Process research is about studying how things happen over time, especially the events, decisions, and activities that unfold in organizations. To do this, researchers rely on rich process data which is one of the three critical ingredients. This  includes a combination of memories, observations, and records. This section breaks down the key aspects of collecting and using process data in international business (IB), where complexity is amplified by global operations, cultural differences, and multiple layers of decision-making.

People’s Memories and Interpretations

One of the primary ways to collect rich process data is by tapping into people’s memories and interpretations. This method involves asking individuals to recall past events, describe their experiences, and share how they perceived or felt about the events. Here’s a detailed breakdown of what this entails and its significance in process research:

A. What Does This Data Include?

Data collected through people’s memories and interpretations captures two critical dimensions:

• Cognitive Reactions (Thoughts):
  • How individuals thought about a situation, including their understanding, reasoning, and judgments.
  • Example: A manager might explain their rationale behind a decision to adopt a new strategy or how they interpreted the reactions of their team.
• Emotional Reactions (Feelings):
  • How individuals felt about the events, including their emotional states like excitement, frustration, anxiety, or satisfaction.
  • Example: An employee might describe feeling anxious during a corporate restructuring or empowered after being given more responsibilities.

This type of data helps researchers uncover not just what happened, but why it mattered to the people involved and how they interpreted the process.

B. How is This Data Collected?

Researchers use several methods to gather people’s memories and interpretations:

• Interviews:
  
  • One-on-one conversations where researchers ask individuals to reflect on specific events or processes.
  • Example: An executive might be asked to describe how their team handled an international merger and what challenges they faced during the transition.
• Focus Groups:
  
  • Group discussions where participants share their collective experiences and perspectives.
  • Example: A focus group of subsidiary managers might discuss how they adapted to a new policy from headquarters.
• Personal Diaries or Journals:
  
  • Researchers analyze written accounts where individuals document their thoughts and emotions about ongoing processes.
  • Example: Employees might keep diaries about their experiences adapting to a new technology rollout over several months.

C. Why is This Data Valuable?

Collecting people’s memories and interpretations offers unique insights that other data sources, like archival documents or real-time observations, may miss. Here’s why:

• Subjective Understanding:
  
  • This data reveals how people made sense of events, including their personal interpretations, which may differ from the “official” narrative.
  • Example: A company’s official report might describe a policy change as successful, but employee interviews could reveal resistance, confusion, or dissatisfaction.
• Emotional Context:
  
  • People’s emotional reactions provide a deeper understanding of how processes were experienced at a human level, which can significantly influence the success or failure of initiatives.
  • Example: A frustrated employee’s feelings about a poorly communicated restructuring effort might explain why the change faced resistance.
• Uncovering Hidden Dynamics:
  
  • Interviews and reflections often reveal informal or behind-the-scenes dynamics that aren’t documented in official records.
  • Example: A manager might disclose how informal discussions among team members influenced a critical decision.

D. Example in Practice

Imagine a company that implemented a new global strategy requiring its subsidiaries to adopt a standardized process. To understand how this process unfolded, a researcher might:

• Interview Headquarters Executives: To gather their thoughts on why the strategy was introduced and how they expected it to work.
• Talk to Subsidiary Managers: To learn how they interpreted the new strategy and how they adjusted it to fit local conditions.
• Ask Employees for Personal Reflections: To capture how they felt about the changes—whether they embraced them, found them confusing, or resisted them altogether.

E. Challenges of Using People’s Memories

While this approach is valuable, it comes with limitations:

• Inaccurate Memories:
  
  • People may forget details or remember events incorrectly, especially if significant time has passed.
  • Example: An executive might misremember the exact timeline of decisions made during a crisis.
• Hindsight Bias:
  
  • Individuals may reconstruct events to align with what they now know, leading to biased interpretations.
  • Example: A manager might describe a failed project as “doomed from the start,” even if they initially supported it.
• Impression Management:
  
  • People often present themselves in a positive light, downplaying mistakes or exaggerating their contributions.
  • Example: An employee might overstate their role in the success of a project to appear more competent.

F. Mitigating These Challenges

Researchers use several strategies to overcome these limitations and ensure the reliability of the data:

• Cross-Checking with Other Sources:
  
  • Comparing interview data with archival records, meeting minutes, or other participants’ accounts to validate details.
  • Example: An employee’s claim about a delayed decision can be cross-checked with email logs or official timelines.
• Focus on Facts Over Opinions:
  
  • Asking specific, fact-based questions to avoid overreliance on interpretations.
  • Example: Instead of asking, “Why do you think the project failed?” researchers might ask, “What steps were taken during the project’s final phase?”
• Using Multiple Perspectives:
  
  • Interviewing different people involved in the same process to get a balanced view.
  • Example: Speaking to both managers and employees about the rollout of a new technology to understand how it was perceived at different levels.

G. A Real-Life Example

A study on Unilever’s transformation from a transnational to a neo-global form combined 68 retrospective interviews with archival materials to capture the structural and cultural changes that occurred between 2000 and 2012.

• Interviews provided insights into how managers and employees interpreted the changes and how these perceptions influenced the process.
• Archival data filled gaps by providing factual details about timelines and official decisions.

This combination allowed researchers to piece together a complete picture of both the objective events and the subjective experiences. Collecting data through people’s memories and interpretations is an essential component of process research. It offers a human-centered perspective, capturing both the cognitive and emotional dimensions of processes. While challenges like memory inaccuracies and bias exist, these can be mitigated through thoughtful research design, such as cross-checking data, focusing on facts, and gathering diverse perspectives. When combined with other sources like real-time observations and archival documents, this approach provides a rich, nuanced understanding of complex organizational processes.

Direct Observations:

Direct observations involve researchers immersing themselves in real-world settings to watch and document events, meetings, and activities as they happen. Unlike retrospective methods, which rely on people’s memories, this approach allows researchers to see processes unfold in real time and capture details that may not be easily recalled or recorded in official documents. This hands-on method is invaluable for understanding what actually happens, as opposed to what people say happened.

1. What Does Direct Observation Involve?

Direct observation means researchers act as a “fly on the wall,” witnessing events and interactions as they take place. Here’s what it typically includes:

• Attending Meetings: Observing how decisions are made, who participates, and what dynamics shape the discussion.
• Shadowing Employees: Following individuals or teams during their daily work to see how they perform tasks, make decisions, and interact with others.
• Noting Real-Time Reactions: Watching how participants respond to announcements, challenges, or unexpected events.
• Recording Informal Interactions: Capturing casual conversations, body language, and subtle cues that provide context beyond formal documentation.

2. Benefits of Direct Observations

a. Access to Unfiltered Information

When researchers observe events directly, they gain access to unfiltered, real-time details that are often missing from interviews or archival records. People’s descriptions of past events are often colored by memory biases or personal perspectives, but observations reveal what actually happens in the moment.

• Example: A researcher attending a meeting where a headquarters decision is announced can see:
  • How the decision is communicated (e.g., confidently, hesitantly, or defensively).
  • Immediate reactions from employees (e.g., excitement, confusion, or frustration).
  • Subtle, non-verbal cues like body language or side conversations that may reveal underlying tensions or support.

b. Capturing Contextual Nuances

Direct observations allow researchers to capture the context of a process—things like the setting, timing, and relationships between participants. These details often provide clues about why events unfold the way they do.

• Example: A headquarters meeting might reveal how power dynamics between executives influence decisions, such as one dominant voice steering the conversation or a marginalized group being ignored.

c. Identifying Informal Processes

Not all processes are formally documented. By observing people in action, researchers can identify informal practices, such as workarounds, unwritten rules, or backchannel communications, that play a critical role in how things actually get done.

• Example: Shadowing employees during a software rollout might reveal that, while the official training materials focus on one approach, employees rely on informal peer-to-peer learning to adapt to the new system.

d. Minimizing Memory Bias

By documenting events as they happen, researchers avoid the memory issues common in retrospective methods, such as forgetting details or reconstructing events inaccurately. Observations capture the raw, real-time experience of participants.

3. Challenges of Direct Observation

While this method has many benefits, it also presents unique challenges:

a. Researcher Visibility

The presence of a researcher can sometimes influence participant behavior, a phenomenon known as the Hawthorne Effect. People may behave differently when they know they are being observed, such as becoming more formal or cautious.

• Example: Employees might adhere more strictly to company policies when they know a researcher is shadowing them, masking informal practices or workarounds.

b. Practical Constraints

Direct observation requires significant time and access. Researchers must often spend weeks or months embedded in an organization to capture meaningful data, which can be resource-intensive.

• Example: A study of a multinational’s headquarters relocation process might require attending dozens of meetings and following various teams over several years to understand the full scope of the decision-making process.

c. Interpreting Complexity

Processes observed in real time can be highly complex, with many overlapping activities and participants. Researchers must carefully document these interactions and later analyze them to identify patterns or insights.

• Example: A relocation decision might involve a mix of formal presentations, informal side discussions, and last-minute adjustments, making it challenging to piece together a coherent narrative.

d. Open-Ended Outcomes

When observing ongoing processes, researchers may not know in advance how events will unfold or what the outcome will be. This open-endedness requires researchers to remain flexible and patient, tolerating ambiguity.

• Example: A researcher observing a team’s response to a new strategic initiative may find that the process evolves in unexpected ways or takes longer than anticipated, delaying conclusions.

4. Example of Direct Observation in Action

Imagine a multinational company is relocating its headquarters from one country to another. A researcher embedded in the organization might:

• Attend Key Meetings: Sit in on discussions between executives about the strategic reasons for the move, potential challenges, and logistical considerations.
  • Observation: An executive’s body language during the discussion might reveal discomfort with the decision, even if they verbally agree.
• Shadow Employees: Follow managers tasked with coordinating the move to see how they communicate with their teams, handle obstacles, and adapt to feedback.
  • Observation: A middle manager may express frustration with unclear guidance from the headquarters, highlighting a potential gap in communication.
• Record Informal Interactions: Watch employees during breaks or casual conversations to understand their authentic reactions and concerns about the relocation.
  • Observation: An overheard discussion among junior employees might reveal fears about job security or changes to work-life balance.

5. Combining Direct Observation with Other Methods

To overcome the challenges of direct observation and strengthen their findings, researchers often pair it with other methods:

• Interviews: After observing events, researchers can interview participants to gather their interpretations and clarify any ambiguities.
  • Example: Asking an employee about their reaction to a decision observed during a meeting might reveal hidden motivations or additional context.
• Archival Data: Observations can be cross-checked with historical records, such as meeting minutes or official announcements, to verify facts and build a comprehensive timeline.
  • Example: Comparing observed meeting discussions with official reports can reveal discrepancies or unspoken dynamics.
• Real-Time and Retrospective Approaches: Observing a process as it unfolds while also interviewing participants about past events provides a holistic view.
  • Example: Observing a product launch while also gathering retrospective accounts of the planning process offers both immediate and historical perspectives.

Direct observation is a powerful method for collecting rich process data because it allows researchers to:

1. Witness events as they happen, minimizing reliance on memory and interpretation.
2. Capture unfiltered, real-time reactions and subtle, contextual details.
3. Identify informal practices and dynamics that are often missing from formal records.
4. Gain a deeper understanding of processes by combining observations with interviews and archival data.

Despite challenges like researcher visibility, resource demands, and complexity, direct observation provides unique insights into the human and organizational dynamics behind processes, making it an essential tool in process research for international business.

Historical Documents and Artifacts:

Historical documents and artifacts are one of the foundational sources of rich process data in research, particularly in fields like international business. These materials offer valuable insights into the timeline of events, decisions, and strategies that organizations have adopted over time. Let’s explore their purpose, examples, benefits, challenges, and how they fit into process research.

1. What Are Historical Documents and Artifacts?

These are records and materials created during the course of an organization’s operations, often preserved in archives. They provide a factual account of events and decisions that occurred over time. Common examples include:

• Meeting Minutes: Records of discussions, decisions, and action points from organizational meetings.
• Annual Reports: Summaries of a company’s financial performance, key achievements, and strategic goals for each year.
• Emails and Correspondence: Digital or written communications that reveal how decisions were discussed and finalized.
• Internal Memos: Documents that outline policies, updates, or internal communications within an organization.
• Policy and Strategy Documents: Artifacts that capture official plans and guidelines for decision-making and implementation.
• Media Reports and Press Releases: External accounts of significant organizational events, such as mergers, expansions, or controversies.

2. How Historical Documents Support Process Research

Historical documents provide researchers with a long-term perspective on organizational processes. By analyzing these records, researchers can:

• Reconstruct Event Timelines:
  
  • Documents help create a chronological map of key events, showing when decisions were made, what actions were taken, and how these unfolded over time.
  • Example: Analyzing meeting minutes and annual reports from 1990 to 2010 to track a company’s shift from domestic to international markets.
• Understand Strategic Shifts:
  
  • Records like annual reports and policy documents reveal how strategies evolved, which can help researchers pinpoint inflection points where significant changes occurred.
  • Example: Identifying when a company adopted sustainability practices by tracing the language in their annual reports.
• Uncover Patterns of Communication and Decision-Making:
  
  • Emails, memos, and internal documents often shed light on how decisions were discussed and approved, highlighting power dynamics, conflicts, or consensus-building.
  • Example: Reviewing email chains between headquarters and subsidiaries to understand how disagreements about market entry strategies were resolved.
• Examine Framing and Messaging:
  
  • Public documents like press releases or media reports reveal how organizations framed their actions to stakeholders, customers, or regulators.
  • Example: Tracking how a multinational framed its corporate identity in press releases over 20 years, showing how messaging shifted in response to cultural or market pressures.

3. Benefits of Using Historical Documents

Historical documents are particularly valuable in process research for several reasons:

a. Objectivity and Permanence

Unlike interviews or personal accounts, which are subjective and prone to bias, documents provide a stable record of what was officially said or decided at the time.

• Example: Meeting minutes from a merger negotiation capture what was agreed upon, even if participants later forget or reinterpret the events.

b. Long-Term Insight

Documents allow researchers to study processes that span years or decades, something that is challenging to achieve through real-time observation.

• Example: A researcher can analyze 30 years of annual reports to track how a company’s global expansion strategies evolved across different markets.

c. Verifiability

Records can be cross-referenced with other data sources (e.g., interviews or observations) to verify claims or fill in gaps.

• Example: If an executive claims a decision was made in 2005, the researcher can check meeting minutes or emails from that time to confirm.

d. Accessibility

Many organizations keep detailed records, and with advancements in digital storage, archival materials are becoming more accessible than ever before.

• Example: Digital email archives allow researchers to trace communication trends and decision-making patterns more efficiently.

4. Challenges and Limitations of Historical Documents

While valuable, historical documents also come with significant limitations:

a. Reflecting the “Official” Version

Many documents represent the formal, polished version of events, often leaving out informal discussions, dissenting opinions, or behind-the-scenes dynamics.

• Example: An annual report might celebrate the success of a new product launch while omitting internal struggles or controversies that occurred during development.

b. Incomplete Records

Not all documents are preserved, and gaps in the archival record can leave parts of the process undocumented.

• Example: A company may have lost meeting minutes from critical years due to poor record-keeping practices.

c. Lack of Emotional Context

Documents are typically factual and impersonal, making it hard to capture the emotions, tensions, or human experiences associated with events.

• Example: Meeting minutes may show that a decision was approved, but they won’t reveal the anxiety or conflict that led up to that decision.

d. Historical Bias

Documents may reflect the perspectives of those in power at the time, marginalizing alternative voices or interpretations.

• Example: A CEO’s memos might dominate the archival record, while the concerns of middle managers or employees go unrecorded.

5. Strategies to Mitigate Challenges

Researchers can address these limitations by using complementary approaches:

• Combine with Other Data Sources:
  
  • Interviews, focus groups, or observations can provide the emotional and informal context missing from documents.
  • Example: After analyzing meeting minutes, researchers can interview participants to learn about behind-the-scenes discussions or personal reactions.
• Triangulate Data:
  
  • Cross-check findings from documents with other sources, such as news articles, personal diaries, or real-time observations, to ensure a fuller picture.
  • Example: Comparing internal memos with public press releases to identify discrepancies between internal strategy and external messaging.
• Look for Patterns Over Time:
  
  • Instead of relying on a single document, researchers analyze multiple documents across time to identify recurring themes, changes, or turning points.
  • Example: Tracing how sustainability was framed in annual reports over two decades to reveal shifts in corporate priorities.

6. Real-Life Examples of Historical Document Use in Process Research

Case 1: Long-Term Strategic Evolution

• A study analyzed Unilever’s archival materials over 57 years to track how the identity of its Indian subsidiary evolved. Researchers used annual reports, internal communications, and external media to understand how the subsidiary framed its relationship with the parent company over time.

Case 2: Discursive Framing of Climate Change

• Researchers studied how different stakeholders (businesses, governments, NGOs) framed climate change in public documents over 34 years. They combined historical document analysis with interviews to explain how and when a shared global narrative about climate change emerged.

Case 3: De-Internationalization Processes

• A study on companies scaling back international operations used archival meeting minutes, correspondence, and strategy reports from the 1960s and 1970s to document how decisions were made. These records helped reconstruct the sequence of events, revealing the reasoning behind strategic withdrawals.

7. Conclusion

Historical documents and artifacts are a critical source of rich process data because they provide an objective, long-term, and factual record of events and decisions. While they have limitations—such as reflecting only the “official” narrative or lacking emotional context—these can be mitigated by combining documents with other data sources like interviews and observations. When used effectively, historical documents enable researchers to piece together the timelines, strategies, and communication dynamics that define complex organizational processes, offering a detailed and nuanced understanding of how things unfolded over time.

Key Challenges in Collecting Rich Process Data

Timeframe of the Study

The timeframe of the study plays a central role in process research, as it determines the scope and depth of understanding a researcher can achieve about how events, decisions, and activities unfold over time. Choosing the right timeframe ensures that the study fully captures the entire process, including its key phases, turning points, and outcomes.

Importance of the Timeframe in Process Research

a. Capturing the Full Process

The study’s timeframe must be long enough to observe the full sequence of events that make up the process being investigated. Processes often unfold over extended periods, especially in complex settings like international business, where decisions and changes involve multiple actors, geographies, and levels of analysis.

• Long-Term Phenomena:
  Processes such as internationalization, corporate identity evolution, or organizational restructuring often span decades. A short timeframe might miss key stages or historical foundations that shape the process.
  
  • Example: A 57-year study on Unilever’s Indian subsidiary tracked shifts in how the subsidiary framed its identity in relation to its parent company, revealing long-term oscillations in messaging.
• Medium-Term Phenomena:
  Some processes are more bounded in time, occurring over several years. For example, studying the adoption of a new corporate strategy or the transfer of practices across multinational subsidiaries might require a timeframe of 3–10 years to fully capture key activities.
  
  • Example: Researchers analyzed Unilever’s transformation between 2000 and 2012, focusing on structural and cultural changes within a 12-year period.

b. Revealing Key Transitions

A well-designed timeframe helps identify inflection points, where strategies shift, decisions are made, or unexpected events alter the trajectory of the process. Without sufficient time, these transitions might be overlooked.

• Example: A study of a company’s international expansion over 20 years could reveal that its strategy initially relied on partnerships but later shifted to acquisitions as the organization matured and gained experience.

c. Accounting for Delayed Outcomes

Processes in international business often involve delayed or gradual effects. A short timeframe might capture the initial decisions or actions but fail to observe their long-term consequences.

• Example: A company entering a new market might face initial challenges (e.g., cultural adaptation, regulatory barriers), but the full impact of its entry strategy might only become evident years later, when sales stabilize or market share grows.

2. Challenges of Long-Term Studies

Long-term studies are invaluable for understanding complex, slow-moving processes, but they come with specific challenges:

a. Reliance on Archival Data

To study phenomena spanning decades, researchers often depend on archival records, such as meeting minutes, reports, and correspondence. While these records provide factual details, they have limitations:

• Lack of Informal Context: Archival documents often reflect the “official” version of events and may omit informal interactions, behind-the-scenes discussions, or emotional reactions.
  
  • Example: Meeting minutes may document that a decision was approved but won’t capture debates or disagreements leading up to the decision.
• Variable Data Quality: Older records might be incomplete, poorly preserved, or lack critical details.
  
  • Example: A study on organizational change in the 1970s might face gaps in documentation due to inconsistent record-keeping practices.

b. Historical Bias

Archival data often reflects the perspectives of those in power at the time, potentially marginalizing alternative voices.

• Example: A CEO’s memos may dominate the archival record, while the experiences of middle managers or employees remain undocumented.

c. Resource-Intensive

Long-term studies require significant time and effort to analyze large volumes of data from multiple time periods. Researchers must piece together a coherent narrative from fragmented and diverse sources.

3. Challenges of Medium-Term Studies

While medium-term studies (spanning 3–10 years) are more manageable, they have their own limitations:

a. Memory Limitations in Retrospective Interviews

Medium-term studies often rely on retrospective interviews to gather data about events in the recent past. However, participants’ memories can be unreliable:

• Poor Chronological Memory: People may struggle to recall the exact sequence or timing of events.
  
  • Example: An executive might misremember when a specific decision was made or what factors influenced it.
• Hindsight Bias: Participants may reinterpret past events based on what they now know about the outcomes.
  
  • Example: A manager might claim that they anticipated a project’s failure, even if their actions at the time indicated optimism.
• Selective Reporting: Participants may present themselves in a positive light, emphasizing their successes and downplaying mistakes.
  
  • Example: An employee might exaggerate their role in a successful initiative to boost their perceived competence.

b. Limited Access to Preceding Context

Medium-term studies often focus on a specific period but may miss the historical context leading up to it. Without this context, researchers might overlook foundational decisions or conditions that shaped the process.

4. Addressing the Challenges

Researchers can address these challenges by adopting strategies to enhance the quality and depth of their studies:

a. Combining Timeframes

Using a combination of retrospective and real-time data can provide a fuller picture of the process:

• Example: A study on practice transfer across multinational subsidiaries combined real-time observations (2013–2015) with retrospective interviews (2008–2012) to understand both ongoing activities and earlier stages.

b. Triangulating Data Sources

Cross-referencing multiple data sources helps mitigate the limitations of archival data and retrospective interviews:

• Example: Researchers can compare meeting minutes with employee interviews to uncover informal dynamics that are missing from the official record.

c. Leveraging Digital Archives

Advances in digital storage and communication have expanded the availability of detailed archival records, such as emails, chat logs, and online meeting recordings. These resources provide richer insights into recent processes:

• Example: Digital records from the 2000s might include email threads that reveal real-time negotiations and informal decision-making processes.

d. Capturing Multiple Perspectives

Interviewing participants from different levels of the organization (e.g., executives, middle managers, employees) provides a more balanced view:

• Example: A study on a company’s restructuring might combine interviews with senior leaders (to understand strategic intent) and employees (to capture on-the-ground experiences).

5. Real-Life Examples of Timeframe Selection

Long-Term Study

• Unilever’s Indian Subsidiary: A 57-year study tracked shifts in the subsidiary’s identity, revealing oscillations between aligning closely with the parent company and emphasizing its independence.
  • Key Insight: Long-term patterns in messaging reflected broader changes in corporate strategy and global market dynamics.

Medium-Term Study

• Organizational Transformation: A study of Unilever’s transformation between 2000 and 2012 used retrospective interviews and archival data to analyze cultural and structural changes.
  • Key Insight: By focusing on a 12-year period, researchers captured the key phases of the transformation without losing focus.

Short-Term Study

• Practice Transfer: A 3-year study observed real-time interactions between headquarters and subsidiaries during a practice transfer, while retrospective data provided context for earlier stages.
  • Key Insight: Combining timeframes allowed researchers to document both the initiation and unfolding of the transfer process.

6. Conclusion

The timeframe of a study is not just a technical decision—it shapes the depth and scope of insights researchers can generate. Long-term studies are essential for capturing slow-moving, evolutionary processes, while medium-term and short-term studies are better suited for examining focused phenomena or specific phases of a process. By carefully designing the timeframe, combining data sources, and addressing challenges like memory limitations and archival bias, researchers can create a comprehensive and nuanced understanding of organizational processes over time.

Retrospective vs. Real-Time Data Collection

In process research, one of the most critical design decisions is whether to collect retrospective data (looking backward at past events) or real-time data (observing events as they unfold). Each approach offers unique advantages and faces specific challenges. In many cases, researchers use a combination of the two methods to create a more complete understanding of the process being studied.

1. Retrospective Data Collection

What It Is:

Retrospective data collection involves gathering information about past events through interviews, surveys, focus groups, or archival records. Participants are asked to recall their experiences, actions, and decisions, while researchers analyze historical materials like meeting minutes, reports, and emails.

Examples:

• Interviews: Asking executives to explain why certain strategic decisions were made over the past decade.
• Archival Records: Analyzing documents like policy memos, email correspondence, or annual reports to reconstruct a timeline of events.

Advantages:

1. Convenience:
   
   • Retrospective data is easier to collect because the events have already occurred. Researchers don’t need to wait for the process to unfold in real-time.
   • Example: A company that recently completed a merger can provide immediate access to executives and documents, allowing researchers to analyze the entire process after the fact.
2. Comprehensive Overview:
   
   • By looking at the full timeline of past events, researchers can see the outcomes of the process and analyze how earlier actions influenced later results.
   • Example: Examining how a multinational company’s decision to enter a new market five years ago impacted its current market share and financial performance.
3. Access to Rare or Long-Term Processes:
   
   • For processes that span decades or involve rare events, retrospective methods are often the only option.
   • Example: Studying how a company’s corporate identity evolved over 50 years by analyzing old press releases and internal communications.

Challenges:

1. Memory Issues:
   
   • People often have poor chronological memory and may forget important details or misremember the sequence of events.
   • Example: An executive might struggle to recall whether a specific policy decision was made in 2015 or 2016.
2. Hindsight Bias:
   
   • Participants tend to view past events through the lens of their current knowledge, framing decisions and outcomes in a way that aligns with the present narrative.
   • Example: An employee may claim they “always knew” a project would succeed, even if their initial reactions were uncertain or negative.
3. Impression Management:
   
   • Participants may present themselves in a positive light, exaggerating their contributions or downplaying mistakes.
   • Example: A manager might describe their role in a successful project as more significant than it was, omitting details of collaborative efforts.
4. Limited Access to Informal Context:
   
   • Retrospective data often misses the informal or emotional aspects of events, which participants may not remember or may hesitate to share.
   • Example: An archival report may document a major decision but omit the conflicts, debates, or personal tensions that influenced the outcome.

2. Real-Time Data Collection

What It Is:

Real-time data collection involves observing events, activities, and interactions as they happen. Researchers immerse themselves in the process, attending meetings, shadowing participants, and documenting their observations in real time.

Examples:

• Meetings: A researcher attends board meetings during a corporate restructuring to observe how decisions are made and communicated.
• Shadowing: Following employees as they implement new practices to see how they adapt and respond to challenges.

Advantages:

1. Capturing Unfiltered Details:
   
   • Real-time data provides access to the raw, unfiltered actions, conversations, and reactions of participants, reducing the reliance on memory or interpretation.
   • Example: Observing an executive team debating a policy change captures their arguments, body language, and decision-making dynamics as they happen.
2. Access to Informal Interactions:
   
   • Researchers can document informal conversations, non-verbal cues, and behind-the-scenes dynamics that are often omitted from formal records or retrospective accounts.
   • Example: Observing how employees discuss a new strategy informally during lunch breaks can reveal resistance or enthusiasm that isn’t expressed in official meetings.
3. Understanding Processes as They Evolve:
   
   • By observing events in real-time, researchers can track how decisions are made, challenges are addressed, and outcomes are shaped, providing a rich understanding of the process.
   • Example: Watching how a team adapts to a sudden market disruption provides insights into real-time problem-solving and decision-making.

Challenges:

1. Unpredictability:
   
   • Real-time processes are open-ended, meaning researchers don’t always know what events will occur or how they’ll unfold. This requires flexibility and patience.
   • Example: A researcher studying an organizational change initiative may need to wait months before significant developments occur.
2. Time and Resource Intensive:
   
   • Real-time studies often require long periods of observation, making them more time-consuming and costly than retrospective methods.
   • Example: Following a company through a three-year restructuring process requires continuous access and significant commitment.
3. Limited Scope:
   
   • Real-time studies often focus on a narrow timeframe, which may miss the historical context or long-term consequences of the process.
   • Example: Observing a product launch captures the immediate reactions of employees and customers but doesn’t reveal the planning or long-term impact.
4. Researcher Influence:
   
   • The presence of a researcher may alter participants’ behavior, a phenomenon known as the Hawthorne Effect.
   • Example: Employees might act more formally or positively when they know they’re being observed.

3. Combining Retrospective and Real-Time Data Collection

To address the limitations of each approach, many studies use a combination of retrospective and real-time methods. This hybrid approach provides a more comprehensive and balanced view of the process.

How It Works:

• Researchers observe ongoing events in real time while also conducting interviews or analyzing archival data to reconstruct the earlier stages of the process.

Examples:

• Multinational Practice Transfer:
  
  • Researchers observed real-time interactions between headquarters and subsidiaries during the practice transfer (2013–2015) while conducting retrospective interviews to understand the process’s initiation (2008–2012).
  • Benefit: This combination allowed the researchers to track the entire lifecycle of the practice transfer, from planning to implementation.
• Corporate Relocation Decision:
  
  • Real-time observations documented employees’ reactions to a headquarters relocation decision, while retrospective interviews provided insights into the strategic discussions that preceded the announcement.
  • Benefit: Researchers captured both the strategic intent and the real-time impact of the decision.

Advantages of Combining Methods:

1. Holistic View: Captures both historical context and ongoing dynamics.
2. Reduced Bias: Cross-checking retrospective accounts with real-time observations ensures accuracy and depth.
3. Flexibility: Researchers can adapt to the complexity of the process, focusing on past, present, or both as needed.

4. Conclusion

Both retrospective and real-time data collection have distinct strengths and challenges. While retrospective methods provide a broad overview and long-term perspective, they are prone to memory bias and selective reporting. Real-time methods, on the other hand, offer raw, unfiltered insights but require significant time and flexibility. By combining the two approaches, researchers can create a richer, more nuanced understanding of processes, capturing both their historical roots and real-time evolution. This hybrid approach is particularly valuable in complex contexts like international business, where processes unfold across multiple levels, geographies, and timeframes.

Perspective on Time in Process Research

In process research, time is more than just a measure of chronological order; it shapes how we study and understand events, activities, and changes. Researchers adopt different perspectives on time depending on their goals and the nature of the phenomena they are studying. Two main perspectives dominate: objective time and subjective time. Each offers unique insights into how processes unfold, and together, they allow for a richer understanding of temporal dynamics.

What is Objective Time?

Objective time views time as linear, measurable, and fixed. It relies on external, standardized units like seconds, minutes, days, or years to track the chronological progression of events. This perspective assumes that:

• Time flows in a consistent, measurable manner.
• Events happen in a sequential order that can be tracked and quantified.

Features of Objective Time:

1. Chronological Focus:
   
   • Researchers examine the exact timing and order of events.
   • Example: Measuring the number of months between a company’s entry into a new market and its first sale.
2. Quantifiable Data:
   
   • This approach uses timelines, calendars, and other objective markers to define when and how events occurred.
   • Example: Tracking how long it takes for an organization to implement a new technology after the decision to adopt it.
3. Linear Process:
   
   • Events are studied as part of a step-by-step progression with a clear beginning, middle, and end.
   • Example: A product launch may involve stages like design, testing, marketing, and release, each measured in terms of duration.

Benefits of Objective Time:

1. Precision:
   
   • Timeframes and sequences can be documented with high accuracy, making it easier to compare processes across different cases.
   • Example: Researchers can compare how quickly two different companies entered and established themselves in similar markets.
2. Generalizability:
   
   • Objective measures of time allow researchers to identify patterns or trends that are consistent across multiple organizations or industries.
   • Example: Studies might reveal that successful market entries consistently occur within 18–24 months of strategic planning.
3. Focus on Efficiency:
   
   • This perspective helps assess the speed and efficiency of processes, which is valuable for organizations seeking to optimize operations.
   • Example: Analyzing how long it takes to transition employees to a new organizational structure.

Challenges of Objective Time:

1. Overlooks Human Experience:
   
   • This perspective treats time as external and uniform, ignoring how individuals or groups perceive and experience it.
   • Example: A merger might take one year to complete on paper, but employees may feel like it dragged on for much longer due to stress and uncertainty.
2. Misses Contextual Depth:
   
   • The focus on measurable units may overlook the social, emotional, or cultural factors influencing the process.
   • Example: A company’s rapid international expansion might appear efficient but fail to account for the internal challenges faced by employees adapting to the change.

2. What is Subjective Time?

Subjective time views time as fluid, personal, and interpretive. It focuses on how people experience, perceive, and interpret time, rather than treating it as an external, fixed construct. This perspective acknowledges that time is:

• Shaped by emotions, perceptions, and social interactions.
• Non-linear, meaning past, present, and future can blend together in how people understand events.

Features of Subjective Time:

1. Perception-Based:
   
   • Time is understood through the lens of personal or collective experiences, rather than strict chronological measures.
   • Example: Employees may feel that an organizational change took “forever,” even if it lasted only a few months.
2. Non-Linear Interpretations:
   
   • People connect the past, present, and future in ways that are unique to their experiences.
   • Example: A manager might interpret a failed project in the past as a critical lesson that shapes their current decision-making.
3. Endogenous to the Process:
   
   • Time is influenced by the process itself and the people involved, making it highly context-dependent.
   • Example: During a crisis, time might feel compressed as decisions are made rapidly, whereas during routine operations, it might feel slower.

Benefits of Subjective Time:

1. Captures Emotional and Social Dynamics:
   
   • By focusing on how people experience time, this perspective reveals the emotional and social aspects of processes.
   • Example: Employees’ sense of “time dragging” during layoffs might explain low morale or reduced productivity.
2. Uncovers Meaning-Making:
   
   • Subjective time helps researchers understand how people interpret events and construct narratives about their experiences.
   • Example: A study might explore how employees frame a reorganization as either a positive opportunity or a stressful disruption.
3. Rich Contextual Insights:
   
   • This perspective highlights the cultural, organizational, or psychological factors shaping how people relate to time.
   • Example: In some cultures, long-term planning might feel natural and rewarding, while in others, it may feel frustrating and unnecessary.

Challenges of Subjective Time:

1. Difficult to Measure:
   
   • Unlike objective time, subjective time is inherently personal and variable, making it harder to quantify or standardize.
   • Example: Two employees might experience the same event very differently—one feeling it was quick, another feeling it was drawn out.
2. Requires Deep Engagement:
   
   • Researchers must conduct in-depth interviews or observations to fully understand participants’ perceptions of time.
   • Example: Capturing subjective experiences of time during a product launch might involve repeated interviews with employees at various stages of the process.

3. Using Both Perspectives Together

Combining objective and subjective perspectives on time provides a more comprehensive understanding of processes. While objective time offers a clear, measurable framework for tracking events, subjective time reveals the lived experiences and emotional realities of those involved.

Example of Combination:

• Objective Time: A company takes 18 months to restructure its supply chain, based on documented timelines.
• Subjective Time: Employees report feeling like the restructuring process was rushed and chaotic, leading to stress and reduced morale.

By integrating both perspectives, researchers can identify gaps between the formal timeline and employees’ experiences, offering insights into how to improve the process in the future.

4. Real-Life Applications of Time Perspectives

Objective Time Example:

• International Market Entry: A researcher measures the exact duration between a company’s decision to enter a new market and its first sale. This helps identify how fast different companies can establish operations and whether speed correlates with success.

Subjective Time Example:

• Employee Experience During a Merger: Researchers collect narratives from employees about how they experienced the merger process. Some might describe the process as never-ending due to constant changes, while others might feel it went by quickly because they were less affected.

Combining Both in Research:

• Climate Change Framing: A study might analyze how long (objectively) it took for stakeholders like governments, NGOs, and businesses to align on a common climate change narrative while also examining how each group perceived the urgency (subjectively) of the issue over time.

5. Conclusion

The perspective on time shapes how researchers approach process studies. Objective time provides measurable, consistent data on the sequence and duration of events, making it ideal for tracking efficiency, patterns, and outcomes. Subjective time, on the other hand, reveals how people experience and interpret processes, uncovering emotional, social, and cultural dimensions. By integrating these perspectives, researchers can create a richer, more nuanced understanding of processes, balancing measurable facts with human experiences. This dual approach is especially valuable in complex settings like international business, where timelines and perceptions often diverge.

The Role of Space (Geographical Spread) in Process Research

In international business (IB), the geographical spread of processes introduces a layer of complexity that is both a challenge and a source of rich insights for researchers. Unlike processes confined to a single location, IB processes often span multiple countries, regions, and cultures, requiring researchers to address the unique dynamics that emerge when operations are spread across diverse contexts. Here’s a detailed exploration of why geographical spread matters and how it shapes process research.

1. What Does Geographical Spread Mean?

Geographical spread refers to the fact that many international business processes are distributed across multiple locations, each with its own:

• Cultural Context: Differences in language, norms, and values that influence how processes are understood and implemented.
• Economic Environment: Variations in economic conditions, infrastructure, and market maturity that affect operational decisions.
• Regulatory Frameworks: Local laws and policies that impose unique constraints or opportunities in each region.
• Organizational Structures: Differences in how multinational corporations (MNCs) operate across headquarters, regional offices, and subsidiaries.

Example:
A multinational corporation (MNC) with headquarters in the United States may have subsidiaries in Europe, Asia, and Africa. Each subsidiary operates in a unique local context, adapting global strategies to meet regional needs while maintaining alignment with headquarters’ objectives.

2. Why Is Geographical Spread Important in Process Research?

a. Capturing Local Adaptations

In international business, processes must often be adapted to fit the specific conditions of each location. Studying geographical spread helps researchers understand:

• How local teams modify global strategies to align with cultural, economic, or regulatory realities.
• The balance between global standardization and local customization.

Example:
A global marketing campaign designed by headquarters may need to be adjusted by subsidiaries to reflect local consumer preferences and cultural norms.

b. Uncovering Cross-Border Interactions

Processes that span multiple locations involve coordination, communication, and negotiation across borders. Geographical spread reveals how these interactions shape the process:

• How information flows between headquarters and subsidiaries.
• The role of cross-cultural communication in decision-making and implementation.
• How conflicts or misunderstandings are resolved across regions.

Example:
In a study on knowledge transfer, researchers might examine how best practices developed in a European subsidiary are shared with teams in Asia, highlighting barriers like language differences or time zone challenges.

c. Addressing Regional Differences

Geographical spread highlights the impact of regional diversity on processes. This diversity can influence:

• Decision-making speed and effectiveness.
• The feasibility of implementing a one-size-fits-all approach.
• Variations in stakeholder expectations.

Example:
A company expanding into both developed and emerging markets might face faster adoption in regions with advanced infrastructure but slower progress in areas with regulatory or logistical challenges.

d. Examining External Stakeholder Influences

Each location often has unique external stakeholders—governments, NGOs, customers, or local communities—who influence the process. Understanding these influences requires data collection across all relevant regions.

Example:
In a study on sustainability practices, researchers might observe how NGOs in Europe push for stricter environmental policies, while stakeholders in emerging markets focus on economic development priorities.

3. Challenges of Studying Geographical Spread

a. Data Collection Complexity

Collecting data across multiple countries involves logistical challenges, such as:

• Traveling to or coordinating with distant locations.
• Navigating different time zones, languages, and communication styles.
• Ensuring consistency in data collection methods across sites.

Example:
A study on global supply chains might require on-site visits to factories in Asia, interviews with logistics teams in Europe, and surveys of end customers in North America.

b. Cultural Barriers

Cultural differences can affect how researchers interact with participants and interpret findings. Barriers include:

• Language differences that require translation or interpretation services.
• Variations in openness or willingness to share information.
• Differing perceptions of time, hierarchy, or work practices.

Example:
A hierarchical culture in one region may discourage employees from openly discussing challenges with researchers, while another region’s egalitarian culture may promote candid feedback.

c. Balancing Global and Local Perspectives

Processes often involve tension between global objectives (headquarters) and local needs (subsidiaries). Researchers must navigate these differing perspectives:

• Headquarters may view processes from a strategic, global perspective.
• Subsidiaries often focus on practical, operational challenges in their specific context.

Example:
Headquarters might emphasize cost efficiency in a new technology rollout, while subsidiaries highlight the need for additional resources to train local teams.

d. Fragmented Data

Data collected from different regions may be incomplete, inconsistent, or difficult to compare. Researchers must integrate fragmented information into a coherent narrative.

Example:
While one subsidiary may provide detailed operational data, another may have limited documentation due to resource constraints or differing priorities.

4. Strategies for Addressing Geographical Spread

a. Use Local Partnerships

Collaborating with researchers or organizations in the regions being studied can improve access to data and cultural understanding.

• Example: Partnering with a local university to collect data from subsidiaries in emerging markets.

b. Standardize Data Collection Methods

To ensure consistency, researchers should develop clear guidelines for data collection across all locations.

• Example: Using the same interview protocol and data collection templates for headquarters and subsidiaries.

c. Triangulate Data Sources

Cross-checking data from multiple sources helps address inconsistencies and enriches findings.

• Example: Comparing employee interviews in one region with company records and stakeholder feedback from the same location.

d. Leverage Technology

Technology tools, such as virtual interviews, cloud-based data sharing, and analytics software, can help manage the challenges of geographical spread.

• Example: Using online platforms to conduct interviews across time zones or store multilingual data in a centralized repository.

5. Real-Life Examples of Geographical Spread in Process Research

Example 1: Knowledge Transfer Across Subsidiaries

A study examines how a multinational transfers best practices from its headquarters in the United States to subsidiaries in Europe, Asia, and Africa.

• Researchers document the challenges of adapting practices to local cultural and regulatory contexts.
• The study reveals that subsidiaries in Asia achieve faster adoption due to strong local leadership, while subsidiaries in Europe face resistance due to cultural differences.

Example 2: Global Product Launch

A global beverage company designs a new product at its headquarters but adapts marketing campaigns in different regions.

• Researchers study how regional teams modify the campaign to suit local tastes and preferences.
• Findings highlight how cultural nuances influence the success of the product in different markets.

Example 3: Sustainability Practices

A study investigates how a multinational implements sustainability initiatives across its operations in North America, Europe, and Asia.

• Researchers observe that European subsidiaries align quickly with global sustainability goals due to regulatory pressures, while North American subsidiaries prioritize cost considerations, and Asian subsidiaries face logistical challenges.

6. Conclusion

Geographical spread is a defining characteristic of processes in international business, offering both challenges and opportunities for researchers. By studying how processes unfold across multiple locations, researchers can capture the rich interplay of global strategies, local adaptations, and cross-border interactions. Despite challenges like cultural barriers, fragmented data, and logistical constraints, strategies like local partnerships, standardized methods, and triangulation can help researchers navigate the complexity. Ultimately, understanding geographical spread provides valuable insights into the dynamics of global operations, enabling organizations to design more effective and context-sensitive strategies.

Multiple Levels of Analysis in Process Research

In international business (IB) process research, it is critical to examine the interactions that occur at multiple organizational levels because processes rarely happen in isolation. The dynamics between different groups—headquarters, subsidiaries, and external stakeholders—shape how decisions are made, implemented, and experienced. Each level plays a unique role, and understanding their interplay provides a holistic view of the process.

1. What Are the Multiple Levels in Process Research?

a. Headquarters

The headquarters of a multinational corporation (MNC) is typically the strategic hub, where high-level decisions are made. Researchers studying the headquarters level focus on:

• Strategic Planning: Headquarters designs overarching policies and plans for global operations, such as expansion strategies, structural changes, or market entries.
• Global Coordination: Headquarters manages relationships with subsidiaries and ensures alignment with corporate objectives.
• Decision-Making Dynamics: Internal power struggles, leadership styles, or organizational politics can influence how decisions are reached.

Example:
In a study on global market entry, researchers might interview executives at the headquarters to understand:

• Why they chose specific countries for expansion.
• How they balanced global standardization with local adaptation.
• The strategic goals behind the process.

b. Subsidiaries

Subsidiaries operate in local markets and are responsible for adapting headquarters’ strategies to fit regional conditions. Researchers studying subsidiaries examine:

• Local Adaptation: How subsidiaries modify global strategies to align with local regulations, cultures, and consumer preferences.
• Execution Challenges: Subsidiaries often face challenges like resource constraints, misaligned goals, or insufficient support from headquarters.
• Autonomy vs. Control: The degree of independence a subsidiary has often affects how effectively strategies are implemented.

Example:
In a study on marketing campaigns, researchers might observe:

• How subsidiary teams tailor a global advertising strategy to suit local cultural norms.
• The challenges they face when headquarters enforces rigid branding guidelines that don’t resonate with local audiences.

c. External Stakeholders

External stakeholders include customers, governments, NGOs, suppliers, and community groups. These groups influence or are affected by organizational processes and often play a critical role in shaping outcomes. Researchers studying external stakeholders focus on:

• Regulatory Compliance: How government policies or international trade agreements impact the process.
• Social and Cultural Dynamics: External stakeholders’ expectations, perceptions, or resistance can shape how processes are implemented locally.
• Collaboration and Negotiation: NGOs, partners, or suppliers might collaborate with subsidiaries to adapt strategies to local needs.

Example:
In a study on corporate social responsibility (CSR) initiatives, researchers might examine:

• How NGOs influenced a multinational to adopt sustainability practices in a specific region.
• Local community responses to the implementation of CSR programs.
• Government regulations that shaped the design of the initiative.

2. Why Is It Important to Study Multiple Levels?

a. Comprehensive Understanding

Studying multiple levels ensures a full picture of the process. Headquarters, subsidiaries, and stakeholders often experience the same process differently, and their interactions can reveal hidden dynamics.

• Example: In a study on a multinational’s headquarters relocation, the headquarters might focus on cost-saving benefits, while subsidiaries experience disruptions, and local governments worry about economic impacts. Analyzing all levels provides a richer narrative.

b. Interactions and Interdependencies

The levels are not isolated—they constantly interact and influence one another. Researchers must examine how these interactions shape the overall process.

• Example: A subsidiary’s resistance to a headquarters directive might lead to renegotiations or adjustments in the global strategy.

c. Conflict and Alignment

Processes often involve conflicts or misalignments between levels. Understanding these tensions can provide insights into why processes succeed or fail.

• Example: A global sustainability initiative might succeed in Europe, where local stakeholders are aligned with headquarters’ goals, but face resistance in Asia, where cultural or economic priorities differ.

d. Local Nuances

Subsidiaries and external stakeholders operate within unique local contexts that significantly impact how processes unfold. Studying these levels helps researchers capture the nuances of regional adaptations.

• Example: A subsidiary in India might approach employee retention differently than one in Germany due to differences in labor laws and cultural norms.

3. Challenges of Studying Multiple Levels

a. Data Collection Complexity

Collecting data from headquarters, subsidiaries, and external stakeholders requires significant effort and coordination. Researchers must navigate:

• Logistical Challenges: Scheduling interviews and accessing sites across different countries and time zones.
• Cultural Barriers: Language differences and cultural norms can affect communication and data quality.
• Data Fragmentation: Information from different levels might be inconsistent or incomplete, making it hard to piece together a coherent narrative.

b. Conflicting Perspectives

Participants at different levels may have conflicting views or priorities, making it challenging to reconcile their narratives.

• Example: Headquarters might view a new policy as a success, while subsidiaries see it as overly rigid and poorly suited to local conditions.

c. Resource-Intensive

Studying multiple levels requires more time, funding, and resources compared to single-level studies.

4. Strategies for Effective Multi-Level Analysis

a. Triangulation

Using data from multiple sources (e.g., interviews, observations, and archival records) helps validate findings and capture diverse perspectives.

• Example: Comparing headquarters meeting minutes, subsidiary interviews, and stakeholder surveys to identify alignment or discrepancies.

b. Mixed-Methods Approach

Combining qualitative methods (e.g., interviews, ethnography) with quantitative ones (e.g., surveys, performance data) provides a more holistic view.

• Example: Conducting in-depth interviews with executives and employees while analyzing sales data to understand the impact of a market entry process.

c. Local Partnerships

Collaborating with researchers or organizations in the regions being studied can improve access to data and enhance cultural understanding.

• Example: Partnering with a local university to study subsidiary operations in Asia.

d. Iterative Analysis

Analyzing data iteratively—regularly revisiting and refining findings as new data is collected—helps researchers manage the complexity of multi-level studies.

• Example: Revising initial interpretations of headquarters decisions after gathering additional data from subsidiaries.

5. Real-Life Example

Multinational Headquarters Relocation Study

A multinational corporation relocates its headquarters to another country, and researchers study the process at multiple levels:

1. Headquarters: Interviews with executives reveal that the relocation decision was driven by cost savings and access to new talent pools.
2. Subsidiaries: Observations show that subsidiary managers struggle to adjust to new reporting structures and feel disconnected from the relocated headquarters.
3. External Stakeholders: Local government officials express concerns about the economic impact of the relocation, while NGOs criticize the lack of transparency in the process.

Insights:

• The study highlights how the headquarters prioritized financial benefits, subsidiaries experienced operational challenges, and stakeholders focused on ethical concerns. These tensions explain why the relocation was met with mixed success.

6. Conclusion

Multi-level analysis in process research is essential for understanding the complex interactions and interdependencies that shape organizational processes. By studying headquarters, subsidiaries, and external stakeholders, researchers can capture the full scope of the process, including strategic intent, local adaptations, and external influences. Despite challenges like data collection complexity and conflicting perspectives, strategies like triangulation, mixed methods, and local partnerships can help overcome these barriers. This approach is especially valuable in international business, where processes span diverse geographies, cultures, and organizational levels, offering rich insights into the dynamics of global operations.

Simplified and Highly Detailed Explanation of Process Research in International Business

Process research is about studying how things happen over time, especially the events, decisions, and activities that unfold in organizations. To do this, researchers rely on rich process data, which includes a combination of memories, observations, and records. This section breaks down the key aspects of collecting and using process data in international business (IB), where complexity is amplified by global operations, cultural differences, and multiple layers of decision-making.

Three Main Sources of Rich Process Data

To study processes effectively, researchers gather data from three key sources:

1. People’s Memories and Interpretations:
   • Researchers interview individuals, conduct focus groups, or review personal diaries to understand what people remember about past events.
   • This data reflects individuals’ cognitive (thoughts) and emotional (feelings) reactions to processes.
   • Example: An employee may describe how they felt when a new policy was implemented, explaining how they adapted or resisted it.
2. Direct Observations:
   • Researchers observe events, meetings, or daily activities in real time.
   • By shadowing participants (e.g., following employees as they work), researchers see processes as they unfold rather than relying on people’s recollections.
   • Example: A researcher might attend meetings where a headquarters decision is announced and watch how employees react.
3. Historical Documents and Artifacts:
   • Researchers examine documents like meeting minutes, annual reports, emails, or memos. These provide a timeline of factual events and decisions.
   • Example: Reviewing decades of archival records to understand how a company shifted its international strategy over time.
   • Challenge: These documents often reflect an “official” version of events and may omit informal discussions or emotional reactions.

Key Challenges in Collecting Rich Process Data

1. Time and Temporality

Time is central to process research because the goal is to understand how events unfold over different timeframes. Researchers consider three aspects of time:

• Timeframe of the Study:
  • The study must cover enough time to fully capture the process.
  • Long-term studies might span decades, such as understanding how a company evolved over 50 years.
  • Example: One study examined Unilever’s Indian subsidiary over 57 years to track shifts in its relationship with the parent company.
• Challenges:
  • Longer studies often require reliance on archival data, which can provide factual details but may lack depth about emotions or informal interactions.
  • Historical data quality varies; older records may not capture nuances or may reflect biased “official” perspectives.
• Retrospective vs. Real-Time Data Collection:
  • Retrospective Data: Looking backward by asking people to recall past events or reviewing old records.
    • Example: Interviews with executives about decisions made over the last 10 years.
    • Challenges: People’s memories may be inaccurate, and they may frame events to present themselves in a positive light (hindsight bias).
  • Real-Time Data: Observing events as they happen.
    • Example: A researcher attending meetings during a corporate restructuring to document how decisions are made and implemented in real-time.
    • Challenges: Researchers don’t always know in advance what events will occur, requiring flexibility and patience.
  • Combination: Some studies mix both approaches to get a complete view.
    • Example: Researchers studying a multinational’s practice transfer observed events in real-time (2013–2015) while also interviewing participants about earlier stages (2008–2012).
• Perspective on Time:
  • Objective Time: Viewing time as measurable and linear, focusing on precise dates and sequences of events.
    • Example: Measuring how long it takes for a company to achieve its first sale in a new market.
  • Subjective Time: Understanding time as experienced by individuals, focusing on their perceptions and emotions.
    • Example: Exploring how employees interpret their roles and changes in the organization, such as how they connect past experiences to future expectations.

2. Geographical Spread

In international business, processes often span multiple countries, which creates unique challenges:

• Researchers must gather data from diverse locations, each with its own cultural, economic, and regulatory context.
• Example: Studying a multinational might require data from the headquarters in one country, subsidiaries in several others, and stakeholders in various regions.

This geographical spread increases logistical complexity, such as:

• Dealing with time zone differences.
• Adapting to local languages and cultural practices.
• Ensuring data from all locations is comparable and consistent.

3. Multiple Levels of Analysis

Processes in IB often involve interactions between different groups at multiple levels, including:

• Headquarters: Strategic decision-making (e.g., planning global expansions).
• Subsidiaries: Adapting strategies to fit local markets (e.g., tailoring marketing campaigns).
• External Stakeholders: Customers, governments, NGOs, or partners who influence or are affected by the process.

Example:

• A study on a multinational’s decision to relocate its headquarters might involve:
  • Interviews with executives at the headquarters about strategic motivations.
  • Observations of how subsidiary managers and employees react to the relocation.
  • Data from local stakeholders (e.g., community groups or governments) about the impact of the move.

Collecting data from all these levels ensures a comprehensive view but requires significant effort and coordination.

Examples of Process Research

Here are some real-world examples that illustrate how process research is conducted:

1. Long-Term Archival Data:
   • Researchers used meeting minutes, reports, and correspondence from the 1960s and 1970s to study how companies scaled back their global operations.
   • Insight: This revealed official decisions over time but had limitations in capturing informal dynamics.
2. Tracking Shifts in Corporate Identity:
   • A study analyzed 57 years of documents to track how executives in an Indian subsidiary described their relationship with the parent company.
   • Insight: The subsidiary’s messaging shifted over time, reflecting evolving corporate strategies and priorities.
3. Combining Retrospective and Real-Time Data:
   • Researchers observed the transfer of practices from a multinational’s headquarters to subsidiaries over three years (real-time) while also interviewing employees about earlier stages (retrospective).
   • Benefit: This provided a detailed timeline of both past decisions and ongoing activities.
4. Real-Time Observation:
   • A study observed employee reactions to a headquarters relocation decision over three years, using interviews, focus groups, and meeting observations.
   • Challenge: The researchers didn’t know in advance how the relocation would unfold, requiring flexibility and tolerance for ambiguity.

Essential Points

Process research requires diverse methodologies to capture the nuances of international business, given the complexity and multifaceted nature of the challenges involved. Rich data sources, such as interviews, observations, and historical documentation, are essential for comprehensive analysis. The combination of retrospective and real-time approaches often yields the most valuable insights into phenomena that span multiple levels of interaction, occur over extended durations, and involve global operations. Researchers can adopt varied temporal perspectives, focusing on either objective timelines or subjective interpretations of time, based on individual experiences.

By navigating these complexities, process research provides a deep understanding of how and why things happen in international business, offering insights that are both practical and theoretically valuable.