What Are the Core Conditions of Good Leadership?
In many organizations, leadership is often equated with physical presence—a good posture, a strong voice—and quick decision-making. However, it is evident that effective leadership is less about appearance or speed and more about clarity. This includes how precisely goals are articulated, how well the cultural context is understood, and how measures will enable realization and continuous improvement.
This piece elaborates on these ideas by looking at three case studies. First, the case of Microsoft’s entry into the smartphone market exemplifies the consequences of a lack of open communication. Second, Uber’s expansion into Germany shows the importance of understanding local laws and culture. Third, Toyota’s production system demonstrates how explicit models enable better process inspection.
The argument is simple. Good leadership is not about presence and influence. In fact, it is about making reasons public.
Microsoft and Clear Communication
In the research literature, good leadership is not simply defined as “having a vision.” Rather, it is the demonstration of normatively appropriate conduct and the promotion of such conduct through communication, reinforcement, and decision-making. For example, it involves transforming goals into commitments (targets, binding constraints, and monitoring rules) that can be verified by others. Vision must be expressed as testable claims.
Microsoft’s mobile pivot illustrates this well. The company re-entered the smartphone market late with the February 15, 2010 launch of Windows Phone 7 at Mobile World Congress. On September 3, 2013, Microsoft announced an agreement to acquire Nokia’s Devices & Services unit. Shares fell as much as 6% intraday on the news. In the end, support for Windows 10 Mobile ended on December 10, 2019.
Microsoft’s attempt is an example of what the research warns: a vision is inert until it is institutionalized as observable and auditable commitments, such as clear targets, constraints, and monitoring that outsiders can track. Without such testable claims, stakeholders cannot evaluate ability or reliability. As a result, coordination rests on personalities, and trust erodes.
From an ethical standpoint, individuals who are asked to take on risk should be given reasons that they can understand without insider knowledge. When organizations lack clearly stated purposes, they often rely on informal channels and personalities. In practice, this means that when things go wrong, the nearest or weakest teams tend to take the blame because the conditions for success and who is responsible for what were never made public.
The case also reflects the organizational factors that were documented at the time. Contemporary reporting emphasized that entrenched internal politics, especially “stack ranking,” harmed coordination and innovation despite public rhetoric about vision. Product failures, such as Windows Vista, reinforced the perception of strategic and executional drift, even as enterprise franchises performed well.
Clear direction, testable statements, binding constraints, and potential disconfirmations create common knowledge and support translation across jurisdictions. This does not replace vision, but rather gives it a form that engineers, customers, and critics can evaluate against the same world.
Uber and Institutional Translation
A strategic direction must be a testable claim. Crossing borders requires an additional step to ensure this is possible. The claim must be expressed in the other country’s legal terms. In other words, in its own language. By “language,” I mean the locally recognized categories, such as who is considered the employer, what licenses are required, and which duties allocate risk.
Consider the case of Uber’s bumpy entrance into Germany. According to the German Passenger Transport Act, taxis are different from private-hire vehicles (Mietwagen). Private-hire vehicles must go back to their home base after each trip (“return-to-base” rule), while taxis do not.
When Uber first arrived in Germany, the courts treated its UberPop private driver service as unlicensed passenger transportation and banned it. Uber then began collaborating with licensed private-hire operators and, later, taxi partners. From the rider’s perspective, the experience was similar. Institutionally, the service became legible by operating through licensed private-hire vehicle and taxi channels and complying with return-to-base requirements.
This is not a case of “innovation versus regulation.” Rather, it is about whether actions can be expressed in terms that workers, regulators, and competitors already understand. If they cannot, the entire business can be jeopardized and may be perceived as a threat to the local culture.
A practical rule is to formulate claims in a way that allows a critical outsider in the host system to reconstruct them without insider knowledge. If the existing forms are unjust, it is advisable to publicly advocate changes to them. This kind of translation makes claims legible and more durable.
Toyota and the Ethics of Inspectability
Once the claims have been clarified and translated, the next step is to make them workable. Operational models accomplish this.
In Toyota’s system, kanban (かんばん), takt time, and standardized work provide concise, testable descriptions of flow, capacity, and method. They specify how the system should behave, what constitutes normal variation, and even when intervention is necessary.
Under the Toyota Production System pillar of automation with a human touch (jidōka 自働化), abnormalities trigger a stop so that the causes can be found and corrected before defects spread. These artifacts are quantitative and visual, so they can be understood across languages with comparatively little loss.
The point is, then, inspectability, which involves shifting judgment from people to shared objects. Without explicit models, interpretation relies on status and rhetoric. With models, however, a supervisor can identify a problem without assigning blame, a worker can document an exceeded capacity, and an external reviewer can reconstruct events without insider knowledge. This ties into Toyota’s emphasis on visual management and built-in quality, which makes “normal vs. abnormal” visible to everyone.
This pattern is generalizable. In software operations, for instance, reliability becomes an auditable target. These is represented by service-level objectives with thresholds. Control charts are used in statistical process control to distinguish between common and special causes and define intervention rules.
In each case, models operationalize public claims (e.g., safety, reliability, and sustainability) in everyday work and generate testable, revisable hypotheses — something slogans cannot do.
Conclusion
In practice, leadership involves making reasons accessible. The three cases above illustrate a simple method for achieving this goal. First, transform vision into public, testable claims (Microsoft). Second, translate those claims into the host system’s legal and institutional terms so outsiders can audit them (Uber). Third, operationalize the claims in models that distinguish normal from abnormal for non-insiders (Toyota).
In high-reliability settings, whether public or private, the same structure appears: clear rules, explicit responsibilities, and visible consequences support coordination without reliance on status or personality.
Although sectors differ in content, their underlying structures are similar. If you are a leader, write your plan in a way that an outsider could understand. Include the intended outcome, the constraints, and what would count as disconfirmation. Translate these points into the local language of roles and duties. Link them to models, such as kanban boards, service-level objectives, and control charts, so people can see when and why to intervene.
These are modest requirements — clarity, translation, and inspectability. They do not replace vision, but rather give it a form that engineers, customers, regulators, and critics can evaluate across borders and over time.