a series of photos of an organization at different times and comparing how it looked before and after a change.
- Performative View in Research:
- Researchers using this view study the everyday actions that create and sustain an organization.
- Example: They might look at how employees interact during meetings, how decisions are made in real-time, or how the organization’s culture is reproduced daily.
- It’s like filming a video that captures how people work, interact, and adapt to keep the organization running.
Why This Distinction Matters
This difference changes how we think about and study organizations.
- The synoptic view is better for studying big-picture changes or trends over time.
- The performative view is better for understanding the nitty-gritty, day-to-day dynamics of how organizations actually function.
For example:
- If you’re studying a company’s response to a crisis, the synoptic view might focus on what policies or strategies the company implemented to adapt.
- The performative view would dig into how employees communicated, what decisions were made in real-time, and how people adapted their behavior during the crisis.
A Final Analogy
Imagine a car:
- In the synoptic view, you see the car as an object that changes over time. You study when it gets a new engine, new tires, or a fresh coat of paint. It’s still the same car, but it evolves.
- In the performative view, you focus on what keeps the car running. It’s the constant actions of the driver turning the wheel, pressing the gas pedal, or filling the tank with fuel. Without those actions, the car wouldn’t move—it’s not just an object; it’s a process in motion.
Organizations, like cars, can be seen either as stable entities that evolve (synoptic) or as dynamic processes sustained by constant actions (performative).
Most process research in management relies on the synoptic view, where organizations are treated as stable entities that experience change over time. This view assumes that while organizations may evolve, their core structure or identity remains relatively intact. For example, when researchers study how a company restructures after a merger or how it adapts to a new market, they treat the organization as a constant “thing” that is undergoing changes. These changes are seen as events happening to the organization, such as implementing a new strategy, adopting new technology, or expanding into a new region.
This approach is useful for studying large-scale, long-term trends or shifts in organizational structure. It allows researchers to capture patterns and outcomes, like how leadership transitions impact performance or how digital transformation affects productivity. However, this view often misses the dynamic, lived experiences of the people within the organization who are actively shaping these changes on a day-to-day basis.
In contrast, the performative view of process is often adopted by ethnographers or researchers using qualitative methods. This view focuses on how organizations are constantly being created and recreated through daily actions, decisions, and interactions. Instead of treating organizations as stable entities, the performative view sees them as fluid and dynamic, where their existence depends on the ongoing work of the people involved.
For example:
- An ethnographer studying organizational culture might observe how employees’ daily behaviors, rituals, and interactions contribute to reproducing and reshaping that culture.
- They might examine how small actions—like the way meetings are conducted, how decisions are debated, or how employees interact in informal settings—collectively sustain or change the organization over time.
This approach captures the micro-level processes that drive the organization, focusing on the messy, real-time dynamics that are often overlooked in the synoptic view.
Why Ethnographers Use the Performative View
Ethnographers aim to understand the human side of organizations—how individuals and groups contribute to shaping their environment. Unlike the synoptic view, which tends to focus on broad outcomes or structural changes, the performative view digs deeper into the everyday practices and lived realities of the people who make up the organization.
For example:
- In a company undergoing a major change, the synoptic view might document the official steps of the change (e.g., announcing new policies, restructuring teams).
- The performative view, however, would explore how employees react to these changes, how they interpret and adapt to new policies, and how their daily work reflects or resists the intended transformation.
This view acknowledges that organizations are not just static entities responding to external forces—they are constantly evolving systems shaped by human actions.
A Complementary Relationship
While the synoptic view is dominant in process research, both views offer valuable insights. The synoptic view provides a macro-level perspective, showing how large-scale changes occur and what outcomes they produce. Meanwhile, the performative view offers a micro-level lens, revealing the intricate and dynamic processes that drive those changes from within.
For example, in studying how a company implements a new technology:
- The synoptic view might focus on the technology adoption process as a series of planned stages: identifying the need, selecting a solution, and rolling it out across teams.
- The performative view would examine how employees interact with the technology on a daily basis, what challenges they face, and how they adapt their workflows to integrate it effectively.
Combining both views allows researchers to paint a more complete picture of organizations, showing both the big picture changes and the nuanced, everyday realities that shape and sustain those changes.
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