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Despite the increasing demand for an effective XML document repository, many are still reluctant to store XML documents in their natural tree form. One main reason is the inadequacy of XML query languages to update the tree‐form XML documents. Even though some of the languages have supported minimum update facilities, they do not concern on preserving the documents constraints. The results are updated documents with very low database integrity. In this paper, we propose a methodology to accommodate XML Update without violating the conceptual constraints of the documents. The method takes form as a set of functions that perform checking mechanisms before update operations. In this paper we discuss the conceptual constraints embedded in three different relationship structures: association, aggregation and inheritance relationship. We highlight four constraints related with association relationship (nuber of participants, referential integrity, cardinality, and adhesion), five constraints related with aggregation relationship (cardinality, adhesion, ordering, homogeneity and share‐ability) and two constraints related to inheritance relationship (disjoint and number of super‐class). In addition, a specific constraint, which is collection type of children, will also be discussed. The proposed method can be implemented in different ways, for example in this paper we use XQuery language. Since the XML update requires schema, in this paper we also propose the mapping of the these constraints in the conceptual level to the XML Schema. We use XML Schema for structure validation, even though the algorithm can be used by any schema languages.

This paper aims to develop insight into the impact the coronavirus disease 2019 (COVID-19) crisis had on female entrepreneurs in Ghana’s beauty industry and their ability to recognize opportunities. The authors also ascertained characteristics of the women that seemed to have influenced opportunity recognition.

Using the case study approach, the authors collected data via in-depth interviews and open-ended questionnaires from sixteen women entrepreneurs in the beauty industry. The data were analyzed using a thematic analysis.

The data show that women entrepreneurs encountered challenges during the pandemic; however, some of the women were able to identify opportunities. Creativity and social media inclination were perceived to have influenced opportunity recognition. It was also observed from the demographic data that women who identified opportunities and women who could not differ in the level of education, age and firm age.

Women entrepreneurs are encouraged to capitalize on social media and virtual platforms to enhance their marketing, services and operations. COVID-19 aid for businesses must be distributed equitably to all entrepreneurs in need.

The findings from this study provide novel insights into opportunity recognition during a crisis, focusing on a specific industry in a developing country.

This study is part of a series aimed at improving the city's environment, as fully restoring the past soundscape is hardly feasible. The initial study aims to uncover the city's sound characteristics, including iconic sounds that have shaped the city's environment for decades, contributing to its status as Indonesia's second most popular tourist destination. This stage is critical for informing policymaking to carefully manage and enhance the urban acoustic environment in alignment with the preserved culture.

The city's sound profile was examined using standard urban sound taxonomies. The study used quantitative methods, including (1) sound pressure level (SPL) measurements and sound recordings, (2) in situ surveys and (3) memory-based surveys. The first set of data were compared to current standards and standard urban sound taxonomies, while the second set was analysed to determine the median rating score for determining the soundscape dimensions. The third data set was used to identify the specific acoustic aspects inherent in Yogyakarta.

Yogyakarta's acoustic environment was bustling, with traffic noise and human activities dominating the soundscape, surpassing the standard levels. Many sounds not classified in standard urban sound taxonomies were present, showing the diverse nature of urban sound classification, particularly in a cultural and traditional city like Yogyakarta. The memory-based survey unveils Yogyakarta's two most remarkable soundmarks, “gamelan” and “andong”, which support the findings of prior studies. The in situ survey rated the city's acoustic environment as eventful, pleasurable and generally appropriate, emphasising the presence of cultural sounds unique to Yogyakarta, even though they are not fully audible in the current environment.

The standard sound taxonomies used in urban areas need to be adjusted to include the unique sounds produced by cultural and traditional activities in developing countries. The ordinates and subordinates of the taxonomies also need to be updated. When cultural and daily activities are massively seen in a particular city, the sounds they produce can be recalled exclusively as the city's signature. It is urgent to implement policies to safeguard the few remaining soundmarks before they disappear entirely.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.