Mining People Analytics from StackOverflow Job Advertisements

Cited by 39 publications

(27 citation statements)

References 24 publications

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“…Workforce analytics is a process--one that is continuously advanced by improving problem solving through sound measurement, appropriate research methods, systematic data analyses, and technology to support organizational decision making. Ramamurthy et al, 2015;Angrave et al, 2016;Malisetty et al, 2017) Education (e.g., Ramamurthy et al, 2015;Wei et al, 2015;Angrave et al, 2016) Participation in Social Networks (e.g., Malisetty et al, 2017;Sinha et al, 2012) The position of the employee in the organization Type of hire (e.g., Ramamurthy et al, 2015) Status of the position (e.g., Ramamurthy et al, 2015;Wei et al, 2015) Salary and compensations (e.g., Ramamurthy et al, 2015;Wei et al, 2015;Malisetty et al, 2017) Changes in the organization (e.g., Ramamurthy et al, 2015) Date of the last promotion (e.g., Ramamurthy et al, 2015) The work carried out in the organization Individual performance (e.g., Davenport et al, 2010;Ramamurthy et al, 2015;Wei et al, 2015) Performance evaluations (e.g., Ramamurthy et al, 2015;Malisetty et al, 2017) Sentiments assessments (e.g., Hans and Mnkandla, 2017) Content and the receivers of their messages in organizational platforms (e.g., Angrave et al, 2016) The employee himself/herself Personality traits (e.g., Russell and Bennett, 2015;Papoutsoglou et al, 2017) Cognitive abilities and skills (mainly, communication and negotiation skills) (e.g., Angrave et al, 2016;Malisetty et al, 2017;Russell and Bennett, 2015;Papoutsoglou et al, 2017) Expertise (e.g., Ramamurthy et al, 2015;Wei et al, 2015;Angrave et al, 2016;Papoutsoglou et al, 2017) T...…”

Section: Tablesmentioning

confidence: 99%

Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption

Fernández¹,

Gallardo-Gallardo

2020

126

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Purpose This paper aims to contribute to the literature on human resources (HR) digitalization, specifically on HR analytics, disentangling the concept of analytics applied to HR and explaining the factors that hinder companies from moving to analytics. Therefore, the central research questions addressed in this study are: what does HR analytics encompass? What impedes the adoption of analytics in HR within organizations? Design/methodology/approach The authors performed a comprehensive literature review on analytics as applied in HR. The authors relied on two of the major multidisciplinary publication databases (i.e. Scopus and WoS). A total of 64 manuscripts from 2010 to 2019 were content analyzed. Findings The results reveal that there is an ongoing confusion on HR analytics conceptualization. Yet, it seems that there is an emerging consensus on what HR analytics encompasses. The authors have identified 14 different barriers for HR analytics adoption grouped into four categories, namely, data and models, software and technology, people and management. Grounding on them the authors propose a set of 14 key factors to help to successfully adopt HR Analytics in companies. Originality/value This paper brings clarity over the conceptualization of HR analytics by offering a comprehensive definition. Additionally, it facilitates business and HR leaders in making informed decisions on adopting and implementing HR analytics. Moreover, it assists HR researchers in positioning their paper more explicitly in current debates and encouraging them to develop some future avenues of research departing from some questions posed.

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Section: Tablesmentioning

confidence: 99%

Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption

Fernández¹,

Gallardo-Gallardo

2020

126

View full text Add to dashboard Cite

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“…Moreover, an ontology allows multidimensional relationships to These skill bases contain different information about the skills and competencies of different occupations. According to [15], competence or tacit knowledge or soft skill is the "ability to use knowledge, skills and personal, social and/or methodological abilities, in work or study situations and professional and personal development". Skill or competency or implicit hard skill or explicit hard skill is the "ability to apply knowledge and use know-how to complete tasks and solve problems".…”

Section: B Skill Basesmentioning

confidence: 99%

“…Several studies have examined the job skills required by the job market using an exact match of skills in job ads to compute the skill frequency. In order to identify skills in job ads, prior work [15], [26], [35] relied on an exact match with the used skill base or the defined list of skills. The exact match can be done through the exact name of the skill, the alternative labels or aliases of the skill.…”

Section: • Skill Count Using Skill Basesmentioning

confidence: 99%

“…Sector specific [15], [21], [22], [39], [40], [62], [63], [66] Soft and Hard skills Sector specific [69], [125] Soft and Hard skills All sectors [33], [47], [64] Soft skills Sector specific Job ads and Resumes [26] Soft and Hard skills All sectors [44] Soft skills sentences All sectors [27] Soft and Hard skills All sectors [25] soft and Hard skills All sectors Job ads and Interviews [34] Soft and Hard skills Sector specific LinkedIn resumes [32] Soft and Hard skills All sectors Curricula development job ads [68] soft and hard skills sector specific [67] Soft and hard skills Sector specific Job ads and University programs [89] Hard skills Sector specific…”

Section: ) Skill Extraction and Job Market Analysismentioning

confidence: 99%

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A Survey on Skill Identification From Online Job Ads

2021

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“…They even also attained reasonable results while training the classifier on one language and testing on another language. In another interesting work, 18 SoF data are exploited to mine people analytic and perform job recommendations based on the identified skills or expertise.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

Ali

Mushtaq

Rasheed

et al. 2021

Concurrency and Computation

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Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions.The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning-based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K-Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni-gram feature set provides best classification results and attains 85.80% accuracy.For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni-gram feature set proved to be the most suitable. K E Y W O R D Sarchitectural knowledge management, stack overflow, crowd-sourced communities, text mining, classification INTRODUCTIONSoftware architectural design appears to have a significant importance in Software Development Life Cycle (SDLC). Its manifests are the early design decisions which help to determine the system development, deployment, and evaluation. To develop a quality project, better architectural decisions are essential and these decisions pose vast challenges for software engineers. Architectural Knowledge Management (AKM) is used to capture

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Mining People Analytics from StackOverflow Job Advertisements

Cited by 39 publications

References 24 publications

Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption

Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption

A Survey on Skill Identification From Online Job Ads

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

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