Information Systems Services | Processes |...

Information Systems Services | Processes | Intelligence

Using ESN Data Analytics for Supporting Decisions in Human Resources Management

Wednesday, November 29, 2017

Janine Hacker i-KNOW 2017, Workshop on Data-Driven Decision Support for Digitized Work Environments Graz, 12 October 2017

Janine Hacker, 12 October 2017

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University of Erlangen-Nuremberg Information Systems Services | Processes | Intelligence

Agenda

│ Motivation and relevance

│ Analysis of ESN data to assess job-relevant personality

character traits

│ Conclusions

│ Discussion


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Motivation and relevance

The current leader of your data science team is going to quit his job. Who‘s gonna replace

him? What does it take to lead a data science team?

Who's going to manage your data science team?

What will HR do to support you in finding the right candidate?


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Human Resource Information

Systems (HRIS) support routine

tasks in HRM

Many decisions in HRM require

human judgement

Human resources management is driven by gut feeling rather than data

Human resources management (HRM)

Provision Internal staffing External recruitment

Development Training Mentoring

Performance Goal setting Controlling

Remuneration Compensation Immaterial benefits

Source: Strohmeier 2015, Piazza 2010, Dulebohn & Johnson 2013


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Data regarding job-relevant personality character traits (“soft skills”) are difficult to observe and hardly available

Human Resources Intelligence and Analytics (HRIA) provides HR-related information and facilitates decision support for management

Data analysis methods and applications

Data

Decision problem

? HRIA

application scenario

Selection of personnel Succession planning Identification of talented

personnel …

Classification Segmentation Prediction …

Personnel master data

Employee history Performance ratings …

Source: Strohmeier 2015, Piazza 2010, Drumm 2008


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Business-focused Inventory of Personality (BIP)

Psychometrical tests facilitate the measuring of personality traits

Source: Hogrefe Ltd 2015


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Profile pages

Following

Activity streams

Searching

Group capabilities

Discussion threads

Tagging

Bookmarks

Blog and wiki capabilities

Social analytics

Records of user activities on Enterprise Social Networks (ESN) are stored in the platform backend

ESN user

ESN user

ESN user

ESN user

ESN user

ESN user

ESN user

ESN data

Activities (usage data)

Content (user-generated

data)

Relations (structural data)

Quantitative analysis

Qualitative and quan-

titative analysis

Social network analysis

Source: Drakos et al. 2014; Koplowitz 2014; Behrendt et al. 2014

How can job-relevant personality character traits be determined using ESN data?

How should I deal with this

client?

Please have a look at my proposal.

Who knows someone in the

marketing department?

Have a look at this article on digital trans-formation:

Our next meeting takes

place on Friday. Who‘s in?


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Analysis of ESN data to assess job-relevant personality character traits

Process overview

Specifica-tion of role require-ments

Mapping of role-based requirements with BIP scales

Mapping of BIP scales with ESN use cases

Metrics deve-lopment

Metrics implemen-tation and calculation

Comparison of individual scores with role requirements

ESN data

Activities (usage data)

Content (user-generated

data)

Relations (structural data)

Quantitative analysis

Qualitative analysis

Social network analysis

Target profile As-is profile

Comparison


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Mapping of role-based requirements with BIP scales

Knowledge and experience

University and school degree Master‘s degree

Technical skills Project management, Data analytics

Work experience Min. 5 years

Skills and abilities

Planning and organisation skills

Collaboration

Dealing with information

Articulateness

Ability to solve problems

Creativity and innovativeness

Leadership qualities

Personality traits

Determination

Self-confidence

Reliability

Sense of responsibility

Ability to take criticism

Decisiveness

Ability to work under pressure

Networking skills

Flexibility / adaptability

BIP scales

Source: Bisani 1989, Hogrefe Ltd 2015


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Mapping of BIP scales with ESN use cases

ESN use case Feature Message content Message length

Asking for an opinion Status update Question mark / word

Discussing Status update, comment Question mark / word Above average

Asking for ideas Status update Question mark / word

Providing input for generating ideas Comment

Asking for help to solve a problem Status update Question mark / word; thank-you

Providing help to solve a problem Comment URL, person tag (CC) Above average

Asking for task-related updates Status update

Question mark / word, person tag (CC)

Short

Coordinating tasks Status update

Person tag (CC) Short

Providing event notifications Status update

URL, numbers Short

Engaging in informal talk Status update, comment Emoticons, congratulations

Storing information Status update URL, topic tag

Generating input Status update URL

Organising meetings Status update, comment Question mark / word, person / topic tag, numbers

Thanking and praising Status update, comment Person tag, positive words

Providing a status update Status update No question word Short

Leadership motivation

Team orien- tation

Openness to contact

Sociability

Source: Richter & Riemer, 2013

Digital traces


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Providing input for generating ideas

No. of replies created / no. of messages created

No. of replies created / no. of status updates created

No. of threads starting with a question contributed to / no. of threads contributed to

Design of ESN metrics based on accumulated digital traces

Source: Hacker 2017


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Use of ESN data to assess job-relevant personality character traits

Metrics development

Trait Example metrics

Team orien-tation

No. of replies created / no. of messages created No. of replies created / no. of status updates created No. of threads starting with a question contributed to / no. of threads contributed to No. of replies containing a URL created / no. of replies created No. of replies containing a file created / no. of replies created No. of replies containing a person tag created / no. of replies created No. of replies containing a topic tag created / no. of replies created No. of thanks messages created / no. of messages created No. of praise messages created / no. of messages created

Leader-ship motiva-tion

No. of status updates created / no. of messages created No. of status updates created / no. of replies created No. of questions (in initial messages) created / no. of (initial) messages created No. of threads starting with a question created / no. of initial messages created No. of status updates containing a person tag created / no. of status updates created No. of status updates containing a topic tag created / no. of status updates created Avg. no. of words / message created No. of replies created / no. of messages created No. of replies created / no. of status updates created No. of replies containing a URL created / no. of replies created No. of replies containing a file created / no. of replies created No. of replies containing numbers created / no. of replies created

Composite score

Composite score

Source: Hacker 2017


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Use of ESN data to assess job-relevant personality character traits

Data collection

Export of ESN data

Exploration of data

Specification of a sample of users

Implementation of developed metrics

E.g. using SQL statements

Amendment of metrics if required

Calculation of metrics for users in the sample

Individual metrics

Creation of composite scores

Assessment of individual users in terms of traits

Identification of levels of scores of individual users in comparison to other users in the sample

Comparison of individual scores with role requirements

Metrics implementation and calculation / Comparison of individual scores with role requirements

As-is profile

Comparison

Target profile


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Conclusions

Implications

Possibility to observe of job-relevant personality character traits

Potential to obtain more objective assessments of job-relevant personality character traits

Facilitation of more evidence-based decision making in HRM, especially concerning personnel

selection

Limitations

Mapping of traits to ESN use cases subjective

Metrics development on a conceptual basis

No consideration of real-life interactions

Implications and limitations


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Discussion

To what extent is the proposed approach feasible in terms of data protection laws?

Would HR be willing to integrate such an approach? How could it be usefully combined with human

judgement?

How could such an approach be communicated in the organisations? How will employee behaviour

on an ESN change if employees feel observed?

How could the approach be improved, e.g. using text mining?

Discussion and next steps

Information Systems Services | Processes | Intelligence


Wednesday, November 29, 2017

Janine Hacker i-KNOW 2017, Workshop on Data-Driven Decision Support for Digitized Work Environments Graz, 12 October 2017

Information Systems Services | Processes |...

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