# Investigating Social Contextual Factors
## 1 Introduction  
Effectively predicting process outcomes in operational business management is essential for customer relationship management (e.g., 'will this customer's order be completed on time? '), enterprise resource planning (e.g., 'what level of resourcing will be required to manage running cases/process instances?') and operational process improvement (e.g., 'what are the common attributes of cases that consistently complete late? '), among others. Predicting the remaining time of a process instance is also very useful. It is essential for effective scheduling of sequentially dependent processes and is a crucial determinant of consumer choice (e.g., where two or more services are identical in price and quality). Reference [1] highlights the importance of contextual factors in predictive process monitoring and identifies four pertinent contextual types:  
* Case context: the properties or attributes of a case. * Process context: similar cases that may be competing for the same resources. * Social context: the way human resources collaborate in an organisation to work on the process of interest. * External context: factors in the broader ecosystem that impacts the process, e.g., weather, legislation, and location. Figure 1 shows the relationship between the various contextual types. To highlight the importance of social contextual factors, [1] argues that \"activities are executed by people that operate in a social network. Friction between individuals may delay process instance, and the speed at which people work may vary.\" It further adds that \"process mining techniques tend to neglect the social context even though it is clear that this context directly impacts the way cases are handled\". The same argument could be made about process monitoring. This study aims to address that gap by empirically investigating the impact of social contextual factors in the predictive process monitoring workflow. To date, numerous approaches have been used to predict process remaining time, including deep learning [2], queuing theory [3] and annotated transition systems [4], among others. This study uses the survival analysis approach, also referred to as \"time-to-event\" analysis. This approach dates back to work by [PERSON] published in his 1662 book 'Natural and Political Observations upon the Bill of Mortality' which suggested that the time of death should be considered an event that deserved systematic study [5]. While the majority of applications of the approach have been in the healthcare research field, by replacing the event of \"death\" with other events, the approach has been successfully applied in others fields, such as human resources management to determine the time-to-employee-attrition [6], marketing to model time-to-customer-churn [7], and credit risk management to determine the time-to-default [8]. However, as yet, time-to-event methods have not been utilised in predictive process monitoring. This study intends to address that gap by proposing an approach that uses survival analysis to predict the remaining time for process instances from several event logs. The primary advantage survival analysis offers over other approaches is that it can deal well with censored observations, i.e., observations where time to the event of interest is unavailable. This contrasts with standard regression approaches which tend to produce results that are neither accurate nor reliable if a high percentage of cases are incomplete. Moreover, survival analysis approaches can handle covariates well. We propose a method for censoring event log for use in survival analysis by treating the completion of a process instance as the event of interest. A review of the literature reveals three primary predictive process monitoring approaches: model-based approaches [4; 9], sequence-to-feature encoding (STEP) approaches [10; 11], and simulation-based approaches [12; 13]. STEP approaches encode event log into feature-outcome pairs using a variety of techniques such as last state, aggregation, index-based or tensor encoding [11; 14].