Search

This research paper focuses on examining the relationship between the cash conversion cycle (CCC) and the profitability of Food and Beverage (F&B) companies in Vietnam by utilizing quantitative models. Analyzing a sample of 23 companies in the period from 2015 to 2021, the study revealsthat a reduction in the CCC positively influences profitability, as measured by the return on assets (ROA) ratio. The study further dissects the impact of CCC’s components, including days payable outstanding (DPO), days inventory outstanding (DIO), and days sales outstanding (DSO), on profitability. Based on regression analysis results, the study recommends that companies under study endeavor to reduce the durations of DPO, DIO, DSO, and CCC to improve profitability. Implementing strategies to tighten credit policies and optimize capital utilization is suggested as a means to achieve this objective.

This article examines the association between media attention and debt financing through a number of channels associated with information asymmetry reduction and whether this relationship changes with cross-country institutional environment characteristics. Focusing on a comprehensive international dataset, we find that overall media coverage is negatively related to firm leverage levels. Media-focused variables directly associated with information asymmetry levels, including press-initiated news, non-financial news, and the extent of positive news sentiment, are also negatively related to firm leverage ratios. Furthermore, the country-level institutional environment provided a substituting rather than legitimizing influence on the relationship between media news coverage and firm financing decision-making. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities. Design/methodology/approach – A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity. Findings – An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches. Originality/value – The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm. [ABSTRACT FROM AUTHOR]

Vandalism, the malicious modification of articles, is a serious problem for open access encyclopedias such as Wikipedia. The use of counter-vandalism bots is changing the way Wikipedia identifies and bans vandals, but their contributions are often not considered nor discussed. In this paper, we propose novel text features capturing the invariants of vandalism across five languages to learn and compare the contributions of bots and users in the task of identifying vandalism. We construct computationally efficient features that highlight the contributions of bots and users, and generalize across languages. We evaluate our proposed features through classification performance on revisions of five Wikipedia languages, totaling over 500 million revisions of over nine million articles. As a comparison, we evaluate these features on the small PAN Wikipedia vandalism data sets, used by previous research, which contain approximately 62,000 revisions. We show differences in the performance of our features on the PAN and the full Wikipedia data set. With the appropriate text features, vandalism bots can be effective across different languages while learning from only one language. Our ultimate aim is to build the next generation of vandalism detection bots based on machine learning approaches that can work effectively across many languages. [ABSTRACT FROM PUBLISHER]

Purpose – This paper aims to solve the stated limitations through (i) exploring usages of scenarios and (ii) proposing a lightweight iterative requirement exploration process that utilizes scenarios to transform the vague ideas of the developers into concrete application’s vision. The explosive growth in pervasiveness, programmability and computational capability of smartphones and tablets unlocks the possibility for many uses of technology, including end-user development (EUD) where end-users create applications as pragmatic tools for their work and hobbies. An extensive amount of research effort has been done to solve EUD challenges; however, the focus of such research lies mainly on designing and implementing software applications. Design/methodology/approach – Based on review on existing application of usage scenario in software application development, we proposed SRE – a scenario-based requirement exploration method and MobiPSE – a lightweight software process that applies SRE. Two EUD projects performed by undergraduate students were used as case studies to test and fine-tune methods. Findings – This paper proposes MobiPSE – a lightweight iterative software process that resolves around our proposed SRE method. Through case studies on MobiPSE, we confirmed that MobiPSE works in real-life scenarios and emphasized two points: ad hoc training has limited effect and lack of technical expertise can still leave a negative impact on requirement exploration process. Research limitations/implications – Because of the limited number of case studies performed for testing and tuning purpose, the proposed methods may not be able to satisfy all cases of EUD. MobiPSE can be fine-tuned further with additional case studies of different scale and team structure. Originality/value – This paper fulfils the need for a lightweight method for exploring requirements and ideas in a EUD project based on creating and analyzing possible usage scenarios. [ABSTRACT FROM AUTHOR]