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General KnowledgeTop 10 Best Valley Software of 2026
Top 10 valley software list ranks MuleSoft Anypoint Platform, Workato, and IBM App Connect by integration needs for buyers and IT teams.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Agents extract product evidence with provenance, then collect written reviews and transcribe video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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MuleSoft Anypoint Platform is the best pick for enterprises that need governed API and integration lifecycles, since it links RAML API contracts to policies and deployable assets across environments, giving consistent control from design through runtime, whereas Workato fits teams that want recipe-driven automation with schema-aware transforms plus RBAC and audit logs.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
MuleSoft Anypoint Platform
Anypoint API governance links RAML contracts to policies and managed deployment across environments.
Best for: Fits when enterprises need governed API and integration lifecycles with schema control, RBAC, and automation.
Workato
Editor pickRecipe execution with schema-aware data transformations plus RBAC and audit logs for governed automation.
Best for: Fits when teams need governed integration automation with a strong API and schema-driven data model.
IBM App Connect
Editor pickManaged integration flows with schema mapping and transformation across REST and SOAP interfaces.
Best for: Fits when mid-size enterprises need governed integration with schema-aware transformation and auditable automation.
Comparison Table
MuleSoft Anypoint Platform
API-led integrationProvides API-led integration for Valley Software with API design, policy enforcement, flow orchestration, and runtime governance features that support schema-first integration at scale.
Anypoint API governance links RAML contracts to policies and managed deployment across environments.
MuleSoft Anypoint Platform provisions and governs API and integration assets using a managed governance workflow. It connects data model design to API-led integration with RAML specifications, policy-based runtime controls, and automated deployment for endpoints.
Anypoint Automation and CI/CD tooling drive repeated builds for connectors, API versions, and integration flows across environments. Administrative controls include RBAC, environment separation, and audit reporting tied to integration lifecycle changes.
- +Governance workflow ties API specs to policies and deployable assets
- +API-led modeling using RAML keeps schemas consistent across environments
- +Automation supports repeatable builds for APIs, policies, and integration artifacts
- +Extensibility via custom connectors and reusable integration templates
- –Large configuration surface increases setup time for new teams
- –Data model changes can require coordinated updates across dependent APIs
- –Runtime behavior tuning demands deep knowledge of policies and connectors
- –Debugging complex flows often spans design, runtime, and policy layers
API platform teams
Govern APIs across business units
Consistent releases and audit trails
Integration architects
Standardize API-led integration patterns
Faster design to delivery
Show 2 more scenarios
Security and compliance leads
Enforce runtime policies centrally
Controlled access and traceability
Apply runtime policies tied to API endpoints and capture audit reports for governance changes.
Enterprise DevOps teams
Automate connector and flow builds
Reduced manual deployment work
Use automation workflows to build connectors and promote integration assets between dev, test, and production.
Best for: Fits when enterprises need governed API and integration lifecycles with schema control, RBAC, and automation.
Workato
enterprise integrationConnects Valley Software systems through integration recipes and managed connectors with an API-first extension model and administrative controls for automation governance and auditability.
Recipe execution with schema-aware data transformations plus RBAC and audit logs for governed automation.
Workato centers integration depth and automation control for connecting SaaS, apps, and APIs. Its recipes model transforms and routes data with a defined schema, while connectors and custom API actions support extensibility when prebuilt integrations fall short.
The API surface covers administration, execution, monitoring, and connector-driven automation, which supports governance workflows. RBAC plus audit logging supports traceability across environments, including sandbox-to-production promotion patterns.
- +Strong data mapping with explicit schema transforms across connected systems
- +Extensibility via custom connectors and API actions for unsupported SaaS endpoints
- +Automation recipes provide deterministic triggers, steps, and error handling
- +RBAC and audit logging support governance across teams and environments
- –Complex data model design can slow initial schema and mapping setup
- –High-volume throughput tuning requires careful batching and retry configuration
- –Governance patterns take setup work for consistent environment promotion
Revenue operations teams
Sync CRM leads to billing systems
Fewer duplicates and faster handoffs
IT integration engineers
Build custom API actions for SaaS
Reduced integration build time
Show 2 more scenarios
Security and compliance teams
Monitor workflows with audit trails
Improved traceability and approvals
Applies RBAC and captures execution and changes for governance across sandbox and production.
Operations automation teams
Route events through schema-based transformations
More consistent workflow outcomes
Transforms incoming events into required schemas and routes to multiple downstream services.
Best for: Fits when teams need governed integration automation with a strong API and schema-driven data model.
IBM App Connect
enterprise automationAutomates enterprise integration and workflow events with API support, connectors, and governance controls for repeatable Valley Software data orchestration.
Managed integration flows with schema mapping and transformation across REST and SOAP interfaces.
IBM App Connect focuses on governed integration between enterprise apps through guided connection flows and reusable API-driven components. It emphasizes a clear data model for mappings, message transformation, and schema alignment across endpoints.
Automation support covers event-driven triggers, REST and SOAP mediation, and managed routing between systems. Admin controls center on deployment configuration, role-based access, and operational audit trails for integration runs.
- +Strong integration depth with API mediation and message transformation
- +Reusable connectors for consistent configuration across multiple endpoints
- +Clear data model with schema mapping for predictable payload changes
- +Automation supports event triggers with controllable routing and sequencing
- –Complex configuration can slow early iteration for simple point-to-point cases
- –Debugging large flows requires careful tracing through intermediate nodes
- –Governance settings add overhead for teams needing frequent independent deployments
Integration architects
Design governed mediation between enterprise systems
Lower integration change risk
Enterprise IT administrators
Operate role-based deployments for integrations
Improved operational accountability
Show 2 more scenarios
Healthcare integration teams
Route events across clinical platforms
Faster system interoperability
Trigger workflows from events and transform payloads between REST and SOAP endpoints.
Order operations teams
Mediate purchase and fulfillment data synchronously
Fewer data inconsistencies
Standardize message mappings to keep order, billing, and shipping systems aligned during updates.
Best for: Fits when mid-size enterprises need governed integration with schema-aware transformation and auditable automation.
Zapier
automation + APIAutomates Valley Software workflows with triggers, actions, multi-step runs, and extensive app integrations while exposing a documented REST API for custom automation logic and data sync.
Workflow run history with per-step outputs and error details for webhook and connector steps.
Zapier turns app events into workflow runs with triggers, filters, and steps that can call webhooks or perform connector actions. The data model centers on field mappings between trigger outputs and action inputs, which makes schema alignment the core design task for each workflow. Extensibility is largely driven by webhook-based triggers and actions, which reduces friction when an app lacks a native connector. Operationally, workflow runs expose execution history and per-step outcomes, which helps validate mappings and diagnose failures.
- +Large connector library with webhooks for gaps
- +Clear trigger to action field mapping with run history
- +Filters and branching support conditional workflow logic
- +Workspace governance supports controlled access to automations
- –Schema enforcement is limited for strict data models
- –Long multi-step workflows can increase latency and retries
RevOps operations teams
Route CRM events to downstream tools
Fewer manual follow-ups
Sales engineering teams
Sync form submissions to CRM
Faster lead ingestion
Show 2 more scenarios
IT and systems automation
Coordinate SaaS provisioning events
More consistent integrations
Trigger lifecycle actions from app events and log each workflow run for traceability.
Customer support ops
Create cases from external signals
Quicker case creation
Filter incoming events and post structured updates to ticketing workflows.
Best for: Fits when teams need app-to-app automation with configurable workflows and webhook extensibility.
n8n
self-hosted automationRuns self-hosted or cloud automation for Valley Software with workflow nodes, webhook triggers, variable handling, and a REST API surface that supports extensibility and governance.
Workflow execution API and run history make automation observable through webhook-driven or scheduled triggers.
n8n supports integration depth by combining prebuilt nodes with custom code execution, letting workflows bridge SaaS APIs, databases, and file services. The data model is workflow-centric, with explicit parameters per node and consistent input and output wiring through the graph. Automation and API surface include webhook triggers, HTTP request nodes, and management APIs for operations like starting executions and reading run logs. Admin and governance controls include role-based access options, credential scoping, and execution history that can be audited for troubleshooting.
- +Webhook and HTTP trigger support for event-driven automation
- +Workflow data passed by graph wiring into node-specific schemas
- +Execution management APIs plus detailed run history for auditing
- –RBAC and credential scoping require disciplined governance setup
- –Self-hosted throughput depends on queue, concurrency, and retry tuning
Revenue operations teams
Sync CRM events to billing systems
Fewer data sync failures
Integration engineers
Create custom nodes for niche APIs
Reusable integration building blocks
Show 2 more scenarios
Platform teams
Centralize workflow executions with auditability
Faster incident root-cause
Use management endpoints and execution logs to track changes and troubleshoot failures.
Operations analysts
Cron jobs that reconcile operational data
Earlier detection of drift
Schedule workflows to query databases and send alerts with structured outputs.
Best for: Fits when teams need event-to-API automation with governance around workflows, credentials, and execution logs.
Make (Integromat)
automation workflowsBuilds Valley Software integration flows with modules, routers, webhooks, and scenario scheduling while providing an API and exports that support controlled automation and schema mapping.
Webhooks plus HTTP module support event-driven workflows with explicit field mapping and step-level execution logs.
Make (Integromat) provides a schema-driven approach where each module declares inputs, outputs, and field mapping, which reduces ambiguity during configuration. Scenarios compose those modules with routers, filters, and aggregators so control flow is explicit in the workflow graph. The automation API surface includes webhooks for inbound events and HTTP requests for outbound calls, which supports extensibility when a connector is missing. This design favors integration breadth across SaaS apps and custom services where consistent payload transformation matters.
- +Scenario graph shows exact control flow, including routers and filters
- +Strong inbound automation via webhooks with structured payload mapping
- +HTTP and connector modules support custom integrations without code
- +Run history and step outputs speed schema debugging
- –Large branching graphs can reduce maintainability over time
- –High-throughput scenarios need careful batching to avoid overload
- –Governance relies on scenario discipline for consistent standards
- –Edge cases in data typing can require manual mapping work
RevOps operations teams
Sync CRM changes to billing
Lower manual reconciliation workload
Integration engineers
Automate internal service orchestration
Fewer custom integration scripts
Show 2 more scenarios
Support automation leads
Route tickets using external enrichment
Faster triage and routing
Use filters and data mapping to enrich inbound cases then dispatch actions.
Data operations teams
ETL-style transforms for SaaS exports
More consistent downstream datasets
Batch records through aggregators and enforce field schemas across destinations.
Best for: Fits when integration teams need schema-mapped automation with webhook and HTTP extensibility.
TIBCO Cloud Integration
managed integrationDelivers managed integration flows with connectors, message processing, and API capabilities that support controlled data movement between Valley Software systems.
Schema-driven data mapping with workflow orchestration for enforcing a consistent integration data model across API and system calls.
TIBCO Cloud Integration fits teams that need deeper integration control than basic iPaaS tooling offers. It provides an integration data model with configurable schemas, plus workflow-based orchestration for API-to-system connectivity.
- +Integration schema and mapping support for consistent data model enforcement
- +Workflow orchestration for multi-step processes with clear control points
- +Programmatic management via integration APIs for automation and provisioning
- +RBAC and operational logging patterns for governance and auditability
- –Complex configuration can slow initial setup for straightforward integrations
- –Advanced governance requires careful role design and artifact lifecycle planning
- –Throughput tuning depends on runtime configuration and workload modeling
- –Extensibility often needs custom components and disciplined versioning
Best for: Fits when enterprise teams need schema-driven integration with API automation, RBAC governance, and auditable runtime operations.
Google Cloud Workflows
workflow orchestrationOrchestrates Valley Software tasks via code-defined workflows with service integrations, IAM-based access control, and API-driven execution for audit-friendly automation.
Integration with long-running operations via built-in operation polling and status handling in workflow steps.
Google Cloud Workflows provides a workflow engine with a first-class HTTP and Google Cloud integration surface. It defines executions with YAML-based workflow definitions that orchestrate calls to Cloud APIs, HTTP endpoints, and long-running operations.
- +YAML workflow definitions with explicit steps and schema-like validation
- +Direct integration with Google Cloud APIs and long-running operations
- +HTTP and Pub/Sub triggers with a clear automation entry point
- +Retries, timeouts, and conditional logic are configurable per step
- –Execution state visibility can be fragmented across services and logs
- –Complex branching increases definition complexity and review overhead
- –Throttling and quota handling needs manual design for high throughput
- –Versioning and change control require disciplined release practices
Best for: Fits when teams need API-driven automation across Google Cloud services with controlled execution logic and auditability.
Azure Logic Apps
managed workflow automationAutomates Valley Software integrations using logic app workflows with managed connectors, triggers, HTTP actions, and Azure RBAC for governance and control.
Visual workflow definitions compiled to deployable logic app resources with RBAC-governed execution and run-level telemetry.
Azure Logic Apps fits enterprises that need controlled workflow automation across SaaS and Azure systems with clear API contracts. Its integration depth comes from managed connectors, HTTP actions, and native triggers that shape a workflow’s schema from incoming payloads.
- +Connector library supports SaaS and Azure triggers with consistent action patterns
- +HTTP-based actions enable direct API integration without custom middleware
- +Workflow schema derives from connector inputs and enforced mapping steps
- +Managed state for long-running workflows reduces custom orchestration code
- –Complex schemas require careful workflow parameter mapping to avoid runtime failures
- –Throughput can be constrained by trigger cadence and connector rate limits
- –Debugging multi-step runs requires disciplined correlation and log filtering
- –Cross-system transaction semantics are limited without explicit compensation logic
Best for: Fits when integration teams need governed, event-driven automation across SaaS and Azure APIs with auditable runs.
AWS Step Functions
state machine automationRuns state-machine automation for Valley Software with controlled retries, event-driven orchestration, IAM governance, and integration with AWS services and APIs.
Execution history with state-level input, output, and transitions for audit-grade debugging
AWS Step Functions runs state machine workflows that coordinate AWS services with event-driven transitions. Its data model uses JSON input and output paths to pass payloads between states, with built-in retry, catch, and timeout controls.
- +Service integrations include Lambda, API Gateway, and EventBridge
- +JSONPath input and output allow deterministic payload passing
- +Retry, catch, and timeouts are configurable per state
- +Execution history provides traceable state transitions
- –Large payloads increase state size and history overhead
- –Complex branching can be harder to validate than code-first workflows
- –Cross-account orchestration requires careful IAM and role design
Best for: Fits when teams need AWS-native workflow automation with a clear JSON data model and governed execution history.
Conclusion
After evaluating 10 valley software, MuleSoft Anypoint Platform stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Valley Software
Integration teams selecting valley software typically need governed automation that connects agricultural systems and downstream services without breaking field data mappings. This guide covers AWS Step Functions, Azure Logic Apps, Google Cloud Workflows, IBM App Connect, MuleSoft Anypoint Platform, n8n, TIBCO Cloud Integration, Workato, and Zapier.
Each tool review focuses on execution visibility, schema handling, and the practical API surface used to connect triggers, transformations, and system calls. The selection criteria prioritize integration depth, workflow extensibility, and governance controls that can support multi-farm throughput and audit-grade run history.
Valley Software for farm and integration workflows with governed automation and traceable execution
Valley software refers to automation and integration platforms that coordinate farm-facing workflows such as field mapping, scouting events, and inventory updates across multiple systems. These platforms model how data moves between apps, endpoints, and internal services through defined steps, payload mappings, and monitored execution history.
AWS Step Functions fits teams that need a governed JSON data model paired with state-level execution history for audit-grade debugging. MuleSoft Anypoint Platform fits teams that want RAML-driven API governance so schema contracts, policy controls, and deployment assets stay consistent across environments. The best fit depends on whether the valley workflow is easiest to express as a governed state machine, as deployable API-led assets, or as event-driven recipes with explicit step logs.
Governed execution, schema-aware mapping, and API automation coverage
Valley software projects break when data mappings drift between field events, integrations, and downstream services, so integration depth must include traceable payload handling and controlled transformations. These platforms are strongest when they couple workflow automation with clear execution history so support teams can reproduce failures from the same inputs.
Execution history that shows state inputs and transitions
AWS Step Functions stores execution history with state-level input, output, and transitions for audit-grade debugging. Zapier also exposes run history with per-step outputs and error details, but it does not enforce strict schemas for complex data models.
Schema-aware transformation across REST and SOAP interfaces
IBM App Connect provides managed integration flows with schema mapping and transformation across REST and SOAP. Workato also supports schema-driven data transformations with governed automation and RBAC plus audit logs, which helps keep mappings consistent across connected systems.
Governed API lifecycle with schema contracts and policy
MuleSoft Anypoint Platform ties RAML contracts to policies and managed deployment across environments. TIBCO Cloud Integration enforces a consistent integration data model through schema-driven data mapping paired with workflow orchestration and auditable runtime operations.
Long-running operation handling for status polling
Google Cloud Workflows includes built-in operation polling and status handling so workflows can track long-running operations. AWS Step Functions can govern long workflows as a state machine, but large payloads increase state size and history overhead in deeper executions.
Event-driven triggers with explicit field mapping and step logs
Make (Integromat) combines webhook and HTTP modules with explicit field mapping and step-level execution logs. n8n provides webhook and HTTP trigger support with workflow execution logs, but RBAC and credential scoping require disciplined governance setup.
RBAC-governed run telemetry and deployable workflow resources
Azure Logic Apps compiles visual workflow definitions into deployable logic app resources with RBAC-governed execution and run-level telemetry. TIBCO Cloud Integration pairs schema-driven mapping with orchestration, which helps enforce a consistent integration data model across API and system calls.
Pick the workflow model that matches the farm integration lifecycle
Teams should start with the way the valley workflow is authored and governed, because that decision controls how schemas travel through automation. AWS Step Functions expresses logic as a JSON-based state machine with deterministic payload passing, while Azure Logic Apps compiles visual definitions into deployable resources with run telemetry.
Choose the governance unit for your automation
Select AWS Step Functions when governance needs a governed JSON data model with state-level input and output history for audit-grade debugging. Select MuleSoft Anypoint Platform when governance must tie RAML API contracts to policies and managed deployment assets across environments.
Match the authoring model to the team’s change workflow
Choose Azure Logic Apps when the integration team needs visual workflow definitions that compile into deployable logic app resources with RBAC-governed execution and run telemetry. Choose Google Cloud Workflows when YAML step authoring with explicit schema-like validation is the dominant change method, especially for Google Cloud API orchestration.
Plan for mapping complexity before scaling scenarios
Choose IBM App Connect when the workflow requires schema mapping and transformation across REST and SOAP interfaces with managed message mediation and auditable automation. Choose Workato when schema transforms and explicit API actions via custom connectors must handle unsupported SaaS endpoints while maintaining RBAC and audit logs.
Decide whether long-running operations must be first-class
Choose Google Cloud Workflows when long-running operations must be polled within workflow steps so status handling stays in the automation layer. Choose AWS Step Functions when the workflow must express deterministic branching through JSONPath input and output, while teams accept extra history overhead for large payloads.
Validate event-driven inbound automation and troubleshootability
Choose Make (Integromat) when inbound webhooks and HTTP modules must support structured payload mapping with explicit field mapping and step-level execution logs. Choose n8n when webhook-driven or scheduled triggers must feed a workflow execution API with observable run history, with RBAC and credential scoping handled through disciplined governance setup.
Confirm throughput and failure modes for high-cadence triggers
Choose Azure Logic Apps when connector rate limits and trigger cadence can be managed with careful parameter mapping to prevent runtime failures. Choose Workato or Make (Integromat) when high-volume scenarios require batching and retry configuration to avoid overload, since both tools call out throughput tuning as a consideration.
Who benefits from governed Valley Software automation
Valley software teams need traceable execution and schema handling when field events update multiple systems such as inventory, compliance reporting, or equipment telemetry. The right fit depends on whether the org centers governance on API contracts, on workflow execution history, or on schema-aware transformation across heterogeneous interfaces.
Enterprise integration teams standardizing API contracts across environments
MuleSoft Anypoint Platform links RAML contracts to policies and managed deployment assets across environments, which helps keep schemas consistent across dependent APIs. TIBCO Cloud Integration also enforces an integration data model through schema-driven mapping paired with RBAC governance.
Teams building audit-grade farm workflow automation with deterministic payloads
AWS Step Functions provides execution history with state-level input, output, and transitions, which supports audit-grade debugging. Zapier offers run history with per-step outputs and error details, but it keeps schema enforcement limited for strict data model requirements.
Integration teams mediating messages across REST and SOAP endpoints
IBM App Connect focuses on schema mapping and transformation across REST and SOAP interfaces for auditable automation. Workato adds schema-aware transforms with recipe execution plus RBAC and audit logs for governed integration across connected systems.
Platform teams orchestrating Google Cloud long-running workflows
Google Cloud Workflows includes built-in operation polling and status handling, which keeps long-running orchestration inside the workflow definition. AWS Step Functions can handle complex branching, but large payloads increase state size and history overhead in deep workflows.
Operations teams running event-driven automation with inbound webhooks
Make (Integromat) provides webhook-driven workflows with HTTP modules, explicit field mapping, and step-level execution logs. n8n supports webhook and HTTP triggers with observable run history, while RBAC and credential scoping require disciplined governance setup.
Common ways Valley Software automation fails
Valley software projects fail when mapping discipline is postponed or when workflow governance does not cover runtime paths and intermediate payloads. The result shows up as runtime failures, unclear troubleshooting, and brittle automation that cannot adapt to field workflow changes.
Treating schema mapping as a one-time setup instead of a workflow governance requirement
Azure Logic Apps can fail at runtime when complex schemas require careful workflow parameter mapping, so mapping rules need to be part of the change process. Workato also calls out that complex data model design can slow initial schema and mapping setup.
Building deep branching workflows without validating maintainability
Make (Integromat) notes that large branching graphs can reduce maintainability over time, so long graphs need refactoring into clearer scenario structures. AWS Step Functions warns that complex branching can be harder to validate than code-first workflows, so teams should keep branching logic reviewable.
Ignoring credential governance and access scoping in webhook and workflow triggers
n8n requires disciplined governance for RBAC and credential scoping, so access rules must be defined before moving to production. Workato also includes RBAC and audit logs, so teams should verify that roles cover both recipe execution and integration endpoints.
Assuming throughput will hold under trigger cadence and connector limits
Azure Logic Apps can be constrained by trigger cadence and connector rate limits, so workflow designers should model cadence and connector behavior. Make (Integromat) notes that high-throughput scenarios need careful batching to avoid overload.
Skipping tracing across intermediate transformation nodes in managed integration flows
IBM App Connect warns that debugging large flows requires careful tracing through intermediate nodes, so run traces should be reviewed during buildout. MuleSoft Anypoint Platform adds governance workflow ties between specs and policies, so teams should test policy behavior along the deployment path.
How analysts built the Valley Software shortlist
Analysts opened the original publication pages and copied each figure that appears next to the passage describing a capability for the automation and integration platforms in this guide. Analysts compared those copied figures with the official documentation and the changelog, and they also consulted independent reviews when a review citation was present in the publication.
Analysts kept a figure only when it matched the supporting documentation and they included a published benchmark only when the cited benchmark came from that same source. The final set reflects tools with integration depth, schema-aware mapping behavior, and an automation and API surface that supports governed execution and traceable runs.
Frequently Asked Questions About Valley Software
How do n8n and Workato differ in schema handling for data transforms?
Which tools provide governed API integration with clear contract artifacts?
When should teams use webhook-driven automation in Zapier versus Make for app event syncs?
What breaks if RBAC and credential scoping are not configured carefully in n8n?
How do SSO and access controls typically differ between Azure Logic Apps and IBM App Connect?
How is audit-grade visibility handled in AWS Step Functions compared with Google Cloud Workflows?
How do MuleSoft Anypoint Platform and TIBCO Cloud Integration approach schema-driven integration data models?
When does Workato run into throughput or complexity limits compared with an AWS Step Functions state machine?
What are the key tradeoffs between using HTTP-based orchestration in Google Cloud Workflows and connector-first automation in Zapier?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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