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This paper aims to examine the emergence of digital entrepreneurship in a conflict environment to explore the emancipatory potential of digital technology in a rare context. The purpose of this paper is to take a micro-level investigation of digital entrepreneurs operating in a violent in-conflict area.

The analysis is based on multiple case studies from a violent conflict zone in India. This study used interview data from 10 digital entrepreneurs from the state of Jammu and Kashmir. Field data was collected with progressive rounds of interviews with the entrepreneurs, supplemented by published statistics and secondary data sources, and analysis was done with the help of qualitative data analysis software NVivo.

The context directed entrepreneurial behaviour and the motivation to start digital ventures. Entrepreneurs realized the potential of technology but its successful use was contingent upon their social positionalities. The digital entrepreneurial engagement of the entrepreneurs is classified into four dimensions: social digital entrepreneurship; business entrepreneurship; knowledge entrepreneurship; and institutional entrepreneurship.

The findings of the study contribute to the digital entrepreneurship literature by illustrating how a rare context influences venturing into technology sectors. The study advances the creative technology use, these in-conflict entrepreneurs with their existing resources maneuvered to create socially ingrained digital ventures. The study provides evidence to “conditional emancipation” attributable to digital entrepreneurship owing to the conflictual context in question.

Digital entrepreneurship is a highly topical, sub-specialist and contemporary branch of entrepreneurship which is providing ever-increasing range of platforms for pursuing entrepreneurship-related career options. While the academic discourses in this area are increasingly growing, there is an equal level of scant attention paid to inquire how proclivity toward this career choice is developed and influenced. In the present study, this gap has been addressed by identifying major antecedents of digital entrepreneurship intentions under the aegis of capital theory.

Using the survey data from 287 respondents, structural equation modeling was utilized to explore the association of four antecedents: digital entrepreneurial competence, innovative cognition, social media adroitness and digital entrepreneurship role models on the goal intentions. Subsequently the association between goal intention and implementation intention to start a digital venture was also examined.

The analysis confirms the significant impact of the identified antecedents on the digital entrepreneurial volition of potential entrepreneurs. Our results demonstrate that innovativeness has the greatest impact on the goal intentions to create new digital ventures followed by the presence of role models. Digital competence is also a significant contributor in enhancing the propensity to start digital ventures while being digitally adroit has the least impact on such inclination. Lastly, our study provides empirical evidence to the linkage between goal and implementation intentions.

This paper informs practice on entrepreneurship education especially the role of skill-based education programs to enhance the information technology–related knowledge of students and incubation support for hands-on-training on the various dimensions of digital ventures. At policy-level institutions providing entrepreneurial education can design special tasks and learning activities that are focused on acquainting students with design thinking perspectives and lean start-up approaches.

Malnutrition is one of the serious public health problems especially for children and pregnant women in developing countries such as Bangladesh. This study aims to identify the risk factors associated with child nutrition for both male and female children in Bangladesh.

This study was conducted among 23,099 mothers or caretakers of children under five years of age from a nationally representative survey named Bangladesh Multiple Indicator Cluster Survey, 2019. This study used chi-square test statistic for bivariate analysis and multinomial logistic regression was used to evaluate the adjusted effects of those covariates on child nutritional status.

The prevalence of severely malnourished, nourishment was higher for males than females (5.3% vs 5.1%, 77.4% vs 76.8%) while moderately malnourished were higher for females (18.1% vs 17.4%). The findings from the multinomial model insinuated that the mother’s education level, wealth index, region, early child development, mother’s functional difficulties, child disability, reading children's books and diarrhea had a highly significant effect on moderate and severe malnutrition for male children. For the female children model, factors such as mother’s education level, wealth index, fever, child disability, rural, diarrhea, early child development and reading less than three books were significant for moderate and severe malnutrition.

There is a solution to any kind of problem and malnutrition is not an exceptional health problem. So, to overcome this problem, policymakers should take effective measures to improve maternal education level, wealth status, child health.

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.