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This study aims to examine the impact of an organization’s internal and external corporate social responsibility (CSR) initiatives on employee job satisfaction and organizational commitment.

Drawing on the social identity perspective, the authors proposed and tested a mediation model to understand the psychological mechanisms underlying the effects of CSR. The study sample comprised 263 employees from Italian manufacturing firms.

Our findings indicate that external CSR orientation is positively associated with employee job satisfaction and organizational commitment. Furthermore, the mediating effect of job satisfaction partially explains the positive relationship between external CSR orientation and organizational commitment. Moreover, we found that the positive impact of external CSR on employee outcomes is strengthened when combined with internal CSR.

This research has practical and theoretical implications for organizations seeking to enhance employee engagement and commitment through CSR initiatives and sheds light on how CSR can shape employee attitudes and behaviors toward the organization.

This study brings a novel contribution to the field by examining the impact of both internal and external CSR initiatives on employee job satisfaction and organizational commitment.

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.